Bar-shaped sensor abnormal driving weighing method and device

Abstract

The invention relates to a bar-shaped sensor abnormal driving weighing method and device, and the method comprises the steps: constructing at least one virtual lane based on the distribution of bar-shaped sensors on a target road; the target road comprises at least one physical lane, each physical lane is provided with two columns of strip-shaped sensors, and the constructed at least one virtual lane covers all possible distribution positions where vehicles pass through the strip-shaped sensors on the target road; when the target vehicle passes through the target road, the driving state of the target vehicle is monitored; performing virtual lane matching according to the driving state of the target vehicle; matching the driving state of the target vehicle with the at least one virtual lane to obtain a target virtual lane matched with the driving state of the target vehicle; and calculating and obtaining weight data of the target vehicle based on a target calculation rule corresponding to the target virtual lane. The technical problem that an existing dynamic weighing system is inaccurate in weighing result and even abnormal when facing an abnormal driving condition is mainly solved.

Description

Technical Field

[0001] The present application relates to the technical field of non-stop weighing, and in particular to a method and device for weighing vehicles with abnormal driving using a bar sensor. Background Art

[0002] With the rapid development of the logistics industry, the problem of vehicle overloading is becoming increasingly serious, which not only affects the safe service life of roads and bridges, but also increases the risk of traffic accidents. Therefore, dynamic weighing (Weigh-in-Motion, WIM) technology is widely used in vehicle overload detection as an effective management method. However, in actual operation, due to the operating habits of drivers and the behavior of evading inspection, the WIM system faces a variety of complex situations, especially when the vehicle passes through the weighing area in a non-standard way, which will have a significant impact on the weighing results.

[0003] A common abnormal situation is the "pressing seam" phenomenon. Pressing seams refer to when a vehicle passes through the weighing area, the tires on one or both sides simultaneously or alternately roll over two adjacent bar sensors, causing the wheel weight that should be borne by a single sensor to be distributed to two adjacent rows of sensors. This situation can be further divided into two types: single-sided pressing seams and double-sided pressing seams according to the force distribution of the vehicle tires. Either situation will affect the accuracy of weighing.

[0004] Another common situation is "lane crossing". Lane crossing means that when a vehicle passes through the weighing area, it crosses the solid lane line of the road, causing the tires on both sides of the vehicle to travel on two adjacent lanes. This behavior will make the weighing system originally designed for independent lanes unable to accurately distribute the vehicle weight, resulting in deviations in the weighing results.

[0005] In addition, "going in an S shape" is also a common behavior to avoid weighing. When the steering axis or coupling part of the vehicle passes through the scale platform at a large angle, the S shape will occur. During this process, the scale platform will continue to be loaded and may even crush two or three rows of sensors at the same time. According to the specific path of the vehicle, the S shape can be further divided into several situations such as normal lane S shape, cross-lane S shape and three-row S shape.

[0006] In the face of the above abnormal driving conditions, the traditional dynamic weighing system is difficult to effectively deal with and ensure the accuracy of the weighing results. Therefore, it is necessary to develop a new strip sensor abnormal driving weighing method that can adapt to different driving modes and match the actual vehicle driving state by building a virtual lane model, thereby improving the weighing accuracy under various abnormal driving conditions. The application of this technology is of great significance to improving the non-site law enforcement capabilities and the level of scientific and technological control of overweight vehicles. Summary of the invention

[0007] The present application mainly solves the technical problem that the existing dynamic weighing system has inaccurate or even abnormal weighing results when facing abnormal driving conditions.

[0008] An embodiment of the present application provides a bar sensor abnormal driving weighing method, the method comprising:

[0009] Based on the distribution of strip sensors on the target road, at least one virtual lane is constructed; the target road includes at least one physical lane, each of the physical lanes is provided with two rows of strip sensors, and the at least one virtual lane constructed covers all possible distribution positions of the strip sensors that vehicles pass by on the target road;

[0010] When a target vehicle passes through a target road, the driving status of the target vehicle is monitored;

[0011] Perform virtual lane matching according to the driving status of the target vehicle;

[0012] Matching the driving state of the target vehicle with at least one virtual lane to obtain a target virtual lane matching the driving state of the target vehicle;

[0013] Based on the target calculation rule corresponding to the target virtual lane, the weight data of the target vehicle is calculated and obtained.

