Overload Warning Management Method for Logistics Vehicles Based on Load

Abstract

The present invention discloses an overloading warning management method for logistics vehicles based on load, which relates to the technical field of logistics vehicle management. In order to solve the problems of inaccurate acquisition of vehicle load data and incorrect overloading judgment. Through instantaneous load determination, the present invention can quickly capture the immediate change of 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 overloading judgment, and 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 cargo weight when going downhill, thereby enhancing the driving safety.

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 No. 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 the 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 the 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:

[0004] 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.

[0005] 2. When determining whether a vehicle is overloaded, a more accurate determination mode is not adopted, resulting in inaccurate overloading judgment.

[0006] 3. There is no targeted dynamic adjustment according to the impact of the surrounding traffic flow conditions of the vehicle on load safety, resulting in a biased load threshold.

[0007] 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

[0008] 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 immediate change in 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 over 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 cargo weight when going downhill, thus enhancing the driving safety and solving the problems in the prior art.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] An overloading warning management method for logistics vehicles based on load, including:

[0011] Real-time monitor the load information of the vehicle and transmit the monitored vehicle load information to the data processing center for data processing, and the data processing includes data preprocessing and calibration;

[0012] Set the dynamic threshold for the processed load data, and after the setting is completed, perform overloading judgment, and perform warning processing according to the overloading judgment result, and finally generate a statistical report on the warning result;

[0013] The process of setting the dynamic threshold is as follows:

[0014] First, retrieve the vehicle identification information, query the vehicle feature database according to the vehicle identification information, wherein the rated load parameters of different vehicle models are pre-stored in the database, and obtain the reference load data of the vehicle according to the vehicle feature database;

[0015] 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 gradient of the vehicle driving section;

[0016] Calculate the dynamic safety load threshold according to the reference load data, driving speed and road gradient of the vehicle.

[0017] Preferably, real-time monitoring of the load information of the vehicle includes:

[0018] Obtain the load information of the vehicle;

[0019] The load information includes the total vehicle weight, axle load, wheel load, cargo weight and vehicle basic information;

[0020] The gross vehicle weight is the self-weight of the vehicle itself and the weight of the carried goods, 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 goods weight is the weight of the carried goods, and the weight of the carriage or pallet is monitored in real time through an on-vehicle electronic scale; the basic vehicle information is the vehicle type, vehicle identification information, and timestamp;

[0021] Finally, the real-time monitored vehicle load information is obtained.

[0022] Preferably, the monitored vehicle load information is transmitted to a data processing center for data processing, including:

[0023] Integrate the collected vehicle load information, and format the data after integration;

[0024] Use wireless communication technology to transmit the vehicle load information with formatted data;

[0025] The data processing center receives the vehicle load information and performs data verification after receiving it;

[0026] Data verification includes checking of data packets, timestamps, and duplicate data;

[0027] Store the verified data in a database or cloud platform.

[0028] Preferably, transmitting the monitored load information to the data processing center for data processing also includes:

[0029] Perform data preprocessing on the data in the database or cloud platform;

[0030] Data preprocessing includes data denoising, missing value processing, outlier detection, and data standardization;

[0031] After data preprocessing, perform data calibration, and data calibration includes sensor error correction, sensor calibration, data fusion, and multi-sensor calibration;

[0032] After data calibration is completed, perform consistency and integrity verification, and the processed load data is obtained after verification.

[0033] Preferably, setting dynamic thresholds for the processed load data also includes:

[0034] Among them, 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;

[0035] Finally, a dynamic threshold of the vehicle is obtained, and this dynamic threshold is the reference threshold for judging whether overloading occurs.

[0036] Preferably, in the process of calculating the dynamic safe load threshold, it further includes:

[0037] When the remaining space of the parallel lane of the vehicle is less than the set remaining threshold, the compensation coefficient of the front traffic flow and the compensation coefficient of the rear traffic flow are increased. Specifically:

[0038] 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 remaining space 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;

[0039] 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;

[0040] Aggregate the first driving vehicles with the same safety distance level to obtain a first safety distance - vehicle combination;

[0041] From the preset vehicle - distance compensation mapping table, determine the first compensation coefficient according to the safety distance level of the first safety distance - vehicle combination and the number of vehicles included correspondingly;

[0042] Sum up the first compensation coefficients of all first safety distance - vehicle combinations to obtain the compensation coefficient of the front traffic flow;

[0043] Obtain the second distance between each second driving vehicle and the current vehicle, and determine the safety distance level between each second driving vehicle and the current vehicle by comparing the second distance with the second preset distance threshold range;

[0044] Aggregate the second driving vehicles with the same safety distance level to obtain a second safety distance - vehicle combination;

[0045] From the preset vehicle - distance compensation mapping table, determine the second compensation coefficient according to the safety distance level of the second safety distance - vehicle combination and the number of vehicles included correspondingly;

[0046] Sum up the second compensation coefficients of all second distance - vehicle combinations to obtain the compensation coefficient of the rear traffic flow.

