Vehicle weighing method, device, equipment and storage medium

Abstract

The present application discloses a vehicle weighing method, apparatus, device, and storage medium, relating to the field of vehicle weighing, including: obtaining first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature; filtering the first weighing signal to obtain a first gross weight; obtaining the tire pressure of the current vehicle based on the vehicle model, and combining the tire pressure and the vehicle speed to obtain a second gross weight; inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight; and obtaining the current gross weight of the current vehicle based on the first gross weight, the second gross weight, and the third gross weight. The present application can solve the technical problems of low accuracy and large errors in dynamic vehicle weighing.

Description

Technical Field

[0001] The present application relates to the field of vehicle weighing, and in particular to a vehicle weighing method and apparatus, equipment and storage medium. Background Art

[0002] Overloading of vehicles will endanger road safety, so it is necessary to weigh the vehicles to determine whether they are overloaded. In the prior art, static weighing or dynamic weighing is usually used to weigh vehicles. Since the static weighing method requires the vehicle to be driven into the measuring and weighing area and stationary before weighing the vehicle, it may cause delays in the corresponding journey of the vehicle, thereby causing unnecessary losses; the dynamic weighing method does not require the vehicle to slow down or stop, and will not delay the vehicle's journey, but the current dynamic weighing method usually uses the method of analyzing the data of the weighing sensor for weighing, and has a high tolerance for the vehicle speed range, but the corresponding weighing accuracy is low, the weighing data is unstable, and is easily disturbed by factors such as vehicle vibration. Therefore, how to improve the accuracy of dynamic weighing of vehicles remains a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The present application provides a vehicle weighing method and apparatus, equipment and storage medium to solve the technical problems of low precision and large error in dynamic vehicle weighing.

[0004] According to a first aspect of the embodiments of the present application, a vehicle weighing method is provided, comprising:

[0005] Acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature;

[0006] filtering the first weighing signal to obtain a first total weight;

[0007] Obtaining a tire pressure of the current vehicle based on the vehicle model, and obtaining a second total weight by combining the tire pressure and the vehicle speed;

[0008] inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight;

[0009] The current total weight of the current vehicle is obtained according to the first total weight, the second total weight and the third total weight.

[0010] The present application first obtains the first measurement data of the current vehicle to be weighed, and filters the first weighing signal therein to obtain the first total weight. It can filter out the interference signal in the weighing signal, improve the accuracy of the weighing signal, and perform a preliminary measurement of the total weight of the vehicle. Then, the tire pressure is obtained based on the vehicle model, and the second total weight is obtained by combining the tire pressure and the vehicle speed. The total weight of the vehicle calculated by the tire pressure can be corrected according to the vehicle speed to reduce the weighing error. The first total weight, the second total weight and the tire temperature are then input into the first total weight prediction model to obtain the third total weight. The influence of tire temperature on the vehicle total weight measurement is fitted by the prediction model, which can improve the accuracy of vehicle weighing. When the current total weight of the current vehicle is obtained based on the first total weight, the second total weight and the third total weight, the error of the current total weight relative to the actual total weight of the vehicle is reduced, thereby improving the accuracy of dynamic vehicle weighing.

[0011] In certain embodiments of the present application, obtaining the first measurement data of the current vehicle to be weighed specifically includes:

[0012] Acquire a first weighing signal of the current vehicle collected by a sensor; and acquire multiple frames of monitoring images of the current vehicle collected by multiple cameras;

[0013] Performing recognition processing on the multiple frames of monitoring images to obtain the vehicle speed, vehicle model and tire temperature of the current vehicle;

[0014] The first weighing signal, the vehicle speed, the vehicle model, and the tire temperature are combined to obtain first measurement data.

[0015] This application first obtains the first weighing signal and multiple frames of monitoring images, and identifies and processes the multiple frames of monitoring images to obtain the vehicle speed, vehicle model and tire temperature, thereby obtaining the first measurement data, which can provide a data basis for the subsequent calculation of the first total weight.

[0016] In certain embodiments of the present application, the performing recognition processing on the multiple frames of monitoring images to obtain the vehicle speed, vehicle model, and tire temperature of the current vehicle specifically includes:

[0017] The multiple monitoring images include multiple first monitoring images of the front of the vehicle taken by a high-speed camera and multiple second monitoring images of the left and right sides of the vehicle taken by an infrared camera;

[0018] Recognize each frame of the first monitoring image according to a preset first recognition model to obtain multiple sets of recognition results; and use the vehicle model corresponding to the maximum recognition probability among the multiple sets of recognition results as the vehicle model of the current vehicle; wherein the recognition result includes the vehicle model and the corresponding recognition probability;

[0019] Segmenting each frame of the second monitoring image according to a preset first segmentation model to obtain a plurality of first regions; obtaining temperatures of the plurality of first regions, and using an average of the temperatures of the plurality of first regions as the tire temperature of the current vehicle; wherein the first region is a region in the second monitoring image that only includes the tire;

[0020] According to a preset second recognition model, each frame of the second monitoring image is identified to obtain multiple second areas; and the displacement of the second areas between each frame of the second monitoring image is calculated, and the vehicle speed of the current vehicle is calculated based on the displacement of the multiple second areas; wherein the second area is the area in the second monitoring image that only contains the vehicle body.

