Logistics vehicle overload alarm management method based on load

Abstract

The invention discloses a load-based logistics vehicle overload alarm management method, relates to the technical field of logistics vehicle management, and aims to solve the problems of inaccurate vehicle load data acquisition and wrong overload judgment. According to the method, the instant change of the vehicle load can be quickly captured through the instant load judgment, the sliding window judgment reflects the long-term load trend of the vehicle by calculating the average load within a period of time, the overload judgment accuracy can be remarkably improved by combining the instant load judgment and the sliding window judgment, and the threshold value better conforms to the actual driving condition through dynamic adjustment; the adaptability is improved, the inertia force is compensated by adding an additional load compensation coefficient on the downhill road section, the overload risk caused by superposition of the self weight of the vehicle and the weight of goods during downhill can be prevented, and therefore the driving safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics vehicle management, and specifically to an overloading warning management method for logistics vehicles based on load Background Art

[0002] Logistics vehicle management refers to the effective organization, command, coordination and control of vehicles used in the logistics transportation process to ensure the efficiency, safety and economy of transportation activities.

[0003] Chinese Patent with publication number CN110766947A discloses a management system, method and device for overloaded vehicles. It mainly monitors the status data of traffic facilities in real time through traffic facility monitoring devices, so that the comprehensive management platform can determine the latest restricted load of the traffic facility according to the latest status data, realizing real-time monitoring of the traffic facility status and determining a reasonable restricted load; measures the load of the vehicle on one side of the vehicle through an on-vehicle terminal to determine in advance whether the vehicle is overloaded, improving the efficiency of overloaded vehicle management and facilitating the vehicle to determine a reasonable driving route; measures the load of the vehicle on one side of the traffic facility through a vehicle information collection device to determine whether the vehicle about to pass through the traffic facility is overloaded, and warns or restricts the overloaded vehicle through the comprehensive management platform, realizing accurate monitoring and effective restriction of overloaded vehicles. Although the above Chinese patent solves the problem of overloaded vehicle management, there are still the following problems in actual operation: 1. When classifying the overloading level of a vehicle, the reference overloading score determined based on the calculation result of the difference in the overloading threshold of the overloaded vehicle is inaccurate, resulting in unscientific overloading level classification.

[0004] 2. When determining whether a vehicle is overloaded, a more accurate determination mode is not adopted, resulting in inaccurate overloading judgment.

[0005] 3. No targeted dynamic adjustment is made according to the impact of the surrounding traffic flow conditions of the vehicle on load safety, resulting in a deviation of the load threshold.

[0006] 4. After obtaining the load data of the vehicle, no further data processing and calibration are performed, resulting in inaccurate original data. Summary of the Invention

[0007] The object of the present invention is to provide an overloading warning management method for logistics vehicles based on load. Through instantaneous load determination, the instantaneous change of the vehicle load can be quickly captured, while the sliding window determination reflects the long-term load trend of the vehicle by calculating the average load within a period of time. The combination of the two can significantly improve the accuracy of overloading judgment, and the dynamic adjustment makes the threshold more in line with the actual driving conditions, improving the adaptability. In the downhill section, an additional load compensation coefficient is added to compensate for the inertial force, which helps to prevent the overloading risk caused by the superposition of the vehicle's own weight and the cargo weight when going downhill, thereby enhancing the driving safety and solving the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions: An overloading warning management method for logistics vehicles based on load, comprising: Real-time monitoring of the vehicle's load information, and transmitting the monitored vehicle load information to the data processing center for data processing, where the data processing includes data preprocessing and calibration; Performing dynamic threshold setting on the processed load data, making an overloading judgment after the setting is completed, performing early warning processing according to the overloading judgment result, and finally generating a statistical report on the early warning result; The process of dynamic threshold setting is as follows: First, retrieve the vehicle identification information, query the vehicle characteristic database according to the vehicle identification information, where the database pre-stores the rated load parameters of different vehicle models, and obtain the reference load data of the vehicle according to the vehicle characteristic database; Then, use the on-vehicle GPS to monitor the current driving speed of the vehicle in real time, and use the GPS and road map data to detect the road slope of the vehicle driving section; Calculate the dynamic safety load threshold according to the vehicle's reference load data, driving speed and road slope.

[0009] Preferably, real-time monitoring of the vehicle's load information includes: Obtaining the vehicle's load information; The load information includes the total vehicle weight, axle load, wheel load, cargo weight and vehicle basic information; The total vehicle weight is the sum of the vehicle's own weight and the weight of the carried cargo, and real-time data collection is carried out through on-vehicle sensors; the axle load is the load on each axle, and data collection is carried out through pressure sensors on each axle; the wheel load is the weight borne by each wheel, and data collection is carried out through pressure sensors on each wheel; the cargo weight is the weight of the carried cargo, and the weight of the carriage or pallet is monitored in real time through an on-vehicle electronic scale; the vehicle basic information is the vehicle type, vehicle identification information and time stamp; Finally, obtain the real-time monitored vehicle load information.

[0010] Preferably, the monitored vehicle load information is transmitted to a data processing center for data processing, including: Integrate the collected vehicle load information, and format the data after integration; Use wireless communication technology to transmit the vehicle load information after data formatting; The data processing center receives the vehicle load information and performs data verification after receiving it; Data verification includes checking data packets, timestamps, and duplicate data; Store the qualified verified data in a database or cloud platform.

[0011] Preferably, transmitting the monitored load information to a data processing center for data processing further includes: Perform data preprocessing on the data in the database or cloud platform; Data preprocessing includes data denoising, missing value processing, outlier detection, and data standardization; After data preprocessing, perform data calibration, which includes sensor error correction, sensor calibration, data fusion, and multi-sensor calibration; After data calibration is completed, perform consistency and integrity verification, and obtain the processed load data after verification.

[0012] Preferably, setting a dynamic threshold for the processed load data further includes: Among them, when the vehicle is on a downhill section, an additional load compensation coefficient is added, and the increase range of the load compensation coefficient is between 8% and 15% to compensate for the inertial force; Finally, obtain the dynamic threshold of the vehicle, which is the reference threshold for judging whether it is overloaded.