[0047] Preferably, after the setting is completed, overloading judgment is carried out, and early warning processing is carried out according to the overloading judgment result, including:

[0048] Carry out overloading judgment on the vehicle according to the set dynamic threshold;

[0049] Overload judgment is a dual judgment mechanism, and the dual judgment mechanism combines instantaneous load and average load of a sliding window;

[0050] The instantaneous load judgment is to retrieve the real-time collected data from the database of the data processing center or the cloud platform every 0.5 seconds;

[0051] Compare the retrieved real-time collected data with the dynamic threshold. If the instantaneous load is greater than the dynamic threshold, the overload judgment is triggered in this cycle. If the instantaneous load is less than or equal to the dynamic threshold, the overload judgment is not triggered in this cycle;

[0052] 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;

[0053] Compare the average load with the dynamic threshold. If the average load is greater than the dynamic threshold, it is determined as overloaded; if the average load is less than the dynamic threshold, the overload judgment is not triggered.

[0054] Preferably, after the setting is completed, overload judgment is performed, and early warning processing is carried out according to the overload judgment result, and it also includes:

[0055] For the results of sliding window judgment and instantaneous load judgment, if it exceeds the dynamic threshold in three consecutive cycles, it is determined as overloaded;

[0056] Finally, overloaded vehicles and non-overloaded vehicles are obtained;

[0057] Calculate the difference of the overload threshold of overloaded vehicles, and determine the overload level according to the calculation result of the difference;

[0058] Among them, the larger the threshold of the calculation result of the difference, the higher the overload level;

[0059] The overload levels are divided into slight overload, moderate overload and severe overload;

[0060] Carry out different-intensity early warning processing according to different overload levels;

[0061] Among them, the early warning processing for slight overload is to give an overload prompt through the in-vehicle display screen; the early warning processing for moderate overload is to start an audible and visual alarm and lock the vehicle speed limit when over the limit; the early 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.

[0062] Preferably, determining the overload level according to the calculation result of the difference includes:

[0063] If, based on 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;

[0064] If, based on the sliding window determination result of the current overloaded vehicle, the average loads in three consecutive cycles all exceed the dynamic threshold, then use the average loads in the current three cycles and the dynamic threshold to calculate the reference overload score;

[0065] Use the obtained reference overload score as the difference calculation result to determine the overload level.

[0066] Preferably, it further includes:

[0067] Obtain and analyze the historical load information of overloaded vehicles within a set time period. If there is a historical overload record, then obtain the total number of historical overloads, the overload level of each historical overload, and the historical overload time;

[0068] 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;

[0069] Based on the reference time interval from the current time, assign corresponding time decay weights to each historical overload;

[0070] If the total number of historical overloads of the overloaded vehicle does not exceed the preset number of overloads, then do not adjust the corresponding reference overload score;

[0071] If the total number of historical overloads of the overloaded vehicle exceeds the preset number of overloads, then divide the historical overloads of different overload levels according to the comparison result between the reference time interval and the set time length constraint to obtain the corresponding first historical overload combination and second historical overload combination;

[0072] Obtain the reference decay weights of the first historical overload combination and the second historical overload combination corresponding to different overload levels;

[0073] Input the historical overload times 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 results corresponding to the overload levels;

[0074] According to the pattern recognition results, assign pattern adjustment weights to each overload level included in the current overloaded vehicle;

[0075] 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;

[0076] Adjust the reference overload score of the current overloaded vehicle by using the obtained target adjustment value.

[0077] Preferably, finally generate a statistical report on the warning result, including:

[0078] Integrate the overload information, overload level information, and warning processing information of the vehicle with the basic information of the vehicle;

[0079] 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 handling results, and the relationship of safety risks;

[0080] Among them, the presentation form of the vehicle statistical report is charts, tables, images, and text reports;

[0081] Automatically send the generated vehicle statistical report to the corresponding staff through email, text message, and system notification methods.

