CN120445374B - Intelligent vehicle weighing monitoring method and system, electronic equipment and storage medium - Google Patents
Intelligent vehicle weighing monitoring method and system, electronic equipment and storage mediumInfo
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- CN120445374B CN120445374B CN202510960772.8A CN202510960772A CN120445374B CN 120445374 B CN120445374 B CN 120445374B CN 202510960772 A CN202510960772 A CN 202510960772A CN 120445374 B CN120445374 B CN 120445374B
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Abstract
The application provides a vehicle weighing intelligent monitoring method and system, electronic equipment and a storage medium, and belongs to the technical field of vehicle intelligent monitoring; the method comprises the steps of determining first weighing data from vehicle weighing sequence data based on vehicle size information and vehicle running positions, wherein data except the first weighing data in the vehicle weighing sequence data are second weighing data, reliability of the first weighing data is higher than that of the second weighing data, denoising the first weighing data based on vehicle running speeds to obtain denoised target weighing data, and determining vehicle weight based on the target weighing data. The intelligent vehicle weighing monitoring method and system, the electronic equipment and the storage medium can improve the accuracy of vehicle weighing monitoring.
Description
Technical Field
The application belongs to the technical field of intelligent monitoring of vehicles, and particularly relates to an intelligent monitoring method and system for weighing a vehicle, electronic equipment and a storage medium.
Background
Along with the rapid development of the logistics transportation industry, the weighing of vehicles is used as a key link of scenes such as cargo metering, traffic law enforcement and the like, and the accuracy and the reliability of symmetrical weight data are required to be higher.
At present, a common vehicle weighing monitoring system mainly collects vehicle weighing data through a plurality of scales and performs simple analysis processing on the data. However, in practical application, the running states of the vehicle in the weighing process are different, which can cause noise and interference in the acquired weighing data to influence the accuracy of the weighing result. Meanwhile, the positions of the scales are fixed, the reliability of final weighing data is difficult to ensure, and a large error exists in a vehicle weight measurement result, so that the requirement of industries such as logistics transportation and the like on accurate weighing cannot be met.
Disclosure of Invention
The application aims to provide a vehicle weighing intelligent monitoring method and system, electronic equipment and a storage medium, so as to improve the accuracy of vehicle weighing monitoring.
In a first aspect of an embodiment of the present application, there is provided a method for intelligently monitoring weighing of a vehicle, including:
acquiring vehicle size information, vehicle driving positions, vehicle driving speeds and vehicle weighing sequence data corresponding to a plurality of scales in response to the vehicle entering a target weighing area;
Determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle running position, wherein data except the first weighing data in the vehicle weighing sequence data are second weighing data, and the reliability of the first weighing data is higher than that of the second weighing data;
denoising the first weighing data based on the vehicle running speed to obtain denoised target weighing data, and determining the vehicle weight based on the target weighing data.
In a second aspect of the embodiment of the present application, there is provided a vehicle weighing intelligent monitoring system, including:
The data acquisition module is used for acquiring vehicle size information, vehicle running positions, vehicle running speeds and vehicle weighing sequence data corresponding to a plurality of scales in response to the vehicle entering the target weighing area;
The data screening module is used for determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position, wherein the data except the first weighing data in the vehicle weighing sequence data are second weighing data, and the reliability of the first weighing data is higher than that of the second weighing data;
the weight calculation module is used for denoising the first weighing data based on the vehicle running speed to obtain denoised target weighing data, and determining the weight of the vehicle based on the target weighing data.
In a third aspect of the embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the steps of the above-mentioned intelligent vehicle weighing monitoring method are implemented when the processor executes the computer program.
In a fourth aspect of the embodiments of the present application, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the vehicle weighing intelligent monitoring method described above.
The intelligent vehicle weighing monitoring method and system, the electronic equipment and the storage medium have the beneficial effects that the embodiment of the application can accurately screen the first weighing data with high reliability from the multi-scale sequence data based on the vehicle size, the driving position, the speed and other multidimensional information by acquiring the vehicle size, the driving position, the speed and other multidimensional information, so that invalid data interference is avoided, and the accuracy of data calculation is further improved. Meanwhile, the embodiment of the application carries out targeted denoising treatment on reliable data by combining the running speed of the vehicle, effectively eliminates data noise caused by fluctuation of the running state of the vehicle, and obviously improves the accuracy and reliability of weighing data.
