Vehicle axle load determination method, device and equipment

Abstract

The invention provides a vehicle axle load determination method, device and equipment, and the method comprises the steps: building an axle load prediction model of a vehicle according to the pressure intensity of an air spring, the rigidity change coefficient of the air spring and the height information of the vehicle; and determining an axle load prediction result of the vehicle according to the axle load prediction model of the vehicle. According to the method, the accuracy of the vehicle axle load prediction result can be effectively improved, and the problem that the error of vehicle axle load calculation based on the mass center and the wheelbase is large is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle axle load determination, and in particular to a vehicle axle load determination method, device and equipment. Background Art

[0002] Axle load refers to the maximum load that each axle can bear, also known as axle weight. It indicates the total vehicle weight that can be shared by each axle under specific circumstances.

[0003] In the related art, the calculation of vehicle axle load is mostly based on the measurement of center of mass and wheelbase. The center of mass estimation error is large, and multiple tests and wheelbase measurements are required, which is cumbersome and costly. Summary of the invention

[0004] The present invention provides a method, device and equipment for determining a vehicle axle load. Based on vehicle height information and air spring pressure, and taking into account changes in spring stiffness, an axle load prediction model is jointly established to accurately and comprehensively reflect the factors affecting the vehicle axle load, thereby effectively improving the accuracy of the vehicle axle load prediction results and effectively overcoming the problem of large errors in calculating the vehicle axle load based on the center of mass and wheelbase.

[0005] The present invention provides a method for determining a vehicle axle load, comprising the following steps.

[0006] A vehicle axle load prediction model is established based on the air spring pressure, the air spring stiffness variation coefficient and the vehicle height information; The axle load prediction result of the vehicle is determined according to the axle load prediction model of the vehicle.

[0007] According to a vehicle axle load determination method provided by the present invention, the vehicle axle load prediction model includes: F = α × (h-h0) × β × P Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle; β represents the stiffness change coefficient of the air spring; and P represents the pressure of the air spring.

[0008] According to a vehicle axle load determination method provided by the present invention, after determining the vehicle axle load prediction result, the method further includes: Determining a correction term for a vehicle axle load prediction result; the correction term is used to correct the effect of the weight of vehicle components on the vehicle axle load; The axle load prediction result of the vehicle is updated according to the correction item of the axle load prediction result of the vehicle.

[0009] According to a vehicle axle load determination method provided by the present invention, the method further includes: Establishing a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring; The cross-sectional area of ​​the air spring is determined according to the first fitting relationship and the height information of the vehicle measured by the sensor.

[0010] According to a vehicle axle load determination method provided by the present invention, the method further includes: Establishing a second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring; The stiffness variation coefficient of the air spring is determined according to the second fitting relationship and the height information of the vehicle measured by the sensor.

[0011] According to a vehicle axle load determination method provided by the present invention, the method further includes: When the predicted axle load of the vehicle is greater than the preset vehicle load weight, an alarm message is reported.

[0012] The present invention also provides a vehicle axle load determination device, comprising the following modules: Establishing a module for establishing a vehicle axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information; The prediction module is used to determine the axle load prediction result of the vehicle according to the axle load prediction model of the vehicle.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any of the above-mentioned vehicle axle load determination methods is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the vehicle axle load determination method as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the vehicle axle load determination method as described above is implemented.

[0016] The vehicle axle load determination method, device and equipment provided by the present invention are based on vehicle height information and air spring pressure, and take into account changes in spring stiffness to jointly establish an axle load prediction model, which accurately and comprehensively reflects the factors affecting the vehicle axle load, thereby effectively improving the accuracy of the vehicle axle load prediction results and effectively overcoming the problem of large errors in calculating the vehicle axle load based on the center of mass and wheelbase. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is one of the flow charts of the vehicle axle load determination method provided by the present invention.

[0019] Figure 2 It is a fitting schematic diagram of the height information of the vehicle and the cross-sectional area of ​​the air spring provided by the present invention.