[0082] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0083] 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 their operation within a safe range and reduce potential safety hazards.

[0084] 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 the actual driving conditions, improves adaptability, and compensates for inertial force 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.

[0085] 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

[0086] Figure 1 It is a schematic diagram of the steps for managing overload warnings of logistics vehicles of the present invention;

[0087] 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

[0088] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 creative efforts shall fall within the protection scope of the present invention.

[0089] 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:

[0090] A method for overloading warning management of logistics vehicles based on load includes:

[0091] Real-time monitoring of the load information of the vehicle and transmitting the monitored vehicle load information to a data processing center for data processing, where the data processing includes data preprocessing and calibration;

[0092] 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.

[0093] 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 dealing with 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.

[0094] Real-time monitoring of the load information of the vehicle includes:

[0095] Obtain the load information of the vehicle;

[0096] The load information includes the total vehicle weight, axle load, wheel load, cargo weight, and vehicle basic information;

[0097] The total vehicle weight is the self-weight of the vehicle itself and the weight of the carried cargo, and real-time data is collected through on-vehicle sensors; the axle load is the load on each axle, and data is collected through pressure sensors on each axle; the wheel load is the weight borne by each wheel, and data is collected 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;

[0098] Finally, the real-time monitored vehicle load information is obtained.

[0099] 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. High-precision sensors and electronic scales are used for data collection, improving the accuracy of the data and 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 their operation within a safe range and reduce potential safety hazards. The automated data collection and processing process reduces manual intervention and improves work efficiency. The real-time monitored data can be immediately fed back to managers or drivers, facilitating their timely adjustment and optimization of 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.

[0100] Transmit the monitored vehicle load information to the data processing center for data processing, including:

[0101] Integrate the collected vehicle load information, and format the data after integration;

[0102] Use wireless communication technology to transmit the vehicle load information with formatted data;

[0103] The data processing center receives the vehicle load information and performs data verification after receiving it;

[0104] Data verification includes the inspection of data packets, timestamps, and duplicate data;

[0105] Store the verified data in a database or cloud platform.

[0106] Perform data preprocessing on the data in the database or cloud platform;

[0107] Data preprocessing includes data denoising, missing value handling, outlier detection, and data normalization;

[0108] After data preprocessing, perform data calibration, which includes sensor error correction, sensor calibration, data fusion, and multi-sensor calibration;

[0109] After data calibration is completed, perform consistency and integrity verification, and the processed load data is obtained after verification.

[0110] 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 steps further improve the quality of the data. 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.

[0111] 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 goes up and down slopes and a deviation in the load threshold, please refer to Figure 1 and Figure 2 This embodiment provides the following technical solutions:

[0112] Set dynamic thresholds for the processed load data, including:

[0113] The process of dynamic threshold setting is as follows:

[0114] First, retrieve the vehicle identification information, and 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 baseline load data of the vehicle is obtained according to the vehicle feature database;

[0115] Then, use the on-vehicle GPS to monitor the current driving speed of the vehicle in real time, and detect the road slope of the vehicle's driving section using GPS and road map data;

[0116] Calculate the dynamic safety load threshold according to the vehicle's baseline load data, driving speed, and road slope;

[0117] 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% - 15% to compensate for the inertial force;

[0118] Finally, obtain the dynamic threshold of the vehicle, and this dynamic threshold is the reference threshold for judging whether it is overloaded.

[0119] Specifically, by retrieving the vehicle identification information and querying the vehicle feature database, the solution can obtain specific baseline load data for each vehicle. This personalized processing is more accurate than using a unified standard, can better adapt to the actual load capacity of different vehicle models, use the on-vehicle GPS to monitor the vehicle driving speed in real time, and combine with road map data to detect the road slope. 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, improves the adaptability. Especially on the downhill section, the solution compensates for the inertial force by increasing an additional load compensation coefficient, which helps to prevent the overloading risk caused by the superposition of the vehicle's own weight and 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 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 judging whether it is overloaded, the solution helps to prevent the occurrence of overloading behavior in advance. This can not only protect the road infrastructure but also reduce the risk of traffic accidents.

