CN116358678A - Vehicle weight detection method and device - Google Patents
Vehicle weight detection method and device Download PDFInfo
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- CN116358678A CN116358678A CN202111630990.3A CN202111630990A CN116358678A CN 116358678 A CN116358678 A CN 116358678A CN 202111630990 A CN202111630990 A CN 202111630990A CN 116358678 A CN116358678 A CN 116358678A
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G3/00—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances
- G01G3/12—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing
- G01G3/13—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing having piezoelectric or piezoresistive properties
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The application is applicable to the technical field of weighing, and provides a vehicle weight detection method and device, wherein the method comprises the following steps: the method comprises the steps of obtaining weighing information of a vehicle, wherein the weighing information comprises weighing data and acquisition time acquired by a strip-shaped weighing sensor; obtaining a time-weight curve of the vehicle axle passing through the weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve; determining weighing characteristic information according to the displacement-weight curve; based on the weighing characteristic information, a prediction result of the vehicle weight is obtained by inputting the preset weighing model, the accurate vehicle weight can be obtained through a small amount of the weighing characteristic information, the vehicle weight is not required to be determined based on all weighing data, the cost of hardware equipment can be reduced, the robustness of the weighing model is improved, the accurate vehicle weight can be obtained when the weighing data is partially lost, meanwhile, the detailed characteristic of the weighing data can be highlighted through the extraction of the weighing characteristic information, and the weighing accuracy is further improved.
Description
Technical Field
The application belongs to the technical field of weighing, and particularly relates to a vehicle weight detection method and device.
Background
The strip weighing sensor at present mainly comprises a strip weighing sensor and a quartz type weighing sensor, and the strip dynamic truck scale and the quartz type dynamic truck scale formed by the strip weighing sensor have the characteristics of small deformation and quick response, and can be more suitable for dynamic weighing of vehicles at higher speeds (more than 20 km/h) compared with the whole-vehicle dynamic truck scale, axle group dynamic truck scale and axle weight dynamic truck scale, so that the strip weighing sensor is widely applied to overtravel and overload enforcement of vehicles in open road sections such as national province.
The weighing method of the dynamic truck scale composed of the strip-shaped sensor mainly comprises the product of speed and weighing curve integration. However, the technical means needs to adopt all acquired weighing data, and if the acquired weighing data are interfered during acquisition and the data are lost, inaccurate weighing results can occur; moreover, the method of adopting all weighing data ignores the detail characteristics acquired by the strip-shaped weighing sensor, so that higher weighing accuracy cannot be achieved.
Disclosure of Invention
The embodiment of the application provides a vehicle weight detection method and device, which can solve the problems of missing weighing data and improving weighing accuracy.
In a first aspect, an embodiment of the present application provides a vehicle weight detection method, including:
acquiring weighing information of a vehicle, wherein the weighing information comprises acquisition time of a strip-shaped weighing sensor and acquired weighing data corresponding to the acquisition time;
obtaining a time-weight curve of a vehicle wheel axle passing through a weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve;
determining weighing characteristic information according to the displacement-weight curve;
and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
In one embodiment, the converting the time-weight curve into a displacement-weight curve comprises:
obtaining a time-displacement curve of a vehicle wheel axle passing through a weighing area according to the time-weight curves of different strip-shaped weighing sensors;
the displacement-weight curve is determined from a time-weight curve and a time-displacement curve of the vehicle axle through the weighing area.
In one embodiment, determining weighing characteristic information from the displacement-weight curve includes:
according to the displacement-weight curve, n displacement points and corresponding weighing data are selected as weighing characteristic information, wherein n is more than or equal to 5.
In one embodiment, the n displacement points are selected by random sampling, hierarchical sampling, systematic sampling, or whole group sampling.
In one embodiment, an average value of distances between two adjacent displacement points in the n displacement points is greater than a preset threshold value.
