CN115235597A - Dynamic weighing angular difference compensation method - Google Patents

Dynamic weighing angular difference compensation method Download PDF

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Publication number
CN115235597A
CN115235597A CN202210828156.3A CN202210828156A CN115235597A CN 115235597 A CN115235597 A CN 115235597A CN 202210828156 A CN202210828156 A CN 202210828156A CN 115235597 A CN115235597 A CN 115235597A
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weighbridge
moment
dynamic weighing
scale
corner
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郑书礼
陈小琴
叶新舟
林小芬
林晓明
姜俊
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Zhejiang Dong Ding Electronic Ltd By Share Ltd
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Zhejiang Dong Ding Electronic Ltd By Share Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
    • G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/457—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by analysing connectivity, e.g. edge linking, connected component analysis or slices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Abstract

The invention relates to the technical field of dynamic weighing, in particular to a dynamic weighing angular difference compensation method. The method comprises the following steps: obtaining an initial shape difference amount and a stable shape difference amount of the weighbridge; obtaining deformation quantity of corner points of the weighbridge at the sampling moment and first distance variable quantity of the corner points of the weighbridge at the sampling moment; obtaining the abnormal degree of the weighbridge at the sampling moment according to the deformation quantity of the corner points of all the weighbridges at the sampling moment and the first distance variable quantity; weighting and summing the abnormal degrees of the weighbridge at each sampling moment to obtain the abnormal degree of the weighbridge in the dynamic weighing process; obtaining the necessity of angular difference compensation according to the initial shape abnormal amount, the stable shape abnormal amount and the abnormal degree of the weighbridge in the dynamic weighing process; and if the angular difference compensation necessity is greater than a preset threshold value, compensating the vehicle weight obtained in the dynamic weighing process to obtain the real weight of the vehicle. The invention realizes the intelligent compensation of the weighing result when the angle difference error of the weighbridge is larger in the dynamic weighing process.

Description

Dynamic weighing angular difference compensation method
Technical Field
The invention relates to the technical field of dynamic weighing and artificial intelligence, in particular to a dynamic weighing angular difference compensation method based on computer vision.
Background
When the flat-plate ground scale is not abnormal, the objects with the same weight are weighed at the corner points of the four ground scales, the weight is the same, when the four support leg foundations of the flat-plate ground scale are uneven or have foundation subsidence, the weights weighed at the corner points of the four ground scales are different, so that errors often exist when a vehicle is weighed, and the weight indication result given by the ground scale at the moment is unreliable.
When the abnormity happens, each weighing sensor is required to be manually adjusted to enable the angle difference not to exceed a division value degree, or the ground scale is detached, the foundation is rebuilt and re-hardened, the precision of the ground scale during weighing can reach the requirement through manual adjustment or maintenance, and the ground scale can be reused. And when such anomalies are not found, or no manual adjustment or maintenance of angular differences is found, all weighing results are still subject to errors.
Disclosure of Invention
In order to solve the above technical problems, the present invention aims to provide a dynamic weighing angular difference compensation method based on computer vision, and the adopted technical scheme is as follows:
one embodiment of the invention provides a dynamic weighing angular difference compensation method based on computer vision, which comprises the following steps:
obtaining the abnormal amount of the initial shape by the deformation of the ground scale when the vehicle is not dynamically weighed; obtaining the abnormal amount of the stable shape by the deformation of the ground scale when the weight is stable in the dynamic weighing process of the vehicle;
obtaining deformation quantity of the corner points of the weighbridge at the sampling moment by using the position variation quantity of the corner points of the weighbridge at the sampling moment and the adjacent historical moment in the dynamic weighing process;
obtaining a first distance variable quantity of the corner points of the weighbridge at the sampling moment by using the difference of first distances between the sampling moment and the corner points of the weighbridge at the adjacent historical moments in the dynamic weighing process, wherein the first distance of the corner points of the weighbridge is the distance between the corner points of the weighbridge at the corresponding moment and the wheel supporting points;
obtaining the abnormal degree of the weighbridge at the sampling moment according to the deformation quantity of the corner points of all the weighbridges at the sampling moment and the first distance variable quantity; weighting and summing the abnormal degrees of the weighbridge at each sampling moment to obtain the abnormal degree of the weighbridge in the dynamic weighing process; obtaining the necessity of angular difference compensation according to the initial shape abnormal amount, the stable shape abnormal amount and the abnormal degree of the weighbridge in the dynamic weighing process;
and if the angular difference compensation necessity is greater than a preset threshold value, compensating the vehicle weight obtained in the dynamic weighing process to obtain the real weight of the vehicle.
