CN118759553B - A method and system for real-time information positioning and tracking of goods based on international transportation - Google Patents
A method and system for real-time information positioning and tracking of goods based on international transportation Download PDFInfo
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Abstract
本发明公开了一种基于国际运输的货物实时信息定位追踪方法及系统，具体涉及实时定位技术领域，通过在目标监测上安装定位数据采集设备和环境信息采集设备，实时收集货物的位置信息和周围环境信息；基于卷积神经网络构建预测模型，将数据中心的环境信息输入该模型，预测当前环境下GPS信号的准确率；根据GPS信号准确率的预测值，合理分配GPS信号修正资源，即调整和优化用于提高GPS信号准确率的技术和计算资源；利用惯性导航系统提供的短期高精度位置、速度和姿态信息判断GPS信号的准确性；若GPS信号异常，则生成并执行运动轨迹替换指令，用惯性导航系统的数据修正GPS定位信息，确保定位的准确性。
The invention discloses a method and system for real-time information positioning and tracking of goods based on international transportation, and specifically relates to the field of real-time positioning technology. By installing positioning data acquisition equipment and environmental information acquisition equipment on target monitoring, the location information and surrounding environment information of the goods are collected in real time; a prediction model is constructed based on a convolutional neural network, and the environmental information of a data center is input into the model to predict the accuracy of the GPS signal in the current environment; according to the predicted value of the GPS signal accuracy, GPS signal correction resources are reasonably allocated, that is, the technology and computing resources used to improve the GPS signal accuracy are adjusted and optimized; the accuracy of the GPS signal is judged by using the short-term high-precision position, speed and attitude information provided by an inertial navigation system; if the GPS signal is abnormal, a motion trajectory replacement instruction is generated and executed, and the GPS positioning information is corrected by using the data of the inertial navigation system to ensure the accuracy of positioning.
Description
Technical Field
The invention relates to the technical field of real-time positioning, in particular to a cargo real-time information positioning and tracking method and system based on international transportation.
Background
The international transportation is increasingly frequent, and the real-time information positioning tracking of goods becomes an important means for ensuring the transportation efficiency, improving the customer satisfaction and guaranteeing the goods safety.
In the prior art with the application number of CN202410058019.5 and the name of real-time tracking method and system based on asset cargo positioning information, the real-time tracking method and system based on asset cargo positioning information is disclosed, and particularly relates to the technical field of real-time positioning, and the real-time tracking method and system comprises a data acquisition module, a model construction module, an analysis verification module and an accurate processing module; and shooting and recording the running scenes of surrounding vehicles through image sensors in the motorcade, and acquiring weather information, GPS coordinate data information and vehicle-mounted gyroscope information of the corresponding scenes. The invention is based on the monitoring and analysis of vehicle image parameters in the cargo transportation process, the difference between the vehicle running image and the GPS positioning parameters is evaluated by constructing a GPS real-time positioning evaluation model and a convolutional neural network model so as to obtain the difference between a GPS positioning system and the actual position, a regression model is constructed by using a target detection algorithm to carry out position correction and safety detection on the calculated difference, and finally more accurate positioning real-time tracking information is obtained according to the over-fitting phenomenon in a regularized optimization model; the GPS real-time positioning evaluation index is formed by acquiring the GPS receiving interval coefficient, the Wi-Fi signal interference coefficient, the weather interference coefficient and the inertial navigation instantaneous difference coefficient, and whether the GPS real-time positioning is available is judged by the relation between the GPS real-time positioning evaluation index and the threshold value.
However, in practical use, there are still many drawbacks, for example, the GPS correction method may not flexibly adjust the resource input according to the real-time environmental change, resulting in a problem of resource waste or deficiency; the prior art needs to judge whether the GPS real-time positioning is accurate or not, and has complex process and inconvenient operation.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a cargo real-time information positioning tracking method and system based on international transportation, wherein a real-time running track of a target monitoring cargo is acquired through an inertial navigation system and recorded as a first running track; the method comprises the steps of obtaining a positioning signal of a target monitoring cargo from a GPS positioning system, obtaining a second running track, obtaining a GPS real-time positioning evaluation index by analyzing the first running track and the second running track, taking corresponding measures based on the relation between the GPS real-time positioning evaluation index DP and a threshold value, and judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index DP and the threshold value, so that the method is more convenient and concise, and the problem in the background technology is solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: a cargo real-time information positioning and tracking method based on international transportation comprises the following steps:
Firstly, acquiring data, namely marking the target monitoring international transported goods as target monitoring goods, and acquiring real-time positioning data and environment information by installing positioning data acquisition equipment and environment information acquisition equipment on the target monitoring goods; pasting an RFID tag on the object monitoring goods, and recording basic information, a destination and a planned transportation route of the goods to a database through the RFID tag; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
step two, data transmission, namely transmitting positioning data and environment information to a data center by utilizing a mobile communication network or satellite communication;
Step three, building a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value;
Step four, distributing GPS signal correction resources based on the GPS signal accuracy rate predicted value; the GPS signal correction resource refers to a technology and a computing resource for improving the accuracy of the GPS signal;
Step five, using short-term high-precision position, speed and gesture information provided by the inertial navigation system to assist positioning, improving GPS signal accuracy, comprising:
Acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a real-time running track of the target monitoring goods in the GPS positioning system, and marking the real-time running track as a second running track;
Acquiring GPS real-time positioning evaluation indexes by analyzing the first moving track and the second moving track;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
and executing a motion trail replacement instruction and outputting corrected motion trail information.