[0013] Preferably, in the process of calculating the dynamic safe load threshold, it further includes: When the available margin of the parallel lane of the vehicle is less than the set available threshold, increase the front traffic flow compensation coefficient and the rear traffic flow compensation coefficient. Specifically: According to the driving conditions of adjacent vehicles within the set monitoring range monitored by the on-vehicle monitoring device, determine the available margin of the parallel lane of each vehicle based on the driving conditions, the first driving vehicle in front of the current vehicle, and the second driving vehicle behind the current vehicle; Obtain the first distance between each first driving vehicle and the current vehicle, and determine the safety distance level between each first driving vehicle and the current vehicle by comparing the first distance with the first preset distance threshold range; Group the first driving vehicles with the same safety distance level to obtain a first safety distance-vehicle combination; Determine a first compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the first safety distance-vehicle combination and the corresponding number of vehicles included; Summarize the first compensation coefficients of all the first safety distance-vehicle combinations to obtain a compensation coefficient for the oncoming traffic flow; Obtain the second distance between each second moving vehicle and the current vehicle, and determine the safety distance level between each second moving vehicle and the current vehicle by comparing the second distance with a second preset distance threshold range; Group the second moving vehicles with the same safety distance level to obtain a second safety distance-vehicle combination; Determine a second compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the corresponding number of vehicles included; Summarize the second compensation coefficients of all the second distance-vehicle combinations to obtain a compensation coefficient for the following traffic flow.

[0014] Preferably, after the setting is completed, an overloading judgment is performed, and early warning processing is carried out according to the overloading judgment result, including: Perform an overloading judgment on the vehicle according to the set dynamic threshold; The overloading judgment is a dual judgment mechanism, and the dual judgment mechanism combines the instantaneous load and the average load of the sliding window; The instantaneous load judgment is to retrieve the real-time collected data from the database or cloud platform of the data processing center every 0.5 seconds; Compare the retrieved real-time collected data with the dynamic threshold. If the instantaneous load is greater than the dynamic threshold, the overloading judgment is triggered in this cycle. If the instantaneous load is less than or equal to the dynamic threshold, the overloading judgment is not triggered in this cycle; The sliding window judgment is to use a 60-second sliding window as a cycle, and calculate the average load within 60 seconds in each cycle; Compare the average load with the dynamic threshold. If the average load is greater than the dynamic threshold, it is determined as overloading; if the average load is less than the dynamic threshold, the overloading judgment is not triggered.

[0015] Preferably, after the setting is completed, an overloading judgment is performed, and early warning processing is carried out according to the overloading judgment result, and it also includes: For the results of the sliding window judgment and the instantaneous load judgment, if they exceed the dynamic threshold in three consecutive cycles, it is determined as overloading; Finally, obtain overloaded vehicles and non-overloaded vehicles; Calculate the difference of the overloading threshold of the overloaded vehicles, and perform an overloading level judgment according to the difference calculation result; Among them, the larger the threshold of the difference calculation result, the higher the overloading level; The overloading levels are divided into slight overloading, moderate overloading, and severe overloading; Warning processing of different intensities is carried out according to different overloading levels; Among them, the warning processing for slight overloading is to give an overloading prompt through the in-vehicle display screen; the warning processing for moderate overloading is to activate the audible and visual alarm and lock the vehicle speed limit when overloaded; the warning processing for severe overloading is to automatically send an encrypted alarm message to the supervision platform after continuous overloading for 5 minutes, synchronously cut off the power output and generate a violation event log.

[0016] Preferably, the overloading level is determined according to the difference calculation result, including: If, according to the instantaneous load determination result of the current overloaded vehicle, the instantaneous loads in three consecutive cycles all exceed the dynamic threshold, then the reference overloading score is calculated using the instantaneous loads in the current three cycles and the dynamic threshold; If, according to the sliding window determination result of the current overloaded vehicle, the average loads in three consecutive cycles all exceed the dynamic threshold, then the reference overloading score is calculated using the average loads in the current three cycles and the dynamic threshold; The obtained reference overloading score is used as the difference calculation result for overloading level determination.

[0017] Preferably, it further includes: Obtain and analyze the historical load information of overloaded vehicles within a set time period. If there is a historical overloading record, obtain the total number of historical overloading times, the overloading level of each historical overloading, and the historical overloading time; Determine the time interval between the historical overloading time of each historical overloading and the current time, and mark it as the reference time interval; Based on the reference time interval from the current time, assign corresponding time decay weights to each historical overloading; If the total number of historical overloading times of the overloaded vehicle does not exceed the preset overloading times, the corresponding reference overloading score is not adjusted; If the total number of historical overloading times of the overloaded vehicle exceeds the preset overloading times, then according to the comparison result between the reference time interval and the set time length constraint, the historical overloadings of different overloading levels are divided to obtain the corresponding first historical overloading combination and second historical overloading combination; Obtain the reference decay weights of the first historical overloading combination and the second historical overloading combination corresponding to different overloading levels; Input the historical overloading times of all historical overloadings corresponding to each overloading level included in the current overloaded vehicle into a pre-established pattern recognition model to obtain the pattern recognition result corresponding to the overloading level; According to the pattern recognition result, assign pattern adjustment weights to each overloading level included in the current overloaded vehicle; Combine the mode adjustment weights of different overload levels included in the current overloaded vehicle with the benchmark decay weights of the corresponding first historical overload combination and the benchmark decay weights of the second historical overload combination to obtain the target adjustment value; Use the obtained target adjustment value to adjust the reference overload score of the current overloaded vehicle.

[0018] Preferably, finally generate a statistical report on the warning result, including: Integrate the overload information, overload level information, and warning processing information of the vehicle with the basic information of the vehicle; After the data integration is completed, generate a vehicle statistical report. The generated statistical report includes the basic information of each vehicle, overload events, overload processing results, and safety risk relationships; Among them, the presentation methods of the vehicle statistical report are charts, tables, images, and text reports; Automatically send the generated vehicle statistical report to the corresponding staff through email, text message, and system notification methods.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The method for managing overload warnings of logistics vehicles based on load provided by the present invention, steps such as data integration, formatting, verification, and preprocessing ensure the accuracy and reliability of the data, reduce false alarms or missed alarms caused by human factors or equipment errors. Real-time monitoring of vehicle load helps prevent overloading and reduces the risk of traffic accidents caused by overloading. For vehicles transporting dangerous goods, real-time monitoring of the load can ensure that they operate within a safe range and reduce potential safety hazards.

[0020] 2. The method for managing overload warnings of logistics vehicles based on load provided by the present invention, dynamic adjustment makes the threshold more in line with actual driving conditions, improves adaptability, and compensates for inertial forces by increasing an additional load compensation coefficient on downhill sections, which helps prevent the overload risk caused by the superposition of the vehicle's own weight and cargo weight when going downhill, thereby enhancing driving safety.