Preferably, a neural network is constructed to obtain the corner points and the wheel supporting points of the ground scale, the neural network comprises two branches, and the corner point obtaining branch of the ground scale and the wheel supporting point estimating branch of the ground scale are constructed; the neural network inputs a vehicle weighing process image, the corner points of the weighbridge acquire the corner points of the branch output weighbridge, and the wheel supporting points estimate the wheel supporting points of the branch output weighbridge.
Preferably, the deformation of the ground scale is obtained by using the difference between a quadrangle formed by corner points of the ground scale in the ground scale image and an ideal rectangle: and acquiring coordinates of the corner points of the weighbridge in the weighbridge image, and acquiring the difference between a quadrangle formed by the corner points of the weighbridge in the weighbridge image and an ideal rectangle by utilizing the transverse coordinate difference of the corner points of the longitudinally adjacent weighbridge and the longitudinal coordinate difference of the corner points of the transversely adjacent weighbridge so as to acquire the deformation of the weighbridge.
Preferably, the deformation quantity calculation formula of the corner point of the ground scale at the sampling moment is as follows:
Figure 470858DEST_PATH_IMAGE002
wherein
Figure 594671DEST_PATH_IMAGE004
Sampling the corner point A1 of the weighbridge at the moment
Figure 347864DEST_PATH_IMAGE006
The amount of deformation at a moment; the number of the historical moments adjacent to the sampling moment is K; g is the attenuation coefficient; (
Figure 353866DEST_PATH_IMAGE008
，
Figure 850706DEST_PATH_IMAGE010
) Is the coordinates of the corner point A1 of the ground scale at the sampling moment (a)
Figure 625764DEST_PATH_IMAGE012
，
Figure 284279DEST_PATH_IMAGE014
) The coordinates of the corner point A1 of the platform balance at the k-th adjacent historical moment.
Preferably, the first distance is in particular:
Figure 279042DEST_PATH_IMAGE016
wherein
Figure 313994DEST_PATH_IMAGE018
Sampling the corner point A1 of the weighbridge at the moment
Figure 474717DEST_PATH_IMAGE006
A first distance of a time; p is the number of the wheel supporting points at the sampling moment;
Figure 304133DEST_PATH_IMAGE020
is as follows
Figure 284727DEST_PATH_IMAGE006
The p-th wheel support point at time;
Figure 123370DEST_PATH_IMAGE022
the pixel distance between the p-th wheel supporting point at the nth moment and the corner point A1 of the ground scale is shown.
Preferably, the first distance variation from the corner point of the platform balance to the wheel supporting point is specifically:
Figure 607441DEST_PATH_IMAGE024
wherein
Figure 873337DEST_PATH_IMAGE026
Sampling the corner points of the A1 platform scale at the moment
Figure 577113DEST_PATH_IMAGE006
The number of the first distance variable quantity from the moment to the wheel supporting point and the historical moment adjacent to the sampling moment is K; g is the attenuation coefficient;
Figure DEST_PATH_IMAGE028
the first distance of the corner point A1 of the ground scale at the nth moment;
Figure DEST_PATH_IMAGE030
is the first distance of the corner point A1 of the ground scale at the k-th adjacent historical moment.
Preferably, the degree of abnormality of the floor scale at the sampling time is as follows:
Figure DEST_PATH_IMAGE032
wherein
Figure DEST_PATH_IMAGE034
The abnormal degree of the floor scale at the sampling time, namely the nth time is represented;
Figure DEST_PATH_IMAGE036
representing corner point of weighbridge at nth moment
Figure DEST_PATH_IMAGE038
The amount of deformation of (a) is,
Figure DEST_PATH_IMAGE040
representing corner point of ground scale at nth sampling moment
Figure DEST_PATH_IMAGE042
The amount of change in (c);
Figure DEST_PATH_IMAGE044
is the corner point of the weighbridge at the nth moment
Figure 704600DEST_PATH_IMAGE038
A first distance variation amount to the wheel supporting point,
Figure DEST_PATH_IMAGE046
is the corner point of the weighbridge at the nth moment
Figure 777599DEST_PATH_IMAGE042
A first distance variation amount to the wheel supporting point;
Figure DEST_PATH_IMAGE048
is a fault tolerance threshold;
Figure DEST_PATH_IMAGE050
is a linear rectification function.
Preferably, the degree of abnormality of the floor scale in the dynamic weighing process is as follows:
Figure DEST_PATH_IMAGE052
YC is the abnormal degree of the ground scale in the dynamic weighing process;
Figure 870188DEST_PATH_IMAGE053
the abnormal degree of the floor scale at the sampling time, namely the nth time.
