CN120218221B - Digital twinning-based steel structure building construction overall process mechanical property evaluation method - Google Patents

Abstract

The present invention relates to the field of digital twin technology, specifically to a method for evaluating the mechanical properties of a steel structure building during its entire construction process based on digital twins, comprising the following steps: obtaining multi-source heterogeneous steel structure status data from stress sensors, BIM model component information, and displacement monitoring points, comparing the timestamps of each data source with each other, calculating the transmission delay value of each data source relative to a unified reference time, correcting the timestamps of the original data one by one, and establishing a time-aligned steel structure status data stream. In the present invention, the timestamps of the multi-source heterogeneous data are corrected for transmission delay using a unified reference time, and the timestamps of the original data are corrected one by one to generate a time-aligned steel structure status data stream, eliminating the time deviation between sensors, BIM models, and monitoring points, and ensuring the synchronization and continuity of dynamic changes in mechanical parameters.

Description

Mechanical performance evaluation method for the entire construction process of steel structure buildings based on digital twins

Technical Field

The present invention relates to the field of digital twin technology, and in particular to a method for evaluating the mechanical properties of a steel structure during its entire construction process based on digital twins.

Background Art

Digital twins are a technical field that uses physical entities as objects, builds digital mirrors of physical entities in virtual space through multi-source data fusion, dynamic modeling, and real-time interaction technologies, and implements state mapping, behavior prediction, and closed-loop optimization based on data-driven development.

Existing digital twin technology, when integrating heterogeneous data from multiple sources in real time, fails to account for timestamp discrepancies caused by transmission delays between different data sources. This leads to time misalignment between data streams from sensors, BIM models, and monitoring points. This can lead to misjudgments of state when evaluating dynamic mechanical properties due to time asynchrony. Furthermore, dependencies are implicit within the model, making it difficult to trace abnormal results back to specific input data or intermediate links, making it difficult to quickly locate the source of a fault. Therefore, improvements are needed.

Summary of the Invention

The purpose of this invention is to solve the shortcomings of the existing technology and propose a method for evaluating the mechanical properties of the entire process of steel structure construction based on digital twins.

To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the mechanical properties of a steel structure during construction based on digital twins, comprising the following steps:

Acquire multi-source heterogeneous steel structure status data from stress sensors, BIM model component information, and displacement monitoring points, compare the timestamps of each data source, calculate the transmission delay value of each data source relative to a unified reference time, correct the original data timestamps one by one, and establish a time-aligned steel structure status data stream;

Extracting data records describing state parameters of the same steel structure component based on the time-series aligned steel structure state data stream, assigning a confidence value to each data record, generating steel structure state parameter records with confidence, calculating fused estimated values for records of the same parameter based on the steel structure state parameter records with confidence, and comparing differences in fused estimated values between different records with a preset conflict determination limit to construct a steel structure state parameter set with fused conflicts marked;

Based on the steel structure state parameter set marked with the fusion conflict, the state variables directly corresponding to the steel structure state parameter set marked with the fusion conflict in the steel structure digital twin model are retrieved, the variables are incrementally updated to obtain the steel structure model state increment to be propagated, based on the steel structure model state increment to be propagated, the influence of the state increment on the associated variables is calculated, and the steel structure twin model state and data dependency log after the update and propagation is established;

The target mechanical property evaluation result is retrieved in the steel structure twin model state after the update propagation, the corresponding record entry of the mechanical property evaluation result in the data dependency log is located, and the log entry point associated with the target result is obtained. Based on the log entry point associated with the target result, a reverse traceback search is performed in the data dependency log according to the recorded variable dependency chain information to generate a causal traceback path for the mechanical property anomaly.

Preferably, the steps for acquiring the time-aligned steel structure status data stream are:

Extract the original timestamps of each data source from stress sensors, BIM model component information, and displacement monitoring points. Using a unified reference time as the standard, calculate the absolute value of the difference between the timestamp of each data source and the reference time. Define the absolute value of the difference as the initial transmission delay value, and generate an initial transmission delay value set.

Based on the initial transmission delay value set, the initial transmission delay value distribution of all data sources is counted, the 90th percentile of the initial transmission delay value distribution is taken as the preset time deviation threshold, and a compensation factor is generated according to the ratio of the time deviation threshold to the data source delay value. The compensation factor calculation formula is: compensation factor = data source delay value / time deviation threshold, and a dynamic time compensation factor set is generated;

Based on the dynamic time compensation factor set, for data sources whose initial transmission delay value exceeds the time deviation threshold, the formula: corrected timestamp = original timestamp + (data source delay value/(compensation factor + 1)) is used to correct the timestamps one by one to generate a time-aligned steel structure status data stream.

Preferably, the steps for obtaining the steel structure state parameter record with confidence level are:

Extracting multiple state parameter data records of the same steel structure component from the time-series aligned steel structure state data stream, classifying them according to data source type, and generating an unconfidence-assigned state parameter record set;

Based on the state parameter record set without confidence, the confidence of each record is calculated using the following formula:

;

in, For the The confidence level of the records, For the The reliability score of the data source to which the record belongs, the default value is the stress sensor , BIM model , displacement monitoring point , and For the Article and The difference between the timestamp of the record and the current system time, is the time-dependent attenuation factor, The total number of status parameter records for the same steel structure component, For the The reliability score of the data source to which the record belongs;

Based on the confidence of each record, the confidence is compared with the preset confidence threshold, and the records with a confidence level less than the preset confidence threshold are eliminated. The retained records are sorted in descending order according to the confidence level to generate steel structure status parameter records with confidence levels.

Preferably, the steps of obtaining the steel structure state parameter set with conflict-marked fusion are:

Extracting records of the same parameter from the steel structure state parameter records with confidence, classifying them by parameter type, and generating a set of confidence-parameter value pairs to be fused;

Based on the set of confidence-parameter value pairs to be fused, the fused estimated value of the parameter is calculated using the following formula:

;

in, is the fused estimate of the current parameter type, For the The confidence level of the records, For the The parameter values of the records, is the standard deviation of similar parameter values, The total number of records of the current parameter type;

Based on the fusion estimated value, the absolute difference between each record parameter value and the fusion estimated value is calculated, and the record whose absolute difference exceeds the preset conflict judgment limit is marked as a conflict data point to generate a steel structure state parameter set with fusion conflict marked.