[0021] The present application first identifies each frame of the first monitoring image according to the first recognition model, and obtains the vehicle model according to the recognition result corresponding to the maximum recognition probability, which can accurately identify the vehicle model of the current vehicle, and then segments each frame of the second monitoring image according to the first segmentation model to obtain multiple first areas, and calculates the tire temperature according to the temperature of the first area, which can accurately identify the area where the tires of the current vehicle are located, and then calculate the tire temperature of the current vehicle, and then identify each frame of the second monitoring image according to the second recognition model to obtain multiple second areas, and calculate the vehicle speed according to the displacement of the second area between each frame of the second monitoring image, which can accurately calculate the displacement of the current vehicle between frames, and then calculate the vehicle speed of the current vehicle, thereby improving the accuracy of the acquired vehicle speed, vehicle model, and tire temperature.

[0022] In certain embodiments of the present application, filtering the first weighing signal to obtain the first total weight specifically includes:

[0023] performing high-pass filtering on the first weighing signal according to a preset frequency threshold to obtain a second weighing signal;

[0024] performing Kalman filtering on the second weighing signal to obtain a third weighing signal;

[0025] Perform sliding average sampling on the third weighing signal to obtain a first total weight.

[0026] The present application first performs high-pass filtering to filter out high-frequency mechanical vibrations in the signal, then performs Kalman filtering, and then performs sliding average sampling, which can improve the accuracy of signal sampling and reduce the error of the first total weight when obtaining the first total weight.

[0027] In certain embodiments of the present application, obtaining the tire pressure of the current vehicle based on the vehicle model and combining the tire pressure and the vehicle speed to obtain the second total weight specifically includes:

[0028] According to the vehicle model, the calibrated tire pressure of the current vehicle is obtained from a preset vehicle database;

[0029] Obtaining the tire pressure of the current vehicle based on the vehicle model and the calibrated tire pressure;

[0030] A second total weight of the current vehicle is calculated according to the tire pressure and the vehicle speed.

[0031] This application first calibrates the tire pressure according to the vehicle model and then obtains the tire pressure of the current vehicle, taking into account the design parameters of the current vehicle, and calculates the second gross weight in combination with the tire pressure and vehicle speed. At the same time, the impact of tire pressure and vehicle speed on the vehicle gross weight is taken into account, and the error of the second gross weight can be reduced when calculating the second gross weight.

[0032] In certain embodiments of the present application, calculating the second gross weight of the current vehicle based on the tire pressure and the vehicle speed specifically includes:

[0033] Calculating a second weight of each tire of the current vehicle according to the tire pressure, the vehicle speed, and the vehicle model;

[0034] The second weight of each tire is specifically:

[0035]

[0036] in, is the second weight of the i-th tire from front to back on side d, where side d includes the left and right sides; is the first stiffness coefficient, β is the second stiffness coefficient; is the vertical deflection of the i-th tire from front to back on side d; P is the tire pressure; v c is the rated maximum speed of the current vehicle; v is the vehicle speed;

[0037] A second total weight of the current vehicle is obtained according to the second partial weights of all tires of the current vehicle.

[0038] The present application first calculates the second partial weight of each tire of the current vehicle, and then obtains the second total weight based on the second partial weight of each tire, taking into account the contribution of each tire to the total weight of the vehicle, and then reducing the error of the second total weight when calculating the second total weight through the second partial weight of each tire.

[0039] In certain embodiments of the present application, inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight specifically includes:

[0040] Acquire a historical weighing data set; wherein the historical weighing data set includes several sets of weighing data; the weighing data includes first historical weighing data, second historical weighing data, and tire temperature of the historical vehicle during weighing;

[0041] Iteratively training an initial gross weight prediction model with preset initial parameters using the historical weighing data set, updating the model parameters during iteration until the model parameters meet a preset threshold, thereby stopping the training and obtaining a first gross weight prediction model;

[0042] The first gross weight, the second gross weight, and the tire temperature are input into the first gross weight prediction model to obtain a third gross weight of the current vehicle.

[0043] This application first obtains a first gross weight prediction model through training of a historical weighing data set, and then obtains a third gross weight by combining the first gross weight, the second gross weight and the tire temperature, taking into account the impact of tire temperature on the vehicle's gross weight. When the third gross weight is obtained from the first gross weight prediction model, it is more in line with the actual situation.

