CN113159431A - Analysis early warning method and device based on ground disaster data - Google Patents
Analysis early warning method and device based on ground disaster data Download PDFInfo
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Abstract
The invention discloses a method and a device for analyzing and early warning based on ground disaster data, wherein the method comprises the following steps: acquiring and preprocessing slope online safety real-time monitoring data; acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data; and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result. According to the method, various monitoring data in historical monitoring data are integrated to establish a disaster analysis early warning model for comprehensive analysis early warning, so that real-time geological risks are reflected more accurately; a disaster analysis early warning model is established through an Adaboost algorithm, the weight of each weak classifier is optimized through an improved pathfinder optimization algorithm, and the prediction performance of the disaster analysis early warning model is further improved.
Description
Technical Field
The invention belongs to the technical field of geological disaster analysis, and particularly relates to a method, a device, equipment and a storage medium for analyzing and early warning based on geological disaster data.
Background
The side slope is a frequent traffic quality and safety accident work point, and how to prevent and monitor the state of the side slope in advance when landslide sliding or debris flow outbreak occurs can give an alarm in advance and transmit information to related personnel, thereby providing precious time for evacuation and relocation in advance and reducing the harm of disasters to lives and properties of people to become a problem which is considered by related management departments first. Therefore, the construction of the slope safety monitoring and early warning system is the trend of information construction of modern industries and is not slow.
Most of traditional disaster monitoring technologies need manual work to acquire data on site regularly, the workload is large, the monitoring timeliness is poor, and the monitoring cannot be implemented under severe weather conditions. Most of slope deformation instability occurs under extreme conditions such as rainstorm and earthquake, and the like, so that the life of monitoring personnel can be seriously threatened by actual measurement of the tissue site. The development of the online monitoring technology is good, the defects in the existing manual monitoring are overcome, the safety and health conditions of the side slope can be known in time through online monitoring, early warning is carried out on potential disasters, most of the existing online monitoring technologies carry out simple statistical analysis and threshold value overrun early warning on single monitoring data, and in fact, certain relevance exists in the influence of each item of monitoring data on geological disasters, and comprehensive analysis and disaster early warning are not carried out on the whole in the prior art.
Disclosure of Invention
In view of the above, the invention provides an analysis and early warning method, an analysis and early warning device, equipment and a storage medium based on ground disaster data, which are used for solving the problem that the existing disaster analysis technology does not perform comprehensive analysis and prediction on the whole.
In a first aspect of the present invention, a method for analyzing and warning based on ground disaster data is disclosed, the method comprising:
acquiring and preprocessing slope online safety real-time monitoring data;
acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data;
and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
Preferably, the slope online safety real-time monitoring data and the slope online safety historical monitoring data comprise earth surface deformation, earth surface displacement, depth displacement, rainfall, soil water content and soil pressure.
Preferably, the preprocessing comprises setting a maximum value, a mean value, a variance and a standard deviation of the monitoring data within a certain period, and taking at least two of the maximum value, the mean value, the variance and the standard deviation of the corresponding monitoring data as the input of the disaster analysis early warning model.
Preferably, the establishing of the disaster analysis early warning model based on the slope online safety historical monitoring data specifically comprises:
randomly extracting at least two monitoring items in the slope online safety historical monitoring data in a non-repeated sampling mode;
respectively taking historical monitoring data corresponding to the extracted monitoring items as input and corresponding risk grade assessment as output, and training a plurality of weak classifiers;
the method comprises the steps of constructing an Adaboost cascade strong classifier by taking a plurality of weak classifiers as base classifiers, and taking the Adaboost cascade strong classifier as a disaster analysis early warning model.
Preferably, each weak classifier is composed of a plurality of LR classifiers, one-VS-rest form is adopted for classification, and the disaster grade with the maximum probability value is used as the output of each weak classifier; the number of LR classifiers in each weak classifier is equal to the number of classes of disaster level.
Preferably, in the process of constructing the Adaboost cascade strong classifier by taking a plurality of weak classifiers as base classifiers, the weight of each weak classifier is optimized by adopting an improved pathfinder optimization algorithm, and the Adaboost cascade strong classifier is retrained again based on slope online safety history monitoring data.
