Long platform dynamic vehicle scale convergence iteration weighing calculation method

Abstract

The application discloses a long-platform dynamic vehicle scale convergence iteration weighing calculation method, which combines the characteristics of long-platform dynamic vehicle weighing, collects a section of data when a vehicle passes through a weighing platform, and captures a most stable section of weight values in a dynamic running process after the vehicle is on the scale, so that the accurate measurement of the vehicle weight is realized. The application adopts the convergence iteration calculation method to capture the most stable section of weight values, and in the iteration process, a stable satisfaction value P is set in combination with the vehicle speed, the estimated weight value and the axle group, so that the accurate measurement of the vehicle weight is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of dynamic weighing, and relates to a long-platform dynamic vehicle weighing calculation method, in particular to a long-platform dynamic vehicle scale convergence iteration weighing calculation method. BACKGROUND

[0002] In the transportation industry, overloaded vehicles not only increase the safety risk of drivers, but also have a negative impact on roads and bridges. Therefore, it is very important to accurately measure the load of vehicles.

[0003] Currently, the weighing equipment for the load of vehicles in China mainly includes two types, one is static weighing equipment, and the other is dynamic weighing equipment. The static weighing equipment is a relatively traditional weighing method. The static detection equipment needs the vehicle to stop on the weighing platform, and uses the static detection principle to accurately weigh the total weight of the truck. This weighing method has the advantages of high precision and is suitable for weight measurement occasions with strict weight requirements. The dynamic weighing equipment uses the dynamic detection principle. When the vehicle dynamically drives through the weighing platform, the detection equipment dynamically monitors the driving state of the vehicle on the weighing platform, and obtains the total weight of the truck through weighing calculation. This weighing method has the advantages of fast measurement speed and is suitable for weight measurement occasions with large traffic flow.

[0004] Due to the dynamic monitoring process, the vehicle is always in the driving process. Due to factors such as road conditions and wind speed, the vehicle will have speed fluctuations in the driving process. When the vehicle load is uneven or the vehicle body design is unreasonable, the vehicle center of gravity will be offset. When the vehicle passes through a bumpy or undulating or other uneven road surface, the driver's sudden braking, sudden acceleration, steering operation and many other factors in the driving process will cause weight fluctuations in the weighing process, which greatly affects the accuracy of the weighing result of the dynamic truck scale.

[0005] To address the aforementioned issues, existing technologies typically employ curve fitting for data processing, which wastes significant computation time and resources. While simpler data processing methods are used to obtain weight results, their accuracy is often too low. For example, patent CN200810181128.7 proposes a dynamic weighing axle load system for truck scales. In this patent, a load-bearing device carries the vehicle and transmits the gravity signal to a weighing sensor. The signal is amplified by a linear amplifier and then converted from analog to digital by a fast A / D converter. This digital signal is stored in a buffer on the weighing processor. After initial low-order filtering, a relatively smooth waveform is obtained. The waveform, after passing through the weighing processor, is compared by a comparator to determine if the ratio of peaks to troughs exceeds a set value. This set value should not exceed 1.4; in practice, a ratio of 1.2-1.4 can be selected. If it exceeds this value, a new sampling point is selected; otherwise, the sampling point is saved. Simultaneously, the data saved by the A / D converter is retrieved based on this sampling point. This data undergoes a low-pass secondary filter to obtain the axle load and total weight data. This patent only performs a single judgment on all data. The method described in this patent is not to capture the most stable weight value during the dynamic movement of the vehicle after it is placed on the scale. It only performs a simple data processing, resulting in low weight detection accuracy. Furthermore, if the judgment conditions are not met, the data needs to be collected again, making the operation cumbersome. Moreover, the patent sets the set value to a fixed value, while it is well known that the fluctuation error produced by vehicles of different models, weights, and driving conditions is not the same. Generally speaking, larger models, heavier vehicles, and vehicles traveling at higher speeds will produce greater fluctuation errors. Setting the set value to a fixed value can easily lead to problems such as the set value being too large or too small for different models, weights, and driving conditions. If the set value is too large, the allowable fluctuation data will be too large, resulting in inaccurate weighing. If the set value is too small, the sampled weighing data will not meet the set value requirements even after repeated weighings. Summary of the Invention

