Vehicle weighing intelligent monitoring method and system, electronic equipment, and storage medium

Abstract

The present application provides a method and system for intelligently monitoring vehicle weighing, an electronic device, and a storage medium, belonging to the field of intelligent vehicle monitoring technology. The method includes: in response to a vehicle entering a target weighing area, obtaining vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to multiple scales; determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and vehicle driving position; the data in the vehicle weighing sequence data other than the first weighing data is second weighing data; the reliability of the first weighing data is higher than the reliability of the second weighing data; denoising the first weighing data based on the vehicle driving speed to obtain denoised target weighing data; and determining the vehicle weight based on the target weighing data. The method and system for intelligently monitoring vehicle weighing, the electronic device, and the storage medium provided in the present application can improve the accuracy of vehicle weighing monitoring.

Description

Technical Field

[0001] The present application belongs to the field of vehicle intelligent monitoring technology, and more specifically, relates to a vehicle weighing intelligent monitoring method and system, electronic equipment, and storage medium. Background Art

[0002] With the rapid development of the logistics and transportation industry, vehicle weighing, as a key link in scenarios such as cargo measurement and traffic law enforcement, has put forward higher requirements on the accuracy and reliability of weighing data.

[0003] Currently, common vehicle weighing monitoring systems primarily use multiple scales to collect vehicle weighing data and perform simple data analysis and processing. However, in practice, the varying driving conditions of vehicles during weighing can lead to noise and interference in the collected weighing data, affecting the accuracy of the weighing results. Furthermore, the fixed positions of multiple scales make it difficult to guarantee the reliability of the final weighing data, resulting in significant errors in vehicle weight measurements and failing to meet the precise weighing requirements of industries like logistics and transportation. Summary of the Invention

[0004] The purpose of this application is to provide a vehicle weighing intelligent monitoring method and system, electronic equipment, and storage medium to improve the accuracy of vehicle weighing monitoring.

[0005] A first aspect of an embodiment of the present application provides a method for intelligently monitoring vehicle weighing, comprising:

[0006] In response to a vehicle entering a target weighing area, obtaining vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to a plurality of scales;

[0007] determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; data in the vehicle weighing sequence data other than the first weighing data is second weighing data; and reliability of the first weighing data is higher than reliability of the second weighing data;

[0008] The first weighing data is subjected to denoising processing based on the vehicle travel speed to obtain denoised target weighing data; and the vehicle weight is determined based on the target weighing data.

[0009] A second aspect of the embodiments of the present application provides an intelligent vehicle weighing monitoring system, comprising:

[0010] A data acquisition module, configured to acquire vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to a plurality of scales in response to a vehicle entering a target weighing area;

[0011] a data screening module, configured to determine first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; data in the vehicle weighing sequence data other than the first weighing data is second weighing data; and reliability of the first weighing data is higher than reliability of the second weighing data;

[0012] The weight calculation module is configured to perform denoising on the first weighing data based on the vehicle's travel speed to obtain denoised target weighing data; and determine the vehicle weight based on the target weighing data.

[0013] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned vehicle weighing intelligent monitoring method when executing the computer program.

[0014] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned vehicle weighing intelligent monitoring method are implemented.

[0015] The beneficial effects of the intelligent vehicle weighing monitoring method and system, electronic device, and storage medium provided in the embodiments of the present application are as follows: by acquiring multi-dimensional information such as vehicle size, driving position, and speed, the embodiments of the present application can accurately filter out highly reliable first weighing data from multiple scale sequence data based on vehicle size and driving position, avoiding interference from invalid data and thereby improving the accuracy of data calculations. Furthermore, the embodiments of the present application perform targeted denoising on reliable data in combination with vehicle driving speed, effectively eliminating data noise caused by fluctuations in vehicle driving conditions and significantly improving the accuracy and reliability of weighing data.

[0016] In summary, the embodiments of the present application realize intelligent screening and noise reduction of weighing data, can accurately determine vehicle weight, reduce measurement errors, improve weighing efficiency and weighing accuracy, and better meet the needs of high-precision weighing in scenarios such as logistics and transportation, and traffic law enforcement. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 A flow chart of a vehicle weighing intelligent monitoring method provided in one embodiment of the present application;

[0019] Figure 2 This is a structural block diagram of a vehicle weighing intelligent monitoring system provided in one embodiment of the present application;

[0020] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0022] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for intelligent monitoring of vehicle weighing provided in one embodiment of the present application. The method can be executed by an electronic device. Specifically, the method can include S101 to S103.

