A vehicle intelligent axle weight detection method, device, equipment and storage medium

Abstract

The present application discloses a method, device, equipment and storage medium for intelligent vehicle axle weight detection, the method comprising: acquiring a vehicle image of a target vehicle through a camera module to obtain a target vehicle image; performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles and n axle spacings of the target vehicle; the target number of axles is m; determining an axle weight distribution coefficient of each axle according to the target vehicle type, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients; acquiring first axle weight data of each axle of the target vehicle through a metering sensor to obtain m first axle weight data; determining target metering data according to the m first axle weight data; determining second axle weight data of each axle according to the m axle weight distribution coefficients and the target metering data to obtain m second axle weight data; determining axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data.

Description

Technical Field

[0001] The present application relates to the field of vehicle detection technology, and in particular to a vehicle intelligent axle weight detection method, device, equipment and storage medium. Background Art

[0002] At the entrance of the expressway, large vehicles such as trucks or special vehicles are usually measured and inspected to prevent overloading or over-limit. However, traditional inspection methods often focus on detecting whether the vehicle is overloaded. In some cases, due to the uneven distribution of the weight of the vehicle or its cargo, the axle weight of the vehicle is unevenly distributed. If the axle weight is too large, it will cause excessive ground pressure and increase the risk of damage to the road or bridge. Secondly, excessive axle weight will also cause the body of the vehicle to be compressed and damaged, seriously threatening the safety of personnel. Existing vehicle inspection technology is easily restricted by various factors, making the measured axle weight inaccurate.

[0003] Therefore, how to accurately measure the load-bearing of each axle in the vehicle becomes an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a vehicle intelligent axle weight detection method, device, equipment and storage medium, which can achieve accurate measurement of the load-bearing capacity of each axle in the vehicle.

[0005] In a first aspect, an embodiment of the present application provides a vehicle intelligent axle weight detection method, which is applied to a processing module of a vehicle metering device, wherein the vehicle metering device further includes: a metering sensor and a camera module, and the method includes:

[0006] The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle;

[0007] Performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles, and a distance between n axles of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1;

[0008] Determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients;

[0009] The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section;

[0010] Determine target measurement data according to the m first axle weight data;

[0011] Determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data, and obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data;

[0012] The axle weight data of each axle of the target vehicle is determined according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data.

[0013] In a second aspect, an embodiment of the present application provides a vehicle intelligent axle weight detection device, which is applied to a processing module of a vehicle metering device, wherein the vehicle metering device further includes: a metering sensor, a camera module, and the vehicle intelligent axle weight detection device includes: a vehicle identification unit, a data processing unit, and an axle weight determination unit, wherein:

[0014] The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle;

[0015] The vehicle identification unit is used to identify the vehicle type of the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacing of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1;

[0016] The data processing unit is used to determine the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients;

[0017] The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section;

[0018] The data processing unit is further used to determine target measurement data according to the m first axle weight data;

[0019] The axle weight determination unit is used to determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data to obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data;

[0020] The axle weight determination unit is further used to determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data, so as to obtain m target axle weight data.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0024] By implementing the embodiments of the present application, a vehicle image of a target vehicle in a preset road section can be collected by a camera module to obtain a target vehicle image; the target vehicle image can be identified to obtain a target vehicle model, a target number of axles and n axle spacings of the target vehicle; the target number of axles is m; the axle weight distribution coefficient of each axle is determined according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients; the first axle weight data of each axle of the target vehicle is collected by a metering sensor to obtain m first axle weight data; the target metering data is determined according to the m first axle weight data; the second axle weight data of each axle is determined according to the m axle weight distribution coefficients and the target metering data to obtain m second axle weight data; the axle weight data of each axle of the target vehicle is determined according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data, thereby achieving accurate measurement of the load-bearing capacity of each axle in the vehicle, and judging whether the vehicle is overloaded or has an uneven weight distribution according to the axle weight distribution of the vehicle, and providing corresponding prompts according to the axle weight distribution of the vehicle to ensure the safety of vehicle driving and the durability of the road. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0026] Figure 1 It is a flow chart of a vehicle intelligent axle weight detection method provided in an embodiment of the present application;

[0027] Figure 2It is a schematic diagram of an application scenario of a vehicle intelligent axle weight detection method provided in an embodiment of the present application;

[0028] Figure 3 It is a structural schematic diagram of a vehicle intelligent axle weight detection device provided in an embodiment of the present application;

[0029] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0034] See also Figure 1 , Figure 1 1 is a flow chart of a vehicle intelligent axle weight detection method provided in an embodiment of the present application. The method is applied to a processing module of a vehicle metering device. The vehicle metering device also includes: a metering sensor and a camera module. The method includes but is not limited to the following steps:

[0035] S101. Capturing a vehicle image of a target vehicle in a preset road section through the camera module to obtain an image of the target vehicle.

[0036] In the embodiment of the present application, the preset road section may include one of the following: a highway entrance and exit, a provincial road, a national road, an airport, a railway station, a high-speed railway station, a designated measurement unit, etc., which is not limited here.

[0037] In the embodiment of the present application, the target vehicle may include one of the following: a van, a car, a truck, a bus, a special vehicle, etc., which is not limited here.

[0038] In a specific embodiment, the target vehicle may be a truck, and the preset road section may be the entrance of a highway. Specifically, when the truck drives to the entrance of the highway, the truck enters the field of view of the camera module, and the camera is automatically triggered to shoot, thereby obtaining a vehicle image of the truck, i.e., the target vehicle image.

[0039] Among them, the target vehicle image is a complete vehicle image, which can show the overall outline and features of the vehicle. By analyzing the target vehicle image, the vehicle size, model, wheels, vehicle cargo condition, etc. can be obtained. The data based on image analysis provides basic data for axle weight detection.