[0006] Analysis of existing technologies reveals that current weighing methods generally suffer from computational complexity or low accuracy. Therefore, this invention, taking advantage of the characteristics of dynamic vehicle weighing on long platforms, collects data as the vehicle passes over the weighing platform and captures the most stable weight value during the vehicle's dynamic movement on the scale, thereby achieving accurate vehicle weight measurement. This invention employs a convergent iterative calculation method to capture the most stable weight value. Furthermore, during the iteration process, it combines vehicle speed, estimated weight, and axle group data to set a stable and satisfactory value P, significantly improving the accuracy of vehicle weight measurement.

[0007] The present invention adopts the following technical solution to solve the technical problem:

[0008] A convergent iterative weighing calculation method for a dynamic vehicle scale with a long platform, comprising the following steps:

[0009] S1. Setting up a weighing platform: A first vertical light curtain perpendicular to the driving direction is set at the vehicle entry end of the weighing platform, and a second vertical light curtain perpendicular to the driving direction is set at the vehicle exit end of the weighing platform. The distance between the first vertical light curtain and the second vertical light curtain is S. A vehicle axle group identification area is set on the platform surface of the weighing platform.

[0010] S2. Calculation Method Starting Point: Waiting for the vehicle to arrive, the weighing platform is unloaded, and all equipment is idle. The vehicle arrives and blocks the first facade light curtain. The vehicle begins to enter the weighing platform, and this time is recorded as t1. The vehicle is no longer blocked by the first facade light curtain and has fully entered the weighing control area. This is the starting point of the calculation method. The weight change of the vehicle is obtained through the weighing platform as a weight fluctuation curve, and the vehicle model code is identified using the vehicle axle group identification area. When the vehicle blocks the second facade light curtain, i.e., the vehicle leaves the weighing control area, the acquisition of the weight fluctuation curve stops, and this time is recorded as t2. The average driving speed V of the vehicle on the weighing platform is calculated as V = S / (t2 - t1). V needs to be equal to the set speed V0. n When comparing, when V <V n If the calculation is successful, proceed with the subsequent calculations; otherwise, stop the calculation.

[0011] S3. Determination of the stable satisfactory value P: Based on the weight fluctuation curve obtained in step S2, the estimated weight T is derived, T = (T max +T min ) / 2, T max T represents the maximum value in the weight fluctuation curve. min This represents the minimum value in the weight fluctuation curve. The corresponding stability satisfaction coefficient α is obtained from the vehicle model code acquired in step S2. The stability satisfaction value P is then obtained using the estimated weight T, average driving speed V, and stability satisfaction coefficient α, where P = α * T * (V / V). m ) 1 / 5 V m For reference speed;

[0012] S4. Set up data groups: Divide the data width A of the weight fluctuation curve obtained in step S2 into M equal parts, each part is a data group, and the data width of each data group is L=A / M;

[0013] S5. Weighing Processing Loop: Set the width of the data window to be the same as the width of the data group. Sample the weight fluctuation curve data in the first data group corresponding to the data window, and determine its fluctuation value B, where B = max1 - min1, and max1 and min1 are the maximum and minimum values ​​of the first data group, respectively. Move the data window using a sliding method, shifting the data window by L data width each time, and recalculate the data fluctuation value B. i =max i -min i max i The min value represents the largest value in the current data window. i This represents the smallest value in the current data window. If B i If less than B, then B = B i If B i If the value is greater than or equal to B, the value of B remains unchanged; compare the value of B with the value of P. If B is less than P, exit the iteration loop and proceed to step S6. This data set is a stable window that meets the expectations. Otherwise, continue to move the data window and perform iterative calculation of the fluctuation value B; if B is still greater than P when i=M, it means that the obtained weight fluctuation curve does not meet the stable expectations, the vehicle needs to be re-inspected, and the loop will proceed to step S2.

