Vehicle-mounted mobile detection system and method based on embedded industrial computer

Abstract

The invention relates to the technical field of vehicle-mounted movement detection, and specifically discloses a vehicle-mounted movement detection system and method based on an embedded industrial computer, comprising: a front vehicle camera obtains road surface image information of a front vehicle and collects gesture images of the front vehicle; the vehicle-mounted industrial computer collects, analyzes and transmits vehicle dynamic driving state information through multiple sensors and executes vehicle control instructions; the vehicle-mounted industrial computer comprises: an on-board information acquisition module, an on-board movement analysis module and an on-board safety detection module; the on-board movement analysis module can ensure that whether the vehicle-mounted movement path is offset is determined, and can also distinguish the influence of road surface flatness and path change on the offset; the on-board safety detection module mainly determines the on-board safety state through the on-board movement error state and the front vehicle path state, so as to further avoid vehicle-mounted safety hazards; by timely and detailed analysis of the offset situation in the vehicle-mounted transportation system, the risk of derailment and non-safety offset is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted motion detection, and in particular to a vehicle-mounted motion detection system and method based on an embedded industrial computer. Background Art

[0002] Industrial control locomotive vehicle-mounted mobile detection generally refers to the use of vehicle-mounted mobile equipment in the field of industrial control to conduct real-time detection, data collection and analysis of the operation and transportation of various machinery and systems. In addition, the detection system technology of industrial control locomotive vehicle-mounted mobile equipment can also effectively improve the level of automation control in the production process, thereby reducing labor operation costs and improving product quality.

[0003] Existing vehicle-mounted mobile detection systems usually combine the wireless communication technology of vehicle navigation machines to comprehensively manage the collected data. The vehicle-mounted industrial computer monitors and records the transportation status of the moving vehicle, the consignment status of the onboard equipment, and the surrounding environment information in real time, and transmits data in real time for management, thereby realizing control of the vehicle's operating status.

[0004] However, in the process of large truck transport vehicles carrying goods, since the front vehicle is the main power system, the automated vehicle control system can transport according to the preset trajectory, while the rear transport equipment is usually large in size and is usually driven by traction or connection or wireless transmission; due to the large transport volume, the rear consignment car is usually long or the consignment car is divided into more sectioned transport cars, and then there is a large offset between the vehicle and the front vehicle during the pick-up and transfer or node transportation and cargo distribution in large factories, resulting in a larger deviation error from the vehicle's established route, which is prone to cargo transportation safety risks, derailment or deviation, etc., which have an adverse impact on the surrounding environment and personnel and are prone to safety hazards. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle-mounted mobile detection system and method based on an embedded industrial computer to solve the following technical problems:

[0006] How to conduct timely and detailed analysis of the deviation conditions in the vehicle-mounted transportation system to reduce the risk of derailment and unsafe deviation.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] Preferably, the vehicle-mounted mobile detection system based on the embedded industrial computer comprises:

[0009] Front vehicle camera, used to obtain road image information and gesture images of the front vehicle;

[0010] The on-board industrial computer is used to collect, analyze and transmit vehicle dynamic driving status information through multiple sensors and execute vehicle control instructions;

[0011] The vehicle-mounted industrial computer includes: a vehicle-mounted information collection module, a vehicle-mounted movement analysis module and a vehicle-mounted safety detection module;

[0012] The vehicle information collection module is used to collect road surface image information of the vehicle in front and collect vehicle moving path information, and the vehicle moving path information includes: preset measurement point information and the edge characteristic line width of the moving path corresponding to the measurement point;

[0013] The vehicle movement analysis module is used to analyze the vehicle movement path information to determine the vehicle movement error state, and to determine the path state of the preceding vehicle in combination with the preceding vehicle road surface image information;

[0014] The vehicle safety detection module is used to determine the vehicle safety status according to the vehicle movement error status and the path status of the preceding vehicle;

[0015] The gesture image sensing terminal is arranged on both sides of the front end of the front vehicle and is used to issue vehicle control instructions and early warning signals based on the gesture images of preset measurement points and the recognition of gesture information.