In one embodiment, the step of inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight comprises,
substituting the displacement X of the weighing characteristic information and the weighing information Z into a deflection equation to obtain a spring constant k z An overdetermined equation set of rotational inertia I, tension T and elastic coefficient E, wherein the deflection equation is as follows;
at the spring constant k z Selecting an optimal combination in a preset value range of the moment of inertia I, the tension T and the elastic coefficient E to obtain an optimal solution of the overdetermined equation set;
the spring constant k in the optimal solution z Substituting the moment of inertia I, the tension T and the elastic coefficient E into a preset formula to obtain a predicted result of the vehicle weight, wherein the preset formula is as follows:
wherein F is z Z is a result of prediction of the vehicle weight o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
In one embodiment, the method further comprises:
and obtaining at least two prediction results, and obtaining the weight of the vehicle based on the prediction results.
In one embodiment, the method further comprises:
and acquiring preset weighing models with different n values and corresponding prediction results, and obtaining the weight of the vehicle based on the prediction results.
In one embodiment, the obtaining the optimal solution of the set of overdetermined equations includes:
and solving an optimal solution of the overdetermined equation set by using a machine learning model.
In one embodiment, the weighing characteristic information is used as a model characteristic and the low-speed site data is used as a model label, and a weighing model between the model characteristic and the model label is established to obtain the preset weighing model.
In a second aspect, an embodiment of the present application provides a vehicle weight detecting device, including:
the acquisition module is used for acquiring weighing information of the vehicle, wherein the weighing information comprises weighing data acquired by the strip-shaped weighing sensor and acquisition time;
the data processing module is used for obtaining a time-weight curve of the vehicle wheel axle when the vehicle wheel axle passes through the weighing area according to the weighing information, and converting the time-weight curve into a displacement-weight curve;
the weighing characteristic information is determined according to the displacement-weight curve;
and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
In one embodiment, the data processing module is specifically configured to:
bits of the weighing characteristic informationSubstituting the displacement X and the weighing information Z into a deflection equation to obtain a relative spring constant k z An overdetermined equation set of rotational inertia I, tension T and elastic coefficient E, wherein the deflection equation is as follows;
at the spring constant k z Selecting an optimal combination in a preset value range of the moment of inertia I, the tension T and the elastic coefficient E to obtain an optimal solution of the overdetermined equation set;
the spring constant k in the optimal solution z Substituting the moment of inertia I, the tension T and the elastic coefficient E into a preset formula to obtain a predicted result of the vehicle weight, wherein the preset formula is as follows:
wherein F is z Z is a result of prediction of the vehicle weight o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
In one embodiment, the length of the strip-shaped weighing sensor along the running direction of the vehicle is less than 100mm, and the strip-shaped weighing sensor can be one or a combination of a narrow strip-shaped weighing sensor, a quartz-type weighing sensor and a piezoelectric-type weighing sensor.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the method according to any one of the first aspects when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method as in any one of the first aspects above.
In a fifth aspect, embodiments of the present application provide a computer program product for, when run on a terminal device, causing the terminal device to perform the method of any one of the first aspects.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
according to the method and the device, weighing information of the vehicle is obtained, wherein the weighing information comprises weighing data and collecting time collected by a strip-shaped weighing sensor; obtaining a time-weight curve of the vehicle axle passing through the weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve; determining weighing characteristic information according to the displacement-weight curve; based on the weighing characteristic information, inputting the weighing characteristic information into a preset weighing model to obtain a prediction result of the vehicle weight, obtaining the accurate vehicle weight through a small amount of the weighing characteristic information, and not needing to determine the vehicle weight based on all weighing data, so that the data processing amount can be reduced, the cost of hardware equipment can be reduced, the robustness of the weighing model can be improved, and the accurate vehicle weight can be obtained when the weighing data is partially lost; meanwhile, through extraction of weighing characteristic information, detailed characteristics of weighing data can be highlighted, and then weighing accuracy is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for detecting vehicle weight according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of an arrangement of strip load cells provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of a time-weight curve provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a time-displacement curve provided by an embodiment of the present application;
FIG. 5 is a schematic illustration of a displacement-weight curve provided by an embodiment of the present application;
fig. 6 is a schematic structural view of a vehicle weight detecting device provided in an embodiment of the present application;
fig. 7 is a schematic structural 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 particular system configurations, 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.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Fig. 1 is a flow chart of a vehicle weight detection method according to an embodiment of the present application. By way of example and not limitation, as shown in fig. 1, the method includes:
s101: the weighing information of the vehicle tire to the ground is obtained through a strip-shaped weighing sensor arranged on the road surface.