[0020] Figure 3 It is a fitting schematic diagram of the height information of the vehicle and the stiffness variation coefficient of the air spring provided by the present invention.

[0021] Figure 4 This is the second flow chart of the vehicle axle load determination method provided by the present invention.

[0022] Figure 5 It is a structural schematic diagram of a vehicle axle load determination device provided by the present invention.

[0023] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Combine the following Figure 1-Figure 6 The vehicle axle load determination method, device and apparatus of the present invention are described.

[0026] In order to facilitate a clearer understanding of the technical solutions of the embodiments of the present application, some technical contents related to the embodiments of the present application are first introduced.

[0027] At present, axle load calculation is mostly based on the measurement of center of mass and wheelbase. The estimation error of center of mass is large, and multiple tests and wheelbase measurements are required. The process of calculating axle load by determining wheelbase by center of mass is cumbersome and increases costs. There are also methods to calculate axle load based on air spring pressure. This method does not take into account the influence of factors such as height and the trend of spring stiffness change when it rises and falls, and the error is large.

[0028] In summary, the existing axle load calculation scheme has the following disadvantages: A. Based on the center of mass and wheelbase, the center of mass estimation error is large.

[0029] B. Based on the center of mass and wheelbase, the process is cumbersome and increases costs.

[0030] C. The axle load is estimated based on pressure, without considering the effects of factors such as height on the axle load.

[0031] D. The influence of factors such as the rise or fall of the spring and the different trends of the spring stiffness changes are not taken into account.

[0032] Figure 1 is one of the flow charts of the vehicle axle load determination method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Establish a vehicle axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information.

[0033] Specifically, in the embodiment of the present application, a vehicle axle load prediction model is established based on the pressure of the air spring, the stiffness change coefficient of the air spring and the vehicle height information. That is, the present application is different from the existing method of calculating the vehicle axle load based on the center of mass and wheelbase. By considering the rise and fall of the height of the ECAS air spring of a single axle, the different trends of its spring stiffness characteristics, and the characteristics of the pressure change accordingly, a vehicle axle load prediction model is established, so that the influencing factors of the vehicle axle load can be more accurately and comprehensively reflected, and the vehicle axle load can be predicted more accurately.

[0034] For example, the present application takes into account the problem of large errors in calculating the vehicle axle load based on the center of mass and wheelbase, and turns to the vehicle height information and air spring pressure, and comprehensively considers the change in spring rigidity to jointly establish an axle load calculation model, thereby effectively improving the accuracy of the vehicle axle load prediction results. Optionally, the height information of the vehicle can be determined by a potentiometer height sensor. Optionally, the height sensor mechanically connects the axle and the frame, and drives the potentiometer slider to move when the height changes. The initial median height is 860mm, and the rise and fall adjustment is performed. The collected height signal is sent to the vehicle's axle load prediction model as input. Optionally, the pressure signal of the air spring is monitored by a pressure detection unit, and the collected signal is sent to the vehicle's axle load prediction model block as input. It should be noted that the height sensor and the pressure sensor work together to ensure the dynamic balance between the vehicle height and the air spring pressure. For example, keep the vehicle body at a certain height. When the height sensor detects that the vehicle body has dropped, the ECU will monitor the air pressure through the pressure sensor and inflate the air spring to restore the height.

[0035] Step 102: Determine the axle load prediction result of the vehicle according to the axle load prediction model of the vehicle.

[0036] Specifically, after the embodiment of the present application establishes the vehicle's axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information, the vehicle's axle load prediction model can be used to accurately predict the vehicle's axle load, effectively overcoming the problem of large errors in calculating the vehicle's axle load based on the center of mass and wheelbase.