[0014] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, the step of constructing at least one virtual lane includes:

[0015] According to all driving states of the vehicle passing through the strip sensor on the target road, obtaining at least one virtual lane corresponding to all driving states;

[0016] The calculation rules of each virtual lane are set respectively.

[0017] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, the driving state includes normal physical lane driving, single-side pressure seam driving, double-side pressure seam driving, cross-lane driving, normal lane S-shaped driving, cross-lane S-shaped driving, and three-row S-shaped driving;

[0018] The number of physical lanes of the target road is N, the strip sensors of the target road are respectively the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors, and the weighing data measured by the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors are W1, W2, ..., W2N-1 and W2N respectively;

[0019] The number of the virtual lanes is 4N-3, where N is a positive integer.

[0020] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, when the driving state of the target vehicle is driving in a normal physical lane, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows:

[0021] The weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is located.

[0022] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, when the driving state of the target vehicle is single-side seam driving, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows:

[0023] The weight data W of the target vehicle is the sum of the data of the three adjacent columns of strip sensors rolled over by the target vehicle;

[0024] When the target vehicle driving state is double-side seam driving, the calculation rule of the virtual lane corresponding to the target vehicle driving state is as follows:

[0025] The weight data W of the target vehicle is the sum of the data of four adjacent columns of strip sensors rolled over by the target vehicle.

[0026] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, when the driving state of the target vehicle is crossing lanes, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows:

[0027] The weight data W of the target vehicle is the sum of the data of two adjacent columns of strip sensors rolled over by the target vehicle;

[0028] The two adjacent columns of strip sensors rolled over by the target vehicle are located in different physical lanes.

[0029] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, when the driving state of the target vehicle is S-shaped driving in a normal lane, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows:

[0030] The weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is located and which is successively rolled over within the first time threshold;

[0031] When the target vehicle is in an S-shaped driving state across lanes, the calculation rule of the virtual lane corresponding to the target vehicle is as follows:

[0032] The weight data W of the target vehicle is the sum of the data of two adjacent columns of strip sensors in different physical lanes that the target vehicle successively rolls over within the first time threshold;

[0033] When the target vehicle is in an S-shaped driving state across three columns, the calculation rule of the virtual lane corresponding to the target vehicle is as follows:

[0034] The weight data W of the target vehicle is the sum of the data of three adjacent columns of strip sensors that are successively rolled over by the target vehicle within the second time threshold;

[0035] The first time threshold is not greater than the second time threshold, and both the first time threshold and the second time threshold are not greater than 0.1s.

[0036] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, after monitoring the driving state of the target vehicle, it also includes:

[0037] Determine whether there is a parallel vehicle when the target vehicle passes through the target road: if not, perform virtual lane matching according to the driving state of the target vehicle; if yes, determine whether the target vehicle and the parallel vehicle run over the same row of strip sensors together:

[0038] If not, then performing virtual lane matching according to the driving state of the target vehicle;

[0039] If so, determine the ownership of the jointly crushed strip sensor. If it belongs to the target vehicle, the data of the jointly crushed strip sensor will be recorded in the target vehicle; if it does not belong to the target vehicle, the data of the jointly crushed strip sensor will not be recorded in the target vehicle.

[0040] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, the determination of the ownership of the strip sensors that are jointly crushed includes:

[0041] Determine whether the strip sensor data on the target vehicle side is coaxial with the strip sensor data being rolled over together:

[0042] If yes, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula:

[0043] W A-total =W A +W AB ;

[0044] W B-total =W B +(W AB -W A );

[0045] If not, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula:

[0046] W A-total =W A +(W AB -W B );

[0047] W B-total =W B +W AB ;

[0048] Among them, W A-total is the weight data of the target vehicle, W B-total is the weight data of the parallel vehicle, W A is the strip sensor data on the target vehicle side, W B is the strip sensor data on the parallel vehicle side, W AB The bar sensor data is crushed together.