Preferably, the optimizing the weight of each weak classifier by using the improved pathfinder optimization algorithm specifically includes:
encoding the weight of each weak classifier into an individual position vector of a pathfinder optimization algorithm and initializing a population;
taking the error rate of the Adaboost cascade strong classifier as a fitness function of each particle of the particle swarm;
calculating the fitness of each individual, and taking the minimum fitness as an explorer and the rest as followers;
updating the position of the seeker and updating the position of the follower according to the position of the seeker;
calculating a fitness value and updating a global optimal value;
and judging whether the end condition is reached, if so, outputting the optimal individual position as the optimized weight combination, otherwise, re-determining the pathfinder and carrying out iterative operation until the end condition is reached.
In a second aspect of the present invention, an analysis and early warning apparatus based on ground disaster data is disclosed, the apparatus comprising:
a data acquisition module: acquiring and preprocessing slope online safety real-time monitoring data;
a model construction module: acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data;
the ground disaster prediction module: and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
In a third aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor which are invoked by the processor to implement the method of the first aspect of the invention.
In a fourth aspect of the invention, a computer-readable storage medium stores computer instructions that cause a computer to implement the method of the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) the invention integrates various monitoring data of surface deformation, surface displacement, depth displacement, rainfall, soil water content and soil pressure in historical monitoring data to establish a disaster analysis early warning model for comprehensive analysis early warning, and more accurately reflects real-time geological risks;
2) according to the disaster analysis early warning model, the Adaboost algorithm is used for establishing the disaster analysis early warning model, the weak classifiers are formed by the LR classifiers, the weight of each weak classifier is optimized by adopting the improved pathfinder optimization algorithm, and the strong classifiers are weighted and established to serve as the disaster analysis early warning model, so that the weak classifiers with high accuracy obtain larger weight, and the useless or redundant weak classifiers obtain smaller weight, and the prediction performance of the disaster analysis early warning model is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an analysis and early warning method based on ground disaster data according to the present invention;
fig. 2 is a schematic flow chart of a method for establishing a disaster analysis early warning model by using an Adaboost algorithm according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, the present invention provides a method for analyzing and warning based on disaster data, including:
s1, acquiring and preprocessing slope online safety real-time monitoring data and slope online safety historical monitoring data;
the slope online safety real-time monitoring data and the slope online safety historical monitoring data comprise earth surface deformation, earth surface displacement, depth displacement, rainfall, soil water content and soil pressure.
The preprocessing comprises setting the maximum value, the mean value, the variance and the standard deviation of the monitoring data in a certain period, and taking at least two of the maximum value, the mean value, the variance and the standard deviation of the corresponding monitoring data as the input of the disaster analysis early warning model.
S2, establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data; referring to fig. 2, step S2 specifically includes the following sub-steps:
and S21, randomly extracting at least two monitoring items in the slope online safety historical monitoring data in a non-repeated sampling mode.
And S22, training a plurality of weak classifiers by respectively taking the historical monitoring data corresponding to the extracted monitoring items as input and the corresponding risk level assessment as output.
Each weak classifier is composed of a plurality of LR classifiers, one-VS-rest form is adopted for classification, and the disaster grade with the maximum probability value is used as the output of each weak classifier; the number of LR classifiers in each weak classifier is equal to the number of classes of disaster level.
S23, constructing an Adaboost cascade strong classifier by taking the weak classifiers as base classifiers, and taking the Adaboost cascade strong classifier as a disaster analysis early warning model.