Preferably, the building process of the GPS signal accuracy prediction model comprises the following steps:
step S11, historical data collection and processing: collecting a large amount of GPS signal data and corresponding environmental parameter time sequence data; the environmental parameters include meteorological parameters, geospatial parameters and GPS positioning system performance parameters;
The meteorological parameters include: ambient temperature, humidity, wind speed, air pressure; the geospatial parameters include: elevation, latitude and longitude, and surrounding building density; the performance parameters of the GPS positioning system comprise satellite number, signal strength and multipath effect; processing GPS signal data and corresponding environmental parameter timing data, comprising: data cleaning, data standardization or normalization and feature selection (features which have influence on GPS signal accuracy are screened based on correlation analysis and principal component analysis methods);
Step S12, screening relevant characteristic parameters: based on a statistical method and a machine learning algorithm, carrying out feature importance assessment, and selecting features with obvious influence on GPS signal accuracy;
Step S13, model selection and training: initializing neural network model parameters, calculating a GPS signal accuracy prediction value by using forward propagation, updating the neural network model parameters by using a backward propagation algorithm, and minimizing a loss function; the loss function is used for measuring the function of the difference between the GPS signal accuracy predicted value and the GPS signal accuracy true value, such as any one of a mean square error, a root mean square error or an average absolute error, and is set according to actual conditions;
step S14, model evaluation and optimization: using cross verification to evaluate the generalization capability of the neural network model, adjusting the structure, the feature set or the super parameter of the neural network model according to the evaluation result, repeating the training process until reaching the satisfactory performance, and outputting a GPS signal accuracy prediction model;
Step S15, model deployment and application: deploying the trained GPS signal accuracy prediction model into an actual application scene, periodically collecting new data, evaluating the performance of the GPS signal accuracy prediction model on the new data, and carrying out model updating or retraining according to the requirement so as to adapt to the environment or parameter change.
Preferably, the acquiring manner of the GPS real-time positioning evaluation index is as follows:
Aligning the first motion trail information and the second motion trail information, acquiring trail shape difference parameters Gx and speed accuracy parameters Sy of the aligned first motion trail information and second motion trail information, and passing through a formula Calculating to obtain a GPS real-time positioning evaluation index, wherein Jbc represents a distortion matrix norm of the first motion trail information and the second motion trail information, alpha represents a trail shape influence index, beta represents a speed influence index, and Form (·) represents a linear normalization function used for converting the content in brackets into a range of 0 to 1 based on actual situation setting.
Preferably, the obtaining method of the distortion matrix includes the following steps:
Step S21, quality check of the first running track: the quality check of the first running track is completed through data integrity check, internal consistency check and external reference comparison operation; the data integrity check: checking whether the data output by the INS system is complete or not, and not having missing time points or data segments; verifying the continuity of the data, ensuring that there are no abrupt breaks or jumps; the internal consistency check includes: analyzing the acceleration and angular velocity data to check if they are physically reasonable (e.g., the acceleration should not exceed some reasonable threshold), using internal algorithms of the INS system (such as kalman filtering) to evaluate the smoothness and consistency of the trajectories; the external reference comparison refers to comparing the INS trace with known accuracy data (e.g., millimeter wave radar); evaluating the difference between the INS track and the reference tracks, and adjusting or correcting the INS track according to the difference;
S22, checking the strength, the signal-to-noise ratio and the multipath effect index of the GPS signals, removing GPS data points with poor signal quality, and selecting data in a time period when the accuracy of the GPS signals is 1 or is close to 1 to form a second motion track set;
Step S23, matching motion trail: time synchronization is carried out on the first moving track and the second moving track, and the first moving track information and the second moving track information are aligned;
Step S24, calculating a distortion matrix: for each pair of matched alignment points, computing a transformation matrix from the first motion trajectory and the second motion trajectory; solving a transformation matrix by a least square method; averaging all the calculated transformation matrixes to obtain a distortion matrix; the distortion matrix reflects an average difference between the first motion profile and the second motion profile.