Preferably, the angular difference compensation necessity is:
Figure 61261DEST_PATH_IMAGE055
WE is the angular difference compensation necessity of the dynamic weighing process of the vehicle;
Figure 507285DEST_PATH_IMAGE057
in order to balance the difference quantity of the initial shape of the land scale,
Figure 965949DEST_PATH_IMAGE059
the stable shape difference of the weighbridge is obtained; u is the influence coefficient of the initial and stable shape difference of the ground scale; v is an influence coefficient of the abnormal degree of the ground scale in the dynamic weighing process of the vehicle; a is a scaling factor.
Preferably, the compensating the weight of the vehicle obtained in the dynamic weighing process to obtain the true weight of the vehicle comprises: constructing a weight compensation neural network, inputting the initial abnormal shape amount, the stable abnormal shape amount, the abnormal degree of the weighbridge in the dynamic weighing process and the weight values of all the corner points of the weighbridge, and outputting the compensation weight; the sum of the dynamic weighing weight and the compensation weight of the floor scale is the real weight of the vehicle.
The embodiment of the invention at least has the following beneficial effects:
utilize computer vision, change to dull and stereotyped weighbridge corner point and wheel strong point in the dynamic weighing process is analyzed, whether there is the angular difference error in judgement dull and stereotyped weighbridge that can be quick accurate, and utilize angular difference compensation necessity to judge whether the vehicle need carry out the angular difference compensation in the dynamic weighing process, if need compensate then carry out weight compensation to vehicle dynamic weighing weight, needn't artifically adjust dull and stereotyped weighbridge and eliminate the angular difference error, save artifically, make weighbridge can continue to use and have certain accuracy in certain error range, realize dull and stereotyped weighbridge and carry out intelligent compensation to the weighing result when the vehicle dynamic weighing in-process weighbridge angular difference error is great.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a dynamic weighing angular difference compensation method based on computer vision.
FIG. 2 is a schematic view of a dynamic weighing camera position.
Fig. 3 is a diagram of a neural network structure for acquiring wheel supporting points and corner points of a floor scale.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the dynamic weighing angular difference compensation method based on computer vision according to the present invention with reference to the accompanying drawings and preferred embodiments shows the following detailed descriptions. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of a dynamic weighing angular difference compensation method based on computer vision in detail with reference to the accompanying drawings.
Example 1
The main application scenarios of the invention are as follows: the vehicle bears the weight of the goods, enter and weigh the area, carry on the dynamic weighing, because the dull and stereotyped weighbridge, the vehicle weighs on the weighbridge with different position attitudes in the dynamic weighing process, if the weighbridge presents the great angular difference, then the angle point atress of the weighbridge that presents the angular difference is different with the atress when not presenting the angular difference, lead to the result that the vehicle dynamic weighing is inaccurate, need the symmetrical weight result to compensate, obtain the true dynamic weighing result of vehicle. The reason for the angular difference in the invention is mainly the uneven foundation below the weighbridge.
Referring to fig. 1, a flow chart of a dynamic weighing angular difference compensation method based on computer vision according to an embodiment of the present invention is shown, the method includes the following steps:
firstly, constructing a neural network to obtain a corner point and a wheel supporting point of the ground scale, wherein the neural network comprises two branches, namely a ground scale corner point obtaining branch and a wheel supporting point estimating branch; the neural network inputs a vehicle weighing process image, the corner points of the weighbridge acquire the corner points of the branch output weighbridge, and the wheel supporting points estimate the wheel supporting points of the branch output weighbridge.
The embodiment needs to acquire the RGB image of the dynamic weighing process in the current scene through the camera. In which the arrangement of cameras and the like is shown in figure 2. The camera is positioned on the vertical line of the central point of the floor scale and can completely shoot an overlooking image of the vehicle in the dynamic weighing process. And processing the dynamic weighing image acquired from the overlooking visual angle by using a neural network to obtain corner points of the ground scale in the image, and estimating to obtain support points of the vehicle on wheels of the ground scale. The weight of the vehicle in the dynamic weighing process is concentrated on a plurality of wheel supporting points, then a plurality of supporting points are in contact with the ground scale to generate pressure on the ground scale, the pressure sensors at four corner points of the ground scale obtain weight data according to the pressure, and then weighing results are obtained comprehensively.
