Vehicle debugging and detecting system

Abstract

A vehicle debugging and detecting system disclosed by the present invention comprises a background management system, a PAD operating system and a data acquisition system, the data acquisition system comprises a T-Box, an external sensor and a detection controller, and the PAD operating system issues a debugging and detecting instruction to a vehicle body and the detection controller through the T-Box of the data acquisition system. After a debugging detection instruction is received, the vehicle body sends all state data to the T-Box, meanwhile, the detection controller sends information collected by the external sensor to the T-Box, the T-Box transmits the received detection data to the PAD operating system, the PAD operating system downloads and obtains detection item information from the background management system, and the vehicle body is subjected to debugging detection according to the detection data obtained in real time. The system has the advantages that debugging and detecting personnel can be assisted in efficiently completing debugging and detecting work, the interchangeability of the personnel is improved, the qualified rate is automatically detected, paperless office work is achieved, and process data analysis and storage are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle commissioning and detection, and specifically relates to a vehicle commissioning and detection system. Background Art

[0002] With the continuous increase in the types and quantities of airport special vehicles, the following problems exist in the current commissioning and detection stage of products: First, there is a high dependence on the experience of commissioning and detection personnel, and strict requirements are imposed on the personal technical level of commissioning and detection personnel; second, the interchangeability of commissioning and detection personnel between different projects is poor. When there is a shortage of personnel or a large number of commissioning and detection personnel need to be equipped, the labor cost is increased; third, the commissioning and detection process is manual measurement. Due to different manual reaction speeds and tool usage methods, there will be certain deviations in the measurement results, affecting the commissioning consistency; fourth, the existing commissioning and detection reports are mainly filled in by hand on paper, which is not conducive to storage and retrieval; fifth, currently, only the result data is recorded in the commissioning and detection, and there is no technical means to record the process data. The summary of problems occurring during the commissioning process relies on the manual recording of commissioning and detection personnel, resulting in low efficiency.

[0003] Therefore, there is an urgent need to design a commissioning and detection system that can assist commissioning and detection personnel to efficiently complete commissioning and detection work, automatically detect the pass rate, achieve paperless office, and perform process data analysis and storage. Summary of the Invention

[0004] The purpose of the present invention is to solve the above-mentioned deficiencies of the prior art, and provide a vehicle commissioning and detection system that can assist commissioning and detection personnel to efficiently complete commissioning and detection work, improve personnel interchangeability, automatically detect the pass rate, achieve paperless office, and perform process data analysis and storage.

[0005] The technical solution adopted by the present invention to solve its technical problems is:

[0006] A vehicle commissioning and detection system, characterized in that it includes a background management system, a PAD operating system, and a data acquisition system. The data acquisition system includes a T-Box, an external sensor, and a detection controller;

[0007] The PAD operating system issues commissioning and detection instructions to the vehicle body and the detection controller through the T-Box of the data acquisition system. After receiving the commissioning and detection instructions, the vehicle body sends all status data to the T-Box. At the same time, the detection controller sends the information collected by the external sensor to the T-Box, and the T-Box transmits the received detection data to the PAD operating system;

[0008] The PAD operating system downloads and obtains detection project information from the background management system. According to the real-time obtained detection data, the PAD operating system completes the detection and uploads the detection data to the background management system;

[0009] The cooperation of the background management system, the PAD operating system and the data acquisition system helps personnel complete the debugging and detection work efficiently, realizes the digitization and paperless of the debugging and detection stage, ensures the consistency of the debugging and detection results, reduces the dependence on the experience of debugging and detection personnel, and improves the interchangeability of debugging and detection personnel.

[0010] In the present invention, the CAN bus communication method is adopted between the data acquisition system and the vehicle body, the MQTT communication method is adopted between the data acquisition system and the PAD operating system, and the 4G IoT card communication is adopted between the PAD operating system and the background management system.

[0011] The background management system of the present invention has functions of dictionary configuration, permission management and data query. The type dictionary defines options for vehicle models, detection items, types of detection sensors, sensor models, and judgment basis conditions for the entire debugging and detection system;

[0012] After the type dictionary is established, the editing and matching of each vehicle model and the corresponding detection items are carried out in a mapping association manner. Each detection item configured according to the above mapping relationship will be stored in the server. When it is necessary to detect the corresponding vehicle body model, the detection items are retrieved according to the vehicle body model and specifications and downloaded to the PAD operating system for subsequent detection;

[0013] Permission management sets permissions for personnel according to their division of labor;

[0014] Data query includes process data query and result data query. The process data query retrieves the data collected during the debugging and detection process and the number of debugging and detection times of the detection items during the debugging and detection process according to the time axis. The result data query retrieves the final detection report. The process data query is beneficial for technical and quality inspection engineers to analyze the product data according to their respective needs and provides direct data support for improving product quality.