[0040] Optionally, the above step S101, collecting a vehicle image of the target vehicle in a preset road section by the camera module to obtain the target vehicle image, may include the following steps:

[0041] A11. When the target vehicle is in the preset road section, instruct the camera module to shoot the target vehicle to obtain a first vehicle image;

[0042] A12. When the first vehicle image satisfies a first preset condition, determining the first vehicle image as the target vehicle image;

[0043] A13. When the first vehicle image does not satisfy the first preset condition, acquiring environmental data of the preset road section to obtain target environmental data;

[0044] A14, determining a target adjustment parameter of the camera module according to the target environment data;

[0045] A15. Adjusting the initial camera parameters of the camera module based on the target adjustment parameters to obtain target shooting parameters;

[0046] A16, controlling the camera module to shoot the target vehicle with the target shooting parameters to obtain a second vehicle image;

[0047] A17. When the second vehicle image meets the first preset condition, the second vehicle image is determined as the target vehicle image.

[0048] Among them, the first preset condition refers to that the image is clear, the image contains a complete vehicle view, the lighting is sufficient, etc. The first preset condition can be set in advance or the system defaults. When the vehicle image meets the first preset condition, the vehicle image can be feature extracted and analyzed. Otherwise, the vehicle image captured by the camera module is not suitable for image analysis.

[0049] In a specific embodiment, when the target vehicle is in a preset road section, the processing module instructs the camera module to shoot the target vehicle to obtain a first vehicle image. When the first vehicle image meets the first preset condition, the first vehicle image is determined as the target vehicle image, and the next image analysis is performed; when the first vehicle image does not meet the first preset condition, it may be due to environmental influences, such as weather or road conditions, which cause the camera module to fail to capture a clear image, and it is necessary to obtain environmental data around the preset road section to obtain target environmental data, wherein the target environmental data may include at least one of the following: lighting conditions, weather conditions, road conditions, etc., which are not limited here, and the camera parameters of the camera module are adjusted according to the target environmental data to re-shoot.

[0050] Next, by analyzing the target environment data, the target adjustment parameters of the camera module can be determined, wherein the target adjustment parameters may include at least one of the following: exposure time, sensitivity, lens focal length, brightness, contrast, etc., which are not limited here. The initial camera parameters of the camera module are adjusted based on the target adjustment parameters, such as increasing the exposure time to compensate for low light conditions, adjusting the sensitivity to reduce image noise, etc., and then obtaining the target shooting parameters, and controlling the camera module to shoot the target vehicle with the target shooting parameters to obtain the second vehicle image. When the second vehicle image meets the first preset condition, the second vehicle image is determined as the target vehicle image, otherwise, the camera parameters of the camera module are re-adjusted to ensure that a clear vehicle image is obtained for image analysis.

[0051] S102, performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles and a distance between n axles of the target vehicle; the target number of axles is m, where m=n+1.

[0052] Wherein, n is an integer greater than or equal to 1, the target number of axles is m, m=n+1, and the axle spacing represents the distance between every two adjacent axles. For example, when the target vehicle is a 4-axle vehicle, the target vehicle has 3 axle spacings.

[0053] In a specific embodiment, the target vehicle image can be identified by a computer vision algorithm, such as a trained deep learning model. Feature data in the target vehicle image can also be extracted and matched with a preset vehicle model database to ultimately obtain the target vehicle model, target number of axles, and n axle spacings of the target vehicle.

[0054] Optionally, the above step S102, performing vehicle type recognition on the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacings of the target vehicle, may include the following steps:

[0055] A201, performing a preprocessing operation on the target vehicle image to obtain a third vehicle image; the preprocessing operation includes at least one of the following: adjusting brightness, adjusting contrast, reducing noise, and enhancing edges;

[0056] A202, extracting feature data of the third vehicle image to obtain target feature data;

[0057] A203, matching the target feature data with feature data of each vehicle model in a preset vehicle model database to obtain a plurality of matching degrees;

[0058] A204, selecting a maximum value among the multiple matching degrees, and determining the vehicle model corresponding to the maximum value as the target vehicle model;

[0059] A205. Determine the wheels of the target vehicle located on the same side and used for bearing and traveling according to the target characteristic data, and obtain m wheels;

[0060] A206. Determine the center position corresponding to each of the m wheels to obtain m center positions;

[0061] A207, determining the pixel distance between every two adjacent center positions of the m center positions in the third vehicle image, to obtain n first pixel distances;

[0062] A208, determining a pixel distance of the target vehicle in the third vehicle image to obtain a second pixel distance;

[0063] A209, determining the length data of the target vehicle according to the target characteristic data to obtain target length data;

[0064] A210. Determine the n interaxial distances according to the n first pixel distances, the second pixel distances and the target length data; the interaxial distances correspond to the first pixel distances one by one.

[0065] Among them, the preset vehicle model database refers to an electronic database that stores information related to various vehicle types. The preset vehicle model database may include the following information: vehicle model identification, vehicle outline, vehicle size, number of vehicle axles, window layout, headlight position, etc., which are not limited here.

[0066] In a specific embodiment, a preprocessing operation is performed on the target vehicle image to obtain a third vehicle image, wherein the preprocessing operation includes at least one of the following: adjusting brightness, adjusting contrast, reducing noise, enhancing edges, etc., which are not limited here. By performing a preprocessing operation on the target vehicle image, the image quality can be further improved so that the features of the target vehicle can be clearly identified.

[0067] Furthermore, feature data of the third vehicle image is extracted to obtain target feature data, and the target feature data is matched with feature data of each vehicle model in a preset vehicle model database to obtain multiple matching degrees, which are used to determine the similarity between the target feature data of the target vehicle and the feature data of each vehicle model in the preset vehicle model database, and the maximum value of the multiple matching degrees is selected, and the vehicle model corresponding to the maximum value is determined as the target vehicle model.

[0068] Next, the same side of the target vehicle is analyzed according to the target feature data to determine the wheels of the target vehicle located on the same side and used for carrying and traveling, and m wheels are obtained, wherein the number of wheels corresponds to the target number of axles of the target vehicle. At the same time, the center position corresponding to each wheel of the m wheels is determined to obtain m center positions, and n first pixel distances are obtained by measuring the pixel distance between each two adjacent center positions in the third vehicle image, and the pixel distance of the target vehicle in the third vehicle image can be determined according to the third vehicle image to obtain the second pixel distance, wherein the first pixel distance is different from the second pixel distance, the first pixel distance represents the pixel distance between axles, and the second pixel distance represents the pixel distance of the entire body length of the target vehicle. The length data of the target vehicle is determined according to the target feature data to obtain the target length data, and the n axle spacings are determined according to the n first pixel distances, the second pixel distances and the target length data, that is, the n axle spacings corresponding to the n first pixel distances can be determined by the proportional relationship between the second pixel distances and the target length data. By performing vehicle type recognition on the target vehicle image, the target vehicle type, target number of axles and n axle spacings of the target vehicle can be obtained, and the axle weight distribution coefficient of the target vehicle can be further determined based on these data.