In the embodiment, a DNN neural network is constructed to identify four corner points corresponding to the ground scale in the image, and estimation and reasoning of the vehicle wheel supporting points are carried out simultaneously. The neural network is specifically as follows as shown in fig. 3: the structure of the network is an Encoder-Decoder structure with branches, namely one Encoder (Encoder) is shared to extract image characteristics, and two different decoders (decoders) are utilized to realize different task requirements of the two branches. Namely, the network has two branches, namely a branch for acquiring the corner point of the ground scale and a branch for estimating the wheel supporting point.
And (3) obtaining branches for corner points of the weighbridge: the branch classifies each pixel in the image in a semantic Segmentation mode to obtain a semantic Segmentation map (Segmentation) so as to realize the identification of the weighbridge area. The training data set used by the network is the image of the flat-plate weighbridge collected under the overlooking visual angle, the corresponding weighbridge needs to be manually marked, and a single-channel label image with the same size is established. If the pixel at the position of the acquired RGB image belongs to the background class, the class ID of the pixel at the corresponding position of the label image needs to be marked as 0, if the pixel belongs to the ground scale class, the pixel is marked as 1, if the pixel belongs to the vehicle class, the pixel is marked as 2, and the loss function adopts a cross entropy loss function to realize a classification task. Carrying out linear detection on the edge of a ground scale connected domain, wherein intersection points of linear extension lines are four ground scale corner points, ID numbering is carried out according to the clockwise direction, and the ground scale corner points are marked as: a1 A2, A3, A4.
For the wheel support point estimation branch: the branch is used for estimating the position of the vehicle supporting point of the vehicle back and forth in a key point mode, and the vehicle wheel image can be influenced by a vehicle body shielding part in a overlooking view. The training data adopts the vehicle images collected from the overlooking visual angles, and the marking of the wheel supporting points is carried out manually. A pixel point is marked at a corresponding position of a wheel supporting point, and then a corresponding heatmap hot spot image is obtained by utilizing Gaussian blur. Because the label is a class of target key points, the label dimension can be a single channel, and the loss function adopts a mean square error loss function to complete a regression task. Obtaining the positions of the wheel supporting points of the vehicle, carrying out ID numbering according to the clockwise direction, and marking the wheel supporting points as: c1 C2, C3, \ 8230;, cp. The above is an embodiment of obtaining the wheel supporting points, and the implementer may set a camera on the side to collect the vehicle weighing image and obtain the wheel supporting points, and when a plurality of cameras are used, it is necessary to align the images collected by the two cameras at the same time.
So far, the collected overlook images in the dynamic weighing process of the vehicle can be processed by the corresponding neural network, and four corner points of the ground scale and a plurality of vehicle wheel supporting points are obtained.
Then, in the dynamic weighing process of the vehicle, the ground scale is changed due to stress, and the stress of the pivot of the ground scale is unbalanced, so that the change of the four corner points of the ground scale in the image is different. And the relative change difference of the corner points of the weighbridge is related to the received pressure, so that whether the angular difference is abnormal or not can be judged according to the change of the four corner points of the weighbridge and the change of the wheel supporting points, and the angular difference compensation necessity is obtained. In this embodiment, the deformation of the inflection point of the floor scale means that the inflection point of the floor scale moves in images in different states (moments) under the view angle of the fixed overhead camera, and the change of the support point is caused by different positions of the vehicle.
Obtaining the abnormal amount of the initial shape by the deformation of the ground scale when the vehicle is not dynamically weighed; the abnormal amount of the stable shape is obtained by the deformation of the ground scale when the weight is stable in the dynamic weighing process of the vehicle. The initial shape abnormality amount and the stable shape abnormality amount are obtained specifically as follows:
when the vehicle is not dynamically weighed, the ground scale is in an initial state, and whether the ground scale is initially abnormal or not can be judged according to the imaging of the ground scale in the image.
Calculate initial shape anomaly amount:
Figure 573647DEST_PATH_IMAGE061
in the formula (I), the compound is shown in the specification,
Figure 750551DEST_PATH_IMAGE063
represents the transverse coordinates of the corner point A1 of the ground scale at the 0 th moment in the image coordinate system,
Figure 469108DEST_PATH_IMAGE065
and the longitudinal coordinate of the corner point A1 of the ground scale at the 0 th moment is shown.
When the weight of the vehicle is stable in the dynamic weighing process, judging the shape difference of corner points of four weighers in the current state, wherein the shape difference is abnormal for stabilizing the shape:
Figure 47857DEST_PATH_IMAGE067
in the formula (I), the compound is shown in the specification,
Figure 826457DEST_PATH_IMAGE069
the difference between the four inflection points corresponding to the nth time and the standard rectangle is shown.
