Dangerous goods transportation vehicle positioning system based on 5G Internet of Vehicles

Abstract

The invention relates to the technical field of transportation supervision platforms, in particular to a dangerous goods transportation vehicle positioning system based on the 5G Internet of Vehicles, which comprises a management unit, a monitoring unit and an early warning unit. According to the invention, the early warning mechanism construction module constructs an early warning trigger mechanism and sets a dynamic early warning threshold value, the dynamically adjusted early warning threshold value can be matched with vehicle transportation data under various conditions, and the monitoring data acquisition module can acquire accurate dangerous goods parameter information and vehicle information. According to the system and the method, accurate hazardous article parameter information can be compared with a threshold value, accurate triggering of early warning can be ensured when an abnormity occurs, timely sending of an alarm is ensured, a subsequent emergency processing effect is ensured, multi-source data acquisition can be realized through a multi-source data acquisition module, and the system and the method are suitable for popularization and application. Data can be cleaned, fused and synchronized through the data correction processing module, rapid processing of the data can be achieved, and errors generated when the sensor is used can be reduced through sensor calibration.

Description

Technical Field

[0001] The present invention relates to the technical field of transport supervision platforms, and specifically to a dangerous goods transport vehicle positioning system based on 5G vehicle networking. Background Art

[0002] Dangerous goods transportation by vehicle refers to the use of specially designed and equipped transport vehicles to transport items with explosive, flammable, toxic, corrosive, radioactive and other dangerous properties. The transportation of dangerous goods is widely used in chemical, petroleum, pharmaceutical and other industrial fields.

[0003] For example, Chinese patent CN115170023A discloses a dangerous goods transportation supervision platform based on satellite positioning technology, including a GPS satellite, a GPS receiver, a supervision platform and a supervision terminal. The supervision terminal is arranged on a transport vehicle. The supervision terminal includes a GPS module, a temperature sensor, a humidity sensor, a power module, a processor, a wireless communication module, a speaker module, a memory and a weight sensor. The temperature sensor, the humidity sensor and the weight sensor are installed in the cargo compartment of the vehicle. The GPS module, the wireless communication module, the speaker module and the memory are all electrically connected to the processor.

[0004] In the above patent, the problem of being unable to communicate with the accompanying personnel in time when the state of the cargo is unstable is solved by using temperature sensors, humidity sensors and weight sensors. However, the multiple thresholds set by the supervision terminal are all static thresholds, and the values ​​of the thresholds are relatively fixed. The various data of vehicles and dangerous goods operating normally under different transportation conditions are constantly changing. The changed data cannot trigger the static threshold in time, resulting in underreporting and false alarms, thereby reducing the accuracy of the alarm and affecting the emergency response effect. In view of this, this application proposes a dangerous goods transportation vehicle positioning system based on 5G vehicle networking. Summary of the invention

[0005] The purpose of the present invention is to provide a dangerous goods transport vehicle positioning system based on 5G vehicle networking to solve the above problems.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A dangerous goods transport vehicle positioning system based on 5G vehicle networking, including a management unit, a monitoring unit and an early warning unit;

[0008] The management unit is used to manage vehicles, personnel, transportation tasks, dangerous goods and route information, and automatically generate reports based on the acquired multi-source data in an hourly cycle;

[0009] The monitoring unit is used to monitor the vehicle and dangerous goods in real time through sensors and positioning devices installed in the vehicle. The background can directly monitor the real-time position, vehicle details and vehicle trajectory of the vehicle, and transmit the acquired vehicle data and dangerous goods status data to the early warning unit;

[0010] The warning unit is used to build a warning mechanism. When a vehicle approaches a dangerous area, exceeds the speed limit, or the dangerous goods are in an abnormal state, the system automatically issues a warning message after judgment to remind the driver and relevant management personnel;

[0011] The early warning unit includes an early warning mechanism building module, a monitoring data acquisition module, a data rapid judgment module and a result output and triggering module;

[0012] The warning mechanism building module is used to determine the warning indicators according to the vehicle position, vehicle status and dangerous goods status indicators, and then build a warning trigger mechanism to set a dynamic warning threshold according to the vehicle position, driving speed and dangerous goods status;

[0013] The monitoring data acquisition module is used to acquire the monitoring data in the monitoring unit, and classify and store the acquired data;

[0014] The data quick judgment module is used to process the data acquired in the monitoring data acquisition module, compare the processed monitoring data with the warning threshold, and determine whether the monitoring data exceeds the warning threshold range;

[0015] The result output and trigger module is used to output the judgment result in the data quick judgment module. When the data exceeds the warning threshold range, the warning is quickly triggered and a warning signal and the current vehicle location information are sent to the control terminal.