[0049] The present application also provides a bar sensor abnormal driving weighing device, the identification device comprising:

[0050] A virtual lane construction module, configured to construct at least one virtual lane based on the distribution of strip sensors on a target road; the target road includes at least one physical lane, each of the physical lanes is provided with two rows of strip sensors, and the constructed at least one virtual lane covers all possible distribution positions of the strip sensors on the target road where vehicles pass by;

[0051] A target vehicle monitoring module is used to monitor the driving status of a target vehicle when the target vehicle passes through the target road;

[0052] A virtual lane matching module is used to perform virtual lane matching according to the driving status of the target vehicle;

[0053] A target virtual lane acquisition module, used to match the driving state of the target vehicle with at least one virtual lane, and acquire a target virtual lane matching the driving state of the target vehicle;

[0054] The target vehicle weight calculation module is used to calculate and obtain the weight data of the target vehicle based on the target calculation rule corresponding to the target virtual lane.

[0055] According to the strip sensor abnormal driving weighing method and device of the above-mentioned embodiment, two rows of strip sensors are set on each physical lane on the target road to form at least one virtual lane, and the vehicle weight calculation rules of each virtual lane are designed respectively, so that even if the vehicle is in abnormal situations such as pressing seams, crossing lanes or S-shaped driving, the vehicle weight data can be accurately collected, thereby improving the accuracy of the weighing result; through the pre-set virtual lane model and the corresponding calculation rules, accurate weight data can be quickly obtained without stopping the vehicle, which not only simplifies the law enforcement procedure, but also speeds up the law enforcement speed and reduces traffic delays caused by inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flow chart of the abnormal driving weighing method of the strip sensor provided in the embodiment of the present application (I);

[0057] Figure 2 Flow chart (II) of the abnormal driving weighing method of the strip sensor provided in the embodiment of the present application;

[0058] Figure 3 Flow chart (I) of the strip sensor abnormal driving weighing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The present application is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are intended to enable the present application to be better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0060] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0061] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0062] Please refer to Figure 1 In order to solve the technical problem that the weighing results of the existing dynamic weighing system are inaccurate or even abnormal when facing abnormal driving conditions, an abnormal driving weighing method of a strip sensor is provided in an embodiment of the present application, and the method comprises the following steps:

[0063] Step S101: construct at least one virtual lane based on the distribution of strip sensors on a target road. The target road includes at least one physical lane, each of which is provided with two rows of strip sensors, and the constructed at least one virtual lane covers all possible distribution positions of the strip sensors that vehicles pass on the target road.

[0064] In some embodiments, the target road is typically distributed with two, three, or four physical lanes.

[0065] If there are two physical lanes on the target lane, four columns of strip sensors are distributed on the target lane;

[0066] If there are three physical lanes on the target lane, six columns of strip sensors are distributed on the target lane;

[0067] If there are four physical lanes on the target lane, eight columns of strip sensors are distributed on the target lane.

[0068] In some embodiments, two rows of strip sensors are installed on each physical lane, one for the front row and the other for the rear row, to cover all possible tire positions when the vehicle passes. By analyzing the data from these sensors, the system can build multiple virtual lane models, each of which represents a possible vehicle driving path. These virtual lanes cover all possibilities from normal straight driving to various abnormal driving (such as seam pressing, lane crossing, S-shaped driving, etc.), ensuring that no matter how the vehicle drives, it can find a virtual lane model that matches it.

[0069] Step S102: When a target vehicle passes through the target road, the driving state of the target vehicle is monitored.

[0070] In some embodiments, when the target vehicle enters the monitoring range, the state of the target vehicle is monitored. The monitoring content includes but is not limited to the vehicle's speed, acceleration, steering angle, tire contact with the ground, etc. These data can be collected by cameras, radars and other sensors installed around the road and transmitted to the central control system in real time. With this information, the system can determine the current driving state of the vehicle and provide a basis for subsequent virtual lane matching.