[0015] After the PAD operating system of the present invention imports all the CAN bus data of the vehicle, it preprocesses the data. The specific preprocessing method is as follows:

[0016] Perform frame separation to split the data stream into individual message frames;

[0017] Use cyclic redundancy check CRC to detect and filter transmission errors, extract the data field according to the data length code, and retain the time stamp.

[0018] According to the CAN bus data parsing rules of the present invention, the digital quantity data of the vehicle CAN bus is parsed. According to the data configuration required for each detection item and the physical quantity data obtained through parsing, each detection item can obtain the required physical quantity data, and the specified data is input into the judgment algorithm model of the detection item to complete the detection judgment.

[0019] In the PAD operating system of the present invention, a determination algorithm model is used to determine whether the performance of the detection item is qualified. During the detection process of the "forward driving braking performance inspection" item, the RTK integrated navigation system transmits time, vehicle speed, and longitude and latitude information in real time, which is transmitted to the PAD operating system through the T-Box. When the brake pedal is depressed, the vehicle body speed begins to decrease, and the vehicle body starts to brake. When the vehicle speed stabilizes at ≤N km / h, the vehicle body stops;

[0020] Select the longitude and latitude coordinates at the moment of sudden speed drop and the longitude and latitude coordinates when the speed stabilizes at N km / h, and use the haversine formula to calculate the distance between the two points to obtain the forward braking distance. When the obtained forward braking distance value is within the qualified range, it indicates that the detection is qualified.

[0021] The specific calculation method of the forward braking distance in the present invention is as follows:

[0022] Step S1: During the process of the vehicle driving from start to brake stop, collect a set of discrete time points [t 1 , t 2 , …, t n of the RTK and the corresponding speed values [v(t 1 ), v(t 2 ), …, v(t n )];

[0023] Step S2: Preprocess the original data;

[0024] Step S3: Calculate the first-order difference of the speed values between adjacent time points, and set 2 times the standard deviation of the difference sequence as the threshold. The calculation formula is:

[0025] Δv(t i ) = v(t i+1 ) - v(t i );

[0026]

[0027] T = 2σ;

[0028] In the formula, i = 0, 1, 2, …, n - 1, Δv(t i ) is the first-order difference of the speed value, μ is the mean of the difference sequence, σ is the standard deviation of the difference sequence, T is the set threshold, which is 2 times the standard deviation of the difference sequence;

[0029] When the first-order difference reaches or exceeds this threshold, that is, |Δv(t i )| ≥ T, the speed v(t i ) at this time is the speed at the moment of starting to brake, that is, v brake ;

[0030] When the vehicle speed is stable at ≤ N km / h, it is determined that the vehicle body has stopped, and the vehicle speed at this time is v stop ;

[0031] Step S4: After obtaining the longitude and latitude of the braking start point and end point, calculate the forward braking distance. The calculation formula is:

[0032]

[0033] Δlat = lat2 - lat1;

[0034] Δlon = lon2 - lon1;

[0035] In the formula, R is the radius of the earth, Δlat is the difference in latitude between the start point and the end point, Δlon is the difference in longitude between the start point and the end point, and the vehicle speed v(t i ) = v brake When, the longitude and latitude coordinates are (lon1, lat1), and the vehicle speed v(t i ) = v stop When, the longitude and latitude coordinates are (lon2, lat2).

[0036] In the PAD operating system of the present invention, it is determined whether the performance of the detection item is qualified through a determination algorithm model. During the detection process of the "minimum left / right turning radius inspection" item, the PAD operating system receives the vehicle longitude and latitude coordinates sent by RTK in real time. First, the original GPS data is smoothed, and multiple groups of data points are selected as sample points. Then, the UTM projection method is used to convert the longitude and latitude coordinates into coordinates in a plane direct coordinate system. Finally, the arc fitting and the least squares method are used for calculation to obtain the minimum left / right turning radius. When the obtained minimum left / right turning radius is within the qualified range, it indicates that the detection is qualified.

[0037] The specific calculation method of the minimum left / right turning radius of the present invention is as follows:

[0038] Create an equation about the center of the circle (x c , y c ) and the radius R turn . Linearize the equation and use the least squares method to obtain the optimal solution, that is, the turning radius. The calculation formula is:

[0039] z i = (x i - x c ) 2 + (y i - y c ) 2 ;

[0040]

[0041] Aθ = B;

[0042] Wherein,

[0043] θ=(A T A) -1 A T B;

[0044]

[0045] In the formula, z i is an intermediate variable of the circle equation, and there are n sampling points (x i , y i ), where i = 1, 2,..., n, and finally the minimum turning radius R turn .