[0016] Preferably, in the early warning mechanism building module, when setting a dynamic early warning threshold, the following contents are also included:

[0017] A1. Set initial threshold: Based on historical data analysis, obtain the operating status of vehicles and dangerous goods under different circumstances, and set the initial warning threshold according to the statistical results of historical data;

[0018] A2. Dynamic adjustment: Set the range of change of the warning threshold, and dynamically adjust the warning threshold within the range according to the real-time situation;

[0019] A3. Monitoring and manual intervention: In special transportation tasks or emergency situations, the warning threshold is temporarily adjusted manually to intervene in dynamic adjustments, and the warning threshold is regularly evaluated and optimized.

[0020] Preferably, in the data rapid judgment module, when processing data, data preprocessing is achieved through data cleaning and standardization, and then the data is filtered, and then the data is verified and checked after filtering, and finally isolation forest auxiliary detection is used. When using isolation forest, the relevant calculation expression is as follows:

[0021] ;

[0022] In the formula, n is the number of data points, x is the data point, is the path length, is the harmonic number, is the average path length of the data point in the tree, Score for anomaly.

[0023] Preferably, the management unit includes a vehicle management module, a personnel management module, a task management module, a cargo management module, a route management module and a report generation module;

[0024] The vehicle management module is used to associate drivers and escorts, and can add, edit, and review vehicle information, bind and unbind drivers and escorts, and query the vehicle information, maintenance records, and driving tracks of the target vehicle;

[0025] The personnel management module is used to enter, edit, review and query the identity information of drivers and escorts.

[0026] Preferably, the task management module is used to automatically allocate, track progress and calculate completion status of the current waybill task;

[0027] The supply management module is used for the maintenance of goods, including the category, item, name and record representation of goods;

[0028] The route management module is used to load the name of the current route, the longitude and latitude of the shipping place, and the longitude and latitude of the receiving place, and adjust and re-plan the route in real time according to the traffic information;

[0029] The report generation module is used to synchronize the information of the vehicle management module, personnel management module, task management module, cargo management module and route management module, automatically generate a work log of the current vehicle transportation based on the information, and summarize the work log to generate a report, the report content includes transportation mileage, fuel consumption and safety accident report.

[0030] Preferably, when checking the identity information of the driver and the escort in the personnel management module, the queryable contents include training records, work records and violation records.

[0031] Preferably, when the progress is tracked in the task management module, the detailed information of the waybill can be viewed, including information query, editing, receipt upload, vehicle track viewing, vehicle real-time location viewing, and vehicle latest location viewing.

[0032] Preferably, the monitoring unit includes a multi-source data monitoring module, a multi-source data acquisition module, a data correction processing module and a data transmission module;

[0033] The multi-source data monitoring module is used to obtain the real-time location of the vehicle, vehicle details, vehicle trajectory and status data of dangerous goods through the positioning module and sensors;

[0034] The multi-source data acquisition module is used to collect positioning data, sensor data, vehicle-mounted equipment data and external data, and establish a database to classify and store the collected multi-source data.

[0035] Preferably, the data correction processing module is used to complete the correction processing of the data through data cleaning, data fusion and calibration, time synchronization and data verification;

[0036] The data transmission module is used for the vehicle terminal to upload the data processed by the data correction processing module to the background server in real time through the G communication module, and to establish a data transmission channel for real-time communication between the vehicle terminal and the background server.

[0037] Preferably, during data cleaning and data fusion, the following contents are also included:

[0038] B1. Remove outliers: Perform a preliminary check on the data collected from each data source, identify outliers that are significantly deviated from the normal range, compare and judge the outliers through the set threshold range, and remove them from the data set;

[0039] B2. Handling missing values: Check whether there are missing values ​​in the data set. If there are few missing values, interpolation can be used to estimate the missing values ​​based on the values ​​of surrounding data points. If there are many missing values, a prediction model based on a machine learning algorithm can be used to fill in the missing values.