[0016] Preferably, the vehicle-mounted mobility analysis module includes:

[0017] Obtain a standard curve W0(t) of the change in the edge characteristic line width of the historical preceding vehicle moving path at the preset measurement point in the historical preset time period [t0, t] over time; wherein t0 is the measurement start time point, t is the measurement end time point, and t>t0≥0;

[0018] Take T as the acquisition period in the time period [t0, t] and Δt as the preset time interval to obtain the vehicle-mounted The set of curves of the edge characteristic line width of the moving path of the group of measurement points changing with time {W l1 (t), ..., W ln (t)};

[0019] The curve W corresponding to each group of measurement points is drawn in chronological order. l (t)′ is established in the same coordinate system as the standard curve W0(t), and the curve W is obtained. l The area difference ΔS between W0(t)′ and the corresponding W0(t)′ in each group of measurement points in the time period i .

[0020] Preferably, the vehicle-mounted movement error detection method is:

[0021] Calculation of ΔS i Compare with the preset standard area difference thresholds S1 and S2, and S1 <S2:

[0022] If ΔS i <S1, then it is judged that the vehicle movement error is extremely small;

[0023] If ΔS i > S2, then it is judged that the vehicle movement error is large, and the path status of the preceding vehicle is obtained;

[0024] If S1≤ΔS i ≤S2, then the vehicle movement error is judged to be small and the road surface flatness is calculated.

[0025] Preferably, whether the path of the preceding vehicle has changed is determined by real-time road surface image information of the preceding vehicle:

[0026] If it does not change, calculate the road surface flatness;

[0027] If it changes, the vehicle deviation coefficient is obtained and the vehicle safety status is determined.

[0028] Preferably, the calculation process of the vehicle-mounted deviation coefficient is:

[0029] Get the edge characteristic line width value W of the front vehicle moving path at the current measurement point l0 And the edge characteristic line width value W of the vehicle moving path at the corresponding measurement point lj ;

[0030] By formula Calculate the vehicle deviation coefficient V d ; Wherein, f is a preset function; θ is the weight coefficient; ΔW is the preset deviation value of the edge characteristic line width of the vehicle moving path; D e The offset angle for path change; D0 is the offset angle for standard path change.

[0031] Preferably, the vehicle deviation coefficient V d The deviation coefficient threshold value from the preset vehicle standard [V A , V B ] to compare the size:

[0032] If V d ∈[V A , V B ], the vehicle safety is judged to be low and a vehicle control command is issued;

[0033] If V d <V A , then the vehicle safety is judged to be high;

[0034] If V d >V B , it is judged that the vehicle safety is very low, a warning signal is issued, the vehicle is stopped and a vehicle safety check is performed.

[0035] Preferably, the system further comprises:

[0036] Visual interface, used to display the status of on-board equipment, historical data of on-board equipment and alarm information, and to set parameters and adjust the monitoring range of staff according to industrial control needs;

[0037] Road condition detection module, used to detect road conditions and calculate road surface flatness;

[0038] The wireless communication module is used for data transmission between the vehicle-mounted industrial computer and the measuring point end through wireless communication technology.

[0039] Preferably, the road surface flatness acquisition and calculation method includes:

[0040] Scan the road surface image information of the vehicle in front to obtain the lane wheel track with cloud point data and perform preprocessing: extract road surface points through cloth simulation algorithm, and remove noise points through statistical filtering algorithm;

[0041] Create wheel track vector lines along the wheel track strip direction of the lane, and take the road surface points within the preset width range on both sides as the point cloud data to be calculated;

[0042] The point cloud data is uniformly sampled and the road surface roughness is calculated segmentally according to the IRI value calculation method;

[0043] The road surface flatness is compared with the current road surface flatness preset threshold. If it is greater than the preset threshold, the flatness is judged to be poor, and the defect location is determined for inspection and repair. Otherwise, continue.

[0044] Preferably, the gesture information includes: a forward turn indication, a forward U-turn indication, a forward straight-ahead indication, and a forward uphill and downhill indication.

[0045] The vehicle-mounted mobile detection method based on embedded industrial computer has the following specific steps:

[0046] Step 1: Obtain the road image information of the vehicle in front and collect the vehicle moving path information according to the camera of the vehicle in front;

[0047] Step 2: Analyze the vehicle movement path information to determine whether the vehicle movement error state is normal. If not, obtain the path state of the preceding vehicle, calculate the road surface flatness, and determine the path state of the preceding vehicle in combination with the road surface image information of the preceding vehicle.