[0046] The PAD operating system described in the present invention determines whether the performance of the detection item is qualified through a determination algorithm model. The specific method for detecting the item "Check the delay time of the rear wheel following in the four-wheel walking mode" is as follows:

[0047] Step S1: In the four-wheel walking mode, obtain the front and rear wheel angle values during the detection process;

[0048] Step S2: Perform data preprocessing, use the IQR interquartile range statistical method to remove outliers or error points, and synchronize the timestamps to ensure the time alignment of the front and rear wheel angle data;

[0049] Step S3: Select a time interval that includes the entire steering process, intercept the front wheel angle curve segment within this time interval for calculation. By fitting a third-order polynomial function, fit the sample curve segment into a smooth curve, use the least squares method to calculate the sum of squared residuals SSE to minimize, and then find the best fitting parameters a, b, c, d of the front wheel angle fitting function. The solution method for the fitting curves of the front wheel angle and the rear wheel angle is the same. The solution formula for the front wheel angle fitting curve is:

[0050] f Fangle (t)=at 3 +bt 2 +ct + d;

[0051]

[0052] In the formula, t i and f Fangle (t i ) are the timestamp and the front wheel angle measurement value respectively, n is the number of data points, SSE is the sum of the squares of the differences between the predicted value and the true value (i.e., the residuals) of each data point. By minimizing the sum of squared residuals, the optimal fitting parameters are obtained;

[0053] The process of calculating the minimized sum of squared residuals SSE uses the Apache Commons Math library in Java to perform least squares fitting to obtain the optimal fitting parameters and the optimal fitting curve.

[0054] Step S4: After obtaining the optimal fitting curve, determine the average angle before the front wheel starts to turn as the reference line, find the time point where the front wheel fitting curve intersects the reference line, and at the same time determine the average angle before the rear wheel starts to turn as the reference line, find the time point where the rear wheel fitting curve intersects the reference line, and take the difference between the two time points to calculate the rear wheel following delay time.

[0055] f Fangle (t F ) - reference_angle_f = 0;

[0056] f Rangle (t R ) - reference_angle_r = 0;

[0057] t delay = t R - t F ;

[0058] In the formula, f Fangle (t) and f Rangle (t) are the third-order polynomial functions of the front wheel angle and the rear wheel angle respectively, reference_angle_f and reference_angle_r are the average angles of the front wheel and the rear wheel before starting to turn respectively, t F is the time point where the third-order polynomial function curve of the front wheel angle intersects its reference line, t R is the time point where the third-order polynomial function curve of the rear wheel angle intersects its reference line, and t delay is the rear wheel following delay time;

[0059] Step S5: When the obtained rear wheel following delay time is within the qualified range, it is considered qualified for detection.

[0060] The beneficial effects of the present invention are as follows: The cooperation of the background management system, the PAD operating system, and the data acquisition system helps personnel to efficiently complete the debugging and detection work, realizes the digitization and paperless of the debugging and detection stage, ensures the consistency of the debugging and detection results, reduces the dependence on the experience of debugging and detection personnel, and improves the interchangeability of debugging and detection personnel. Description of the Drawings

[0061] Figure 1 is the communication diagram of the debugging and detection system of the present invention.

[0062] Figure 2It is the function configuration diagram of the background management system of the present invention.

[0063] Figure 3 It is the data analysis flow chart of the PAD operating system of the present invention.

[0064] Figure 4 They are the front and rear wheel angle values obtained in the four-wheel walking mode. Detailed implementation manners

[0065] The present invention will be described below in conjunction with the accompanying drawings and embodiments.

[0066] As shown in the attached Figure 1 figure, a vehicle debugging and detection system includes a background management system, a PAD operating system and a data acquisition system. This debugging and detection system can be mounted on an aircraft tractor to assist debugging and detection personnel to efficiently complete debugging and detection work. The data acquisition system includes a T-Box, an external sensor and a detection controller;

[0067] The PAD operating system issues debugging and detection instructions to the vehicle body and the detection controller through the T-Box of the data acquisition system. After receiving the debugging and detection instructions, the vehicle body sends all status data to the T-Box. At the same time, the detection controller sends the signals collected by the external sensor to the T-Box, and the T-Box transmits all the received detection data to the PAD operating system in real time;

[0068] The PAD operating system downloads and obtains detection project information from the background management system. In this embodiment, the PAD operating system downloads and obtains a detection report template from the background management system. According to the real-time obtained detection data, the PAD operating system completes the detection of all mechanical detection items and electrical detection items. After being signed and confirmed by mechanical debuggers and electrical debuggers, a detection report is generated. The PAD operating system uploads the detection data, detection records and detection reports to the background management system;