The weighing information of the tire to the ground comprises the acquisition time of the strip-shaped weighing sensor and the weighing data of the acquired corresponding acquisition time.
S102: and obtaining a time-weight curve of the vehicle wheel axle passing through the weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve.
The weighing area is a data acquisition area of the strip-shaped weighing sensor.
Specifically, the tire is elastically deformed under the combined action of the gravity of the vehicle and the road surface supporting force.
When a vehicle passes through the strip-shaped weighing sensor, the strip-shaped weighing sensorThe sensor generates deformation, the length of the strip-shaped weighing sensor along the running direction of the vehicle is far smaller than the length of the tyre contacting the ground, the envelope characteristic is related to the supporting force of the ground to the vehicle and the radial deformation of the tyre, the deformation characteristic under load is used as an evaluation index, and the spring constant k is constructed z The relation between the moment of inertia I, the tension T, and the elastic coefficient E and the deformation amount in the longitudinal direction of any displacement point, the deflection equation of the tire envelope characteristic is as follows:
where X is positional information of any point on the contact surface of the tire, and Z is a deformation amount of the sensor in the longitudinal direction.
General solution of the flex equation in the machine direction:
wherein, the liquid crystal display device comprises a liquid crystal display device,
ε＝4EIk z /T 2 。
then, the spring constant k in the optimal solution z Substituting the rotational inertia I, the tension T and the elastic coefficient E into a preset formula to obtain the vehicleThe predicted result of the vehicle weight is given by the following formula:
wherein F is z Z is the result of predicting the weight of the vehicle o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
The weighing information acquired by the strip-shaped weighing sensor is positively correlated with the deformation of the sensor, so that points on the displacement-weight curve all conform to the deflection equation.
Based on the analysis results described above, the time-weight curve can be correlated with the time-displacement curve, thereby converting the time-weight curve of the vehicle weighing information into the displacement-weight curve.
S103: and determining weighing characteristic information according to the displacement-weight curve.
S104: and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
The method comprises the steps of obtaining weighing information of a vehicle, wherein the weighing information comprises weighing data and acquisition time acquired by a strip-shaped weighing sensor; obtaining a time-weight curve of the vehicle axle passing through the weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve; determining weighing characteristic information according to the displacement-weight curve; based on the weighing characteristic information, the prediction result of the vehicle weight is obtained by inputting the preset weighing model, the accurate vehicle weight can be obtained through a small amount of weighing characteristic information, the vehicle weight is not required to be determined based on all weighing data, the cost of hardware equipment can be reduced, the robustness of the weighing model is improved, and the accurate vehicle weight can be obtained when the weighing data is partially lost, so that the weighing precision is improved.
In another embodiment, converting the time-weight curve into a displacement-weight curve includes:
firstly, according to the time-weight curves of different strip-shaped weighing sensors, a time-displacement curve of a vehicle wheel axle passing through a weighing area is obtained.
Fig. 2 is a schematic view of an arrangement of a strip load cell provided in an embodiment of the present application. As shown in fig. 2, the bar-shaped load cell 101 is disposed in a road with its upper surface flush with the road, and the bar-shaped load cell 101 can collect the pressure of the vehicle axle against it when the vehicle passes through the road on which the bar-shaped load cell 101 is mounted. It should be noted that the arrangement of the bar sensors shown in fig. 2 is only one embodiment of the present invention, and a person skilled in the art may obtain different bar sensor combinations by increasing or decreasing the number of bar sensors and adjusting the positional relationship of the bar sensors.
FIG. 3 is a schematic representation of the time-weight curve provided by the bar sensor in the embodiment of FIG. 2. As shown in fig. 3, a time-weight curve is shown after the front and rear axles of the vehicle pass through two bar-shaped load cells. The time-weight curve of the solid line is the condition that the two strip-shaped weighing sensors acquire the weighing data of the vehicle after the front axle passes through the two strip-shaped weighing sensors; the time-weight curve of the broken line is the case of the weighing data of the vehicle acquired by the two strip-shaped weighing sensors after the rear axle passes through the two strip-shaped weighing sensors. The characteristic information of the moment when the vehicle enters the strip-shaped weighing sensor, the moment when the vehicle leaves the strip-shaped weighing sensor, the peak value moment and the like can be obtained from the time-weight curve.