The ground scale is closer to an ideal rectangle in a quadrangle formed by corner points of the ground scale in an image, and the smaller the abnormal amount of the shape is, the smaller the abnormity of the ground scale is. Namely, the more the plane of the weighbridge is not inclined, the more the plane is vertical to the optical axis direction of the camera, and the less the abnormity is.
And obtaining the deformation quantity of the corner points of the weighbridge at the sampling moment by utilizing the position variation quantity of the corner points of the weighbridge at the sampling moment and the adjacent historical moment in the dynamic weighing process. The normal weighbridge is that four angles steadily sink and change, and the weighbridge that has the angular difference can take place deformation by a relatively large margin when weight is partial to unusual weighbridge corner point. And (3) calculating deformation of corner points of the ground scale at different sampling moments, specifically:
recording the coordinate sequence of the corner points of the weighbridge at each moment in the whole dynamic weighing process as follows:
Figure 631602DEST_PATH_IMAGE071
the above is a sequence of coordinates of the corner point A1 of the platform scale in each frame of image in the weighing process. Similarly, the corner points of the four weighers all have corresponding coordinate sequences.
Obtaining a deformation quantity sequence of each sampling time corresponding to the corner point of the A1 ground scale:
Figure 779949DEST_PATH_IMAGE073
wherein the content of the first and second substances,
Figure 88571DEST_PATH_IMAGE004
the deformation quantity of the corner point A1 of the platform scale at the sampling moment, namely the nth moment, compared with the historical moment is represented by the following specific calculation formula:
Figure 162706DEST_PATH_IMAGE074
wherein
Figure 923988DEST_PATH_IMAGE004
Sampling the corner point A1 of the weighbridge at the moment
Figure 374561DEST_PATH_IMAGE006
The amount of deformation at a moment; the number of the historical moments adjacent to the sampling moment is K, preferably, K is 5 in this embodiment; g is an attenuation coefficient which can be adjusted according to actual operation, and preferably, g is e in the embodiment; (
Figure 803269DEST_PATH_IMAGE008
，
Figure 48305DEST_PATH_IMAGE010
) Is the coordinates of the corner point A1 of the ground scale at the sampling moment (a)
Figure 562463DEST_PATH_IMAGE076
，
Figure 52613DEST_PATH_IMAGE078
) The coordinates of the corner point A1 of the weighbridge at the k-th adjacent historical moment
In the formula, the damping coefficient and the position variation of the corner point A1 of the ground scale are divided into two parts. In order to avoid reducing the abrupt change degree due to short interval time, the present embodiment needs to calculate the position variation of the coordinates A1 of the corner point of the weighbridge at the sampling time and at the adjacent historical time, and comprehensively determine the deformation amount of the corner point A1 of the weighbridge at the sampling time. And similarly, the deformation quantity of corner points of different weighers at each sampling moment can be obtained.
And obtaining a first distance variable quantity of the corner point of the weighbridge at the sampling moment by using the difference of first distances between the sampling moment and the corner points of the weighbridge at adjacent historical moments in the dynamic weighing process, wherein the first distance of the corner point of the weighbridge is the distance between the corner point of the weighbridge at the corresponding moment and a wheel supporting point.
In the dynamic weighing process of the vehicle, if no angular difference exists, the pixel distance from the wheel supporting point to the corner point of the ground scale changes along with the change of the supporting point position and the deformation of the corner point of the ground scale, and the change is normal; if the angle difference of the ground scale is large, the change of the pixel distance from the wheel supporting point to the corner point of the ground scale is different from the normal situation.
Whether the deformation condition of the corner points of the ground scale is normal can be indirectly reflected by utilizing the first distance variable quantity corresponding to the corner points of the ground scale, and the calculation formula of the first distance is as follows:
Figure DEST_PATH_IMAGE080
wherein
Figure 663722DEST_PATH_IMAGE018
Sampling time for corner point A1 of the weighbridge
Figure 220606DEST_PATH_IMAGE006
A first distance of a time; p is the number of the wheel supporting points at the sampling moment;
Figure 81114DEST_PATH_IMAGE081
is a first
Figure 14435DEST_PATH_IMAGE006
At the moment, the p-th wheel support point;
Figure DEST_PATH_IMAGE082
the pixel distance between the p-th wheel supporting point at the nth moment and the corner point A1 of the ground scale is shown.