In summary, the embodiment of the application realizes intelligent screening and noise reduction of weighing data, can accurately determine the weight of the vehicle, reduces measurement errors, improves weighing efficiency and weighing precision, and can better meet the requirements of high-precision weighing in scenes such as logistics transportation, traffic law enforcement and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for intelligently monitoring weighing of a vehicle according to an embodiment of the application;
FIG. 2 is a block diagram of a vehicle weighing intelligent monitoring system according to an embodiment of the application;
fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of a method for intelligently monitoring a weighing of a vehicle according to an embodiment of the application, where the method may be performed by an electronic device, and specifically, the method may include S101 to S103.
And S101, acquiring vehicle size information, vehicle driving positions, vehicle driving speeds and vehicle weighing sequence data corresponding to a plurality of scales in response to the vehicle entering the target weighing area.
In this embodiment, the target weighing area refers to a pre-defined specific road section containing a weighing device (scale). The boundary of the target weighing area may be defined by a ground sensing coil, an infrared correlation device or a video recognition technology, and when the front wheel of the vehicle triggers the detection device of the boundary entrance of the target weighing area, the embodiment may determine that the vehicle enters the target weighing area and start the data acquisition process.
The vehicle size information may include length, width, front-rear axle spacing, left-right wheel spacing, axle count, etc. of the vehicle for determining the type of vehicle and the matching relationship of the vehicle to the scale array. The vehicle size information can be obtained by installing laser radars on two sides of a weighing area, scanning the vehicle outline in real time, and calculating the three-dimensional size and wheelbase, or shooting a vehicle side image through a high-definition camera, analyzing the vehicle type by combining an image recognition algorithm, and matching with a preset size library.
The vehicle travel position refers to the lateral (whether centered) and longitudinal (whether front/rear wheels are on the scale) coordinates of the vehicle within the target weighing zone. The method for acquiring the running position of the vehicle can comprise paving a plurality of ground induction coils on the surface and the periphery of the platform scale, positioning the wheel axle position of the vehicle through triggering combination of different coils, or detecting the transverse offset of the vehicle through a pre-installed lateral radar, and determining whether the vehicle is stopped or passes through the platform scale at a constant speed by combining the triggering sequence of the longitudinal ground induction coils.
The method for acquiring the running speed of the vehicle can comprise the steps of continuously shooting a moving picture of the vehicle by using a camera, and calculating the instantaneous speed by an optical flow method or a characteristic point tracking algorithm. The vehicle weighing sequence data corresponding to the scales refer to weight data acquired by the distributed scales according to time sequence when the vehicle passes through, and each group of data comprises a time stamp, a scale number and a weight value measured in real time.
According to the embodiment, the dynamic parameters of the vehicle are obtained in real time through multi-sensor fusion, and a basis is provided for subsequent screening of high-reliability weighing data.
When the front wheel of the vehicle presses the entrance ground induction coil, the embodiment can judge that the vehicle enters the target area, immediately activate detection devices such as a laser radar, a camera, a radar and the like, synchronously acquire the size, the position and the speed of the vehicle, and trigger all scales to start real-time sampling. The embodiment can ensure that the size, the position and the speed are consistent with the time stamp of the platform scale data through the hardware clock, and avoid data dislocation. The method and the device can fit the vehicle outline by utilizing laser radar point cloud data, calculate the length, width, height, wheelbase and other size information of the vehicle, calculate the running speed by utilizing the ground induction coil triggering sequence and time difference, and record the running position of the vehicle in real time according to the camera, such as the coordinates of all wheels in the running process. The embodiment can buffer the weight data of all scales in time sequence.
And S102, determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position, wherein the data except the first weighing data in the vehicle weighing sequence data are second weighing data, and the reliability of the first weighing data is higher than that of the second weighing data.
In the embodiment, the first weighing data is determined from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position, and specifically the method comprises the steps of determining a target weighing area based on the vehicle size information and the vehicle driving position, and taking the vehicle weighing sequence data corresponding to the scales in the target weighing area as the first weighing data.
In this embodiment, the first weighting data refers to the high reliability weighting data collected by the corresponding scale when the vehicle is within the target weighing area. The high reliability is characterized in that the target weighing area is an effective weighing range dynamically defined based on the vehicle size and the driving position and is used for screening high-reliability data of the vehicle wheel axle which completely covers the core stress area of the platform scale.
The second weight data refers to low reliable weight data collected by the scale outside the target weighing area. The scales outside the target weighing area are not effectively covered by the vehicle, such as the wheel axle only partially presses the scales, the lateral deviation of the vehicle body is too large, and the like, which can cause the interference that data comprise unbalanced load components, edge effects and the like, and the actual weight of the vehicle cannot be accurately reflected. For example, for scales outside the target weighing area, the vehicle axle is only pressed to the edge of the scale or far away, and the data is unreliable due to uneven stress.