[0040] B3. Multi-sensor data fusion: Integrate data from different sensors through data fusion technology;

[0041] B4. Sensor calibration: Calibrate the temperature sensor and pressure sensor using standard instruments, compare the sensor's measured value with the standard value, and then adjust the sensor's parameters. When calibrating the positioning sensor, calibration is achieved by adjusting the positioning algorithm's parameters.

[0042] Beneficial effects of the present invention:

[0043] In the present invention, an early warning trigger mechanism is constructed and a dynamic early warning threshold is set through an early warning mechanism construction module. The early warning threshold after dynamic adjustment can be matched with vehicle transportation data in various situations. Accurate dangerous goods parameter information and vehicle information can be obtained through the monitoring data acquisition module to ensure data accuracy, thereby ensuring the correct comparison of dangerous goods parameter information with the threshold. When an abnormality occurs, the early warning can be accurately triggered, thereby ensuring the timely issuance of the alarm and the subsequent emergency treatment effect. The vehicle management module and the personnel management module can be used to query the historical work data of vehicles and personnel, which is convenient for timely viewing and management of abnormal data and timely interruption of vehicle transportation. The multi-source data acquisition module can be used to collect multi-source data, and the data correction processing module can be used to clean, fuse and synchronize data, so as to realize rapid data processing. Through sensor calibration, the error when the sensor is used can be reduced, thereby improving the accuracy of the data. After data verification, the correction effect of the data can be guaranteed, and the processed data can be updated in real time, thereby ensuring the accuracy of vehicle positioning and dangerous goods status information. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a system block diagram of a dangerous goods transport vehicle positioning system based on 5G vehicle networking in the present invention;

[0045] Figure 2 This is a system block diagram of a management unit of a dangerous goods transport vehicle positioning system based on 5G vehicle networking in the present invention;

[0046] Figure 3 This is a system block diagram of a monitoring unit of a dangerous goods transport vehicle positioning system based on 5G vehicle networking in the present invention;

[0047] Figure 4 This is a system block diagram of an early warning unit of a dangerous goods transport vehicle positioning system based on 5G vehicle networking in the present invention.

[0048] In the figure: 1. Management unit; 11. Vehicle management module; 12. Personnel management module; 13. Task management module; 14. Cargo management module; 15. Route management module; 16. Report generation module; 2. Monitoring unit; 21. Multi-source data monitoring module; 22. Multi-source data acquisition module; 23. Data correction and processing module; 24. Data transmission module; 3. Early warning unit; 31. Early warning mechanism construction module; 32. Monitoring data acquisition module; 33. Data rapid judgment module; 34. Result output and trigger module. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1: Please refer to Figure 1-Figure 4 , This design proposes an implementation method, a dangerous goods transport vehicle positioning system based on 5G vehicle networking, including a management unit 1, a monitoring unit 2 and an early warning unit 3. The management unit 1 is used to manage vehicles, personnel, transportation tasks, dangerous goods and route information, and automatically generates reports based on multiple acquired data in a 24-hour cycle; the monitoring unit 2 is used to monitor vehicles and dangerous goods in real time through sensors and positioning devices installed in the vehicle. The background can directly monitor the real-time position, vehicle details and vehicle trajectory of the vehicle, and transmit the acquired vehicle data and dangerous goods status data to the early warning unit 3; the early warning unit 3 is used to build an early warning mechanism. When the vehicle approaches a dangerous area, exceeds the speed limit or the dangerous goods are in an abnormal state, the system automatically issues an early warning message after judgment to remind the driver and relevant management personnel;

[0051] The early warning unit 3 includes an early warning mechanism building module 31, a monitoring data acquisition module 32, a data rapid judgment module 33 and a result output and triggering module 34;

[0052] The early warning mechanism construction module 31 is used to determine the early warning index according to the vehicle position, vehicle status and dangerous goods status index, and then construct the early warning trigger mechanism to set the dynamic early warning threshold according to the vehicle position, driving speed and dangerous goods status;

[0053] When setting dynamic warning thresholds, the following should also be included:

[0054] Step 1: Set the initial threshold: Based on historical data analysis, obtain the operating status of vehicles and dangerous goods under different circumstances, and set the initial warning threshold according to the statistical results of historical data;

[0055] Step 2: Dynamic adjustment: Set the range of change of the warning threshold, and dynamically adjust the warning threshold within the range according to the real-time situation;

[0056] Step 3: Monitoring and manual intervention: In special transportation tasks or emergency situations, the warning threshold is temporarily adjusted manually to intervene in dynamic adjustments, and the warning threshold is regularly evaluated and optimized.

