CN114674403A

CN114674403A - Target vehicle detection method and device, storage medium and electronic equipment

Info

Publication number: CN114674403A
Application number: CN202111663145.6A
Authority: CN
Inventors: 郝杰鹏; 吴石恩; 韩青山; 王平
Original assignee: Beijing Wanji Technology Co Ltd
Current assignee: Beijing Wanji Technology Co Ltd
Priority date: 2021-12-30
Filing date: 2021-12-30
Publication date: 2022-06-28
Anticipated expiration: 2041-12-30
Also published as: CN114674403B

Abstract

The invention discloses a target vehicle detection method and device, a storage medium and electronic equipment. Wherein, the method comprises the following steps: acquiring target vehicle information of a target vehicle, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. Therefore, the problems that the vehicle-mounted weighing system in the prior art cannot timely perform overload and overrun detection on the target vehicle escaping from the detection station, and the vehicle-mounted weighing system is low in detection precision, poor in adaptability and the like can be solved.

Description

Target vehicle detection method and device, storage medium and electronic equipment

Technical Field

The invention relates to the field of vehicle-mounted weighing, in particular to a target vehicle detection method and device, a storage medium and electronic equipment.

Background

When a vehicle in overload and overrun runs on the road, great harm exists, the detection efficiency of the non-stop and overrun detection station provided in the related technology is greatly improved compared with the traditional detection modes such as a road overrun detection station, but the non-stop and overrun detection station still belongs to the detection mode of a fixed point position essentially. Because the road network is complicated, the completely closed detection is difficult to realize, and the over-limit overloaded freight vehicles avoid detection stations by means of detouring and the like, so that the detection is avoided;

further, the vehicle-mounted weighing system in the related art receives load information of the vehicle; judging whether the vehicle is in a static state or not according to the received load information of the vehicle; if the vehicle is judged to be in the static state, judging whether the load of the vehicle is in the static state according to the received load information of the vehicle; and if the load of the vehicle is judged to be in the non-static state, controlling the second sensor to enter a normal working state from a dormant state so as to detect the load information of the vehicle in real time. However, since the above method may require post-loading of the vehicle with sensors, there is a possibility that the vehicle body structure may be damaged; moreover, the calibration of a common vehicle-mounted weighing system is usually carried out under an ideal condition, but the road condition is complex, and the interference factors are many, so that the detection error of the vehicle-mounted weighing system is large, and the adaptability is poor.

In view of the above problems, the vehicle-mounted weighing system in the prior art cannot timely perform overload and overrun detection on a target vehicle escaping from a detection station, and has low detection precision, poor adaptability and other problems, and an effective solution is not proposed at present.

Disclosure of Invention

The embodiment of the invention provides a target vehicle detection method and device, a storage medium and electronic equipment, and aims to at least solve the problems that a vehicle-mounted weighing system in the prior art cannot timely perform overload and overrun detection on a target vehicle escaping from a detection station, and the vehicle-mounted weighing system is low in detection precision, poor in adaptability and the like.

According to an aspect of an embodiment of the present invention, there is provided a target vehicle detection method including: acquiring target vehicle information of a target vehicle, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

According to another aspect of the embodiments of the present invention, there is also provided a target vehicle detection method apparatus, including: the device comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring target vehicle information of a target vehicle, and the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; the determining module is used for determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and the judging module is used for judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

According to a further aspect of an embodiment of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method in any one of the method embodiments when executed.

According to yet another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory and a processor, where the memory stores therein a computer program, and the processor is configured to execute the method in any one of the method embodiments described above by the computer program.

In an embodiment of the present invention, target vehicle information of a target vehicle is acquired, wherein the target vehicle information includes: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and judging whether the target vehicle is overloaded and exceeds the limit or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. Namely, a vehicle load estimation model of the target vehicle is formed by acquiring corresponding data in advance, then the load weight corresponding to the real-time target vehicle information of the target vehicle can be determined by the vehicle load estimation model, and indeed compared to the maximum allowable total mass limit to determine whether the target vehicle is overrun, and, therefore, can solve the problems that the vehicle-mounted weighing system in the prior art can not timely carry out overload and overrun detection on the target vehicle escaping from the detection station, and the vehicle-mounted weighing system has the problems of low detection precision, poor adaptability and the like, and then the target weight of the target vehicle under different working conditions, different positions and different road conditions can be determined through the vehicle load estimation model, so that the detection precision of the vehicle-mounted weighing system on the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system on the target vehicle in different environments are greatly improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention to a proper form. In the drawings:

fig. 1 is a block diagram of a hardware configuration of a computer terminal of a detection method of a target vehicle according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method of detecting a target vehicle according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a freight vehicle overrun overload transport detection system in accordance with an alternative embodiment of the present invention;

fig. 4 is a schematic configuration diagram of a detection device of a target vehicle according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The method provided by the embodiment of the application can be executed in a computer terminal, a mobile terminal or a similar operation device. Taking the example of being operated on a computer terminal, fig. 1 is a hardware structure block diagram of a computer terminal of a detection method of a target vehicle according to an embodiment of the present invention. As shown in fig. 1, the computer terminal 10 may include one or more (only one shown in fig. 1) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.