Obtaining a corresponding first distance variable quantity sequence for reflecting whether the deformation condition of the corner point of the ground scale is normal or not:
Figure 480052DEST_PATH_IMAGE084
the first distances corresponding to the corner points A1 of the ground scale at different moments are different, and a calculation formula of the first distance variation from the corner points of the ground scale to the wheel supporting points specifically comprises the following steps:
Figure 207836DEST_PATH_IMAGE086
wherein
Figure 57106DEST_PATH_IMAGE026
Sampling the corner points of the A1 platform scale at the moment
Figure 262959DEST_PATH_IMAGE006
The number of the first distance variation from the moment to the wheel supporting point and the historical moments adjacent to the sampling moment is K, preferably K is 5 in the embodiment; g is an attenuation coefficient, preferably, g is e in the embodiment;
Figure DEST_PATH_IMAGE087
the first distance of the corner point A1 of the ground scale at the nth moment;
Figure DEST_PATH_IMAGE089
the first distance of the corner point A1 of the ground scale at the kth historical moment.
Obtaining the abnormal degree of the weighbridge at the sampling moment according to the deformation quantity of the corner points of all the weighbridges at the sampling moment and the first distance variable quantity; and weighting and summing the abnormal degrees of the weighbridge at each sampling moment to obtain the abnormal degree of the weighbridge in the dynamic weighing process.
The abnormal degree of the ground scale in the weighing process is reflected by the difference between the deformation quantity of the corner point of the ground scale and the variable quantity of the first distance, and the corresponding calculation formula is as follows:
first, the degree of abnormality at the sampling time (nth time) is obtained:
Figure DEST_PATH_IMAGE091
in the formula (I), the compound is shown in the specification,
Figure 910978DEST_PATH_IMAGE034
the abnormal degree of the floor scale at the sampling time, namely the nth time is represented;
Figure 934298DEST_PATH_IMAGE036
representing corner point of weighbridge at nth moment
Figure 644765DEST_PATH_IMAGE038
The amount of deformation of (a) is,
Figure 292127DEST_PATH_IMAGE040
representing corner point of weighbridge at nth sampling moment
Figure 404439DEST_PATH_IMAGE042
The amount of change in (c);
Figure 333081DEST_PATH_IMAGE044
is the corner point of the weighbridge at the nth moment
Figure 530844DEST_PATH_IMAGE092
The first distance to the wheel support point varies,
Figure 344079DEST_PATH_IMAGE046
is the corner point of the floor scale at the nth moment
Figure 435532DEST_PATH_IMAGE042
A first distance variation to a wheel support point;
Figure DEST_PATH_IMAGE093
is corresponding toPreferably, rc is 0.15 in this embodiment. The deformation quantity of the corner points of the ground scale and the first distance variable quantity corresponding to the corner points of the ground scale are in an inverse relation.
Figure DEST_PATH_IMAGE095
Representing input data
Figure DEST_PATH_IMAGE097
In the function.
Figure DEST_PATH_IMAGE098
YC is the abnormal degree of the ground scale in the dynamic weighing process;
Figure 302119DEST_PATH_IMAGE053
the abnormal degree of the floor scale at the sampling time, namely the nth time, the abnormal degree of the floor scale at each sampling time is weighted and accumulated, and the weight is the proportion of the numerical value of the abnormal degree of the floor scale at each sampling time to the total numerical value of the abnormal degree of the floor scale, namely
Figure 111812DEST_PATH_IMAGE053
The larger the value, the higher the occupancy ratio at the time of superimposition.
And obtaining the necessity of angular difference compensation according to the initial abnormal shape amount, the stable abnormal shape amount and the abnormal degree of the ground scale in the dynamic weighing process.
Figure DEST_PATH_IMAGE100
}
In the formula, WE is the angular difference compensation necessity of the weighing, and u and v are influence coefficients of two factors and are used for reflecting different influence degrees of the two factors; a is a scaling factor for mapping the angular difference compensation necessity to the [0,1] range while performing dimensional conversion (for dimensional agreement, each factor may be subjected to normalization processing before combination).
After the necessity of angular difference compensation in the dynamic weighing process is obtained, the necessity of compensation can be further judged. Because the present embodiment uses the deep learning neural network to perform weighing compensation, and the neural network is used as a black box fitter, no matter what data is input into the neural network, the corresponding compensation data can be obtained.
When the WE is larger than the preset threshold value, the current dynamic weighing result is considered to be greatly influenced by the abnormal angular difference, and the weight measurement compensation is needed. The preset threshold can be set according to actual operation, and preferably, the preset threshold of this embodiment is 0.7.
And finally, if the angular difference compensation necessity is greater than a preset threshold value, compensating the vehicle weight obtained in the dynamic weighing process to obtain the real weight of the vehicle.