[0048] Step 3: Determine the path change status of the preceding vehicle, obtain the vehicle deviation coefficient, and judge the vehicle safety status;

[0049] Step 4: Determine whether the vehicle safety status is normal. If not, issue a vehicle control command, send out a warning signal, stop the vehicle and perform a vehicle safety check.

[0050] Beneficial effects of the present invention: The present invention sets a vehicle-mounted movement analysis module to analyze the vehicle-mounted movement path information acquired in real time, that is, the measurement point information and the real-time movement path edge feature line width data recorded at the measurement point, and compares it with the standard movement path information data acquired by the preceding vehicle operation in the historical state to judge the vehicle-mounted movement error state. In order to ensure that the comparison between the two has an intuitive basis for comparison, the preceding vehicle path state is judged by combining the preceding vehicle road surface image information, and the deviation of the vehicle-mounted movement is judged according to the preceding vehicle path state. Setting the vehicle-mounted movement analysis module can ensure that whether the vehicle-mounted movement path is offset can be determined, and the influence of the road surface flatness and path change on the deviation can also be distinguished. A vehicle-mounted safety detection module is also set, which mainly judges the vehicle-mounted safety state through the vehicle-mounted movement error state and the preceding vehicle path state, so as to further avoid vehicle-mounted safety hazards.

[0051] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 It is a schematic diagram of a vehicle-mounted mobile detection system based on an embedded industrial computer of the present invention;

[0054] Figure 2 This is a step diagram of the vehicle-mounted motion detection method based on an embedded industrial computer of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In the prior art, in the process of large truck transport vehicles carrying goods, since the front vehicle is the main power system, the automated vehicle control system can transport according to the preset trajectory, while the rear transport equipment is usually large in size and is usually driven by traction or connection or wireless transmission. Due to the large transport volume, the rear consignment car is usually long or the consignment car is divided into more sectioned transport cars, and then there is a large offset between the vehicle and the front vehicle during the pick-up and transfer or node transportation and cargo distribution in large factories, resulting in a larger deviation error from the vehicle's established route, which is prone to cargo transportation safety risks, derailment or deviation, etc., which have an adverse effect on the surrounding environment and personnel and are prone to safety hazards.

[0057] In order to solve the above technical problems, the present invention is a vehicle-mounted mobile detection system based on an embedded industrial computer. Figure 1 As shown, the system mainly includes:

[0058] Front vehicle camera, used to obtain road image information and gesture images of the front vehicle;

[0059] The on-board industrial computer is used to collect, analyze and transmit vehicle dynamic driving status information through multiple sensors and execute vehicle control instructions;

[0060] The vehicle-mounted industrial computer includes: vehicle-mounted information collection module, vehicle-mounted mobile analysis module and vehicle-mounted safety detection module;

[0061] The vehicle information collection module is used to collect the road surface image information of the vehicle in front and the vehicle moving path information. The vehicle moving path information includes: preset measurement point information and the moving path edge feature line width corresponding to the measurement point;

[0062] The vehicle movement analysis module is used to analyze the vehicle movement path information to determine the vehicle movement error state, and to determine the path state of the preceding vehicle in combination with the preceding vehicle road surface image information;

[0063] The vehicle safety detection module is used to determine the vehicle safety status according to the vehicle movement error status and the path status of the preceding vehicle;

[0064] The gesture image sensing terminal is arranged on both sides of the front end of the front vehicle and is used to issue vehicle control instructions and early warning signals based on the gesture images of preset measurement points and the recognition of gesture information.