The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the detection method of the target vehicle in the embodiment of the present invention, and the processor 102 executes the computer programs stored in the memory 104 to perform various functional applications and data processing, so as to implement the method described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

In the present embodiment, a method for detecting a target vehicle is provided, and fig. 2 is a flowchart of a method for detecting a target vehicle according to an embodiment of the present invention, the flowchart including the steps of:

step S202, target vehicle information of a target vehicle is obtained, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle;

step S204, determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information;

it can be understood that, before the vehicle real-time vehicle information of the target vehicle is acquired, a vehicle load estimation model matched with the target vehicle is generated according to data information of the target vehicle passing through a target detection station in a cloud platform corresponding to the target vehicle or a vehicle-mounted terminal or a transportation detection system installed on the target vehicle.

And step S206, judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

Through the steps, target vehicle information of a target vehicle is acquired, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. Namely, a vehicle load estimation model of the target vehicle is formed by acquiring corresponding data in advance, then the load weight corresponding to the real-time target vehicle information of the target vehicle can be determined by the vehicle load estimation model, and indeed compared to the maximum allowable total mass limit to determine whether the target vehicle is overrun and, therefore, can solve the problems that the vehicle-mounted weighing system in the prior art can not timely carry out overload and overrun detection on the target vehicle escaping from the detection station, and the vehicle-mounted weighing system has the problems of low detection precision, poor adaptability and the like, and then the target weight of the target vehicle under different working conditions, different positions and different road conditions can be determined through the vehicle load estimation model, so that the detection precision of the vehicle-mounted weighing system on the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system on the target vehicle in different environments are greatly improved.

It should be noted that, in the vehicle load estimation model in the above manner, the vehicle-mounted sensor is not additionally added by using the engine state; the existing overrun station data is used as calibration data, and position correction is performed, so that the calibration result is richer, and the detection result is more accurate. The problem that the existing detection means are mostly fixed-point detection, road network full coverage cannot be achieved, and the problem that the existing middle and rear mounted sensors possibly damage the vehicle body structure is solved; calibration of the vehicle-mounted weighing system is usually carried out under ideal conditions, but the road conditions are complex, and interference factors are many, so that the vehicle-mounted weighing system has large detection error and poor adaptability; the vehicle-mounted weighing system is higher in detection precision and stronger in adaptability.

Optionally, the building process of the vehicle load estimation model includes: obtaining historical operating data of the target vehicle, wherein the historical operating data comprises: the vehicle weight information detected when the target vehicle passes through the detection station, the engine running state data when the target vehicle passes through the detection station, the vehicle running state when the target vehicle passes through the detection station, the detection time period when the target vehicle passes through the detection station, and the detection position when the target vehicle passes through the detection station; and establishing the vehicle load estimation model according to the historical operation data.

As an optional embodiment, the obtaining of the historical operating data of the target vehicle comprises: determining a plurality of pieces of vehicle weight information of a target vehicle passing through a plurality of detection stations; analyzing the plurality of pieces of vehicle weight information to determine a plurality of detection time periods for weighing and detecting the target vehicle at the detection station and a plurality of detection positions of the target vehicle; intercepting multiple groups of data information from the real-time vehicle information uploaded to the data platform by the target vehicle according to the detection time periods and the detection positions, wherein the multiple groups of data information are corresponding vehicle information of the target vehicle in a weighing state; and uploading the real-time vehicle information through a vehicle-mounted terminal arranged on the target vehicle. And constructing a vehicle load estimation model of the target vehicle according to the plurality of sets of data information and the plurality of pieces of vehicle weight information.

That is, in order to ensure that the constructed vehicle load estimation model is consistent with the actual situation, by acquiring the vehicle weight information of the target vehicle passing through the detection station, the vehicle weight information is obtained by a weighing system of the detection station, and the vehicle weight information not only comprises the weight information of the vehicle at the moment, but also comprises the position information of the position of the target vehicle at the moment and the detection time period of the target vehicle in the weighing system of the detection station, and because the target vehicle is in a stable state during weighing detection, corresponding multiple groups of data information can be intercepted from the real-time vehicle information uploaded to the data platform by the vehicle-mounted terminal on the target vehicle based on the detection time interval and the detection position, so that the correlation between the weight information and the vehicle information is realized, and a vehicle load estimation model of the target vehicle is constructed according to the correlation condition.