[0021] 3. The method for managing overload warnings of logistics vehicles based on load provided by the present invention, instantaneous load determination can quickly capture the immediate change in vehicle load, while sliding window determination reflects the long-term load trend of the vehicle by calculating the average load over a period of time. The combination of the two can significantly improve the accuracy of overload judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps for managing overload warnings of logistics vehicles of the present invention; Figure 2 It is a schematic diagram of the process for managing overload warnings of logistics vehicles of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0024] In order to solve the problem in the prior art that after obtaining the load data of a vehicle, no further data processing and calibration are performed, resulting in inaccurate original data, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment: An overloading warning management method for logistics vehicles based on load includes: Real-time monitoring of the load information of the vehicle and transmitting the monitored vehicle load information to the data processing center for data processing, where the data processing includes data preprocessing and calibration; Performing dynamic threshold setting on the processed load data, making an overloading judgment after the setting is completed, performing early warning processing according to the overloading judgment result, and finally generating a statistical report of the early warning result.

[0025] Specifically, by real-time monitoring of the load information of the vehicle, over-limit and overloading behaviors can be discovered and warned in a timely manner, effectively avoiding omissions in the traditional sampling inspection method. The system has a fast response speed and can complete data collection, analysis, and warning in a short time, improving the governance efficiency. By adopting advanced weighing sensors and data processing technologies, the accuracy and reliability of the monitoring data are ensured. The data preprocessing step can remove noise and outliers in the data, reduce the risk of model overfitting, and improve the generalization ability of the model, thereby enhancing the reliability and accuracy of the data. It allows for dynamic adjustment of the threshold according to actual needs to adapt to different application scenarios and regulatory requirements. This flexibility enables the management method to more precisely adapt to various situations and improve the accuracy of early warning. Through the automated data processing and early warning mechanism, the burden of manual management is greatly reduced, and the management efficiency is improved. At the same time, by timely warning and handling overloading behaviors, it helps to reduce road damage and traffic accidents caused by overloading, thereby reducing maintenance costs and accident handling costs. The finally generated early warning statistical report can provide strong decision-making support for the management department. Through the analysis and reporting of data, the management department can more accurately understand the road transportation situation and provide a basis for formulating more scientific traffic management strategies.

[0026] Real-time monitoring of the load information of the vehicle includes: Obtaining the load information of the vehicle; The load information includes the total vehicle weight, axle load, wheel load, cargo weight, and vehicle basic information; The total vehicle weight is the self-weight of the vehicle itself and the weight of the carried cargo, and real-time data collection is carried out through on-vehicle sensors; the axle load is the load on each axle, and data collection is carried out through pressure sensors on each axle; the wheel load is the weight borne by each wheel, and data collection is carried out through pressure sensors on each wheel; the cargo weight is the weight of the carried cargo, and the weight of the carriage or pallet is monitored in real time through an on-vehicle electronic scale; the vehicle basic information is the vehicle type, vehicle identification information, and timestamp; Finally, the real-time monitored vehicle load information is obtained.

[0027] Specifically, real-time data collection through on-vehicle sensors and on-vehicle electronic scales ensures the timeliness of vehicle load information. Whether it is the total vehicle weight, axle load, wheel load, or cargo weight, the latest data can be obtained in a short time. Using high-precision sensors and electronic scales for data collection improves the accuracy of the data, avoiding errors caused by manual measurement or estimation. It covers multiple aspects of vehicle load, including the total vehicle weight, axle load, wheel load, cargo weight, and vehicle basic information, providing comprehensive load information. The addition of vehicle basic information (such as vehicle type, vehicle identification information, and timestamp) makes the data more complete, facilitating subsequent analysis and management. Real-time monitoring of vehicle load helps prevent overloading and reduces the risk of traffic accidents caused by overloading. For vehicles transporting dangerous goods, real-time monitoring of the load can ensure that they operate within a safe range, reducing potential safety hazards. The automated data collection and processing process reduces manual intervention and improves work efficiency. The real-time monitoring data can be immediately fed back to managers or drivers, facilitating them to make timely adjustments and optimize the transportation plan. The real-time collected data can be stored in a database for subsequent data analysis and mining. Through data analysis, the load distribution and transportation efficiency of the vehicle can be understood, providing strong support for optimizing transportation strategies.

[0028] Transmit the monitored vehicle load information to the data processing center for data processing, including: Integrate the collected vehicle load information, and format the data after integration; Use wireless communication technology to transmit the vehicle load information with formatted data; The data processing center receives the vehicle load information and conducts data verification after receiving it; Data verification includes the inspection of data packets, timestamps, and duplicate data; Store the verified data in a database or cloud platform.

[0029] Preprocess the data in the database or cloud platform; Data preprocessing includes data denoising, missing value handling, outlier detection, and data normalization; After data preprocessing, data calibration is performed, which includes sensor error correction, sensor calibration, data fusion, and multi-sensor calibration; After data calibration is completed, consistency and integrity verification are carried out, and the processed load data is obtained after verification.

[0030] Specifically, through automated data collection and wireless communication technologies, real-time monitoring and transmission of vehicle load information can be achieved, greatly improving the detection efficiency, reducing manual intervention and waiting time. The real-time monitoring function can effectively prevent overloaded vehicles from passing, ensuring the safety and smoothness of road traffic. Steps such as data integration, formatting, verification, and preprocessing ensure the accuracy and reliability of the data, reducing false alarms or missed reports caused by human factors or equipment errors. Data denoising, missing value handling, outlier detection, and data normalization in the data preprocessing step further improve the data quality. An intelligent data processing flow is adopted, including steps such as data integration, formatting, transmission, verification, preprocessing, and calibration, realizing the automated processing of data. Sensor error correction, sensor calibration, data fusion, and multi-sensor calibration in the data calibration step improve the accuracy and consistency of the data. The processed load data can provide strong decision-making support for traffic management departments, helping them better understand the road traffic conditions and formulate corresponding management measures. Through data analysis, the patterns and characteristics of traffic violations can also be discovered, providing a scientific basis for law enforcement. By real-time monitoring and managing the load conditions of vehicles, the solution helps to ensure the safety and compliance of road traffic, reducing traffic accidents and violations caused by overloading.

[0031] To solve the problem in the prior art that there is no targeted dynamic adjustment according to the actual situation of the vehicle, resulting in no compensation for the load coefficient when the vehicle is going uphill or downhill and a deviation in the load threshold, please refer to Figure 1 and Figure 2 This embodiment provides the following technical solutions: Set dynamic thresholds for the processed load data, including: The process of setting dynamic thresholds is as follows: First, retrieve the vehicle identification information, query the vehicle feature database according to the vehicle identification information. Among them, the rated load parameters of different vehicle models are pre-stored in the database, and the reference load data of the vehicle is obtained according to the vehicle feature database; Then, use the on-vehicle GPS to monitor the current driving speed of the vehicle in real time, and use GPS and road map data to detect the road slope of the vehicle's driving section; Calculate the dynamic safety load threshold based on the vehicle's reference load data, driving speed, and road gradient; When the vehicle is on a downhill section, an additional load compensation coefficient is added, and the range of increase in the load compensation coefficient is between 8% - 15% to compensate for the inertial force; Finally, the dynamic threshold of the vehicle is obtained, and this dynamic threshold is the reference threshold for determining whether there is overloading.

