LU500570B1

LU500570B1 - Method for vehicle load weight prediction, system, electronic equipment, and medium thereof

Info

Publication number: LU500570B1
Application number: LU500570A
Authority: LU
Inventors: Yan Yan; Kunqi Ma; Mengliang Li; Yu Liu; Hanzhengnan Yu; Tieqiang Fu; Jingyuan Li; Xiaopan An
Original assignee: China Automotive Tech & Res Ct; Catarc Automotive Test Center Tianjin Co Ltd
Priority date: 2021-01-04
Filing date: 2021-08-24
Publication date: 2022-02-24
Also published as: CN112819031B; CN112819031A

Abstract

A method and system for vehicle load weight prediction, electronic equipment, and a medium. The method for vehicle load weight prediction includes the following steps: (a) acquiring vehicle traveling data and calculating a parameter: acquiring the engine speed, engine load percentage, and ECU vehicle speed of a vehicle in a traveling process under different load conditions; and calculating to obtain a current torque and a current transmission gear ratio; (b) segment cutting and constant speed segment screening; (c) relevant vector machine model training; and (d) load prediction: for a vehicle with unknown load, inputting the average transmission gear ratio, the average vehicle speed and the average torque of the screened segments into the relevant vector machine model to obtain a load prediction result of each segment, and to further obtain a prediction result of the vehicle load.

Description

DESCRIPTION 00057 METHOD FOR VEHICLE LOAD WEIGHT PREDICTION, SYSTEM, ELECTRONIC EQUIPMENT, AND MEDIUM THEREOF

FIELD OF THE INVENTION The invention relates to the field of transportation, in particular to a method for vehicle load weight prediction, system, electronic equipment, and a medium.

BACKGROUND OF THE INVENTION With the continuous progress of China's science and technology, the road transportation industry has also achieved rapid development. But due to the lack of powerful methods of the supervision by traffic authorities and the relatively weak awareness of national security, the situation of China's traffic security is increasingly severe. In recent years, although the investment and attention to highway construction and maintenance by the country are increasing year by year, especially in the aspect of highways, the overload phenomenon still repeatedly emerges under forbiddance. The commercial vehicle overload not only damages the highway construction of the country, but also increases the risk of traveling. Many serious traffic accidents are caused by overload. The existing vehicle load testing method is limited by factors such as technology, costs, sites, and the like, and cannot effectively monitor the actual traveling load of the vehicle. As for the whole vehicle enterprise, the development and matching of the vehicle require a large amount of load data in the actual traveling process of the vehicle. If the load estimation is unreasonable, the matching and calibration of the vehicle power system can be greatly influenced, and the oil consumption, emission and power performance in the actual traveling of the vehicle are influenced. In addition, if the actual load of the vehicles can be monitored in real time, it is also important to distribute the load of different vehicles reasonably, improve the transportation efficiency of the vehicle fleet, and reduce the transportation cost.

Accordingly, the present invention has been proposed with particularity. 0500570

SUMMARY OF THE INVENTION In a first aspect, the present invention aims to provide a method for vehicle load weight prediction which is simple and convenient to implement, low in cost and high in efficiency, is not limited by site conditions, and can effectively monitor the load in the actual traveling of the vehicle. In a second aspect, the present invention aims to provide electronic equipment. In a third aspect, the present invention aims to provide a medium. In a fourth aspect, the present invention aims to provide a vehicle load weight prediction system. In order to achieve the above objects, the invention adopts the following technical solutions. In the first aspect, the present invention provides a method for vehicle load weight prediction, which comprises the following steps: Step a: acquiring vehicle traveling data and calculating parameters; Step a1. acquiring engine speed, engine load percentage and ECU vehicle speed of a vehicle in a traveling process under different load conditions; Step a2. according to an engine external characteristic curve and the engine speed, obtaining a maximum torque of the engine at the engine speed, and obtaining a current torque according to the maximum torque of the engine and the engine load percentage; and Step a3. obtaining a current transmission gear ratio according to a wheel radius, a final driver ratio, the engine speed and the ECU vehicle speed; Step b: segment cutting and constant speed segment screening: dividing the vehicle traveling data into time segments and removing idle speed time segments to obtain a continuous motion segment, and screening out a constant speed segment from the continuous motion segment; Step c: relevant vector machine model training: taking an average transmission gear ratio, average vehicle speed and average torque of each constant speed segment as an input, taking a load as an output, and training a relevant vector machine to obtain a relevant vector machine model; and Step d: predicting the load: for a vehicle with unknown load, acquiring the engine speed, the engine load percentage and the ECU vehicle speed, 0500570 obtaining the current torque according to step a2, and obtaining the current transmission gear ratio according to step a3; then performing segment cutting and constant speed segment screening according to step b, and inputting the average transmission gear ratio, the average vehicle speed and the average torque of screened segments into the relevant vector machine model to obtain a load prediction result of each segment to further obtain a prediction result of a vehicle load.

