Vehicle Overlimit Detection Method, Device, Storage Medium and Electronic Device

Abstract

The present application discloses a vehicle over-limit detection method, device, storage medium and electronic device. Among them, the above method includes: obtaining pressure information of a vehicle passing through a road surface through a road surface sensor disposed below the road surface, and determining first axle information of the vehicle based on the pressure information; obtaining vehicle outline information through a non-contact sensor disposed on the roadside, and determining second axle information of the vehicle according to the vehicle outline information; judging whether the vehicle has a floating axle according to the first axle information and the second axle information to obtain a floating axle judgment result; judging whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle identification, and in particular, to a vehicle over-limit detection method, device, storage medium, and electronic device. Background Art

[0002] A suspension axle is a floating axle in a multi-axle truck with a load, which can be lifted or lowered at will by one or more airbags or hydraulic controls. When a vehicle with a suspension axle travels empty, the suspension axle can be lifted to reduce energy consumption and tire wear. When loading goods, the suspension axle can be lowered to reduce the pressure on the road surface caused by the goods.

[0003] In vehicle over-limit control applications, corresponding over-limit standards and toll standards are set for different axle types. The maximum weight that a vehicle can carry is related to the number of axles and the axle type. For example, for each additional double-tire axle, the upper limit of the weight that the vehicle can carry increases accordingly. In addition, if the suspension axle is located in the part of the triple-axle or double-axle, the upper limit of the weight that can be carried also has different increases. For example, the maximum carrying capacity of a double-axle double-tire is 18t, while the maximum carrying capacity of a triple-axle double-tire is 22t. Therefore, in vehicle over-limit control applications, the specific information of the suspension axle needs to be considered. Summary of the Invention

[0004] Embodiments of this application provide a vehicle over-limit detection method, device, storage medium, and electronic device, so as to at least solve the technical problem in the related art that since the vehicle suspends the suspension axle in the air, it is impossible to accurately identify whether the vehicle uses the suspension axle, and thus it is difficult to determine whether the vehicle has an over-limit behavior.

[0005] According to one aspect of the embodiments of this application, a vehicle over-limit detection method is provided, including: obtaining pressure information of a vehicle passing through a road surface by a road surface sensor disposed under the road surface, and determining first axle information of the vehicle based on the pressure information; obtaining vehicle outline information by a non-contact sensor disposed on the roadside, and determining second axle information of the vehicle according to the vehicle outline information; judging whether the vehicle has a suspension axle according to the first axle information and the second axle information to obtain a suspension axle judgment result; and judging whether the vehicle is over-limit according to the suspension axle judgment result and the vehicle weight of the vehicle.

[0006] Optionally, it is determined whether the vehicle is overweight based on the suspension axle determination result and the vehicle weight of the vehicle, including: calculating the vehicle weight of the vehicle based on the pressure information; in the case where the suspension axle determination result indicates that the vehicle has a suspension axle, determining the position of the suspension axle of the vehicle according to the pressure information and the vehicle outline information, and determining whether it is overweight based on the position of the suspension axle and the vehicle weight of the vehicle; in the case where the suspension axle determination result indicates that the vehicle does not have a suspension axle, determining the contact state between each axle of the vehicle and the road surface according to the pressure information of each axle in the suspension axle determination result, where the contact state includes a full contact state and a semi-contact state, and the semi-contact state means that the pressure information of the axle does not reach the weighing threshold, and the weighing threshold is the minimum value of the pressure information determined by the road surface sensor as the axle; when there is an axle in the semi-contact state, determining whether it is overweight based on the position of the axle in the semi-contact state and the vehicle weight of the vehicle.

[0007] Optionally, the method further includes: when there is an axle in the semi-contact state, correcting the vehicle weight based on all the pressure information corresponding to the axle in the semi-contact state and the effective pressure information corresponding to other axles; and determining whether there is an overweight behavior according to the corrected vehicle weight.

[0008] Optionally, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle outline information includes vehicle point cloud data or vehicle image data; in the case where the non-contact sensor is a laser sensor, vehicle point cloud data is obtained through the laser sensor; in the case where the non-contact sensor is an image sensor, vehicle image data is obtained through the image sensor.

[0009] Optionally, determining the first axle information of the vehicle based on the pressure information includes: generating a pressure change diagram of the road surface sensor in a target period according to the pressure information; determining a plurality of peaks in the pressure change diagram, and determining the moment corresponding to each peak; in the case where the peak is greater than or equal to the weighing threshold, determining the peak as the target peak corresponding to when the axle passes; determining the first axle information according to the number of target peaks, the difference between the moments corresponding to adjacent target peaks, and the vehicle speed.