For example, when the real-time vehicle information includes the engine torque α of the target vehicle, the engine speed β of the target vehicle, the vehicle speed υ of the target vehicle, the geographic position information η of the target vehicle, and the recorded accurate weight w of the vehicle. Combining the acquired information to construct a characteristic matrix of accurate weight w, engine torque alpha, engine rotating speed beta, vehicle speed upsilon of the target vehicle and the corresponding characteristic matrix of the engine torque of the target vehicle, wherein the characteristic matrix is { alpha, beta, upsilon }; suppose constructed as h_θ(x)＝θ₀+θ₁α+θ₂β+θ₃V, expressed in a matrix: h is a total of_θ(x) X θ; wherein a set (theta) is found for the determination₀,θ₁,θ₂,θ₃) Let m be the number of samples and n be the parameter to be solved. The second moment of the accurately measured vehicle weight and the predicted weight is taken as a loss function J (θ), which is defined as:

by a loss functionDetermining an optimal optimum (theta)₀,θ₁,θ₂,θ₃) And (5) combining to obtain the product. And the loss function means the relationship between the real vehicle weight and the predicted vehicle weight, and if and only if the loss function value is minimum, the obtained parameter is the model parameter.

Optionally, the target vehicle information further includes: the determining the weight of the target vehicle according to the target vehicle information of the target vehicle and a preset vehicle load estimation model comprises: inputting the engine operating state data of the target vehicle into a vehicle load estimation model of the target vehicle to determine the weight of the target vehicle; and correcting the weight of the target vehicle based on the current position information of the target vehicle and the detection position corresponding to the vehicle load estimation model of the target vehicle.

In short, because the target vehicle is in a moving state, in order to ensure that the weight of the target vehicle estimated by the vehicle load estimation model better conforms to the actual weight of the target vehicle, the weight needs to be corrected according to the detection position corresponding to the weight estimation and the current position information of the target vehicle by using the vehicle load estimation model, so as to determine the corrected weight conforming to the actual scene.

Optionally, the method comprises: determining a mapping relation between the current position information and a detection position corresponding to a vehicle load estimation model of the target vehicle; and determining a correction coefficient corresponding to the current position information of the target vehicle according to the mapping relation.

Optionally, the process of establishing the mapping relationship includes: associating first weighing information detected when the target vehicle passes through the detection station with second weighing information obtained based on the preset vehicle load estimation model; the first weighing information is vehicle weight information determined by a weighing detection system when the target vehicle passes through the detection position of each detection station, and the second weighing information is the weight of the target vehicle obtained by the preset vehicle load estimation model when the target vehicle passes through each detection related detection road section; under the condition that a predicted weight error is determined, determining correction coefficients of different detection road sections according to the predicted weight error, the first weighing information and the second weighing information; and carrying out one-to-one correspondence on the correction coefficient, the detection position of the detection station and the different detection road sections to obtain the mapping relation.

It should be noted that the related detected road section in this embodiment refers to a road section where the vehicle load does not change before and after the target vehicle passes through the detection position, and the specific confirmation manner includes a road section where the vehicle speed is continuously not zero before and after passing through the detection position, or a change of the second weighing information of the vehicle before and after passing through the detection position does not exceed a set threshold.

Optionally, the correcting the weight of the target vehicle based on the current position information of the target vehicle and the detected position corresponding to the vehicle load estimation model of the target vehicle includes: determining whether the weight of the target vehicle needs to be corrected based on the current position information of the target vehicle; and correcting the weight of the target vehicle by using the correction coefficient of the target road section corresponding to the current position.

For example, when it is determined that the running state of the target vehicle corresponding to the current position information is stable and the current position of the target vehicle is in an abnormal weight region, it is determined that the weight of the target vehicle needs to be corrected; correcting the weight of the target vehicle by using the correction coefficient of the target road section corresponding to the current position; and determining that the weight of the target vehicle does not need to be corrected when the running state of the target vehicle corresponding to the current position information is determined to be stable and the current position of the target vehicle is not in an abnormal weight region.

As an alternative embodiment, as the state of the vehicle in the driving process changes, the correlation between the predicted vehicle weight and the driving state is acquired, and the position information is combined; the distribution of the abnormal vehicle weight along with the driving position of the vehicle is obtained; an example of position correction is as follows:

optionally, the method one: the abnormal weight distribution and the running speed of the vehicle are related through the geographical position information of the vehicle, the vehicle speed in an abnormal area is judged to have no obvious change, the abnormal change of the running weight of the vehicle is considered to be related to the geographical position change (such as a concave road surface, an ascending slope and a descending slope), the abnormal change of the running weight of the vehicle is considered to be marked as an interval to be optimized, and then the real weight w of different types of vehicles is combined with the difference of abnormal values in different vehicle passing states_οAnd predicted weight error

The following model is established:

k is a correction factor and W is an estimated weight determined by the vehicle load estimation model.