In a further preferred technical solution, in step a3, the current transmission i, =0.377 522 gear ratio is calculated by adopting the following method: le XV ; where vis the ECU vehicle speed, n is the engine speed, r is the wheel radius, io is the transmission gear ratio; and ig is the final driver ratio.

According to a further preferred technical solution, in step b, the method for dividing time segments comprises: adopting a moving window method to divide the second-by-second vehicle speed into different time segments.

According to a further preferred technical solution, in step b, the method for screening a constant speed segment comprises: calculating the variation coefficient of the vehicle speed of the continuous motion segment, sequencing the variation coefficient of the vehicle speed from large to small, and reserving the last 20% segment as a constant speed segment.

According to a further preferred technical solution, the variation coefficient of the vehicle speed is calculated by the following method: = 5Std) - where, cv is the variation coefficient of the vehicle speed, std (v) is the standard deviation of the vehicle speed, and ¥ is the average vehicle speed.

According to a further preferred technical solution, in step c, the parameter of the relevant vector machine is obtained by adopting an artificial bee colony algorithm.

Preferably, the total number of bees of the artificial bee colony algorithm is 20-50, the number of bees gathering honey is 10-25, the maximum searching times is 50-100, and the maximum iteration times is 50-150. Preferably, the total number of bees is 30-50, more preferably 40.

Preferably, the number of bees gathering honey is 15-25, more preferably 20. 0500570 Preferably, the maximum number of searching times is 50-80, more preferably

50. Preferably, the maximum number of iteration times is 80-120, more preferably

100. According to a further preferred technical solution, in step d, after the load prediction results for each segment are obtained, the load prediction results for each segment are sequenced, and the average value of the load results between 5% and 95% of the lane place lines is calculated as the prediction result of the vehicle load. In the second aspect, the present invention provides electronic equipment comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores an instruction executable by the at least one processor, and the instruction is executed by the at least one processor to enable the at least one processor to execute the method for vehicle load weight prediction described above. In the third aspect, the present invention provides a medium on which a computer instruction is stored, and the computer instruction is used for causing a computer to execute the method for vehicle load weight prediction. In the fourth aspect, the invention provides a vehicle load weight prediction system, comprising a vehicle traveling data acquisition module, a parameter calculation module, a segment cutting module, a constant speed segment screening module, a relevant vector machine training module and a load prediction module; wherein the vehicle traveling data acquisition module, the parameter calculation module, the segment cutting module, the constant speed segment screening module and the relevant vector machine training module are sequentially connected; the load prediction module is also connected to the constant speed segment screening module. Compared with the prior art, the invention has the following beneficial effects. According to the method for vehicle load weight prediction provided by the present invention, the traveling data of a vehicle with a known load is acquired 0500570 and the parameter is calculated, segment cutting and constant speed segment screening are performed, a relevant vector machine model is obtained through training, and then the vehicle with the unknown load is subjected to load prediction through the relevant vector machine model so that the dynamic monitoring of the vehicle load is realized. The method is simple and convenient to implement, low in cost and high in efficiency, is not limited by site conditions, can effectively test the load in the actual traveling of the vehicle, is convenient for the government to effectively supervise the load of the heavy commercial vehicle, provides support for the dynamic property design and matching optimization of the vehicle type of the whole vehicle enterprise, and improves the carrying efficiency of a vehicle fleet.