[0014] S6. Calculate vehicle weight: Obtain the overall data within the expected stable window, and calculate the average value of the data within that window, which is the weight of the vehicle.

[0015] Further, in step S2, V n =30km / h.

[0016] Further, in step S3, V m =10km / h.

[0017] Further, in step S1, the vehicle axle identification area is set as follows: the 4m area at the front end of the weighing platform is set as the vehicle axle identification area. Specifically, the vehicle axle identification area is divided into 10 segments along the driving direction. The starting points of each segment are denoted as S00, S04, S08, S12, S16, S20, S24, S28, S32, and S36, respectively. Among them, S00 is the upper boundary of the vehicle axle identification area, S16 is about 1.6 meters away from the upper boundary, S20 is about 2.0 meters away from the upper boundary, S32 is about 3.2 meters away from the upper boundary, S36 is about 3.6 meters away from the upper boundary, and S20 is roughly located at the center of the vehicle axle identification area. Several load sensors are arranged along the travel direction and correspond one-to-one with each segment on the upper boundary of the vehicle axle identification area to determine whether there is a load in each area.

[0018] Furthermore, identifying the vehicle model using the vehicle axle group identification area in step S2 includes the following steps:

[0019] SS1. The initial state is when no vehicle enters the vehicle axle group recognition area. The system waits for a vehicle to enter. When the vehicle enters and blocks the first facade light curtain, and the first axle completely enters the upper boundary of the vehicle axle group recognition area along the driving direction, the axle group recognition calculation method starts and the initial vehicle model code value is set to empty.

[0020] SS2. Loop Processing: If the first facade light curtain has no end, it continues to move with the vehicle and enters SS4 for axis following processing; if the first facade light curtain ends, it enters SS5 to obtain the vehicle model code.

[0021] SS3. Axle Following Processing: Axle following processing is divided into three cases: SS4.1: Only the first axle is unloaded until the vehicle axle group identification area is reached. The vehicle model string is incremented by "2", and the process recursively returns to SS3; SS4.2: Before the first axle reaches point N20, the second axle is loaded, and the process enters SS4 for axle coupling processing; SS4.3: After the first axle passes S20, the second axle is loaded. The vehicle model string is incremented by "2", and the second axle becomes the new first axle. The vehicle continues to move forward, and after the original first axle leaves the vehicle axle group identification area, the process recursively returns to SS2;

[0022] SS4. Coupling Processing: After the second axle is placed on the scale, it is pre-processed as a two-axle coupling. If a new axle is added to the weighing platform before the first axle reaches N30, it is processed as a three-axle coupling, and the vehicle model code is appended with "7". If no new axle is placed on the weighing platform before the first axle reaches N30, it is a two-axle coupling, and the vehicle model code is appended with "5". The vehicle continues to move, and subsequent axles are treated as the new first axle. After all the couplings mentioned above are removed from the weighing platform, the process recursively returns to SS3.

[0023] SS5. Obtain vehicle model code: If "2" appears at the beginning of the string, replace it with "1"; if "5" appears at the beginning of the string, replace it with "11". This completes the processing of one vehicle model code and obtains the vehicle model code.

[0024] Furthermore, in step S4, M = 8~16.

[0025] Furthermore, in step S4, M=10.

[0026] The beneficial effects of this invention are as follows:

[0027] 1. This invention combines the characteristics of dynamic vehicle weighing on a long platform. When a vehicle passes through the weighing platform, a segment of data is collected, and the most stable weight value is captured during the dynamic movement of the vehicle after it is on the scale, thereby achieving accurate measurement of the vehicle's weight.

[0028] 2. This invention employs a convergent iterative weighing calculation method, which allows for multiple iterations during data processing, improving the accuracy of weight measurement. Furthermore, the system design fully considers the influence of various factors, such as vehicle speed, vehicle type, and estimated weight value, thereby enhancing the system's accuracy.