In the embodiment, when the vehicle is weighted, the measurement accuracy of the weight scale is directly related to the covering position of the wheel axle, namely, when the wheel axle is completely pressed on the core area of the weight scale, the sensors are uniformly stressed, the data stability is high, and if the wheel axle is laterally offset too much, the data can be disturbed due to uneven stress or introduction of lateral component force. Therefore, the embodiment dynamically delimits the target weighing area through the vehicle size and the driving position, and can accurately screen high-reliability data.
In an exemplary embodiment, the lateral coordinate ranges of all scales may be pre-stored, the scales are traversed in the divided target weighing areas, scales with the lateral coordinate ranges falling in the target weighing areas are screened out, the collected data of the scales are marked as first weighing data, and the collected data of the rest scales are marked as second weighing data.
The scales in the target weighing area are completely covered by the wheel axles of the vehicle, the stress is uniform and no unbalanced load exists, the acquired data can truly reflect the weight, and the scales outside the target weighing area are interfered due to the fact that the wheel axles are too tightly pressed at the edges or are too far away from each other, the stress is uneven, and the data are unreliable. Through the process, high-reliability data accurate screening is realized.
And S103, denoising the first weighing data based on the running speed of the vehicle to obtain denoised target weighing data, and determining the weight of the vehicle based on the target weighing data.
In the embodiment, denoising the first weighing data based on the vehicle running speed to obtain target weighing data, which specifically comprises determining a noise threshold based on the vehicle running speed, wherein the vehicle running speed is positively correlated with the noise threshold, and denoising the first weighing data based on the noise threshold.
In this embodiment, the noise threshold is a critical value for measuring the rationality of the fluctuation of the weighted data, and the value of the noise threshold is positively correlated with the speed, so as to distinguish the real dynamic load from the random noise.
The dynamic load characteristics of the vehicle when the vehicle is weighted are considered to be influenced by the running speed of the vehicle, for example, the vehicle is in stable contact with the weight scale at low speed and the data fluctuation is small, and at high speed, the natural fluctuation of the data is increased due to vibration and impact of the vehicle. For example, in a low-speed driving scene where the driving speed of the vehicle is lower than 5km/h, the vehicle slowly passes through the scale at a constant speed, the contact time between the wheel axle and the scale is long, the weight data should be smooth and stable, and the tiny fluctuation is mostly sensor noise or road micro-jolt.
For example, the present embodiment may establish a map of the vehicle running speed and the noise threshold. Specifically, the embodiment can count reasonable fluctuation ranges in different vehicle running speed intervals through historical weighting data. For example, the normal weight fluctuation range may be 3% when the speed is below 5km/h, the noise threshold may be 3%, the normal weight fluctuation range may be 5% when the speed is between 5 and 10km/h, the noise threshold may be 5%, and the normal weight fluctuation range may be 8% when the speed is above 10km/h, the noise threshold may be 8%.
In the actual measurement process, the embodiment can continuously shoot the moving picture of the vehicle through the camera, calculate the displacement difference between adjacent frames by utilizing the characteristic point tracking algorithm, and calculate the instantaneous speed of the vehicle in combination with the frame rate. The present embodiment may divide the first weighting data into successive windows in time and calculate a weight average within each window. In this embodiment, the deviation rate between each data point in the window and the reference value may be calculated, and if the deviation rate is greater than the noise threshold corresponding to the current speed, the data point is determined to be a noise point. After noise points are removed, the embodiment can fill the gaps by adopting an adjacent point interpolation method, so that the data continuity is ensured.
It can be obtained from the above that, in this embodiment, through obtaining multidimensional information such as a vehicle size, a driving position, and a speed, the first weighing data with high reliability can be accurately screened out from the multi-scale sequence data based on the vehicle size and the driving position, so that invalid data interference is avoided, and further, accuracy of data calculation is improved. Meanwhile, the embodiment carries out targeted denoising treatment on reliable data by combining the vehicle running speed, effectively eliminates data noise caused by fluctuation of the vehicle running state, and remarkably improves the accuracy and reliability of weighing data.
To sum up, this embodiment has realized the intelligent screening and the noise reduction of weighing data, can accurately confirm vehicle weight, reduces measuring error, promotes weighing efficiency and weighing accuracy, can satisfy the demand of scene such as commodity circulation transportation, traffic law enforcement to high accuracy weighing better.
In one embodiment of the application, before acquiring the vehicle weighing sequence data corresponding to the scales, the method further comprises the steps of acquiring a first wheel position and a first driving direction 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 a plurality of scales into a first scale set and a second scale set based on a wheel driving route, and adjusting the data acquisition frequency of the scales in the first scale set to obtain a first acquisition frequency;
The vehicle weighing sequence data corresponding to the scales comprises vehicle weighing sequence data corresponding to a first scale set acquired based on a first acquisition frequency and vehicle weighing sequence data corresponding to a second scale set acquired based on a second acquisition frequency, wherein the second acquisition frequency is the initial acquisition frequency of the scales.