[0024] S101: In response to a vehicle entering a target weighing area, obtaining vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to a plurality of scales.

[0025] In this embodiment, the target weighing area refers to a pre-defined road section containing weighing equipment (scales). The boundaries of this target weighing area can be defined using ground sensors, infrared beam sensors, or video recognition technology. When a vehicle's front wheels trigger detection equipment at the entrance to the target weighing area, this embodiment determines that the vehicle has entered the target weighing area and initiates the data collection process.

[0026] Vehicle dimensional information, including length, width, height, front and rear axle spacing, left and right wheel spacing, and number of axles, is used to determine vehicle type and its compatibility with the scale array. Vehicle dimensional information can be obtained by installing lidar sensors on both sides of the weighing area to scan the vehicle's profile in real time and calculate its three-dimensional dimensions and wheelbase. Alternatively, a high-definition camera can be used to capture the vehicle's side profile, using image recognition algorithms to analyze the vehicle model and match it to a pre-set dimensional library.

[0027] Vehicle position refers to the vehicle's lateral (centering) and longitudinal (front / rear wheels on the scale) coordinates within the target weighing area. Vehicle position can be determined by placing multiple ground-sensing coils on and around the scale surface, locating the vehicle's wheel axle position by triggering different coils in combination. Alternatively, pre-installed lateral radar can be used to detect the vehicle's lateral offset, combined with the triggering sequence of the longitudinal ground-sensing coils to determine whether the vehicle is stationary or moving at a constant speed as it passes through the scale.

[0028] Vehicle speed can be acquired by continuously capturing images of the vehicle's movement with a camera and calculating the instantaneous speed using optical flow or feature point tracking algorithms. Vehicle weighing sequence data corresponding to multiple scales refers to weight data collected chronologically by distributed scales as vehicles pass through. Each data set includes a timestamp, scale ID, and real-time weight value.

[0029] This embodiment acquires vehicle dynamic parameters in real time through multi-sensor fusion, providing a basis for subsequent screening of highly reliable weighing data.

[0030] For example, when the front wheels of a vehicle pass over the entrance ground sensor coil, this embodiment can determine that the vehicle has entered the target area, immediately activate the lidar, camera, radar and other detection equipment, synchronously collect the vehicle size, position, speed, and trigger all scales to start real-time sampling. This embodiment can use the hardware clock to ensure that the size, position, speed and the timestamp of the scale data are consistent to avoid data misalignment. This embodiment can use the lidar point cloud data to fit the vehicle contour and calculate the vehicle's length, width, height and wheelbase and other dimensional information; this embodiment can calculate the driving speed by the triggering sequence and time difference of the ground sensor coil; this embodiment can record the vehicle's driving position in real time based on the camera, such as the coordinates of all wheels during driving. This embodiment can cache the weight data of all scales in chronological order.

[0031] S102: Determine first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; data in the vehicle weighing sequence data other than the first weighing data is second weighing data; and reliability of the first weighing data is higher than reliability of the second weighing data.

[0032] In this embodiment, the first weighing data is determined from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position, which can specifically include: determining the target weighing area based on the vehicle size information and the vehicle driving position; and using the vehicle weighing sequence data corresponding to the scale in the target weighing area as the first weighing data.

[0033] In this embodiment, the first weighing data refers to the highly reliable weighing data collected by the scale when the vehicle is within the target weighing area. This high reliability is reflected in the fact that the target weighing area is a dynamically defined effective weighing range based on vehicle size and driving position, and is used to select highly reliable data when the vehicle's axle completely covers the scale's core load-bearing area.

[0034] Secondary weighing data refers to less reliable weighing data collected from scales outside the target weighing area. Scales outside this target weighing area are not effectively covered by the vehicle, for example, due to the axle being only partially pressed or the vehicle body having excessive lateral deflection. This can cause the data to contain interference from eccentric load components and edge effects, making it inaccurately reflecting the vehicle's actual weight. For example, for scales outside the target weighing area, the vehicle's axle may only press against the edge of the scale or be far away. In this case, the data is unreliable due to uneven force distribution.