Preferably, the track shape difference parameter is obtained by the following steps:
calculating to obtain track shape difference parameters of the first motion track information and the second motion track information;
Setting n alignment points of the first motion track information and the second motion track information after alignment, and using i to represent the sequence numbers of the alignment points;
acquiring the coordinates of the alignment points of the first motion trail information as X, y and z respectively represent longitude and latitude elevation information, and a represents first motion track information alignment point identification information; acquiring the coordinates of the alignment points of the second motion trail information asRespectively representing longitude and latitude elevation information, and b represents second motion trail information alignment point identification information;
by the formula Calculating to obtain a track shape difference parameter Gx; wherein δ represents an influence coefficient of elevation information, the value of δ is (0, 1), based on user setting, the closer δ to 0 indicates that the elevation information of the international shipment is not considered, and the closer δ to 1 indicates that the elevation information of the international shipment is as important as the longitude and latitude information.
Preferably, the speed accuracy parameter is obtained by the following steps:
acquiring a velocity vector V and an acceleration vector A of a first motion trail information alignment point, which are respectively recorded as ; Acquiring a velocity vector V and an acceleration vector A of a second motion trail information alignment point, which are respectively recorded as; By the formulaAnd calculating to obtain a speed accuracy parameter Sy.
Preferably, the aligning of the first motion trajectory information and the second motion trajectory information includes:
If the time stamps of the acquired time sequence data of the GPS signals and the inertial navigation data are not completely matched, aligning the data by using an interpolation or resampling technology, aligning the first motion trail information and the second motion trail information based on time, and finding a matched alignment point between the first motion trail and the second motion trail by using nearest neighbor search; removing data points with larger noise and error, and ensuring that the calculation is accurate and reliable; mapping the first moving track and the second moving track to the same geographic coordinate system, recording a transformation matrix of the second moving track in the mapping process, wherein the coordinate origins of the first moving track information and the second moving track information are the same, and minimizing the difference between the first moving track information and the second moving track information through a least square method.
Preferably, the process of executing the motion trail replacement instruction includes:
And after the mapping process transformation matrix is used for matching the longitude and latitude elevation information with the first running track, replacing the second running track corresponding to the time, and finishing the correction and update of the historical running track.
Preferably, the cargo real-time information positioning and tracking method further comprises the following steps:
Displaying and early warning the target monitoring cargo track: after optimizing the corrected motion trail information, transmitting the motion trail information to a data display platform and a trail early warning platform; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can check the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP, so that rich inquiry functions are provided, such as screening according to time, place and goods numbering conditions and inquiring the target monitoring goods; the track early warning platform is used for monitoring cargo track data in real time and timely finding and processing abnormal conditions.
In order to achieve the above purpose, the present invention provides the following technical solutions: an international transportation-based cargo real-time information positioning and tracking system, comprising:
The data acquisition module is used for marking the target monitoring international transportation goods as target monitoring goods, and acquiring real-time positioning data and environment information by installing positioning data acquisition equipment and environment information acquisition equipment on the target monitoring goods; pasting an RFID tag on the object monitoring goods, and recording basic information, a destination and a planned transportation route of the goods to a database through the RFID tag; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
the data transmission module is used for transmitting the positioning data and the environment information to the data center;
The GPS signal accuracy prediction module is used for constructing a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value; allocating GPS signal correction resources based on the GPS signal accuracy prediction value;
a positioning signal correction module: positioning is assisted using short term high accuracy position, velocity and attitude information provided by an inertial navigation system to improve GPS signal accuracy, including:
acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a positioning signal of a target monitoring cargo in a GPS positioning system, marking the positioning signal as a second running track, and analyzing the first running track and the second running track to obtain a GPS real-time positioning evaluation index;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
executing a motion trail replacement instruction and outputting corrected motion trail information;
The data display module is used for transmitting the corrected motion trail information to the data display platform and the trail early warning platform after optimizing the motion trail information; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can check the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP, so that rich inquiry functions are provided, such as screening according to time, place and goods numbering conditions and inquiring the target monitoring goods; the track early warning platform is used for monitoring cargo track data in real time and timely finding and processing abnormal conditions.
The invention has the technical effects and advantages that:
(1) According to the cargo real-time information positioning tracking method based on international transportation, a GPS signal accuracy prediction model is established by using a convolutional neural network, the accuracy of GPS signals can be predicted according to environment information, and corrected resources are reasonably allocated according to the accuracy, so that the optimal configuration of the resources is realized, and the problem that the traditional GPS correction method can not flexibly adjust the resource investment according to the real-time environment change, so that the resources are wasted or insufficient is solved.