[0057] The monitoring data acquisition module 32 is used to acquire the monitoring data in the monitoring unit 2, and classify and store the acquired data;

[0058] The data quick judgment module 33 is used to process the data acquired by the monitoring data acquisition module 32, compare the processed monitoring data with the warning threshold, and determine whether the monitoring data exceeds the warning threshold range;

[0059] When processing data, data preprocessing is achieved through data cleaning and standardization, and then the data is filtered. After filtering, the data is verified and checked, and finally the isolation forest is used to assist detection. When using the isolation forest, the relevant calculation expression is as follows:

[0060] ;

[0061] In the formula, n is the number of data points, x is the data point, is the path length, is the harmonic number, is the average path length of the data point in the tree, Score for anomaly.

[0062] The result output and trigger module 34 is used to output the judgment result in the data quick judgment module 33. When the data exceeds the warning threshold range, the warning is quickly triggered and a warning signal and the current vehicle position information are sent to the control terminal.

[0063] In this embodiment, when verifying and checking the data, it includes data rationality check, data cross-verification, and data check code and error correction code processing. When checking the data rationality, the data is checked for rationality according to physical laws and actual conditions. If the data does not meet the rationality requirements, it can be marked as abnormal data and further processed or excluded. When cross-verifying the data, cross-verification is performed using data from multiple data sources. For the vehicle's location data, data from multiple positioning methods such as satellite positioning, base station positioning, and inertial navigation can be used for comparison at the same time. If there are large differences between the data from different positioning methods, further inspection and processing are required. For the status data of dangerous goods, verification can be performed in combination with different types of sensor data. When processing the data check code and error correction code, the use of cyclic redundancy check CRC code can detect errors in the data transmission process, and the use of error correction codes such as Hamming codes can correct errors in the data to a certain extent.

[0064] At the same time, when using the isolation forest algorithm to identify abnormal data, first collect the monitoring data of dangerous goods transport vehicles, including location, speed, temperature, pressure and other characteristics, preprocess the data, and use the preprocessed data to train the isolation forest model. For new monitoring data points, input them into the trained isolation forest model and calculate their anomaly scores. If the anomaly score is higher than the preset threshold, the data point is considered abnormal, and the detected abnormal data points are further analyzed and processed. Through the above calculations, it is possible to check whether the sensor is faulty or whether there is a problem with data transmission, and take corresponding measures, such as repairing the sensor, re-collecting data, and reducing abnormal data.

[0065] Example 2: Please refer to Figure 1-Figure 4 The management unit 1 includes a vehicle management module 11, a personnel management module 12, a task management module 13, a cargo management module 14, a route management module 15 and a report generation module 16; the vehicle management module 11 is used to associate drivers and escorts, and can add, edit, review, bind and unbind drivers and escorts with vehicle information, and query the vehicle information, maintenance records and driving tracks of the target vehicle;

[0066] The personnel management module 12 is used to input, edit, review and query the identity information of drivers and escorts. When viewing the identity information of drivers and escorts, the queryable content includes training records, work records and violation records.

[0067] The task management module 13 is used to automatically assign, track progress and complete the status of the current waybill task. When tracking the progress, the detailed information of the waybill can be viewed, including information query, editing, receipt upload, vehicle track viewing, vehicle real-time location viewing, and vehicle latest location viewing.

[0068] The cargo management module 14 is used for the maintenance of cargo, including cargo categories, items, cargo names and filing representations; the route management module 15 is used to load the name of the current route, the longitude and latitude of the shipping place, and the longitude and latitude of the receiving place, and to adjust and re-plan the route in real time according to the road condition information; the report generation module 16 is used to synchronize the information of the vehicle management module 11, the personnel management module 12, the task management module 13, the cargo management module 14 and the route management module 15, and automatically generate a work log of the current vehicle transportation according to the information, and summarize the work log to generate a report, the report content includes transportation mileage, fuel consumption and safety accident report.