[0032] Specifically, by retrieving the vehicle identification information and querying the vehicle feature database, the solution can obtain specific reference load data for each vehicle. This personalized processing is more accurate than using a unified standard and can better adapt to the actual load capacity of different vehicle models. By using the on-vehicle GPS to monitor the vehicle's driving speed in real time and combining with the road map data to detect the road gradient, the solution can adjust the safety load threshold in real time. This dynamic adjustment makes the threshold more in line with the actual driving conditions and improves the adaptability. Especially on the downhill section, the solution compensates for the inertial force by increasing an additional load compensation coefficient, which helps prevent the overloading risk caused by the superposition of the vehicle's own weight and the cargo weight when going downhill, thereby enhancing the driving safety. Combining multiple technical means such as vehicle identification, GPS monitoring, and map data, it realizes the intelligent management of the load data. This not only improves the management efficiency but also reduces the possibility of human intervention and misjudgment. By setting the dynamic threshold as the reference for determining whether there is overloading, the solution helps prevent the occurrence of overloading behavior in advance. This can not only protect the road infrastructure but also reduce the risk of traffic accidents.

[0033] To solve the problem in the prior art that there is no targeted dynamic adjustment according to the impact of the surrounding traffic flow conditions of the vehicle on the load safety, resulting in a deviation in the load threshold, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solution: During the process of calculating the dynamic safety load threshold, it further includes: When the available space in the parallel lane of the vehicle is less than the set available threshold, increase the front traffic flow compensation coefficient and the rear traffic flow compensation coefficient. Specifically: According to the driving conditions of adjacent vehicles within the set monitoring range monitored by the on-vehicle monitoring device in real time, determine the available space in the parallel lane of each vehicle, including the first driving vehicle in front of the current vehicle and the second driving vehicle behind the current vehicle; Obtain the first distance between each first driving vehicle and the current vehicle, and by comparing the first distance with the first preset distance threshold range, determine the safety distance level between each first driving vehicle and the current vehicle; Group the first driving vehicles with the same safety distance level to obtain the first safety distance - vehicle combination; Determine a first compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the first safety distance-vehicle combination and the corresponding number of vehicles included. Sum up the first compensation coefficients of all first safety distance-vehicle combinations to obtain a compensation coefficient for the oncoming traffic flow. Obtain the second distance between each second moving vehicle and the current vehicle, and determine the safety distance level between each second moving vehicle and the current vehicle by comparing the second distance with a second preset distance threshold range. Group the second moving vehicles with the same safety distance level to obtain a second safety distance-vehicle combination. Determine a second compensation coefficient from the preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the corresponding number of vehicles included. Sum up the second compensation coefficients of all second distance-vehicle combinations to obtain a compensation coefficient for the following traffic flow.

[0034] In this embodiment, the on-vehicle monitoring device refers to a monitoring device installed on a vehicle for capturing images of the front, rear, left, and right of the vehicle in real time, generally referring to a high-definition camera; the set monitoring range refers to the area range that is pre-determined for the effective monitoring of the on-vehicle monitoring device; the driving conditions of adjacent vehicles refer to the relative positions and the number of vehicles of other vehicles around the current vehicle; the spare space in the parallel lane refers to the space margin between the lane where the current vehicle is located and the adjacent lane, that is, the space where the current vehicle can move horizontally without colliding with other vehicles; the set spare threshold is a pre-determined value for judging whether the spare space in the parallel lane is sufficient, generally set to the body width of the current vehicle.

[0035] In this embodiment, the first moving vehicle refers to a vehicle driving in front of the current vehicle; the second moving vehicle refers to a vehicle driving behind the current vehicle; the first distance refers to the distance between the current vehicle and the first moving vehicle; the first preset distance threshold range is a preset distance range for classifying the distance between the first moving vehicle and the current vehicle into different levels; the safety distance levels include three levels: close distance, medium and long distance, and long distance.

[0036] In this embodiment, for example, there is a first preset distance threshold range of , where m is in meters, and the first distances between the first moving vehicles a1, a2, and a3 and the current vehicle are 1.3 m, 2.3 m, and 4.1 m respectively. At this time, the safety distance level between the first moving vehicle a1 and the current vehicle is the close distance level, the safety distance level between the first moving vehicle a2 and the current vehicle is the medium and long distance level, and the safety distance level between the first moving vehicle a3 and the current vehicle is the long distance level.

[0037] In this embodiment, the first safety distance - vehicle combination refers to the combination obtained by aggregating the first moving vehicles of the same safety distance level; the preset vehicle - distance compensation mapping table is a pre - set table composed of different safety distance levels, vehicle quantity ranges, and corresponding compensation coefficients (the value range is (0, 1)). Among them, for each safety distance level, the preset vehicle - distance compensation mapping table lists different vehicle quantity ranges, and different vehicle quantity ranges correspond to different compensation coefficients. For example, in the short - distance level, the vehicle quantity range is divided into 1 - 2 vehicles, 3 - 5 vehicles, and 6 or more vehicles, and the corresponding compensation coefficients are 0.5, 0.55, and 0.7 respectively.

[0038] In this embodiment, the first compensation coefficient is the compensation coefficient found and determined from the preset vehicle - distance compensation mapping table according to the distance level and the number of vehicles included in the first safety distance - vehicle combination; the front - traffic - flow compensation coefficient is obtained by summing the corresponding first compensation coefficients of all the first safety distance - vehicle combinations obtained, and is used to adjust the load threshold of the current vehicle based on considering the traffic flow situation in front of the current vehicle. Among them, for example, if the front - traffic - flow compensation coefficient of vehicle 1 is c1, then the adjusted load threshold , where, represents the load threshold of vehicle 1 before adjustment; e represents the base of the natural logarithm, with a value of 2.7; Among them, for example, when c1 = 0.5, , which means that when the front traffic flow is dense, the load threshold is reduced by 37%.

[0039] In this embodiment, for example, there is a dynamic threshold adjustment process as follows: Input: Load threshold before adjustment tons, downhill slope 5°, compensation coefficient + 10% → Output tons; Input: Front traffic flow c1 = 0.5 → Output dynamic threshold tons.

[0040] In this embodiment, the second distance refers to the distance between the current vehicle and the second moving vehicle; the second preset distance threshold range is a preset distance range used to divide the distance between the second moving vehicle and the current vehicle into different levels; the second safety distance - vehicle combination refers to the combination obtained by aggregating the second moving vehicles of the same safety distance level.