[0069] S103, determining an axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings, to obtain m axle weight distribution coefficients.

[0070] In the embodiment of the present application, the axle weight distribution coefficient refers to the proportion of weight borne by each axle of the vehicle. The axle weight data of multiple vehicles of the same type can be collected and statistically analyzed to determine the axle weight distribution of different axles and then determine the axle weight distribution coefficient of the current vehicle model. A mathematical model method can also be used to establish a mathematical model based on factors affecting axle weight distribution, such as vehicle type, load, road conditions, etc., and calculate the axle weight distribution coefficient of the target vehicle through a mathematical model.

[0071] In a specific embodiment, multiple sample vehicle data can be obtained based on the target vehicle model, target number of axles and n axle spacings, and the sample vehicle data is analyzed to determine the axle weight distribution coefficient of each axle of the sample vehicle to obtain m axle weight distribution coefficients. At the same time, the m axle weight distribution coefficients are also applicable to the target vehicle in the embodiment of the present application. The axle weight of each axle in the target vehicle can be determined based on the m axle weight distribution coefficients.

[0072] Optionally, the above step S103, determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients, may include the following steps:

[0073] A31. Obtain a sample vehicle set; the sample vehicle set includes p sample vehicles; p is an integer greater than 1; each sample vehicle in the sample vehicle set is the target vehicle model, and each sample vehicle includes m axles, and each sample vehicle includes the n axle spacings;

[0074] A32. Obtain a sample axle weight data set of each sample vehicle in the sample vehicle set to obtain p sample axle weight data sets;

[0075] A33. Determine an axle weight data set corresponding to each of the m axles according to the p sample axle weight data sets, to obtain m axle weight data sets;

[0076] A34. Determine average axle weight data corresponding to each axle according to the m axle weight data sets to obtain m average axle weight data;

[0077] A35. Normalize the m average axle weight data to obtain the m axle weight distribution coefficients; the axle weight distribution coefficients correspond to the average axle weight data one by one.

[0078] In a specific embodiment, after obtaining the target vehicle model, the target number of axles and the n wheel spacings, a sample vehicle set can be collected based on the target vehicle model, the target number of axles and the n wheel spacings. Each sample vehicle in the sample vehicle set is the target vehicle model, and each sample vehicle includes m axles, and each sample vehicle includes n wheel spacings. Ultimately, p sample vehicles can be collected, where p is an integer greater than 1.

[0079] Next, a sample axle weight data set of each sample vehicle in the sample vehicle set is obtained to obtain p sample axle weight data sets, each of which includes the weight of each axle of the sample vehicle, that is, m axle weight data sets. Based on the p sample axle weight data sets, the axle weight data set corresponding to each axle in the m axles can be determined to obtain m axle weight data sets, each of which includes p axle weight data of the current axle. Based on the m axle weight data sets, the average axle weight data corresponding to each axle is calculated to obtain m average axle weight data, which reflects the typical load condition of each axle in the sample vehicle.

[0080] Furthermore, the m average axle weight data are normalized to obtain m axle weight distribution coefficients, wherein the normalization process includes adjusting the m average axle weight data in proportion so that the sum of the proportions of each axle weight is 1, that is, the sum of the m axle weight distribution coefficients is 1. Each axle weight distribution coefficient represents the proportion of the corresponding axle in the weight of the vehicle. According to the axle weight distribution coefficient obtained from the sample vehicle set, the axle weight distribution coefficient of the target vehicle in actual conditions can be obtained, that is, according to the vehicle model signal and vehicle design parameters, the general axle weight of each axle can be designed.

[0081] In one possible embodiment, the sample vehicle set includes 100 sample vehicles, and the current target vehicle is a 4-axle truck, which includes 3 axle spacings. Then, each sample vehicle is a 4-axle truck, and each sample vehicle includes 3 axle spacings. The axle spacings of the sample vehicles correspond to the axle spacings of the target vehicle one by one. Then, the axle weight data set corresponding to each of the 4 axles can be determined based on the 100 sample axle weight data sets, and 4 axle weight data sets are obtained, each axle weight data set includes 100 axle weight data. The axle weight distribution coefficient corresponding to each axle can be determined based on the 4 axle weight data sets.

[0082] S104. Collect the first axle weight data of each axle of the target vehicle through the measuring sensor to obtain m first axle weight data.

[0083] Among them, the metering sensor is set under the preset road section. The metering sensor can be set inside the scale platform, and the scale platform is embedded in the preset road section, so that when the vehicle passes, the wheels are pressed on the scale platform. The metering sensor can detect the pressure of the wheels on the scale platform and calculate the axle weight of the vehicle.

[0084] For ease of understanding, Figure 2 As shown, Figure 2 Schematic diagram of an application scenario of a vehicle intelligent axle weight detection method provided in an embodiment of the present application. Figure 2As shown, a weighing platform is embedded on the ground of a preset road section, and a metering sensor is arranged inside the weighing platform. When the target vehicle travels to the preset road section, each axle of the target vehicle passes through the weighing platform, and the axle weight data of the axle of the target vehicle is collected by the metering sensor. In the preset road section, a camera module is also arranged. According to the camera module, the shooting time of the target vehicle passing through the weighing platform and the time of the metering sensor detection are calculated, and the axle weight data corresponding to each axle of the target vehicle can be obtained.

[0085] In a specific embodiment, the first axle weight data of each axle of the target vehicle can be collected by a metering sensor to obtain m first axle weight data, that is, the actual axle weight of each axle of the target vehicle.

[0086] S105. Determine target measurement data according to the m first axle weight data.