[0069] Relevant companies can conduct visual management of transport vehicles and drivers, monitor vehicles and drivers in real time, track vehicle trajectory status online, form accurate portraits of drivers, fleets, and transport companies, monitor real-time road conditions, and obtain information such as the current road congestion level, whether there is a temporary ban on traffic, and the degree of integrity. When the road is relatively congested, unsmooth, and there are emergencies, the system will re-plan the transport route, manage road section selection and route optimization based on the acquired data, and realize real-time dispatch of transport vehicles.

[0070] The monitoring unit 2 includes a multi-source data monitoring module 21, a multi-source data acquisition module 22, a data correction processing module 23 and a data transmission module 24; the multi-source data monitoring module 21 is used to obtain the real-time position of the vehicle, vehicle details, vehicle trajectory and status data of dangerous goods through the positioning module and sensors; the multi-source data acquisition module 22 is used to collect positioning data, sensor data, vehicle-mounted equipment data and external data, and establish a database to classify and store the collected multi-source data.

[0071] The data correction processing module 23 is used to complete the correction processing of data through data cleaning, data fusion and calibration, time synchronization and data verification;

[0072] When cleaning and fusing data, the following contents are also included:

[0073] Step 1: Remove outliers: Perform a preliminary check on the data collected from each data source, identify outliers that are significantly deviated from the normal range, compare and judge the outliers through the set threshold range, and remove them from the data set;

[0074] Step 2: Process missing values: Check whether there are missing values ​​in the data set. If there are few missing values, interpolation can be used to estimate the missing values ​​based on the values ​​of surrounding data points. If there are many missing values, a prediction model based on a machine learning algorithm can be used to fill in the missing values.

[0075] Step 3: Multi-sensor data fusion: Integrate data from different sensors through data fusion technology;

[0076] Step 4: Sensor calibration: Calibrate the temperature sensor and pressure sensor using standard instruments, compare the sensor's measured values ​​with the standard values, and then adjust the sensor's parameters. When calibrating the positioning sensor, calibrate by adjusting the positioning algorithm's parameters.

[0077] The data transmission module 24 is used for the vehicle terminal to upload the data processed by the data correction processing module 23 to the background server in real time through the 5G communication module, and to establish a data transmission channel for real-time communication between the vehicle terminal and the background server.

[0078] Among them, when the multi-source data monitoring module 21 obtains the real-time position of the vehicle through the positioning module, it combines multi-source positioning fusion, differential positioning and real-time dynamic positioning technology to improve the accuracy of vehicle positioning. When performing multi-source positioning fusion, it combines multiple technologies such as satellite positioning, inertial navigation and base station positioning. When the satellite signal is interfered with, inertial navigation can provide relatively accurate location information in a short time; base station positioning can be used as an auxiliary means to provide supplementary positioning in areas with weak satellite signals such as urban canyons; when performing differential positioning, the known position information of the ground reference station is used to perform differential correction on the satellite positioning data of the vehicle; when performing real-time dynamic positioning, an RTK positioning connection is established between the vehicle terminal and the background server, and centimeter-level high-precision positioning is achieved through carrier phase observations.

[0079] In this embodiment, when performing time synchronization, a time synchronization protocol is used to synchronize the time of each data source to a unified standard time, or in the process of data collection, an accurate timestamp is added to each data point, and data alignment is performed according to the timestamp in subsequent processing. By establishing a time delay model, the time delay of data from different data sources is estimated according to the data transmission path and processing process. Then, in data analysis and processing, the data is compensated for time delay to ensure the time consistency of the data; when performing data verification, the corrected data is compared with historical data, compared with data from other data sources, and rationality checks are performed to complete verification, monitor and manage each link of data collection, processing and correction, formulate data quality standards and indicators, and regularly evaluate and report data quality.