[0035] In this embodiment, the scale's measurement accuracy is directly related to the axle's coverage during vehicle weighing. When the axle is fully pressed against the scale's core area, the force applied to each sensor is even, resulting in highly stable data. Excessive lateral displacement of the axle can lead to data interference due to uneven force distribution or the introduction of lateral force components. Therefore, this embodiment dynamically defines the target weighing area based on vehicle size and driving position, enabling precise selection of highly reliable data.

[0036] Illustratively, this embodiment can pre-store the horizontal coordinate ranges of all scales, traverse the scales in the divided target weighing area, filter out the scales whose horizontal coordinate ranges fall within the target weighing area, and mark the data collected by them as the first weighing data; the data collected by the remaining scales are marked as the second weighing data.

[0037] Scales within the target weighing area are completely covered by the vehicle's axles, receiving even force without unbalanced loads, and the data collected accurately reflects the weight. Scales outside the target weighing area, however, experience uneven force due to the axles pressing against their edges or being excessively offset, leading to interference and unreliable data. This process enables highly reliable and accurate data screening.

[0038] S103: Denoising the first weighing data based on the vehicle's travel speed to obtain denoised target weighing data; and determining the vehicle weight based on the target weighing data.

[0039] In this embodiment, the first weighing data is denoised based on the vehicle speed to obtain target weighing data, which may specifically include: determining a noise threshold based on the vehicle speed; the vehicle speed is positively correlated with the noise threshold; and denoising the first weighing data based on the noise threshold.

[0040] In this embodiment, the noise threshold is a critical value used to measure the rationality of the fluctuation of the weighing data. The value of the noise threshold is positively correlated with the speed and is used to distinguish between real dynamic load and random noise.

[0041] Consider that vehicle speed affects the dynamic load characteristics during weighing. For example, at low speeds, the vehicle's contact with the scale is stable, resulting in minimal data fluctuations. At high speeds, vibrations and impacts can naturally cause greater data fluctuations. For example, in a scenario where a vehicle is traveling at a slow, steady speed, passing the scale, the wheel axle maintains contact with the scale for a long time, and the weight data should be smooth and stable. Small fluctuations in this situation are likely due to sensor noise or minor road bumps.

[0042] Exemplarily, this embodiment can establish a mapping relationship between vehicle speed and noise threshold. Specifically, this embodiment can use historical weighing data to count the reasonable fluctuation range within different vehicle speed ranges. For example: when the speed is lower than 5km / h, the normal weight fluctuation range is ±3%, and the noise threshold can be set to 3%; when the speed is between 5-10km / h, the normal weight fluctuation range is ±5%, and the noise threshold can be set to 5%; when the speed is higher than 10km / h, the normal weight fluctuation range is ±8%, and the noise threshold can be set to 8%.

[0043] During the actual measurement process, the present embodiment can continuously capture the moving images of the vehicle through a camera, calculate the displacement difference between adjacent frames using a feature point tracking algorithm, and calculate the instantaneous speed of the vehicle in combination with the frame rate. The present embodiment can divide the first weighing data into continuous windows according to time, and calculate the average weight in each window. The present embodiment can calculate the deviation rate of each data point in the window from the reference value. If the deviation rate is greater than the noise threshold corresponding to the current speed, it is determined to be a noise point. After eliminating the noise points, the present embodiment can use the adjacent point interpolation method to fill in the gaps to ensure data continuity.

[0044] As can be seen from the above, this embodiment, by acquiring multi-dimensional information such as vehicle size, driving position, and speed, can accurately filter out highly reliable first weighing data from multiple scale sequence data based on vehicle size and driving position, avoiding interference from invalid data and thereby improving data calculation accuracy. Furthermore, this embodiment performs targeted denoising on reliable data based on vehicle driving speed, effectively eliminating data noise caused by fluctuations in vehicle driving conditions and significantly improving the accuracy and reliability of weighing data.

[0045] In summary, this embodiment realizes the intelligent screening and noise reduction of weighing data, can accurately determine the vehicle weight, reduce measurement errors, improve weighing efficiency and weighing accuracy, and better meet the needs of high-precision weighing in scenarios such as logistics and transportation, and traffic law enforcement.