(2) According to the cargo real-time information positioning tracking method based on international transportation, provided by the invention, the abnormity of the GPS signal is automatically detected by analyzing the track data provided by the GPS and the inertial navigation system, and the replacement instruction is generated to correct the motion track, so that the self-repairing capability of the system is enhanced, and the problem that the abnormity of the GPS signal cannot be found in time or the GPS signal can be processed only by manual intervention in the prior art is solved.
Drawings
Fig. 1 is a flowchart of a cargo real-time information positioning and tracking method according to the present invention.
FIG. 2 is a flow chart of assisted positioning according to the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
Example 1
Referring to the flow chart of the cargo real-time information positioning and tracking method of fig. 1 and the auxiliary positioning flow chart of fig. 2, the invention provides a cargo real-time information positioning and tracking method based on international transportation as shown in fig. 1, which comprises the following steps:
firstly, acquiring data, namely marking the target monitoring international transported goods as target monitoring goods, and acquiring real-time positioning data and environment information by installing positioning data acquisition equipment and environment information acquisition equipment on the target monitoring goods;
The method comprises the steps of explaining, sticking an RFID tag to target monitoring goods, and recording basic information, a destination and a planned transportation route of the goods to a database through the RFID tag; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
step two, data transmission, namely transmitting positioning data and environment information to a data center by utilizing a mobile communication network or satellite communication;
Step three, building a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value;
Step four, distributing GPS signal correction resources based on the GPS signal accuracy rate predicted value; the GPS signal correction resource refers to a technology and a computing resource for improving the accuracy of the GPS signal;
Step five, using short-term high-precision position, speed and gesture information provided by the inertial navigation system to assist positioning, improving GPS signal accuracy, referring to FIG. 2, the assisted positioning process comprises:
Acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a real-time running track of the target monitoring goods in the GPS positioning system, and marking the real-time running track as a second running track;
Acquiring GPS real-time positioning evaluation indexes by analyzing the first moving track and the second moving track;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
and executing a motion trail replacement instruction and outputting corrected motion trail information.
In the embodiment of the present invention, it is further explained that the process of building the GPS signal accuracy prediction model includes the following steps:
step S11, historical data collection and processing: collecting a large amount of GPS signal data and corresponding environmental parameter time sequence data; the environmental parameters include meteorological parameters, geospatial parameters and GPS positioning system performance parameters;
The meteorological parameters include: ambient temperature, humidity, wind speed, air pressure; the geospatial parameters include: elevation, latitude and longitude, and surrounding building density; the performance parameters of the GPS positioning system comprise satellite number, signal strength and multipath effect; processing GPS signal data and corresponding environmental parameter timing data, comprising: data cleaning, data standardization or normalization and feature selection (features which have influence on GPS signal accuracy are screened based on correlation analysis and principal component analysis methods);
Step S12, screening relevant characteristic parameters: based on a statistical method and a machine learning algorithm, carrying out feature importance assessment, and selecting features with obvious influence on GPS signal accuracy;
Step S13, model selection and training: initializing neural network model parameters, calculating a GPS signal accuracy prediction value by using forward propagation, updating the neural network model parameters by using a backward propagation algorithm, and minimizing a loss function; the loss function is used for measuring the function of the difference between the GPS signal accuracy predicted value and the GPS signal accuracy true value, such as any one of a mean square error, a root mean square error or an average absolute error, and is set according to actual conditions;
step S14, model evaluation and optimization: using cross verification to evaluate the generalization capability of the neural network model, adjusting the structure, the feature set or the super parameter of the neural network model according to the evaluation result, repeating the training process until reaching the satisfactory performance, and outputting a GPS signal accuracy prediction model;
Step S15, model deployment and application: deploying the trained GPS signal accuracy prediction model into an actual application scene, periodically collecting new data, evaluating the performance of the GPS signal accuracy prediction model on the new data, and carrying out model updating or retraining according to the requirement so as to adapt to the environment or parameter change.
It should be further explained in the embodiments of the present invention that the GPS signal correction resources include, but are not limited to:
Multipath effects due to signal reflection and refraction are reduced through a multipath suppression algorithm; correcting a positioning error of the GPS receiver by receiving differential correction information from a reference station of known position; the positioning precision is further improved by using a real-time dynamic differential technology; positioning is aided using short-term high-precision position, velocity and attitude information provided by inertial navigation systems; in embodiments of the present invention, an inertial navigation system is used to assist in positioning.
