CN114674403B

CN114674403B - Target vehicle detection method and device, storage medium and electronic equipment

Info

Publication number: CN114674403B
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: 2024-05-14
Anticipated expiration: 2041-12-30
Also published as: CN114674403A

Abstract

The invention discloses a method and a device for detecting a target vehicle, a storage medium and electronic equipment. Wherein the method comprises the following steps: 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and judging whether the target vehicle is overloaded or out of limit according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. Therefore, the problems that in the prior art, the vehicle-mounted weighing system cannot timely detect overload and overrun of a target vehicle escaping from a detection site, 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 method and a device for detecting a target vehicle, a storage medium and electronic equipment.

Background

When the vehicle in overload overrun runs on the road, the detection efficiency of the non-stop overrun detection station proposed in the related technology is greatly improved compared with the traditional detection modes such as the highway overrun detection station, but the non-stop overrun detection station basically still belongs to the detection mode of fixed point positions. Because the road network is complex, the full closed detection is difficult to achieve, and the overrun overload freight car avoids the detection site by means of detour and the like, so that the detection is avoided;

In addition, 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 in the stationary state, judging whether the load of the vehicle is in the stationary state according to the received load information of the vehicle; and if the load of the vehicle is judged to be in a non-stationary 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 mounting a sensor to the vehicle, the vehicle body structure may be damaged; in addition, the calibration of the common vehicle-mounted weighing system is usually carried out under ideal conditions, however, the road conditions are complex, and the interference factors are large, so that the vehicle-mounted weighing system has large detection error and poor adaptability.

Aiming at the problems, the vehicle-mounted weighing system in the prior art cannot timely detect overload overrun of a target vehicle escaping from a detection site, and has low detection precision, poor adaptability and the like, and no effective solution is proposed at present.

Disclosure of Invention

The embodiment of the invention provides a method and a device for detecting a target vehicle, a storage medium and electronic equipment, which at least solve the problems that a vehicle-mounted weighing system in the prior art cannot timely detect overload and overrun of the target vehicle escaping from a detection site, 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 method for detecting a target vehicle, including: 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and judging whether the target vehicle is overloaded 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 embodiment of the present invention, there is also provided a detection method apparatus for a target vehicle, including: the system comprises an acquisition module, a control module and a control 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and the judging module is used for judging whether the target vehicle is overloaded or out of limit according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

According to a further aspect of embodiments of the present invention, there is also provided a computer readable storage medium having stored therein a computer program, wherein the computer program is arranged to perform the method of any of the method embodiments when run.

According to a further aspect of the embodiments of the present invention, there is also provided an electronic device comprising a memory in which a computer program is stored, and a processor arranged to perform the method of any of the method embodiments described above by means of the computer program.

In an embodiment of the present invention, target vehicle information of a target vehicle is acquired, where 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and judging whether the target vehicle is overloaded or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. The vehicle load estimation model of the target vehicle is formed by collecting corresponding data in advance, the load weight corresponding to real-time target vehicle information of the target vehicle can be determined through the vehicle load estimation model, and the load weight is compared with the maximum allowable total mass limit to determine whether the target vehicle is overrun, so that the problems that in the prior art, the target vehicle escaping from a detection site cannot be timely detected in overload overrun by a vehicle-mounted weighing system, the detection precision of the vehicle-mounted weighing system is low, the adaptability is poor and the like can be solved, 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, and the detection precision of the vehicle-mounted weighing system to the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system to the target vehicle under different environments are greatly improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation of the application. In the drawings:

Fig. 1 is a hardware block diagram 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 invention;

FIG. 3 is a schematic structural view of a freight vehicle overrun overload detection system according to an alternative embodiment of the present invention;

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

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, 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 embodiments provided by the embodiments of the present application may be performed in a computer terminal, a mobile terminal, or similar computing device. Taking a computer terminal as an example, fig. 1 is a block diagram of a hardware structure of a computer terminal of a method for detecting a target vehicle according to an embodiment of the present application. As shown in fig. 1, the computer terminal 10 may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as 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 appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, a computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than the equivalent functions shown in FIG. 1 or more than the functions shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for detecting a target vehicle in an embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-described method. Memory 104 may include high-speed random access memory, but 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 means 106 is arranged to receive or transmit data via a network. The 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 (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