[0037] The method of the above embodiment is based on vehicle height information and air spring pressure, and takes into account the change in spring stiffness, to jointly establish an axle load prediction model, which accurately and comprehensively reflects the factors affecting the vehicle axle load, thereby effectively improving the accuracy of the vehicle axle load prediction results and effectively overcoming the problem of large errors in calculating the vehicle axle load based on the center of mass and wheelbase.

[0038] In some embodiments, the axle load prediction model of the vehicle includes: F = α × (h-h0) × β × P Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle; β represents the stiffness change coefficient of the air spring; and P represents the pressure of the air spring.

[0039] Specifically, in the embodiment of the present application, the relationship between the axle load and the air spring characteristics and the pressure and height changes is taken into account to establish an axle load prediction model, which is simple, easy to implement and has strong real-time performance. Optionally, the axle load prediction model includes: F = α × (h-h0) × β × P; Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle, h represents the current height of the vehicle, and h0 represents the initial median height; β represents the rigidity change coefficient of the air spring; and P represents the pressure of the air spring. That is, this application is based on the vehicle height information and the air spring pressure, and takes into account the change of spring rigidity to jointly establish an axle load prediction model, which can more accurately and comprehensively reflect the influencing factors of the vehicle axle load, and can also more accurately predict the vehicle axle load.

[0040] The method of the above embodiment is based on vehicle height information and air spring pressure, and takes into account the change in spring stiffness to jointly establish an axle load prediction model, which can more accurately and comprehensively reflect the factors affecting the vehicle axle load, and can also more accurately predict the vehicle axle load.

[0041] In some embodiments, after determining the axle load prediction result of the vehicle, the method further includes: Determine the correction item of the vehicle axle load prediction result; the correction item is used to correct the influence of the weight of vehicle parts on the vehicle axle load; The axle load prediction result of the vehicle is updated according to the correction item of the axle load prediction result of the vehicle.

[0042] Specifically, after determining the axle load prediction result of the vehicle based on the axle load prediction model of the vehicle, the present application further considers the influence of unsprung component factors, adds unsprung component correction items, and updates the axle load prediction result, so that the vehicle axle load can be predicted more accurately. Optionally, there are wheels and other sensors under the air spring, which will affect the axle load prediction result. Therefore, the present application can effectively improve the accuracy of the vehicle axle load prediction result by adding correction items to the axle load prediction result of the vehicle. Optionally, the axle load prediction result output by the axle load prediction model can be compared with the axle load measured by the axle load meter, and the difference between the two can be used as a correction item for the axle load prediction result of the vehicle.

[0043] For example, the predicted axle load results when the vehicle height rises and falls are fitted and checked with the values ​​measured in the actual axle load meter to obtain the correction error, and the load correction value f is updated iteratively multiple times until the model calculation results are within the allowable range of axle load error.

[0044] The method of the above embodiment takes into account the influence of unsprung components, adds unsprung component correction items, corrects the influence of the weight of vehicle components on the vehicle axle load, updates the axle load prediction results, and effectively improves the accuracy of the vehicle axle load prediction results.

[0045] In some embodiments, the vehicle axle load determination method further includes: Establishing a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring; The cross-sectional area of ​​the air spring is determined according to the first fitting relationship and the height information of the vehicle measured by the sensor.

[0046] Specifically, Figure 2 As shown, in the implementation of the present application, according to the membrane air spring used in the vehicle, the change trend of the effective coefficient of the cross-sectional area in the airbag with the increase or decrease of the height is determined. Through multiple increases and decreases in height, the first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring is established. Then, when predicting the axle load of the vehicle, the cross-sectional area of ​​the air spring can be determined based on the current height information of the vehicle and the first fitting relationship, and substituted into the axle load prediction model of the vehicle, and the axle load prediction result of the vehicle can be obtained.

[0047] The method of the above embodiment establishes a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring by causing the vehicle to rise and fall in height multiple times. Then, when predicting the axle load of the vehicle, the cross-sectional area of ​​the air spring and the axle load prediction result of the vehicle can be accurately determined based on the current vehicle height information and the first fitting relationship.