The inertial navigation system obtains the speed, yaw angle and position information of the carrier by measuring the acceleration of the carrier in an inertial reference system, integrating the acceleration in time and simultaneously converting the acceleration into a navigation coordinate system; accelerometers in inertial navigation systems are key components for measuring acceleration changes, and are located on different axes (such as an X axis, a Y axis and a Z axis) of a vehicle and are used for measuring acceleration information on each axis; when the height of the vehicle changes, the corresponding acceleration change is detected by the accelerometer in the vertical direction, and the height change of the vehicle is obtained by time integration of the acceleration change.
The embodiment of the invention needs to be further explained, wherein the data preprocessing operation is performed on the collected multi-source positioning data in the data center, and comprises the steps of checking, cleaning, smoothing, recovering and supplementing the preprocessed data, compressing the preprocessed data, and storing the compressed data into a cloud data platform so as to ensure the safety and accessibility of the data; the data preprocessing operation includes:
Performing preliminary verification on the received positioning data, and checking the integrity, rationality and consistency of the data; identifying and eliminating abnormal values such as position coordinates exceeding a physical range and timestamp errors; the random noise is reduced by adopting a data smoothing technology, and the data quality is improved; establishing a data recovery mechanism, and recovering or supplementing by using historical data or a prediction model when the data is lost or damaged; for missing data, interpolation methods (such as linear interpolation and polynomial interpolation) or prediction models based on machine learning are adopted for filling.
In the embodiment of the present invention, it needs to be further explained that the method for acquiring the GPS real-time positioning evaluation index is as follows:
Aligning the first motion trail information and the second motion trail information, acquiring trail shape difference parameters Gx and speed accuracy parameters Sy of the aligned first motion trail information and second motion trail information, and passing through a formula Calculating to obtain a GPS real-time positioning evaluation index, wherein Jbc represents a distortion matrix norm of the first motion trail information and the second motion trail information, alpha represents a trail shape influence index, beta represents a speed influence index, and setting is based on actual conditions; form (-) represents a linear normalization function for converting the contents in brackets into a range of 0 to 1.
In the embodiment of the present invention, it is further explained that the obtaining manner of the distortion matrix includes the following steps:
Step S21, quality check of the first running track: the quality check of the first running track is completed through data integrity check, internal consistency check and external reference comparison operation; the data integrity check: checking whether the data output by the INS system is complete or not, and not having missing time points or data segments; verifying the continuity of the data, ensuring that there are no abrupt breaks or jumps; the internal consistency check includes: analyzing the acceleration and angular velocity data to check if they are physically reasonable (e.g., the acceleration should not exceed some reasonable threshold), using internal algorithms of the INS system (such as kalman filtering) to evaluate the smoothness and consistency of the trajectories; the external reference comparison refers to comparing the INS trace with known accuracy data (e.g., millimeter wave radar); evaluating the difference between the INS track and the reference tracks, and adjusting or correcting the INS track according to the difference;
S22, checking the strength, the signal-to-noise ratio and the multipath effect index of the GPS signals, removing GPS data points with poor signal quality, and selecting data in a time period when the accuracy of the GPS signals is 1 or is close to 1 to form a second motion track set;
Step S23, matching motion trail: time synchronization is carried out on the first moving track and the second moving track, and the first moving track information and the second moving track information are aligned;
Step S24, calculating a distortion matrix: for each pair of matched alignment points, computing a transformation matrix from the first motion trajectory and the second motion trajectory; solving a transformation matrix by a least square method; averaging all the calculated transformation matrixes to obtain a distortion matrix; the distortion matrix reflects an average difference between the first motion profile and the second motion profile.
In the embodiment of the present invention, it should be further explained that the track shape difference parameter is obtained by:
calculating to obtain track shape difference parameters of the first motion track information and the second motion track information;
Setting n alignment points of the first motion track information and the second motion track information after alignment, and using i to represent the sequence numbers of the alignment points;
acquiring the coordinates of the alignment points of the first motion trail information as X, y and z respectively represent longitude and latitude elevation information, and a represents first motion track information alignment point identification information; acquiring the coordinates of the alignment points of the second motion trail information asRespectively representing longitude and latitude elevation information, and b represents second motion trail information alignment point identification information;
by the formula Calculating to obtain a track shape difference parameter Gx; wherein δ represents an influence coefficient of elevation information, the value of δ is (0, 1), based on user setting, the closer δ to 0 indicates that the elevation information of the international shipment is not considered, and the closer δ to 1 indicates that the elevation information of the international shipment is as important as the longitude and latitude information.
In the embodiment of the present invention, it needs to be further explained that the speed accuracy parameter is obtained by:
acquiring a velocity vector V and an acceleration vector A of a first motion trail information alignment point, which are respectively recorded as ; Acquiring a velocity vector V and an acceleration vector A of a second motion trail information alignment point, which are respectively recorded as; By the formulaAnd calculating to obtain a speed accuracy parameter Sy.