[0010] Optionally, in the case where the non-contact sensor is an image sensor, determining the second axle information of the vehicle according to the vehicle outline information includes: extracting the feature data of each frame of a plurality of frames of pictures of the vehicle to obtain a plurality of feature data; calculating the mutual information between any two of the plurality of feature data through a mutual information registration algorithm to obtain a plurality of mutual information; performing image registration on the plurality of frames of pictures according to the plurality of mutual information to obtain a target picture; inputting the target picture into a preset neural network model to obtain the second axle information, where the preset neural network model is trained by multiple groups of training samples, and each group of training samples includes a historical vehicle picture and axle information.

[0011] Optionally, determining whether a vehicle has a floating axle based on the first axle information and the second axle information includes: determining whether the first axle information and the second axle information are the same; in the case where the first axle information and the second axle information are the same, determining that the vehicle does not have a floating axle; in the case where the first axle information and the second axle information are different, determining that there is a floating axle in the vehicle, and determining the axle serial number of the floating axle according to the first axle information and the second axle information, where the axle serial number is the position information of the floating axle installed on the vehicle.

[0012] According to another aspect of the embodiments of the present application, there is also provided a vehicle over-limit detection device, including: a first acquisition unit, configured to acquire pressure information of a vehicle passing through a road surface through a road surface sensor disposed below the road surface, and determine first axle information of the vehicle based on the pressure information; a second acquisition unit, configured to acquire vehicle outline information through a non-contact sensor disposed on the roadside, and determine second axle information of the vehicle according to the vehicle outline information; a first judgment unit, configured to judge whether the vehicle has a floating axle according to the first axle information and the second axle information, and obtain a floating axle judgment result; a second judgment unit, configured to judge whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle.

[0013] According to still another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the above-mentioned vehicle over-limit detection method when running.

[0014] According to still another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the above-mentioned processor executes the above-mentioned vehicle over-limit detection method through the computer program.

[0015] In the embodiments of the present application, pressure information of a vehicle passing through a road surface is acquired through a road surface sensor disposed below the road surface, and first axle information of the vehicle is determined based on the pressure information; vehicle outline information is acquired through a non-contact sensor disposed on the roadside, and second axle information of the vehicle is determined according to the vehicle outline information; it is judged whether the vehicle has a floating axle according to the first axle information and the second axle information, and a floating axle judgment result is obtained; it is judged whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle, achieving the technical effect of accurately identifying whether the vehicle uses a floating axle, so as to determine whether the vehicle has an over-limit behavior, and further solving the problem in the related art that it is difficult to accurately identify whether the vehicle uses a floating axle due to the vehicle suspending the floating axle, and thus it is difficult to judge whether the vehicle has an over-limit behavior. Description of the Drawings

[0016] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the hardware environment of an optional vehicle over-limit detection method according to an embodiment of the present application;

[0019] Figure 2 It is a schematic flowchart of an optional vehicle over-limit detection method according to an embodiment of the present application;

[0020] Figure 3 It is an optional pressure change diagram according to an embodiment of the present application;

[0021] Figure 4 It is a structural diagram of an optional vehicle over-limit detection device according to an embodiment of the present application;

[0022] Figure 5 It is a structural block diagram of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0023] In order to enable those skilled in the art of the present technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to one aspect of the embodiments of the present application, a vehicle over-limit detection method is provided. Optionally, in this embodiment, the above vehicle over-limit detection method can be applied to a hardware environment including a detection component 102 and a data processor 104 as shown in Figure 1 the figure. As shown in Figure 1 the figure, the data processor 104 is connected to the detection component 102 through a network, and can be used to identify vehicle information based on the detection data of the detection component 102. For example, the contour information of the vehicle, the weight information of the vehicle, etc. can be identified. A data storage component (the data can be stored through a database) can be set on or independent of the data processor to provide data storage services for the data processor 104. Here, the detection component 102 and the data processor 104 can both belong to the vehicle information detection system.

[0026] The above network can include but is not limited to at least one of the following: wired network, wireless network. The above wired network can include but is not limited to at least one of the following: wide area network, metropolitan area network, local area network. The above wireless network can include but is not limited to at least one of the following: WIFI (Wireless Fidelity), Bluetooth. In addition to being connected through a network, the detection component 102 and the data processor 104 can also be connected through a network cable or a serial port. The detection component 102 can include two lateral scanning components, one longitudinal scanning component and a row of weighing sensors. Among them, the lateral scanning component and the longitudinal scanning component can be but are not limited to scanning laser sensors, and the weighing sensors can include but are not limited to narrow strip sensors, etc.