For example, after the vehicle axle passes through the two strip-shaped weighing sensors, a time-displacement curve of the vehicle axle passing through the weighing area is obtained according to a distance between the two strip-shaped weighing sensors, a speed of the vehicle axle passing through the two strip-shaped weighing sensors and a time-weight curve of the two strip-shaped weighing sensors.
The time difference passing through the two bar-shaped weighing sensors is obtained from the time-weight curve of the two bar-shaped weighing sensors corresponding to one axle, the time difference passing through the two bar-shaped weighing sensors can be obtained from two peak values, and the speed of the vehicle axle passing through the two bar-shaped weighing sensors is calculated according to the time difference and the distance between the two bar-shaped weighing sensors, so that the time-displacement curve of the vehicle axle passing through the weighing area is obtained according to the distance between the two bar-shaped weighing sensors, the speed of the vehicle axle passing through the two bar-shaped weighing sensors and the time difference.
FIG. 4 is a schematic diagram of a time-displacement curve provided in an embodiment of the present application. As shown in fig. 4, the change in vehicle displacement from entering the strip load cell to exiting the strip load cell is illustrated.
Next, a displacement-weight curve is determined from the time-weight curve and the time-displacement curve of the vehicle axle through the weighing region.
Specifically, based on the displacement of the vehicle in the relationship between time and displacement from entering the bar-shaped load cell to exiting the bar-shaped load cell, the time-weight curve of the vehicle passing through the bar-shaped load cell is correlated with the position-weight curve of the vehicle passing through the bar-shaped load cell to convert the time-weight curve into the displacement-weight curve.
And extracting data with the weight larger than a certain preset value from the displacement-weight curve, and expanding the data forwards and backwards by a certain displacement to obtain a plurality of effective displacement-weight curves.
FIG. 5 is a schematic representation of a displacement-weight curve provided by an embodiment of the present application. As shown in fig. 5, the change in vehicle weight as the vehicle passes the displacement of the bar load cell is illustrated. The displacement point on the displacement-weight curve and the corresponding weighing data meet the longitudinal deflection equation, and the characteristic information such as the displacement point of the vehicle driving into the bar-shaped weighing sensor, the displacement point of the vehicle driving out of the bar-shaped weighing sensor and the like can be obtained from the displacement-weight curve.
In another embodiment, determining weighing characteristic information from a displacement-weight curve includes:
according to the displacement-weight curve, n displacement points and corresponding weighing data are selected as weighing characteristic information, wherein n is more than or equal to 5.
Wherein the deflection equation in the longitudinal direction is based on the spring constant k z Equations for moment of inertia I, tension T, and elastic coefficient E and deformation, with spring constant k z Four variables of moment of inertia I, tension T and elastic coefficient EAt least five weighing characteristic information are selected to obtain an accurate prediction result.
The difficulty and the computational complexity of solving the variable can be considered, the maximum value of n is set based on the specific use scene and the condition of hardware equipment before n pieces of weighing characteristic information are selected, and meanwhile, the value of n is not set to be equal to the total amount of the acquired weighing data, so that the accuracy of solving the optimal variable is prevented from being influenced by the uniformity of the data.
For example, the weighing data of the corresponding characteristic moment can be determined based on the state condition that the whole vehicle passes through the strip-shaped weighing sensor, so that the corresponding weighing characteristic information is selected from the displacement-weight curve, and the weighing characteristic information of the characteristic moment corresponding to the waveform peak value, the characteristic moment corresponding to the entering strip-shaped weighing sensor, the characteristic moment of the exiting strip-shaped weighing sensor and the characteristic moment of the nonlinear change of the stress displacement is selected. The characteristic time of the nonlinear change of the stress displacement can be selected according to the requirement of an actual use scene, or the characteristic time of the nonlinear change of the driving-in stress displacement or the characteristic time of the nonlinear change of the driving-out stress displacement can be selected, or the characteristic time of the nonlinear change of the driving-in stress displacement and the characteristic time of the nonlinear change of the driving-out stress displacement can be selected at the same time.