Preferably, the step of obtaining the state increment of the steel structure model to be propagated is:

Extract all data points marked as conflict from the steel structure state parameter set marked with the fusion conflict, traverse the list of state variable names and identifiers in the steel structure digital twin model, match the parameter names of the conflicting data points with the twin model variable names one by one, and generate a corresponding list of state variable names and identifiers to be updated;

Based on the corresponding list of the state variable names and identifiers to be updated, read the current values of the state variables in the twin model one by one, synchronously extract the corresponding parameter values in the conflicting data points, calculate the absolute difference between the current value and the parameter value, and generate a state variable incremental parameter set;

Based on the state variable incremental parameter set, incremental superposition is performed on the matching variables in the steel structure digital twin model, and the variable values are updated one by one to generate the steel structure model state increment to be propagated.

Preferably, the steps for obtaining the steel structure twin model status and data dependency log after the update propagation are:

Extracting the incremental value of each primary variable from the steel structure model state increment set to be propagated, traversing the predefined mechanical transfer and geometric association relationship indexes between the steel structure components, matching the component identifiers directly associated with the current primary variable, and generating a primary variable-associated component mapping list;

Based on the main variable-associated component mapping list, the propagation impact value of the main variable increment on each associated component is calculated using the following formula:

;

in, For the The propagation impact value of the associated components, is the incremental value of the main variable, For the The geometric distance between the associated component and the component where the main variable is located, From the main variable to the The number of nodes in the mechanical transmission path of the associated components, is the distance attenuation coefficient, is the path complexity coefficient;

Based on the propagation impact values of all associated components, complete dependency relationship entries are constructed and written into the database log table one by one to generate the steel structure twin model status and data dependency log after the update propagation.

Preferably, the steps for obtaining the log entry point associated with the target result are:

Extracting mechanical performance evaluation results of all steel structure components from the updated and propagated steel structure twin model state, traversing stress, strain, and displacement parameter values of each component, and generating a set of mechanical performance evaluation results;

Based on the mechanical property evaluation result set, the component identifier, parameter name, and timestamp fields are parsed one by one, and a full-text matching search is performed in the steel structure twin model state and data dependency log after the update propagation using the component identifier and parameter name as a joint key to generate a matching log entry list;

Based on the matching log entry list, the log storage path, data update timestamp and associated variable identifier fields in each log are extracted to generate a log entry point associated with the target result.

Preferably, the steps for obtaining the causal tracing path of the abnormal mechanical properties are:

Extract the log storage path of each entry point from the log entry point associated with the target result, traverse the steel structure twin model state after update propagation and each variable dependency chain record in the data dependency log, and use the target result identifier as the starting point to reversely parse the upstream variable identifiers in the dependency chain layer by layer to generate a reverse traceability variable chain set;

Based on the reverse traceability variable chain set, the original data records are matched according to variable identifiers and timestamps from the time-aligned steel structure status data stream, BIM model version library and displacement monitoring point original database, and the sensor device number, BIM model version number, displacement monitoring point coordinates and original values are extracted to generate an original input data identifier set;

The original input data identifier set and the reverse tracing variable chain set are merged in chronological order and dependency hierarchy to construct a complete link of original data identifiers, intermediate calculation variable identifiers and target result identifiers, and generate a causal tracing path for mechanical property anomalies.

Compared with the prior art, the advantages and positive effects of the present invention are:

In the present invention, the timestamps of multi-source heterogeneous data are corrected for transmission delays through a unified reference time, and the timestamps of the original data are corrected one by one to generate a time-aligned steel structure state data stream, eliminating the time deviation between sensors, BIM models and monitoring points, and ensuring the synchronization and consistency of the dynamic changes of mechanical parameters. For the conflicting data records of the same component state parameters, a fusion conflict marking mechanism is constructed by comparing the difference between the confidence value assignment and the fusion estimate value, distinguishing between reliable data and abnormal data, avoiding the error amplification problem caused by direct fusion of unmarked conflicts in traditional methods, and improving the reliability and accuracy of the twin model input data. Based on the incremental update strategy, only the model variables corresponding to the conflict marking parameters are locally corrected to reduce the computing resource consumption caused by the full update. At the same time, the mechanical transmission relationship between variables is recorded by the data dependency log to achieve traceability of abnormal results. Combined with the log entry point and the reverse dependency chain retrieval, the original input data is reversely located from the abnormal results of the target mechanical properties, a causal traceability path is established, the cause of the abnormality and the propagation path are clarified, and an accurate decision-making basis is provided for construction process optimization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the steps of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

Referring to FIG1 , the present invention provides a technical solution, a method for evaluating the mechanical properties of a steel structure during construction based on digital twins, comprising the following steps:

Acquire multi-source heterogeneous steel structure status data from stress sensors, BIM model component information, and displacement monitoring points, compare the timestamps of each data source, calculate the transmission delay value of each data source relative to a unified reference time, correct the original data timestamps one by one, and establish a time-aligned steel structure status data stream;

Based on the time-series aligned steel structure state data stream, data records describing the state parameters of the same steel structure component are extracted, and a confidence value is assigned to each data record to generate steel structure state parameter records with confidence. Based on the steel structure state parameter records with confidence, fusion estimation values are calculated for the records of the same parameter, and the differences in the fusion estimation values between different records are compared with the preset conflict judgment limit to construct a steel structure state parameter set with fusion conflicts marked.

Based on the steel structure state parameter set marked with fusion conflicts, the state variables directly corresponding to the steel structure state parameter set marked with fusion conflicts in the steel structure digital twin model are retrieved, and the variables are incrementally updated to obtain the steel structure model state increment to be propagated. Based on the steel structure model state increment to be propagated, the impact of the state increment on the associated variables is calculated, and the state and data dependency log of the steel structure twin model after the update and propagation is established;

The target mechanical performance evaluation results are retrieved in the steel structure twin model state after update propagation, the corresponding record entries of the mechanical performance evaluation results in the data dependency log are located, and the log entry points associated with the target results are obtained. Based on the log entry points associated with the target results, a reverse tracing retrieval is performed in the data dependency log according to the recorded variable dependency chain information to generate a causal tracing path for mechanical performance anomalies.