[0041] In this embodiment, the second compensation coefficient is determined by searching the preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the number of vehicles included; the rear traffic compensation coefficient is obtained by summing the corresponding second compensation coefficients of all acquired second safety distance-vehicle combinations, and is used to adjust the load threshold of the current vehicle based on the consideration of the rear traffic situation of the current vehicle, where, for example, if the rear traffic compensation coefficient of vehicle 2 is c2, the adjusted load threshold ,in, It represents the load threshold of vehicle 2 before adjustment; e represents the base of the natural logarithm, and its value is 2.7.

[0042] In this embodiment, the construction of the front / rear traffic flow compensation formula refers to the idea of ​​the car-following model, which specifically means that the driving conditions of adjacent vehicles are monitored in real time by the on-board monitoring equipment, and the parallel lane vacancy of the vehicle is determined according to the driving conditions. This process is actually simulating the perception and response of the rear vehicle to the driving state of the front vehicle in the car-following model; According to the safety distance level between the vehicle in front and the current vehicle, if the traffic in front is dense, the current vehicle may face greater driving pressure and potential risks. Therefore, the compensation coefficient of the traffic in front is appropriately increased to reflect the potential limitation of the traffic in front on the load capacity of the current vehicle. Similarly, according to the safety distance level between the vehicle behind and the current vehicle, if the traffic in the rear is dense, the compensation coefficient of the traffic in the rear is appropriately increased to consider the impact of the traffic in the rear on the driving state of the current vehicle.

[0043] In this embodiment, the adjustment formula of the front / rear traffic flow compensation coefficient is based on the normal distribution transformation. Through the combination of the exponential function and the square root, the change of the load threshold follows the transformed normal distribution, thereby better describing the load changes in the actual traffic flow; wherein, the exponential function is used to smoothly show the change of the load threshold with the front / rear traffic flow compensation coefficient; the square root is used to perform a square root operation on the result of the exponential function, thereby further making the change of the load threshold smoother and more in line with the characteristics of the normal distribution, and in line with the gradual change characteristics in the actual traffic flow, thereby better describing the load changes in the actual traffic flow.

[0044] The working principle of the above technical solution is as follows: First, determine the available margin of the parallel lane of the current vehicle and identify the vehicles traveling in front and behind. Then, when the available margin of the parallel lane is less than the set threshold, calculate the distances between the vehicles in front and behind and the current vehicle respectively, and classify these vehicles into different safe distance levels according to the preset distance threshold range; perform set processing on the vehicles in the same safe distance level to form a combination of safe distance - vehicle; then, from the preset vehicle - distance compensation mapping table, find and determine the corresponding compensation coefficients according to the safe distance levels and the number of vehicles in these combinations; finally, summarize the compensation coefficients of all safe distance - vehicle combinations to obtain the compensation coefficients of the traffic flow in front and the traffic flow behind respectively, so as to adjust the load threshold of the current vehicle.

[0045] The beneficial effects of the above technical solution are as follows: By monitoring the driving conditions around the vehicle in real time and considering the influence of the traffic flow in front and behind on the load safety, the driving environment of the current vehicle can be evaluated more accurately, so as to dynamically adjust the load threshold, ensure that the vehicle can maintain safe driving in a complex traffic environment, enhance the adaptability of the vehicle and effectively prevent traffic accidents.

[0046] To solve the problem in the prior art that when determining overloading of a vehicle, a more accurate determination mode is not adopted, resulting in inaccurate overloading judgment, please refer to Figure 1 and Figure 2 , the following technical solution is provided in this embodiment: After setting, perform overloading judgment, and perform early warning processing according to the overloading judgment result, including: Perform overloading judgment on the vehicle according to the set dynamic threshold; The overloading judgment is a dual - judgment mechanism, and the dual - judgment mechanism combines the instantaneous load and the average load of the sliding window; The instantaneous load judgment is to retrieve the real - time collected data from the database or cloud platform of the data processing center every 0.5 seconds; Compare the retrieved real - time collected data with the dynamic threshold. If the instantaneous load is greater than the dynamic threshold, the overloading judgment is triggered in this cycle. If the instantaneous load is less than or equal to the dynamic threshold, the overloading judgment is not triggered in this cycle; The sliding window judgment is to use a 60 - second sliding window as a cycle, and calculate the average load within 60 seconds in each cycle; Compare the average load with the dynamic threshold. If the average load is greater than the dynamic threshold, it is determined as overloading; if the average load is less than the dynamic threshold, the overloading judgment is not triggered.

[0047] Regarding the results of the sliding window judgment and the instantaneous load judgment, if they exceed the dynamic threshold in three consecutive cycles, it is determined as overloading; Finally, overloaded vehicles and non-overloaded vehicles are obtained; Calculate the difference value of the overload threshold of the overloaded vehicle, and determine the overload level according to the calculation result of the difference value; Among them, the larger the threshold value of the difference calculation result, the higher the overload level; The overload levels are divided into slight overload, moderate overload and severe overload; Perform warning processing with different intensities according to different overload levels; Among them, the warning processing for slight overload is to give an overload prompt through the in-vehicle display screen; the warning processing for moderate overload is to start the audible and visual alarm and lock the vehicle speed limit when overloaded; the warning processing for severe overload is to automatically send an encrypted alarm message to the supervision platform after continuous overload for 5 minutes, synchronously cut off the power output and generate a violation event log.

[0048] In this embodiment, the initial reference value of the dynamic threshold refers to the maximum safe load calibrated at the vehicle factory.

[0049] Specifically, a dual mechanism combining instantaneous load determination and sliding window determination is adopted, which can more comprehensively and accurately evaluate the load situation of the vehicle. Instantaneous load determination can quickly capture the immediate change of the vehicle load, while sliding window determination reflects the long-term load trend of the vehicle by calculating the average load within a period of time. The combination of the two can significantly improve the accuracy of overload judgment. Using a dynamic threshold for overload judgment means that the threshold can be adjusted according to the actual situation to adapt to different roads, different vehicle types or different transportation requirements. This flexibility enables the solution to be more widely applied to various scenarios and improves its practicality. Performing warning processing with different intensities according to the overload level can ensure that measures are taken in a timely manner when the vehicle is overloaded to prevent potential safety hazards. At the same time, hierarchical warning processing can also take appropriate measures according to different situations to avoid overreaction or ignoring problems. Using the database or cloud platform of the data processing center for real-time data collection and analysis reflects advanced technical means. By obtaining and processing data in real time, the solution can achieve real-time monitoring and judgment of the vehicle load, improving work efficiency and accuracy. In the case of severe overload, measures such as cutting off the power output and generating a violation event log are taken, which can ensure the safety of the vehicle in extreme situations. At the same time, automatically sending an encrypted alarm message to the supervision platform can also notify the relevant departments for processing in a timely manner, further improving the safety of the solution.