[0087] In the embodiment of the present application, the target measurement data may be one of the following: the total weight of the vehicle, axle weight distribution, overload condition, etc., which are not limited here.

[0088] In a specific embodiment, the target measurement data may be the total weight of the target vehicle. Specifically, the m first axle weight data are added together to obtain the total weight of the vehicle, that is, the target measurement data.

[0089] S106. Determine second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data to obtain m second axle weight data.

[0090] In a specific embodiment, the second axle weight data of each axle of the target vehicle can be determined according to the m axle weight distribution coefficients and the target measurement data, that is, the axle weight of each axle of the target vehicle is determined according to the proportion of the axle weight of each axle of the target vehicle to the total weight of the target vehicle and the total weight of the target vehicle.

[0091] Among them, the second axle weight data is different from the first axle weight data. The second axle weight data is the theoretical axle weight data of the target vehicle, and the first axle weight data is the actually measured axle weight data of the target vehicle. However, the axle corresponding to the second axle weight data corresponds one-to-one with the axle corresponding to the first axle weight data.

[0092] In one possible embodiment, the target vehicle is a 4-axle truck. According to the design of the vehicle and the calculation of the sample vehicle, when the total weight of the target vehicle is 10 tons, the axle weight distribution coefficient is set to 10% for the front axle, 35% for each of the two middle axles, and 20% for the rear axle, that is, the front axle weight is 1 ton, the two middle axles are 3.5 tons each, and the rear axle weight is 2 tons.

[0093] S107 . Determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data.

[0094] Among them, the target axle weight data is determined by combining the first axle weight data and the second axle weight data. The first axle weight data is measured by a measuring sensor, and the accuracy of the data may be affected by factors such as sensor error and environmental conditions, such as road inclination, rainy weather, and rapid passage of vehicles. The second axle weight data is obtained by theoretically analyzing the axle weight of the target vehicle, but the analysis process is based on historical data or mathematical models, etc., which may not fully reflect the actual load situation of the target vehicle. Therefore, the first axle weight data and the second axle weight data are analyzed and compared, and the target axle weight data of each axle is calculated by combining the first axle weight data and the second axle weight data, so as to obtain more accurate axle weight data of the target vehicle.

[0095] In a specific embodiment, the m first axle weight data and the m second axle weight data can be weighted to determine the axle weight data of each axle of the target vehicle, and m target axle weight data can be obtained. The final axle weight data of each axle of the target vehicle can be determined based on the m target axle weight data, that is, the target axle weight data is accurate axle weight data.

[0096] Optionally, the above step S107, determining the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data, may include the following steps:

[0097] A701. Obtain a target speed and a target acceleration of a first axle of the target vehicle when the first axle passes through the metering sensor; the first axle is any axle among the m axles of the target vehicle;

[0098] A702. Adjust the target first axle weight data of the first axle according to the target speed and the target acceleration to obtain third axle weight data; the target first axle weight data is the axle weight data corresponding to the first axle among the m first axle weight data;

[0099] A703, obtaining a first weight coefficient corresponding to target second axle weight data of the first axle; the target second axle weight data is axle weight data corresponding to the first axle among the m second axle weight data;

[0100] A704. Determine a second weight coefficient corresponding to the third axle weight data according to the first weight coefficient;

[0101] A705. Perform a weighted operation based on the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient to obtain the target axle weight data corresponding to the first axle among the m target axle weight data.

[0102] In a specific embodiment, the target speed and target acceleration of the target vehicle are obtained when the first axle of the target vehicle passes through the metering sensor, wherein the first axle is any axle among the m axles of the target vehicle. Then, the target first axle weight data of the first axle is adjusted according to the target speed and target acceleration to obtain the third axle weight data, that is, when the vehicle passes through the metering sensor, if the vehicle accelerates, the rear axle of the target vehicle may bear more weight, and the target first axle weight data measured by the metering sensor may become larger. Therefore, the target first axle weight data needs to be adjusted according to the current speed and acceleration of the target vehicle to obtain the third axle weight data.

[0103] Further, the first weight coefficient corresponding to the second axle weight data is obtained, and the second weight coefficient corresponding to the third axle weight data is determined according to the first weight coefficient. The first weight coefficient represents the proportion of the target second axle weight data of the first axle to the target axle weight data corresponding to the first axle. The sum of the first weight coefficient and the second weight coefficient is 1, wherein the first weight coefficient can be preset or the system defaults, and can be set specifically according to the actual situation. For example, the higher the sensor accuracy and the better the stability, the lower the first weight coefficient can be set, indicating that the first axle weight data measured by the metering sensor has a greater impact on the target axle weight data, that is, the second weight coefficient is higher. If the sensor accuracy is low, the first weight coefficient can be increased. Then, a weighted operation is performed according to the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient, and the target axle weight data corresponding to the first axle can be obtained.

[0104] Optionally, the above step A702, adjusting the target first axle weight data of the first axle according to the target speed and the target acceleration to obtain the third axle weight data, may include the following steps:

[0105] B711. Obtain reference speed and reference acceleration;

[0106] B712. determining a speed offset coefficient according to the reference speed and the target speed;

[0107] B713. determining an acceleration offset coefficient according to the reference acceleration and the target acceleration;

[0108] B714. Obtaining the influence of the target speed on the first axle to obtain a speed influence coefficient;

[0109] B715. Obtaining the influence degree of the target acceleration on the first axle to obtain an acceleration influence coefficient;

[0110] B716. Determine an adjustment coefficient corresponding to the target first axle weight data of the first axle according to the speed offset coefficient, the speed influence coefficient, the acceleration offset coefficient, and the acceleration influence coefficient to obtain a target adjustment coefficient;

[0111] B717. Adjust the target first axle weight data of the first axle according to the target adjustment coefficient to obtain the third axle weight data.