BRIEF DESCRIPTION OF THE DRAWINGS In order to explain the preferred embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings that need to be used in the preferred embodiments or the description of the prior art. Obviously, the drawings in the following description are some implementations of the present invention. For those of ordinary skills in the art, other drawings can be obtained based on these drawings without creative work. FIG. 1 is a basic flowchart of a method for vehicle load weight prediction provided by the present invention; FIG. 2 is a schematic view showing a structure of a vehicle load weight prediction system provided by the present invention; FIG. 3 is a schematic view showing a vehicle traveling data acquisition mode in Embodiment 1 of the present invention: FIG. 4 is an external characteristic curve of an engine according to Embodiment 1 of the present invention: FIG. 5 is a fitness curve in Embodiment 1 of the present invention; FIG. 6 is a result of vehicle load prediction in Embodiment 1 of the present invention. In which:

1: vehicle traveling data acquisition module; 0500570 2: parameter calculation module; 3: segment cutting module; 4: constant speed segment screening module; 5: relevant vector machine training module; 6: load prediction module; 7: driver; 8: load information; 9: vehicle-mounted terminal; 10: vehicle ODB interface; 11: vehicle traveling data; 12: mobile base station; and 13: remote platform.

DETAILED DESCRIPTION OF THE EMBODIMENTS In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, it is to be understood that the described embodiments are only a few, but not all, embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skills in the art without involving any inventive effort are within the scope of the present invention. According to one aspect of the present invention, as shown in FIG. 1, the present invention provided a method for vehicle load weight prediction, comprising the following steps: Step a: acquiring vehicle traveling data and calculating parameters: al. acquiring the engine speed, the engine load percentage and the ECU vehicle speed of a vehicle in the traveling process under different load conditions; a2. according to an engine external characteristic curve and the engine speed, obtaining the maximum torque of the engine at the engine speed, and obtaining the current torque according to the maximum torque of the engine and the engine load percentage; and a3. obtaining the current transmission gear ratio according to the wheel radius,

the final driver ratio, the engine speed and the ECU vehicle speed; 0500570 Step b: segment cutting and constant speed segment screening: dividing the vehicle traveling data into time segments and removing idle speed time segments to obtain a continuous motion segment, and screening out a constant speed segment from the continuous motion segment; Step c: relevant vector machine model training: taking the average transmission gear ratio, the average vehicle speed and the average torque of each constant speed segment as an input, taking the load as an output, and training a relevant vector machine to obtain a relevant vector machine model: and Step d: predicting the load: for a vehicle with unknown load, acquiring the engine speed, the engine load percentage and the ECU vehicle speed, obtaining the current torque according to the step a2, and obtaining the current transmission gear ratio according to the step a3; then performing segment cutting and constant speed segment screening according to the step b, and inputting the average transmission gear ratio, the average vehicle speed and the average torque of the screened segment into the relevant vector machine model to obtain a load prediction result of each segment to further obtain a prediction result of a vehicle load.

According to the method for vehicle load weight prediction, the traveling data of a vehicle with a known load is acquired and the parameter is calculated, segment cutting and constant speed segment screening are performed, a relevant vector machine model is obtained through training, and then the vehicle with the unknown load is subjected to load prediction through the relevant vector machine model so that the dynamic monitoring of the vehicle load is realized.

The method is simple and convenient to implement, low in cost and high in efficiency, is not limited by site conditions, can effectively test the load in the actual traveling of the vehicle, is convenient for the government to effectively supervise the load of the heavy commercial vehicle, provides support for the dynamic property design and matching optimization of the vehicle type of the whole vehicle enterprise, and improves the carrying efficiency of a vehicle fleet.

It should be noted that:

the above-mentioned "average transmission gear ratio" refers to the arithmetic 0500570 average value of each current transmission gear ratio in a constant speed segment.

The above-mentioned "average vehicle speed" refers to the arithmetic average value of the respective ECU vehicle speeds in the constant speed segment.

The above-mentioned "average torque" refers to the arithmetic average value of each current torque in the constant speed segment.

In a preferred implementation, in step a3, the current transmission gear ratio is i, =0.377 522 calculated by adopting the following method: fe XV ; where vis the ECU vehicle speed, n is the engine speed, r is the wheel radius, io is the transmission gear ratio; and ig is the final driver ratio.

The /o (transmission gear ratio) is determined by the output gear.

In a preferred implementation, in the step b, the method for dividing time segments comprises: adopting a moving window method to divide the second-by-second vehicle speed into different time segments.

In a preferred implementation, in the step b, the method for screening a constant speed segment comprises: calculating the variation coefficient of the vehicle speed of the continuous motion segment, sequencing the variation coefficient of the vehicle speed from large to small, and reserving the last 20% segment as a constant speed segment; or, calculating the variation coefficient of the vehicle speed of the continuous motion segments, sequencing the variation coefficient of the vehicle speed from small to large, and reserving the first 20% of the segments as constant speed segments.