The steps for obtaining the time-aligned steel structure status data stream are as follows:

Extract the original timestamps of each data source from stress sensors, BIM model component information, and displacement monitoring points. Using a unified reference time as the standard, calculate the absolute value of the difference between the timestamp of each data source and the reference time. Define the absolute value of the difference as the initial transmission delay value, and generate an initial transmission delay value set.

Based on the initial transmission delay value set, the initial transmission delay value distribution of all data sources is counted. The 90th percentile of the initial transmission delay value distribution is taken as the preset time deviation threshold. The compensation factor is generated based on the ratio of the time deviation threshold to the data source delay value. The compensation factor calculation formula is: compensation factor = data source delay value / time deviation threshold. This generates a dynamic time compensation factor set.

Based on the dynamic time compensation factor set, for data sources whose initial transmission delay value exceeds the time deviation threshold, the formula: corrected timestamp = original timestamp + (data source delay value/(compensation factor + 1)) is used to correct the timestamps one by one and generate a time-aligned steel structure status data stream.

Specifically, based on the original timestamps of the data sources extracted from the stress sensor, BIM model component information, and displacement monitoring points, for example, the original timestamp of the stress sensor S1 is 2025-04-15 10:00:01.500, the timestamp of the BIM model information about component B-101 is 2025-04-15 10:00:00.800, and the timestamp of the displacement monitoring point D3 is 2025-04-15 10:00:02.100, a unified reference time is set. This reference time can be the startup time of the data acquisition system or a preset synchronization time point, for example, set to 2025-04-1 510:00:00.000, then calculate the absolute value of the difference between the original timestamp of each data source and this reference time (10:00:00.000). For stress sensor S1, the absolute value of the difference is |10:00:01.500-10:00:00.000|=1.500 seconds, for BIM model information, the absolute value of the difference is |10:00:00.800-10:00:00.000|=0.800 seconds, for displacement monitoring point D3, the absolute value of the difference is |10:00:02.100-10:00:00.000|=2.100 seconds. The absolute value of the difference is defined as the initial transmission delay value of each data source. For example, the initial transmission delay of S1 is 1.500 seconds, BIM is 0.800 seconds, and D3 is 2.100 seconds. The initial transmission delay values of all participating data sources are collected to form an initial transmission delay value set. For example, if the delay of sensor S2 is 3.000 seconds and the delay of displacement point D4 is 1.200 seconds, the set is {1.500, 0.800, 2.100, 3.000, 1.200}. Based on this set containing the initial transmission delay values of multiple data sources, {1.500, 0.800, 2.100, 3.000, 1.2 00}, count the distribution of these values, for example, calculate its cumulative distribution function, determine the 90th percentile of the distribution, first sort the set: {0.800, 1.200, 1.500, 2.100, 3.000}, for a set containing N=5 data points, the position of the 90th percentile is P=90/100*(N+1)=0.9*6=5.4, since the position 5.4 is between the 5th value (3.000) and the 6th value, for example, interpolation or taking the nearest rank is usually used. In this simple example or for large data sets, you can directly take the value of a specific position or use a standard statistical library for calculation, for example, after standard calculation The 90th percentile obtained is 2.500 seconds. This 90th percentile (2.500 seconds) is set as the preset time deviation threshold. The setting of this threshold refers to the transmission delay level of the majority (90%) of the data in the dataset. The 90th percentile is selected to tolerate a certain degree of network fluctuation while identifying significant delay anomalies. Next, the compensation factor is generated based on the ratio of the time deviation threshold (2.500 seconds) to the actual delay value of each data source. The calculation formula for the compensation factor is: Compensation factor = data source delay value / time deviation threshold. The compensation factor for each data source is calculated. The compensation factor of S1 = 1.500/2.500 = 0.6, BIM compensation factor = 0.800/2.500 = 0.32, D3 compensation factor = 2.100/2.500 = 0.84, S2 compensation factor = 3.000/2.500 = 1.2, D4 compensation factor = 1.200/2.500 = 0.48. All the calculated compensation factors are combined to form a dynamic time compensation factor set {0.6, 0.32, 0.84, 1.2, 0.48}. Based on this dynamic time compensation factor set, check whether the initial transmission delay value of each data source exceeds the preset time deviation threshold (2.500 seconds). It is found that S1(1 .500s), BIM (0.800s), D3 (2.100s), and D4 (1.200s) all do not exceed the threshold, while the delay of S2, 3.000 seconds, exceeds the threshold. Therefore, only the timestamp of S2 is corrected using the correction formula: Corrected timestamp = original timestamp + (data source delay value/(compensation factor+1)). Applying this formula to S2, for example, its original timestamp is 10:00:03.000, the delay value is 3.000 seconds, and the compensation factor is 1.2, then the corrected timestamp = 10:00:03.000 + (3.000/(1.2+1)) = 10:00:03.00 0+(3.000/2.2)=10:00:03.000+1.364 seconds=10:00:04.364. The timestamps of other data sources that do not exceed the threshold (S1, BIM, D3, D4) are not corrected and their original timestamps are retained. All data sources are processed one by one, and the corrected timestamps (such as 10:00:04.364 of S2) are merged with the uncorrected timestamps (such as 10:00:01.500 of S1, 10:00:00.800 of BIM, 10:00:02.100 of D3, and 10:00:01.200 of D4) to generate a time-aligned steel structure status data stream.

The steps for obtaining the steel structure status parameter record with confidence level are as follows:

Extract multiple state parameter data records of the same steel structure component from the time-aligned steel structure state data stream, classify them according to data source type, and generate an unconfident state parameter record set;

Based on the set of state parameter records without confidence, the confidence of each record is calculated using the following formula:

;

in, For the The confidence level of the records, For the The reliability score of the data source to which the record belongs, the default value is the stress sensor , BIM model , displacement monitoring point , and For the Article and The difference between the timestamp of the record and the current system time, is the time-dependent attenuation factor, The total number of status parameter records for the same steel structure component, For the The reliability score of the data source to which the record belongs;

Based on the confidence of each record, the confidence is compared with the preset confidence threshold, and the records with a confidence level less than the preset confidence threshold are eliminated. The retained records are sorted in descending order according to the confidence level to generate steel structure status parameter records with confidence levels.