[0027] The vehicle over-limit detection method of the embodiments of the present application can be executed by the data processor 104, or can be jointly executed by the data processor 104 and the detection component 102. Taking the vehicle over-limit detection method in this embodiment being executed by the data processor 104 as an example, Figure 2 is a schematic flowchart of an optional vehicle over-limit detection method according to the embodiments of the present application. As shown in Figure 2 the figure, the process of this method can include the following steps:

[0028] Step S202, obtain the pressure information of the vehicle passing through the road surface through the road surface sensor arranged under the road surface, and determine the first axle information of the vehicle based on the pressure information.

[0029] Specifically, the road surface sensor can be a strain pressure sensor. By collecting the deformation information of the road surface when the vehicle passes above the strain pressure sensor through the strain pressure sensor, the pressure information is then generated based on the deformation information. The strain pressure sensor is a type of sensor based on measuring the strain generated by an object under force. It uses a resistance strain gauge to convert the strain change into a resistance change and outputs the pressure change value collected by the pressure sensor through the resistance change. By using the strain sensor to continuously sense the pressure information of the vehicle rolling above the road surface, the axle type information, axle distance information, and number of axles of the vehicle passing above the road surface can be calculated, that is, the first axle information.

[0030] Step S204: Obtain the vehicle outline information through a non-contact sensor set on the roadside, and determine the second axle information of the vehicle based on the vehicle outline information.

[0031] Specifically, the non-contact sensor can be a laser sensor or an image sensor. When the non-contact sensor is an image sensor, through the image sensor installed beside the road surface, multiple frames of pictures of the vehicle are taken by the image sensor when the vehicle passes the road surface. Each frame of the collected pictures needs to include the axle part of the vehicle. Since one frame of picture cannot cover all the axles of the vehicle, multiple frames of pictures of the respective axle parts of the vehicle collected need to be subjected to image registration to obtain a target picture containing all the axles of the vehicle. By inputting the target picture into a pre-trained deep learning model, the position information of each axle of the vehicle in the target picture is automatically recognized, and the axle type information, axle distance information, and number of axles of the vehicle are output, that is, the second axle information.

[0032] Step S206: Determine whether the vehicle has a floating axle based on the first axle information and the second axle information, and obtain the floating axle judgment result.

[0033] Specifically, compare the first axle information collected through the road surface sensor with the second axle information collected through the non-contact sensor to determine whether the first axle information and the second axle information are the same. If the comparison result shows that the first axle information and the second axle information are the same, it means that the vehicle does not use a floating axle. If the comparison result shows that the first axle information and the second axle information are different, it means that the vehicle uses a floating axle.

[0034] Step S208: Determine whether the vehicle is overloaded based on the floating axle judgment result and the vehicle weight of the vehicle.

[0035] Specifically, the load-bearing limit of a vehicle using a floating axle is different from that of a vehicle not using a floating axle. Some vehicles use floating axles to save fuel during transportation. However, these vehicles using floating axles may be overloaded. Therefore, determine the current load-bearing limit of the vehicle based on the floating axle judgment result of the vehicle, and then determine whether the vehicle is overloaded based on the current load-bearing limit and the vehicle weight.

[0036] In this embodiment, the axle information may include the number of axles, and may also include the axle type and the axle spacing, which can be specifically set according to actual needs. Through the above steps S202 to S208, the pressure information of the vehicle passing through the road surface is obtained by the road surface sensor arranged under the road surface, and the first axle information of the vehicle is determined based on the pressure information; the outer contour information of the vehicle is obtained by the non-contact sensor arranged on the roadside, and the second axle information of the vehicle is determined according to the outer contour information of the vehicle; whether the vehicle has a floating axle is judged according to the first axle information and the second axle information, and a floating axle judgment result is obtained; whether the vehicle is overweight is judged based on the floating axle judgment result and the vehicle weight of the vehicle, which solves the problem in the related technology that since the vehicle suspends the floating axle in the air, it is impossible to accurately identify whether the vehicle uses the floating axle, and thus it is difficult to judge whether the vehicle has an overweight behavior, and achieves the technical effect of accurately identifying whether the vehicle uses the floating axle, and thus judging whether the vehicle is overweight.

[0037] In an exemplary embodiment, optionally, judging whether the vehicle is overweight based on the floating axle judgment result and the vehicle weight of the vehicle includes: calculating the vehicle weight based on the pressure information; when the floating axle judgment result indicates that the vehicle has a floating axle, determining the position of the floating axle of the vehicle according to the pressure information and the outer contour information of the vehicle, and judging whether it is overweight based on the position of the floating axle and the vehicle weight of the vehicle; when the floating axle judgment result indicates that the vehicle does not have a floating axle, judging the contact state between each axle of the vehicle and the road surface according to the pressure information of each axle in the floating axle judgment result, where the contact state includes a full contact state and a semi-contact state, and the semi-contact state means that the pressure information of the axle does not reach the weighing threshold, and the weighing threshold is the minimum value of the pressure information determined by the road surface sensor as the axle; when there is an axle in the semi-contact state, judging whether it is overweight based on the position of the axle in the semi-contact state and the vehicle weight of the vehicle.