In this 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, where the flowchart includes the following steps:

Step S202, obtaining 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;

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 the weight information and the target vehicle information when the target vehicle passes through the detection station;

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

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

Through the steps, the target vehicle information of the 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and judging whether the target vehicle is overloaded or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. The vehicle load estimation model of the target vehicle is formed by collecting corresponding data in advance, the load weight corresponding to real-time target vehicle information of the target vehicle can be determined through the vehicle load estimation model, and the load weight is compared with the maximum allowable total mass limit to determine whether the target vehicle is out of limit, so that the problems that in the prior art, the vehicle-mounted weighing system cannot timely detect overload and overrun of the target vehicle escaping from a detection site, the detection precision of the vehicle-mounted weighing system is low, the adaptability is poor and the like can be solved, 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, and the detection precision of the vehicle-mounted weighing system to the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system to the target vehicle under different environments are greatly improved.

The vehicle load estimation model in the above mode uses the engine state without adding an additional vehicle-mounted sensor; the existing overrun station data is used as calibration data, and the position correction is added, so that the calibration result is richer and the detection result is more accurate. The method solves the problems that most of existing detection means are fixed-point detection, full coverage of road network cannot be achieved, and supervision loopholes and the existing sensor installed at the middle and rear part are possibly damaged; the calibration of the vehicle-mounted weighing system is usually carried out under ideal conditions, however, the road conditions are complex, and the interference factors are large, 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 process for establishing the vehicle load estimation model includes: acquiring historical operation data of the target vehicle, wherein the historical operation data comprises: the method comprises the steps of detecting vehicle weight information when the target vehicle passes through a detection station, engine running state data when the target vehicle passes through the detection station, vehicle running state when the target vehicle passes through the detection station, detection time period when the target vehicle passes through the detection station and 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 alternative embodiment, acquiring the historical operating data of the target vehicle includes: determining a plurality of pieces of vehicle weight information of a target vehicle passing through a plurality of detection stations; analyzing the plurality of vehicle weight information to determine a plurality of detection periods of weighing detection of the target vehicle at the detection station and a plurality of detection positions of the target vehicle; intercepting a plurality of groups of data information in real-time vehicle information uploaded to a data platform by the target vehicle according to the detection periods and the detection positions, wherein the plurality of 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 data information and the plurality of vehicle weight information.

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

For example, when the real-time vehicle information includes the engine torque α of the target vehicle, the engine rotational speed β of the target vehicle, the vehicle speed v of the target vehicle, the geographic position information η of the target vehicle, and the recorded vehicle accurate weight w. Combining the acquired information to construct a characteristic matrix of the accurate weight w corresponding to the engine torque alpha, the engine rotating speed beta, the vehicle speed upsilon of the target vehicle and the target vehicle engine torque as { alpha, beta, upsilon }; assuming that h _θ(x)＝θ₀+θ₁α+θ₂β+θ₃ v is constructed, the matrix expression is: h _θ (X) =xθ; in order to determine that a group (θ ₀,θ₁,θ₂,θ₃) is found, the number of samples is assumed to be m, and the parameter to be solved is n. The second moment, which accurately measures the weight of the vehicle and the predicted weight, is defined as the loss function J (θ), which is defined as: And determining the optimal (theta ₀,θ₁,θ₂,θ₃) combination by the loss function, and obtaining the optimal (theta ₀,θ₁,θ₂,θ₃) combination. The loss function means the relation between the real vehicle weight and the predicted vehicle weight, and the obtained parameters are the obtained model parameters if and only if the loss function value is minimum.

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 includes: inputting 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.

In short, since 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 more accords with the actual weight of the target vehicle, the weight of the target vehicle needs to be corrected according to the detected position corresponding to the weight estimation performed by the vehicle load estimation model and the current position information of the target vehicle, and the corrected weight which accords with the actual scene is determined.

Optionally, the method comprises the following steps: 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 for establishing the mapping relationship includes: correlating 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 through the preset vehicle load estimation model when the target vehicle passes through each detection section related to detection; under the condition that the 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 point and the different detection road sections to obtain the mapping relation.

It should be noted that, in the related detection road sections described in this embodiment, the road sections where the load of the vehicle does not change before and after the target vehicle passes through the detection position, and the specific confirmation method includes the road sections where the speed of the vehicle is continuously not zero before and after passing through the detection position, or the second weighing information of the vehicle does not change beyond the set threshold before and after passing through the detection position.