In the embodiment, the data acquisition frequency of the first centralized scale is adjusted, which specifically includes acquiring a first running speed when the vehicle enters the target weighing area, adjusting the data acquisition frequency of the first centralized scale based on the first running speed and the vehicle size information to obtain a first acquisition frequency, and making the first running speed and the vehicle size information directly related to the first acquisition frequency.
In the embodiment, the data acquisition frequency of the first centralized scale is adjusted based on the first running speed and the vehicle size information to obtain a first acquisition frequency, which specifically includes adjusting the data acquisition frequency of the first centralized scale by using a first formula based on the first running speed and the vehicle size information to obtain the first acquisition frequency;
The first formula is: ;
Wherein, the For the first acquisition frequency of the first acquisition,For the second acquisition frequency of the first acquisition,Is a preset frequency gradient which is set to be equal to the frequency gradient,AndAs the weight coefficient of the light-emitting diode,,For the running speed of the vehicle,As the preset vehicle running speed reference value,As the information on the size of the vehicle,As the preset vehicle size information reference value,Is a preset minimum acquisition frequency.
In the present embodiment of the present invention, in the present embodiment,For the speed correction term, the higher the vehicle running speed, the shorter the contact time between the vehicle and the platform scale, the stronger the dynamic fluctuation of the data, and the frequency needs to be increased to capture the instantaneous weight change.For the size correction term, the larger the vehicle size, the more the axle number of the wheel cover scale and the more complex the weight distribution, the frequency needs to be increased to record the multiaxial dynamic load. Using a max function for lower limiting the data acquisition frequencyConstraint, low frequency caused by low speed and trolley is avoided, and missing of key data is prevented. The first formula is used for calculating specific acquisition frequencies, wherein parameters are dimensionless.
Illustratively, in a logistics freight scenario, a heavy truck with size information of 12 enters the weighing zone at a speed of 20 km/h. Preset parameters are known:=100Hz,=50Hz,=0.6,=0.4,=10km/h,=6, =80 Hz. The values of the above parameters are substituted into a formula to calculate that the speed correction term is 0.6x50× (20-10)/10=30, the size correction term is 0.4x50× (12-6)/6=20, and f=max (100+30+20, 80) =150. The final adjusted first acquisition frequency is thus 150Hz.
In this embodiment, the first wheel position refers to the initial coordinates of the front wheel (or the head axle wheel) when the vehicle enters the target weighing zone. The first driving direction refers to the driving direction angle of the vehicle when the vehicle enters, for example, an included angle of 5 degrees with the center line of the scale array, and the first driving direction represents the driving trend of the vehicle. The wheel travel path refers to a predicted wheel movement path based on the initial position and direction for determining which scales the wheels will pass. The first scale set refers to scales which are covered by wheels on a predicted wheel driving route, and if the front wheels pass through the No. 1 and No. 2 scales, the No. 1 and No. 2 scales need high-frequency sampling to capture key weight data. The second scale set refers to scales outside the predicted wheel travel path, and if the rear wheel does not pass through scale No. 3, the scale No. 3 maintains the initial low frequency sampling to reduce the load. The first acquisition frequency refers to a high-frequency sampling rate dynamically adjusted according to the vehicle running speed and the vehicle size information, and the higher the vehicle running speed is, the larger the vehicle size is, the higher the first acquisition frequency is.
In this embodiment, only part of the scales are actually covered by the wheels when the vehicle is weighted, the weight data of these scales are key to calculating the total weight, and the uncovered scale data have little effect on the result. By predicting the wheel route, the key scales (first scale set) are identified in advance, and the sampling frequency is adjusted according to the vehicle running speed and the vehicle size information, so that the calculation and storage pressure can be reduced while the accuracy of the key data is ensured.
For example, when the front wheel of the vehicle presses the ground sensing coil, the embodiment can determine the initial transverse position through the triggered coil number, for example, the triggered coil number 10 corresponds to x=2 meters, and the longitudinal position can be calculated through the timestamp shot by the entrance camera and the length of the vehicle, for example, the longitudinal y=0 meters of the front wheel when the vehicle enters. In this embodiment, the contour of both sides of the vehicle may be captured by the entrance camera at the entrance, the connection angle between the front wheel center and the rear wheel center may be calculated, such as the front wheel center (X1, Y1), the rear wheel center (X2, Y2), the direction angle θ=arctan ((Y2-Y1)/(X2-X1)), and whether the vehicle is straight or deviated may be determined according to the two coordinates and the direction angle.