[0044] According to a second aspect of the embodiments of the present application, there is provided a vehicle weighing device, comprising a vehicle data acquisition module, a first total weight acquisition module, a second total weight acquisition module, a third total weight acquisition module, and a current total weight acquisition module;

[0045] The vehicle data acquisition module is configured to acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature;

[0046] The first total weight acquisition module is configured to filter the first weighing signal to obtain a first total weight;

[0047] The second total weight acquisition module is configured to obtain the tire pressure of the current vehicle based on the vehicle model, and obtain a second total weight by combining the tire pressure and the vehicle speed;

[0048] The third total weight acquisition module is configured to input the first total weight, the second total weight, and the tire temperature into a preset first total weight prediction model to obtain a third total weight;

[0049] The current total weight acquisition module is used to obtain the current total weight of the current vehicle according to the first total weight, the second total weight and the third total weight.

[0050] The present application first obtains the first measurement data of the current vehicle to be weighed, and filters the first weighing signal therein to obtain the first total weight. It can filter out the interference signal in the weighing signal, improve the accuracy of the weighing signal, and perform a preliminary measurement of the total weight of the vehicle. Then, the tire pressure is obtained based on the vehicle model, and the second total weight is obtained by combining the tire pressure and the vehicle speed. The total weight of the vehicle calculated by the tire pressure can be corrected according to the vehicle speed to reduce the weighing error. The first total weight, the second total weight and the tire temperature are then input into the first total weight prediction model to obtain the third total weight. The influence of tire temperature on the vehicle total weight measurement is fitted by the prediction model, which can improve the accuracy of vehicle weighing. When the current total weight of the current vehicle is obtained based on the first total weight, the second total weight and the third total weight, the error of the current total weight relative to the actual total weight of the vehicle is reduced, thereby improving the accuracy of dynamic vehicle weighing.

[0051] According to a third aspect of the embodiments of the present application, there is provided a computer device, comprising: a processor; a memory; a computer program stored in the memory and configured to be executed by the processor;

[0052] When the processor executes the computer program, a vehicle weighing method described in the present application is implemented.

[0053] According to a fourth aspect of the embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute a vehicle weighing method described in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 : A schematic flow chart of a vehicle weighing method according to certain embodiments of the present application;

[0055] Figure 2 : A module structure diagram of a vehicle weighing device shown in certain embodiments of the present application. DETAILED DESCRIPTION

[0056] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of the present application and should not be understood as limiting the embodiments of the present application. Based on the embodiments shown in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0057] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, unless otherwise clearly specified, "multiple" and "several" mean two or more.

[0058] Existing vehicle weigh-in-motion (WIM) methods typically analyze data from load cells. These methods have limited requirements for vehicle speed range, but also result in low weighing accuracy. The weighing data is unstable and easily affected by factors such as vehicle vibration. Therefore, a vehicle weighing method is needed that can improve the accuracy and reduce WIM errors.

[0059] Based on the above technical background, please refer to Figure 1 The embodiment of the present application provides a vehicle weighing method, including steps S101 to S105, each of which is as follows:

[0060] Step S101: Acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature.

[0061] In certain embodiments of the present application, obtaining the first measurement data of the current vehicle to be weighed specifically includes:

[0062] Acquire a first weighing signal of the current vehicle collected by a sensor; and acquire multiple frames of monitoring images of the current vehicle collected by multiple cameras;

[0063] Performing recognition processing on the multiple frames of monitoring images to obtain the vehicle speed, vehicle model and tire temperature of the current vehicle;

[0064] The first weighing signal, the vehicle speed, the vehicle model, and the tire temperature are combined to obtain first measurement data.

[0065] This application first obtains the first weighing signal and multiple frames of monitoring images, and identifies and processes the multiple frames of monitoring images to obtain the vehicle speed, vehicle model and tire temperature, thereby obtaining the first measurement data, which can provide a data basis for the subsequent calculation of the first total weight.

[0066] In certain optional embodiments of the present application, the sensor is preferably installed at the bottom of the road weighing area, and is preferably a gravity sensor. The multiple cameras are preferably installed on both sides of and above the road weighing area, and are preferably of a type including, but not limited to, high-speed cameras and infrared cameras. Specifically, the gravity sensor can acquire the vehicle's weighing signal, thereby determining the vehicle's gross weight; the high-speed camera can capture an image of the vehicle, thereby determining the vehicle's model; and the infrared camera can capture an image of the vehicle, thereby acquiring the vehicle's temperature, identifying the tires and vehicle body in the image, and determining the tire temperature and vehicle body displacement, thereby calculating the vehicle's speed.