S24, in the process of constructing the Adaboost cascade strong classifier by taking the weak classifiers as the base classifier, the weight of each weak classifier is optimized by adopting an improved pathfinder optimization algorithm, and the Adaboost cascade strong classifier is retrained again based on the slope online safety history monitoring data. Specifically, the method comprises the following steps:
s241, encoding the weight of each weak classifier into an individual position vector of a pathfinder optimization algorithm and initializing a population N; the dimensionality of the individual position vector is the same as the number of weak classifiers;
s242, taking the error rate of the Adaboost cascade strong classifier as a fitness function of each particle of the particle swarm;
the fitness function formula is as follows:
wherein h ist(xi) Denotes a weak classifier, I (h)t(xi)≠yi) Representing the classification error rate of the weak classifier, m being the number of samples, wtiDenotes the t weight value, y, of the ith particleiRepresenting the true class of the ith sample
S243, calculating the fitness of each individual, taking the minimum fitness as an explorer, and taking the rest as followers;
s244, updating the position of the seeker, and updating the position of the follower according to the position of the seeker;
the seeker position update formula is as follows:
where p denotes the seeker subscript, K denotes the current number of iterations of the algorithm, KmaxIndicates the current number of iterations of the algorithm,
indicating the location of the current generation of the pathfinder,
indicating the location of the previous generation of pathfinder,
representing the updated position of the pathfinder; r is1Step size factor for pathfinder movement, and in the range [0,1]The oral administration is uniformly distributed; a represents the multi-directionality and randomness of the pathfinder movement: u. of1∈[-1,1]。
The follower location update formula is:
wherein i represents the follower subscript,
indicating the current position of the follower and,
indicating its updated position, R1＝αr2,R2＝βr3,ε＝(1-K/Kmax)u2Dij,Dij＝||Xi-XjThe | |, alpha represents the interaction coefficient between followers, beta represents the attraction coefficient of the pathfinder to the followers, and the two coefficients are all [1,2 ]]Uniform distribution is obeyed; r is2、r3Step-size factors of movement with other followers and pathfinder respectively are [0, 1%]A random number within a range; ε denotes the randomness of the follower's movement, DijIs the distance between the current follower and other followers
S245, calculating a fitness value and updating a global optimal value;
and S246, judging whether the end condition is reached, if so, outputting the optimal individual position as the optimized weight combination, otherwise, returning to the step S243 to determine the pathfinder and carrying out iterative operation until the end condition is reached.
And S3, inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
According to the disaster analysis early warning model, the Adaboost algorithm is used for establishing the disaster analysis early warning model, the improved pathfinder optimization algorithm is used for optimizing the weight of each weak classifier, and the strong classifiers are weighted and established to serve as the disaster analysis early warning model, so that the weak classifiers with high accuracy obtain larger weight, the useless or redundant weak classifiers obtain smaller weight, and the prediction performance of the disaster analysis early warning model is further improved.
When a disaster analysis early warning model is established, at least two monitoring items in slope online safety historical monitoring data are randomly extracted in a non-repeated sampling mode, historical monitoring data corresponding to the extracted monitoring items are used as input, corresponding risk grade evaluation is used as output, and a plurality of weak classifiers are trained; the Adaboost cascade strong classifier is constructed by taking a plurality of weak classifiers as base classifiers, the implicit relevance of the influence of each monitoring data on the geological disaster is considered in the model establishment and training mode, and comprehensive analysis and disaster early warning can be carried out on the whole. And finally, inputting the slope on-line safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result, so that the defects of simple statistical analysis and threshold overrun early warning on single monitoring data can be overcome.
Corresponding to the above method embodiment, the present invention further provides an analysis and early warning device based on ground disaster data, where the device includes:
a data acquisition module: acquiring and preprocessing slope online safety real-time monitoring data;
a model construction module: acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data;
the ground disaster prediction module: and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
The above method embodiments and apparatus embodiments correspond, and reference may be made to the method embodiments for a brief description of the apparatus embodiments.
The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor, which invokes the program instructions to implement the methods of the invention described above.
The invention also discloses a computer readable storage medium which stores computer instructions for causing the computer to implement all or part of the steps of the method of the embodiment of the invention. The storage medium includes: u disk, removable hard disk, ROM, RAM, magnetic disk or optical disk, etc.
The above-described system embodiments are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Some or all of the modules may be selected according to the actual Xian to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An analysis early warning method based on ground disaster data is characterized by comprising the following steps:
acquiring and preprocessing slope online safety real-time monitoring data;
acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data;
and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
2. The analytic warning method based on ground disaster data as claimed in claim 1, wherein the slope online safety real-time monitoring data and the slope online safety historical monitoring data respectively comprise earth surface deformation, earth surface displacement, depth displacement, rainfall, soil water content and soil pressure.