[0069] The cooperation of the background management system, the PAD operating system and the data acquisition system assists personnel to efficiently complete debugging and detection work. This debugging and detection system uses sensors externally connected by tooling or installed on the vehicle body itself to control the triggering of data acquisition through a handheld PAD terminal, and outputs measurement results externally through a prefabricated algorithm. At the same time, it records the debugging process data, summarizes the items that need to be debugged multiple times, facilitates production assembly personnel or technicians to improve and optimize, reduces the professional technical requirements for debuggers, only requires training on equipment use, improves personnel interchangeability, alleviates the problem of insufficient personnel, realizes paperless work, and directly generates an electronic report after debugging, which is convenient for storage and review.

[0070] The data acquisition system communicates with the vehicle body via the CAN bus, and communicates with the PAD operating system via MQTT. The PAD operating system communicates with the background management system via a 4G IoT card, with fast transmission speed, for uploading inspection reports and process data as well as downloading inspection report templates. The T-Box of the data acquisition system performs data transfer and reception work, and has 4G communication function, WiFi self-organizing network function, two-way CAN bus interfaces and local data storage function. In this embodiment, the T-Box communicates with the vehicle body and the T-Box communicates with the inspection controller via the CAN bus.

[0071] The background management system includes an operable Web page and a server. The background management system has functions such as dictionary configuration, permission management, data query, inspection item management and inspection record viewing. The type dictionary defines options for the vehicle models, inspection items, types of inspection sensors, sensor models, and judgment basis conditions of the entire debugging and inspection system.

[0072] After the type dictionary is established, the editing and matching of each vehicle model and the corresponding inspection items are carried out in a mapping association manner. Each inspection item configured according to the above mapping relationship will be stored in the server. When it is necessary to perform inspections on the corresponding vehicle body model, the inspection items are retrieved according to the vehicle body model and specifications and downloaded to the PAD operating system for subsequent inspections.

[0073] Permission management sets permissions for personnel according to their job assignments, including administrator permissions, engineer permissions, debugging personnel permissions, quality inspection personnel permissions, after-sales personnel permissions, etc.

[0074] Data query includes process data query and result data query. The process data query retrieves the data collected during the debugging and inspection process and the number of debugging and inspection times of the inspection items during the debugging and inspection process according to the time axis. The process data query is beneficial for technical and quality inspection engineers to analyze product data according to their respective needs and provides direct data support for improving product quality. The result data query retrieves the final inspection report and displays it in a formatted report form.

[0075] The server is used to store inspection data and realizes access to the inspection platform and content configuration.

[0076] The matching of inspection items and vehicle models of this debugging and inspection system is editable. The configuration end is designed in a B / S (Brower / Sever, browser / server mode) structure. Through operations on the Web page, the self-editing of inspection item content and the binding of inspection sensors are realized.

[0077] The PAD operating system is a on-site handheld operation tool for debug testers, capable of inspecting the vehicle body status, calculating the detected data using a judgment algorithm model, determining whether each inspection item is qualified, displaying an inspection operation interface, and viewing inspection records at any time.

[0078] The PAD operating system has established two links in total. One is to interact with the server through a 4G interface. Its main function is to download the inspection items and requirements of the device under test configured to the PAD side for item-by-item inspection. Its secondary function is to upload the results after inspection to the server to generate an inspection report for storage and retrieval. The other is to establish communication with the on-site T-Box side, read the detection parameters, and determine whether the performance inspection items are qualified through the algorithm built into the PAD side.

[0079] After the debugger logs in to the PAD operating system, first connect to the T-Box gateway of the data acquisition system through the WIFI interface. After the gateway connection is successful, click on the "Vehicle Status Bar" to connect to the vehicle body. Then click on "Synchronize Inspection Items" to obtain inspection item information from the background management system. According to the model and specification number information of the vehicle to be inspected, the debugger selects the corresponding inspection report. The inspection items include 6 categories such as pre-power-on item inspection and post-power-on item inspection. According to the inspection method, the inspection items can also be divided into manually confirmed inspection items and automatically confirmed inspection items. Each inspection item is marked with "Item Description, Operation Specification, and Judgment Standard" for reference. Photos or videos can be uploaded during the inspection.

[0080] During the inspection process, the PAD operating system receives the CAN bus digital quantity data transmitted by the T-box and performs data analysis according to the CAN bus communication protocol between the debug detection system and the aircraft tractor. The data received by the PAD operating system includes product number, product specification number, and all CAN bus digital quantity data. Before the inspection starts, the PAD operating system downloads the inspection report templates corresponding to all product specification numbers from the background management system, and at the same time receives the vehicle physical quantity data types and data parsing rules corresponding to all product specification numbers. According to the product number in the vehicle's all CAN bus data, the inspection report template with the same product number can be found. The inspection report template carries the product specification number, and at the same time, the physical quantity data required for specific inspection items can be queried in the inspection report template. According to the product specification number in the inspection report template, the CAN bus data parsing rule with the same product specification number can be found.