Preferably, the variation coefficient of the vehicle speed is calculated by the following method: cv=std(V)/V- Where, cv is the variation coefficient of the vehicle speed, std (v) is the standard deviation of the speed, and ¥ is the average vehicle speed.

In a preferred implementation, in the step c, the parameter of the relevant vector machine is obtained by adopting an artificial bee colony algorithm.

The artificial bee colony algorithm can adaptively select the optimal parameter, has high convergence speed and is not easy to fall into a locally optimal solution. 0500570 Preferably, the total number of bees of the artificial bee colony algorithm is 20-50, the number of bees gathering honey is 10-25, the maximum searching times is 50-100, and the maximum iteration times is 50-150. The total number of the bees is, for example, 20, 25, 30, 35, 40, 45 or 50; the number of bees gathering honey is, for example, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25, etc. ; the maximum number of searching times is for example 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100; the maximum number of iteration times is, for example, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, or 150, etc. Preferably, the total number of bees is 30-50, more preferably 40. Preferably, the number of bees gathering honey is 15-25, more preferably 20. Preferably, the maximum number of searching times is 50-80, more preferably

100. In a preferred implementation, in the step d, after the load prediction results for each segment are obtained, the load prediction results for each segment are sequenced, and the average value of the load results between 5% and 95% of the lane place lines is calculated as the prediction result of the vehicle load. Directly taking the average value of the load prediction results of all segments as the prediction result of the vehicle load, the error is relatively large. Taking the average value of the load results between 5% and 95% of the lane place lines as the prediction result of the vehicle load can greatly reduce the error, and the prediction accuracy can be enhanced. According to another aspect of the present invention, there is provided electronic equipment, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores an instruction executable by the at least one processor, and the instruction is executed by the at least one processor to enable the at least one processor to execute the method described above. At least one processor in the electronic equipment can execute the above method, thereby having at least the same advantages as the above method. According to another aspect of the present invention, the present invention provides a medium on which a computer instruction is stored.

The computer 0500570 instruction in the medium can be used to cause a computer to execute the method described above, thereby having at least the same advantages as the method described above.

According to another aspect of the present invention, as shown in FIG. 2, there is provided a vehicle load weight prediction system including a vehicle traveling data acquisition module 1, a parameter calculation module 2, a segment cutting module 3, a constant speed segment screening module 4, a relevant vector machine training module 5, and a load prediction module 6; the vehicle traveling data acquisition module 1, the parameter calculation module 2, the segment cutting module 3, the constant speed segment screening module 4, and the relevant vector machine training module 5 are sequentially connected; the load prediction module 6 is also connected to the constant speed segment screening module 4. In the vehicle load weight prediction system, the following is included. (1) The vehicle traveling data acquisition module is used for acquiring the engine speed, the engine load percentage and the ECU vehicle speed in the traveling process of a vehicle under different load conditions, and the engine speed, the engine load percentage and the ECU vehicle speed in the traveling process of the vehicle with the unknown load. (2) The parameter calculation module is used for obtaining the current torque and the current transmission gear ratio of the known load-carrying vehicle and the unknown load-carrying vehicle, specifically, obtaining the maximum torque of the engine at the engine speed according to the acquired engine speed and the external characteristic curve of the engine, obtaining the current torque according to the maximum torque of the engine and the engine load percentage, and simultaneously obtaining the current transmission gear ratio according to the wheel radius, the final driver ratio, the engine speed and the ECU vehicle speed. (3) The segment cutting module is used for dividing time segments, removing idle time segments and obtaining a continuous motion segment. (4) The constant speed segment screening module is used for screening constant speed segments from the continuous motion segments of the known load-carrying vehicle and unknown load-carrying vehicle. 0500570 (5) The relevant vector machine training module is used for training the relevant vector machine according to the average transmission gear ratio, the average vehicle speed, the average torque and the load of each constant speed segment to obtain a relevant vector machine model. (6) The load prediction module is used for inputting the average transmission gear ratio, the average vehicle speed and the average torque of the constant speed segments obtained by the constant speed segments screening module into the relevant vector machine model obtained by the relevant vector machine training module to obtain a load prediction result of each segment to further obtain a prediction result of the vehicle load.

The vehicle load weight prediction system corresponds to the method for vehicle load weight prediction, and as the method is executed, it at least has the same advantages as that of the method.