[0120] 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 solutions:

[0121] During the process of calculating the dynamic safety load threshold, it further includes:

[0122] When the available space in the parallel lane of the vehicle is less than the set available threshold, increase the compensation coefficient for the traffic flow ahead and the compensation coefficient for the traffic flow behind. Specifically:

[0123] According to the driving conditions of adjacent vehicles within the set monitoring range as monitored by the on-vehicle monitoring device in real time, determine the available space in the parallel lane of each vehicle based on the driving conditions, including the first vehicle driving ahead of the current vehicle and the second vehicle driving behind the current vehicle;

[0124] Obtain the first distance between each first vehicle driving ahead and the current vehicle, and determine the safety distance level between each first vehicle driving ahead and the current vehicle by comparing the first distance with the first preset distance threshold range;

[0125] Group the first vehicles driving ahead with the same safety distance level to obtain the first safety distance - vehicle combination;

[0126] From the pre-set vehicle - distance compensation mapping table, determine the first compensation coefficient according to the safety distance level of the first safety distance - vehicle combination and the number of vehicles included;

[0127] Sum up the first compensation coefficients of all first safety distance - vehicle combinations to obtain the compensation coefficient for the traffic flow ahead;

[0128] Obtain the second distance between each second vehicle driving behind and the current vehicle, and determine the safety distance level between each second vehicle driving behind and the current vehicle by comparing the second distance with the second preset distance threshold range;

[0129] Group the second vehicles driving behind with the same safety distance level to obtain the second safety distance - vehicle combination;

[0130] From the pre-set vehicle - distance compensation mapping table, determine the second compensation coefficient according to the safety distance level of the second safety distance - vehicle combination and the number of vehicles included;

[0131] Sum up the second compensation coefficients of all second distance - vehicle combinations to obtain the compensation coefficient for the traffic flow behind.

[0132] In this embodiment, the on-vehicle monitoring device refers to the monitoring device installed on the vehicle, which is used to capture the 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 and within which the on-vehicle monitoring device can effectively monitor; the driving conditions of adjacent vehicles refer to the relative positions and the number of other vehicles around the current vehicle; the available space in the parallel lane refers to the space between the lane where the current vehicle is located and the adjacent lane, that is, the space where the current vehicle can move laterally without colliding with other vehicles; the set available threshold is a pre-determined value used to judge whether the available space in the parallel lane is sufficient, generally set as the body width of the current vehicle.

[0133] In this embodiment, the first moving vehicle refers to the vehicle moving in front of the current vehicle; the second moving vehicle refers to the vehicle moving 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 used to divide 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.

[0134] In this embodiment, for example, there is a first preset distance threshold range of , where m is in meters. 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.

[0135] 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 close distance level, the vehicle quantity range is divided into 1 - 2 vehicles, 3 - 5 vehicles, and 6 vehicles and above, and the corresponding compensation coefficients are 0.5, 0.55, and 0.7 respectively.

[0136] 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 - flowing vehicle compensation coefficient is the sum of 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 the consideration of the front - flowing vehicle situation in front of the current vehicle. Among them, for example, if the front - flowing vehicle 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;

[0137] Among them, for example, when c1 = 0.5, , which means that when the front - flowing vehicle is dense, the load threshold is reduced by 37%.

[0138] In this embodiment, for example, there is a dynamic threshold adjustment process as follows:

[0139] Input: Adjusted front load threshold Tons, downhill slope 5°, compensation factor +10% → output ton;

[0140] Input: Traffic ahead c1=0.5 → Output dynamic threshold ton.

[0141] 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, which is 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 gathering second moving vehicles of the same safety distance level.

[0142] 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.

[0143] 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;

[0144] 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.

[0145] In this embodiment, the adjustment formula for the forward / rear traffic flow compensation coefficient is based on a normal distribution transformation. Through the combination of an exponential function and a square root, the change in the load threshold follows the transformed normal distribution, thus better describing the load change in the actual traffic flow. Among them, the exponential function is used to smoothly show the change in the load threshold with the forward / rear traffic flow compensation coefficient; the square root is used to take the square root of the result of the exponential function, further making the change in the load threshold smoother and more in line with the characteristics of the normal distribution, conforming to the gradual change characteristics in the actual traffic flow, and thus being able to better describe the load change in the actual traffic flow.

[0146] The working principle of the above technical solution is as follows: First, determine the available space in the parallel lanes of the current vehicle and identify the vehicles traveling in front and behind. Then, when the available space in the parallel lanes is less than the set threshold, calculate the distances between the vehicles in front and behind and the current vehicle respectively, and divide 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 safe distance-vehicle combination; then, from the preset vehicle-distance compensation mapping table, find and determine the corresponding compensation coefficient according to the safe distance levels and the number of vehicles in these combinations; finally, sum up the compensation coefficients of all safe distance-vehicle combinations to obtain the forward traffic flow compensation coefficient and the rear traffic flow compensation coefficient respectively, so as to adjust the load threshold of the current vehicle.