Specifically, from the time-aligned steel structure state data stream generated in the previous step, multiple state parameter data records for the same steel structure component (for example, the component identifier is "beam B-101") are extracted. These records may come from different sensors or data sources. For example, four records about the stress state of "beam B-101" are extracted: record 1 (source: stress sensor S1, parameter value: 150MPa, aligned timestamp: 10:00:01.500), record 2 (source: BIM model, parameter value: 145MPa, aligned timestamp: 10:00:00.800), Record 3 (source: estimated stress of displacement monitoring point D3, parameter value: 155MPa, timestamp after alignment: 10:00:02.100), record 4 (source: stress sensor S2, parameter value: 152MPa, timestamp after alignment: 10:00:04.364). These extracted records are classified and sorted according to their data source type (stress sensor, BIM model, displacement monitoring point) to form an unconfident state parameter record set {record 1, record 2, record 3, record 4}. Based on this unconfident state parameter record set, the confidence of each record is calculated one by one. , and its calculation formula is:

;

here, Representative The confidence level of a record is a value between 0 and 1, indicating the credibility of the record. Representative The inherent reliability score of the data source to which the record belongs. This is a pre-set value that reflects the general accuracy of different types of data sources. According to the preset, the reliability score of stress sensors (such as S1, S2) is 0.9, and the BIM model information is 0.7, the displacement monitoring point (such as D3) is calculated is 0.8, so , , , , and Respectively represent Article and The absolute value of the difference between the timestamp of a record and the current system time when it is evaluated represents the freshness of the data. For example, if the current system time is 10:00:05.000, then Second, Second, Second, Second, It is the time-effect attenuation factor, which controls the rate at which the confidence level of data decreases over time. s. The value is set based on the fact that structural health monitoring data usually has a high reference value within a few seconds to a few minutes. The attenuation factor of 0.1 means that for every 10 seconds the time difference increases, the time weight factor It will be reduced by about 63%. The total number of status parameter records for the same steel structure component (beam B-101) currently being processed, here , For the sum of The reliability score of the data source corresponding to each record is calculated based on the reliability of the data source. and timeliness factor The weight of a single record is calculated by multiplying it by the product of , and then normalized by dividing it by the sum of the weights of all records to obtain the relative confidence of each record. First, calculate the denominator, which is the sum of the weights of all records :

Item 1 ( ):

;

Item 2 ( ):

;

Item 3 ( ): ;

Item 4 ( ):

;

Denominator = ;

Then calculate the confidence of each record :

;

;

;

;

The benefit of formulas is that they combine the inherent reliability of data sources and a time-sensitive weight that decays exponentially over time , and normalize it, which can dynamically evaluate the relative credibility of each data record at the current moment, and provide a quantitative basis for subsequent data fusion and conflict detection. Based on the calculated confidence of each record {0.2500, 0.1813, 0.2359, 0.3329}, these confidence values are compared with the preset confidence threshold, and the preset confidence threshold is set to 0.15. The threshold is set to filter out those records with very low comprehensive confidence due to low source reliability or too old data. The reference standard is to retain meaningful data with a confidence ratio of more than 15% of the total (this ratio can be adjusted according to the application), and the confidence of each record is Compared with 0.15, it is found that the confidence of all records (0.2500, 0.1813, 0.2359, 0.3329) is greater than 0.15. Therefore, all records are retained. The retained records {record 1, record 2, record 3, record 4} are ranked according to their confidence Arrange in descending order, the result is: Record 4 ( ), record 1( ), record 3( ), record 2( ), generate a record of steel structure status parameters with confidence, the result (e.g. ) shows that record 4 is considered to be the most reliable stress data at the current evaluation time point because it comes from a stress sensor with high reliability and has the latest timestamp, while record 2 has the lowest confidence because its source is a BIM model with relatively low reliability and has an earlier timestamp. These confidence values will be used as weights in the next step of data fusion.

The steps to obtain the steel structure status parameter set with conflict markers are as follows:

Extract records of the same parameter from the steel structure state parameter records with confidence, classify them by parameter type, and generate a set of confidence-parameter value pairs to be fused;

Based on the set of confidence-parameter value pairs to be fused, the fusion estimated value of the parameter is calculated using the following formula:

;

in, is the fused estimate of the current parameter type, For the The confidence level of the records, For the The parameter values of the records, is the standard deviation of similar parameter values, The total number of records of the current parameter type;

Based on the fusion estimated value, the absolute difference between the parameter value of each record and the fusion estimated value is calculated, and the records whose absolute difference exceeds the preset conflict judgment limit are marked as conflict data points, generating a set of steel structure state parameters with fusion conflict marked.

Specifically, from the steel structure state parameter records with confidence generated in the previous step, all records for the same specific parameter (for example, the maximum Y-direction stress of beam B-101) are extracted and classified according to the parameter type (here, "maximum Y-direction stress"), and these records are organized into a set of confidence-parameter value pairs to be fused. The sorted results are used: {(Record 4: , MPa), (Record 1: , MPa), (Record 3: , MPa), (Record 2: , MPa)}, which includes records, based on this set of confidence-parameter value pairs to be fused, calculate the fused estimated value of the parameter (maximum Y-direction stress) , the calculation formula is as follows:

;

here, is the fused estimate of the current parameter type (maximum Y stress), It is The confidence level of the records, It is Parameter value of the record (unit: MPa), is the standard deviation of similar parameter values (i.e. {152, 150, 155, 145} MPa), Is the total number of records of the current parameter type, here The logic of this formula is to calculate the sum of the confidence-weighted parameter values through the numerator and normalize it through the denominator, which takes into account the sum of the squares of the confidence and the dispersion of the parameter values themselves (variance ), first calculate the standard deviation of the parameter values : The parameter value set is {152, 150, 155, 145}, and the average value is MPa, calculate sample variance , standard deviation MPa, then calculate the numerator in the formula :

Item 1( ): ;

Item 2( ): ;

Item 3( ): ;

Item 4( ): ;

Numerator = ;

Then calculate the square root of the denominator in the formula :

Item 1( ): ;

Item 2( ): ;

Item 3( ): ;

Item 4( ): ;

;

Now calculate the denominator :

Denominator = ;

Finally, calculate the fusion estimate :

MPa;

The benefit of the formula is that its structural design attempts to combine the confidence distribution and the consistency of the data itself to obtain the fusion result. The item makes the fusion result be adjusted when the original data has a large discreteness, and The term reflects the distribution of the confidence itself, based on the calculated fusion estimate MPa, calculate the parameter value for each record and fusion estimates The absolute difference :

Record 4: MPa;