[0046] In one embodiment of the present application, before obtaining vehicle weighing sequence data corresponding to a plurality of scales, the method further includes: obtaining a first wheel position and a first driving direction of the vehicle when the vehicle enters a target weighing area, and predicting a wheel driving route based on the first wheel position and the first driving direction;

[0047] dividing the plurality of scales into a first scale set and a second scale set based on the wheel travel route, and adjusting the data collection frequency of the scales in the first scale set to obtain a first collection frequency;

[0048] The vehicle weighing sequence data corresponding to multiple scales include vehicle weighing sequence data corresponding to a first set of scales collected based on a first collection frequency and vehicle weighing sequence data corresponding to a second set of scales collected based on a second collection frequency; the second collection frequency is the initial collection frequency of multiple scales.

[0049] In this embodiment, the data collection frequency of the first centralized weighing scale is adjusted, which may specifically include: obtaining the first driving speed of the vehicle when entering the target weighing area; adjusting the data collection frequency of the first centralized weighing scale based on the first driving speed and vehicle size information to obtain the first collection frequency; the first driving speed and vehicle size information are both positively correlated with the first collection frequency.

[0050] In this embodiment, adjusting the data collection frequency of the first centralized weighing scale based on the first driving speed and the vehicle size information to obtain the first collection frequency may specifically include: adjusting the data collection frequency of the first centralized weighing scale based on the first driving speed and the vehicle size information using a first formula to obtain the first collection frequency;

[0051] The first formula is: ;

[0052] in, is the first acquisition frequency, is the second acquisition frequency, is the preset frequency gradient, and is the weight coefficient, , is the vehicle speed, is the preset vehicle speed reference value, is the vehicle size information, is the preset vehicle size information reference value, The preset minimum acquisition frequency.

[0053] In this embodiment, This is a speed correction term. The higher the vehicle speed, the shorter the contact time between the vehicle and the scale, and the stronger the dynamic fluctuation of the data. The frequency needs to be increased to capture instantaneous weight changes. This is a size correction term. The larger the vehicle size, the more axles the wheels cover and the more complex the weight distribution. This requires a higher frequency to record multi-axis dynamic loads. The max function is used to set a lower limit on the data acquisition frequency. Constraints are set to avoid low frequencies due to low speeds and small vehicles, thus preventing key data from being missed. The first formula is used to calculate the specific acquisition frequency, where all parameters are dimensionless.

[0054] For example, in a logistics freight scenario, a heavy truck with a size of 12 enters the weighing area at a speed of 20 km / h. The preset parameters are known: =100Hz, =50Hz, =0.6, =0.4, =10km / h, =6, = 80Hz. Substituting the above parameter values ​​into the formula, the speed correction term is: 0.6 × 50 × (20-10) / 10 = 30, the size correction term is: 0.4 × 50 × (12-6) / 6 = 20, and f = max (100 + 30 + 20, 80) = 150. Therefore, the final adjusted first acquisition frequency is 150Hz.

[0055] In this embodiment, the first wheel position refers to the initial coordinates of the front wheels (or the lead axle wheels) when the vehicle enters the target weighing area. The first driving direction refers to the vehicle's direction of travel upon entry. For example, if the angle is 5° with the centerline of the scale array, the first driving direction represents the vehicle's direction of travel. The wheel path refers to the predicted wheel motion trajectory based on the initial position and direction, and is used to determine which scales the wheel will pass through. The first scale set refers to the scales along the predicted wheel path that will be covered by the wheel. For example, if the front wheel will pass through scales 1 and 2, scales 1 and 2 require high-frequency sampling to capture critical weight data. The second scale set refers to the scales outside the predicted wheel path. For example, if the rear wheel will not pass through scale 3, scale 3 will maintain its initial low-frequency sampling to reduce load. The first acquisition frequency refers to the high-frequency sampling rate that is dynamically adjusted based on vehicle speed and vehicle size information. The faster the vehicle speed and the larger the vehicle size, the higher the first acquisition frequency.

[0056] In this embodiment, only a portion of the scales are actually covered by the wheels during vehicle weighing. The weight data from these scales is crucial for calculating the total weight; the data from the uncovered scales has minimal impact on the result. By predicting wheel paths, identifying key scales (the first set of scales) in advance and adjusting their sampling frequency based on vehicle speed and dimensions, this approach reduces computational and storage requirements while ensuring critical data accuracy.