[0065] Through the above technical solution, this embodiment sets a front vehicle camera to obtain the image of the front vehicle's running path and the gesture image sensed by the front vehicle, so that the front vehicle can make advance adjustments according to the specific road conditions and the real-time path transportation direction, thereby improving the transmission efficiency of the vehicle-mounted transportation system; sets an on-board industrial control computer to facilitate the acquisition of the overall transportation status of the vehicle and transmit the vehicle's running status through multiple sensors, thereby ensuring timely control of the acquired vehicle's dynamic driving status; the specific on-board industrial control computer includes three modules, namely:

[0066] The on-board information collection module ensures the movement path information obtained under the historical operating status of the leading vehicle and the real-time movement path information collected by the sensor. The on-board movement path information includes: preset measurement point information and the movement path edge characteristic line width corresponding to the measurement point; it should be explained here that the movement path information obtained under the historical operating status of the leading vehicle is the status information obtained by using the leading vehicle alone to perform the preset path operation in the original transportation simulation stage, which includes the pre-set historical measurement point information and the historical movement path edge characteristic line width recorded at the historical measurement points, and these historical data are the result of data simulation fitting.

[0067] The vehicle movement analysis module analyzes the real-time vehicle movement path information, that is, the measurement point information and the real-time movement path edge feature line width data recorded at the measurement point, and compares it with the standard movement path information data obtained from the preceding vehicle operation in the historical state to determine the vehicle movement error state. In order to ensure that the comparison between the two has an intuitive basis for comparison, the preceding vehicle path state is determined by combining the preceding vehicle road surface image information, and the deviation of the vehicle movement is determined by the preceding vehicle path state. The vehicle movement analysis module can ensure that the vehicle movement path is deviated, and can also distinguish the impact of road surface flatness and path changes on the deviation.

[0068] The vehicle safety detection module mainly determines the vehicle safety status through the vehicle movement error status and the path status of the preceding vehicle, so as to further avoid vehicle safety hazards.

[0069] The gesture image sensing terminal obtains the gesture images configured on both sides of the front end of the front vehicle and ensures that the gesture images and gesture information are based on the preset measurement points, and issues vehicle control instructions, and sends a warning signal to the control terminal based on the vehicle safety.

[0070] As an embodiment of the present invention, the vehicle-mounted mobility analysis module includes:

[0071] Obtain a standard curve W0(t) of the change in the edge characteristic line width of the historical preceding vehicle moving path at the preset measurement point in the historical preset time period [t0, t] over time; wherein t0 is the measurement start time point, t is the measurement end time point, and t>t0≥0;

[0072] Take T as the acquisition period in the time period [t0, t] and Δt as the preset time interval to obtain the vehicle-mounted The set of curves of the edge characteristic line width of the moving path of the group of measurement points changing with time {W l1 (t), ..., W ln (t)};

[0073] The curve W corresponding to each group of measurement points is drawn in chronological order. l (t)′ is established in the same coordinate system as the standard curve W0(t), and the curve W is obtained. l The area difference ΔS between W0(t)′ and the corresponding W0(t)′ in each group of measurement points in the time period i .

[0074] Through the above technical solution, in this embodiment, the collected data is further processed by the vehicle-mounted movement analysis module. Specifically, first, by obtaining the standard curve W0(t) of the change of the edge characteristic line width of the historical front vehicle movement path at the preset measurement point in the historical preset time period [t0, t] with time; wherein t0 is the measurement start time point, t is the measurement end time point, and t>t0≥0; and T is the collection period in the time period [t0, t], and Δt is the preset time interval, the vehicle-mounted The set of curves of the edge characteristic line width of the moving path of the group of measurement points changing with time {W l1 (t), ..., W ln (t)}; wherein the collection period T is determined according to the distribution of measurement points during the preset vehicle transportation path. This collection period is pre-simulated in the historical preceding vehicle movement path according to the preset measurement points and will not be described in detail here.

[0075] It should be noted that the preset time period [t0, t] and the preset interval period Δt can be set according to the actual operating status; Where i is a positive integer, and the purpose of collecting measurement point data by dividing into i groups is set according to the total length of the preset overall vehicle transportation path, and is also affected by the collection cycle and time interval.

[0076] Then, the curve W corresponding to each set of measurement points is arranged in chronological order. l (t)′ is established in the same coordinate system as the standard curve W0(t), and the curve W is obtained. l The area difference ΔS between W0(t)′ and the corresponding W0(t)′ in each group of measurement points in the time period i ; By collecting the area difference ΔS in different groups of measurement points i The size of the vehicle transportation process is guaranteed to be analyzed in stages, and a comprehensive judgment is made based on the side path and surrounding environment information obtained from the actual vehicle transportation.