[0071] Step S103: performing virtual lane matching according to the driving state of the target vehicle.

[0072] In some embodiments, after sufficient vehicle status information is collected, the vehicle status information data is analyzed to identify the vehicle's current driving mode; this information is compared with a previously constructed virtual lane model to find the virtual lane that best matches the current vehicle driving state.

[0073] In some embodiments, during the virtual lane matching process, pattern recognition technology is used to ensure the accuracy of the matching results.

[0074] In some embodiments, pattern recognition employs the following methods:

[0075] First, data collection and preprocessing:

[0076] First, a large amount of data needs to be collected when the vehicle passes through the strip sensor, including but not limited to vehicle speed, acceleration, sensor pressure distribution, etc. It is also necessary to collect data on the actual driving status of the vehicle, such as whether it presses the seam, crosses the lane, or takes an S-shaped turn.

[0077] Second, perform data preprocessing:

[0078] Clean the collected data to remove noise and invalid data. Discretize, standardize or normalize continuous data to facilitate subsequent feature extraction and model training.

[0079] Third, perform feature extraction and selection:

[0080] Extract key features when the vehicle passes the sensor, such as the location of the tire contact point, pressure change trend, speed change, etc. Signal processing techniques (such as Fourier transform, wavelet transform, etc.) can be used to extract useful frequency domain features.

[0081] Fourth, perform feature selection:

[0082] Use feature selection techniques (such as principal component analysis PCA, LASSO regression, etc.) to reduce feature dimensions and select the features that contribute most to classification.

[0083] Fifth, train the model:

[0084] Use machine learning algorithms (such as decision trees, support vector machines (SVMs), random forests, deep learning, etc.) to train the model so that it can distinguish different vehicle driving states from the features. Use cross-validation and other techniques to evaluate the generalization ability of the model and adjust the model parameters to achieve optimal performance.

[0085] Sixth, make predictions about the application model in advance:

[0086] When a new vehicle passes by, the system will collect vehicle data and extract features in real time; these features will be input into the trained model, and the model will output the vehicle's driving status category; based on the output results, the system can quickly find a virtual lane model that matches it.

[0087] Seventh, verify the results and provide feedback:

[0088] The prediction results of the model are verified to ensure that the matched virtual lanes are consistent with the actual driving status of the vehicle. If an incorrect match occurs, the model parameters can be adjusted through the feedback mechanism to further optimize the model performance.

[0089] Step S104: Match the driving state of the target vehicle with at least one virtual lane to obtain a target virtual lane that matches the driving state of the target vehicle.

[0090] In some embodiments, after detailed analysis and calculation, the system will select a best matching virtual lane as the target virtual lane. This target virtual lane represents the closest model of the actual driving path of the vehicle. Through this matching process, the system can effectively combine the actual driving situation with the theoretical model to prepare for the next weighing calculation.

[0091] Step S105: Calculate and obtain the weight data of the target vehicle based on the target calculation rule corresponding to the target virtual lane.

[0092] In some embodiments, the system will calculate the vehicle's weight data based on the selected target virtual lane and its corresponding calculation rules. The so-called calculation rules here refer to the method of adjusting the weighing algorithm according to the driving state of the vehicle on a specific virtual lane. For example, when driving in an S-shape, the algorithm will take into account the changes in the center of gravity of the vehicle and the pressure distribution of the wheels on the sensor at different time points. In this way, the system can obtain more accurate vehicle weight information, thereby solving the problem of inaccurate weighing results of existing dynamic weighing systems when facing abnormal driving conditions.

[0093] Please refer to Figure 2In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, the construction of at least one virtual lane in step S101 includes the following steps:

[0094] Step S201: Acquire at least one virtual lane corresponding to all driving states according to all driving states of the vehicle passing the strip sensor on the target road.

[0095] Step S202: corresponding to each virtual lane, respectively set the calculation rules of the virtual lane.