In the embodiment of the present invention, it is further explained that the aligning process of the first motion trail information and the second motion trail information includes:
If the time stamps of the acquired time sequence data of the GPS signals and the inertial navigation data are not completely matched, aligning the data by using an interpolation or resampling technology, aligning the first motion trail information and the second motion trail information based on time, and finding a matched alignment point between the first motion trail and the second motion trail by using nearest neighbor search; removing data points with larger noise and error, and ensuring that the calculation is accurate and reliable; mapping the first moving track and the second moving track to the same geographic coordinate system, recording a transformation matrix of the second moving track in the mapping process, wherein the coordinate origins of the first moving track information and the second moving track information are the same, and minimizing the difference between the first moving track information and the second moving track information through a least square method.
In the embodiment of the present invention, it needs to be further explained that the process of executing the motion trail replacement instruction includes:
And after the mapping process transformation matrix is used for matching the longitude and latitude elevation information with the first running track, replacing the second running track corresponding to the time, and finishing the correction and update of the historical running track.
In the embodiment of the present invention, it is further explained that the method for positioning and tracking real-time information of goods further includes:
Displaying and early warning the target monitoring cargo track: after optimizing the corrected motion trail information, transmitting the motion trail information to a data display platform and a trail early warning platform; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can check the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP, so that rich inquiry functions are provided, such as screening according to time, place and goods numbering conditions and inquiring the target monitoring goods; the track early warning platform is used for monitoring cargo track data in real time and timely finding and processing abnormal conditions.
The cargo transportation track optimization process comprises the following steps: the accuracy and the readability of track data are improved through track optimization and map matching technology; the jitter and jump phenomena in the track are eliminated by adopting a smoothing algorithm, so that the track is smoother and more continuous; mapping the optimized track data onto an actual road network by using high-precision map data; the track deviation is further corrected by comparing the matching degree of the track and the road network, so that the track accuracy is improved; the optimized and matched track data are displayed in a graphical mode, so that a user can intuitively understand the cargo transportation condition;
The early warning platform includes: defining abnormal events (such as deviation from a preset route, long-time stationary state, abnormal speed and the like) according to business requirements; real-time monitoring the cargo track data by utilizing a real-time data processing technology; the abnormal event is identified by statistical analysis, machine learning model and the like. For example, using a clustering algorithm to identify trace points of the outlier group, or using time series analysis to detect speed mutations; and once an abnormal event is detected, an alarm mechanism is immediately triggered, related personnel are notified in modes of short messages, mails, APP pushing and the like, and detailed abnormal information and processing suggestions are provided.
Example 2
The embodiment of the invention provides a cargo real-time information positioning and tracking system based on international transportation, which comprises the following components:
The data acquisition module is used for marking the target monitoring international transportation goods as target monitoring goods, and acquiring real-time positioning data and environment information by installing positioning data acquisition equipment and environment information acquisition equipment on the target monitoring goods; pasting an RFID tag on the object monitoring goods, and recording basic information, a destination and a planned transportation route of the goods to a database through the RFID tag; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
the data transmission module is used for transmitting the positioning data and the environment information to the data center;
The GPS signal accuracy prediction module is used for constructing a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value; allocating GPS signal correction resources based on the GPS signal accuracy prediction value;
a positioning signal correction module: positioning is assisted using short term high accuracy position, velocity and attitude information provided by an inertial navigation system to improve GPS signal accuracy, including:
acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a positioning signal of a target monitoring cargo in a GPS positioning system, marking the positioning signal as a second running track, and analyzing the first running track and the second running track to obtain a GPS real-time positioning evaluation index;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
executing a motion trail replacement instruction and outputting corrected motion trail information;
The data display module is used for transmitting the corrected motion trail information to the data display platform and the trail early warning platform after optimizing the motion trail information; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can check the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP, so that rich inquiry functions are provided, such as screening according to time, place and goods numbering conditions and inquiring the target monitoring goods; the track early warning platform is used for monitoring cargo track data in real time and timely finding and processing abnormal conditions.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The cargo real-time information positioning and tracking method based on international transportation is characterized by comprising the following steps of:
step one, data acquisition, namely installing positioning data acquisition equipment and environment information acquisition equipment on a target monitoring cargo to acquire real-time positioning data and environment information; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
step two, data transmission, namely transmitting positioning data and environment information to a data center by utilizing a mobile communication network or satellite communication;
Step three, building a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value;
Step four, distributing GPS signal correction resources based on the GPS signal accuracy rate predicted value; the GPS signal correction resource refers to a technology and a computing resource for improving the accuracy of the GPS signal;
Step five, using short-term high-precision position, speed and gesture information provided by the inertial navigation system to assist positioning, improving GPS signal accuracy, comprising:
Acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a real-time running track of the target monitoring goods in the GPS positioning system, and marking the real-time running track as a second running track;
acquiring GPS real-time positioning evaluation indexes by analyzing the first moving track and the second moving track; the GPS real-time positioning evaluation index is obtained by the following steps:
Aligning the first motion trail information and the second motion trail information, acquiring trail shape difference parameters Gx and speed accuracy parameters Sy of the aligned first motion trail information and second motion trail information, and passing through a formula Calculating to obtain a GPS real-time positioning evaluation index, wherein Jbc represents a distortion matrix norm of the first motion trail information and the second motion trail information, alpha represents a trail shape influence index, beta represents a speed influence index, and Form (·) represents a linear normalization function based on actual condition setting; the obtaining mode of the distortion matrix comprises the following steps:
Step S21, quality check of the first running track: the quality check of the first running track is completed through data integrity check, internal consistency check and external reference comparison operation;
S22, checking the strength, the signal-to-noise ratio and the multipath effect index of the GPS signals, removing GPS data points with poor signal quality, and selecting data in a time period with the GPS signal accuracy rate of 1 to form a second motion track set;
Step S23, matching motion trail: time synchronization is carried out on the first moving track and the second moving track, and the first moving track information and the second moving track information are aligned;
Step S24, calculating a distortion matrix: for each pair of matched alignment points, computing a transformation matrix from the first motion trajectory and the second motion trajectory; solving a transformation matrix by a least square method; averaging all the calculated transformation matrixes to obtain a distortion matrix; the distortion matrix reflects the average difference between the first motion trail and the second motion trail;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
and executing a motion trail replacement instruction and outputting corrected motion trail information.
2. The method for positioning and tracking cargo real-time information based on international transportation according to claim 1, wherein the process for constructing the GPS signal accuracy prediction model comprises the following steps:
step S11, historical data collection and processing: collecting a large amount of GPS signal data and corresponding environmental parameter time sequence data; the environmental parameters include meteorological parameters, geospatial parameters and GPS positioning system performance parameters;
Step S12, screening relevant characteristic parameters: based on a statistical method and a machine learning algorithm, carrying out feature importance assessment, and selecting features with obvious influence on GPS signal accuracy;
Step S13, model selection and training: initializing neural network model parameters, calculating a GPS signal accuracy prediction value by using forward propagation, updating the neural network model parameters by using a backward propagation algorithm, and minimizing a loss function;
step S14, model evaluation and optimization: using cross verification to evaluate the generalization capability of the neural network model, adjusting the structure, the feature set or the super parameter of the neural network model according to the evaluation result, repeating the training process until reaching the satisfactory performance, and outputting a GPS signal accuracy prediction model;
step S15, model deployment and application: and deploying the trained GPS signal accuracy prediction model into an actual application scene.
3. The method for positioning and tracking cargo real-time information based on international transportation according to claim 1, wherein the track shape difference parameter is obtained by the following steps:
calculating to obtain track shape difference parameters of the first motion track information and the second motion track information;
Setting n alignment points of the first motion track information and the second motion track information after alignment, and using i to represent the sequence numbers of the alignment points;
acquiring the coordinates of the alignment points of the first motion trail information as X, y and z respectively represent longitude and latitude elevation information, and a represents first motion track information alignment point identification information; acquiring the coordinates of the alignment points of the second motion trail information asRespectively representing longitude and latitude elevation information, and b represents second motion trail information alignment point identification information;
by the formula Calculating to obtain a track shape difference parameter Gx; wherein δ represents an influence coefficient of elevation information, and the value of δ is (0, 1) based on user settings.
4. The method for positioning and tracking real-time information of goods based on international transportation according to claim 1, wherein the speed accuracy parameter is obtained by the following steps:
acquiring a velocity vector V and an acceleration vector A of a first motion trail information alignment point, which are respectively recorded as Acquiring a velocity vector V and an acceleration vector A of a second motion trail information alignment point, which are respectively recorded asBy the formulaAnd calculating to obtain a speed accuracy parameter Sy.