[0038] Specifically, the vehicle weight is calculated through the pressure information of each axle of the vehicle collected by the road surface sensor. The maximum weight that the vehicle can carry is linearly related to the number of axles. For each additional double-tire axle, the upper limit that the vehicle can carry increases accordingly. If the position of the floating axle is in the triple-axle or double-axle part, the upper limit that can be carried also has different increases. For example, the maximum carrying mass of a double-axle double-tire is 18t, while the maximum carrying mass of a triple-axle double-tire is 22t. Therefore, it is necessary to determine the load-bearing upper limit of the vehicle according to whether the vehicle uses a floating axle and the position of the floating axle, and then judge whether the vehicle weight exceeds the load-bearing upper limit to determine whether the vehicle is overweight.

[0039] Since the existence of a floating axle is an important basis for determining whether a vehicle is overloaded, when there is a floating axle, the contact state between the floating axle and the road end is also crucial. Since there is a situation where the vehicle lowers the floating axle and semi-contacts the road surface, that is, the axle is in a semi-contact state with the road surface. At this time, the judgment result based on the floating axle may show that there is no floating axle. In this case, it is necessary to further judge the contact state between each axle of the vehicle and the road surface. If there is an axle of the vehicle in a semi-contact state, when calculating the vehicle weight based on the pressure information, the weight of the axle in the semi-contact state will not be calculated, that is, the vehicle weight is inaccurate. Therefore, it is necessary to recalculate the vehicle weight based on the pressure information of the axle in the semi-contact state, and then judge whether the vehicle is overloaded according to the recalculated vehicle weight and the load limit.

[0040] In an exemplary embodiment, optionally, the method further includes: when there is an axle in a semi-contact state, correcting the vehicle weight based on all the pressure information corresponding to the axle in the semi-contact state and the effective pressure information corresponding to other axles; and judging whether there is an overloading behavior according to the corrected vehicle weight.

[0041] It can be understood that currently, when calculating the vehicle weight using pressure information, the obtained pressure information will be first denoised to obtain effective pressure information; this denoising process generally can be achieved by setting a pressure threshold, and taking the part of the pressure signal amplitude in all the pressure information that is greater than the pressure threshold as the effective pressure information, so as to determine the vehicle data. However, when the vehicle has abnormal driving behaviors such as deliberately jumping on the scale, the tire may not be in full contact with the pressure sensor, resulting in a relatively small pressure data corresponding to the tire, which cannot be used for calculating the vehicle weight, thus causing a deviation between the calculated vehicle weight and the actual vehicle weight. In this application, the weight of the axle in the semi-contact state is calculated based on the pressure information corresponding to the vehicle in the semi-contact state, and the weight of the axle in the semi-contact state is added to the total weight of the axles in the full-contact state to obtain the corrected vehicle weight.

[0042] In an exemplary embodiment, optionally, the non-contact sensor includes any one of a laser sensor and an image sensor; the vehicle outline information includes vehicle point cloud data or vehicle image data; when the non-contact sensor is a laser sensor, vehicle point cloud data is obtained through the laser sensor; when the non-contact sensor is an image sensor, vehicle image data is obtained through the image sensor.

[0043] Specifically, the vehicle outline information, that is, the appearance information of the vehicle, is obtained by obtaining the image data of the vehicle to get the second axle information of the vehicle. If the non-contact sensor is a laser sensor, the point cloud data of the vehicle is collected through the laser sensor, and the second axle information is obtained by analyzing the point cloud data. If the non-contact sensor is an image sensor, the second axle information is obtained by collecting multiple frames of pictures of the vehicle through the image sensor.

[0044] Generate a pressure change graph based on pressure information, and calculate the first axle information according to the peak characteristics in the pressure change graph. In an exemplary embodiment, optionally, determining the first axle information of the vehicle based on pressure information includes: generating a pressure change graph of the road surface sensor within a target period according to the pressure information; determining multiple peaks in the pressure change graph and determining the moment corresponding to each peak; when the peak is greater than or equal to the weighing threshold, determining the peak as the target peak corresponding to when the axle passes; determining the first axle information according to the number of target peaks, the difference between the moments corresponding to adjacent target peaks, and the vehicle speed.