[0067] In certain embodiments of the present application, the performing recognition processing on the multiple frames of monitoring images to obtain the vehicle speed, vehicle model, and tire temperature of the current vehicle specifically includes:

[0068] The multiple monitoring images include multiple first monitoring images of the front of the vehicle taken by a high-speed camera and multiple second monitoring images of the left and right sides of the vehicle taken by an infrared camera;

[0069] Recognize each frame of the first monitoring image according to a preset first recognition model to obtain multiple sets of recognition results; and use the vehicle model corresponding to the maximum recognition probability among the multiple sets of recognition results as the vehicle model of the current vehicle; wherein the recognition result includes the vehicle model and the corresponding recognition probability;

[0070] Segmenting each frame of the second monitoring image according to a preset first segmentation model to obtain a plurality of first regions; obtaining temperatures of the plurality of first regions, and using an average of the temperatures of the plurality of first regions as the tire temperature of the current vehicle; wherein the first region is a region in the second monitoring image that only includes the tire;

[0071] According to a preset second recognition model, each frame of the second monitoring image is identified to obtain multiple second areas; and the displacement of the second areas between each frame of the second monitoring image is calculated, and the vehicle speed of the current vehicle is calculated based on the displacement of the multiple second areas; wherein the second area is the area in the second monitoring image that only contains the vehicle body.

[0072] In certain optional embodiments of the present application, a preferred embodiment of the first recognition model is a tree model based on machine learning, including but not limited to a decision tree model, a random forest model, a gradient boosting tree model, an XGBoost model, and an AdaBoost model; a preferred embodiment of the first segmentation model includes but is not limited to a YOLO v3 model or an R-CNN model; a preferred embodiment of the second recognition model includes but is not limited to a YOLO v3 model or an R-CNN model.

[0073] The present application first identifies each frame of the first monitoring image according to the first recognition model, and obtains the vehicle model according to the recognition result corresponding to the maximum recognition probability, which can accurately identify the vehicle model of the current vehicle, and then segments each frame of the second monitoring image according to the first segmentation model to obtain multiple first areas, and calculates the tire temperature according to the temperature of the first area, which can accurately identify the area where the tires of the current vehicle are located, and then calculate the tire temperature of the current vehicle, and then identify each frame of the second monitoring image according to the second recognition model to obtain multiple second areas, and calculate the vehicle speed according to the displacement of the second area between each frame of the second monitoring image, which can accurately calculate the displacement of the current vehicle between frames, and then calculate the vehicle speed of the current vehicle, thereby improving the accuracy of the acquired vehicle speed, vehicle model, and tire temperature.

[0074] Step S102: filtering the first weighing signal to obtain a first total weight.

[0075] In certain embodiments of the present application, filtering the first weighing signal to obtain the first total weight specifically includes:

[0076] performing high-pass filtering on the first weighing signal according to a preset frequency threshold to obtain a second weighing signal;

[0077] performing Kalman filtering on the second weighing signal to obtain a third weighing signal;

[0078] Perform sliding average sampling on the third weighing signal to obtain a first total weight.

[0079] In certain optional embodiments of the present application, the preferred solution for the frequency threshold is 60 Hz.

[0080] The reason for considering high-pass filtering of the first weighing signal is that when a vehicle passes through the sensor in the road weighing area, the vehicle's wheel rotation and the sensor system will generate high-frequency vibrations. If the sensor responds to these high-frequency vibrations during detection, it will generate high-frequency vibration signal interference, thereby affecting the actual weighing signal; considering performing Kalman filtering again is that random high- and low-frequency noise interference will inevitably be mixed in during the sensor acquisition process. Kalman filtering can effectively eliminate noise interference, and then performing sliding average sampling can improve the accuracy of signal sampling, thereby reducing the error of the weighing signal.

[0081] The present application first performs high-pass filtering to filter out high-frequency mechanical vibrations in the signal, then performs Kalman filtering, and then performs sliding average sampling, which can improve the accuracy of signal sampling and reduce the error of the first total weight when obtaining the first total weight.

[0082] Step S103: obtaining the tire pressure of the current vehicle based on the vehicle model, and obtaining a second total weight by combining the tire pressure and the vehicle speed.

[0083] In certain embodiments of the present application, obtaining the tire pressure of the current vehicle based on the vehicle model and combining the tire pressure and the vehicle speed to obtain the second total weight specifically includes:

[0084] According to the vehicle model, the calibrated tire pressure of the current vehicle is obtained from a preset vehicle database;

[0085] Obtaining the tire pressure of the current vehicle based on the vehicle model and the calibrated tire pressure;

[0086] A second total weight of the current vehicle is calculated according to the tire pressure and the vehicle speed.