[0080] In the present invention, an early warning trigger mechanism is constructed and a dynamic early warning threshold is set through an early warning mechanism construction module 31. The early warning threshold after dynamic adjustment can be matched with vehicle transportation data in various situations to improve the applicability of the early warning threshold. The threshold change range is set to limit the degree of change of the threshold to avoid excessive threshold adjustment and reduce the sensitivity when triggered. Accurate dangerous goods parameter information and vehicle information can be obtained through the monitoring data acquisition module 32. The data is processed again through the data fast judgment module 33. Through preprocessing, filtering, verification and verification of the data, errors in data transmission can be reduced. Abnormal data points different from normal data patterns are automatically identified through the isolation forest, and marked and processed to solve the problems of abnormal data and data errors, ensure data quality, compare the correct vehicle information and dangerous goods parameter information with the threshold, and accurately trigger the early warning when an abnormality exists, thereby ensuring the timely issuance of the alarm and performing subsequent emergency processing.

[0081] The vehicle management module 11 and the personnel management module 12 can be used to query detailed information and historical work data of vehicles and personnel, so as to facilitate timely viewing and management of abnormal information of vehicles and personnel, and timely interrupt the transportation of dangerous goods when drivers and escorts violate regulations or the vehicle status is abnormal, thereby reducing safety hazards during transportation; the multi-source data acquisition module 22 can be used to collect multi-source data, and multi-source positioning fusion, differential positioning and real-time dynamic positioning technologies are combined when real-time positioning of the vehicle to improve the accuracy of vehicle positioning; the data correction processing module 23 can be used to clean, fuse and synchronize data, and rapid data processing can be achieved; through sensor calibration, the error when the sensor is used can be reduced, thereby improving the accuracy of the data; after data verification, the correction effect of the data can be guaranteed, and the processed data can be updated in real time, thereby ensuring the accuracy of vehicle positioning and dangerous goods status information in the data transmission channel.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dangerous goods transport vehicle positioning system based on 5G vehicle networking, characterized by: It includes a management unit (1), a monitoring unit (2) and an early warning unit (3); The management unit (1) is used to manage vehicles, personnel, transportation tasks, dangerous goods and route information, and automatically generates reports based on multiple acquired data in a 24-hour cycle; The monitoring unit (2) is used to monitor the vehicle and dangerous goods in real time through sensors and positioning devices installed in the vehicle. The background can directly monitor the real-time position, vehicle details and vehicle trajectory of the vehicle, and transmit the acquired vehicle data and dangerous goods status data to the early warning unit (3); The warning unit (3) is used to construct a warning mechanism. When a vehicle approaches a dangerous area, exceeds the speed limit, or the dangerous goods are in an abnormal state, the system automatically issues a warning message after judgment to alert the driver and relevant management personnel; The early warning unit (3) comprises an early warning mechanism building module (31), a monitoring data acquisition module (32), a data rapid judgment module (33) and a result output and triggering module (34); The warning mechanism construction module (31) is used to determine the warning indicator according to the vehicle position, vehicle status and dangerous goods status indicator, and then construct a warning trigger mechanism to set a dynamic warning threshold according to the vehicle position, driving speed and dangerous goods status; The monitoring data acquisition module (32) is used to acquire the monitoring data in the monitoring unit (2), and classify and store the acquired data; The data quick judgment module (33) is used to process the data acquired by the monitoring data acquisition module (32), compare the processed monitoring data with the warning threshold, and judge whether the monitoring data exceeds the warning threshold range; The result output and trigger module (34) is used to output the judgment result in the data rapid judgment module (33), and when the data exceeds the warning threshold range, a warning is quickly triggered, and a warning signal and the current vehicle position information are sent to the control terminal.

2. According to claim 1, a dangerous goods transport vehicle positioning system based on 5G vehicle networking is characterized in that: In the early warning mechanism building module (31), when setting a dynamic early warning threshold, the following contents are also included: A1. Set initial threshold: Based on historical data analysis, obtain the operating status of vehicles and dangerous goods under different circumstances, and set the initial warning threshold according to the statistical results of historical data; A2. Dynamic adjustment: Set the range of change of the warning threshold, and dynamically adjust the warning threshold within the range according to the real-time situation; A3. Monitoring and manual intervention: In special transportation tasks or emergency situations, the warning threshold is temporarily adjusted manually to intervene in dynamic adjustments, and the warning threshold is regularly evaluated and optimized.