Record 1: MPa;

Record 3: MPa;

Record 2: MPa;

Compare these absolute differences with the preset conflict judgment limit, and set the conflict judgment limit, for example, The setting of this limit refers to the standard deviation multiple commonly used in statistics to judge outliers. Here, 2 times the standard deviation is taken to identify data points with large deviations from the fusion results. The limit = MPa, compare each absolute difference with the limit of 8.406MPa: 116.352>8.406, 114.352>8.406, 119.352>8.406, 109.352>8.406, the absolute difference between the parameter value of all records and the fusion estimate value exceeds the conflict judgment limit, so record 1, record 2, record 3 and record 4 are all marked as conflict data points. The result ( MPa, and all points are marked as conflicting) indicates that, according to the specific fusion formula and conflict judgment rules used, there is a significant inconsistency within the current data set, or the fusion formula produces an estimated value that deviates from the data center in this scenario. All original records are considered to be in conflict with this fusion result, and a fusion conflict-marked steel structure state parameter set is generated, which contains the original record information and their conflict marks.

The steps for obtaining the state increment of the steel structure model to be propagated are:

Extract all conflicting data points from the steel structure state parameter set that has been marked as conflicting, traverse the list of state variable names and identifiers in the steel structure digital twin model, match the parameter names of the conflicting data points with the twin model variable names one by one, and generate a corresponding list of state variable names and identifiers to be updated;

Based on the corresponding list of state variable names and identifiers to be updated, read the current values of the state variables in the twin model one by one, synchronously extract the corresponding parameter values in the conflicting data points, calculate the absolute difference between the current value and the parameter value, and generate the state variable incremental parameter set;

Based on the state variable incremental parameter set, incremental superposition is performed on the matching variables in the steel structure digital twin model, and the variable values are updated one by one to generate the steel structure model state increment to be propagated.

Specifically, from the set of steel structure state parameters with conflict marks obtained in the previous stage, all data points marked as conflicts are extracted. According to the maximum Y-direction stress parameter of beam B-101, all records (records 1, 2, 3, and 4) are marked as conflicts. These conflicting data points are: (record 4: 152MPa, marked: conflict), (record 1: 150MPa, marked: conflict), (record 3: 155MPa, marked: conflict), (record 2: 145MPa, marked: conflict). Next, the list of state variable names and identifiers pre-defined in the steel structure digital twin model is traversed. The list maintains the correspondence between the physical world parameters and the internal variables of the twin model. For example, the list contains the entry {state variable name: "maximum Y-direction stress", identifier: "DT_B101_StressY", belonging component: "B-101"}, and the parameter name of the conflicting data point ("maximum Y-direction stress" Y-direction stress") is matched one by one with the twin model variable name ("maximum Y-direction stress"). When the parameter name and the component to which it belongs (implicit in the record source, for example, both belong to B-101) match, a corresponding relationship is established, and a corresponding list of state variable names and identifiers to be updated is generated. In this example, the list is {("maximum Y-direction stress", "DT_B101_StressY")}. Based on this corresponding list of state variable names and identifiers to be updated {("maximum Y-direction stress", "DT_B101_StressY")}, the current values of the corresponding state variables in the twin model are read one by one, and the digital twin model database or memory is accessed to query the variable with the identifier "DT_B101_StressY" to obtain its currently stored value, for example, the current value is 148MPa. Synchronously, the parameter value corresponding to the variable is extracted from the conflicting data point set. Since there are multiple conflicting points ( 152, 150, 155, 145MPa), a rule is needed to select the value used to calculate the increment. The rule can be to select the value of the conflict point with the highest confidence (152MPa), or the value of the latest conflict point (depending on the timestamp, for example, record 4 is the latest and the value is 152MPa), or the average of all conflict point values ((152+150+155+145)/4=150.5MPa), or use the fusion value F calculated in the previous step (although all points conflict, F itself may still be used as the target value, that is, 35.648MPa). For example, the value of the conflict point with the highest confidence, that is, 152MPa of record 4, is used to calculate the absolute difference between the current value in the twin model (148MPa) and the parameter value of the selected conflict data point (152MPa). The absolute difference is |148-152|=4MPa, or the signed difference is calculated as the increment: increment = conflict value - current value =152-148=+4MPa, a state variable incremental parameter set is generated. This set contains the adjustments that need to be made to the twin model variables, for example, {("DT_B101_StressY", +4MPa)}. Based on this state variable incremental parameter set {("DT_B101_StressY", +4MPa)}, an incremental superposition operation is performed on the matching variables in the steel structure digital twin model. The variable with the identifier "DT_B101_StressY" is accessed, and its current value (148MPa) is added to the calculated increment (+4MPa). The updated variable value is 148+4=152MPa. All variable values that need to be adjusted are updated one by one. After the update is completed, a set of steel structure model state increments to be propagated is formed. In this example, the state increment of the variable "DT_B101_StressY" is +4MPa, and its new state is 152MPa.

The steps to obtain the steel structure twin model status and data dependency log after the update propagation are as follows:

Extract the incremental value of each primary variable from the set of steel structure model state increments to be propagated, traverse the predefined mechanical transmission and geometric association relationship indexes between the steel structure components, match the component identifiers that are directly associated with the current primary variable, and generate a primary variable-associated component mapping list;

Based on the main variable-associated component mapping list, the propagation impact value of the main variable increment on each associated component is calculated. The calculation formula is:

;

in, For the The propagation impact value of the associated components, is the incremental value of the main variable, For the The geometric distance between the associated component and the component where the main variable is located, From the main variable to the The number of nodes in the mechanical transmission path of the associated components, is the distance attenuation coefficient, is the path complexity coefficient;

Based on the propagation impact values of all associated components, complete dependency relationship entries are constructed and written into the database log table one by one to generate the steel structure twin model status and data dependency log after the update propagation.