[0087] In certain optional embodiments of the present application, obtaining the tire pressure of the current vehicle based on the vehicle model and the calibrated tire pressure specifically includes:

[0088] According to the vehicle model, obtaining the tire pressure multiplier coefficient of the current vehicle from the vehicle database;

[0089] The tire pressure of the current vehicle is obtained according to the tire pressure multiplier coefficient and the calibrated tire pressure.

[0090] For example, if the vehicle type corresponding to the vehicle model of the current vehicle is a truck, the tire pressure ratio coefficient is 1.2; if the vehicle type corresponding to the vehicle model of the current vehicle is a sedan, the tire pressure ratio coefficient is 1.05.

[0091] In certain optional implementations of the present application, obtaining the current tire pressure of the vehicle according to the tire pressure multiplier coefficient and the calibrated tire pressure is specifically as follows:

[0092] The product of the tire pressure multiplier coefficient and the calibrated tire pressure is used as the current tire pressure of the vehicle.

[0093] This application first calibrates the tire pressure according to the vehicle model and then obtains the tire pressure of the current vehicle, taking into account the design parameters of the current vehicle, and calculates the second gross weight in combination with the tire pressure and vehicle speed. At the same time, the impact of tire pressure and vehicle speed on the vehicle gross weight is taken into account, and the error of the second gross weight can be reduced when calculating the second gross weight.

[0094] In certain embodiments of the present application, calculating the second gross weight of the current vehicle based on the tire pressure and the vehicle speed specifically includes:

[0095] Calculating a second weight of each tire of the current vehicle according to the tire pressure, the vehicle speed, and the vehicle model;

[0096] The second weight of each tire is specifically:

[0097]

[0098] in, is the second weight of the i-th tire from front to back on side d, where side d includes the left and right sides; is the first stiffness coefficient, β is the second stiffness coefficient; is the vertical deflection of the i-th tire from front to back on side d; P is the tire pressure; v c is the rated maximum speed of the current vehicle; v is the vehicle speed;

[0099] A second total weight of the current vehicle is obtained according to the second partial weights of all tires of the current vehicle.

[0100] In certain optional embodiments of the present application, obtaining the second total weight of the current vehicle based on the second partial weights of all tires of the current vehicle specifically includes:

[0101] The sum of the second partial weights of all tires of the current vehicle is used as the second total weight of the current vehicle.

[0102] The present application first calculates the second partial weight of each tire of the current vehicle, and then obtains the second total weight based on the second partial weight of each tire, taking into account the contribution of each tire to the total weight of the vehicle, and then reducing the error of the second total weight when calculating the second total weight through the second partial weight of each tire.

[0103] Step S104: inputting the first total weight, the second total weight, and the tire temperature into a preset first total weight prediction model to obtain a third total weight.

[0104] In certain embodiments of the present application, inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight specifically includes:

[0105] Acquire a historical weighing data set; wherein the historical weighing data set includes several sets of weighing data; the weighing data includes first historical weighing data, second historical weighing data, and tire temperature of the historical vehicle during weighing;

[0106] Iteratively training an initial gross weight prediction model with preset initial parameters using the historical weighing data set, updating the model parameters during iteration until the model parameters meet a preset threshold, thereby stopping the training and obtaining a first gross weight prediction model;

[0107] The first gross weight, the second gross weight, and the tire temperature are input into the first gross weight prediction model to obtain a third gross weight of the current vehicle.

[0108] In certain optional embodiments of the present application, the preferred solution of the initial total weight prediction model is the LSTM (Long Short-Term Memory) model and its variants.

[0109] Preferably, the first historical weighing data and the second historical weighing data are weighing data of the same time.

[0110] This application first obtains a first gross weight prediction model through training of a historical weighing data set, and then obtains a third gross weight by combining the first gross weight, the second gross weight and the tire temperature, taking into account the impact of tire temperature on the vehicle's gross weight. When the third gross weight is obtained from the first gross weight prediction model, it is more in line with the actual situation.

[0111] Step S105: Obtaining the current total weight of the current vehicle according to the first total weight, the second total weight, and the third total weight.

[0112] In certain optional embodiments of the present application, obtaining the current gross weight of the current vehicle according to the first gross weight, the second gross weight, and the third gross weight specifically includes:

[0113] An average of the first total weight, the second total weight, and the third total weight is used as the current total weight of the current vehicle.

[0114] In certain optional embodiments of the present application, obtaining the current gross weight of the current vehicle according to the first gross weight, the second gross weight, and the third gross weight specifically includes:

[0115] A weighted average of the first total weight, the second total weight, and the third total weight is used as the current total weight of the current vehicle.

[0116] Preferably, considering the high accuracy of the sensor, the weight of the first total weight is 0.4, the weight of the second total weight is 0.3, and the weight of the third total weight is 0.3; considering the influence of speed correction of the total weight, the weight of the first total weight is 0.35, the weight of the second total weight is 0.45, and the weight of the third total weight is 0.2; considering the influence of temperature correction of the total weight, the weight of the first total weight is 0.3, the weight of the second total weight is 0.3, and the weight of the third total weight is 0.4.