[0048] In some embodiments, the vehicle axle load determination method further includes: Establishing a second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring; The stiffness variation coefficient of the air spring is determined according to the second fitting relationship and the height information of the vehicle measured by the sensor.

[0049] Specifically, Figure 3 As shown, in the implementation of this application, the vehicle is unloaded and fully loaded, and the air spring rigidity change trend is verified through multiple height rises and falls, and the second fitting relationship between the vehicle height information and the air spring rigidity change coefficient is established. Then, when predicting the vehicle's axle load, the air spring rigidity change coefficient can be determined based on the current vehicle height information and the second fitting relationship, and substituted into the vehicle's axle load prediction model, and the vehicle's axle load prediction result can be obtained.

[0050] The method of the above embodiment establishes a second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring. When predicting the axle load of the vehicle, the stiffness variation coefficient of the air spring and the axle load prediction result of the vehicle can be accurately determined based on the current vehicle height information and the second fitting relationship.

[0051] In some embodiments, the vehicle axle load determination method further includes: When the predicted axle load of the vehicle is greater than the preset vehicle load weight, an alarm message is reported.

[0052] Specifically, in the embodiment of the present application, the axle load of the rear axle can be calculated in real time. When the axle load of the rear axle exceeds the vehicle's rated load, it will be sent to the VCU through the CAN line for early warning, and the overload warning light will be reported on the instrument. Optionally, the left and right loads of a single axle can also be calculated separately. If there is a huge difference, it may cause abnormal rollover, which also requires early warning from the VCU and the abnormal load warning light will be reported on the instrument.

[0053] The method of the above embodiment predicts the vehicle axle load through the vehicle axle load prediction model, and reports an alarm message when the vehicle axle load prediction result is greater than the preset vehicle load weight, thereby effectively improving vehicle safety.

[0054] For example, Figure 4 As shown, a method for determining a vehicle axle load is provided in an embodiment of the present application, which is specifically as follows: Based on the vehicle height information and air spring pressure, and taking into account the change in spring stiffness, an axle load prediction model is jointly established: F = α × (h-h0) × β × P Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle; β represents the stiffness change coefficient of the air spring; and P represents the pressure of the air spring. Optionally, the loads of the left and right wheels can be calculated separately for the axle load of a single axle. Since the pressures on the left and right sides may be different, after calculating the left and right loads, they are added together as the axle load of the single axle, and the axle load result is more accurate. For the rise and fall of height, the effective coefficient of the cross-sectional area follows different curves and can be converted into αup and αdown, thereby establishing a calculation model for the rise and fall axle load: F = αup × (h-h0) × β × P + f F = αdown × (h-h0) × β × P + f It should be noted that in the present application, a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring is established; based on the first fitting relationship and the height information of the vehicle measured by the sensor, the cross-sectional area of ​​the air spring is determined. A second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring is established; based on the second fitting relationship and the height information of the vehicle measured by the sensor, the stiffness variation coefficient of the air spring is determined. In addition, the present application takes into account the influence of unsprung component factors, adds unsprung component correction items, corrects the influence of the weight of vehicle components on the vehicle axle load, and updates the axle load prediction results, thereby effectively improving the accuracy of the vehicle axle load prediction results. When the vehicle's axle load prediction result is greater than the preset vehicle load weight, an alarm message is reported, which can effectively improve vehicle safety.

[0055] The vehicle axle load determination device provided by the present invention is described below. The vehicle axle load determination device described below and the vehicle axle load determination method described above can be referred to each other. Figure 5 As shown, including: Establishing module 510, for establishing a vehicle axle load prediction model according to the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information; The prediction module 520 is used to determine the axle load prediction result of the vehicle according to the axle load prediction model of the vehicle.

[0056] Optionally, the vehicle axle load prediction model includes: F = α × (h-h0) × β × P Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle; β represents the stiffness change coefficient of the air spring; and P represents the pressure of the air spring.