The embodiment can default that the vehicle runs at a constant speed in the current direction to obtain the predicted wheel running route. The embodiment can pre-store the transverse and longitudinal coordinate ranges of all scales, traverse all scales, judge whether the longitudinal range of the scales intersects with the longitudinal movement range of the wheel driving route, and if the transverse range of the scales overlaps with the transverse predicted position of the wheel driving route by more than 50 percent (a preset threshold value), the scales are divided into a first scale set, and the rest scales are divided into a second scale set.
The method can preset the basic frequency (second acquisition frequency) to be 100Hz, wherein on the basis of the second acquisition frequency, the frequency is increased by 50Hz when the running speed of the vehicle is increased by 5km/h, the total volume of the vehicle is calculated according to the vehicle size information, and the frequency is increased by 30Hz when the total volume of the vehicle is increased by 1 cubic meter, so that the first acquisition frequency is finally obtained. The embodiment can send the adjusted first acquisition frequency to a scale control center in the first scale set to control the acquisition frequency, and the second scale set still samples at the initial low frequency.
According to the embodiment, the scale set is divided by predicting the driving route of the wheels, the acquisition frequency is dynamically increased based on the speed and the size of the key scale (the first scale set), the instantaneous weight change and the multi-axis load distribution of the high-speed cart can be accurately captured, key data are prevented from being missed, the non-key scale (the second scale set) is kept to be sampled at a low frequency, and invalid data are reduced.
In one embodiment of the application, determining the target weighing area based on the vehicle size information and the vehicle running position comprises marking an area with a first distance to the vertical direction of the vehicle running direction with the vehicle running position as the center and taking the vehicle running position as the center as the target weighing area, marking an area with a second distance to the vertical direction of the vehicle running direction with the vehicle running position as the center and taking the first distance as the target weighing area if the vehicle size information is smaller than the first size threshold.
In this embodiment, the first size threshold is a preset vehicle type distinguishing threshold for determining whether the vehicle belongs to a large vehicle or a small vehicle. The first pitch and the second pitch are lateral distances extending to the left and right sides with the running position as the center.
In this embodiment, the axle completely covers the scale core area when the vehicle is weighted to obtain reliable weighing data. The wheel track difference of vehicles with different sizes causes different ranges of the cover scales, the wheel track of a large-sized vehicle is wide, a large area is needed to ensure that the double wheel shafts are effectively stressed at the same time, and the wheel track of a small-sized vehicle is narrow, so that the small area can meet the requirements. According to the embodiment, the real-time position and the size information are acquired according to the running route of the vehicle, the target weighing area is dynamically defined, the data acquired when the wheel axle is completely positioned in the core stress area of the platform scale can be accurately screened, unbalanced load errors caused by partial coverage or line pressing of the wheel axle are avoided, and the data reliability is improved.
By way of example, the embodiment can acquire the running position of the vehicle in real time through the ground induction coil array, and calculate the track by scanning the side profile of the vehicle through the laser radar. The embodiment can compare the wheel track with the first size threshold, and if the wheel track is larger than or equal to the first size threshold, the wheel track is divided into areas in the vertical direction according to the first interval by taking the current running position as the center, and if the wheel track is smaller than the first size threshold, the wheel track is divided into areas according to the second interval. Finally, according to the coordinate range of each scale, the scales which are completely or mostly (for example, 60%) in the target area are screened, and the corresponding weighing sequence data are marked as first weighing data for subsequent accurate weighing.
According to the embodiment, the target weighing area is dynamically defined according to the vehicle size, the wheel tread difference of the large and small vehicle types is accurately adapted, and unbalanced load errors caused by incomplete wheel axle coverage are avoided. According to the embodiment, invalid interference is effectively removed by screening the weight scale data in the core stress area, compared with the traditional fixed area acquisition, the data reliability can be improved, the accuracy and stability of weighing results are obviously improved, and the manual rechecking cost is reduced.
In one embodiment of the application, determining the vehicle weight based on the target weighing data comprises extracting statistical characteristics 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 are in one-to-one correspondence with the weighing scale number, determining a weight coefficient matrix corresponding to the plurality of weighing sequence subsets based on the vehicle running speed and the weighing scale position, and performing weighted calculation on the plurality of weighing sequence subsets based on the weight coefficient matrix to obtain the vehicle weight.