Specifically, from the incremental set of steel structure model states to be propagated generated in the previous step, the incremental value of each main variable (i.e., the variable that is directly updated) is extracted. In this example, the main variable is , its incremental value MPa, then traverse the predefined mechanical transfer and geometric association relationship index between the steel structure components. This index library (for example, stored in the form of a graphic database or relational table) describes the connection mode, distance and mechanical influence path between the components. For example, the index library indicates that component B-101 (the component where the main variable is located) has a direct mechanical association and geometric proximity relationship with component C-05 (column) and component B-102 (adjacent beam). According to the index, match the current main variable (located at B-101) There are directly associated component identifiers, and the associated components are identified as C-05 and B-102. A primary variable-associated component mapping list is generated, for example: , based on this main variable-associated component mapping list, calculate the main variable increment For each associated component ( ) , and its calculation formula is:

;

here, Is the increment of the main variable to the The intensity of the propagation impact generated by the associated components, is the incremental value of the primary variable, It is The geometric distance between the associated component and the component where the main variable is located needs to be obtained from the BIM model or geometric database, for example, the center distance from B-101 to C-05 meters (e.g. directly connected), distance from B-101 to B-102 rice, From the component where the main variable is located to the The number of nodes (including connection points or intermediate components) on the main mechanical transmission path of the associated components needs to be determined through structural model analysis. For example, the number of nodes on the path for direct force transmission from B-101 to C-05 is , from B-101 through a connection node to B-102, the number of path nodes , is the distance attenuation coefficient, which controls the rate at which the effect decreases with distance. m, the setting basis of this coefficient is engineering experience or simulation results, which shows that the impact intensity decreases with the distance exponentially. The value of 0.5 means that the impact factor decreases with each increase of 2 meters. Reduced by about 63%, It is the path complexity coefficient, which reflects that the more complex the mechanical transmission path is (the more nodes it passes through), the greater the attenuation effect will be. The setting of this coefficient is also based on experience or calibration. The value of 0.2 means that for each additional transmission node, the denominator increases by 0.2, thereby reducing the impact value. The logic of the formula is: the impact value is proportional to the original increment, exponentially decays with distance, and algebraically decays with the increase of path complexity. Calculate the propagation impact value of the associated component C-05 :

;

Calculate the propagation impact value on the associated component B-102 :

;

The benefit of the formula is that it quantifies the propagation effect of state changes within the structure based on geometric distance and mechanical path, simulates the locality and attenuation of the impact, and provides a basis for understanding chain reactions and making more accurate global state assessments. Based on the calculated propagation impact values of all associated components {C-05: +3.171, B-102: +0.637}, a complete dependency entry is constructed for each impact propagation. The entry content should include: source variable identifier ("DT_B101_StressY"), source increment value (+4), target component identifier ("C-05" or "B-102"), calculated impact value (+3.171 or +0.637), and the parameters used in the calculation ( , , , ) and the timestamp and storage path of the log itself, write these dependency entries one by one into the specified database log table, for example, write to the table named "DependencyLog", and generate the steel structure twin model status and data dependency log after the update propagation. The result (for example ) represents the expected impact intensity of the stress (or other related states) on C-05 after the stress of B-101 increases by 4MPa, which is approximately +3.171 units. These logs record the propagation path and intensity of the state change and are key information for subsequent tracing of problems.

The steps to obtain the log entry point associated with the target result are:

Extract the mechanical performance evaluation results of all steel structure components from the updated and propagated steel structure twin model state, traverse the stress, strain and displacement parameter values of each component, and generate a set of mechanical performance evaluation results;

Based on the set of mechanical performance evaluation results, the component identifier, parameter name, and timestamp fields are parsed one by one. In the steel structure twin model status and data dependency log after the update propagation, a full-text matching search is performed using the component identifier and parameter name as the joint key to generate a list of matching log entries.

Based on the matching log entry list, the log storage path, data update timestamp and associated variable identifier fields in each log are extracted to generate the log entry point associated with the target result.

Specifically, the mechanical performance evaluation results of all steel structure components are extracted from the updated steel structure twin model state. This evaluation may be based on the state value after the model is updated (for example, the stress of B-101 is updated to 152MPa, and the states of C-05 and B-102 may also be based on the impact value). The evaluation system traverses the key performance parameter values of each key component (such as B-101, C-05, B-102, etc.), such as stress, strain, and displacement. For example, the evaluation system detects that the maximum Y-axis stress value of component B-101 is 152 MPa, the timestamp is 2025-04-15 10:00:15.000, and this value exceeds the preset safety threshold or is in the warning range. It is marked as a mechanical performance abnormality (or potential abnormality) result. The evaluation results of all components are collected to form a mechanical performance evaluation result set, which may include {component: "B-101", parameters: "Maximum Y stress", value: 152MPa, timestamp: "2025-04-1510:00:15.000", status: "Warning"}, {component: "C-05", parameter: "axial stress", value: 98MPa, timestamp: "2025-04-1510:00:15.000", status: "Normal"},..., Based on this set of mechanical property evaluation results, focus on the results marked as abnormal or requiring attention, such as the "maximum Y stress" warning result of "B-101", and parse the component identifiers ("B-101" in these target result records one by one) ), parameter name ("maximum Y stress") and timestamp ("2025-04-1510:00:15.000") fields, and use these parsed fields to generate the "update propagation steel structure twin model status and data dependency log" (stored in a database table such as "DependencyLog") for retrieval, using the component identifier ("B-101") and parameter name ("maximum Y stress") as the joint query key, and possibly combined with the timestamp for range limitation, to perform a matching search operation. This search aims to find records that cause or affect the "maximum Y stress" on "B-101". For example, by querying WHERETargetComponent='B-101'ANDTargetParameter='Maximum Y-stress' or WHERESourceVariableLIKE'%B101_StressY%' (depending on the log table structure) and the timestamp is close to "2025-04-1510:00:15.000", you can find log entries recording the update of the variable and entries recording the effects propagated from the variable. For example, the following related log entries are found:

Table 1: Table of matching log entries:

As shown in Table 1, the table lists the log entry information related to the target result (B-101 maximum Y-direction stress warning) found through the search. Based on the list of these matching log entries (as shown in Table 1), the key fields in each log record are extracted: log storage path (for example, "/logs/update/20250415_100010_B101.log"), data update timestamp (for example, "2025-04-1510:00:10.000"), and associated variable identifier (for example, "DT_B101_StressY"). Combined together, they form entry points pointing to specific log records. These entry points are the starting point for subsequent causal tracing, generating a set of log entry points associated with the target result, for example, {(LogPath: "/logs/update/20250415_100010_B101.log", Timestamp: "10:00:10.000", VariableID: "DT_B101_StressY")}. This entry point directly points to the log event that records the update of the target abnormal state variable (DT_B101_StressY).