For example, n displacement points may be selected by random sampling, hierarchical sampling, systematic sampling or whole group sampling, so as to determine weighing characteristic information. For example, displacement points are selected through layered sampling, weighing data acquired by the strip-shaped weighing sensor are equally divided into n parts, and one displacement point is selected from each part of data.
In another embodiment, the average value of the distances between two adjacent displacement points in the n displacement points is greater than a preset threshold value, so as to avoid the situation that the accuracy of the prediction result is reduced due to the fact that the two adjacent displacement points are too close.
The preset threshold value can be set according to the requirements of actual use scenes.
In another embodiment, based on the weighing characteristic information, inputting a preset weighing model to obtain a predicted result of the weight of the vehicle, including,
first of all,substituting the displacement X of the weighing characteristic information and the weighing information Z into a deflection equation to obtain a relative spring constant k z An overdetermined system of equations for moment of inertia I, tension T and elastic coefficient E.
Wherein the deflection equation is
Because the quantity of the weighing data acquired by the strip-shaped weighing sensor is large, an overdetermined equation set is obtained, so that an accurate spring constant k is acquired z The moment of inertia I, the tension T and the modulus of elasticity provide the basis.
Then, at the spring constant k z And selecting an optimal combination in a preset value range of the moment of inertia I, the tension T and the elastic coefficient E to obtain an optimal solution of the overdetermined equation set.
Wherein the spring constant k z The preset value ranges of the moment of inertia I, the tension T and the elastic coefficient E can be determined according to the parameters of the existing tire, and the optimal combination is convenient to select.
Then, the spring constant k in the optimal solution z Substituting the moment of inertia I, the tension T and the elastic coefficient E into a preset formula to obtain a predicted result of the vehicle weight, wherein the preset formula is as follows:
wherein F is z Z is the result of predicting the weight of the vehicle o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
Wherein Z is o Can be determined from the peaks in the displacement-weight curve.
In another embodiment, the method further comprises:
at least two prediction results are obtained, and the vehicle weight is obtained based on the prediction results.
Specifically, based on a set n value, at least two sets of weighing characteristic information are obtained, each set of n weighing characteristic information is selected by adopting random sampling, hierarchical sampling, system sampling or whole group sampling, the n weighing characteristic information of each set is input into a preset weighing model, a prediction result of the vehicle weight is obtained, and the vehicle weight is obtained according to all the prediction results.
For example, n values are set to be six, three sets of weighing characteristic information are obtained, and each set adopts random sampling, hierarchical sampling, system sampling or whole group sampling to select six weighing characteristic information. And inputting six weighing characteristic information in each group into a preset weighing model aiming at each group of weighing characteristic information to obtain a predicted result of the vehicle weight, and obtaining the vehicle weight according to the three predicted results.
The embodiment obtains more accurate vehicle weight by obtaining at least two prediction results and obtaining the vehicle weight based on the prediction results.
In another embodiment, the method further comprises:
and acquiring preset weighing models with different n values and corresponding prediction results, and obtaining the weight of the vehicle based on the prediction results.
For example, the corresponding prediction result is data of the vehicle in a free flow state, and the data can be acquired by a strip-shaped weighing sensor in a detection station such as a no-stop overrun detection station, a highway overrun detection station and the like. In addition, the weighing information may also include vehicle type, vehicle axle number, speed, etc. For example, five weighing characteristic information is obtained, a corresponding preset weighing model is input, and a prediction result of the vehicle weight is obtained; six weighing characteristic information is obtained, a corresponding preset weighing model is input, and a prediction result of the vehicle weight is obtained; nine weighing feature models are obtained, corresponding preset weighing models are input, a predicted result of the vehicle weight is obtained, and the vehicle weight is obtained according to all the predicted results.
According to the embodiment, the vehicle weight is obtained by obtaining the preset weighing models with different n values and the corresponding prediction results and obtaining the vehicle weight based on the prediction results, so that the more accurate vehicle weight is obtained.
In another embodiment, obtaining an optimal solution to the set of overdetermined equations includes:
and solving an optimal solution of the overdetermined equation set by using a machine learning model.