[0117] The present application first obtains the first measurement data of the current vehicle to be weighed, and filters the first weighing signal therein to obtain the first total weight. It can filter out the interference signal in the weighing signal, improve the accuracy of the weighing signal, and perform a preliminary measurement of the total weight of the vehicle. Then, the tire pressure is obtained based on the vehicle model, and the second total weight is obtained by combining the tire pressure and the vehicle speed. The total weight of the vehicle calculated by the tire pressure can be corrected according to the vehicle speed to reduce the weighing error. The first total weight, the second total weight and the tire temperature are then input into the first total weight prediction model to obtain the third total weight. The influence of tire temperature on the vehicle total weight measurement is fitted by the prediction model, which can improve the accuracy of vehicle weighing. When the current total weight of the current vehicle is obtained based on the first total weight, the second total weight and the third total weight, the error of the current total weight relative to the actual total weight of the vehicle is reduced, thereby improving the accuracy of dynamic vehicle weighing.

[0118] Corresponding to the above method, see Figure 2 , an embodiment of the present application provides a vehicle weighing device, including a vehicle data acquisition module 210, a first total weight acquisition module 220, a second total weight acquisition module 230, a third total weight acquisition module 240 and a current total weight acquisition module 250;

[0119] The vehicle data acquisition module 210 is configured to acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature;

[0120] The first total weight acquisition module 220 is configured to filter the first weighing signal to obtain a first total weight;

[0121] The second total weight obtaining module 230 is configured to obtain the tire pressure of the current vehicle based on the vehicle model, and obtain a second total weight by combining the tire pressure and the vehicle speed;

[0122] The third total weight acquisition module 240 is configured to input the first total weight, the second total weight, and the tire temperature into a preset first total weight prediction model to obtain a third total weight;

[0123] The current total weight obtaining module 250 is configured to obtain the current total weight of the vehicle according to the first total weight, the second total weight, and the third total weight.

[0124] In certain embodiments of the present application, the vehicle data acquisition module 210 includes a data acquisition submodule 211 , an image recognition submodule 212 , and a data combination submodule 213 ;

[0125] The data acquisition submodule 211 is configured to acquire a first weighing signal of the current vehicle collected by a sensor; and acquire multiple frames of monitoring images of the current vehicle collected by multiple cameras;

[0126] The image recognition submodule 212 is configured to perform recognition processing on the multiple monitoring images to obtain the vehicle speed, vehicle model, and tire temperature of the current vehicle;

[0127] The data combination submodule 213 is configured to combine the first weighing signal, the vehicle speed, the vehicle model, and the tire temperature to obtain first measurement data.

[0128] In certain embodiments of the present application, the multiple frames of monitoring images include multiple frames of first monitoring images in front of the vehicle taken by a high-speed camera and multiple frames of second monitoring images to the left and right of the vehicle taken by an infrared camera, and the image recognition submodule 212 includes a model recognition unit 2121, a temperature calculation unit 2122, and a speed calculation unit 2123;

[0129] The model recognition unit 2121 is configured to recognize each frame of the first monitoring image according to a preset first recognition model to obtain multiple sets of recognition results; and to use the vehicle model corresponding to the maximum recognition probability among the multiple sets of recognition results as the vehicle model of the current vehicle; wherein the recognition result includes the vehicle model and the corresponding recognition probability;

[0130] The temperature calculation unit 2122 is configured to segment each frame of the second monitoring image according to a preset first segmentation model to obtain a plurality of first regions; obtain temperatures of the plurality of first regions, and use an average of the temperatures of the plurality of first regions as the tire temperature of the current vehicle; wherein the first region is a region in the second monitoring image that only includes the tire;

[0131] The speed calculation unit 2123 is used to identify each frame of the second monitoring image according to a preset second recognition model to obtain multiple second areas; and calculate the displacement of the second areas between each frame of the second monitoring image, and calculate the vehicle speed of the current vehicle based on the displacement of the multiple second areas; wherein the second area is the area in the second monitoring image that only contains the vehicle body.

[0132] In some embodiments of the present application, the first total weight acquisition module 220 includes a first filtering submodule 221, a second filtering submodule 222 and a signal sampling submodule 223;

[0133] The first filtering submodule 221 is configured to perform high-pass filtering on the first weighing signal according to a preset frequency threshold to obtain a second weighing signal;

[0134] The second filtering submodule 222 is configured to perform Kalman filtering on the second weighing signal to obtain a third weighing signal;

[0135] The signal sampling submodule 223 is configured to perform sliding average sampling on the third weighing signal to obtain a first total weight.