Optionally, the method two: the method comprises the steps of obtaining statistics of vehicle working condition abnormity changing along with a geographical position state by counting abnormal driving road sections and combining vehicle torque changing conditions, and quantifying the relation between the real weight and the error of a vehicle by combining a vehicle error distribution curve along with a geographical position.

Optionally, building the vehicle load estimation model according to the historical operating data includes: determining corresponding historical operating data under the stable driving state of the vehicle according to the operating state of the vehicle; and establishing the vehicle load estimation model according to the determined corresponding historical running data of the vehicle in the stable running state.

In short, in order to ensure the accuracy and stability of the established vehicle load estimation model, after determining the historical operating data, data filtering needs to be performed on the historical operating data according to preset data screening conditions to determine the historical vehicle information for establishing the vehicle load estimation model. Wherein the preset data screening condition comprises at least one of the following conditions: the current position is a target road section where the target vehicle runs for multiple times, the target vehicle is not in a braking state, and the target vehicle is not switched to a gear.

For example, data that has an important influence on the estimation accuracy of the vehicle weight is removed by data screening. The specific screening conditions were as follows: 1. the data of the fixed road section is selected, the road conditions such as the gradient of the fixed road section are unchanged, and the influence of inconsistent gradient resistance is eliminated. 2. The speed of the vehicle when traveling on a fixed road segment is recorded because the speed of the vehicle is related to the wind resistance of the vehicle. 3. The number of samples has a minimum limit: the number of samples is too small and random errors due to data fluctuations are magnified, so that a sufficient number of samples is necessary to ensure the accuracy of the data source. 4. Ensuring that the vehicle is not in a braking state: the brake torque collected from the CAN line is inaccurate. 5. Data at gear shift is not available: since the connection between the engine and the drive train is cut off at the time of gear shifting, the calculation accuracy in this case is poor.

Optionally, determining that the weight of the target vehicle needs to be corrected, and correcting the weight of the target vehicle using the correction coefficient of the target road segment corresponding to the current position includes: when the correction coefficient is positive, determining that the weight of the target vehicle is greater than or equal to the actual load of the target vehicle by the vehicle load estimation model, and subtracting the product of the correction coefficient and a preset error weight from the weight to obtain the corrected weight of the target vehicle; and when the correction coefficient is negative, determining that the weight of the target vehicle is smaller than the actual load of the target vehicle by the vehicle load estimation model, and obtaining the corrected weight of the target vehicle by adding the product of the weight and the correction coefficient and a preset error weight.

Optionally, after determining whether the target vehicle is overloaded and overrun according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle, the method further includes: determining an unloaded torque and a loaded torque of the target vehicle; determining a torque percentage of the target vehicle from the unloaded torque and the fully loaded torque; verifying a maximum allowable total mass limit of the target vehicle based on the torque percentage.

It is understood that, since the vehicle ages due to the gradual increase of the usage time of the vehicle, the maximum allowable total mass limit of the vehicle changes according to the vehicle condition, and therefore, the no-load torque and the full-load torque of the target vehicle need to be obtained, and then the maximum allowable total mass limit of the target vehicle needs to be changed, so that the target weight determined by the vehicle load estimation model corresponds to the real-time vehicle condition of the target vehicle.

In order to better understand the technical solutions of the embodiments and the alternative embodiments of the present invention, the following explains the flow of the detection method of the target vehicle with reference to the example, but is not limited to the technical solution of the embodiments of the present invention.

In order to better understand technical solutions of the embodiments and the alternative embodiments of the present invention, application scenarios that may occur in the embodiments and the alternative embodiments of the present invention are described below, but the application scenarios are not limited to the following scenarios.

Overrun overload definition: the overrun means that the external dimension, the axle load and the total mass of the freight vehicle exceed the limit of the national safety technical standard of the motor vehicle or exceed the load limit, height limit, width limit and length limit standards marked by a road traffic sign. Overload means that the load of the freight vehicle exceeds the load mass approved by the vehicle driving license.

Optionally, the source is controlled to be in an overload state, and the overload detection is carried out on the delivery freight vehicles in the important freight source area, and the overload vehicles are forbidden to run out. The detection efficiency is low, and full coverage detection is difficult to realize;

optionally, the mobile detection is performed, and related workers perform spot-check detection on passing load-carrying vehicles on the road. Although the requirements of mobility and maneuverability can be met, a large amount of manpower is needed for road surface law enforcement, and the detection efficiency is low as well;

optionally, the road overrun detection station is usually built at the roadside, and a vehicle needs to be guided to enter the station to be detected. The contradiction between the detection station limitation and immobility and the maneuverability of illegal over-limit transportation is still quite prominent;

optionally, the non-stop and over-limit detection station is built at a key road section, non-stop and over-limit detection can be performed on all vehicles passing by, and efficiency is greatly improved.