[0045] Specifically, the first axle information may include the number of first axles and the first axle spacing, and the target period may be the duration when the vehicle completely passes through the image acquisition device. Figure 3 It is an optional pressure change graph according to an embodiment of the present application. As Figure 3 shown, the horizontal axis in the pressure change graph represents the acquisition time of the pressure information, and the vertical axis represents the magnitude of the pressure borne by the road surface. Figure 3 The pressure information when two wheels of the vehicle pass through the road surface is intercepted. Each wheel passing through the pressure sensor represents an axle passing through the pressure sensor. The dotted lines indicated by t1 and t2 are also the positions where the target peaks are located. Since there will also be pressure changes on the road surface itself, there will also be smaller peaks in the pressure change graph. By setting a first threshold, the pressure changes caused by non-axle passing are excluded. The peaks greater than the first threshold are used as target peaks, and the number of target peaks is the number of first axles. By calculating the difference between t1 and t2, the time difference between the two axles passing through the pressure sensor is obtained. Combining with the driving speed of the vehicle, the product of the difference between the moments corresponding to the target peaks and the vehicle speed is calculated to obtain the first axle spacing. Determine the first axle information through the pressure change graph, so as to provide analysis data for judging whether the vehicle uses a floating axle.

[0046] In order to obtain the second axle information, it is necessary to first perform image registration on multiple frames of pictures to obtain a target picture, and then determine the second axle information based on the target picture. Optionally, when the non-contact sensor is an image sensor, determining the second axle information of the vehicle according to the vehicle outline information includes: extracting the feature data of each frame of picture in multiple frames of pictures of the vehicle to obtain multiple feature data; calculating the mutual information between any two of the multiple feature data through the mutual information registration algorithm to obtain multiple mutual information; performing image registration on multiple frames of pictures according to the multiple mutual information to obtain a target picture; inputting the target picture into a preset neural network model to obtain the second axle information, where the preset neural network model is trained by multiple groups of training samples, and each group of training samples includes a historical vehicle picture and axle information.

[0047] Specifically, the second axle information can be the number of second axles and the second axle spacing. The feature data can be the set of pixel blocks in each frame of the picture. The mutual information refers to a measure of the mutual dependence between two frames of pictures. Image registration refers to the process of matching and superimposing two or more pictures obtained at different times, by different sensors, or under different conditions. The mutual information registration algorithm is also the image registration method based on mutual information. Calculate the mutual information between any two sets of pixel blocks in the multiple sets of pixel blocks in multiple frames of pictures. Use the mutual information as the matching criterion between images, that is, superimpose the pixel blocks corresponding to the same axle in multiple frames of pictures, and fuse the pixel blocks corresponding to all the same vehicle structures. Finally, obtain the target picture that completely displays all the structural features of the vehicle. Obtain the target picture containing all the axle information of the vehicle through image registration for obtaining the second axle information.

[0048] It should be noted that when comparing the first axle information and the second axle information, it is necessary to determine that they correspond to the same vehicle. Therefore, when obtaining multiple frames of pictures, it is necessary to obtain pictures at the same time as the target acquisition time period corresponding to the acquisition of the first axle information.

[0049] The target neural network model can be a deep learning model. Use the historical vehicle picture data and the corresponding number of vehicle axles and axle spacing as the training set to input into the deep learning model for training, so that the target neural network model can output the number of vehicle axles and the axle spacing of the vehicle in the target picture according to the input target picture. The deep learning model identifies the position of the tires in the target image, detects the position of the tires, and counts the second axle information and the second axle spacing of the vehicle. By inputting the target picture into the target neural network model, obtain the second axle information and the second axle spacing, so as to provide comparison data for judging whether the vehicle uses a floating axle.

[0050] Optionally, judging whether the vehicle has a floating axle according to the first axle information and the second axle information includes: judging whether the first axle information and the second axle information are the same; in the case where the first axle information and the second axle information are the same, determining that the vehicle does not have a floating axle; in the case where the first axle information and the second axle information are different, determining that there is a floating axle in the vehicle, and determining the axle serial number of the floating axle according to the first axle information and the second axle information, where the axle serial number is the position information of the floating axle installed on the vehicle.

[0051] Specifically, determining whether a vehicle uses a floating axle is mainly based on the number of axles and the axle spacing of the vehicle. Therefore, whether the first axle information and the second axle information are the same is used as the comparison result. When the vehicle uses a floating axle, if the vehicle retracts the floating axle, the first axle information detected by the pressure sensor is less than the actual number of vehicle axles, while the second axle information collected by the image acquisition device is the complete actual number of vehicle axles, so that the first axle information and the second axle information are different. If the vehicle slightly floats the axle, that is, the pressure of the axle using the floating axle collected by the pressure sensor is quite different from the pressure of other axles. By comparing the pressure change diagrams of each axle passing through the pressure sensor, it is determined whether the vehicle uses a floating axle. If the first axle information and the second axle information are the same, it means that the vehicle does not use a floating axle. The accuracy of vehicle over-limit detection is improved by judging whether the vehicle uses a floating axle according to the comparison result.