[0050] Determine the overload level according to the calculation result of the difference value, including: If, according to the instantaneous load determination result of the current overloaded vehicle, the instantaneous loads in three consecutive cycles all exceed the dynamic threshold, then use the instantaneous loads in the current three cycles and the dynamic threshold to calculate the reference overload score; If, according to the determination result of the sliding window of the current overloaded vehicle, the load average values of three consecutive cycles all exceed the dynamic threshold, then use the load average value of the current three cycles and the dynamic threshold to calculate the reference overload score. Use the obtained reference overload score as the difference calculation result to determine the overload level.

[0051] In this embodiment, the calculation formula of the reference overload score determined according to the instantaneous load determination result or the sliding window determination result is as follows: ; In the formula, represents the reference overload score determined according to the instantaneous load determination result of the current overloaded vehicle or the sliding window determination result; represents the instantaneous load of the current overloaded vehicle in the i-th cycle determined according to the instantaneous load result, or the load average value of the current overloaded vehicle in the i-th cycle determined according to the sliding window determination result, where i = 1, 2, 3; represents the dynamic threshold; e represents the base of the natural logarithm, with a value of 2.7; Among them, for example, there is a reference overload score calculation example as follows: Input: → Output .

[0052] In this embodiment, calculating the reference overload score according to the instantaneous load determination result or the sliding window determination result is based on the principle of accumulating the load differences between the instantaneous loads or load average values of three consecutive cycles and the dynamic threshold. By using the exponential model, the reference overload score is converted into a value between 0 and 1; among them, the exponential model is used to smoothly show the change of the reference overload score with the accumulation of load differences.

[0053] The beneficial effects of the above technical solution are: By determining the reference overload score according to the instantaneous load determination result or the sliding window determination result, it can ensure that whether it is an instantaneous high load or a continuous high load, the load status of the vehicle can be accurately captured and quantified, and the introduction of the reference overload score provides a more refined measurement standard for overload determination, making the division of overload levels more scientific and reasonable, and providing strong data support for subsequent early warning processing.

[0054] To solve the problem in the prior art that when dividing the overload level of a vehicle, the reference overload score determined based on the difference calculation result of the overload threshold of the overloaded vehicle is inaccurate, resulting in an unscientific division of the overload level, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solution: Obtain and analyze the historical load information of overloaded vehicles within a set time period. If there are historical overloading records, obtain the total number of historical overloads, the overload level of each historical overload, and the historical overload time; Determine the time interval between the historical overload time of each historical overload and the current time, and mark it as the reference time interval; Based on the reference time interval from the current time, assign corresponding time decay weights to each historical overload; If the total number of historical overloads of the overloaded vehicle does not exceed the preset number of overloads, do not adjust the corresponding reference overload score; If the total number of historical overloads of the overloaded vehicle exceeds the preset number of overloads, divide the historical overloads of different overload levels according to the comparison result between the reference time interval and the set time length constraint, and obtain the corresponding first historical overload combination and second historical overload combination; Obtain the reference decay weights of the corresponding first historical overload combination and the second historical overload combination for different overload levels; Input the historical overload time of all historical overloads corresponding to each overload level included in the current overloaded vehicle into a pre-established pattern recognition model to obtain the pattern recognition result corresponding to the overload level; According to the pattern recognition result, assign pattern adjustment weights to each overload level included in the current overloaded vehicle; Combine the pattern adjustment weights of different overload levels included in the current overloaded vehicle with the reference decay weights of the corresponding first historical overload combination and the second historical overload combination to obtain the target adjustment value; Use the obtained target adjustment value to adjust the reference overload score of the current overloaded vehicle.

[0055] In this embodiment, the set time period refers to a specific time period preset for obtaining historical load information for evaluating overloading behavior; historical load information refers to the data records of each load of an overloaded vehicle in the past period, including the weight, load time, load location, etc. of each load.

[0056] In this embodiment, historical overloading records refer to the overloading records of vehicles in historical load information, including the time of overloading, the weight of overloading, and the level of overloading, etc.; the total number of historical overloads refers to the total number of all overloading records of a vehicle in the past period; the historical overload time refers to the specific time point when each overloading occurs; historical overloading refers to overloading events that occurred in the past.

[0057] In this embodiment, the reference time interval refers to the time difference between each historical overload time and the current time; the time decay weight is expressed as , where e represents a constant with a value of 2.7; Denoted as the attenuation coefficient, which controls the rate of weight reduction; Denoted as the reference time interval; the preset overloading times is a threshold set in advance to determine whether a vehicle is frequently overloaded, such as 5 times.

[0058] In this embodiment, the historical overloads with a reference time interval not exceeding the set time length constraint for each overload level are classified as the first historical overload combination, and the historical overloads with a reference time interval exceeding the set time length constraint are classified as the second historical overload combination; the set time length constraint refers to the limit on the time difference between the occurrence time point of the historical load behavior and the current moment set in advance, such as 1 month.

[0059] In this embodiment, the reference attenuation weight is obtained by averaging the average value of the maximum and minimum time attenuation weights within the current historical overload combination and the average time attenuation weight.

[0060] In this embodiment, the pattern recognition model is a model obtained by training a neural network using a large amount of collected historical load data for identifying overload patterns. In terms of the neural network structure, a deep neural network including multiple hidden layers is designed. Among them, the input layer is responsible for receiving historical load data, and the dimension of the input data is determined according to specific load records, including multiple features such as timestamps and load weights; after the input data is preprocessed, it is sent to the hidden layer for feature extraction and transformation; the hidden layer consists of multiple fully connected layers, each layer contains a certain number of neurons, and an activation function is used to introduce non-linearity. By gradually extracting and transforming features layer by layer, the neural network can gradually learn the complex patterns in the load data; the output layer uses the Softmax activation function to convert the output of the neural network into a probability distribution, so as to judge which overload pattern the input data belongs to. Among them, the output dimension corresponds to the number of categories of overload patterns, and the output dimension is 3, corresponding to the low-frequency pattern, high-frequency pattern, and high-low frequency mixed pattern respectively.

[0061] In this embodiment, the pattern recognition result, that is, the overload pattern includes the low-frequency pattern, high-frequency pattern, and high-low frequency mixed pattern; the low-frequency pattern refers to frequent overloads within multiple consecutive cycles (such as 3 overloads within a week); the high-frequency pattern refers to overloads at long intervals (such as once a month but lasting for half a year); the high-low frequency mixed pattern refers to the overload behavior showing both high-frequency and low-frequency characteristics in time. For example, there may be a vehicle that frequently overloads within a few months (such as 2-3 times a month), but the overload behavior decreases in the following months and only occurs occasionally (such as once every two or three months).