[0096] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred scheme, the driving status includes driving in normal physical lanes, driving with a single-side seam, driving with a double-side seam, driving across lanes, S-shaped driving in normal lanes, S-shaped driving across lanes, and S-shaped driving across three rows; the number of physical lanes of the target road is N, and the strip sensors of the target road are respectively the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors, and the weighing data measured by the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors are W1, W2, ..., W2N-1 and W2N respectively; the number of virtual lanes is 4N-3; wherein N is a positive integer.

[0097] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred scheme, when the driving state of the target vehicle is driving in a normal physical lane, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows: the weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is located.

[0098] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred scheme, when the driving state of the target vehicle is single-sided seam driving, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows: the weight data W of the target vehicle is the sum of the three adjacent columns of strip sensor data crushed by the target vehicle.

[0099] In some embodiments, when the target vehicle is in a double-side seam driving state, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: the weight data W of the target vehicle is the sum of the adjacent four columns of strip sensor data rolled over by the target vehicle.

[0100] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred scheme, when the driving state of the target vehicle is cross-lane driving, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows: the weight data W of the target vehicle is the sum of the data of the two adjacent columns of strip sensors rolled over by the target vehicle; wherein, the two adjacent columns of strip sensors rolled over by the target vehicle are located in different physical lanes respectively.

[0101] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred scheme, when the driving state of the target vehicle is S-shaped driving in a normal lane, the calculation rule of the virtual lane corresponding to the driving state of the target vehicle is as follows: the weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is successively rolled over within the first time threshold.

[0102] In some embodiments, when the target vehicle is in an S-shaped driving state across lanes, the calculation rule of the virtual lane corresponding to the target vehicle's driving state is as follows: the weight data W of the target vehicle is the sum of the data of two adjacent columns of strip sensors in different physical lanes that are successively rolled over by the target vehicle within the first time threshold.

[0103] In some embodiments, when the target vehicle is in an S-shaped driving state across three columns, the calculation rule of the virtual lane corresponding to the target vehicle's driving state is as follows: the weight data W of the target vehicle is the sum of the three adjacent columns of strip sensor data that the target vehicle has successively rolled over within the second time threshold.

[0104] The first time threshold is not greater than the second time threshold, and both the first time threshold and the second time threshold are not greater than 0.1s.

[0105] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, after monitoring the driving state of the target vehicle, it also includes:

[0106] Determine whether there is a parallel vehicle when the target vehicle passes through the target road: if not, perform virtual lane matching according to the driving state of the target vehicle; if yes, determine whether the target vehicle and the parallel vehicle run over the same row of strip sensors together:

[0107] If not, then performing virtual lane matching according to the driving state of the target vehicle;

[0108] If so, determine the ownership of the jointly crushed strip sensor. If it belongs to the target vehicle, the data of the jointly crushed strip sensor will be recorded in the target vehicle; if it does not belong to the target vehicle, the data of the jointly crushed strip sensor will not be recorded in the target vehicle.

[0109] In the above-mentioned strip sensor abnormal driving weighing method, as a preferred solution, the determination of the ownership of the strip sensors that are jointly crushed includes:

[0110] Determine whether the strip sensor data on the target vehicle side is coaxial with the strip sensor data being rolled over together:

[0111] If yes, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula:

[0112] W A-total =W A +W AB ;

[0113] W B-total =W B +(W AB -W A );

[0114] If not, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula:

[0115] W A-total =W A +(W AB -W B );

[0116] W B-total =W B +W AB ;

[0117] Among them, W A-total is the weight data of the target vehicle, W B-total is the weight data of the parallel vehicle, W A is the strip sensor data on the target vehicle side, W B is the strip sensor data on the parallel vehicle side, W AB The bar sensor data is crushed together.

[0118] In the implementation manner of the present application, the compression seam, the cross-lane and the S-shape are defined as follows respectively.