[0057] Exemplarily, a ground sensor coil array is installed at the entrance of the weighing area. When the front wheel of the vehicle passes over the ground sensor coil, this embodiment can determine the initial lateral position by the number of the triggered coil, such as triggering coil No. 10, corresponding to X=2 meters; the longitudinal position can be inferred by the timestamp captured by the entrance camera and the length of the vehicle, such as the longitudinal Y=0 meter of the front wheel when the vehicle enters. This embodiment can use the entrance camera at the entrance to capture the contours of both sides of the vehicle, calculate the angle of the line connecting the center of the front wheel and the center of the rear wheel, such as the front wheel center (X1, Y1), the rear wheel center (X2, Y2), and the direction angle θ=arctan((Y2-Y1) / (X2-X1))), and judge whether the vehicle is going straight or deviating based on these two coordinates and the direction angle.

[0058] This embodiment can assume that the vehicle maintains a constant speed in its current direction to obtain a predicted wheel travel path. This embodiment can pre-store the horizontal and vertical coordinate ranges of all scales, traverse all scales, and determine whether their longitudinal ranges intersect with the longitudinal movement range of the wheel travel path. If the horizontal range of a scale overlaps the predicted horizontal position of the wheel travel path by more than 50% (a preset threshold), the scale is assigned to the first scale set; the remaining scales are assigned to the second scale set.

[0059] In this embodiment, a base frequency (second acquisition frequency) of 100 Hz can be preset. Based on the second acquisition frequency, the frequency is increased by 50 Hz for every 5 km / h increase in vehicle speed. The gross vehicle volume is calculated based on vehicle dimensions, and the frequency is increased by 30 Hz for every 1 cubic meter increase in gross vehicle volume, ultimately resulting in the first acquisition frequency. In this embodiment, the adjusted first acquisition frequency can be sent to the scale control center in the first scale set to control its acquisition frequency. The second scale set continues sampling at the initial low frequency.

[0060] This embodiment divides the scale sets by predicting the wheel travel routes, and dynamically increases the collection frequency of key scales (the first scale set) based on vehicle speed and size. This can accurately capture the instantaneous weight changes and multi-axle load distribution of high-speed vehicles, avoiding missing key data; non-critical scales (the second scale set) maintain low-frequency sampling to reduce invalid data.

[0061] In one embodiment of the present application, a target weighing area is determined based on vehicle size information and a vehicle driving position, including: if the vehicle size information is greater than or equal to a first size threshold, an area of ​​a first spacing is drawn with the vehicle driving position as the center in a direction perpendicular to the vehicle driving direction as the target weighing area; if the vehicle size information is less than the first size threshold, an area of ​​a second spacing is drawn with the vehicle driving position as the center in a direction perpendicular to the vehicle driving direction as the target weighing area; the first spacing is greater than the second spacing.

[0062] In this embodiment, the first size threshold is a preset vehicle type distinction threshold value used to determine whether the vehicle is a large vehicle or a small vehicle. The first spacing and the second spacing are lateral distances extending to the left and right sides with the driving position as the center.

[0063] In this embodiment, when a vehicle is weighed, reliable weighing data can only be obtained when the axle completely covers the core area of ​​the scale. The difference in wheelbase of vehicles of different sizes results in different coverage of the scale. Large vehicles have wide wheelbases, requiring a larger area to ensure that both axles are effectively stressed at the same time; small vehicles have narrow wheelbases, so a smaller area can meet the needs. This embodiment obtains real-time position and size information based on the vehicle's traveled route, dynamically delineates the target weighing area, and can accurately filter out data collected when the axle is completely in the core stress area of ​​the scale, avoiding eccentric load errors caused by partial coverage or line pressing of the axle, and improving data reliability.

[0064] For example, this embodiment can use a ground sensor coil array to obtain the vehicle's real-time position and a lidar scanner to scan the vehicle's side profile to calculate the wheelbase. This embodiment can compare the wheelbase with a first size threshold. If it is greater than or equal to the first size threshold, a region is vertically delineated at a first spacing, centered on the current driving position. If it is less than the first size threshold, a region is vertically delineated at a second spacing. Finally, based on the coordinate range of each scale, scales that are completely or mostly (e.g., 60%) within the target area are selected, and their corresponding weighing sequence data is marked as the first weighing data for subsequent precision weighing.