In the embodiment, the nonlinear complex relationship between the scene parameters and the compensation data is fitted in a neural network manner, that is, the corresponding neural network can be trained to deduce and obtain the corresponding compensation weighing data by processing the scene parameters.
The weight compensation neural network comprises the following specific contents:
1) The network adopts the structure of the full connection network (FC), and the quantity of input neuron is the quantity of influence parameter, and the embodiment needs to input 7 data altogether, includes: the abnormal amount of initial shape of the weighbridge, the abnormal amount of stable shape, the abnormal degree of the weighbridge in the dynamic weighing process and the weight numerical values of all the corner points of the weighbridge, and the number of the neurons of the network output layer are the number of the inference data, so that the embodiment only needs to infer the corresponding compensation weight, and only needs one neuron.
2) The acquisition of training data used by the network is as follows:
the method comprises the following steps of repeatedly stopping a vehicle with the known weight of G on a floor scale at different poses for weighing, carrying out random corner hardening on the floor scale, acquiring the abnormal amount of the initial shape, the abnormal amount of the stable shape, the abnormal degree of the floor scale in the dynamic weighing process and the weight values of all corner points of the floor scale according to the mode, and taking mG as corresponding output data:
Figure DEST_PATH_IMAGE102
wherein G is the actual weight of the vehicle,
Figure DEST_PATH_IMAGE104
in order to measure the weight of the floor scale,
Figure DEST_PATH_IMAGE106
the weight value corresponding to the pressure at the corner of the ith floor scale is obtained.
3) The network uses a mean square error loss function to accomplish the numerical regression task.
After the weight compensation neural network outputs the weight mG, the compensated weight can be obtained by adding the mG to the current weighbridge weighing result, namely the real weight of the vehicle in the dynamic weighing process.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A dynamic weighing angular difference compensation method is characterized by comprising the following steps:
obtaining the abnormal amount of the initial shape by the deformation of the ground scale when the vehicle is not dynamically weighed; obtaining the abnormal amount of the stable shape by the deformation of the ground scale when the weight is stable in the dynamic weighing process of the vehicle;
obtaining deformation quantity of the corner points of the weighbridge at the sampling moment by using the position variation quantity of the corner points of the weighbridge at the sampling moment and the adjacent historical moment in the dynamic weighing process;
obtaining a first distance variable quantity of the corner points of the weighbridge at the sampling moment by using the difference of first distances between the sampling moment and the corner points of the weighbridge at the adjacent historical moments in the dynamic weighing process, wherein the first distance of the corner points of the weighbridge is the distance between the corner points of the weighbridge at the corresponding moment and the wheel supporting points;
obtaining the abnormal degree of the weighbridge at the sampling moment according to the deformation quantity and the first distance variable quantity of corner points of all the weighbridges at the sampling moment; weighting and summing the abnormal degrees of the weighbridge at each sampling moment to obtain the abnormal degree of the weighbridge in the dynamic weighing process; obtaining the necessity of angular difference compensation according to the initial shape abnormal amount, the stable shape abnormal amount and the abnormal degree of the weighbridge in the dynamic weighing process;
if the angular difference compensation necessity is greater than a preset threshold value, compensating the vehicle weight obtained in the dynamic weighing process to obtain the real weight of the vehicle; the deformation of the corner point of the weighbridge at the sampling moment is as follows:
Figure DEST_PATH_IMAGE002
wherein
Figure DEST_PATH_IMAGE004
Sampling time for corner point A1 of the weighbridge
Figure DEST_PATH_IMAGE006
The amount of deformation at a moment; the number of the historical moments adjacent to the sampling moment is K; g is the attenuation coefficient; (
Figure DEST_PATH_IMAGE008
，
Figure DEST_PATH_IMAGE010
) Is the coordinates of the corner point A1 of the ground scale at the sampling moment (a)
Figure DEST_PATH_IMAGE012
，
Figure DEST_PATH_IMAGE014
) Coordinates of corner points A1 of the weighbridge at the kth adjacent historical moment;
the first distance variation from the corner point of the platform scale to the wheel supporting point is as follows:
Figure DEST_PATH_IMAGE016
wherein
Figure DEST_PATH_IMAGE018
Sampling the corner points of the A1 platform scale at the moment
Figure 16476DEST_PATH_IMAGE006