In this embodiment, the target weight data is a high reliability weight sequence after screening, including real-time measurements of each scale during vehicle weighing. The statistical features are parameters reflecting the quality of the data, such as mean (average weight), variance (data fluctuation amplitude), effective data duty ratio (available data proportion after noise removal), for evaluating the stability and reliability of the single scale data. The subset of the weighing sequence is a separate data set, such as a subset of scales 1, a subset of scales 2, each set containing all valid data for the scale during the weighing of the vehicle, divided by scale number. The weight coefficient matrix is a weight coefficient determined by the running speed of the vehicle and the position of the scales, such as a front axle scale weight of 0.2, a middle axle of 0.3 and a rear axle of 0.5, and is used for quantifying the contribution degree and the reliability of different scale data to the total weight.
In this embodiment, the total weight of the vehicle is formed by overlapping weights of all axles, and the data reliability, dynamic characteristics and position importance of scales corresponding to different axles are different. The reliability is evaluated by extracting data characteristics, focusing single-axis data is grouped according to scales, weights are dynamically distributed according to the combination speed and the positions of the scales, and finally, all the axis data are weighted and synthesized, so that the total weight calculation of the dominant data with high reliability and high contribution is realized, and the measurement accuracy is improved.
For example, the present embodiment may combine a vehicle type (e.g., a three-axle truck) and a scale position (e.g., front/center/rear axle for a scale), and mark the vehicle axle corresponding to each subset of the weighing sequences during different time periods, e.g., scale 1, with three time periods of weighing data corresponding to the front axle, center axle, and rear axle, respectively.
The embodiment can average the continuous measurement value of each scale to reflect the stable bearing of the scale, and can count the fluctuation range of the data, and the smaller the variance, the higher the reliability. The embodiment can split the target weighing data into independent subsets according to the scale number, such as the subset of the scale 1: [1000,1005,998] and the subset of the scale 2: [1500,1510,1495].
Considering that the faster the vehicle is running (e.g., 20 km/h), the greater the data fluctuation, the embodiment may give priority to a subset of the weighing sequences with small variance, such as scale variance 5 number 1, variance 20 number 0.4,2, and 0.2. The present embodiment multiplies the average value of each subset by the corresponding weight, and sums the weighted values of all subsets to obtain the final vehicle weight.
According to the embodiment, reliability is evaluated by extracting data statistics characteristics, focusing single-axis data is grouped according to scales, and weight is dynamically distributed according to the vehicle speed and the positions of the scales, so that the total weight calculation of the high-reliability data is realized. The embodiment can remarkably improve weighing precision, reduce noise interference, adapt to different vehicle speeds and vehicle types, ensure reasonable weighting of data of each shaft, enable final weight calculation to be more accurate and reliable, and be suitable for various weighing scenes.
Corresponding to the vehicle weighing intelligent monitoring method of the above embodiment, fig. 2 is a block diagram of a vehicle weighing intelligent monitoring system according to an embodiment of the application. For convenience of explanation, only portions relevant to the embodiments of the present application are shown. Referring to fig. 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.
The data acquisition module 21 is configured to acquire vehicle size information, a vehicle driving position, a vehicle driving speed and vehicle weighing sequence data corresponding to a plurality of scales in response to a vehicle entering a target weighing area;
the data screening module 22 is configured to determine first weighting data from the vehicle weighting sequence data based on the vehicle size information and the vehicle driving position, where the data in the vehicle weighting sequence data except the first weighting data is second weighting data, and the reliability of the first weighting data is higher than the reliability of the second weighting data;
the weight calculation module 23 is configured to denoise the first weighing data based on the vehicle running speed to obtain denoised target weighing data, and determine the weight of the vehicle based on the target weighing data.
In one embodiment of the present application, the intelligent vehicle weighing monitoring system 20 further includes a data acquisition frequency adjustment module for acquiring a first wheel position and a first driving direction when the vehicle enters the target weighing area, and predicting a wheel driving route based on the first wheel position and the first driving direction;
dividing a plurality of scales into a first scale set and a second scale set based on a wheel driving route, and adjusting the data acquisition frequency of the scales in the first scale set to obtain a first acquisition frequency;
The vehicle weighing sequence data corresponding to the scales comprises vehicle weighing sequence data corresponding to a first scale set acquired based on a first acquisition frequency and vehicle weighing sequence data corresponding to a second scale set acquired based on a second acquisition frequency, wherein the second acquisition frequency is the initial acquisition frequency of the scales.
In one embodiment of the present application, the data acquisition frequency adjustment module is specifically configured to obtain a first travel speed of the vehicle when the vehicle enters the target weighing area;
Adjusting the data acquisition frequency of the first centralized scale based on the first running speed and the vehicle size information to obtain a first acquisition frequency;
the first travel speed and the vehicle size information are both positively correlated with the first acquisition frequency.