[0029] 3. This invention is applicable to dynamic vehicle scale scenarios with long platforms. It can be used not only for weighing large vehicles such as trucks and container trucks, but also for weighing small vehicles. Moreover, this solution can achieve real-time detection of vehicle weight and quickly provide measurement results, improving work efficiency and real-time operation.

[0030] 4. This invention can utilize existing hardware devices, such as light curtains, eliminating the need to purchase additional specialized equipment and reducing system construction costs. Furthermore, the software implementation of this solution is relatively simple, allowing for rapid implementation and easy maintenance.

[0031] 5. This invention adaptively adjusts the window size based on actual data conditions, improving the system's adaptability and flexibility; it uses a sliding method to move the data window and recalculate the data fluctuation value, thereby quickly determining a data window containing stable weight that meets the requirements, greatly improving weighing efficiency and accuracy. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0033] like Figure 1 As shown, a convergent iterative weighing calculation method for a dynamic vehicle scale with a long platform is proposed. The method, when implemented, includes at least the following steps:

[0034] S1. Setting up a weighing platform: A first vertical light curtain perpendicular to the driving direction is set at the vehicle entry end of the weighing platform, and a second vertical light curtain perpendicular to the driving direction is set at the vehicle exit end of the weighing platform. The distance between the first vertical light curtain and the second vertical light curtain is S. A vehicle axle group identification area is set on the platform surface of the weighing platform.

[0035] S2. Calculation Method Starting Point: Waiting for the vehicle to arrive, the weighing platform is unloaded, and all equipment is idle. The vehicle enters the weighing platform, blocking the first facade light curtain. This time is recorded as t1. The vehicle is no longer blocked by the first facade light curtain and has fully entered the weighing control area. This is the starting point of the calculation method. The weight change of the vehicle is obtained through the weighing platform as a weight fluctuation curve, and the vehicle model code is identified using the vehicle axle group identification area. When the vehicle blocks the second facade light curtain, i.e., the vehicle leaves the weighing control area, the acquisition of the weight fluctuation curve stops, and this time is recorded as t2. The average speed V of the vehicle on the weighing platform is calculated as V = S / (t2 - t1). If the speed is too high, the error exceeds the allowable range. The driving speed needs to be within a reasonable range for subsequent weight calculations. V needs to be within the set speed range. n When comparing, when V <V n If the speed is not satisfied, subsequent calculations are performed. If the conditions are not met, it indicates that the vehicle speed is too high, and the calculation stops. The optimal V is selected. n =30km / h;

[0036] S3. Determination of the stable satisfactory value P: Based on the weight fluctuation curve obtained in step S2, the estimated weight T is derived, T = (T max +T min ) / 2, T max T represents the maximum value in the weight fluctuation curve. min This represents the minimum value in the weight fluctuation curve. The corresponding stability satisfaction coefficient α is obtained from the vehicle model code acquired in step S2. The stability satisfaction value P is then obtained using the estimated weight T, average driving speed V, and stability satisfaction coefficient α, where P = α * T * (V / V). m ) 1 / 5 V m For reference speed, as an example of this case, V m =10km / h, the stability satisfaction coefficient α is obtained from the corresponding vehicle model code. It is easy to understand that different vehicle models correspond to different stability satisfaction coefficients α. The correspondence between the stability satisfaction coefficient α and the vehicle model code has been stored in the calculation system. The stability satisfaction coefficient α is set differently according to the size of the vehicle model. It is easy to understand that the larger the vehicle model, the larger the corresponding stability satisfaction coefficient value. In order to ensure the relative stability of the waveform data, as an implementation method, the P / T value needs to be guaranteed to be between 6% and 10%.