[0119] A pressure seam refers to a situation where one or both tires on a vehicle run over two adjacent strip sensors at the same time or alternately, causing the wheel weight to be shared by two adjacent rows of sensors; regardless of whether it is across lanes, it can be divided into two situations: single-sided pressure seams and double-sided pressure seams, according to the pressure seams of the left and right wheels of the vehicle. Across lanes refers to a situation where a vehicle crosses the solid line of a road lane, causing the tires on both sides of the vehicle to travel on two adjacent lanes. An S-shaped movement refers to a situation where the steering axis or coupling part of the vehicle passes at a large angle, causing the scale to be continuously loaded or running over two rows of sensors or even three rows of sensors at the same time; depending on the situation, it can be divided into three situations: an S-shaped movement in a normal lane, an S-shaped movement across lanes, and a three-row S-shaped movement.

[0120] The core of the method of this application is to establish virtual lanes according to the driving conditions of the vehicle and the physical conditions of the lanes. For example, on a road with three physical lanes, nine virtual lanes may be established when passing vehicles. If two columns of strip sensors are installed in each lane, and the three lanes are sequentially installed with six columns of strip sensors numbered 1, 2, 3, 4, 5, and 6, then the virtual lanes are 12, 123, 23, 234, 34, 345, 45, 456, and 56, a total of nine.

[0121] Among the above nine virtual lanes, 12, 34, and 56 correspond to three legal physical lanes. If the sampled waveforms are independent of each other, there is no compression seam or S-shaped feature of the normal lane, then the vehicle is passing normally when driving in this lane.

[0122] Among the above nine virtual lanes, lanes 23 and 45 correspond to cross-lane lanes. If the sampled waveforms are independent of each other and there is no pressure seam or S-shaped feature of cross-lane, then the vehicle is crossing the lane when driving in this lane.

[0123] Among the above nine virtual lanes, 123, 234, 345, and 456 have no corresponding legal physical lanes, nor do they belong to cross-lane or S-shaped cross-lane. They are abnormal driving with double-sided seams or three-row S-shaped passing of vehicles.

[0124] To facilitate the explanation of the method, the method process is also described by taking 3 legal physical lanes and 9 virtual lanes as an example. More or fewer lanes can be added or reduced according to the rules.

[0125] In some embodiments, when the present application method is performing a specific working example, the workflow is as follows:

[0126] 1) When the road is completely idle, various status flags and variables are initialized, the vehicle weight is set to zero, and all sensors are in idle state.

[0127] 2) Determine whether there is a car passing through the adjacent lane. If there is no other car, that is, only one car passes, turn to 3), otherwise turn to 8).

[0128] 3) When a vehicle approaches, the vehicle's driving position is determined based on the action of the front coil. If the vehicle is driving in a normal and legal physical lane, it is handled according to the normal process for a single vehicle, and this method will not be repeated.

[0129] 4) If the vehicle is driving abnormally, first determine whether it meets the cross-lane driving condition, that is, whether it crosses the lane in one of the 23 and 45 virtual lanes. If so, add the weighing results of all sensors in the corresponding sensor column to get the vehicle weight. Otherwise, go to 5).

[0130] 5) Then determine whether the single-side wheel pressure gap condition is met, including the cross-lane pressure gap condition, that is, whether the vehicle is driving in one of the 12, 23, 34, 45, and 56 virtual lanes. If so, add the weighing results of all sensors in the corresponding sensor column to get the vehicle weight. Otherwise, go to 6).

[0131] 6) Then determine whether the vehicle meets the S-shaped condition in the normal lane, including the S-shaped condition across the lane, that is, whether the vehicle is traveling in an S-shaped manner in one of the virtual lanes 12, 23, 34, 45, and 56. If so, add the weighing results of all sensors in the corresponding sensor column to get the vehicle weight. Otherwise, turn to 7).

[0132] 7) If not, it must be a double-side wheel gap or three-row S-shaped situation, that is, double-side wheel gap or three-row S-shaped driving in one of the four virtual lanes 123, 234, 345, 456. If so, add the weighing results of all sensors in the corresponding sensor row to get the vehicle weight.

[0133] 8) Entering this process indicates that there are vehicles running in parallel in adjacent lanes. For an open road, there may be two or three or even more parallel vehicles. But for one of the vehicles, there are only two situations: one with a parallel vehicle on the left and one with a parallel vehicle on the right. If there is no shared sensor column, the virtual lane method mentioned above can be used to handle it separately.