[0081] After the PAD operating system imports all the CAN bus data of the vehicle, it preprocesses the data. The specific preprocessing method is as follows:

[0082] Perform frame separation to split the data stream into individual message frames;

[0083] Cyclic Redundancy Check (CRC) is used to detect and filter transmission errors. The data field is extracted according to the data length code, and the timestamp is retained.

[0084] According to the CAN bus data parsing rules, the digital quantity data of the vehicle CAN bus is parsed. The data parsing rules specify the position (starting bit), size (number of bits), scaling factor, offset, etc. of which bits of the CAN bus digital quantity data represent what physical quantities. Accordingly, the digital quantity data of the vehicle CAN bus can be converted into specific physical quantity data;

[0085] Physical_Value = f(Raw_Data);

[0086] In the formula, Raw_Data is the preprocessed digital quantity data read from the CAN bus, Physical_Value is the physical quantity data required for the detection item, and f(Raw_Data) is the CAN bus data parsing function.

[0087] The digital quantity data and physical quantity data are respectively:

[0088] Digital quantity data: In the CAN bus, the data field encapsulated in the CAN message frame. The maximum digital quantity that each CAN message can transmit is 64 bits;

[0089] Physical quantity data: Data of actual physical phenomena measured by sensors or actuators and converted into digital format, such as angles, pressures, speeds, position coordinates, etc.

[0090] According to the data configuration required for each detection item in the detection report template and the physical quantity data obtained by parsing the vehicle CAN bus data, each detection item in the detection report template can obtain the required physical quantity data. The specified data is input into the decision algorithm model of the detection item to complete the detection decision.

[0091] The PAD operating system determines whether the performance of the detection item is qualified through the decision algorithm model. The specific detection items and the detection and decision process are as follows:

[0092] 1. Detection item "Forward driving braking performance inspection", the operation specification is:

[0093] ① Keep the device powered on with high voltage;

[0094] ② Engage the forward gear;

[0095] ③ Control the vehicle speed at 20 km / h with the accelerator pedal;

[0096] ④ The detection terminal selects the start detection option;

[0097] ⑤ Release the accelerator pedal;

[0098] ⑥The brake pedal is depressed to the maximum limit position;

[0099] ⑦The equipment comes to a complete stop;

[0100] ⑧The detection terminal selects the end detection option;

[0101] ⑨The system automatically confirms the result;

[0102] ⑩The detection terminal displays the actual braking distance.

[0103] During the project detection process, the RTK integrated navigation system sends real-time time, vehicle speed, longitude and latitude information, which is transmitted to the PAD operating system via T-Box. When the brake pedal is depressed, the vehicle body speed begins to decrease, and the vehicle body starts to brake. When the vehicle speed stabilizes at ≤N km / h, the vehicle body stops; in this embodiment, it is set that when the vehicle speed stabilizes at ≤0.3 km / h, the vehicle body stops;

[0104] Select the longitude and latitude coordinates at the moment of sudden speed drop and the longitude and latitude coordinates when the speed stabilizes at N km / h, and use the haversine formula to calculate the distance between the two points to obtain the forward braking distance. When the obtained forward braking distance value is within the qualified range, it means the detection is qualified.

[0105] The specific calculation method of the forward braking distance is as follows:

[0106] Step S1: During the process of the vehicle driving at a speed of 20 km / h until it brakes to a stop, collect a set of discrete time points [t 1 , t 2 , …, t n of RTK and the corresponding speed values [v(t 1 ), v(t 2 ), …, v(t n )];

[0107] Step S2: Preprocess the original data and perform smooth data processing using a Kalman filter;

[0108] Step S3: Calculate the first-order difference of the speed values between adjacent time points, and set 2 times the standard deviation of the difference sequence as the threshold. The calculation formula is:

[0109] Δv(t i ) = v(t i+1 ) - v(t i );

[0110]

[0111] T = 2σ;

[0112] where \(i = 0, 1, 2, \cdots, n - 1\), \(\Delta v(t i )\) is the first-order difference of the speed value, \(\mu\) is the mean of the difference sequence, \(\sigma\) is the standard deviation of the difference sequence, \(T\) is the set threshold, which is 2 times the standard deviation of the difference sequence;

[0113] When the first-order difference reaches or exceeds this threshold, i.e., \(|\Delta v(t i )| \geq T\), the speed \(v(t i )\) at this time is the speed at the instant when braking starts, i.e., \(v brake ;