[0077] As an embodiment of the present invention, a method for detecting vehicle-mounted movement error is:

[0078] Calculation of ΔS i Compare with the preset standard area difference thresholds S1 and S2, and S1<S2:

[0079] If ΔS i <S1, then it is judged that the vehicle movement error is extremely small;

[0080] If ΔS i > S2, then it is judged that the vehicle movement error is large, and the path status of the preceding vehicle is obtained;

[0081] If S1≤ΔS i ≤S2, then the vehicle movement error is judged to be small and the road surface flatness is calculated.

[0082] Through the above technical solution, the detection of vehicle movement error in this embodiment is based on curve W l The area difference ΔS between W0(t)′ and the corresponding W0(t)′ in each group of measurement points in the time period i The size change is used to make further judgments, specifically: calculate ΔS i Compare with the preset standard area difference thresholds S1 and S2, and S1<S2, where S1 and S2 are not equal to 0, and judge ΔS i If it is less than S1, then the vehicle movement error is judged to be extremely small; ΔS i If it is greater than S2, then it is judged that the vehicle movement error is large and the path state of the preceding vehicle is obtained; ΔS i If it falls within the preset standard area difference threshold range, which is between S1 and S2, then the vehicle movement error is judged to be small and the road surface flatness is calculated.

[0083] As an implementation mode of the present invention, whether the path of the preceding vehicle has changed is determined by using the real-time road surface image information of the preceding vehicle:

[0084] If it does not change, calculate the road surface flatness;

[0085] If it changes, the vehicle deviation coefficient is obtained and the vehicle safety status is determined.

[0086] Through the above technical solution, this embodiment also uses real-time road surface image information of the preceding vehicle to determine whether the path of the preceding vehicle has changed. When the path has changed, it is determined that there is a large deviation in the vehicle during the acquisition period, and first determines whether the deviation is caused by a change in the direction of the preceding vehicle's path. Then, it is necessary to obtain the vehicle deviation coefficient and then determine the vehicle's safety situation. If the path has not changed, there is still a possibility of errors caused by road surface flatness problems.

[0087] As an implementation mode of the present invention, the calculation process of the vehicle deviation coefficient is:

[0088] Get the edge characteristic line width value W of the front vehicle moving path at the current measurement point l0 And the edge characteristic line width value W of the vehicle moving path at the corresponding measurement point lj ;

[0089] By formula Calculate the vehicle deviation coefficient V d ; Wherein, f is a preset function; θ is the weight coefficient; ΔW is the preset deviation value of the edge characteristic line width of the vehicle moving path; D e The offset angle for path change; D0 is the offset angle for standard path change.

[0090] Through the above technical solution, in this embodiment, the vehicle deviation coefficient is calculated to determine the change in the state of the vehicle moving path. Specifically, the following steps are performed: first, the edge characteristic line width value W of the front vehicle moving path at the current measurement point is obtained. l0 And the edge characteristic line width value W of the vehicle moving path at the corresponding measurement point lj ; Then, by the formula Calculate the vehicle deviation coefficient V d ; Where f is a preset function, and f is a function fitted in advance by machine calculation. In order to ensure the vehicle deviation coefficient V d Avoid errors of judgment within a reasonable range; θ is the weight coefficient, which is selected and set based on the empirical data fitting, and will not be described in detail here; ΔW is the preset deviation value of the edge characteristic line width of the vehicle moving path; D e The offset angle for path change; D0 is the offset angle for standard path change.

[0091] It should be explained that the path change of the leading vehicle is usually caused by receiving a path change instruction. In real transportation scenarios, it is usually caused by the leading vehicle turning, turning around, and going uphill. At this time, the carriage carrying the transportation will also change with the change of the path of the leading vehicle. Due to the inertia of the rear load of the truck, when the front vehicle changes direction, the carriage may have a certain deviation within a reasonable range. The angle of different path changes is different. The path change deviation angle D obtained by the angle sensor eThe specific size is obtained according to the specific scenario setting of the current path change, and the edge characteristic line width of the vehicle moving path and the path change offset angle can reflect the approximate situation of the vehicle deviation, and then determine the vehicle transportation safety risk; among them, the standard path change offset angle is the historical data obtained in advance by simulating the path change scenario, which will not be described in detail here; the preset deviation value ΔW is the result obtained by optimizing the control based on the existing machine model according to the change size of the edge characteristic line width of the historical vehicle moving path, which is pre-designed and will not be described in detail here.