[0052] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0053] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0054] According to another aspect of the embodiments of this application, a vehicle over-limit detection device for implementing the above vehicle over-limit detection method is also provided. Figure 4 is a structural diagram of an optional vehicle over-limit detection device according to the embodiments of this application, as Figure 4 shown, the vehicle over-limit detection device may include:

[0055] The first acquisition unit 10 is configured to acquire pressure information of a vehicle passing through a road surface via a road surface sensor disposed below the road surface, and determine first axle information of the vehicle based on the pressure information;

[0056] The second acquisition unit 20 is configured to acquire vehicle outline information via a non-contact sensor disposed on the roadside, and determine second axle information of the vehicle according to the vehicle outline information;

[0057] The first determination unit 30 is configured to determine whether the vehicle has a floating axle based on the first axle information and the second axle information, and obtain a floating axle determination result;

[0058] The second determination unit 40 is configured to determine whether the vehicle is overloaded based on the floating axle determination result and the vehicle weight of the vehicle.

[0059] Through the above vehicle overloading detection device: The first acquisition unit 10 acquires pressure information of a vehicle passing through a road surface via a road surface sensor disposed below the road surface, and determines first axle information of the vehicle based on the pressure information; the second acquisition unit 20 acquires vehicle outline information via a non-contact sensor disposed on the roadside, and determines second axle information of the vehicle according to the vehicle outline information; the first determination unit 30 determines whether the vehicle has a floating axle based on the first axle information and the second axle information, and obtains a floating axle determination result; the second determination unit 40 determines whether the vehicle is overloaded based on the floating axle determination result and the vehicle weight of the vehicle, which solves the problem in the related art that in the vehicle overloading detection method, since the vehicle suspends the floating axle in the air, it is impossible to accurately identify whether the vehicle uses the floating axle, and thus it is difficult to determine whether the vehicle has an overloading behavior, and achieves the technical effect of accurately identifying whether the vehicle uses the floating axle, and thus determining whether the vehicle is overloaded.

[0060] In an exemplary embodiment, the second determination unit 40 includes: a first calculation module configured to calculate the vehicle weight of the vehicle based on the pressure information; a first determination module configured to, when the floating axle determination result indicates that the vehicle has a floating axle, determine the floating axle position of the vehicle according to the pressure information and the vehicle outline information, and determine whether it is overloaded based on the floating axle position and the vehicle weight of the vehicle; a first judgment module configured to, when the floating axle determination result indicates that the vehicle does not have a floating axle, judge the contact state between each axle and the road surface according to the pressure information of each axle in the floating axle determination result, where the contact state includes a full contact state and a semi-contact state, and the semi-contact state is that the pressure information of the axle does not reach the weighing threshold, and the weighing threshold is the minimum value of the pressure information determined by the road surface sensor as the axle; a second judgment module configured to, when there is an axle in the semi-contact state, determine whether it is overloaded based on the position of the axle in the semi-contact state and the vehicle weight of the vehicle.

[0061] In an exemplary embodiment, optionally, the device further includes: a correction unit configured to, when there is an axle in a semi-contact state, correct the vehicle weight based on all the pressure information corresponding to the axle in the semi-contact state and the effective pressure information corresponding to other axles; and determine whether there is an over-limit behavior according to the corrected vehicle weight.

[0062] In an exemplary embodiment, optionally, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle outline information includes vehicle point cloud data or vehicle image data; when the non-contact sensor is a laser sensor, the vehicle point cloud data is obtained by the laser sensor; when the non-contact sensor is an image sensor, the vehicle image data is obtained by the image sensor.

[0063] In an exemplary embodiment, optionally, the first acquisition unit 10 includes: a generation module configured to generate a pressure change graph of the road surface sensor in a target period according to the pressure information; a second determination module configured to determine a plurality of peaks in the pressure change graph and determine the moment corresponding to each peak; a third determination module configured to, when the peak is greater than or equal to the weighing threshold, determine the peak as the target peak corresponding to the passing of the axle; a fourth determination module configured to determine the first axle information according to the number of target peaks, the difference between the moments corresponding to adjacent target peaks, and the vehicle speed.

[0064] In an exemplary embodiment, optionally, when the non-contact sensor is an image sensor, the second acquisition unit 20 includes: an extraction module configured to extract the feature data of each frame of a plurality of frames of pictures of the vehicle to obtain a plurality of pieces of feature data; a second calculation module configured to calculate the mutual information between any two pieces of feature data among the plurality of pieces of feature data by using a mutual information registration algorithm to obtain a plurality of pieces of mutual information; a registration module configured to perform image registration on the plurality of frames of pictures according to the plurality of pieces of mutual information to obtain a target picture; an input module configured to input the target picture into a preset neural network model to obtain the second axle information, where the preset neural network model is trained by multiple groups of training samples, and each group of training samples includes a historical vehicle picture and axle information.