[0134] 9) If the wheels on the adjacent sides of adjacent parallel vehicles run over the same row of weighing sensors, the key to correct weighing is to determine the weight ownership of the sensor row. For the convenience of description, assume that vehicles A and B are parallel and the right tire of vehicle A and the left tire of vehicle B share a row of sensors. Clear the total weight of vehicles A and B and prepare to deal with the weight ownership of the shared sensors.

[0135] 10) The three rows of sensors take a weighing result and record it as A, AB, and B. At this time, first determine whether A and AB are coaxial. If so, AB belongs to car A, otherwise it belongs to car B, and the total weight of A and B is accumulated.

[0136] 11) Receive the signal from car A to finish, and accumulate the total weight of car A. Turn to 12).

[0137] 12) Receive the signal from car B to finish, and accumulate the total weight of car B. Jump out of the small loop and turn to 14).

[0138] 13)Return to 10).

[0139] 14) End parallel vehicle processing.

[0140] 15) Reset state values ​​and variables and re-enter the idle state.

[0141] 16) Return to 2) and start the big cycle.

[0142] Please refer to Figure 3 The present application also provides a bar sensor abnormal driving weighing device, the identification device comprising:

[0143] A virtual lane construction module 301 is used to construct at least one virtual lane based on the distribution of strip sensors on a target road; the target road includes at least one physical lane, each of which is provided with two rows of strip sensors, and the constructed at least one virtual lane covers all possible distribution positions of the strip sensors on the target road where vehicles pass by;

[0144] The target vehicle monitoring module 302 is used to monitor the driving status of the target vehicle when the target vehicle passes through the target road;

[0145] A virtual lane matching module 303 is used to perform virtual lane matching according to the driving state of the target vehicle;

[0146] A target virtual lane acquisition module 304 is used to match the driving state of the target vehicle with at least one virtual lane to acquire a target virtual lane that matches the driving state of the target vehicle;

[0147] The target vehicle weight calculation module 305 is used to calculate and obtain the weight data of the target vehicle based on the target calculation rule corresponding to the target virtual lane.

[0148] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0149] The above specific examples are used to illustrate the present application, which is only used to help understand the present application and is not intended to limit the present application. For technicians in the technical field to which the present application belongs, they can also make some simple deductions, deformations or substitutions based on the ideas of the present application.

Claims

1. A bar sensor abnormal driving weighing method, characterized in that: The method comprises: Based on the distribution of strip sensors on the target road, at least one virtual lane is constructed; the target road includes at least one physical lane, each of the physical lanes is provided with two rows of strip sensors, and the at least one virtual lane constructed covers all possible distribution positions of the strip sensors that vehicles pass by on the target road; When a target vehicle passes through a target road, the driving status of the target vehicle is monitored; Perform virtual lane matching according to the driving status of the target vehicle; Matching the driving state of the target vehicle with at least one virtual lane to obtain a target virtual lane matching the driving state of the target vehicle; Based on the target calculation rule corresponding to the target virtual lane, the weight data of the target vehicle is calculated and obtained.

2. The strip sensor abnormal driving weighing method according to claim 1, characterized in that: The constructing of at least one virtual lane comprises: According to all driving states of the vehicle passing through the strip sensor on the target road, obtaining at least one virtual lane corresponding to all driving states; The calculation rules of each virtual lane are set respectively.

3. The strip sensor abnormal driving weighing method according to claim 2, characterized in that: The driving states include driving in a normal physical lane, driving in a single-side pressure seam, driving in a double-side pressure seam, driving across lanes, driving in an S-shape in a normal lane, driving in an S-shape across lanes, and driving in an S-shape across three lanes; The number of physical lanes of the target road is N, and the strip sensors of the target road are respectively the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors, and the weighing data measured by the first row of strip sensors, the second row of strip sensors, ..., the 2N-1th row of strip sensors and the 2Nth row of strip sensors are W1, W2, ..., W 2N-1 and W 2N ; The number of the virtual lanes is 4N-3, where N is a positive integer.

4. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: When the target vehicle is in a normal physical lane, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: The weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is located.

5. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: When the target vehicle driving state is single-side seam driving, the calculation rule of the virtual lane corresponding to the target vehicle driving state is as follows: The weight data W of the target vehicle is the sum of the data of the three adjacent columns of strip sensors rolled over by the target vehicle; When the target vehicle driving state is double-side seam driving, the calculation rule of the virtual lane corresponding to the target vehicle driving state is as follows: The weight data W of the target vehicle is the sum of the data of four adjacent columns of strip sensors rolled over by the target vehicle.

6. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: When the target vehicle is in a cross-lane driving state, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: The weight data W of the target vehicle is the sum of the data of two adjacent columns of strip sensors rolled over by the target vehicle; The two adjacent columns of strip sensors rolled over by the target vehicle are located in different physical lanes.

7. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: When the target vehicle is in a normal lane S-shaped driving state, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: The weight data W of the target vehicle is the sum of the two columns of strip sensor data in the physical lane where the target vehicle is located and which is successively rolled over within the first time threshold; When the target vehicle is in an S-shaped driving state across lanes, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: The weight data W of the target vehicle is the sum of the data of two adjacent columns of strip sensors in different physical lanes that the target vehicle successively rolls over within the first time threshold; When the target vehicle is in an S-shaped driving state across three columns, the calculation rule of the virtual lane corresponding to the target vehicle is as follows: The weight data W of the target vehicle is the sum of the data of three adjacent columns of strip sensors that are successively rolled over by the target vehicle within the second time threshold; The first time threshold is not greater than the second time threshold, and both the first time threshold and the second time threshold are not greater than 0.1s.

8. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: After monitoring the driving state of the target vehicle, the method further includes: Determine whether there is a parallel vehicle when the target vehicle passes through the target road: if not, perform virtual lane matching according to the driving state of the target vehicle; if yes, determine whether the target vehicle and the parallel vehicle run over the same row of strip sensors together: If not, then performing virtual lane matching according to the driving state of the target vehicle; If so, determine the ownership of the jointly crushed strip sensor. If it belongs to the target vehicle, the data of the jointly crushed strip sensor will be recorded in the target vehicle; if it does not belong to the target vehicle, the data of the jointly crushed strip sensor will not be recorded in the target vehicle.

9. The strip sensor abnormal driving weighing method according to claim 3, characterized in that: The determining of the ownership of the strip sensors that are jointly crushed includes: Determine whether the strip sensor data on the target vehicle side is coaxial with the strip sensor data being rolled over together: If yes, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula: IN A-total =In A +W AB ; IN B-total =In B +(W AB -IN A ); If not, the weight data of the target vehicle and the parallel vehicle are calculated by the following formula: IN A-total =In A +(W AB -IN B ); IN B-total =In B +W AB ; Among them, W A-total is the weight data of the target vehicle, W B-total is the weight data of the parallel vehicles, W A is the strip sensor data on the target vehicle side, W B is the strip sensor data on the parallel vehicle side, W AB The bar sensor data is crushed together.

10. A bar sensor abnormal driving weighing device, characterized in that: The identification device comprises: A virtual lane construction module, configured to construct at least one virtual lane based on the distribution of strip sensors on a target road; the target road includes at least one physical lane, each of the physical lanes is provided with two rows of strip sensors, and the constructed at least one virtual lane covers all possible distribution positions of the strip sensors on the target road where vehicles pass by; A target vehicle monitoring module is used to monitor the driving status of a target vehicle when the target vehicle passes through the target road; A virtual lane matching module is used to perform virtual lane matching according to the driving status of the target vehicle; A target virtual lane acquisition module, used to match the driving state of the target vehicle with at least one virtual lane, and acquire a target virtual lane matching the driving state of the target vehicle; The target vehicle weight calculation module is used to calculate and obtain the weight data of the target vehicle based on the target calculation rule corresponding to the target virtual lane.

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