[0037] S4. Set up data groups: Divide the data width A of the weight fluctuation curve obtained in step S2 into M equal parts, M=8~16, M is preferably 10, each part is a data group, and the data width of each data group is L=A / M;

[0038] S5. Weighing Processing Loop: Set the width of the data window to be the same as the width of the data group. Sample the weight fluctuation curve data in the first data group corresponding to the data window, and determine its fluctuation value B, where B = max1 - min1, and max1 and min1 are the maximum and minimum values ​​of the first data group, respectively. Move the data window using a sliding method, shifting the data window by L data width each time, and recalculate the data fluctuation value B. i =max i -min i max i The min value represents the largest value in the current data window. i This represents the smallest value in the current data window. If B i If less than B, then B = B i If B i If the value is greater than or equal to B, the value of B remains unchanged; compare the value of B with the value of P. If B is less than P, exit the iteration loop and proceed to step S6. This data set is a stable window that meets the expectations. Otherwise, continue to move the data window and perform iterative calculation of the fluctuation value B; if B is still greater than P when i=M, it means that the obtained weight fluctuation curve does not meet the stable expectations, the vehicle needs to be re-inspected, and the loop will proceed to step S2.

[0039] S6. Calculate vehicle weight: Obtain the overall data within the expected stable window, and calculate the average value of the data within that window, which is the weight of the vehicle.

[0040] In step S1, the vehicle axle identification area is set as follows: the 4m area at the front end of the weighing platform is designated as the vehicle axle identification area. Specifically, the vehicle axle identification area is divided into 10 segments along the driving direction. The starting points of each segment are denoted as S00, S04, S08, S12, S16, S20, S24, S28, S32, and S36, respectively. S00 is the upper boundary of the vehicle axle identification area (i.e., the upper boundary of the weighing platform). S16 is approximately 1.6 meters away from the upper boundary, S20 is approximately 2.0 meters away from the upper boundary, S32 is approximately 3.2 meters away from the upper boundary, and S36 is approximately 3.6 meters away from the upper boundary. S20 is approximately located at the center of the vehicle axle identification area. Several load sensors are arranged along the travel direction and correspond one-to-one with each segment in the vehicle axle identification area to determine whether there is a load in each area.

[0041] Vehicles can be classified into single-axle, double-axle, and triple-axle types based on their axle configuration. Vehicles with a wheelbase greater than 1.8 meters between adjacent axles are considered single-axle vehicles, vehicles with a wheelbase greater than 1.5 meters but less than 1.8 meters between adjacent axles are considered double-axle vehicles, and vehicles with a wheelbase less than 1.5 meters between adjacent axles are considered triple-axle vehicles. The vehicle is classified as a single-axle, double-axle, or triple-axle vehicle based on its wheelbase.

[0042] Step S2, which uses the vehicle axle group identification area to identify the vehicle model, includes the following steps:

[0043] SS1. The initial state is when no vehicle enters the vehicle axle group recognition area. The system waits for a vehicle to enter. When the vehicle enters and blocks the first facade light curtain, and the first axle completely enters the upper boundary of the vehicle axle group recognition area along the driving direction, the axle group recognition calculation method starts and the initial vehicle model code value is set to empty.

[0044] SS2. Loop Processing: If the first facade light curtain has no end, it continues to move with the vehicle and enters SS4 for axis following processing; if the first facade light curtain ends, it enters SS5 to obtain the vehicle model code.

[0045] SS3. Axle Following Processing: Axle following processing is divided into three cases: SS3.1: Only the first axle is unloaded until the vehicle axle group identification area is reached. The vehicle model string is incremented by "2", and the process recursively returns to SS2; SS3.2: Before the first axle reaches point N20, the second axle is loaded, and the process enters SS4 for axle coupling processing; SS3.3: After the first axle passes S20, the second axle is loaded. The vehicle model string is incremented by "2", and the second axle becomes the new first axle. The vehicle continues to move forward, and after the original first axle leaves the vehicle axle group identification area, the process recursively returns to SS2.

[0046] SS4. Coupling Processing: After the second axle is on the scale, it is pre-processed as a two-axle coupling. If a new axle is added to the weighing platform before the first axle reaches N30, it is processed as a three-axle coupling, and the vehicle model code is appended with "7". If no new axle is on the weighing platform before the first axle reaches N30, it is a two-axle coupling, and the vehicle model code is appended with "5". The vehicle continues to move, and subsequent axles are treated as the new first axle. After all the couplings mentioned above are removed from the weighing platform, the process recursively returns to SS2.