The dynamic monitoring of the vehicle load can be realized, the system integrity is strong, the dynamic monitoring of the vehicle load can be realized automatically, the efficiency is high, and the cost is low.

The invention will now be described in further detail with reference to the embodiments.

Embodiment 1 A method for vehicle load weight prediction includes steps as follows.

Data Acquisition The actual operating data of 20 tractors of a certain type was acquired by adopting the autonomous traveling method in the test, the acquisition time was from November 1, 2016 to May 31, 2017, and the accumulated traveling mileage was 1.6 million kilometers.

The test system was consisted of a vehicle-mounted data acquisition terminal (sampling frequency 1Hz) and a data management platform.

The vehicle-mounted data acquisition terminal encoded the acquired information according to a unified data protocol and sent the encoded information to a working condition data management platform in real time through a GPRS network.

The vehicle-mounted data acquisition terminal acquisition mode was as shown in FIG. 3. A driver 7 manually input load information 8 to a vehicle-mounted terminal 9, a vehicle ODB interface 10 acquired vehicle traveling data 11 to the vehicle-mounted terminal 9, the vehicle-mounted terminal 9 sent the above two aspects of information to a 0500570 mobile base station 12 through GPRS, and the mobile base station 12 sent the data to a remote platform 13 through the Internet.

Parameters for vehicle load weight prediction included ECU vehicle speed, engine speed, engine load rate, etc.

Data Processing According to the current revolving speed of the engine, the maximum torque of the engine at the revolving speed was acquired by utilizing an engine external characteristic curve (shown in FIG. 4). The engine current torque was obtained by multiplying the engine maximum torque at the revolving speed by the engine load percentage.

The current transmission gear ratio was calculated according to the engine speed, the wheel radius, the final driver ratio and the ECU vehicle speed as the following formula:

i, = 03717 Where v was the current vehicle speed (km/h), n was the engine speed (r/min), r was the wheel radius (mm), io was the transmission gear ratio and was determined by the output gear; ig was the final driver ratio; the window duration was set to be 100 seconds, and the second-by-second vehicle speed was divided into different time segments by utilizing a moving window method.

A time segment containing idle speed was removed, and the variation coefficient of the vehicle speed of each time segment was calculated: cv=stdV)V. where cv was the variation coefficient, std (v) was the vehicle speed standard deviation, and ¥ was the average vehicle speed.

The segments were sequenced according to the variation coefficient from large to small, and the last 20% of the segments of the fluctuation value was reserved as the constant speed segments.

Model Training The relevant vector machine model was trained by taking the average transmission gear ratio, the average vehicle speed and the average torque of each constant speed segment as input and the load as output.

An artificial bee colony algorithm was adopted to optimize the kernel function parameters of the 0500570 relevant vector machine.

The total number of bees in the artificial bee colony algorithm was set as 40, the number of bees gathering honey was set as 20, the maximum number of searching times was set as 50, the maximum number of iteration times was set as 100, and the fitness function was the prediction error percentage.

The fitness curve for the optimization process is as shown in FIG. 5. After optimization, the optimal kernel function parameter was obtained as 1.7. Model Validation 100 groups of samples were selected under each load, and the average transmission gear ratio, the average vehicle speed and the average torque were taken as inputs to obtain the load output of several segments. 100 prediction results under each load were sequenced from large to small, the average value of the load results between 5% and 95% lane place lines was calculated as a prediction result of the load of the vehicle, and the prediction result was as shown in figure 6. It can be seen that the method proposed in the embodiment has a good correlation between the predicted load and the actual load, and the average error is only 6.7%. Model Prediction The engine speed, ECU vehicle speed and engine load percentage data for a vehicle with unknown load were acquired for segment cutting and constant speed segment screening.

The average transmission gear ratio, the average vehicle speed and the average torque of the screened segments were taken as inputs to obtain a prediction result of 100 constant speed segments.

The prediction results were sequenced from large to small, and the average value of the load results between the 5% and 95% lane place lines was calculated as the prediction result of the vehicle load.

It should be understood that the various forms of flow, resequencing, adding or removing steps shown above may be used.

For example, the steps recited in the present application can be executed in parallel or sequentially or may be executed in a different order, so long as the desired results of the technical solutions disclosed in the present application can be achieved, and no limitation is made herein.

The above-mentioned preferred embodiments are not to be construed as limiting the scope of the present application.

It will be apparent to those skilled 0500570 in the art that various modifications, combinations, sub-combinations and substitutions are possible, depending on design requirements and other factors.