The number of the first distance variable quantity from the moment to the wheel supporting point and the historical moment adjacent to the sampling moment is K; g is the attenuation coefficient;
Figure DEST_PATH_IMAGE020
the first distance of the corner point A1 of the ground scale at the nth moment;
Figure DEST_PATH_IMAGE022
the first distance of the corner point A1 of the weighbridge at the kth adjacent historical moment;
the first distance specifically is:
Figure DEST_PATH_IMAGE024
wherein
Figure DEST_PATH_IMAGE025
Sampling time for corner point A1 of the weighbridge
Figure 28556DEST_PATH_IMAGE006
A first distance of a time; p is the number of the wheel supporting points at the sampling moment;
Figure DEST_PATH_IMAGE027
is as follows
Figure 995244DEST_PATH_IMAGE006
The p-th wheel support point at time;
Figure DEST_PATH_IMAGE029
the pixel distance between the p-th wheel supporting point and the corner point A1 of the ground scale at the nth moment is shown;
the abnormal degree of the sampling time floor scale is as follows:
Figure DEST_PATH_IMAGE031
wherein
Figure DEST_PATH_IMAGE033
The abnormal degree of the floor scale at the sampling time, namely the nth time is represented;
Figure DEST_PATH_IMAGE035
representing corner point of weighbridge at nth moment
Figure DEST_PATH_IMAGE037
The amount of deformation of (a) is,
Figure DEST_PATH_IMAGE039
representing corner point of weighbridge at nth sampling moment
Figure DEST_PATH_IMAGE041
The amount of change in (c);
Figure DEST_PATH_IMAGE043
is the corner point of the weighbridge at the nth moment
Figure 970547DEST_PATH_IMAGE037
The first distance to the wheel support point varies,
Figure DEST_PATH_IMAGE045
is the corner point of the floor scale at the nth moment
Figure 722602DEST_PATH_IMAGE041
A first distance variation amount to the wheel supporting point;
Figure DEST_PATH_IMAGE047
is a fault tolerance threshold;
Figure DEST_PATH_IMAGE049
is a linear rectification function.
2. The dynamic weighing angular difference compensation method according to claim 1, further comprising the acquisition of wheel support points and corner points of a floor scale, specifically: constructing a neural network to obtain a corner point and a wheel supporting point of the ground scale, wherein the neural network comprises two branches, namely a ground scale corner point obtaining branch and a wheel supporting point estimating branch; the neural network inputs a vehicle weighing process image, the corner points of the ground scale acquire the corner points of the branch output ground scale, and the wheel supporting points estimate the wheel supporting points of the branch output ground scale.
3. The dynamic weighing angular difference compensation method of claim 2, wherein the deformation of the floor scale is obtained by using the difference between a quadrangle formed by the corner points of the floor scale in the image of the floor scale and an ideal rectangle: and acquiring coordinates of the corner points of the weighbridge in the weighbridge image, and acquiring the difference between a quadrangle formed by the corner points of the weighbridge in the weighbridge image and an ideal rectangle by utilizing the transverse coordinate difference of the corner points of the longitudinally adjacent weighbridge and the longitudinal coordinate difference of the corner points of the transversely adjacent weighbridge so as to acquire the deformation of the weighbridge.
4. The dynamic weighing angular difference compensation method of claim 3, wherein the degree of abnormality of the floor scale during the dynamic weighing process is as follows:
Figure DEST_PATH_IMAGE051
YC is the abnormal degree of the ground scale in the dynamic weighing process;
Figure 358114DEST_PATH_IMAGE033
the abnormal degree of the floor scale at the sampling time, namely the nth time.
5. The dynamic weighing angular difference compensation method of claim 4, wherein the angular difference compensation necessity is:
Figure DEST_PATH_IMAGE053
WE is the angular difference compensation necessity of the dynamic weighing process of the vehicle;
Figure DEST_PATH_IMAGE055
in order to balance the difference quantity of the initial shape of the land scale,
Figure DEST_PATH_IMAGE057
the stable shape difference of the weighbridge is obtained; u is the influence coefficient of the initial and stable shape difference of the weighbridge; v is an influence coefficient of the abnormal degree of the ground scale in the dynamic weighing process of the vehicle; and a is a scaling coefficient.
6. The angular difference compensation method for dynamic weighing according to claim 5, wherein the step of compensating the vehicle weight obtained in the dynamic weighing process to obtain the true weight of the vehicle comprises: constructing a weight compensation neural network, inputting the initial shape abnormal quantity and the stable shape abnormal quantity of the weighbridge, the abnormal degree of the weighbridge in the dynamic weighing process and the weight numerical values of all the corner points of the weighbridge, and outputting compensation weight; the sum of the dynamic weighing weight and the compensation weight of the floor scale is the real weight of the vehicle.
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