Optionally, 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: judging whether the weight of the target vehicle needs to be corrected or not 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 an abnormal weight region, it is determined that the weight of the target vehicle needs to be corrected; and correcting the weight of the target vehicle by using the correction coefficient of the target road section corresponding to the current position; and under the condition 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 not an abnormal weight area, the weight of the target vehicle is determined not to need to be corrected.

As an alternative embodiment, as the state of the vehicle changes during running, the association of the predicted vehicle weight and the running state is acquired and the position information is combined; acquiring the distribution of abnormal vehicle weight along with the running position of the vehicle; examples of position correction are as follows:

Alternatively, mode one: the abnormal distribution of the weight of the vehicle 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, if the abnormal change of the running weight of the vehicle is considered to be related to the geographical position change (such as a concave pavement, an ascending slope and a descending slope), if the abnormal change of the running weight of the vehicle is considered to be marked as a section to be optimized, and then the actual weight w _ο and the predicted weight error of different types of vehicles are combined with the difference distance of abnormal values in different vehicle passing states The following model is established: /(I)K is a correction coefficient, and W is an estimated weight determined by a vehicle load estimation model.

Alternatively, mode two: and acquiring statistics of the change of the vehicle working condition abnormality along with the state of the geographic position by counting the road sections of abnormal driving and combining the change condition of the vehicle torque, and quantifying the relation between the real weight of the vehicle and the error by combining the distribution curve of the vehicle error along with the geographic position.

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

In short, in order to ensure the accuracy and stability of the established vehicle load estimation model, after determining the historical operation data, data filtering is required to be performed on the historical operation data according to preset data filtering conditions so as to determine the historical vehicle information for establishing the vehicle load estimation model. Wherein the preset data screening conditions comprise at least one of the following: the current position is a target road section of the target vehicle running for a plurality of times, the target vehicle is not in a braking state, and the target vehicle is not switched to a gear.

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

Optionally, determining that the weight of the target vehicle needs to be corrected, correcting the weight of the target vehicle by using a correction coefficient of the target road section 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 according to the vehicle load estimation model, and subtracting the product of the correction coefficient and the 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 using the product of the weight and the correction coefficient and the preset error weight.

Optionally, after determining whether the target vehicle is overloaded according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle, the method further includes: determining an empty torque and a full torque of the target vehicle; determining a torque percentage of the target vehicle according to the idle torque and the full torque; and verifying the maximum allowable total mass limit of the target vehicle according to the torque percentage.

It will be appreciated that, as the vehicle ages due to the gradual increase in the time of use of the vehicle, the corresponding maximum allowable total mass limit of the vehicle will also change due to different vehicle conditions, and therefore, it is necessary to acquire the no-load torque and the full-load torque of the target vehicle, and then change the maximum allowable total mass limit of the target vehicle 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 optional embodiments of the present invention, the following description is given with reference to the above-mentioned flow of the detection method of the target vehicle by way of example, but the present invention is not limited to the technical solutions of the embodiments of the present invention.

In order to better understand the technical solutions of the embodiments and optional embodiments of the present invention, the following describes application scenarios that may occur in the embodiments and optional embodiments of the present invention, but is not used to limit the application of the following scenarios.

Overrun overload definition: overrun refers to the fact that the overall size, axle load and total mass of the freight vehicle exceed the limit of national safety technical standards of locomotives or exceed the limit load, limit height, limit width and limit length standards marked by highway traffic signs. Overload refers to the fact that the cargo of a freight vehicle exceeds the approved cargo quality of a vehicle license.

Optionally, the source addresses the overstation, and the overstation detection is carried out on the outgoing freight vehicles at the important freight source places, so that the overstation overload vehicles are forbidden to exit. The detection efficiency is low, and full coverage detection is difficult to achieve;

optionally, the flow detection is performed by related staff on the road for spot check detection of passing load vehicles. Although the requirements of mobility and maneuverability can be met, a great deal of manpower is required for pavement law enforcement, and the detection efficiency is low as well;

Alternatively, a highway overrun detection station, typically built on the road side, may need to guide the vehicle into the station for detection. The contradiction between the limited property, the fixity and the mobility of illegal overrun transportation of the detection site is still highlighted;

Optionally, the non-stop overrun detection station is built on a key road section, and can be used for detecting the non-stop overrun of all vehicles passing through, so that the efficiency is greatly improved.