[0057] Optionally, the prediction module 520 is further configured to: Determine the correction item of the vehicle axle load prediction result; the correction item is used to correct the influence of the weight of vehicle parts on the vehicle axle load; The axle load prediction result of the vehicle is updated according to the correction item of the axle load prediction result of the vehicle.

[0058] Optionally, the establishment module 510 is further used for: Establishing a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring; The cross-sectional area of ​​the air spring is determined according to the first fitting relationship and the height information of the vehicle measured by the sensor.

[0059] Optionally, the establishment module 510 is further used for: Establishing a second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring; The stiffness variation coefficient of the air spring is determined according to the second fitting relationship and the height information of the vehicle measured by the sensor.

[0060] Optionally, the prediction module 520 is further configured to: When the predicted axle load of the vehicle is greater than the preset vehicle load weight, an alarm message is reported.

[0061] Figure 6 An example of a physical structure diagram of an electronic device is provided, and the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute a vehicle axle load determination method, and the method includes: establishing an axle load prediction model of the vehicle according to the pressure of the air spring, the stiffness variation coefficient of the air spring, and the vehicle height information; and determining the axle load prediction result of the vehicle according to the axle load prediction model of the vehicle.

[0062] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0063] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the vehicle axle load determination method provided by the above-mentioned methods, which includes: establishing a vehicle axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information; determining the vehicle axle load prediction result based on the vehicle axle load prediction model.

[0064] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the vehicle axle load determination method provided by the above-mentioned methods. The method includes: establishing a vehicle axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information; determining the vehicle axle load prediction result based on the vehicle axle load prediction model.

[0065] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0066] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining a vehicle axle load, characterized in that: include: A vehicle axle load prediction model is established based on the air spring pressure, the air spring stiffness variation coefficient and the vehicle height information; The axle load prediction result of the vehicle is determined according to the axle load prediction model of the vehicle.

2. The vehicle axle load determination method according to claim 1, characterized in that: The vehicle axle load prediction model includes: F = α × (h-h0) × β × P Among them, F represents the predicted result of the vehicle axle load; α represents the cross-sectional area of ​​the air spring; h-h0 represents the height change information of the vehicle; β represents the stiffness change coefficient of the air spring; and P represents the pressure of the air spring.

3. The vehicle axle load determination method according to claim 1 or 2, characterized in that: After determining the axle load prediction result of the vehicle, the method further includes: Determining a correction term for a vehicle axle load prediction result; the correction term is used to correct the effect of the weight of vehicle components on the vehicle axle load; The axle load prediction result of the vehicle is updated according to the correction item of the axle load prediction result of the vehicle.

4. The vehicle axle load determination method according to claim 1 or 2, characterized in that: The method further comprises: Establishing a first fitting relationship between the height information of the vehicle and the cross-sectional area of ​​the air spring; The cross-sectional area of ​​the air spring is determined according to the first fitting relationship and the height information of the vehicle measured by the sensor.

5. The vehicle axle load determination method according to claim 1 or 2, characterized in that: The method further comprises: Establishing a second fitting relationship between the height information of the vehicle and the stiffness variation coefficient of the air spring; The stiffness variation coefficient of the air spring is determined according to the second fitting relationship and the height information of the vehicle measured by the sensor.

6. The vehicle axle load determination method according to claim 1 or 2, characterized in that: The method further comprises: When the predicted axle load of the vehicle is greater than the preset vehicle load weight, an alarm message is reported.

7. A vehicle axle load determination device, characterized in that: include: Establishing a module for establishing a vehicle axle load prediction model based on the pressure of the air spring, the stiffness variation coefficient of the air spring and the vehicle height information; The prediction module is used to determine the axle load prediction result of the vehicle according to the axle load prediction model of the vehicle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the vehicle axle load determination method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle axle load determination method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle axle load determination method according to any one of claims 1 to 6 is implemented.

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