[0065] This embodiment dynamically delineates the target weighing area based on vehicle size, precisely adapting to the wheelbase differences between large and small vehicles, and avoiding eccentric loading errors caused by incomplete axle coverage. By filtering scale data from the core load-bearing area, this embodiment effectively eliminates invalid interference. Compared to traditional fixed-area data collection, this improves data reliability, significantly enhances the accuracy and stability of weighing results, and reduces manual review costs.

[0066] In one embodiment of the present application, the vehicle weight is determined based on the target weighing data, including: extracting statistical features of the target weighing data; dividing the target weighing data into multiple weighing sequence subsets based on the scale number; the weighing sequence subsets correspond to the scale numbers one-to-one; determining the weight coefficient matrix corresponding to the multiple weighing sequence subsets based on the vehicle speed and the scale position; and performing weighted calculation on the multiple weighing sequence subsets based on the weight coefficient matrix to obtain the vehicle weight.

[0067] In this embodiment, the target weighing data is a highly reliable weight sequence that has been screened and contains the real-time measurements of each scale during the vehicle weighing period. Statistical features are parameters that reflect data quality, such as mean (average weight), variance (data fluctuation range), and effective data ratio (the proportion of available data after noise removal), which are used to evaluate the stability and reliability of single scale data. The weighing sequence subsets are independent data groups divided by scale number, such as scale subset No. 1 and scale subset No. 2. Each group contains all valid data of the scale during the vehicle weighing period. The weight coefficient matrix is ​​a weighting coefficient determined by the vehicle's driving speed and scale position, such as the front axle scale weight is 0.2, the middle axle weight is 0.3, and the rear axle weight is 0.5. The weight coefficient matrix is ​​used to quantify the contribution and reliability of different scale data to the total weight.

[0068] In this embodiment, the gross vehicle weight is calculated by adding up the weights of each axle. The reliability, dynamic characteristics, and location importance of scale data for different axles vary. By extracting data features to assess reliability, focusing on single-axle data by scale group, and dynamically assigning weights based on speed and scale position, this weighted synthesis of all axle data allows for highly reliable, high-contribution data-driven gross weight calculations, improving measurement accuracy.

[0069] Exemplarily, this embodiment can combine the vehicle type (such as a three-axle truck) and the scale location (front / middle / rear axles corresponding to scales) to mark the vehicle axles corresponding to each weighing sequence subset in different time periods. For example, scale No. 1 includes weighing data for three time periods, wherein the weighing data for the three time periods correspond to the front axle, the middle axle, and the rear axle, respectively.

[0070] This embodiment can average the continuous measurement values ​​of each scale to reflect the scale's stable load-bearing capacity. This embodiment can also calculate the amplitude of data fluctuations; the smaller the variance, the higher the reliability. This embodiment can also split the target weighing data into independent subsets based on the scale number, such as scale 1 subset: [1000, 1005, 998], scale 2 subset: [1500, 1510, 1495].

[0071] Considering that faster vehicle speeds (e.g., 20 km / h) result in greater data fluctuations, this embodiment prioritizes weighting subsets of weighing sequences with smaller variances. For example, scale 1, with a variance of 5, is assigned a weight of 0.4, while scale 2, with a variance of 20, is assigned a weight of 0.2. This embodiment multiplies the mean of each subset by the corresponding weight and sums the weighted values ​​of all subsets to obtain the final vehicle weight.

[0072] This implementation evaluates reliability by extracting statistical features from the data, focusing on single-axle data by scale group, and dynamically assigning weights based on vehicle speed and scale position, achieving highly reliable data-driven gross weight calculation. This implementation significantly improves weighing accuracy, reduces noise interference, adapts to different vehicle speeds and vehicle types, ensures appropriate weighting of each axle data, and makes the final weight calculation more accurate and reliable, making it suitable for a variety of weighing scenarios.

[0073] Corresponding to the vehicle weighing intelligent monitoring method of the above embodiment, Figure 2 This is a structural block diagram of a vehicle weighing intelligent monitoring system provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 The vehicle weighing intelligent monitoring system 20 includes: a data acquisition module 21, a data screening module 22 and a weight calculation module 23.