5. The method for locating and tracking real-time information of goods based on international transportation according to claim 1, wherein the aligning process of the first movement track information and the second movement track information comprises: if the time stamps of the acquired time sequence data of the GPS signals and the inertial navigation data are not completely matched, aligning the data by using an interpolation or resampling technology, aligning the first motion trail information and the second motion trail information based on time, and finding a matched alignment point between the first motion trail and the second motion trail by using nearest neighbor search; removing data points with larger noise and error, and ensuring that the calculation is accurate and reliable; mapping the first moving track and the second moving track to the same geographic coordinate system, recording a transformation matrix of the second moving track in the mapping process, wherein the coordinate origins of the first moving track information and the second moving track information are the same, and minimizing the difference between the first moving track information and the second moving track information through a least square method.
6. The method for positioning and tracking real-time information of goods based on international transportation according to claim 1, wherein the process of executing the motion trail replacement instruction comprises:
And after the mapping process transformation matrix is used for matching the longitude and latitude elevation information with the first running track, replacing the second running track corresponding to the time, and finishing the correction and update of the historical running track.
7. The method for locating and tracking real-time information of goods based on international transportation according to claim 1, wherein the method for locating and tracking real-time information of goods further comprises:
Displaying and early warning the target monitoring cargo track: after optimizing the corrected motion trail information, transmitting the motion trail information to a data display platform and a trail early warning platform; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can view the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP.
8. Cargo real-time information positioning tracking system based on international transportation, characterized by comprising:
The data acquisition module is used for marking the target monitoring international transportation goods as target monitoring goods, and acquiring real-time positioning data and environment information by installing positioning data acquisition equipment and environment information acquisition equipment on the target monitoring goods; pasting an RFID tag on the object monitoring goods, and recording basic information, a destination and a planned transportation route of the goods to a database through the RFID tag; the positioning data acquisition device comprises: a GPS positioning system and an inertial navigation system;
the data transmission module is used for transmitting the positioning data and the environment information to the data center;
The GPS signal accuracy prediction module is used for constructing a GPS signal accuracy prediction model based on a convolutional neural network, acquiring environmental information from a data center, inputting the environmental information into the GPS signal accuracy prediction model, and outputting a GPS signal accuracy prediction value; allocating GPS signal correction resources based on the GPS signal accuracy prediction value;
a positioning signal correction module: positioning is assisted using short term high accuracy position, velocity and attitude information provided by an inertial navigation system to improve GPS signal accuracy, including:
Acquiring preprocessed data from a data center, and recording a real-time running track of a target monitoring cargo acquired by an inertial navigation system as a first running track; acquiring a positioning signal of a target monitoring cargo in a GPS positioning system, marking the positioning signal as a second running track, and analyzing the first running track and the second running track to obtain a GPS real-time positioning evaluation index; the GPS real-time positioning evaluation index is obtained by the following steps:
Aligning the first motion trail information and the second motion trail information, acquiring trail shape difference parameters Gx and speed accuracy parameters Sy of the aligned first motion trail information and second motion trail information, and passing through a formula Calculating to obtain a GPS real-time positioning evaluation index, wherein Jbc represents a distortion matrix norm of the first motion trail information and the second motion trail information, alpha represents a trail shape influence index, beta represents a speed influence index, and Form (·) represents a linear normalization function based on actual condition setting; the obtaining mode of the distortion matrix comprises the following steps:
Step S21, quality check of the first running track: the quality check of the first running track is completed through data integrity check, internal consistency check and external reference comparison operation;
S22, checking the strength, the signal-to-noise ratio and the multipath effect index of the GPS signals, removing GPS data points with poor signal quality, and selecting data in a time period with the GPS signal accuracy rate of 1 to form a second motion track set;
Step S23, matching motion trail: time synchronization is carried out on the first moving track and the second moving track, and the first moving track information and the second moving track information are aligned;
Step S24, calculating a distortion matrix: for each pair of matched alignment points, computing a transformation matrix from the first motion trajectory and the second motion trajectory; solving a transformation matrix by a least square method; averaging all the calculated transformation matrixes to obtain a distortion matrix; the distortion matrix reflects the average difference between the first motion trail and the second motion trail;
Judging whether the GPS real-time positioning is accurate or not based on the relation between the GPS real-time positioning evaluation index and the threshold value;
when the GPS real-time positioning evaluation index exceeds a threshold value, indicating that the GPS signal is abnormal, and generating a motion trail replacement instruction; when the GPS real-time positioning evaluation index does not exceed the threshold value, indicating that the GPS signal is normal, and taking no measures;
executing a motion trail replacement instruction and outputting corrected motion trail information;
The data display module is used for transmitting the corrected motion trail information to the data display platform and the trail early warning platform after optimizing the motion trail information; the data display platform is used for displaying the optimized positioning data and track information to a user in a visual mode, and the user can check the position, the transportation state and the historical track information of the target monitoring goods in real time through a Web interface or a mobile APP; the track early warning platform is used for monitoring cargo track data in real time and timely finding and processing abnormal conditions.
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