[0136] In certain embodiments of the present application, the second total weight acquisition module 230 includes a tire pressure matching submodule 231 , a tire pressure acquisition submodule 232 , and a total weight calculation submodule 233 ;

[0137] The tire pressure matching submodule 231 is used to match the calibrated tire pressure of the current vehicle from a preset vehicle database according to the vehicle model;

[0138] The tire pressure acquisition submodule 232 is configured to obtain the tire pressure of the current vehicle based on the vehicle model and the calibrated tire pressure;

[0139] The total weight calculation submodule 233 is configured to calculate a second total weight of the current vehicle according to the tire pressure and the vehicle speed.

[0140] In certain embodiments of the present application, the total weight calculation submodule 233 includes a sub-weight calculation unit 2331 and a total weight calculation unit 2332;

[0141] The weight calculation unit 2331 is configured to calculate the second weight of each tire of the current vehicle according to the tire pressure, the vehicle speed, and the vehicle model;

[0142] The second weight of each tire is specifically:

[0143]

[0144] in, is the second weight of the i-th tire from front to back on side d, where side d includes the left and right sides; is the first stiffness coefficient, β is the second stiffness coefficient; is the vertical deflection of the i-th tire from front to back on side d; P is the tire pressure; v c is the rated maximum speed of the current vehicle; v is the vehicle speed;

[0145] The total weight calculation unit 2332 is configured to obtain a second total weight of the current vehicle based on the second divided weights of all tires of the current vehicle.

[0146] In certain embodiments of the present application, the third total weight acquisition module 240 includes a historical data acquisition submodule 241 , a model training submodule 242 , and a total weight prediction submodule 243 ;

[0147] The historical data acquisition submodule 241 is used to acquire a historical weighing data set; wherein the historical weighing data set includes a plurality of sets of weighing data; the weighing data includes the first historical weighing data of the historical vehicle, the second historical weighing data and the tire temperature of the historical vehicle during weighing;

[0148] The model training submodule 242 is configured to iteratively train an initial gross weight prediction model with preset initial parameters using the historical weighing data set, updating the model parameters during iteration until the model parameters meet a preset threshold, thereby stopping the training and obtaining a first gross weight prediction model;

[0149] The gross weight prediction submodule 243 is configured to input the first gross weight, the second gross weight, and the tire temperature into the first gross weight prediction model to obtain a third gross weight of the current vehicle.

[0150] The present application first obtains the first measurement data of the current vehicle to be weighed, and filters the first weighing signal therein to obtain the first total weight. It can filter out the interference signal in the weighing signal, improve the accuracy of the weighing signal, and perform a preliminary measurement of the total weight of the vehicle. Then, the tire pressure is obtained based on the vehicle model, and the second total weight is obtained by combining the tire pressure and the vehicle speed. The total weight of the vehicle calculated by the tire pressure can be corrected according to the vehicle speed to reduce the weighing error. The first total weight, the second total weight and the tire temperature are then input into the first total weight prediction model to obtain the third total weight. The influence of tire temperature on the vehicle total weight measurement is fitted by the prediction model, which can improve the accuracy of vehicle weighing. When the current total weight of the current vehicle is obtained based on the first total weight, the second total weight and the third total weight, the error of the current total weight relative to the actual total weight of the vehicle is reduced, thereby improving the accuracy of dynamic vehicle weighing.

[0151] Adaptively, the embodiments of the present application further provide a computer device and a computer-readable storage medium.

[0152] The computer device comprises: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor;

[0153] Wherein, when the processor executes the computer program, a vehicle weighing method described in this application is implemented.

[0154] The computer-readable storage medium stores a plurality of instructions, which are suitable for being loaded by a processor to execute a vehicle weighing method described in the present application.

[0155] The above description is a partial embodiment of the present application, which further describes the purpose, technical solutions, and beneficial effects of the present application in detail. It should be understood that the above description of the partial embodiment of the present application is not to be construed as limiting the present application. In particular, it is pointed out that for those skilled in the art, any changes, modifications, equivalent substitutions, and variations made within the spirit and principles of the present application should be included within the scope of protection of the present application.

Claims

1. A vehicle weighing method, characterized in that: include: Acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature; filtering the first weighing signal to obtain a first total weight; Obtaining a tire pressure of the current vehicle based on the vehicle model, and obtaining a second total weight by combining the tire pressure and the vehicle speed; inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight; Obtaining a current gross weight of the current vehicle according to the first gross weight, the second gross weight, and the third gross weight; Obtaining the tire pressure of the current vehicle based on the vehicle model, and obtaining the second gross weight by combining the tire pressure and the vehicle speed, specifically includes: obtaining a calibrated tire pressure of the current vehicle from a preset vehicle database according to the vehicle model; obtaining the tire pressure of the current vehicle according to the vehicle model and combining the calibrated tire pressure; and calculating the second gross weight of the current vehicle according to the tire pressure and the vehicle speed. The calculating the second gross weight of the current vehicle according to the tire pressure and the vehicle speed specifically includes: Calculating a second weight of each tire of the current vehicle according to the tire pressure, the vehicle speed, and the vehicle model; The second weight of each tire is specifically: ； in, for Side from front to back The second weight of each tire, Side includes left and right side; is the first stiffness coefficient, is the second stiffness coefficient; for Side from front to back Vertical deflection of each tire; For tire pressure; is the rated maximum speed of the current vehicle; is the vehicle speed; A second total weight of the current vehicle is obtained according to the second partial weights of all tires of the current vehicle.