[0074] The data acquisition module 21 is used to acquire vehicle size information, vehicle driving position, vehicle driving speed and vehicle weighing sequence data corresponding to multiple scales in response to the vehicle entering the target weighing area;

[0075] The data screening module 22 is configured to determine first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; the data in the vehicle weighing sequence data other than the first weighing data is the second weighing data; and the reliability of the first weighing data is higher than the reliability of the second weighing data;

[0076] The weight calculation module 23 is configured to perform denoising on the first weighing data based on the vehicle's travel speed to obtain denoised target weighing data; and determine the vehicle weight based on the target weighing data.

[0077] In one embodiment of the present application, the vehicle weighing intelligent monitoring system 20 further includes: a data acquisition frequency adjustment module for obtaining a first wheel position and a first driving direction of the vehicle when the vehicle enters a target weighing area, and predicting a wheel driving route based on the first wheel position and the first driving direction;

[0078] dividing the plurality of scales into a first scale set and a second scale set based on the wheel travel route, and adjusting the data collection frequency of the scales in the first scale set to obtain a first collection frequency;

[0079] The vehicle weighing sequence data corresponding to multiple scales include vehicle weighing sequence data corresponding to a first set of scales collected based on a first collection frequency and vehicle weighing sequence data corresponding to a second set of scales collected based on a second collection frequency; the second collection frequency is the initial collection frequency of multiple scales.

[0080] In one embodiment of the present application, the data acquisition frequency adjustment module is specifically used to obtain a first driving speed of the vehicle when entering a target weighing area;

[0081] Adjusting a data collection frequency of a first centralized weighing scale based on the first driving speed and the vehicle size information to obtain a first collection frequency;

[0082] The first driving speed and the vehicle size information are both positively correlated with the first collection frequency.

[0083] In one embodiment of the present application, the data screening module 22 is specifically configured to determine a target weighing area based on vehicle size information and vehicle driving position;

[0084] The vehicle weighing sequence data corresponding to the scale in the target weighing area is used as the first weighing data.

[0085] In one embodiment of the present application, the data screening module 22 is further configured to, if the vehicle size information is greater than or equal to a first size threshold, demarcate an area of ​​a first spacing in a direction perpendicular to the vehicle's driving direction, centered on the vehicle's driving position, as a target weighing area;

[0086] If the vehicle size information is smaller than the first size threshold, an area of ​​a second spacing is drawn in a direction perpendicular to the vehicle's travel direction, centered on the vehicle's travel position, as the target weighing area;

[0087] The first spacing is greater than the second spacing.

[0088] In one embodiment of the present application, the weight calculation module 23 is specifically configured to determine a noise threshold based on a vehicle speed; the vehicle speed is positively correlated with the noise threshold; and perform denoising on the first weighing data based on the noise threshold.

[0089] In one embodiment of the present application, the weight calculation module 23 is further configured to extract statistical features of the target weighing data; divide the target weighing data into a plurality of weighing sequence subsets based on the scale number; and the weighing sequence subsets correspond one-to-one to the scale number;

[0090] Determine a weight coefficient matrix corresponding to a plurality of weighing sequence subsets based on vehicle speed, vehicle size information, and scale location;

[0091] The vehicle weight is obtained by performing weighted calculation on multiple weighing sequence subsets based on the weight coefficient matrix.

[0092] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned system embodiments, such as Figure 2 The functions of the data acquisition module 21, the data screening module 22 and the weight calculation module 23 are shown.

[0093] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0094] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0095] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store vehicle information.

[0096] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the embodiment of the vehicle weighing intelligent monitoring method provided in the embodiment of the present application, and can also execute the implementation method of the electronic device 300 described in the embodiment of the present application, which will not be repeated here.

[0097] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0098] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0099] Those skilled in the art will appreciate that the modules / units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0100] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules, units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or modules / units, or it can be an electrical, mechanical or other form of connection.

[0102] Modules / units described as separate components may or may not be physically separate, and components displayed as modules / units may or may not be physical modules / units, that is, they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0103] In addition, the functional modules / units in the various embodiments of the present application may be integrated into a single processing module / unit, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit. The aforementioned integrated modules / units may be implemented in the form of hardware or software functional modules / units.