[0065] In an exemplary embodiment, optionally, the first determination unit 30 includes: a third determination module configured to determine whether the first axle information and the second axle information are the same; a fifth determination module configured to, when the first axle information and the second axle information are the same, determine that there is no floating axle in the vehicle; a sixth determination module configured to, when the first axle information and the second axle information are different, determine that there is a floating axle in the vehicle and determine the axle serial number of the floating axle according to the first axle information and the second axle information, where the axle serial number is the position information of the floating axle installed on the vehicle.

[0066] It should be noted here that the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a hardware environment such as Figure 1 shown, and can be implemented by software or by hardware, where the hardware environment includes a network environment.

[0067] According to another aspect of the embodiments of the present application, a storage medium is also provided. Optionally, in this embodiment, the above storage medium can be used to execute the program code of any one of the above vehicle over-limit detection methods in the embodiments of the present application.

[0068] Optionally, in this embodiment, the above storage medium can be located on at least one of multiple network devices in the network shown in the above embodiment.

[0069] Optionally, in this embodiment, the storage medium is set to store program code for executing the following steps:

[0070] S1, obtain the pressure information of the vehicle passing through the road surface through the road surface sensor set under the road surface, and determine the first axle information of the vehicle based on the pressure information;

[0071] S2, obtain the external contour information of the vehicle through the non-contact sensor set on the roadside, and determine the second axle information of the vehicle according to the external contour information of the vehicle;

[0072] S3, judge whether the vehicle has a floating axle according to the first axle information and the second axle information, and obtain a floating axle judgment result;

[0073] S4, judge whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle.

[0074] Optionally, the specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be elaborated herein.

[0075] Optionally, in this embodiment, the above storage medium can include but is not limited to: various media such as USB flash drives, ROMs, RAMs, mobile hard disks, magnetic disks or optical discs that can store program code.

[0076] According to another aspect of the embodiments of the present application, an electronic device for implementing the above vehicle over-limit detection method is also provided. The electronic device can be a server, a terminal, or a combination thereof.

[0077] Figure 5 is a structural block diagram of an optional electronic device according to the embodiments of the present application, as Figure 5As shown, it includes a processor 502, a communication interface 504, a memory 506, and a communication bus 508. Among them, the processor 502, the communication interface 504, and the memory 506 complete their mutual communication through the communication bus 508. Among them,

[0078] The memory 506 is used to store computer programs;

[0079] When the processor 502 is used to execute the computer program stored on the memory 506, the following steps are implemented:

[0080] S1, obtain the pressure information of the vehicle passing through the road surface through the road surface sensor set under the road surface, and determine the first axle information of the vehicle based on the pressure information;

[0081] S2, obtain the vehicle outline information through the non-contact sensor set on the roadside, and determine the second axle information of the vehicle according to the vehicle outline information;

[0082] S3, judge whether the vehicle has a floating axle according to the first axle information and the second axle information, and obtain the floating axle judgment result;

[0083] S4, judge whether the vehicle is overweight according to the floating axle judgment result and the vehicle weight of the vehicle.

[0084] Optionally, the communication bus can be a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used for the communication between the above-mentioned electronic device and other devices.

[0085] The memory can include a RAM, and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located far from the aforementioned processor.

[0086] The above-mentioned processor can be a general-purpose processor, which may include but is not limited to: CPU (Central Processing Unit, central processing unit), NP (Network Processor, network processor), etc.; it can also be a DSP (Digital Signal Processing, digital signal processor), ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), FPGA (Field-Programmable Gate Array, field-programmable gate array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0087] Optionally, the specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be elaborated here.

[0088] Those of ordinary skill in the art can understand that Figure 5 The structure shown is only for illustration. The device for implementing the above vehicle over-limit detection method can be a terminal device, and the terminal device can be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a handheld computer, and a Mobile Internet Device (MID), a PAD and other terminal devices. Figure 5 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, a display device, etc.) than those shown Figure 5 in the figure, or have a different configuration from that shown Figure 5 in the figure.

[0089] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a ROM, a RAM, a magnetic disk or an optical disc, etc.

[0090] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages and disadvantages of the embodiments.

[0091] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage media. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing one or more computer devices (which can be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in various embodiments of this application.

[0092] In the above embodiments of this application, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided by this application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0094] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution provided in this embodiment.