[0147] The beneficial effects of the above technical solution are as follows: By real-time monitoring the driving conditions around the vehicle and considering the impact of the forward and rear traffic flows on load safety, the driving environment of the current vehicle can be more accurately evaluated, so as to dynamically adjust the load threshold, ensure that the vehicle can maintain safe driving in a complex traffic environment, while enhancing the adaptability of the vehicle and effectively preventing traffic accidents.

[0148] To solve the problem in the prior art that when determining vehicle overloading, a more accurate determination mode is not adopted, resulting in inaccurate overloading judgment, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solution:

[0149] After setting, perform overloading judgment, and perform warning processing according to the overloading judgment result, including:

[0150] Perform overloading judgment on the vehicle according to the set dynamic threshold;

[0151] 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;

[0152] 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;

[0153] Compare the retrieved real-time acquisition data with the dynamic threshold. If the instantaneous load is greater than the dynamic threshold, an overload determination is triggered for this cycle. If the instantaneous load is less than or equal to the dynamic threshold, no overload determination is triggered for this cycle;

[0154] It is determined that a 60-second sliding window is used as a cycle, and the average load within 60 seconds is calculated within each cycle;

[0155] Compare the average load with the dynamic threshold. If the average load is greater than the dynamic threshold, it is determined as overloaded; if the average load is less than the dynamic threshold, no overload determination is triggered.

[0156] Regarding the results of the sliding window determination and the instantaneous load determination, if it exceeds the dynamic threshold for three consecutive cycles, it is determined as overloaded;

[0157] Finally, overloaded vehicles and non-overloaded vehicles are obtained;

[0158] Calculate the difference in the overload thresholds of overloaded vehicles, and determine the overload level based on the calculation result of the difference;

[0159] Among them, the larger the threshold of the calculation result of the difference, the higher the overload level;

[0160] The overload levels are divided into slight overload, moderate overload, and severe overload;

[0161] Perform warning processing with different intensities according to different overload levels;

[0162] 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 activate the audible and visual alarm and lock the vehicle speed limit when over the limit; 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.

[0163] In this embodiment, the initial reference value of the dynamic threshold refers to the maximum safe load calibrated at the vehicle factory.

[0164] 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 vehicles. Instantaneous load determination can quickly capture the immediate changes 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 overloading judgment. Using a dynamic threshold for overloading judgment means that the threshold can be adjusted according to the actual situation to adapt to different roads, vehicle types, or different transportation needs. This flexibility enables the solution to be more widely applied to various scenarios and improves its practicality. Different intensities of early warning processing are carried out according to the overloading level, which 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 early warning processing can also take appropriate measures according to different situations to avoid overreaction or ignoring problems. Real-time data collection and analysis are carried out using the database or cloud platform of the data processing center, which reflects advanced technical means. By obtaining and processing data in real time, the solution can achieve immediate monitoring and judgment of vehicle load, improving work efficiency and accuracy. In the case of severe overloading, 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.

[0165] Determine the overloading level according to the difference calculation result, including:

[0166] 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, then use the instantaneous loads of the current three cycles and the dynamic threshold to calculate the reference overloading score;

[0167] If, according to the sliding window determination result of the current overloaded vehicle, the average loads of three consecutive cycles all exceed the dynamic threshold, then use the average loads of the current three cycles and the dynamic threshold to calculate the reference overloading score;

[0168] Use the obtained reference overloading score as the difference calculation result to determine the overloading level.

[0169] In this embodiment, the calculation formula for the reference overloading score determined according to the instantaneous load determination result or the sliding window determination result is as follows:

[0170] ;

[0171] In the formula, represents the reference overloading score determined according to the instantaneous load determination result or the sliding window determination result of the current overloaded vehicle; It is expressed as the instantaneous load of the current overloaded vehicle in the i-th cycle determined according to the instantaneous load result, or the average load of the current overloaded vehicle in the i-th cycle determined according to the sliding window determination result, where i = 1, 2, 3; It is expressed as the dynamic threshold; e is expressed as the base of the natural logarithm, with a value of 2.7;

[0172] Among them, for example, there is a reference overload score calculation example as shown below:

[0173] Input: → Output .

[0174] 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 difference between the instantaneous load or the average load 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 the load difference.