[0114] When the vehicle speed stabilizes at \(\leq N\) km / h, it is determined that the vehicle body has stopped, and the vehicle speed at this time is \(v stop ;

[0115] Step S4: After obtaining the latitudes and longitudes of the braking start point and end point, calculate the forward braking distance. The calculation formula is:

[0116]

[0117] \(\Delta lat = lat2 - lat1\);

[0118] \(\Delta lon = lon2 - lon1\);

[0119] where \(R = 6378\) km, \(R\) is the radius of the earth, \(\Delta lat\) is the difference in latitudes between the start point and the end point, \(\Delta lon\) is the difference in longitudes between the start point and the end point, when the vehicle speed \(v(t i ) = v brake \), the latitude and longitude coordinates are \((lon1, lat1)\), and when the vehicle speed \(v(t i ) = v stop \), the latitude and longitude coordinates are \((lon2, lat2)\).

[0120] 2. For the inspection item "Minimum left turning radius inspection", the operation specification is as follows:

[0121] ① Keep the equipment powered on at high voltage;

[0122] ② Rotate the steering wheel to the leftmost extreme position;

[0123] ③ Engage the forward gear;

[0124] ④ Keep the idling state;

[0125] ⑤ The detection terminal selects the start detection option;

[0126] ⑥ The equipment rotates one circle;

[0127] ⑦ The detection terminal selects the end of detection;

[0128] ⑧ The system automatically confirms the result.

[0129] During the project detection process, the PAD operating system receives the vehicle's longitude and latitude coordinates sent by RTK in real time. First, the Kalman filtering method is used to smooth the original GPS data, and multiple groups of data points are selected as sample points. Then, the UTM projection method is used to convert the longitude and latitude coordinates into coordinates in the plane direct coordinate system. Finally, the circular arc fitting and the least squares method are used for calculation to obtain the minimum left turning radius. When the obtained minimum left turning radius is within the qualified range, it indicates that the detection is qualified.

[0130] The specific calculation method of the minimum left turning radius is as follows:

[0131] Create an equation about the center of the circle (x c , y c ) and the radius R turn . Linearize the equation and use the least squares method to obtain the optimal solution, that is, the turning radius. The calculation formula is:

[0132] z i = (x i - x c ) 2 + (y i - y c ) 2 ;

[0133]

[0134] Aθ = B;

[0135] Among them,

[0136] θ = (A T A) -1 A T B;

[0137]

[0138] In the formula, z i is the intermediate variable of the circle equation. There are n sampling points (x i , y i ), where i = 1, 2,..., n. Finally, the minimum turning radius R turn is obtained.

[0139] In this embodiment, the detection, calculation, and determination processes of the "minimum right turning radius inspection" detection item are basically the same as those of the "minimum left turning radius detection".

[0140] 3. For the detection item "check the rear-wheel following delay time in the four-wheel walking mode", the operation specification is:

[0141] ① Keep the device in the high-voltage power-on state;

[0142] ②Switch the walking mode to four-wheel mode;

[0143] ③Straighten the steering wheel and engage the forward gear;

[0144] ④Stabilize the vehicle speed at 10 km / h;

[0145] ⑤The detection terminal selects the start detection option;

[0146] ⑥Wait for the data collection to complete;

[0147] ⑦Then turn the steering wheel one full turn to the left and hold;

[0148] ⑧The detection terminal selects the operation to complete;

[0149] ⑨Wait for the data collection to complete, and the system automatically confirms the result;

[0150] ⑩The detection terminal displays the actual following angle difference.

[0151] "Check the rear-wheel following delay time under the four-wheel walking mode", the specific method for project detection is as follows:

[0152] Step S1: Under the four-wheel walking mode, obtain the front and rear wheel angle values during the detection process;

[0153] Step S2: Perform data preprocessing, use the IQR interquartile range statistical method to remove outliers or error points, synchronize the timestamps, and ensure the time alignment of the front and rear wheel angle data;

[0154] Step S3: Select a time interval that includes the entire steering process, intercept the front wheel angle curve segment within this time interval for calculation, fit the sample curve segment into a smooth curve by fitting a third-order polynomial function, use the least squares method to calculate the sum of squared residuals SSE to minimize, and then find the optimal fitting parameters a, b, c, d of the front wheel angle fitting function. The solution method for the fitting curves of the front wheel angle and the rear wheel angle is the same. The solution formula for the front wheel angle fitting curve is:

[0155] f Fangle (t) = at 3 + bt 2 + ct + d;

[0156]