[0112] In a specific embodiment, firstly, a reference speed and a reference acceleration are obtained. The reference speed and the reference acceleration respectively represent the speed and acceleration of the target vehicle under standard test conditions. The reference speed is used to ensure that the measurement data of the vehicle passing through the reference speed during axle weight measurement is accurate. It should be noted that when the vehicle is traveling at high speed, the contact characteristics between the vehicle tire and the road surface, including tire deformation, friction and grip, may affect the accuracy of the axle weight measurement. If the vehicle speed is too low, the vehicle may not be able to fully trigger or pass all sensors, which may affect the measurement accuracy of the sensor. When the vehicle accelerates or decelerates, the axle weight distribution of the vehicle may change. The reference acceleration is used to evaluate the axle weight under dynamic conditions.

[0113] It should be noted that the embodiment of the present application dynamically detects the axle weight of the vehicle, and therefore sets a reference speed for the target vehicle to pass through the metering sensor. The reference speed can be adjusted. For example, if the reference speed is set to 0, it means that the axle weight data measured when the vehicle stops on the metering sensor is accurate data.

[0114] Furthermore, a speed offset coefficient is determined by comparing the reference speed with the target speed, an acceleration offset coefficient is determined by comparing the reference acceleration with the target acceleration, the influence of the target speed on the first axle is obtained, and the speed influence coefficient is obtained, the influence of the target acceleration on the first axle is obtained, and the acceleration influence coefficient is obtained, wherein the speed influence coefficient and the acceleration influence coefficient are used to compensate for the influence of speed and acceleration when adjusting the axle weight measurement data.

[0115] Next, the adjustment coefficient corresponding to the target first axle weight data of the first axle is determined according to the speed offset coefficient, the speed influence coefficient, the acceleration offset coefficient and the acceleration influence coefficient to obtain the target adjustment coefficient, wherein the target adjustment coefficient can be calculated by the following formula: target adjustment coefficient=1+speed offset coefficient×speed influence coefficient+acceleration offset coefficient×acceleration influence coefficient, wherein the speed offset coefficient=target speed-reference speed, and the acceleration offset coefficient=target acceleration-reference acceleration. The target first axle weight data of the first axle is adjusted according to the target adjustment coefficient to obtain the third axle weight data.

[0116] In a possible embodiment, the base speed is set to 50km / h, the base acceleration is set to 0m / s², when the vehicle passes the metering sensor, the target speed is 60km / h, the target acceleration is 0.1m / s², the speed offset coefficient is 10, the acceleration offset coefficient is 0.1, and it is set that for every increase of 1km / h in speed, the axle weight reading may increase by 0.05%, then the speed influence coefficient is 0.0005, for every increase of 0.1m / s² in acceleration, the axle weight reading may increase by 0.3%, then the acceleration influence coefficient is 0.003, and the target adjustment coefficient is finally 1.035.

[0117] Optionally, the vehicle detection device further includes a prompt module, and the method may further include the following steps:

[0118] C721. When the target measurement data is greater than or equal to a preset first threshold, a first prompt message is generated, and the first prompt message is displayed through the prompt module; the first prompt message is used to prompt that the target vehicle is overweight;

[0119] C722. When the target measurement data is less than the preset first threshold, determining whether there is target axle weight data greater than or equal to a preset second threshold among the m target axle weight data;

[0120] C723. If there is a target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, obtain the target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data to obtain k target axle weight data; k is an integer greater than or equal to 1 and less than or equal to m;

[0121] C724. Determine k axle positions according to the k target axle weight data;

[0122] C725. Generate second prompt information according to the k axle positions, and display the second prompt information through the prompt module; the second prompt information is used to prompt that the axles corresponding to the k axle positions of the target vehicle are overweight;

[0123] C726. If there is no target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, a third prompt information is generated and displayed through the prompt module; the third prompt information is used to prompt that the target vehicle can pass normally.

[0124] The prompt module may be a display screen arranged at a preset road section for displaying prompt information.

[0125] The preset first threshold is preset or system default, indicating that when the target measurement data of the target vehicle is greater than or equal to the threshold, the target vehicle is overweight. The preset second threshold is preset or system default, indicating that when there is an axle exceeding the threshold among the multiple axles of the target vehicle, one of the axles of the target vehicle is overweight.

[0126] In a specific embodiment, when the target measurement data is greater than or equal to a preset first threshold, a first prompt message is generated, and the first prompt message is displayed by a prompt module, wherein the first prompt message is used to prompt that the target vehicle is overweight. When the target measurement data is less than the preset first threshold, it is determined whether there is a target axle weight data greater than or equal to a preset second threshold among the m target axle weight data, if there is a target axle weight data greater than or equal to the preset second threshold among the m target axle weight data, the target axle weight data greater than or equal to the preset second threshold among the m target axle weight data is obtained, and k target axle weight data are obtained, wherein k is an integer greater than or equal to 1 and less than or equal to m, k axle positions are determined according to the k target axle weight data, second prompt messages are generated according to the k axle positions, and the second prompt messages are displayed by a prompt module, wherein the second prompt message is used to prompt that the axles corresponding to the k axle positions of the target vehicle are overweight, if there is no target axle weight data greater than or equal to the preset second threshold among the m target axle weight data, a third prompt message is generated, and the third prompt message is displayed by a prompt module, wherein the third prompt message is used to prompt that the target vehicle can pass normally.

[0127] In summary, by implementing the embodiments of the present application, the vehicle image of the target vehicle in the preset road section can be collected by the camera module to obtain the target vehicle image; the vehicle type recognition is performed on the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacing of the target vehicle; the target number of axles is m; the axle weight distribution coefficient of each axle is determined according to the target vehicle type, target number of axles and n axle spacing, and m axle weight distribution coefficients are obtained; the first axle weight data of each axle of the target vehicle is collected by the metering sensor to obtain m first axle weight data; the target metering data is determined according to the m first axle weight data; the second axle weight data of each axle is determined according to the m axle weight distribution coefficients and the target metering data to obtain m second axle weight data; the axle weight data of each axle of the target vehicle is determined according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data, thereby achieving accurate measurement of the load-bearing capacity of each axle in the vehicle, and judging whether the vehicle is overloaded or has uneven weight distribution according to the axle weight distribution of the vehicle, and providing corresponding prompts according to the axle weight distribution of the vehicle to ensure the safety of vehicle driving and the durability of the road.