3. The analytic warning method based on disaster data as set forth in claim 3, wherein the preprocessing comprises setting a maximum value, a mean value, a variance, and a standard deviation of the monitored data within a time period, and using at least two of the maximum value, the mean value, the variance, and the standard deviation of the corresponding monitored data as inputs of the disaster analytic warning model.
4. The analytic warning method based on the geological disaster data as claimed in claim 3, wherein the establishing of the disaster analytic warning model based on the slope online safety history monitoring data specifically comprises:
randomly extracting at least two monitoring items in the slope online safety historical monitoring data in a non-repeated sampling mode;
respectively taking historical monitoring data corresponding to the extracted monitoring items as input and corresponding risk grade assessment as output, and training a plurality of weak classifiers;
the method comprises the steps of constructing an Adaboost cascade strong classifier by taking a plurality of weak classifiers as base classifiers, and taking the Adaboost cascade strong classifier as a disaster analysis early warning model.
5. The analytic warning method based on ground disaster data as in claim 4, wherein each weak classifier is composed of a plurality of LR classifiers, one-VS-rest form is adopted for classification, and the disaster grade with the maximum probability value is used as the output of each weak classifier; the number of LR classifiers in each weak classifier is equal to the number of classes of disaster level.
6. The analytic warning method based on the ground disaster data as recited in claim 4, wherein in the process of constructing Adaboost cascade strong classifiers by using a plurality of weak classifiers as base classifiers, the weight of each weak classifier is optimized by adopting an improved pathfinder optimization algorithm, and the Adaboost cascade strong classifiers are retrained again based on the slope online safety history monitoring data.
7. The analytic warning method based on ground disaster data according to claim 6, wherein optimizing the weight of each weak classifier by using an improved pathfinder optimization algorithm specifically comprises:
encoding the weight of each weak classifier into an individual position vector of a pathfinder optimization algorithm and initializing a population;
taking the error rate of the Adaboost cascade strong classifier as a fitness function of each particle of the particle swarm;
calculating the fitness of each individual, and taking the minimum fitness as an explorer and the rest as followers;
updating the position of the seeker and updating the position of the follower according to the position of the seeker;
calculating a fitness value and updating a global optimal value;
and judging whether the end condition is reached, if so, outputting the optimal individual position as the optimized weight combination, otherwise, re-determining the pathfinder and carrying out iterative operation until the end condition is reached.
8. An analysis early warning device based on ground disaster data, characterized in that the device comprises:
a data acquisition module: acquiring and preprocessing slope online safety real-time monitoring data;
a model construction module: acquiring slope online safety historical monitoring data, and establishing a disaster analysis early warning model through an Adaboost algorithm based on the slope online safety historical monitoring data;
the ground disaster prediction module: and inputting the slope online safety real-time monitoring data into the disaster analysis early warning model to obtain a current disaster grade prediction result.
9. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which cause a computer to implement the method of any one of claims 1 to 7.
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| CN115426358A (en) * | 2022-09-07 | 2022-12-02 | 河海大学 | Slope safety early warning method and system based on big data and storable medium |
| CN115719178A (en) * | 2022-11-17 | 2023-02-28 | 西北大学 | A geological hazard identification and evolution intelligent perception early warning system and method |
| CN115730193A (en) * | 2022-11-24 | 2023-03-03 | 国能乌海能源信息技术有限公司 | Coal mine disaster data processing method, device and system |
| CN116244593A (en) * | 2022-12-31 | 2023-06-09 | 成都信息工程大学 | Global expansion calculation method and application of geological hazard classification samples, landslide risk classification and early warning method |
| CN115930768A (en) * | 2023-01-16 | 2023-04-07 | 冀东水泥铜川有限公司 | Slope intelligent monitoring device based on Beidou GPS fusion positioning |
| CN116976837A (en) * | 2023-09-25 | 2023-10-31 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
| CN116976837B (en) * | 2023-09-25 | 2024-01-30 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
| CN121163604A (en) * | 2025-11-20 | 2025-12-19 | 国网甘肃省电力公司 | Multi-parameter long-term monitoring device for landslide risk around power grid |
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