Therefore, although the detection efficiency of the non-stop overrun detection station of the current mainstream is greatly improved compared with the traditional detection modes such as the highway overrun detection station, the non-stop overrun detection station basically still belongs to the detection mode of the fixed point location. Because the road network is complex, the full closed detection is difficult to achieve, and the overrun overload freight vehicle avoids the detection site by means of detour and the like, so that the detection is avoided.

As an optional implementation manner, the method for detecting the overrun overload transportation of the freight vehicle can compensate for the detection loopholes of the existing fixed overload detection station to a great extent, and specifically comprises the following steps of;

Step 1, data acquisition is carried out on a freight vehicle, and specifically, weight information of the vehicle is obtained by a non-stop overrun detection station, a highway overrun detection station and the like; the vehicle-mounted terminal obtains vehicle information such as engine torque, rotating speed, vehicle speed, GPS position and the like of the vehicle;

It should be noted that, the vehicle load and the engine state (torque, rotation speed), the vehicle speed, the GPS position and other vehicle information are in a corresponding relation, the motion law of the vehicle in operation still accords with the Newton's second law, and the vehicle running equation can be obtained by carrying out stress analysis on the vehicle: f=f _f+F_w+F_i; the driving force at the time of constant-speed running of the automobile is equal to the running resistance at the time of constant-speed running (F _f+F_w+F_i). Wherein F _w is air resistance; f _f is the rolling resistance and gradient resistance F _i of the vehicle proportional to the total weight of the vehicle, and F is the rolling resistance coefficient, i.e., the running resistance of the vehicle increases proportionally with increasing total weight of the vehicle. The driving force of the automobile is proportional to the output torque of the engine. Therefore, the vehicle load is proportional to the engine torque at a constant gradient and vehicle speed.

Optionally, the vehicle-mounted terminal accesses a whole vehicle CAN (controller Area network, controller area network, abbreviated as CAN) network through a vehicle OBD port, and analyzes data according to a J1939 standard protocol (sampling period is 100 ms). The J1939 protocol is a network protocol supporting high-speed communication of closed-loop control, and is mainly used on trucks or buses. The ECU (Electronic Control Unit, electronic control unit, ECU for short) packages its data into CAN data at a certain frequency according to the data conversion scheme 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 geographic position information of the vehicle in real time through a 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 having an important influence on the estimation accuracy of the vehicle mass is removed by data screening. The specific screening conditions are as follows:

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

2. The speed of the vehicle when traveling on a fixed road is recorded, as it is related to the wind resistance of the vehicle.

3. There is a minimum limit on the number of samples: the number of samples is too small and random errors due to data fluctuations are amplified, so that there must be a sufficient number of samples to ensure the accuracy of the data source.

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

5. Data at the time of gear shift is not available: because the connection between the engine and the power train is cut off at the time of gear shift, the calculation accuracy in this case is poor.

Step 2, data association: acquiring weighing detection time and position from vehicle weight information; intercepting the vehicle information in a certain time period and a certain area of the time and the position of the vehicle in weighing detection; the weight information and the vehicle information are associated and matched, and one or more groups of data points are produced;

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

Optionally, the data model is built based on an automobile motion balance equation, by recording vehicle weight information obtained from a weighing detection device such as a super station, corresponding vehicle steady-state running conditions (road section information) under the weight, and recording key information such as engine speed, engine torque percentage, vehicle speed and the like, and vehicle information such as vehicle brands, models, production dates and the like, and constructing a database to be stored in a controller. When the data stored in the database reaches a certain quantity, a vehicle load estimation equation is constructed by using an estimation algorithm based on recursive least square, and then a data model of the similar vehicle is built by a self-learning method.

Step4, overrun detection: and inquiring the real-time collected vehicle information, 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 overrun suspicion exists.

For example, when the vehicle is running and load identification is suitable (conditions such as fixed road section and stable speed), the starting identification strategy is to query a database by using the information of the current running condition of the engine, namely the output torque or the output torque percentage, the rotating speed and the like, and the current load state of the vehicle is locked through calculation and analysis. The load mass of the vehicle in the current steady state condition can be determined, for example, by the torque percentage relationship between the current torque and the full and no load.