Any modifications, equivalents, and improvements and the like within the spirit and principle of the present application is intended to be included within the scope of the present application.

Claims

Claims LU5005 70

1. A method for vehicle load weight prediction, characterized by comprising the following steps: step a: acquiring vehicle traveling data and calculating a parameter: al. acquiring engine speed, engine load percentage, and ECU vehicle speed of a vehicle in a traveling process under different load conditions; a2. according to an engine external characteristic curve and the engine speed, obtaining a maximum torque of the engine at the engine speed, and obtaining a current torque according to the maximum torque of the engine and the engine load percentage; and a3. obtaining a current transmission gear ratio according to a wheel radius, a final driver ratio, the engine speed, and the ECU vehicle speed; step b: segment cutting and constant speed segment screening: dividing the vehicle traveling data into time segments and removing idle speed time segments to obtain a continuous motion segment, and screening out a constant speed segment from the continuous motion segment; step c: relevant vector machine model training: taking an average transmission gear ratio, average vehicle speed and average torque of each constant speed segment as an input, taking a load as an output, and training a relevant vector machine to obtain a relevant vector machine model; and step d: predicting the load: for a vehicle with unknown load, acquiring the engine speed, the engine load percentage and the ECU vehicle speed, obtaining the current torque according to step a2, and obtaining the current transmission gear ratio according to step a3; then performing segment cutting and constant speed segment screening according to step (b), and inputting the average transmission gear ratio, the average vehicle speed and the average torque of screened segments into the relevant vector machine model to obtain a load prediction result of each segment to further obtain a prediction result of a vehicle load.

2. The method for vehicle load weight prediction according to claim 1, characterized in that in the step a3, the current transmission gear ratio is i, =0377 27 calculated by the following method: fe XV ; where vis the ECU vehicle speed, n is the engine speed, r is the wheel radius, io is the transmission gear ratio; and ig is the final driver ratio.

3. The method for vehicle load weight prediction according to claim 1, characterized in that in the step b, the method for dividing time segments comprises: adopting a moving window method to divide a second-by-second vehicle speed into different time segments.

4. The method for vehicle load weight prediction according to claim 1, characterized in that in the step b, the method for screening constant speed segments comprises: calculating variation coefficient of a vehicle speed of continuous motion segments, sequencing the variation coefficient of the vehicle speed from large to small, and reserving last 20% segment as a constant speed segment.

5. The method for vehicle load weight prediction according to claim 4, characterized in that the variation coefficient of the vehicle speed is calculated by the following method: ©Y=std(")/7- Where, cv is the variation coefficient of the vehicle speed, std (v) is a standard deviation of the vehicle speed, and ¥ is the average vehicle speed.

6. The method for vehicle load weight prediction according to claim 1, characterized in that in the step c, a parameter of the relevant vector machine is obtained by adopting an artificial bee colony algorithm; preferably, a total number of bees of the artificial bee colony algorithm is 20-50, the number of bees gathering honey is 10-25, maximum searching times is 50-100, and maximum iteration times is 50-150; preferably, the total number of bees is 30-50, more preferably 40; preferably, the number of bees gathering honey is 15-25, more preferably 20; preferably, the maximum number of searching times is 50-80, more preferably

50: LU500570 preferably, the maximum number of iteration times is 80-120, more preferably

100.

7. The method for vehicle load weight prediction according to any one of claims 1 to 6, characterized in that in step (d), after the load prediction results of each segment are obtained, the load prediction results of each segment are sequenced, and an average value of the load results between 5% and 95% lane place lines is calculated as the prediction result of the vehicle load.

8. Electronic equipment, characterized by comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores an instruction executable by the at least one processor, and the instruction is executed by the at least one processor to enable the at least one processor to execute the method for vehicle load weight prediction according to any one of claims 1-6.

9. A medium, characterized in that the medium on which a computer instruction are stored, and the computer instruction is used for causing a computer to execute the method for vehicle load weight prediction of any one of claims 1-6.

10. A vehicle load weight prediction system, characterized by comprising a vehicle traveling data acquisition module, a parameter calculation module, a segment cutting module, a constant speed segment screening module, a relevant vector machine training module, and a load prediction module; wherein the vehicle traveling data acquisition module, the parameter calculation module, the segment cutting module, the constant speed segment screening module, and the relevant vector machine training module are sequentially connected; the load prediction module is also connected to the constant speed segment screening module.