[0175] The beneficial effects of the above technical solutions 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 state 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.

[0176] In order 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 calculation result of the difference between the overload thresholds of overloaded vehicles 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 solutions:

[0177] 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;

[0178] 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;

[0179] Based on the reference time interval from the current time, assign corresponding time decay weights to each historical overload;

[0180] If the total number of historical overloads of the overloaded vehicle does not exceed the preset number of overloads, the corresponding reference overload score will not be adjusted;

[0181] If the total historical overloading times of an overloaded vehicle exceed 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 corresponding first historical overload combinations and second historical overload combinations;

[0182] Obtain the reference decay weights of the corresponding first historical overload combinations of different overloading levels and the reference decay weights of the second historical overload combinations;

[0183] Input the historical overload times of each overloading level included in the current overloaded vehicle into a pre-established pattern recognition model to obtain the pattern recognition results corresponding to the overloading levels;

[0184] According to the pattern recognition results, assign pattern adjustment weights to each overloading level included in the current overloaded vehicle;

[0185] Combine the pattern adjustment weights of different overloading levels included in the current overloaded vehicle with the reference decay weights of the corresponding first historical overload combinations and the reference decay weights of the second historical overload combinations to obtain the target adjustment value;

[0186] Use the obtained target adjustment value to adjust the reference overloading score of the current overloaded vehicle.

[0187] In this embodiment, the set time period refers to a specific time period preset for obtaining the historical load information for evaluating overloading behaviors; the historical load information refers to the data records of each load of an overloaded vehicle in the past period of time, including the weight, load time, load location, etc. of each load.

[0188] In this embodiment, the historical overload record refers to the overload record of a vehicle in the historical load information, including the overload time, overload weight, and overload level, etc.; the total historical overloading times refer to the total number of all overload records of a vehicle in the past period of time; the historical overload time refers to the specific time point when each overload occurs; the historical overload refers to the overload events that occurred in the past.

[0189] 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; represents the decay coefficient, which controls the weight decline speed; represents the reference time interval; the preset overloading times is a threshold preset for determining whether a vehicle is frequently overloaded, such as 5 times.

[0190] In this embodiment, the historical overloading within the reference time interval for each overloading level that does not exceed the set time length constraint is classified into the first historical overloading combination, and the historical overloading with a reference time interval exceeding the set time length constraint is classified into the second historical overloading combination; the set time length constraint refers to the pre-set limit on the time difference between the occurrence time point of the historical load behavior and the current moment, such as 1 month.

[0191] In this embodiment, the reference decay weight is obtained by taking the average of the maximum value and the minimum value of the time decay weights within the current historical overloading combination and averaging it with the average value of the time decay weights.

[0192] In this embodiment, the pattern recognition model is a model for identifying overloading patterns obtained by training a neural network using a large amount of collected historical load data. 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-linear characteristics. 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 determine which overloading pattern the input data belongs to. Among them, the output dimension corresponds to the number of overloading pattern categories, and the output dimension is 3, corresponding to the low-frequency pattern, the high-frequency pattern, and the high-low frequency mixed pattern respectively.

[0193] In this embodiment, the pattern recognition results, that is, the overloading patterns include the low-frequency pattern, the high-frequency pattern, and the high-low frequency mixed pattern; the low-frequency pattern refers to frequent overloading within multiple consecutive cycles (such as 3 overloading times within a week); the high-frequency pattern refers to overloading at long intervals (such as 1 time per month but lasting for half a year); the high-low frequency mixed pattern refers to the overloading behavior showing both high-frequency and low-frequency characteristics in time. For example, there may be a vehicle that overloads frequently within a few months (such as 2-3 times per month), but the overloading behavior decreases in the following months and only occurs occasionally (such as 1 time every two or three months).

[0194] In this embodiment, the pattern adjustment weight refers to the adjustment weights assigned to different overloading levels of the vehicle according to the pattern recognition results, where the weights assigned to the low-frequency pattern, the high-frequency pattern, and the high-low frequency mixed pattern respectively and The value ranges of , and It is 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 overload score determined based on the difference calculation result of the overload threshold of the overloaded vehicle.

[0195] In this embodiment, for example, within the set time period of overloaded vehicle 1, there are only two overload levels of slight overload and moderate overload in historical overloads. Among them, after historical overload classification, slight overload includes the first historical overload combination and , and the corresponding reference decay weights are and , and the mode adjustment weight of slight overload is ; Moderate overload includes the first historical overload combination and the second historical overload combination , and the corresponding reference decay weights are and , and the mode adjustment weight of moderate overload is ;

[0196] At this time, the target adjustment value of overloaded vehicle 1 .