[0128] See also Figure 3 , Figure 3: is a structural schematic diagram of a vehicle intelligent axle weight detection device provided in an embodiment of the present application. The vehicle intelligent axle weight detection device 300 is applied to a processing module of a vehicle metering device. The vehicle metering device also includes: a metering sensor, a camera module, and the vehicle intelligent axle weight detection device 300 includes: a vehicle identification unit 301, a data processing unit 302, and an axle weight determination unit 303, wherein:

[0129] The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle;

[0130] The vehicle identification unit 301 is used to identify the vehicle type of the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacing of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1;

[0131] The data processing unit 302 is used to determine the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients;

[0132] The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section;

[0133] The data processing unit 302 is further configured to determine target measurement data according to the m first axle weight data;

[0134] The axle weight determination unit 303 is used to determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data to obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data;

[0135] The axle weight determination unit 303 is further used to determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data, so as to obtain m target axle weight data;

[0136] Wherein, determining the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data includes:

[0137] Obtaining a target speed and a target acceleration of a first axle of the target vehicle when the first axle passes through the metering sensor; the first axle is any axle among the m axles of the target vehicle;

[0138] The target first axle weight data of the first axle is adjusted according to the target speed and the target acceleration to obtain third axle weight data; the target first axle weight data is the axle weight data corresponding to the first axle among the m first axle weight data;

[0139] Acquire a first weight coefficient corresponding to target second axle weight data of the first axle; the target second axle weight data is the axle weight data corresponding to the first axle among the m second axle weight data;

[0140] Determine a second weight coefficient corresponding to the third axle weight data according to the first weight coefficient;

[0141] A weighted operation is performed based on the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient to obtain the target axle weight data corresponding to the first axle among the m target axle weight data.

[0142] Optionally, in the aspect of acquiring the vehicle image of the target vehicle in the preset road section by the camera module to obtain the target vehicle image, the vehicle identification unit 301 is further specifically used for:

[0143] When the target vehicle is in the preset road section, instructing the camera module to shoot the target vehicle to obtain a first vehicle image;

[0144] When the first vehicle image meets a first preset condition, determining the first vehicle image as the target vehicle image;

[0145] When the first vehicle image does not satisfy the first preset condition, acquiring environmental data of the preset road section to obtain target environmental data;

[0146] Determining a target adjustment parameter of the camera module according to the target environment data;

[0147] Adjusting the initial camera parameters of the camera module based on the target adjustment parameters to obtain target shooting parameters;

[0148] Controlling the camera module to shoot the target vehicle with the target shooting parameters to obtain a second vehicle image;

[0149] When the second vehicle image meets the first preset condition, the second vehicle image is determined as the target vehicle image.

[0150] Optionally, in the aspect of performing vehicle type recognition on the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacings of the target vehicle, the vehicle recognition unit 301 is further specifically used for:

[0151] Performing a preprocessing operation on the target vehicle image to obtain a third vehicle image; the preprocessing operation includes at least one of the following: adjusting brightness, adjusting contrast, reducing noise, and enhancing edges;

[0152] Extracting feature data of the third vehicle image to obtain target feature data;

[0153] Matching the target feature data with feature data of each vehicle model in a preset vehicle model database to obtain multiple matching degrees;

[0154] Selecting a maximum value among the multiple matching degrees, and determining the vehicle model corresponding to the maximum value as the target vehicle model;

[0155] Determine the wheels of the target vehicle located on the same side and used for bearing and traveling according to the target characteristic data, and obtain m wheels;

[0156] Determine the center position corresponding to each wheel of the m wheels to obtain m center positions;

[0157] Determine the pixel distance between every two adjacent center positions of the m center positions in the third vehicle image to obtain n first pixel distances;

[0158] Determine a pixel distance of the target vehicle in the third vehicle image to obtain a second pixel distance;

[0159] Determine the length data of the target vehicle according to the target characteristic data to obtain target length data;

[0160] The n interaxial distances are determined according to the n first pixel distances, the second pixel distances and the target length data; the interaxial distances correspond one to one with the first pixel distances.

[0161] Optionally, in determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients, the data processing unit 302 is further specifically used to:

[0162] Acquire a sample vehicle set; the sample vehicle set includes p sample vehicles; p is an integer greater than 1; each sample vehicle in the sample vehicle set is the target vehicle model, and each sample vehicle includes m axles, and each sample vehicle includes the n axle spacings;

[0163] Obtain a sample axle weight data set of each sample vehicle in the sample vehicle set to obtain p sample axle weight data sets;

[0164] Determine an axle weight data set corresponding to each axle of the m axles according to the p sample axle weight data sets, to obtain m axle weight data sets;

[0165] Determine the average axle weight data corresponding to each axle according to the m axle weight data sets to obtain m average axle weight data;

[0166] The m average axle weight data are normalized to obtain the m axle weight distribution coefficients; the axle weight distribution coefficients correspond to the average axle weight data one by one.

[0167] Optionally, in the aspect of adjusting the target first axle weight data of the first axle according to the target speed and the target acceleration to obtain the third axle weight data, the axle weight determination unit 303 is further specifically configured to:

[0168] Get the base speed and base acceleration;

[0169] determining a speed offset coefficient according to the reference speed and the target speed;

[0170] determining an acceleration offset coefficient according to the reference acceleration and the target acceleration;

[0171] Acquire the influence degree of the target speed on the first axle to obtain a speed influence coefficient;

[0172] Acquiring the influence degree of the target acceleration on the first axle to obtain an acceleration influence coefficient;

[0173] determining an adjustment coefficient corresponding to the target first axle weight data of the first axle according to the speed offset coefficient, the speed influence coefficient, the acceleration offset coefficient and the acceleration influence coefficient to obtain a target adjustment coefficient;

[0174] The target first axle weight data of the first axle is adjusted according to the target adjustment coefficient to obtain the third axle weight data.