In one embodiment of the present application, the data screening module 22 is specifically configured to determine a target weighing area based on the vehicle size information and the vehicle driving position;
And taking the vehicle weighing sequence data corresponding to the scales in the target weighing area as first weighing data.
In one embodiment of the present application, the data screening module 22 is specifically further configured to, if the vehicle size information is greater than or equal to the first size threshold, mark a region with a first distance in a direction perpendicular to the vehicle running direction with the vehicle running position as a center, as the target weighing region;
if the vehicle size information is smaller than the first size threshold value, a region with a second interval is marked out in the direction perpendicular to the vehicle running direction by taking the vehicle running position as the center, and the region is taken as a target weighing region;
The first spacing is greater than the second spacing.
In one embodiment of the present application, the weight calculation module 23 is specifically configured to determine a noise threshold based on a vehicle running speed, the vehicle running speed is positively correlated with the noise threshold, and the first weighting data is denoised based on the noise threshold.
In one embodiment of the present application, the weight calculation module 23 is further specifically configured to extract statistical characteristics of the target weighing data, divide the target weighing data into a plurality of weighing sequence subsets based on the scale number;
determining a weight coefficient matrix corresponding to the plurality of weighing sequence subsets based on the vehicle running speed, the vehicle size information and the scale position;
and weighting the plurality of weighing sequence subsets based on the weight coefficient matrix to obtain the weight of the vehicle.
Referring to fig. 3, fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present application. The electronic device 300 in this embodiment as shown in fig. 3 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 processor 301, the input device 302, the output device 303, and the memory 304 communicate with each other via a communication bus 305. The memory 304 is used to store a computer program comprising program instructions. The processor 301 is configured to execute program instructions stored in the memory 304. Wherein the processor 301 is configured to invoke program instructions to perform the functions of the modules of the system embodiments described above, such as the functions of the data acquisition module 21, the data screening module 22, and the weight calculation module 23 shown in fig. 2.
It should be appreciated that in embodiments of the present application, the Processor 301 may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application SPECIFIC INTEGRATED Circuits (ASICs), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a fingerprint collection sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include read only memory and random access memory and provides instructions and data to the processor 301. A portion of memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information of vehicle information.
In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present application may execute the implementation described in the embodiments of the method for intelligently monitoring the weighing of a vehicle provided in the embodiments of the present application, and may also execute the implementation of the electronic device 300 described in the embodiments of the present application, which is not described herein again.
In another embodiment of the present application, a computer readable storage medium is provided, where the computer readable storage medium stores a computer program, where the computer program includes program instructions, where the program instructions, when executed by a processor, implement all or part of the procedures in the method embodiments described above, or may be implemented by instructing related hardware by the computer program, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by the processor, implements the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include any entity or device capable of carrying computer program code, recording medium, USB flash disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media, among others.
The computer readable storage medium may be an internal storage unit of the electronic device of any of the foregoing embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk provided on the electronic device, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the computer-readable storage medium may also include both internal storage units and external storage devices of the electronic device. The computer-readable storage medium is used to store a computer program and other programs and data required for the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the various illustrative modules/units and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device and unit described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed electronic device and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of modules/units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple modules, units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces or modules/units, or may be an electrical, mechanical, or other form of connection.
The modules/units illustrated as separate components may or may not be physically separate, and components shown as modules/units may or may not be physical modules/units, may be located in one place, or may be distributed over multiple network modules/units. Some or all of the modules/units may be selected according to actual needs to achieve the purposes of the embodiments of the present application.
In addition, each functional module/unit in the embodiments of the present application may be integrated into one processing module/unit, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit. The integrated modules/units described above may be implemented either in hardware or in software functional modules/units.
The present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present application, and these modifications and substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (8)
1. An intelligent vehicle weighing monitoring method is characterized by comprising the following steps:
acquiring vehicle size information, vehicle driving positions, vehicle driving speeds and vehicle weighing sequence data corresponding to a plurality of scales in response to the vehicle entering a target weighing area;
Determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle running position, wherein data except the first weighing data in the vehicle weighing sequence data are second weighing data, and the reliability of the first weighing data is higher than that of the second weighing data;
denoising the first weighing data based on the vehicle running 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 the scales, the method further comprises the following steps:
Acquiring a first wheel position and a first driving direction when a 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 scales into a first scale set and a second scale set based on the wheel driving route, and obtaining a first driving speed when the vehicle enters a target weighing area; the data acquisition frequency of the first centralized platform scale is adjusted based on the first running speed and the vehicle size information to obtain a first acquisition frequency, wherein the first running speed and the vehicle size information are positively correlated with the first acquisition frequency;
the vehicle weighing sequence data corresponding to the scales comprises vehicle weighing sequence data corresponding to the first scale set acquired based on the first acquisition frequency and vehicle weighing sequence data corresponding to the second scale set acquired based on the second acquisition frequency, wherein the second acquisition frequency is the initial acquisition frequency of the scales.