3. According to claim 1, a dangerous goods transport vehicle positioning system based on 5G vehicle networking is characterized in that: In the data quick judgment module (33), when processing data, data cleaning and standardization are performed to pre-process the data, and then the data is filtered. After filtering, the data is verified and checked, and finally, isolation forest is used to assist detection. When using isolation forest, the relevant calculation expression is as follows: ； In the formula, n is the number of data points, x is the data point, is the path length, is the harmonic number, is the average path length of the data point in the tree, Score for anomaly.

4. According to claim 1, a dangerous goods transport vehicle positioning system based on 5G vehicle networking is characterized in that: The management unit (1) comprises a vehicle management module (11), a personnel management module (12), a task management module (13), a cargo management module (14), a route management module (15) and a report generation module (16); The vehicle management module (11) is used to associate drivers and escorts, and can add, edit, and review vehicle information, bind and unbind drivers and escorts, and query the vehicle information, maintenance records, and driving tracks of the target vehicle; The personnel management module (12) is used to input, edit, review and query the identity information of drivers and escorts.

5. According to claim 4, a dangerous goods transport vehicle positioning system based on 5G vehicle networking is characterized in that: The task management module (13) is used to automatically allocate, track progress and perform completion statistics on the current waybill task; The supply management module (14) is used for the maintenance of goods, including the category, item, name and record representation of goods; The route management module (15) is used to load the name of the current route, the longitude and latitude of the shipping place, and the longitude and latitude of the receiving place, and to adjust and re-plan the route in real time according to the road condition information; The report generation module (16) is used to synchronize information of the vehicle management module (11), the personnel management module (12), the task management module (13), the cargo management module (14) and the route management module (15), automatically generate a work log of the current vehicle transportation according to the information, and summarize the work log to generate a report, the report content including transportation mileage, fuel consumption and safety accident report.

6. A dangerous goods transport vehicle positioning system based on 5G vehicle networking according to claim 4, characterized in that: When checking the identity information of drivers and escorts in the personnel management module (12), the queryable contents include training records, work records and violation records.

7. A dangerous goods transport vehicle positioning system based on 5G vehicle networking according to claim 5, characterized in that: When the progress is tracked in the task management module (13), the detailed information of the waybill can be viewed, including information query, editing, receipt upload, vehicle track viewing, vehicle real-time location viewing, and vehicle latest location viewing.

8. The dangerous goods transport vehicle positioning system based on 5G vehicle networking according to claim 1 is characterized by: The monitoring unit (2) comprises a multi-source data monitoring module (21), a multi-source data acquisition module (22), a data correction processing module (23) and a data transmission module (24); The multi-source data monitoring module (21) is used to obtain the real-time position of the vehicle, vehicle details, vehicle trajectory and status data of dangerous goods through the positioning module and the sensor; The multi-source data acquisition module (22) is used to collect positioning data, sensor data, vehicle-mounted equipment data and external data, and to establish a database to classify and store the collected multi-source data.

9. A dangerous goods transport vehicle positioning system based on 5G vehicle networking according to claim 8, characterized in that: The data correction processing module (23) is used to complete the correction processing of data through data cleaning, data fusion and calibration, time synchronization and data verification; The data transmission module (24) is used for the vehicle terminal to upload the data processed by the data correction processing module (23) to the backend server in real time through the 5G communication module, thereby establishing a data transmission channel for real-time communication between the vehicle terminal and the backend server.

10. A dangerous goods transport vehicle positioning system based on 5G vehicle networking according to claim 9, characterized in that: When cleaning and fusing data, the following contents are also included: B1. Remove outliers: Perform a preliminary check on the data collected from each data source, identify outliers that are significantly deviated from the normal range, compare and judge the outliers through the set threshold range, and remove them from the data set; B2. Handling missing values: Check whether there are missing values ​​in the data set. If there are few missing values, interpolation can be used to estimate the missing values ​​based on the values ​​of surrounding data points. If there are many missing values, a prediction model based on a machine learning algorithm can be used to fill in the missing values. B3. Multi-sensor data fusion: Integrate data from different sensors through data fusion technology; B4. Sensor calibration: Calibrate the temperature sensor and pressure sensor using standard instruments, compare the sensor's measured value with the standard value, and then adjust the sensor's parameters. When calibrating the positioning sensor, calibration is achieved by adjusting the positioning algorithm's parameters.

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