[0062] In this embodiment, the mode adjustment weight refers to the adjustment weight assigned to different overloading levels of a vehicle according to the pattern recognition result, where the weights assigned to the low-frequency mode, high-frequency mode, and high-low frequency hybrid mode respectively and The value ranges of both are , and are obtained by solving the matrix constructed after pairwise comparison and scoring using the analytic hierarchy process, and ; The target adjustment value is used to adjust the reference overloading score determined based on the difference calculation result of the overloading threshold of the overloaded vehicle.

[0063] In this embodiment, for example, there is an overloaded vehicle 1. During a set time period, there are only two overloading levels in historical overloading, namely slight overloading and moderate overloading. Among them, after historical overloading classification, slight overloading includes the first historical overloading combination and , and the corresponding reference decay weights are and respectively. The mode adjustment weight of slight overloading is ; Moderate overloading includes the first historical overloading combination and the second historical overloading combination , and the corresponding reference decay weights are and respectively. The mode adjustment weight of moderate overloading is ; At this time, the target adjustment value of the overloaded vehicle 1.

[0064] The working principle of the above technical solution is as follows: First, obtain the historical load information of the overloaded vehicle, including the total number of historical overloading times, the overloading level of each overloading, and the overloading moment, and calculate the time interval between each historical overloading and the current moment, that is, the reference time interval; Then, based on the reference time interval, assign time decay weights to the historical overloading of different overloading levels to consider the influence of time factors on overloading behavior; Then, when the total number of historical overloading times exceeds the preset threshold, according to the comparison between the reference time interval and the set time length, divide the historical overloading of different overloading levels into the first historical overloading combination and the second historical overloading combination; Subsequently, use the pre-established pattern recognition model to analyze the historical overloading moments of the overloading levels, identify the low-frequency, high-frequency, or high-low frequency hybrid mode, and assign mode adjustment weights to each overloading level according to the pattern recognition result; Finally, combine the mode adjustment weight and the reference decay weight to determine the target adjustment value and adjust the corresponding reference overloading score.

[0065] The beneficial effects of the above technical solution are as follows: By deeply analyzing the historical overloading records of overloaded vehicles, assigning time-decaying weights to the historical overloading, determining the benchmark decay weights of the first historical overloading combination and the second historical overloading combination corresponding to different overloading levels after division, and using a pattern recognition model to identify the historical overloading patterns of different overloading levels; finally, combining the pattern adjustment weights assigned to different overloading levels according to the recognition results output by the model with the benchmark decay weights to adjust the reference overloading score, the overloading level division can be made more accurate and flexible, which is helpful for subsequent accurate early warning processing.

[0066] Finally, a statistical report will be generated for the early warning results, including: Integrate the overloading information, overloading level information, and early warning processing information of the vehicle with the basic information of the vehicle; After the data integration is completed, a vehicle statistical report will be generated. The generated statistical report includes the basic information, overloading events, overloading processing results, and safety risk relationships of each vehicle; Among them, the presentation forms of the vehicle statistical report are charts, tables, images, and text reports; Automatically send the generated vehicle statistical report to the corresponding staff through email, text message, and system notification methods.

[0067] Specifically, integrating the overloading information, overloading level information, and early warning processing information of the vehicle with the basic information of the vehicle ensures the comprehensiveness and relevance of the data. This integration enables the staff to more easily understand the background and handling situation of overloading events. The generated statistical report includes the basic information, overloading events, overloading processing results, and safety risk relationships of each vehicle, providing a comprehensive overview of the vehicle overloading situation. This exhaustiveness helps the staff to conduct in-depth analysis and effective management of the vehicle overloading problem. The presentation forms of the vehicle statistical report include charts, tables, images, and text reports, meeting the information reception preferences of different staff. This diversity makes the report easier to understand and use, improving the information transmission efficiency. Automatically sending the generated vehicle statistical report to the corresponding staff through email, text message, and system notification methods reduces the tediousness of manual operations and improves work efficiency. The automated sending also ensures the timeliness and accuracy of the report, avoiding information delays or omissions, providing rich data and information support for the staff, and helping them make more scientific and reasonable decisions. Through the statistics and analysis of overloading events, the staff can identify the trends and patterns of overloading problems, so as to formulate more effective management measures. By timely warning and handling overloading events, this solution helps to reduce the safety risks brought by vehicle overloading and improve the safety of road traffic. This is of great significance for protecting people's lives and property.

[0068] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0069] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A load-based logistics vehicle overload alarm management method, characterized in that: include: Real-time monitoring of vehicle load information, and transmission of the monitored vehicle load information to the data processing center for data processing, which includes data preprocessing and calibration; The processed load data is dynamically set with a threshold value, and after the setting is completed, an overload judgment is made, and an early warning is processed according to the overload judgment result, and finally a statistical report is generated based on the early warning result; The process of dynamic threshold setting is as follows: First, the vehicle identification information is retrieved, and the vehicle characteristic database is queried according to the vehicle identification information, wherein the rated load parameters of different vehicle models are pre-stored in the database, and the vehicle's benchmark load data is obtained according to the vehicle characteristic database; The vehicle's current speed is then monitored in real time through the vehicle's onboard GPS, and the road slope of the vehicle's driving section is detected using GPS and road map data; Calculate the dynamic safe load threshold based on the vehicle's baseline load data, driving speed and road slope.

2. The method for managing overload warning of logistics vehicles based on load according to claim 1 is characterized in that: Real-time monitoring of vehicle load information, including: Obtain the vehicle's load information; Load information includes gross vehicle weight, axle load, wheel load, cargo weight and basic vehicle information; The gross vehicle weight is the weight of the vehicle itself and the weight of the cargo it carries, and real-time data is collected through on-board sensors; the axle load is the load on each axle, and data is collected through the pressure sensor on each axle; the wheel load is the weight borne by each wheel, and data is collected through the pressure sensor on each wheel; the cargo weight is the weight of the cargo carried, and the weight of the carriage or pallet is monitored in real time through the on-board electronic scale; the basic vehicle information is the vehicle type, vehicle identification information and timestamp; Finally, the vehicle load information is monitored in real time.

3. The method for managing overload warning of logistics vehicles based on load according to claim 1 is characterized in that: The monitored vehicle load information is transmitted to the data processing center for data processing, including: Integrate the collected vehicle load information and format the data after integration; The vehicle load information after data formatting is transmitted using wireless communication technology; The data processing center receives the vehicle load information and performs data verification after receiving it; Data validation includes checking of data packets, timestamps, and duplicate data; Store the verified data in a database or cloud platform; The monitored load information is transmitted to the data processing center for data processing, which also includes: Preprocess the data in the database or cloud platform; Data preprocessing includes data denoising, missing value processing, outlier detection and data standardization; Data calibration is performed after data preprocessing, which includes sensor error correction, sensor calibration, data fusion and multi-sensor calibration; After the data calibration is completed, the consistency and integrity verification is carried out, and after verification, the processed load data is obtained.