[0104] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A vehicle weighing intelligent monitoring method, characterized in that: include: In response to a vehicle entering a target weighing area, obtaining vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to a plurality of scales; determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; data in the vehicle weighing sequence data other than the first weighing data is second weighing data; and reliability of the first weighing data is higher than reliability of the second weighing data; performing denoising processing on the first weighing data based on the vehicle speed to obtain denoised target weighing data; determining a vehicle weight based on the target weighing data; Before obtaining the vehicle weighing sequence data corresponding to multiple scales, it also includes: Acquiring a first wheel position and a first driving direction of the vehicle when the vehicle enters a target weighing area, and predicting a wheel driving route based on the first wheel position and the first driving direction; dividing the plurality of scales into a first scale set and a second scale set based on the wheel travel route, obtaining a first travel speed of the vehicle when entering a target weighing area; adjusting a data collection frequency of the scales in the first scale set based on the first travel speed and the vehicle size information to obtain a first collection frequency; the first travel speed and the vehicle size information are both positively correlated with the first collection frequency; The vehicle weighing sequence data corresponding to the multiple scales include the vehicle weighing sequence data corresponding to the first set of scales collected based on the first collection frequency and the vehicle weighing sequence data corresponding to the second set of scales collected based on the second collection frequency; the second collection frequency is the initial collection frequency of the multiple scales.

2. The vehicle weighing intelligent monitoring method according to claim 1, characterized in that: The determining the first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position includes: determining a target weighing area based on the vehicle size information and the vehicle driving position; The vehicle weighing sequence data corresponding to the scale in the target weighing area is used as the first weighing data.

3. The vehicle weighing intelligent monitoring method according to claim 2, characterized in that: The determining of the target weighing area based on the vehicle size information and the vehicle driving position includes: If the vehicle size information is greater than or equal to a first size threshold, an area with a first spacing centered on the vehicle's driving position and perpendicular to the vehicle's driving direction is drawn as the target weighing area; If the vehicle size information is smaller than the first size threshold, an area of ​​a second spacing is drawn in a direction perpendicular to the vehicle's driving direction with the vehicle's driving position as the center, as the target weighing area; The first spacing is greater than the second spacing.

4. The vehicle weighing intelligent monitoring method according to claim 1, characterized in that: Denoising the first weighing data based on the vehicle speed to obtain target weighing data includes: Determining a noise threshold based on the vehicle speed; wherein the vehicle speed is positively correlated with the noise threshold; The first weighing data is subjected to denoising processing based on the noise threshold.

5. The vehicle weighing intelligent monitoring method according to claim 1, characterized in that: Determining the vehicle weight based on the target weighing data includes: Extracting statistical features of the target weighing data; dividing the target weighing data into a plurality of weighing sequence subsets based on the weighing scale number; wherein the weighing sequence subsets correspond to the weighing scale numbers in a one-to-one manner; Determining weight coefficient matrices corresponding to the plurality of weighing sequence subsets based on the vehicle speed and the scale position; The plurality of weighing sequence subsets are weightedly calculated based on the weight coefficient matrix to obtain the vehicle weight.

6. A vehicle weighing intelligent monitoring system, characterized in that: include: A data acquisition module, configured to acquire vehicle size information, vehicle driving position, vehicle driving speed, and vehicle weighing sequence data corresponding to a plurality of scales in response to a vehicle entering a target weighing area; a data screening module, configured to determine first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position; data in the vehicle weighing sequence data other than the first weighing data is second weighing data; and reliability of the first weighing data is higher than reliability of the second weighing data; a weight calculation module, configured to perform denoising processing on the first weighing data based on the vehicle's travel speed to obtain denoised target weighing data; determining a vehicle weight based on the target weighing data; a data acquisition frequency adjustment module, configured to obtain a first wheel position and a first driving direction of a vehicle when the vehicle enters a target weighing area, and predict a wheel driving route based on the first wheel position and the first driving direction; Dividing the plurality of scales into a first scale set and a second scale set based on the wheel travel route, obtaining a first travel speed of the vehicle when entering a target weighing area; and adjusting a data collection frequency of the scales in the first scale set based on the first travel speed and the vehicle size information to obtain a first collection frequency; The first driving speed and the vehicle size information are both positively correlated with the first acquisition frequency; The vehicle weighing sequence data corresponding to the multiple scales include the vehicle weighing sequence data corresponding to the first set of scales collected based on the first collection frequency and the vehicle weighing sequence data corresponding to the second set of scales collected based on the second collection frequency; the second collection frequency is the initial collection frequency of the multiple scales.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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