[0157] Where, t i and f Fangle (t i ) are the timestamp and the front wheel angle measurement value respectively, n is the number of data points, SSE is the sum of the squares of the differences between the predicted value and its true value (i.e., the residual) for each data point. By minimizing the sum of squared residuals, the optimal fitting parameters are obtained;

[0158] The process of calculating the minimized sum of squared residuals SSE uses the Apache Commons Math library in Java to perform least squares fitting to obtain the optimal fitting parameters and the optimal fitting curve;

[0159] Step S4: After obtaining the optimal fitting curve, determine the average angle before the front wheel starts to turn as the reference line, find the time point where the front wheel fitting curve intersects the reference line, and at the same time determine the average angle before the rear wheel starts to turn as the reference line, find the time point where the rear wheel fitting curve intersects the reference line, and take the difference between the two time points to calculate the rear wheel following delay time;

[0160] f Fangle (t F ) - reference_angle_f = 0;

[0161] f Rangle (t R ) - reference_angle_r = 0;

[0162] t delay = t R - t F ;

[0163] In the formula, f Fangle (t) and f Rangle (t) are the third-order polynomial functions of the front wheel angle and the rear wheel angle respectively, reference_angle_f and reference_angle_r are the average angles of the front wheel and the rear wheel before starting to turn respectively, t F is the time point where the third-order polynomial function curve of the front wheel angle intersects its reference line, t R is the time point where the third-order polynomial function curve of the rear wheel angle intersects its reference line, and t delay is the rear wheel following delay time;

[0164] Step S5: When the obtained rear wheel following delay time is within the qualified range, it is considered qualified for detection.

[0165] In this embodiment, regarding the mechanical structure of the debugging and detection system, the entire control cabinet of the debugging and detection system is installed on the toolbox trolley, which can be quickly disassembled and can move with the product to be detected. The control cabinet contains electrical components such as a power distribution module, a detection controller, and a T-Box. The wiring harness is connected through quick connectors and is equipped with a data hub for long-distance wiring. The toolbox can be used to store accessories such as sensors of the debugging and detection system and debugging tools for daily use.

[0166] This embodiment takes an aircraft tractor as an example for illustration, but it is not limited to this. Other vehicles can also apply this debugging and detection system.

Claims

1. A vehicle debugging and detection system, characterized in that: It includes a background management system, a PAD operating system and a data acquisition system, wherein the data acquisition system includes a T-Box, an external sensor and a detection controller; The PAD operating system sends debugging and detection instructions to the vehicle body and the detection controller through the T-Box of the data acquisition system. After receiving the debugging and detection instructions, the vehicle body sends all status data to the T-Box, and the detection controller sends the information collected by the external sensor to the T-Box, and the T-Box transmits the received detection data to the PAD operating system; The PAD operating system downloads and obtains the test project information from the background management system. Based on the real-time test data, the PAD operating system completes the test and uploads the test data to the background management system.

2. A vehicle debugging and detection system according to claim 1, characterized in that: The data acquisition system and the vehicle body use the CAN bus communication method, the data acquisition system and the PAD operating system use the MQTT communication method, and the PAD operating system and the background management system use the 4G Internet of Things card communication method.

3. A vehicle debugging and detection system according to claim 1 or 2, characterized in that: The backend management system has dictionary configuration, authority management and data query functions. The type dictionary defines options for the entire debugging and testing system vehicle type, testing items, testing sensor types, sensor models, and judgment criteria; After the type dictionary is established, the mapping association method is used to edit and match each vehicle model and the corresponding test items. Each test item configured according to the above mapping relationship is stored on the server. When the corresponding vehicle model test is required, the test items are retrieved according to the vehicle model and specifications, and downloaded to the PAD operating system for subsequent testing; Permission management sets permissions for personnel according to their division of labor; Data query includes process data query and result data query. Process data query retrieves the data collected during the debugging and testing process and the number of debugging and testing items during the debugging and testing process according to the timeline. Result data query retrieves the final test report.

4. A vehicle debugging and detection system according to claim 1 or 2, characterized in that: After the PAD operating system imports all the CAN bus data of the vehicle, the data is preprocessed. The specific preprocessing method is as follows: Perform frame separation to divide the data stream into independent message frames; Cyclic redundancy check (CRC) is used to detect and filter transmission errors, extract data fields based on data length codes, and retain timestamps.

5. A vehicle debugging and detection system according to claim 4, characterized in that: According to the CAN bus data parsing rules, the vehicle CAN bus digital data is parsed. According to the specific data configuration required by each test item and the physical quantity data obtained through parsing, each test item can obtain the required physical quantity data, and the specified data is input into the judgment algorithm model of the test item to complete the detection judgment.