In another embodiment, weighing characteristic information of a road-mounted bar-type weighing sensor is acquired, while vehicle weight information of an adjacent low-speed station is acquired. The vehicle weight information may also include vehicle type, vehicle axle number, speed, etc.
The weighing characteristic information and the vehicle weight information are matched through the vehicle license plate, and it is obvious to those skilled in the industry that the matching effect can be evaluated through the similarity of the vehicle license plate, the time difference of the vehicle passing through the road surface station and the low-speed station, and the weighing characteristic information and the vehicle weight data.
In another embodiment, the weighing characteristic information is used as a model characteristic and the low-speed site data is used as a model label, and a weighing model between the model characteristic and the model label is established to obtain a preset weighing model.
The weighing model is a deep learning model and comprises an input layer, a BN layer, an intermediate layer and a full-connection layer, wherein the intermediate layer comprises a combination of a plurality of convolution layers and a pooling layer.
The BN layer performs normalization processing on the data to obtain mean variance, the convolution layer of the middle layer multiplies and adds the characteristic value of the position and the characteristic factor, the pooling layer of the middle layer searches the maximum value of adjacent data, so that the data characteristic of the redundant part of the multi-sensor is maximized, and the full connection layer integrates the local information with category differentiation in the convolution layer or the pooling layer.
The loss function of the weighing model is mean square error, and the weight of each layer of neural network is updated by adopting a counter-propagation mode of the error.
In another embodiment, the least square fitting process is performed on the weighing characteristic information, and a weighing model is built to obtain a preset weighing model.
For example, five weighing characteristic information is selected, { Z1, Z2, Z3, Z4, Z5}, the number of samples is assumed to be 100, namely, 100 displacement-weight curves of different vehicles are assumed, 100 equations are established to calculate optimal solutions of (θ1, θ2, θ3, θ4, θ5), and the optimal solutions are substituted into an original weighing model to obtain a preset weighing model. Constructing an original weighing model h θ (Z) =z·θ, i.e. h θ (z)＝θ 0 +θ 1 Z 1 +θ 2 Z 2 +θ 3 Z 3 +θ 4 Z 4 +θ 5 Z 5 . The second-order matrix between the vehicle weight information which is accurately measured by using the low-speed station data and a predicted result obtained by predicting an original weighing model is used as a loss function:
wherein Z.θ is the prediction result, w is the low-speed station data, T is the transpose, and the value of the loss function is the smallest (θ1, θ2, θ3, θ4, θ5) J(θ)min Is the optimal solution.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Corresponding to the methods described in the embodiments above, only the parts relevant to the embodiments of the present application are shown for convenience of explanation.
Fig. 6 is a schematic structural view of a vehicle weight detecting device provided in an embodiment of the present application. By way of example and not limitation, as shown in fig. 6, the apparatus includes:
the acquisition module 10 is used for acquiring weighing information of the vehicle, wherein the weighing information comprises weighing data acquired by the strip-shaped weighing sensor and acquisition time;
the data processing module 11 is used for obtaining a time-weight curve of the vehicle wheel axle passing through the weighing area based on the weighing information and converting the time-weight curve into a displacement-weight curve;
the weighing characteristic information is determined according to the displacement-weight curve;
and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
In another embodiment, the data processing module is specifically configured to:
displacement X of the weighing characteristic information and the weighing informationZ is substituted into the deflection equation to obtain the value of the spring constant k z An overdetermined equation set of moment of inertia I, tension T and elastic coefficient E, and the deflection equation is
At spring constant k z Selecting an optimal combination in a preset value range of the moment of inertia I, the tension T and the elastic coefficient E to obtain an optimal solution of an overdetermined equation set;
the spring constant k in the optimal solution z Substituting the moment of inertia I, the tension T and the elastic coefficient E into a preset formula to obtain a predicted result of the vehicle weight, wherein the preset formula is as follows:
wherein F is z Z is the result of predicting the weight of the vehicle o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic apparatus 2 of this embodiment includes: at least one processor 20 (only one is shown in fig. 7), a memory 21 and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, the processor 20 implementing the steps in any of the various method embodiments described above when executing the computer program 22.