As an alternative implementation manner, a system for detecting overload and transportation of a freight vehicle is provided, as shown in fig. 3, which is a schematic structural diagram of a system for detecting overload and transportation of a freight vehicle according to an alternative embodiment of the present invention, where the system 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 the vehicle passing through the station by the 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 configured to provide the detection system with the obtained vehicle information such as the engine torque, the rotation speed, the vehicle speed, the GPS position, etc. of the vehicle;

the data processing platform 306 comprises a data receiving and storing module 402, a data association load estimating module 404 and an overrun detecting module 406, and is used for processing data provided by the weight detecting station 302 and the vehicle-mounted terminal 304; the weight information of the vehicle obtained from the weighing detection device such as the super station and the like and the corresponding steady running working condition (road section information) of the vehicle under the weight are recorded, key information such as the engine speed, the engine torque percentage and the vehicle speed is recorded, and the key information is constructed into a database and is stored in the controller. When the data stored in the database reaches a certain amount, a vehicle load estimation equation is constructed. When the vehicle runs, when load identification is suitable (conditions such as fixed road section and stable speed), a starting identification strategy is adopted, the current running working condition of the engine, namely the output torque or the output torque percentage, the rotating speed and other information are used for inquiring a database, and the current load state of the vehicle is locked through calculation and analysis. The load mass of the vehicle in the current steady state condition can be determined, for example, by the torque percentage relationship between the current torque and the full and no load.

Optionally, when the over-limit overload transportation detection is required for the freight vehicle, vehicle information of the freight vehicle is collected in real time, then whether the data processing platform 306 is in a data model is queried, if so, the vehicle information is input into the model, the weight of the vehicle is output, and further whether the over-limit suspicion exists is judged.

According to the overrun detection method, based on the automobile motion balance equation, the vehicle weight information obtained from the weighing detection device for the overrun station and the like and the corresponding vehicle steady-state running working conditions (road section information) under the weight are recorded, key information such as the engine rotating speed, the engine torque percentage and the vehicle speed is recorded, the key information is constructed into a database and stored into the controller, and a vehicle load model of the target vehicle is constructed, so that the purpose of timely determining whether the target vehicle escaping from the 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 problems that the vehicle-mounted weighing system in the prior art cannot timely detect overload overrun of the target vehicle escaping from the detection station are solved, the detection precision of the vehicle-mounted weighing system is low, the adaptability is poor and the like are solved, and compared with the prior art, the accuracy and the real-time performance are higher, and the application scene is wider.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

From the above description of embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus necessary general hardware platform, but may alternatively be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

According to another aspect of the embodiment of the present invention, there is also provided a detection apparatus for a target vehicle for implementing the detection method for 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 the weight information and the target vehicle information when the target vehicle passes through the detection station;

And the judging module 506 is configured to judge 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 device, the target vehicle information of the 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 the weight information and the target vehicle information when the target vehicle passes through the detection station; and judging whether the target vehicle is overloaded or not according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle. The vehicle load estimation model of the target vehicle is formed by collecting corresponding data in advance, the load weight corresponding to real-time target vehicle information of the target vehicle can be determined through the vehicle load estimation model, and the load weight is compared with the maximum allowable total mass limit to determine whether the target vehicle is overrun, so that the problems that in the prior art, the vehicle-mounted weighing system cannot timely detect overload overrun of the target vehicle escaping from a detection site, the detection precision of the vehicle-mounted weighing system is low, the adaptability is poor and the like can be solved, 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, and the detection precision of the vehicle-mounted weighing system to the target weight of the target vehicle and the adaptability of the vehicle-mounted weighing system to the target vehicle under different environments are greatly improved.

Optionally, the determining module further includes: the establishing unit is used for acquiring historical operation data of the target vehicle, wherein the historical operation data comprises: the detected vehicle weight information 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.

Optionally, the determining module is further configured to, when the target vehicle information further includes: under the condition of the current position information of the target vehicle, the engine running state data of the target vehicle is input 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: the coefficient unit is used for determining the mapping relation between the current position information and the detection position corresponding to the 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 for establishing the mapping relationship includes: correlating 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 through 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 the 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 site and the different detection road sections to obtain the mapping relation.

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.