4. The method for managing overload warning of logistics vehicles based on load according to claim 1, characterized in that: Set dynamic thresholds for the processed load data. include: When the vehicle is on a downhill section, an additional load compensation factor is added, and the load compensation factor is increased in the range of 8%-15% to compensate for the inertia force; Finally, the dynamic threshold of the vehicle is obtained, which is the reference threshold for judging whether it is overloaded.

5. The method for managing overload warning of logistics vehicles based on load according to claim 4 is characterized in that: The process of calculating the dynamic safe load threshold also includes: When the parallel lane vacancy of the vehicle is less than the set vacancy threshold, the front traffic flow compensation coefficient and the rear traffic flow compensation coefficient are increased, specifically: According to the driving conditions of adjacent vehicles within the set monitoring range monitored in real time by the on-board monitoring device, the parallel lane vacancy of each vehicle is determined according to the driving conditions, a first driving vehicle in front of the current vehicle and a second driving vehicle behind the current vehicle; Obtaining a first distance between each first traveling vehicle and the current vehicle, and determining a safety distance level between each first traveling vehicle and the current vehicle by comparing the first distance with a first preset distance threshold range; The first moving vehicles of the same safety distance level are grouped together to obtain a first safety distance-vehicle combination; Determine a first compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the first safety distance-vehicle combination and the number of vehicles included therein; Summarize the first compensation coefficients of all first safety distance-vehicle combinations to obtain the front traffic compensation coefficient; Obtaining a second distance between each second traveling vehicle and the current vehicle, and determining a safety distance level between each second traveling vehicle and the current vehicle by comparing the second distance with a second preset distance threshold range; The second traveling vehicles of the same safety distance level are grouped together to obtain a second safety distance-vehicle combination; Determine a second compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the number of vehicles included therein; The second compensation coefficients of all second distance-vehicle combinations are summed up to obtain the rear traffic compensation coefficient.

6. The method for managing overload warning of logistics vehicles based on load according to claim 1, characterized in that: After the setting is completed, overload judgment is performed and early warning processing is carried out according to the overload judgment result, including: Determine vehicle overload based on set dynamic thresholds; Overload judgment is a dual judgment mechanism that combines the instantaneous load and the average load of the sliding window; The instantaneous load determination is to retrieve the real-time collected data from the database or cloud platform of the data processing center every 0.5 seconds; The retrieved real-time collected data is compared with the dynamic threshold. If the instantaneous load is greater than the dynamic threshold, the overload determination is triggered. If the instantaneous load is less than or equal to the dynamic threshold, the overload determination is not triggered. The sliding window is determined by using a 60-second sliding window as a cycle, and the average load value within 60 seconds is calculated in each cycle; The average load value is compared with the dynamic threshold. If the average load value is greater than the dynamic threshold, it is judged as overload; if the average load value is less than the dynamic threshold, the overload judgment is not triggered.

7. The method for managing overload warning of logistics vehicles based on load according to claim 6 is characterized in that: After the setting is completed, overload judgment is performed, and early warning processing is performed according to the overload judgment result, which also includes: If the results of the sliding window judgment and instantaneous load judgment exceed the dynamic threshold for three consecutive cycles, it is judged as overload; Finally, we get overloaded vehicles and non-overloaded vehicles; Calculate the difference between the overload thresholds of the overloaded vehicles and determine the overload level based on the difference calculation result; The larger the threshold of the difference calculation result is, the higher the overload level is; Overload levels are divided into slight overload, moderate overload and severe overload; Carry out early warning processing of different intensities according to different overload levels; Among them, the warning process for minor overload is to give an overload prompt through the on-board display screen; the warning process for moderate overload is to activate the sound and light alarm and lock the vehicle speed limit when exceeding the limit; the warning process for severe overload is to automatically send an encrypted alarm message to the supervision platform after 5 minutes of continuous overload, simultaneously cut off the power output and generate a violation event log.

8. The method for managing overload warning of logistics vehicles based on load according to claim 7 is characterized in that: The overload level is determined based on the difference calculation results, including: If, according to the instantaneous load determination result of the current overloaded vehicle, the instantaneous loads of three consecutive cycles all exceed the dynamic threshold, the reference overload score is calculated using the instantaneous loads of the current three cycles and the dynamic threshold; If, according to the sliding window judgment result of the current overloaded vehicle, the average load of three consecutive cycles exceeds the dynamic threshold, the reference overload score is calculated using the average load of the current three cycles and the dynamic threshold; The reference overload score obtained is used as the difference calculation result to determine the overload level.

9. The method for managing overload warning of logistics vehicles based on load according to claim 8, characterized in that: Also includes: Obtain and analyze the historical load information of overloaded vehicles within a set time period. If there is a historical overload record, obtain the total number of historical overloads, the overload level of each historical overload, and the historical overload time; Determine the time interval between the historical overload moment and the current moment of each historical overload, and mark it as a reference time interval; Based on the reference time interval with the current moment, a corresponding time decay weight is assigned to each historical overload; If the total number of historical overloading times of the overloaded vehicle does not exceed the preset number of overloading times, the corresponding reference overloading score will not be adjusted; If the total number of historical overloading times of the overloaded vehicle exceeds the preset number of overloading times, the historical overloading times of different overloading levels are divided according to the comparison result between the reference time interval and the set time length constraint to obtain the corresponding first historical overloading combination and the second historical overloading combination; Obtaining the reference decay weights of the first historical overload combination and the reference decay weights of the second historical overload combination corresponding to different overload levels; Inputting all historical overloads corresponding to each overload level of the current overloaded vehicle and the historical overload time into a pre-established pattern recognition model to obtain a pattern recognition result corresponding to the overload level; According to the pattern recognition result, the weight of each overload level allocation pattern included in the current overloaded vehicle is adjusted; The mode adjustment weights of different overload levels included in the current overloaded vehicle are combined with the reference decay weight corresponding to the first historical overload combination and the reference decay weight corresponding to the second historical overload combination to obtain a target adjustment value; The obtained target adjustment value is used to adjust the reference overload score of the current overloaded vehicle.

10. The method for managing overload warning of logistics vehicles based on load according to claim 1, characterized in that: Finally, the early warning results will be generated into a statistical report, including: Integrate the vehicle's overload information, overload level information and warning processing information with the vehicle's basic information; After data integration is completed, a vehicle statistical report is generated, which includes basic information of each vehicle, overloading events, overloading processing results and safety risk relationships; Among them, the vehicle statistics report is presented in the form of charts, tables, images and text reports; The generated vehicle statistics report will be automatically sent to the corresponding staff via email, SMS and system notification.

Images