[0175] Optionally, the vehicle detection device further includes a prompt module, and the vehicle intelligent axle weight detection device 300 is further specifically used for:

[0176] When the target measurement data is greater than or equal to a preset first threshold, a first prompt message is generated, and the first prompt message is displayed by the prompt module; the first prompt message is used to prompt that the target vehicle is overweight;

[0177] When the target measurement data is less than the preset first threshold, determining whether there is target axle weight data greater than or equal to a preset second threshold among the m target axle weight data;

[0178] If there is a target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, the target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data are obtained to obtain k target axle weight data; k is an integer greater than or equal to 1 and less than or equal to m;

[0179] Determine k axle positions according to the k target axle weight data;

[0180] Generate second prompt information according to the k axle positions, and display the second prompt information through the prompt module; the second prompt information is used to prompt that the axles corresponding to the k axle positions of the target vehicle are overweight;

[0181] If there is no target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, a third prompt information is generated and displayed through the prompt module; the third prompt information is used to prompt that the target vehicle can pass normally.

[0182] The vehicle intelligent axle weight detection device 300 described in the present application can acquire a vehicle image of a target vehicle in a preset road section through a camera module to obtain a target vehicle image; perform vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles and n axle spacing of the target vehicle; the target number of axles is m; determine the axle weight distribution coefficient of each axle according to the target vehicle type, the target number of axles and the n axle spacing to obtain m axle weight distribution coefficients; acquire the first axle weight data of each axle of the target vehicle through a metering sensor to obtain m first axle weight data; and The target measurement data is determined according to the m axle weight distribution coefficients and the target measurement data, and the second axle weight data of each axle is determined to obtain m second axle weight data; the axle weight data of each axle of the target vehicle is determined according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data. Therefore, the load-bearing capacity of each axle in the vehicle is accurately measured, and it can be judged whether the vehicle is overloaded or has an uneven weight distribution according to the axle weight distribution of the vehicle, and corresponding prompts can be provided according to the axle weight distribution of the vehicle to ensure the safety of vehicle driving and the durability of the road.

[0183] See also Figure 4 , Figure 4 : is a structural diagram of an electronic device provided in an embodiment of the present application, the electronic device may include a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface may be interconnected through a bus; the one or more programs are stored in the memory and configured to be executed by the processor; in the embodiment of the present application, the program includes instructions for executing the following steps:

[0184] The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle;

[0185] Performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles, and a distance between n axles of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1;

[0186] Determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients;

[0187] The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section;

[0188] Determine target measurement data according to the m first axle weight data;

[0189] Determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data, and obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data;

[0190] Determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data, to obtain m target axle weight data;

[0191] Wherein, determining the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data includes:

[0192] Obtaining a target speed and a target acceleration of a first axle of the target vehicle when the first axle passes through the metering sensor; the first axle is any axle among the m axles of the target vehicle;

[0193] The target first axle weight data of the first axle is adjusted according to the target speed and the target acceleration to obtain third axle weight data; the target first axle weight data is the axle weight data corresponding to the first axle among the m first axle weight data;

[0194] Acquire a first weight coefficient corresponding to target second axle weight data of the first axle; the target second axle weight data is the axle weight data corresponding to the first axle among the m second axle weight data;

[0195] Determine a second weight coefficient corresponding to the third axle weight data according to the first weight coefficient;

[0196] A weighted operation is performed based on the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient to obtain the target axle weight data corresponding to the first axle among the m target axle weight data.

[0197] The electronic device described in the present application can collect a vehicle image of a target vehicle in a preset road section through a camera module to obtain a target vehicle image; perform vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles and n axle spacing of the target vehicle; the target number of axles is m; determine the axle weight distribution coefficient of each axle according to the target vehicle type, the target number of axles and the n axle spacing to obtain m axle weight distribution coefficients; collect the first axle weight data of each axle of the target vehicle through a metering sensor to obtain m first axle weight data; determine target metering data according to the m first axle weight data; determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target metering data to obtain m second axle weight data; determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data, thereby achieving accurate measurement of the load-bearing capacity of each axle in the vehicle, and judging whether the vehicle is overloaded or has an uneven weight distribution according to the axle weight distribution of the vehicle, and providing corresponding prompts according to the axle weight distribution of the vehicle to ensure the safety of vehicle driving and the durability of the road.

[0198] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0199] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0200] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0201] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist in a terminal device or a management device as discrete components.

[0202] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0203] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. It is implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0204] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A vehicle intelligent axle weight detection method, characterized in that: A processing module applied to a vehicle metering device, wherein the vehicle metering device further comprises: a metering sensor and a camera module, and the method comprises: The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle; Performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles, and a distance between n axles of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1; Determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients; The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section; Determine target measurement data according to the m first axle weight data; Determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data, and obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data; Determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data, to obtain m target axle weight data; Wherein, determining the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data includes: Obtaining a target speed and a target acceleration of a first axle of the target vehicle when the first axle passes through the metering sensor; the first axle is any axle among the m axles of the target vehicle; The target first axle weight data of the first axle is adjusted according to the target speed and the target acceleration to obtain third axle weight data; the target first axle weight data is the axle weight data corresponding to the first axle among the m first axle weight data; Acquire a first weight coefficient corresponding to target second axle weight data of the first axle; the target second axle weight data is the axle weight data corresponding to the first axle among the m second axle weight data; Determine a second weight coefficient corresponding to the third axle weight data according to the first weight coefficient; A weighted operation is performed based on the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient to obtain the target axle weight data corresponding to the first axle among the m target axle weight data.

2. The method according to claim 1, characterized in that The step of collecting a vehicle image of a target vehicle in a preset road section by the camera module to obtain the target vehicle image includes: When the target vehicle is in the preset road section, instructing the camera module to shoot the target vehicle to obtain a first vehicle image; When the first vehicle image meets a first preset condition, determining the first vehicle image as the target vehicle image; When the first vehicle image does not satisfy the first preset condition, acquiring environmental data of the preset road section to obtain target environmental data; Determining a target adjustment parameter of the camera module according to the target environment data; Adjusting the initial camera parameters of the camera module based on the target adjustment parameters to obtain target shooting parameters; Controlling the camera module to shoot the target vehicle with the target shooting parameters to obtain a second vehicle image; When the second vehicle image meets the first preset condition, the second vehicle image is determined as the target vehicle image.