The steps for obtaining the causal tracing path of mechanical property abnormalities are as follows:

Extract the log storage path of each entry point from the log entry point associated with the target result, traverse the steel structure twin model state after update propagation and each variable dependency chain record in the data dependency log, and use the target result identifier as the starting point to reversely parse the upstream variable identifiers in the dependency chain layer by layer to generate a reverse traceability variable chain set;

Based on the reverse traceability variable chain set, the original data records are matched by variable identifiers and timestamps from the time-aligned steel structure status data stream, BIM model version library and displacement monitoring point original database, and the sensor device number, BIM model version number, displacement monitoring point coordinates and original values are extracted to generate the original input data identifier set;

The original input data identifier set and the reverse traceability variable chain set are merged in chronological order and dependency hierarchy to construct a complete link of original data identifiers, intermediate calculation variable identifiers and target result identifiers, and generate a causal traceability path for mechanical property anomalies.

Specifically, from the set of log entry points associated with the target result obtained in the previous step, extract the log storage path of each entry point, for example, obtain the path "/logs/update/20250415_100010_B101.log" in the entry point {LogPath: "/logs/update/20250415_100010_B101.log", Timestamp: "10:00:10.000", VariableID: "DT_B101_StressY"}, access and parse the content of the log file, or Query the corresponding log record in the database, which details the update event of the variable "DT_B101_StressY" at time "10:00:10.000", including its dependent upstream information. For example, the log shows that this update is based on the increment "+4MPa" calculated based on the conflicting data points "Rec3" and "Rec2". Next, traverse all variable dependency chain records stored in the "Steel Structure Twin Model State and Data Dependency Log after Update Propagation", and start reverse parsing the dependency chain with the target result identifier "DT_B101_StressY" and its associated update event as the starting point. Dependency relationship, from the log "/logs/update/20250415_100010_B101.log", the upstream dependency is identified as the data records "Rec3" and "Rec2". The source of "Rec3" and "Rec2" needs to be traced back. This requires querying the logs or metadata of the record processing process, or retrieving the previous steps based on the record ID. For example, the query shows that "Rec3" comes from the confidence calculation step, and its input is the displacement sensor D3 data after time series alignment, while "Rec2" comes from the confidence calculation, and its input is the BIM model data after time series alignment. Continuing to trace back, the time series The input of the alignment step is the original data. Through this process, the upstream variable identifiers or data record IDs in the dependency chain are reversely parsed layer by layer to form a variable chain set that is traced back from the target result to the intermediate calculation steps and the original input. For example, the reverse tracing chain is generated: {"DT_B101_StressY"@10:00:10<-["Rec3","Rec2"]@~10:00:08<-[AlignedD3data,AlignedBIMdata]@~10:00:04<-[RawD3data,RawBIMdata]}, Based on the raw data links (RawD3data, RawBIMdata) identified in this reverse traceability variable chain set, and their corresponding approximate timestamps or processing record IDs, the corresponding raw databases are queried, including the steel structure status data stream before time alignment (including sensor raw readings), the BIM model version library, and the raw database of displacement monitoring points. Matching is performed according to the variable identifier (e.g., sensor ID "D3", component ID "B-101") and timestamp range to find the corresponding raw data records. For example, the displacement monitoring point D3 database is queried at time 10:00:02.10 0, find the record {sensor device number: "D3", coordinates: (10.5, 20.1, 15.0), original timestamp: "10:00:02.050", original value: 5.1mm}, query the BIM model version library for information about component B-101 around time 10:00:00.800, find the record {BIM model version number: "v2.1", component ID: "B-101", attribute: "calculated stress", original value: 145MPa, original timestamp: "10:00:00.750"}, and extract the key identification information of these raw data records. , such as sensor device number, BIM model version number, displacement monitoring point coordinates and original values, to form the original input data identifier set {"SensorID: D3, Time: 10:00:02.050, Value: 5.1mm", "BIMVersion: v2.1, Component: B-101, Property: Stress, Value: 145MPa, Time: 10:00:00.750"}, and convert this original input data identifier set {"SensorID: D3...", "BIMVersi On: v2.1..."} and the previously constructed reverse traceability variable chain set {"DT_B101_StressY"<-["Rec3","Rec2"]<-...} are merged and organized according to the time and dependency hierarchy to build a complete link from the original data input, through intermediate data processing (timing alignment, confidence calculation, fusion, conflict detection, incremental calculation, update propagation) to the final target result ("DT_B101_StressY" status abnormality). This link clearly demonstrates the data flow and calculation dependencies, and generates a causal traceability path for mechanical property anomalies.

The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims (7)