2. The vehicle weighing intelligent monitoring method of claim 1 wherein said determining first weighing data from said vehicle weighing sequence data based on said vehicle size information and said vehicle travel location comprises:
determining a target weighing area based on the vehicle size information and the vehicle driving position;
And taking the vehicle weighing sequence data corresponding to the scales in the target weighing area as the first weighing data.
3. The vehicle weighing intelligent monitoring method according to claim 2, wherein said determining a target weighing area based on said vehicle size information and said vehicle driving position comprises:
If the vehicle size information is greater than or equal to a first size threshold value, a region with a first interval is marked out in the vertical direction of the vehicle running direction by taking the vehicle running position as the center, and the region is taken as the target weighing region;
if the vehicle size information is smaller than the first size threshold value, a region with a second interval is marked out in the vertical direction of the vehicle running direction by taking the vehicle running position as the center, and the region is taken as the target weighing region;
the first pitch is greater than the second pitch.
4. The vehicle weighing intelligent monitoring method according to claim 1, wherein denoising the first weighing data based on the vehicle running speed to obtain target weighing data, comprises:
Determining a noise threshold based on the vehicle travel speed, the vehicle travel speed being positively correlated with the noise threshold;
denoising the first weighing data based on the noise threshold.
5. The vehicle weight intelligent monitoring method of claim 1, wherein the determining the vehicle weight based on the target weight data comprises:
Dividing the target weighing data into a plurality of weighing sequence subsets based on scale numbers, wherein the weighing sequence subsets are in one-to-one correspondence with the scale numbers;
Determining a weight coefficient matrix corresponding to the plurality of weighing sequence subsets based on the vehicle running speed and the scale position;
and weighting calculation is carried out on the plurality of weighing sequence subsets based on the weight coefficient matrix, so that the weight of the vehicle is obtained.
6. A vehicle weighing intelligent monitoring system, comprising:
The data acquisition module is used for acquiring vehicle size information, vehicle running positions, vehicle running speeds and vehicle weighing sequence data corresponding to a plurality of scales in response to the vehicle entering the target weighing area;
The data screening module is used for determining first weighing data from the vehicle weighing sequence data based on the vehicle size information and the vehicle driving position, wherein the data except the first weighing data in the vehicle weighing sequence data are second weighing data, and the reliability of the first weighing data is higher than that of the second weighing data;
The weight calculation module is used for denoising the first weighing data based on the vehicle running speed to obtain denoised target weighing data;
The data acquisition frequency adjustment module is used for acquiring a first wheel position and a first driving direction when the vehicle enters the target weighing area, and predicting a wheel driving route based on the first wheel position and the first driving direction;
Dividing the scales into a first scale set and a second scale set based on the wheel driving route, and obtaining a first driving speed when the vehicle enters a target weighing area; the data acquisition frequency of the first centralized platform scale is adjusted based on the first running speed and the vehicle size information to obtain a first acquisition frequency, wherein the first running speed and the vehicle size information are positively correlated with the first acquisition frequency;
the vehicle weighing sequence data corresponding to the scales comprises vehicle weighing sequence data corresponding to the first scale set acquired based on the first acquisition frequency and vehicle weighing sequence data corresponding to the second scale set acquired based on the second acquisition frequency, wherein the second acquisition frequency is the initial acquisition frequency of the 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 the processor implements the steps of the method according to any of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 5.
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| CN119124326A (en) * | 2024-08-19 | 2024-12-13 | 福建闽光云商有限公司 | Intelligent weighbridge management system and method thereof |
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| CH705762A1 (en) * | 2011-11-04 | 2013-05-15 | Kistler Holding Ag | Method for weighing a vehicle, as well as measuring device and measuring chain for this purpose. |
| CN205843793U (en) * | 2016-07-01 | 2016-12-28 | 重庆智砼信息技术有限公司 | A kind of electronic loadometer system for vehicle weighing |
| CN119756543B (en) * | 2024-12-13 | 2025-11-04 | 广州新流向电子科技有限公司 | A Quartz Dynamic Truck Scale Automatic Weighing System and Weighing Method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118310611A (en) * | 2024-04-09 | 2024-07-09 | 晋能集团信息服务有限公司 | Wagon balance data acquisition method |
| CN119124326A (en) * | 2024-08-19 | 2024-12-13 | 福建闽光云商有限公司 | Intelligent weighbridge management system and method thereof |
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