Therefore, although the detection efficiency of the current mainstream non-stop over-limit detection station is greatly improved compared with the traditional detection modes such as a highway over-limit detection station and the like, the non-stop over-limit detection station still belongs to the detection mode of a fixed point position essentially. Because the road network is complicated, it is difficult to achieve totally enclosed detection, and the overrun overload freight vehicle avoids the detection station by means of detouring and the like, thereby avoiding detection.

As an optional implementation manner, a method for detecting overrun and overload transportation of a freight vehicle is provided, which can make up for a detection leak of a conventional 'fixed overload control detection station' to a great extent, and specifically comprises the following steps;

step 1, carrying out data acquisition on freight vehicles, specifically, acquiring weight information of the vehicles by a non-stop over-limit detection station, a highway over-limit detection station and the like; vehicle information such as engine torque, rotating speed, vehicle speed, GPS position and the like of a vehicle is acquired by a vehicle-mounted terminal;

it should be noted that, the vehicle load and the vehicle information such as the engine state (torque, rotation speed), the vehicle speed, the GPS position, etc. are in a corresponding relationship, the motion law of the vehicle in operation still conforms to the newton's second law, and the vehicle is subjected to stress analysis, so that the vehicle running equation can be obtained: f ═ F_f+F_w+F_i(ii) a The driving force during constant speed running of the automobile is equal to the running resistance (F) during constant speed running_f+F_w+F_i). Wherein, F_wIs the air resistance; f_fRolling and gradient resistances F for motor vehicles_iF is a rolling resistance coefficient in proportion to the total weight of the automobile, namely, the running resistance of the automobile is increased in proportion to the increase of the total weight of the automobile. The driving force of the vehicle is in turn proportional to the output torque of the engine. Therefore, the vehicle load is proportional to the engine torque when the gradient and the vehicle speed are constant.

Optionally, the vehicle-mounted terminal is accessed to a Controller Area Network (CAN) network of the entire vehicle through an OBD port of the vehicle, and performs data analysis (a sampling period of 100ms) according to a J1939 standard protocol. The J1939 protocol is a network protocol supporting closed-loop control high-speed communication, and is mainly used on trucks or buses. An ECU (Electronic Control Unit, ECU for short) packs its data into CAN data at a certain frequency according to a data conversion method described in the J1939 protocol. And collecting and recording the data according to the protocol vehicle-mounted information unit. And acquiring the running time and the geographical position information of the vehicle in real time through the GPS module.

Optionally, before the vehicle information is acquired by the vehicle-mounted terminal, in order to ensure that the accuracy of the determined data is ensured, data that has an important influence on the estimation accuracy of the vehicle quality is removed through data screening. The specific screening conditions were as follows:

1. the data of the fixed road section is selected, the road conditions such as the gradient of the fixed road section are unchanged, and the influence of inconsistent gradient resistance is eliminated.

2. The speed of the vehicle when traveling on a fixed road segment is recorded because the speed of the vehicle is related to the wind resistance of the vehicle.

3. The number of samples has a minimum limit: the number of samples is too small and random errors due to data fluctuations are magnified, so that a sufficient number of samples is necessary to ensure the accuracy of the data source.

4. Ensuring that the vehicle is not in a braking state: the brake torque collected from the CAN line is inaccurate.

5. Data at gear shift is not available: since the connection between the engine and the drive train is cut off during the gear change, the calculation accuracy is poor in this case.

Step 2, data association: acquiring the time and position of weighing detection from the vehicle weight information; intercepting vehicle information of the vehicle in a certain time period and a certain area at the time and position of weighing detection; correlating and matching the weight information and the vehicle information to produce one or more groups of data points;

and step 3, estimating the vehicle load: after a certain amount of data is accumulated, a data model is established, and the weight information of the vehicle is obtained by utilizing the vehicle information;

optionally, the data model is established by recording vehicle weight information obtained from a weighing detection device such as an overtaking control station and the like, corresponding vehicle steady-state driving conditions (road section information) under the weight, recording key information such as engine speed, engine torque percentage, vehicle speed and the like, vehicle information such as vehicle brand, model and production date and the like, constructing a database and storing the database in the controller based on the vehicle motion balance equation. And when the data stored in the database reaches a certain amount, a vehicle load estimation equation is constructed by utilizing an estimation algorithm based on recursive least-squares, and then a data model of the same vehicle is established by a self-learning method.

Step 4, overrun detection: and inquiring the vehicle information collected in real time, inquiring whether a data model is established, inputting the vehicle information into the model if the data model is established, outputting the weight of the vehicle, and further judging whether the suspicion of overrun exists.

For example, when the vehicle is running and load identification is properly performed (conditions such as a fixed road section and a stable vehicle speed), an identification strategy is started, a database is inquired by using information such as the current running condition of the engine, namely output torque or output torque percentage, rotating speed and the like, and the current load state of the vehicle is locked through calculation and analysis. The load mass at the current steady state condition of the vehicle can be determined, for example, by the current torque versus the percentage torque at full load and at no load.