[0197] 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 overloads, the overload level of each overload, and the overload time, and calculate the time interval between each historical overload and the current time, that is, the reference time interval; Then, based on the reference time interval, allocate time decay weights to historical overloads of different overload levels to consider the impact of time factors on overload behavior; Then, when the total number of historical overloads exceeds the preset threshold, according to the comparison between the reference time interval and the set time length, divide the historical overloads of different overload levels into the first historical overload combination and the second historical overload combination; Subsequently, use the pre-established pattern recognition model to analyze the historical overload time of the overload level, identify low-frequency, high-frequency, or low-high frequency mixed patterns, and allocate mode adjustment weights to each overload level according to the pattern recognition results; Finally, combine the mode adjustment weight and the reference decay weight to determine the target adjustment value and adjust the corresponding reference overload score.

[0198] 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 distribution, determining the benchmark decay weights of the first historical overloading combination and the second historical overloading combination corresponding to different overloading levels after classification, 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 classification can be made more accurate and flexible, which in turn helps the subsequent early warning processing to be accurate.

[0199] Finally, a statistical report will be generated for the early warning results, including:

[0200] Integrate the overloading information, overloading level information, and early warning processing information of the vehicle with the basic information of the vehicle;

[0201] 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;

[0202] Among them, the presentation form of the vehicle statistical report is charts, tables, images, and text reports;

[0203] Automatically send the generated vehicle statistical report to the corresponding staff by email, text message, and system notification methods.

[0204] Specifically, integrating the overloading information, overloading level information, warning handling 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 incidents. The generated statistical reports include the basic information of each vehicle, overloading incidents, overloading handling results, and safety risk relationships, providing a comprehensive overview of the vehicle overloading situation. This exhaustiveness helps the staff conduct in-depth analysis and effective management of the vehicle overloading problem. The presentation forms of the vehicle statistical reports include charts, tables, images, and text reports, meeting the information reception preferences of different staff members. This diversity makes the reports easier to understand and use, improving the information transmission efficiency. The generated vehicle statistical reports are automatically sent to the corresponding staff members via email, text message, and system notification, reducing the tediousness of manual operations and improving work efficiency. The automated sending also ensures the timeliness and accuracy of the reports, avoiding information delays or omissions, providing rich data and information support for the staff, and helping them make more scientific and reasonable decisions. By statistically analyzing overloading incidents, the staff can identify the trends and patterns of overloading problems, thereby formulating more effective management measures. By promptly warning and handling overloading incidents, this solution helps reduce the safety risks brought by vehicle overloading and enhance the safety of road traffic. This is of great significance for protecting people's lives and property safety.

[0205] It should be noted that in this article, 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 term "comprising", "including" or any other variant thereof is 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 further includes elements inherent to such process, method, article or device.

[0206] 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 method for overloading warning management of a logistics vehicle based on load, characterized in that It includes: Real-time monitoring of the load information of vehicles, 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 overloading judgments after the setting is completed, conducting early warning processing based on the overloading judgment results, and finally generating a statistical report on the early warning results; The process of dynamic threshold setting 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 benchmark 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 detect the road gradient of the vehicle driving section using GPS and road map data; Calculate the dynamic threshold based on the vehicle's benchmark load data, driving speed, and road gradient; After the setting is completed, make an overloading judgment, and conduct early warning processing based on the overloading judgment result. It also includes: The results of sliding window judgment and instantaneous load judgment. If it exceeds the dynamic threshold for three consecutive cycles, it is determined as overloaded; Finally, obtain overloaded vehicles and non-overloaded vehicles; Calculate the difference between the actual load of the overloaded vehicle and the dynamic threshold, and conduct overloading level judgment based on the difference calculation result; Among them, the larger the value of the difference calculation result, the higher the overloading level; The overloading levels are divided into slight overloading, moderate overloading, and severe overloading; Conduct different-intensity early warning processing according to different overloading levels; Among them, the early warning processing for slight overloading is to give an overloading prompt through the on-vehicle display screen; the early warning processing for moderate overloading is to start an audible and visual alarm and lock the vehicle speed limit when over the limit; the early 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; Conducting overloading level judgment based on the difference calculation result includes: If, according to the instantaneous load judgment result of the current overloaded vehicle, the instantaneous load in three consecutive cycles exceeds the dynamic threshold, then use the instantaneous load and dynamic threshold in the current three cycles to calculate the reference overloading score; If, according to the sliding window judgment result of the current overloaded vehicle, the average load in three consecutive cycles exceeds the dynamic threshold, then use the average load and dynamic threshold in the current three cycles to calculate the reference overloading score; Use the obtained reference overloading score as the difference calculation result to conduct overloading level judgment; 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 overloads, the overloading 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, no adjustment is made to the corresponding reference overloading score; If the total historical overloading times of an overloaded vehicle exceed the preset overloading times, the historical overloadings of different overloading levels are divided according to the comparison result between the reference time interval and the set time length constraint, and the corresponding first historical overloading combination and second historical overloading combination are obtained; Obtain the reference decay weights of the corresponding first historical overloading combinations of different overloading levels and the reference decay weights of the second historical overloading combinations; 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 results corresponding to the overloading levels; Assign pattern adjustment weights to each overloading level included in the current overloaded vehicle according to the pattern recognition results; Combine the pattern adjustment weights of different overloading levels included in the current overloaded vehicle with the reference decay weights of the corresponding first historical overloading combinations and the reference decay weights of the second historical overloading combinations to obtain the target adjustment value; Use the obtained target adjustment value to adjust the reference overloading score of the current overloaded vehicle.