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

In other words, in order to ensure the accuracy and stability of the vehicle load estimation model, after determining the historical operation data, the historical operation data needs to be filtered according to the preset data screening condition, so as to determine the historical vehicle information used for building the vehicle load estimation model. Wherein the preset data screening conditions comprise at least one of the following: the current position is a target road section of the target vehicle running for a plurality of 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 each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; or the above modules may be located in different processors in any combination.

An embodiment of the invention also provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.

Alternatively, in the present embodiment, the above-described storage medium may be configured to store a computer program for performing the steps of:

S1, 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;

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 the weight information and the target vehicle information when the target vehicle passes through the detection station;

and S3, judging whether the target vehicle is overloaded or out of limit according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle.

Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.

Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.

An embodiment of the invention also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

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

Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:

Alternatively, in this embodiment, it will be understood by those skilled in the art that all or part of the steps in the methods of the foregoing embodiments may be performed by a program for instructing a terminal device to execute the method, where the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the method described in the embodiments of the present invention.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this embodiment that is not described in detail, reference may be made to the related descriptions of other embodiments.

In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

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

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

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the 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 running state data of the target vehicle, current position information of the target vehicle, and a vehicle running state of the target vehicle;

inputting the engine operating state data into a vehicle load estimation model of the target vehicle to determine the weight of the target vehicle; determining a mapping relation between the current position information of the target vehicle and a detection position corresponding to a vehicle load estimation model of the target vehicle; wherein the vehicle load estimation model is established based on weight information and target vehicle information of the target vehicle when passing through a detection station;

Determining a correction coefficient corresponding to the current position information of the target vehicle based on the mapping relation to correct the weight of the target vehicle;

Judging whether the target vehicle is overloaded or out of limit according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle;

Wherein the process of establishing the vehicle load estimation model includes:

Acquiring historical operation data of the target vehicle, and establishing the vehicle load estimation model according to the historical operation data, wherein the historical operation data comprises: detected vehicle weight information when the target vehicle passes through a detection station, engine running state data when the target vehicle passes through the detection station, a vehicle running state when the target vehicle passes through the detection station, a detection time period when the target vehicle passes through the detection station, and a detection position when the target vehicle passes through the detection station;

The process for establishing the mapping relation comprises the following steps:

correlating 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 a detection position of each detection station, and the second weighing information is the weight of the target vehicle obtained through the preset vehicle load estimation model when the target vehicle passes through a detection road section related to each detection station;

Under the condition that the 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 point and the different detection road sections to obtain the mapping relation.

2. The method according to claim 1, 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 includes:

Judging whether the weight of the target vehicle needs to be corrected or not 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.

3. The method of claim 1, wherein building the vehicle load estimation model from the historical operating data comprises:

According to the running state of the vehicle, determining corresponding historical running data under the stable running state of the vehicle;

And establishing the vehicle load estimation model according to the corresponding historical operation data under the determined vehicle stable running state.

4. A detection apparatus for a target vehicle, characterized by comprising:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring target vehicle information of a target vehicle, and the target vehicle information comprises: engine running state data of the target vehicle, current position information of the target vehicle, and a vehicle running state of the target vehicle;

a determining module for inputting the engine operating state data into a vehicle load estimation model of the target vehicle to determine a weight of the target vehicle; determining a mapping relation between the current position information of the target vehicle and a detection position corresponding to a vehicle load estimation model of the target vehicle; determining a correction coefficient corresponding to the current position information of the target vehicle based on the mapping relation to correct the weight of the target vehicle; wherein the vehicle load estimation model is established based on the weight information and the target vehicle information when the target vehicle passes through the detection station;

The judging module is used for judging whether the target vehicle is overloaded or out of limit according to the weight of the target vehicle and the maximum allowable total mass of the target vehicle;

The determining module further includes: the building unit is used for obtaining historical operation data of the target vehicle and building the vehicle load estimation model according to the historical operation data, wherein the historical operation data comprises: detected vehicle weight information when the target vehicle passes through a detection station, engine running state data when the target vehicle passes through the detection station, a vehicle running state when the target vehicle passes through the detection station, a detection time period when the target vehicle passes through the detection station, and a detection position when the target vehicle passes through the detection station;

5. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program, when run, performs the method of any one of claims 1 to 3.

6. 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 the claims 1 to 3 by means of the computer program.