The electronic device 2 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 2 may include, but is not limited to, a processor 20, a memory 21. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the electronic device 2 and is not meant to be limiting of the electronic device 2, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 20 may be a central processing unit (Central Processing Unit, CPU), and the processor 20 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 21 may in some embodiments be an internal storage unit of the electronic device 2, such as a hard disk or a memory of the electronic device 2. The memory 21 may in other embodiments also be an external storage device of the electronic device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the electronic device 2. The memory 21 is used for storing an operating system, application programs, boot Loader (Boot Loader), data, other programs, etc., such as program codes of the computer program. The memory 21 may also be used for temporarily storing data that has been output or is to be output.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer readable storage medium storing a computer program, which when executed by a processor, may implement the steps in the above-described method embodiments.
The present embodiments provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform the steps of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. 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.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A vehicle weight detection method, characterized by comprising:
acquiring weighing information of a vehicle, wherein the weighing information comprises acquisition time of a strip-shaped weighing sensor and acquired weighing data corresponding to the acquisition time;
obtaining a time-weight curve of a vehicle wheel axle passing through a weighing area based on the weighing information, and converting the time-weight curve into a displacement-weight curve;
determining weighing characteristic information according to the displacement-weight curve;
and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
2. The method of claim 1, wherein said converting said time-weight curve into a displacement-weight curve comprises:
obtaining a time-displacement curve of a vehicle wheel axle passing through a weighing area according to the time-weight curves of different strip-shaped weighing sensors;
the displacement-weight curve is determined from a time-weight curve and a time-displacement curve of the vehicle axle through the weighing area.
3. The method of claim 1, wherein determining weighing characteristic information from the displacement-weight curve comprises:
according to the displacement-weight curve, n displacement points and corresponding weighing data are selected as weighing characteristic information, wherein n is more than or equal to 5.
4. A method as claimed in claim 3, wherein the n displacement points are selected by random sampling, hierarchical sampling, systematic sampling or whole group sampling.
5. The method of claim 4, wherein an average value of distances between two adjacent displacement points of the n displacement points is greater than a preset threshold.
6. The method of claim 3, wherein inputting the predicted result of the vehicle weight into a preset weighing model based on the weighing characteristic information comprises,
substituting the displacement X of the weighing characteristic information and the weighing information Z into a deflection equation to obtain a spring constant k z An overdetermined equation set of moment of inertia I, tension T and elastic coefficient E, wherein the deflection equation is
At the spring constant k z Selecting an optimal combination in a preset value range of the moment of inertia I, the tension T and the elastic coefficient E to obtain an optimal solution of the overdetermined equation set;
the spring constant k in the optimal solution z Substituting the moment of inertia I, the tension T and the elastic coefficient E into a preset formula to obtain a predicted result of the vehicle weight, wherein the preset formula is as follows:
wherein F is z Z is a result of prediction of the vehicle weight o Being the maximum of radial deformation, ζ is constant, ε=4 EIk z /T 2 。
7. The method of any one of claims 1-6, further comprising:
and obtaining at least two prediction results, and obtaining the weight of the vehicle based on the prediction results.
8. The method of claim 1, wherein the weighing characteristic information is used as a model characteristic and low-speed site data is used as a model tag, and a weighing model between the model characteristic and the model tag is established, so that the preset weighing model is obtained.
9. A vehicle weight detecting apparatus, characterized by comprising:
the acquisition module is used for acquiring weighing information of the vehicle, wherein the weighing information comprises weighing data acquired by the strip-shaped weighing sensor and acquisition time;
the data processing module is used for obtaining a time-weight curve of the vehicle wheel axle passing through the weighing area based on the weighing information and converting the time-weight curve into a displacement-weight curve;
the weighing characteristic information is determined according to the displacement-weight curve;
and inputting the weight characteristic information into a preset weighing model to obtain a predicted result of the vehicle weight.
10. The vehicle weight detecting apparatus according to claim 9, wherein a length of the strip-shaped load cell in a vehicle traveling direction is less than 100mm.
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| CN202111630990.3A CN116358678A (en) | 2021-12-28 | 2021-12-28 | Vehicle weight detection method and device |
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