2. The overload warning management method for a logistics vehicle based on load as claimed in claim 1, wherein Monitor the load information of the vehicle in real time, including: Obtain 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 is collected through on-vehicle sensors; the axle load is the load on each axle, and data is collected through pressure sensors on each axle; the wheel load is the weight borne by each wheel, and data is collected 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.

3. The overload warning management method for a logistics vehicle based on load as claimed in claim 1, wherein 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 formatted vehicle load information; The data processing center receives the vehicle load information and performs data verification after receiving it; The data verification includes checks on data packets, timestamps, and duplicate data; Store the verified data in a database or cloud platform; Transmitting the monitored load information to the data processing center for data processing also 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, and data calibration includes sensor error correction, sensor calibration, data fusion, and multi-sensor calibration; After data calibration is completed, perform consistency and integrity verification, and the processed load data is obtained after verification.

4. The method for overloading warning management of a logistics vehicle based on load as claimed in claim 1, wherein Set dynamic thresholds for the processed load data, and also include: When the vehicle is on a downhill section, an additional load compensation coefficient is added on the basis of the dynamic threshold. The value range of the load compensation coefficient is 8% - 15%, which is used to compensate for the influence of inertia force on the load, and finally a dynamic threshold for overloading judgment is obtained.

5. The overload warning management method for a logistics vehicle based on load as claimed in claim 4, wherein During the calculation of the dynamic threshold, it also includes: When the remaining space in the parallel lane of the vehicle is less than the set remaining 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 by the on-vehicle monitoring device, determine the remaining space in 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; Collect the first driving vehicles with the same safety distance level to obtain a first safety distance - vehicle combination; From the preset vehicle - distance compensation mapping table, determine the first compensation coefficient 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 first safety distance - vehicle combinations to obtain the front traffic flow compensation coefficient; Obtain the second distance between each second driving vehicle and the current vehicle, and determine the safety distance level between each second driving vehicle and the current vehicle by comparing the second distance with the second preset distance threshold range; Collect the second driving vehicles with the same safety distance level to obtain a second safety distance - vehicle combination; From the preset vehicle - distance compensation mapping table, determine the second compensation coefficient 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 second distance - vehicle combinations to obtain the rear traffic flow compensation coefficient.

6. The overloading warning management method for a logistics vehicle based on load as claimed in claim 1, wherein, After setting, overloading judgment is carried out, and early warning processing is carried out according to the overloading judgment result, including: Carry out 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, an overloading judgment is triggered. If the instantaneous load is less than or equal to the dynamic threshold, no overloading judgment is triggered; 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 judged as overloading; if the average load is less than the dynamic threshold, no overloading judgment is triggered.

7. The overload warning management method for a logistics vehicle based on load as claimed in claim 1, wherein, Finally, generate a statistical report on the early warning result, 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 is generated. The generated statistical report includes the basic information of each vehicle, overloading incidents, overloading handling results, and the relationship of safety risks; Among them, the presentation methods of the vehicle statistical report are charts, tables, images, and text reports; The generated vehicle statistical report is automatically sent to the corresponding staff through email, SMS, and system notifications.

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