As an optional implementation manner, a system for detecting transportation of a freight vehicle exceeding or overloading is provided, as shown in fig. 3, which is a schematic structural diagram of a system for detecting transportation of a freight vehicle exceeding or overloading according to an optional embodiment of the present invention, and the system for detecting transportation of a freight vehicle exceeding or overloading includes a weighing detection station 302, a vehicle-mounted terminal 304, a data processing platform 306, and the like.

Wherein the weight detection station 302 determines weight information of vehicles passing through the station by a weight detection system, wherein the weight information at least comprises: real-time weight information of the vehicle, weighing detection time of the vehicle, and weighing detection position of the vehicle.

The vehicle-mounted terminal 304 is used for providing the acquired vehicle information such as the engine torque, the rotating speed, the vehicle speed, the GPS position and the like of the vehicle to the detection system;

the data processing platform 306 comprises a data receiving and storing module 402, a data-associated load estimating module 404 and an overrun detecting module 406, and is used for processing data provided by the weighing detection station 302 and the vehicle-mounted terminal 304; the method comprises the steps of recording vehicle weight information acquired from weighing detection devices such as an overtaking control station and the like, recording corresponding vehicle steady-state driving working conditions (road section information) under the weight, recording key information such as engine speed, engine torque percentage, vehicle speed and the like, constructing a database and storing the database into a controller. When the data stored in the database reaches a certain amount, a vehicle load estimation equation is constructed. When the vehicle runs and load identification is properly carried out (conditions such as a fixed road section and stable vehicle speed), an identification strategy is started, a database is inquired by using information such as the current running working condition of the engine, namely output torque or output torque percentage, rotating speed and the like, and the current load state of the vehicle is locked through calculation and analysis. The load mass at the current steady state condition of the vehicle can be determined, for example, by the current torque versus percentage torque at full and no load.

Optionally, when the overrun overload transportation of the freight vehicle needs to be detected, the vehicle information of the freight vehicle is collected in real time, then the data processing platform 306 is inquired whether the vehicle is already established with a data model, if the vehicle is already established, the vehicle information is input into the model, the weight of the vehicle is output, and then whether overrun suspicion exists is judged.

Through the embodiment, the overrun detection method is provided, based on an automobile motion balance equation, by recording the vehicle weight information acquired from the weighing detection device such as the overload control station and the like, and the corresponding vehicle steady-state driving working condition (road section information) under the weight, recording key information such as the engine speed, the engine torque percentage, the vehicle speed and the like, constructing a database, storing the database into the controller, and constructing the vehicle load model of the target vehicle, so that the purpose of timely determining whether the target vehicle of the escape detection station is overrun or not is achieved, the technical effect of the detection efficiency of the target vehicle is improved, the overload overrun of the target vehicle can be monitored in real time, the problem that the overload detection of the target vehicle of the escape detection station cannot be timely performed by the vehicle-mounted weighing system in the prior art is solved, and the detection precision of the vehicle-mounted weighing system is low, The problem such as adaptability is poor compares with prior art, and rate of accuracy and real-time need all be higher for the application scene is wider.

It should be noted that for simplicity of description, the above-mentioned method embodiments are shown as a series of combinations of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus necessary general hardware platform, but may also be implemented by hardware, but in many cases, the former is a better embodiment. Based on this understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and include instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the methods described in the embodiments of the present invention.

According to another aspect of the embodiments of the present invention, there is also provided a detection apparatus of a target vehicle for implementing the detection method of a target vehicle described above. As shown in fig. 4, the apparatus includes:

an obtaining module 502, configured to obtain target vehicle information of a target vehicle, where the target vehicle information includes: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle;

a determining module 504, configured to determine a weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information;

the determining module 506 is configured to determine whether the target vehicle is overloaded and overrun according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

By the above apparatus, target vehicle information of a target vehicle is acquired, wherein the target vehicle information includes: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle; determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information; and judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. Namely, a vehicle load estimation model of the target vehicle is formed by acquiring corresponding data in advance, then the load weight corresponding to the real-time target vehicle information of the target vehicle can be determined by the vehicle load estimation model, and indeed compared to the maximum allowable total mass limit to determine whether the target vehicle is overrun and, therefore, can solve the problems that the vehicle-mounted weighing system in the prior art can not timely carry out overload and overrun detection on the target vehicle escaping from the detection station, and the vehicle-mounted weighing system has the problems of low detection precision, poor adaptability and the like, and then the target weight of the target vehicle under different working conditions, different positions and different road conditions can be determined through the vehicle load estimation model, so that the detection precision of the vehicle-mounted weighing system on the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system on the target vehicle in different environments are greatly improved.