[0095] In addition, the functional units in the various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or at least two units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0096] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. A method for detecting over-limit vehicles, characterized in that, it includes: Obtaining the pressure information of the vehicle passing through the road surface by means of a road surface sensor arranged below the road surface, and determining the first axle information of the vehicle based on the pressure information; Obtaining the vehicle outline information by means of a non-contact sensor arranged on the roadside, and determining the second axle information of the vehicle according to the vehicle outline information; Judging whether the vehicle has a floating axle according to the first axle information and the second axle information, and obtaining a floating axle judgment result; Judging whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle; wherein, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle outline information includes vehicle point cloud data or vehicle image data; when the non-contact sensor is the laser sensor, obtaining the vehicle point cloud data through the laser sensor; when the non-contact sensor is the image sensor, obtaining the vehicle image data through the image sensor.

2. The method according to claim 1, characterized in that, Judging whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle includes: Calculating the vehicle weight of the vehicle based on the pressure information; When the floating axle judgment result indicates that the vehicle has a floating axle, determining the floating axle position of the vehicle according to the pressure information and the vehicle outline information, and judging whether it is over-limit based on the floating axle position and the vehicle weight of the vehicle; When the floating axle judgment result indicates that the vehicle does not have a floating axle, judging the contact state between each axle of the vehicle and the road surface according to the pressure information of each axle in the floating axle judgment result, wherein the contact state includes a full contact state and a semi-contact state, and the semi-contact state means that the pressure information of the axle does not reach the upper weighing threshold, and the upper weighing threshold is the minimum value of the pressure information determined by the road surface sensor as the axle; When there is an axle in the semi-contact state, judging whether it is over-limit based on the position of the axle in the semi-contact state and the vehicle weight of the vehicle.

3. The method according to claim 2, characterized in that, it further includes: When there is an axle in the semi-contact state, correcting the vehicle weight based on all the pressure information corresponding to the axle in the semi-contact state and the effective pressure information corresponding to other axles; And judging whether there is an over-limit behavior according to the corrected vehicle weight.

4. The method according to claim 1, characterized in that, Determining the first axle information of the vehicle based on the pressure information includes: Generating a pressure change graph of the road surface sensor in a target period according to the pressure information; Determining a plurality of peaks in the pressure change graph, and determining the moment corresponding to each peak; When the peak is greater than or equal to the upper weighing threshold, determining the peak as the target peak corresponding to when the axle passes; Determining the first axle information according to the number of the target peaks, the difference between the moments corresponding to adjacent target peaks and the vehicle speed.

5. The method according to claim 1, characterized in that, When the non-contact sensor is an image sensor, determining the second axle information of the vehicle according to the vehicle outline information includes: Extracting the feature data of each frame of a plurality of frames of pictures of the vehicle to obtain a plurality of feature data; Calculating the mutual information between any two of the plurality of feature data through a mutual information registration algorithm to obtain a plurality of mutual information; Performing image registration on the plurality of frames of pictures according to the plurality of mutual information to obtain a target picture; Inputting the target picture into a preset neural network model to obtain the second axle information, where the preset neural network model is obtained by training with multiple groups of training samples, and each group of training samples includes a historical vehicle picture and axle information.

6. The method according to claim 1, wherein, Judging whether the vehicle has a floating axle according to the first axle information and the second axle information includes: Judging whether the first axle information and the second axle information are the same; When the first axle information and the second axle information are the same, determining that the vehicle does not have a floating axle; When the first axle information and the second axle information are different, determining that there is a floating axle in the vehicle, and determining the axle serial number of the floating axle according to the first axle information and the second axle information, where the axle serial number is the position information of the floating axle installed on the vehicle.

7. A vehicle over-limit detection device, wherein, including: A first acquisition unit, configured to acquire the pressure information of a vehicle passing through the road surface through a road surface sensor arranged under the road surface, and determine the first axle information of the vehicle based on the pressure information; A second acquisition unit, configured to acquire vehicle outline information through a non-contact sensor arranged on the roadside, and determine the second axle information of the vehicle according to the vehicle outline information; A first judgment unit, configured to judge whether the vehicle has a floating axle according to the first axle information and the second axle information to obtain a floating axle judgment result; A second judgment unit, configured to judge whether the vehicle is over-limit according to the floating axle judgment result and the vehicle weight of the vehicle; wherein, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle outline information includes vehicle point cloud data or vehicle image data; when the non-contact sensor is the laser sensor, the vehicle point cloud data is acquired through the laser sensor; when the non-contact sensor is the image sensor, the vehicle image data is acquired through the image sensor.

8. A computer-readable storage medium, wherein, The computer-readable storage medium includes a stored program, wherein the program runs to execute the vehicle over-limit detection method according to any one of claims 1 to 7.

9. An electronic device, including a memory and a processor, wherein, A computer program is stored in the memory, and the processor is configured to execute the vehicle over-limit detection method according to any one of claims 1 to 7 through the computer program.

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