3. The method according to claim 1, characterized in that The step of performing vehicle type recognition on the target vehicle image to obtain a target vehicle type, a target number of axles, and a distance between n axles of the target vehicle includes: Performing a preprocessing operation on the target vehicle image to obtain a third vehicle image; the preprocessing operation includes at least one of the following: adjusting brightness, adjusting contrast, reducing noise, and enhancing edges; Extracting feature data of the third vehicle image to obtain target feature data; Matching the target feature data with feature data of each vehicle model in a preset vehicle model database to obtain multiple matching degrees; Selecting a maximum value among the multiple matching degrees, and determining the vehicle model corresponding to the maximum value as the target vehicle model; Determine the wheels of the target vehicle located on the same side and used for bearing and traveling according to the target characteristic data, and obtain m wheels; Determine the center position corresponding to each wheel of the m wheels to obtain m center positions; Determine the pixel distance between every two adjacent center positions of the m center positions in the third vehicle image to obtain n first pixel distances; Determine a pixel distance of the target vehicle in the third vehicle image to obtain a second pixel distance; Determine the length data of the target vehicle according to the target characteristic data to obtain target length data; The n interaxial distances are determined according to the n first pixel distances, the second pixel distances and the target length data; the interaxial distances correspond one to one with the first pixel distances.

4. The method according to any one of claims 1 to 3, characterized in that: The step of determining the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients includes: Acquire a sample vehicle set; the sample vehicle set includes p sample vehicles; p is an integer greater than 1; each sample vehicle in the sample vehicle set is the target vehicle model, and each sample vehicle includes m axles, and each sample vehicle includes the n axle spacings; Obtain a sample axle weight data set of each sample vehicle in the sample vehicle set to obtain p sample axle weight data sets; Determine an axle weight data set corresponding to each axle of the m axles according to the p sample axle weight data sets, to obtain m axle weight data sets; Determine the average axle weight data corresponding to each axle according to the m axle weight data sets to obtain m average axle weight data; The m average axle weight data are normalized to obtain the m axle weight distribution coefficients; the axle weight distribution coefficients correspond to the average axle weight data one by one.

5. The method according to claim 1, characterized in that The step of adjusting the target first axle weight data of the first axle according to the target speed and the target acceleration to obtain third axle weight data includes: Get the base speed and base acceleration; determining a speed offset coefficient according to the reference speed and the target speed; determining an acceleration offset coefficient according to the reference acceleration and the target acceleration; Acquire the influence degree of the target speed on the first axle to obtain a speed influence coefficient; Acquiring the influence degree of the target acceleration on the first axle to obtain an acceleration influence coefficient; determining an adjustment coefficient corresponding to the target first axle weight data of the first axle according to the speed offset coefficient, the speed influence coefficient, the acceleration offset coefficient and the acceleration influence coefficient to obtain a target adjustment coefficient; The target first axle weight data of the first axle is adjusted according to the target adjustment coefficient to obtain the third axle weight data.

6. The method according to any one of claims 1 to 3, characterized in that: The vehicle metering device further includes a prompt module, and the method further includes: When the target measurement data is greater than or equal to a preset first threshold, a first prompt message is generated, and the first prompt message is displayed by the prompt module; the first prompt message is used to prompt that the target vehicle is overweight; When the target measurement data is less than the preset first threshold, determining whether there is target axle weight data greater than or equal to a preset second threshold among the m target axle weight data; If there is a target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, the target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data are obtained to obtain k target axle weight data; k is an integer greater than or equal to 1 and less than or equal to m; Determine k axle positions according to the k target axle weight data; Generate second prompt information according to the k axle positions, and display the second prompt information through the prompt module; the second prompt information is used to prompt that the axles corresponding to the k axle positions of the target vehicle are overweight; If there is no target axle weight data greater than or equal to the preset second threshold value among the m target axle weight data, a third prompt information is generated and displayed through the prompt module; the third prompt information is used to prompt that the target vehicle can pass normally.

7. A vehicle intelligent axle weight detection device, characterized in that: A processing module applied to a vehicle metering device, wherein the vehicle metering device further comprises: a metering sensor, a camera module, and the vehicle intelligent axle weight detection device comprises: a vehicle identification unit, a data processing unit, and an axle weight determination unit, wherein: The camera module collects a vehicle image of the target vehicle in a preset road section to obtain an image of the target vehicle; The vehicle identification unit is used to identify the vehicle type of the target vehicle image to obtain the target vehicle type, target number of axles and n axle spacing of the target vehicle; the target number of axles is m, m=n+1; n is an integer greater than or equal to 1; The data processing unit is used to determine the axle weight distribution coefficient of each axle according to the target vehicle model, the target number of axles and the n axle spacings to obtain m axle weight distribution coefficients; The first axle weight data of each axle of the target vehicle is collected by the measuring sensor to obtain m first axle weight data; the measuring sensor is arranged below the preset road section; The data processing unit is further used to determine target measurement data according to the m first axle weight data; The axle weight determination unit is used to determine the second axle weight data of each axle according to the m axle weight distribution coefficients and the target measurement data to obtain m second axle weight data; the axles corresponding to the second axle weight data correspond one to one with the axles corresponding to the first axle weight data; The axle weight determination unit is further used to determine the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data, so as to obtain m target axle weight data; Wherein, determining the axle weight data of each axle of the target vehicle according to the m first axle weight data and the m second axle weight data to obtain m target axle weight data includes: Obtaining a target speed and a target acceleration of a first axle of the target vehicle when the first axle passes through the metering sensor; the first axle is any axle among the m axles of the target vehicle; The target first axle weight data of the first axle is adjusted according to the target speed and the target acceleration to obtain third axle weight data; the target first axle weight data is the axle weight data corresponding to the first axle among the m first axle weight data; Acquire a first weight coefficient corresponding to target second axle weight data of the first axle; the target second axle weight data is the axle weight data corresponding to the first axle among the m second axle weight data; Determine a second weight coefficient corresponding to the third axle weight data according to the first weight coefficient; A weighted operation is performed based on the target second axle weight data of the first axle, the third axle weight data, the first weight coefficient and the second weight coefficient to obtain the target axle weight data corresponding to the first axle among the m target axle weight data.

8. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 6.

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