Optionally, the determining module further includes: the device comprises an establishing unit, a storage unit and a control unit, wherein the establishing unit is used for acquiring historical operation data of the target vehicle, and the historical operation data comprises: the vehicle weight information detected when the target vehicle passes through a detection station, the engine running state data when the target vehicle passes through the detection station, the vehicle running state when the target vehicle passes through the detection station, the detection time period when the target vehicle passes through the detection station, and the detection position when the target vehicle passes through the detection station; and establishing the vehicle load estimation model according to the historical operation data.

As an optional embodiment, the obtaining of the historical operating data of the target vehicle comprises: determining a plurality of pieces of vehicle weight information of a target vehicle passing through a plurality of detection stations; analyzing the plurality of pieces of vehicle weight information to determine a plurality of detection time periods for weighing detection of the target vehicle at the detection station and a plurality of detection positions of the target vehicle; intercepting multiple groups of data information from the real-time vehicle information uploaded to the data platform by the target vehicle according to the detection time periods and the detection positions, wherein the multiple groups of data information are corresponding vehicle information of the target vehicle in a weighing state; and uploading the real-time vehicle information through a vehicle-mounted terminal arranged on the target vehicle. And constructing a vehicle load estimation model of the target vehicle according to the plurality of sets of data information and the plurality of pieces of vehicle weight information.

That is, in order to ensure that the constructed vehicle load estimation model conforms to the actual situation, by acquiring the vehicle weight information of the target vehicle passing through the detection station, the vehicle weight information is obtained by a weighing system of the detection station, and the vehicle weight information not only comprises the weight information of the vehicle at the moment, but also comprises the position information of the position of the target vehicle at the moment and the detection time period of the target vehicle in the weighing system of the detection station, and because the target vehicle is in a stable state during weighing detection, corresponding multiple groups of data information can be intercepted from the real-time vehicle information uploaded to the data platform by the vehicle-mounted terminal on the target vehicle based on the detection time interval and the detection position, so that the correlation between the weight information and the vehicle information is realized, and a vehicle load estimation model of the target vehicle is constructed according to the correlation condition.

For example, when the real-time vehicle information includes the engine torque α of the target vehicle, the engine speed β of the target vehicle, the vehicle speed v of the target vehicle, the geographical position information η of the target vehicle, and the recorded w of the precise weight of the vehicle. KnotConstructing a characteristic matrix of accurate weight w, engine torque alpha, engine rotating speed beta, vehicle speed upsilon of the target vehicle and corresponding to the engine torque of the target vehicle, wherein the characteristic matrix is { alpha, beta, upsilon }; suppose constructed as h _θ(x)＝θ₀+θ₁α+θ₂β+θ₃υ, its matrix is expressed as: h is_θ(x) X θ; wherein a set (theta) is found for the determination₀,θ₁,θ₂,θ₃) Let m be the number of samples and n be the parameter to be solved. The second moment of the accurately measured vehicle weight and the predicted weight is taken as a loss function J (θ), which is defined as:

determining an optimal optimum (theta) from the loss function₀,θ₁,θ₂,θ₃) And (5) combining to obtain the product. And the loss function means the relationship between the real vehicle weight and the predicted vehicle weight, and if and only if the loss function value is minimum, the obtained parameter is the model parameter.

Optionally, the determining module is further configured to, in the target vehicle information, further include: under the condition of the current position information of the target vehicle, inputting the engine running state data of the target vehicle into a vehicle load estimation model of the target vehicle to determine the weight of the target vehicle; and correcting the weight of the target vehicle based on the current position information of the target vehicle and the detection position corresponding to the vehicle load estimation model of the target vehicle.

Optionally, the determining module further includes: a coefficient unit for determining a mapping relationship between the current position information and a detection position corresponding to a vehicle load estimation model of the target vehicle; and determining a correction coefficient corresponding to the current position information of the target vehicle according to the mapping relation.

Optionally, the process of establishing the mapping relationship includes: associating first weighing information detected when the target vehicle passes through the detection station with second weighing information obtained based on the preset vehicle load estimation model; the first weighing information is vehicle weight information determined by a weighing detection system when the target vehicle passes through the detection position of each detection station, and the second weighing information is the weight of the target vehicle obtained by the preset vehicle load estimation model when the target vehicle passes through the detection road section relevant to each detection station; under the condition of determining a predicted weight error, determining correction coefficients of different detection road sections according to the predicted weight error, the first weighing information and the second weighing information; and carrying out one-to-one correspondence on the correction coefficient, the detection position of the detection station and the different detection road sections to obtain the mapping relation.

It should be noted that the related detected road segments in this embodiment refer to road segments where the vehicle load does not change before and after the target vehicle passes through the detected position, and the specific confirmation manner includes road segments where the vehicle speed is continuously not zero before and after passing through the detected position, or road segments where the change of the second weighing information of the vehicle does not exceed the set threshold before and after passing through the detected position.