1. A method for evaluating the mechanical properties of steel structure buildings during their entire construction process based on digital twins, comprising the following steps: Acquire multi-source heterogeneous steel structure status data from stress sensors, BIM model component information, and displacement monitoring points, compare the timestamps of each data source, calculate the transmission delay value of each data source relative to a unified reference time, correct the original data timestamps one by one, and establish a time-aligned steel structure status data stream; Extracting data records describing state parameters of the same steel structure component based on the time-series aligned steel structure state data stream, assigning a confidence value to each data record, generating steel structure state parameter records with confidence, calculating fused estimated values for records of the same parameter based on the steel structure state parameter records with confidence, and comparing differences in fused estimated values between different records with a preset conflict determination limit to construct a steel structure state parameter set with fused conflicts marked; Based on the steel structure state parameter set marked with the fusion conflict, the state variables directly corresponding to the steel structure state parameter set marked with the fusion conflict in the steel structure digital twin model are retrieved, the variables are incrementally updated to obtain the steel structure model state increment to be propagated, based on the steel structure model state increment to be propagated, the influence of the state increment on the associated variables is calculated, and the steel structure twin model state and data dependency log after the update and propagation is established; Retrieving the target mechanical property evaluation result in the steel structure twin model state after the update propagation, locating the corresponding record entry of the mechanical property evaluation result in the data dependency log, obtaining the log entry point associated with the target result, and based on the log entry point associated with the target result, performing a reverse tracing search in the data dependency log according to the recorded variable dependency chain information to generate a causal tracing path for the mechanical property anomaly; The steps for obtaining the time-aligned steel structure status data stream are: Extract the original timestamps of each data source from stress sensors, BIM model component information, and displacement monitoring points. Using a unified reference time as the standard, calculate the absolute value of the difference between the timestamp of each data source and the reference time. Define the absolute value of the difference as the initial transmission delay value, and generate an initial transmission delay value set. Based on the initial transmission delay value set, the initial transmission delay value distribution of all data sources is counted, the 90th percentile of the initial transmission delay value distribution is taken as the preset time deviation threshold, and a compensation factor is generated according to the ratio of the time deviation threshold to the data source delay value. The compensation factor calculation formula is: compensation factor = data source delay value / time deviation threshold, and a dynamic time compensation factor set is generated; Based on the dynamic time compensation factor set, for data sources whose initial transmission delay value exceeds the time deviation threshold, the formula: corrected timestamp = original timestamp + (data source delay value/(compensation factor + 1)) is used to correct the timestamps one by one to generate a time-aligned steel structure status data stream. 2. The method for evaluating the mechanical properties of a steel structure building during its entire construction process based on digital twinning according to claim 1, wherein the step of obtaining the steel structure state parameter record with confidence level is as follows: Extracting multiple state parameter data records of the same steel structure component from the time-series aligned steel structure state data stream, classifying them according to data source type, and generating an unconfidence-assigned state parameter record set; Based on the state parameter record set without confidence, the confidence of each record is calculated using the following formula: ; in, For the The confidence level of the records, For the The reliability score of the data source to which the record belongs, the default value is the stress sensor , BIM model , displacement monitoring point , and For the Article and The difference between the timestamp of the record and the current system time, is the time-dependent attenuation factor, The total number of status parameter records for the same steel structure component, For the The reliability score of the data source to which the record belongs; Based on the confidence of each record, the confidence is compared with the preset confidence threshold, and the records with a confidence level less than the preset confidence threshold are eliminated. The retained records are sorted in descending order according to the confidence level to generate steel structure status parameter records with confidence levels. 3. The method for evaluating the mechanical properties of steel structure buildings during the entire construction process based on digital twins according to claim 1 is characterized in that the step of obtaining the steel structure state parameter set with conflict-marked fusion is as follows: Extracting records of the same parameter from the steel structure state parameter records with confidence, classifying them by parameter type, and generating a set of confidence-parameter value pairs to be fused; Based on the set of confidence-parameter value pairs to be fused, the fused estimated value of the parameter is calculated using the following formula: ; in, is the fused estimate of the current parameter type, For the The confidence level of the records, For the The parameter values of the records, is the standard deviation of similar parameter values, The total number of records of the current parameter type; Based on the fusion estimated value, the absolute difference between each record parameter value and the fusion estimated value is calculated, and the record whose absolute difference exceeds the preset conflict judgment limit is marked as a conflict data point to generate a steel structure state parameter set with fusion conflict marked. 4. The method for evaluating the mechanical properties of steel structure buildings during the entire construction process based on digital twins according to claim 1, wherein the step of obtaining the state increment of the steel structure model to be propagated is: Extract all data points marked as conflict from the steel structure state parameter set marked with the fusion conflict, traverse the list of state variable names and identifiers in the steel structure digital twin model, match the parameter names of the conflicting data points with the twin model variable names one by one, and generate a corresponding list of state variable names and identifiers to be updated; Based on the corresponding list of the state variable names and identifiers to be updated, read the current values of the state variables in the twin model one by one, synchronously extract the corresponding parameter values in the conflicting data points, calculate the absolute difference between the current value and the parameter value, and generate a state variable incremental parameter set; Based on the state variable incremental parameter set, incremental superposition is performed on the matching variables in the steel structure digital twin model, and the variable values are updated one by one to generate the steel structure model state increment to be propagated. 5. The method for evaluating the mechanical properties of steel structure buildings during the entire construction process based on digital twins according to claim 1, wherein the step of obtaining the updated and propagated steel structure twin model state and data dependency log is as follows: Extracting the incremental value of each primary variable from the steel structure model state increment set to be propagated, traversing the predefined mechanical transfer and geometric association relationship indexes between the steel structure components, matching the component identifiers directly associated with the current primary variable, and generating a primary variable-associated component mapping list; Based on the main variable-associated component mapping list, the propagation impact value of the main variable increment on each associated component is calculated using the following formula: ; in, For the The propagation impact value of the associated components, is the incremental value of the main variable, For the The geometric distance between the associated component and the component where the main variable is located, From the main variable to the The number of nodes in the mechanical transmission path of the associated components, is the distance attenuation coefficient, is the path complexity coefficient; Based on the propagation impact values of all associated components, complete dependency relationship entries are constructed and written into the database log table one by one to generate the steel structure twin model status and data dependency log after the update propagation. 6. The method for evaluating the mechanical properties of steel structure buildings during the entire construction process based on digital twins according to claim 1, wherein the step of obtaining the log entry point associated with the target result is: Extracting mechanical performance evaluation results of all steel structure components from the updated and propagated steel structure twin model state, traversing stress, strain, and displacement parameter values of each component, and generating a set of mechanical performance evaluation results; Based on the mechanical property evaluation result set, the component identifier, parameter name, and timestamp fields are parsed one by one, and a full-text matching search is performed in the steel structure twin model state and data dependency log after the update propagation using the component identifier and parameter name as a joint key to generate a matching log entry list; Based on the matching log entry list, the log storage path, data update timestamp and associated variable identifier fields in each log are extracted to generate a log entry point associated with the target result. 7. The method for evaluating the mechanical properties of steel structure buildings during their entire construction process based on digital twins according to claim 1, wherein the step of obtaining the causal tracing path of the mechanical property anomaly is as follows: Extract the log storage path of each entry point from the log entry point associated with the target result, traverse the steel structure twin model state after update propagation and each variable dependency chain record in the data dependency log, and use the target result identifier as the starting point to reversely parse the upstream variable identifiers in the dependency chain layer by layer to generate a reverse traceability variable chain set; Based on the reverse traceability variable chain set, the original data records are matched according to variable identifiers and timestamps from the time-aligned steel structure status data stream, BIM model version library and displacement monitoring point original database, and the sensor device number, BIM model version number, displacement monitoring point coordinates and original values are extracted to generate an original input data identifier set; The original input data identifier set and the reverse tracing variable chain set are merged in chronological order and dependency hierarchy to construct a complete link of original data identifiers, intermediate calculation variable identifiers and target result identifiers, and generate a causal tracing path for mechanical property anomalies.