[0047] SS5. Obtain vehicle model code: If "2" appears at the beginning of the string, replace it with "1"; if "5" appears at the beginning of the string, replace it with "11". This completes the processing of one vehicle model code and obtains the vehicle model code.

[0048] This invention combines the characteristics of dynamic vehicle weighing on a long platform. As a vehicle passes over the weighing platform, data is collected, and the most stable weight value is captured during the vehicle's dynamic movement on the scale, thus achieving accurate vehicle weight measurement. The convergent iterative weighing calculation method employed allows for multiple iterations during data processing, improving the accuracy of weight measurement. Simultaneously, the system design fully considers the influence of various factors, such as vehicle speed, vehicle type, and estimated weight, thereby enhancing the system's accuracy. Furthermore, this invention adaptively adjusts the window size based on actual data conditions, improving the system's adaptability and flexibility; a sliding method is used to move the data window and recalculate data fluctuation values, quickly determining a suitable data window containing stable weight, significantly improving weighing efficiency and accuracy.

[0049] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A convergent iterative weighing calculation method for a long-platform dynamic vehicle scale, comprising the following steps: S1. Setting up a weighing platform: A first vertical light curtain perpendicular to the driving direction is set at the vehicle entry end of the weighing platform, and a second vertical light curtain perpendicular to the driving direction is set at the vehicle exit end of the weighing platform. The distance between the first vertical light curtain and the second vertical light curtain is S. A vehicle axle group identification area is set on the platform surface of the weighing platform. S2. Calculation Method Starting Point: Waiting for the vehicle to arrive, the weighing platform is unloaded, and all equipment is idle. The vehicle arrives and blocks the first facade light curtain. The vehicle begins to enter the weighing platform, and this time is recorded as t1. The vehicle is no longer blocked by the first facade light curtain and has fully entered the weighing control area. This is the starting point of the calculation method. The weight change of the vehicle is obtained through the weighing platform as a weight fluctuation curve, and the vehicle model code is identified using the vehicle axle group identification area. When the vehicle blocks the second facade light curtain, i.e., the vehicle leaves the weighing control area, the acquisition of the weight fluctuation curve stops, and this time is recorded as t2. The average driving speed V of the vehicle on the weighing platform is calculated as V = S / (t2 - t1). V needs to be equal to the set speed V0. n When comparing, when V <V n If the calculation is successful, proceed with the subsequent calculations; otherwise, stop the calculation. S3. Determination of the stable satisfactory value P: Based on the weight fluctuation curve obtained in step S2, the estimated weight T is derived, T = (T max +T min ) / 2, T max T represents the maximum value in the weight fluctuation curve. min This represents the minimum value in the weight fluctuation curve. The corresponding stability satisfaction coefficient α is obtained from the vehicle model code acquired in step S2. The stability satisfaction value P is then obtained using the estimated weight T, average driving speed V, and stability satisfaction coefficient α, where P = α * T * (V / V). m ) 1 / 5 V m For reference speed; S4. Set up data groups: Divide the data width A of the weight fluctuation curve obtained in step S2 into M equal parts, each part is a data group, and the data width of each data group is L=A / M; S5. Weighing Processing Loop: Set the width of the data window to be the same as the width of the data group. Sample the weight fluctuation curve data in the first data group corresponding to the data window, and determine its fluctuation value B, where B = max1 - min1, and max1 and min1 are the maximum and minimum values ​​of the first data group, respectively. Move the data window using a sliding method, shifting the data window by L data width each time, and recalculate the data fluctuation value B. i =max i -min i max i The min value represents the largest value in the current data window. i This represents the smallest value in the current data window. If B i If less than B, then B = B i If B i If the value is greater than or equal to B, the value of B remains unchanged; Compare the values ​​of B and P. If B is less than P, exit the iteration loop and proceed to step S6. This data set is a stable window that meets the expectations. Otherwise, continue to move the data window and perform iterative calculation of the fluctuation value B. If B is still greater than P when i=M, it means that the obtained weight fluctuation curve does not meet the stability expectation, the vehicle needs to be re-inspected, and the process will proceed to step S2 for looping. S6. Calculate vehicle weight: Obtain the overall data within the expected stable window, and calculate the average value of the data within that window, which is the weight of the vehicle.