Optionally, the determining module is further configured to determine whether the weight of the target vehicle needs to be corrected based on the current position information of the target vehicle; and correcting the weight of the target vehicle by using the correction coefficient of the target road section corresponding to the current position.

As an alternative embodiment, as the state of the vehicle during running changes, the correlation between the predicted vehicle weight and the running state is obtained, and the correlation is combined with the position information; the distribution of the abnormal vehicle weight along with the vehicle running position is obtained; an example of position correction is as follows:

The following model was established:

Optionally, the establishing unit is further configured to determine, according to the vehicle running state, historical running data corresponding to the vehicle in a stable running state; and establishing the vehicle load estimation model according to the determined corresponding historical running data of the vehicle in the stable running state.

In other words, in order to ensure the accuracy and stability of the established vehicle load estimation model, after determining the historical operating data, the historical operating data needs to be filtered according to preset data screening conditions to determine the historical vehicle information for establishing the vehicle load estimation model. Wherein the preset data screening condition comprises at least one of the following conditions: the current position is a target road section where the target vehicle runs for multiple times, the target vehicle is not in a braking state, and the target vehicle is not switched to a gear.

It should be noted that the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.

Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.

Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the following steps:

s1, obtaining target vehicle information of the target vehicle, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle;

s2, determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information;

and S3, judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.

Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.

Optionally, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware related to the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.

The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be essentially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention.

In the above embodiments of the present invention, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described in detail in a certain embodiment.

In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be implemented in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware mode, and can also be realized in a software functional unit mode.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. A method of detecting a target vehicle, comprising:

obtaining target vehicle information of a target vehicle, wherein the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle;

determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle when passing through a detection station and target vehicle information;

And judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

2. The method of claim 1, wherein the process of building the vehicle load estimation model comprises:

obtaining historical operating data of the target vehicle, wherein the historical operating data comprises: the vehicle weight information detected when the target vehicle passes through the detection station, the engine operating state data when the target vehicle passes through the detection station, the vehicle operating state when the target vehicle passes through the detection station, the detection time period when the target vehicle passes through the detection station, and the detection position when the target vehicle passes through the detection station;

and establishing the vehicle load estimation model according to the historical operation data.

3. The method of claim 1, wherein the target vehicle information further includes current location information of the target vehicle; the determining the weight of the target vehicle according to the target vehicle information of the target vehicle and a preset vehicle load estimation model comprises:

inputting the engine operating state data of the target vehicle into a vehicle load estimation model of the target vehicle to determine the weight of the target vehicle;

And correcting the weight of the target vehicle based on the current position information of the target vehicle and the detection position corresponding to the vehicle load estimation model of the target vehicle.

4. The method of claim 3, wherein the method comprises:

determining a mapping relation between the current position information and a detection position corresponding to a vehicle load estimation model of the target vehicle;

and determining a correction coefficient corresponding to the current position information of the target vehicle according to the mapping relation.

5. The method according to claim 4, wherein the establishing of the mapping relationship comprises:

associating first weighing information detected when the target vehicle passes through the detection station with second weighing information obtained based on the preset vehicle load estimation model; the first weighing information is vehicle weight information determined by a weighing detection system when the target vehicle passes through the detection position of each detection station, and the second weighing information is the weight of the target vehicle obtained by the preset vehicle load estimation model when the target vehicle passes through the detection road section related to each detection station;

Under the condition that a predicted weight error is determined, determining correction coefficients of different detection road sections according to the predicted weight error, the first weighing information and the second weighing information;

and carrying out one-to-one correspondence on the correction coefficient, the detection position of the detection station and the different detection road sections to obtain the mapping relation.

6. The method of claim 3, wherein correcting the weight of the target vehicle based on the current position information of the target vehicle and the detected position corresponding to the vehicle load estimation model of the target vehicle comprises:

determining whether the weight of the target vehicle needs to be corrected based on the current position information of the target vehicle;

and correcting the weight of the target vehicle by using the correction coefficient of the target road section corresponding to the current position.

7. The method of claim 2, wherein building the vehicle load estimation model based on the historical operating data comprises:

determining corresponding historical operating data under the stable driving state of the vehicle according to the operating state of the vehicle;

and establishing the vehicle load estimation model according to the determined corresponding historical running data of the vehicle in the stable running state.

8. A detection device of a target vehicle, characterized by comprising:

the vehicle information acquisition system comprises an acquisition module and a processing module, wherein the acquisition module is used for acquiring target vehicle information of a target vehicle, and the target vehicle information comprises: engine operating state data of the target vehicle and a vehicle operating state of the target vehicle;

the determining module is used for determining the weight of the target vehicle according to the target vehicle information and a preset vehicle load estimation model; wherein the vehicle load estimation model is established based on weight information of the target vehicle passing through the detection station and target vehicle information;

and the judging module is used for judging whether the target vehicle is overloaded and overrun or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.