[0092] As an embodiment of the present invention, the vehicle deviation coefficient V d The deviation coefficient threshold value from the preset vehicle standard [V A , V B ] to compare the size:

[0093] If V d ∈[V A , V B ], the vehicle safety is judged to be low and a vehicle control command is issued;

[0094] If V d <V A , then the vehicle safety is judged to be high;

[0095] If V d >V B , it is judged that the vehicle safety is very low, a warning signal is issued, the vehicle is stopped and a vehicle safety check is performed.

[0096] Through the above technical solution, in this embodiment, the vehicle safety is judged by analyzing the size of the vehicle deviation system. Specifically, the vehicle deviation coefficient V d The deviation coefficient threshold value from the preset vehicle standard [V A , V B ] to compare the size: If V d ∈[V A , V B ], the vehicle safety is judged to be low and a vehicle control command is issued; if V d <V A , then the vehicle safety is judged to be high; if V d >V B , it is judged that the vehicle safety is very low, a warning signal is issued, the vehicle is stopped and a vehicle safety check is performed.

[0097] As an embodiment of the present invention, the system further includes:

[0098] Visual interface, used to display the status of on-board equipment, historical data of on-board equipment and alarm information, and to set parameters and adjust the monitoring range of staff according to industrial control needs;

[0099] Road condition detection module, used to detect road conditions and calculate road surface flatness;

[0100] The wireless communication module is used for data transmission between the vehicle-mounted industrial computer and the measuring point end through wireless communication technology.

[0101] Through the above technical scheme, in order to ensure the smooth management process of the data of the vehicle-mounted mobile detection system of the embedded industrial computer, this embodiment also sets up a visual interface to intuitively display the status of the vehicle-mounted equipment, the historical data of the vehicle-mounted equipment and display alarm information, and sets parameters and adjusts the staff monitoring range according to the needs of industrial control; a road condition detection module is also set up to ensure the detection of road conditions and calculation of the road surface flatness; a wireless communication module is set up to realize the data transmission between the vehicle-mounted industrial computer and the measurement point end through wireless communication technology.

[0102] As an embodiment of the present invention, a method for obtaining and calculating road surface flatness includes:

[0103] First, the road surface image information of the vehicle in front is scanned to obtain the lane wheel track cloud point data and preprocessed: the road surface points are extracted through the cloth simulation algorithm, and the noise points are removed through the statistical filtering algorithm; then, the wheel track vector line is created along the direction of the lane wheel track, and the road surface points within the preset width range on both sides are taken as the point cloud data to be calculated; then, the point cloud data is uniformly sampled and the road surface flatness is calculated in segments according to the IRI value calculation method; finally, the road surface flatness is compared with the current preset threshold of road surface flatness. If it is greater than the preset threshold, it is judged that the flatness is poor, and the defect location is determined for inspection and repair. Otherwise, continue.

[0104] Among them, the point cloud data is obtained through a three-dimensional stereo scanner, specifically through the integrated management of sensors such as the laser scanner of the vehicle-mounted laser scanning system, inertial navigation system (INS), global positioning navigation system (GNSS), inertial measurement unit (IMU), industrial camera, panoramic camera, time synchronization controller, etc.

[0105] The specific steps are: first calculate the fractional segments, calculate the number of valid points n of IRI, according to the sampling interval I, calculate the number of points p in the segment interval of the specified distance E in the specific implementation, and then calculate the number of valid segments N; the calculation formula is:

[0106]

[0107] Where: [·] means rounding up;

[0108] Then set the initial value. According to the IRI calculation standard, record the road slope of 11m as the initial value; record the displacement speed and acceleration recorded by the front vehicle as four dynamic response quantities, namely F i1, F i2 , F i3 , F i4 ; The expression is:

[0109]

[0110] Where: H1 is the elevation value of the first sample point;

[0111] Next, the dynamic response of all sampling points is solved by iteration:

[0112]