2. The convergent iterative weighing calculation method for a long-platform dynamic truck scale according to claim 1, characterized in that, In step S2, V n =30km / h.

3. The convergent iterative weighing calculation method for a long-platform dynamic vehicle scale according to claim 1, characterized in that, In step S3, V m =10km / h.

4. The convergent iterative weighing calculation method for a long-platform dynamic vehicle scale according to claim 1, characterized in that, In step S1, the vehicle axle identification area is set as follows: the 4m area at the front end of the weighing platform is designated as the vehicle axle identification area. Specifically, the vehicle axle identification area is divided into 10 segments along the driving direction. The starting points of each segment are denoted as S00, S04, S08, S12, S16, S20, S24, S28, S32, and S36, respectively. S00 is the upper boundary of the vehicle axle identification area. S16 is approximately 1.6 meters away from the upper boundary, S20 is approximately 2.0 meters away from the upper boundary, S32 is approximately 3.2 meters away from the upper boundary, and S36 is approximately 3.6 meters away from the upper boundary. S20 is approximately located at the center of the vehicle axle identification area. Several load sensors are arranged along the travel direction and correspond one-to-one with each segment on the upper boundary of the vehicle axle identification area to determine whether there is a load in each area.

5. The convergent iterative weighing calculation method for a long-platform dynamic vehicle scale according to claim 4, characterized in that, Step S2, which uses the vehicle axle group identification area to identify the vehicle model, includes the following steps: SS1. The initial state is when no vehicle enters the vehicle axle group recognition area. The system waits for a vehicle to enter. When the vehicle enters and blocks the first facade light curtain, and the first axle completely enters the upper boundary of the vehicle axle group recognition area along the driving direction, the axle group recognition calculation method starts and the initial vehicle model code value is set to empty. SS2. Loop Processing: If the first facade light curtain has no end, it continues to move with the vehicle and enters SS4 for axis following processing; if the first facade light curtain ends, it enters SS5 to obtain the vehicle model code. SS3. Axle Following Processing: Axle following processing is divided into three cases: SS3.1: Only the first axle is unloaded until the vehicle axle group identification area is reached. The vehicle model string is incremented by "2", and the process recursively returns to SS2; SS3.2: Before the first axle reaches point N20, the second axle is loaded, and the process enters SS4 for axle coupling processing; SS3.3: After the first axle passes S20, the second axle is loaded. The vehicle model string is incremented by "2", and the second axle becomes the new first axle. The vehicle continues to move forward, and after the original first axle leaves the vehicle axle group identification area, the process recursively returns to SS2. SS4. Coupling Processing: After the second axle is placed on the scale, it is pre-processed as a two-axle coupling. If a new axle is added to the weighing platform before the first axle reaches N30, it is processed as a three-axle coupling, and the vehicle model code is appended with "7". If no new axle is placed on the weighing platform before the first axle reaches N30, it is a two-axle coupling, and the vehicle model code is appended with "5". The vehicle continues to move, and subsequent axles are treated as the new first axle. After all the couplings mentioned above are removed from the weighing platform, the process recursively returns to SS2. SS5. Obtain vehicle model code: If "2" appears at the beginning of the string, replace it with "1". If "5" appears at the beginning of the string, replace it with "11". This completes the processing of one vehicle model code and obtains the vehicle model code.

6. The convergent iterative weighing calculation method for a long-platform dynamic truck scale according to claim 1, characterized in that, In step S4, M = 8~16.

7. The convergent iterative weighing calculation method for a long-platform dynamic truck scale according to claim 4, characterized in that, In step S4, M=10.

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