2. A vehicle weighing method according to claim 1, characterized in that: The obtaining of the first measurement data of the current vehicle to be weighed specifically includes: Acquire a first weighing signal of the current vehicle collected by a sensor; and acquire multiple frames of monitoring images of the current vehicle collected by multiple cameras; Performing recognition processing on the multiple frames of monitoring images to obtain the vehicle speed, vehicle model and tire temperature of the current vehicle; The first weighing signal, the vehicle speed, the vehicle model, and the tire temperature are combined to obtain first measurement data.

3. A vehicle weighing method according to claim 2, characterized in that: The identifying and processing the multiple monitoring image frames to obtain the vehicle speed, vehicle model, and tire temperature of the current vehicle specifically includes: The multiple monitoring images include multiple first monitoring images of the front of the vehicle taken by a high-speed camera and multiple second monitoring images of the left and right sides of the vehicle taken by an infrared camera; Recognize each frame of the first monitoring image according to a preset first recognition model to obtain multiple sets of recognition results; and use the vehicle model corresponding to the maximum recognition probability among the multiple sets of recognition results as the vehicle model of the current vehicle; wherein the recognition result includes the vehicle model and the corresponding recognition probability; Segmenting each frame of the second monitoring image according to a preset first segmentation model to obtain a plurality of first regions; obtaining temperatures of the plurality of first regions, and using an average of the temperatures of the plurality of first regions as the tire temperature of the current vehicle; wherein the first region is a region in the second monitoring image that only includes the tire; According to a preset second recognition model, each frame of the second monitoring image is identified to obtain multiple second areas; and the displacement of the second areas between each frame of the second monitoring image is calculated, and the vehicle speed of the current vehicle is calculated based on the displacement of the multiple second areas; wherein the second area is the area in the second monitoring image that only contains the vehicle body.

4. A vehicle weighing method according to claim 1, characterized in that: The filtering the first weighing signal to obtain the first total weight specifically includes: performing high-pass filtering on the first weighing signal according to a preset frequency threshold to obtain a second weighing signal; performing Kalman filtering on the second weighing signal to obtain a third weighing signal; Perform sliding average sampling on the third weighing signal to obtain a first total weight.

5. A vehicle weighing method according to claim 1, characterized in that: Inputting the first gross weight, the second gross weight, and the tire temperature into a preset first gross weight prediction model to obtain a third gross weight specifically includes: Acquire a historical weighing data set; wherein the historical weighing data set includes several sets of weighing data; the weighing data includes first historical weighing data, second historical weighing data, and tire temperature of the historical vehicle during weighing; Iteratively training an initial gross weight prediction model with preset initial parameters using the historical weighing data set, updating the model parameters during iteration until the model parameters meet a preset threshold, thereby stopping the training and obtaining a first gross weight prediction model; The first gross weight, the second gross weight, and the tire temperature are input into the first gross weight prediction model to obtain a third gross weight of the current vehicle.

6. A vehicle weighing device, characterized in that: Used to implement a vehicle weighing method according to any one of claims 1 to 5, comprising a vehicle data acquisition module, a first total weight acquisition module, a second total weight acquisition module, a third total weight acquisition module and a current total weight acquisition module; The vehicle data acquisition module is configured to acquire first measurement data of the current vehicle to be weighed; wherein the first measurement data includes a first weighing signal, vehicle speed, vehicle model, and tire temperature; The first total weight acquisition module is configured to filter the first weighing signal to obtain a first total weight; The second total weight acquisition module is configured to obtain the tire pressure of the current vehicle based on the vehicle model, and obtain a second total weight by combining the tire pressure and the vehicle speed; The third total weight acquisition module is configured to input the first total weight, the second total weight, and the tire temperature into a preset first total weight prediction model to obtain a third total weight; The current total weight acquisition module is used to obtain the current total weight of the current vehicle according to the first total weight, the second total weight and the third total weight.

7. A computer device, characterized in that: include: processor; Memory; a computer program stored in the memory and configured to be executed by the processor; When the processor executes the computer program, the vehicle weighing method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute a vehicle weighing method according to any one of claims 1 to 5.

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