[0113] Where H′=(H1-H j-1 ) / dX, indicating the slope output; S and P are both calculation parameter matrices, and the size of their coefficients is related to the value of dX;

[0114] Further, the adjustment slope is calculated, and the adjustment slope value D of each sampling point is calculated. R :

[0115] D R =|F j4 -F j1 |;

[0116] Furthermore, the IRI value I IRI for:

[0117]

[0118] Where p is the total number of sampling points in the calculation interval with a distance of E;

[0119] According to the IRI calculation results, through the formula:

[0120]

[0121] Repeat this formula to calculate the IRI value of the current road section, compare it with the calibrated IRI value calculated by leveling, statistically analyze the systematic deviation between the IRI calculation result based on the point cloud and the calibrated IRI, and subtract the deviation as the final IRI calculation result;

[0122] Finally, according to the road pavement grade and smoothness regulations, determine whether the IRI of the inspected section meets the smoothness requirements of the road pavement at that level; for unqualified road surfaces, use the marking points corresponding to the elevation values ​​to find the mutation location and determine the location of the smoothness defect.

[0123] As an implementation manner of the present invention, the gesture information includes: a forward turn indication, a forward U-turn indication, a forward straight-ahead indication, and a forward uphill and downhill indication.

[0124] Through the above technical solution, gesture information is presented in the form of electronic gesture images and gesture model positions by being set at preset measurement points. It is not limited to a certain recognition form, but is presented in the form of gesture images. In addition, by setting gesture images for recognition, it is ensured that the next path change information is transmitted to the on-board industrial computer for information processing and control of vehicle operation adjustments before the vehicle is about to drive to the set position, thereby improving the transmission efficiency of indication information.

[0125] The present invention also relates to a vehicle-mounted motion detection method based on an embedded industrial computer, the specific steps of which are:

[0126] Step 1: Obtain the road image information of the vehicle in front and collect the vehicle moving path information according to the camera of the vehicle in front;

[0127] Step 2: Analyze the vehicle movement path information to determine whether the vehicle movement error state is normal. If not, obtain the path state of the preceding vehicle, calculate the road surface flatness, and determine the path state of the preceding vehicle in combination with the road surface image information of the preceding vehicle.

[0128] Step 3: Determine the path change status of the preceding vehicle, obtain the vehicle deviation coefficient, and judge the vehicle safety status;

[0129] Step 4: Determine whether the vehicle safety status is normal. If not, issue a vehicle control command, send out a warning signal, stop the vehicle and perform a vehicle safety check.

[0130] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. The vehicle-mounted mobile detection system based on embedded industrial computer is characterized by: The system comprises: Front vehicle camera, used to obtain road image information and gesture images of the front vehicle; The on-board industrial computer is used to collect, analyze and transmit vehicle dynamic driving status information through multiple sensors and execute vehicle control instructions; The vehicle-mounted industrial computer includes: a vehicle-mounted information collection module, a vehicle-mounted movement analysis module and a vehicle-mounted safety detection module; The vehicle information collection module is used to collect road surface image information of the vehicle in front and collect vehicle moving path information, and the vehicle moving path information includes: preset measurement point information and the edge characteristic line width of the moving path corresponding to the measurement point; The vehicle movement analysis module is used to analyze the vehicle movement path information to determine the vehicle movement error state, and to determine the path state of the preceding vehicle in combination with the preceding vehicle road surface image information; The vehicle safety detection module is used to determine the vehicle safety status according to the vehicle movement error status and the path status of the preceding vehicle; The gesture image sensing terminal is arranged on both sides of the front end of the leading vehicle and is used to issue vehicle control instructions and warning signals based on the gesture images of preset measurement points and the recognition of gesture information.

2. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 1 is characterized in that: The vehicle-mounted mobile analysis module comprises: Obtain a standard curve W0(t) of the change in the edge characteristic line width of the historical preceding vehicle moving path at the preset measurement point in the historical preset time period [t0, t] over time; wherein t0 is the measurement start time point, t is the measurement end time point, and t>t0≥0; Take T as the acquisition period in the time period [t0, t] and Δt as the preset time interval to obtain the vehicle-mounted The set of curves of the edge characteristic line width of the moving path of the group of measurement points changing with time {W l1 (t), ..., W ln (t)}; The curve W corresponding to each group of measurement points is drawn in chronological order. l (t)′ is established in the same coordinate system as the standard curve W0(t), and the curve W is obtained. l The area difference ΔS between W0(t)′ and the corresponding W0(t)′ in each group of measurement points in the time period i .

3. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 2 is characterized in that: The detection method of the vehicle-mounted movement error is: Calculation of ΔS i Compare with the preset standard area difference thresholds S1 and S2, and S1<S2: If ΔS i <S1, then it is judged that the vehicle movement error is extremely small; If ΔS i > S2, then it is judged that the vehicle movement error is large, and the path status of the preceding vehicle is obtained; If S1≤ΔS i ≤S2, then the vehicle movement error is judged to be small and the road surface flatness is calculated.

4. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 3 is characterized in that: Determine whether the path of the vehicle ahead has changed by using real-time road image information of the vehicle ahead: If it does not change, calculate the road surface flatness; If it changes, the vehicle deviation coefficient is obtained and the vehicle safety status is determined.

5. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 4 is characterized in that: The calculation process of the vehicle deviation coefficient is: Get the edge characteristic line width value W of the front vehicle moving path at the current measurement point l0 And the edge characteristic line width value W of the vehicle moving path at the corresponding measurement point lj ; By formula Calculate the vehicle deviation coefficient V d ; Wherein, f is a preset function; θ is the weight coefficient; ΔW is the preset deviation value of the edge characteristic line width of the vehicle moving path; D e The offset angle for path change; D0 is the offset angle for standard path change.

6. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 5 is characterized in that: The vehicle deviation coefficient V d The deviation coefficient threshold value from the preset vehicle standard [V A , V B ] to compare the size: If V d ∈[V A , V B ], the vehicle safety is judged to be low and a vehicle control command is issued; If V d <V A , then the vehicle safety is judged to be high; If V d >V B , it is judged that the vehicle safety is very low, a warning signal is issued, the vehicle is stopped and a vehicle safety check is performed.

7. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 1 is characterized in that: The system further comprises: Visual interface, used to display the status of on-board equipment, historical data of on-board equipment and alarm information, and to set parameters and adjust the monitoring range of staff according to industrial control needs; Road condition detection module, used to detect road conditions and calculate road surface flatness; The wireless communication module is used for data transmission between the vehicle-mounted industrial computer and the measuring point end through wireless communication technology.

8. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 1 is characterized in that: The road surface flatness acquisition and calculation method includes: Scan the road surface image information of the vehicle in front to obtain the lane wheel track with cloud point data and perform preprocessing: extract road surface points through cloth simulation algorithm, and remove noise points through statistical filtering algorithm; Create wheel track vector lines along the wheel track strip direction of the lane, and take the road surface points within the preset width range on both sides as the point cloud data to be calculated; The point cloud data is uniformly sampled and the road surface roughness is calculated segmentally according to the IRI value calculation method; The road surface flatness is compared with the current road surface flatness preset threshold. If it is greater than the preset threshold, the flatness is judged to be poor, and the defect location is determined for inspection and repair. Otherwise, continue.

9. The vehicle-mounted mobile detection system based on an embedded industrial computer according to claim 1, characterized in that: The gesture information includes: a forward turn indication, a forward U-turn indication, a forward straight-ahead indication, and a forward uphill and downhill indication.

10. The vehicle-mounted motion detection method based on embedded industrial computer is characterized in that: Applied to the vehicle-mounted mobile detection system based on an embedded industrial computer as described in any of claims 1 to 9, the specific steps are: Step 1: Obtain the road image information of the vehicle in front and collect the vehicle moving path information according to the camera of the vehicle in front; Step 2: Analyze the vehicle movement path information to determine whether the vehicle movement error state is normal. If not, obtain the path state of the preceding vehicle, calculate the road surface flatness, and determine the path state of the preceding vehicle in combination with the road surface image information of the preceding vehicle. Step 3: Determine the path change status of the preceding vehicle, obtain the vehicle deviation coefficient, and judge the vehicle safety status; Step 4: Determine whether the vehicle safety status is normal. If not, issue a vehicle control command, send out a warning signal, stop the vehicle and perform a vehicle safety check.

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