6. A vehicle debugging and detection system according to claim 5, characterized in that: The PAD operating system determines whether the performance of the test item is qualified through the judgment algorithm model. During the "forward driving brake performance check" test, the RTK integrated navigation system sends the time, vehicle speed and longitude and latitude information in real time, which are transmitted to the PAD operating system via the T-Box. When the brake pedal is pressed, the vehicle speed begins to decrease and the vehicle starts to brake. When the vehicle speed stabilizes at ≤N km / h, the vehicle stops. Select the longitude and latitude coordinates when the speed suddenly drops and the longitude and latitude coordinates when the speed stabilizes at N km / h, use the haversine formula to calculate the distance between the two points, and obtain the forward braking distance. When the obtained forward braking distance value is within the qualified range, it means that the test is qualified.

7. A vehicle debugging and detection system according to claim 6, characterized in that: The specific calculation method of forward braking distance is: Step S1: During the process from vehicle driving to braking, a set of discrete time points [t1, t2, …, t n ] and the corresponding velocity values ​​[v(t1),v(t2),…,v(t n )]; Step S2: preprocessing the original data; Step S3: Calculate the first-order difference of the velocity values ​​between adjacent time points, set 2 times the standard deviation of the difference sequence as the threshold, and the calculation formula is: Δv(t i )=v(t i+1 )-v(t i ); Where i = 0, 1, 2, ..., n-1, Δv(t i ) is the first-order difference of the velocity value, μ is the mean of the difference sequence, σ is the standard deviation of the difference sequence, and T is the set threshold, which is twice the standard deviation of the difference sequence; When the first-order difference reaches or exceeds the threshold, |Δv(t i )|≥T, the velocity v(t i ) is the speed at the moment of braking, that is, v brake ; When the vehicle speed is stable at ≤N km / h, the vehicle is judged to have stopped. At this time, the vehicle speed is v stop ; Step S4: After obtaining the latitude and longitude of the braking start and end points, calculate the forward braking distance using the following formula: Δlat=lat2-lat1; Δlon=lon2-lon1; Where R is the radius of the earth, Δlat is the difference in latitude between the starting point and the end point, Δlon is the difference in longitude between the starting point and the end point, and the vehicle speed v(t i )=v brake When the latitude and longitude coordinates are (lon1, lat1), the vehicle speed v(t i )=v stop When , the longitude and latitude coordinates are (lon2,lat2).

8. A vehicle debugging and detection system according to claim 5, characterized in that: The PAD operating system determines whether the performance of the detection item is qualified through the judgment algorithm model. During the "minimum left / right turning radius inspection" item detection process, the PAD operating system receives the vehicle longitude and latitude coordinates sent by RTK in real time, first smoothes the original GPS data, selects multiple groups of data points as sample points, and then uses the UTM projection method to convert the longitude and latitude coordinates into coordinates in the plane direct coordinate system, and finally uses arc fitting and least squares method to calculate the minimum left / right turning radius. When the obtained minimum left / right turning radius is within the qualified range, it means that the detection is qualified.

9. A vehicle debugging and detection system according to claim 8, characterized in that: The specific calculation method for the minimum left / right turning radius is: Create a circle about the center (x c ,y c ) and radius R turn The equation is linearized and the optimal solution, i.e. the turning radius, is obtained using the least squares method. The calculation formula is: z i (x) i -x c ) 2 +(and i -and c ) 2 ; z i -R t 2 urn =2R turn x c +2R turn y c -x i 2 -y i 2 ; θ=(A T A) -1 A T B; In the formula, z i is the intermediate variable of the circle equation, with n sampling points (x i ,y i ), where i = 1, 2, ..., n, and finally the minimum turning radius R is obtained. turn .

10. A vehicle debugging and detection system according to claim 5, characterized in that: The PAD operating system determines whether the performance of the test item is qualified by determining the algorithm model. The specific method for detecting the item "rear wheel following delay time check in four-wheel walking mode" is: Step S1: In the four-wheel walking mode, obtain the angle values ​​of the front and rear wheels during the detection process; Step S2: perform data preprocessing, use the IQR interquartile range statistical method to remove outliers or error points, synchronize timestamps, and ensure that the front and rear wheel angle data are time aligned; Step S3: Select a time interval that includes the entire steering process, intercept the front wheel angle curve segment within the time interval for calculation, fit the sample curve segment into a smooth curve by fitting a third-order polynomial function, use the least squares method to calculate the minimum residual square sum SSE, and then find the best fitting parameters a, b, c, d of the front wheel angle fitting function. The fitting curve solution method of the front wheel angle and the rear wheel angle is the same. The solution formula of the front wheel angle fitting curve is: f Fangle (t)=at 3 +bt 2 +ct+d; Where, t i and f Fangle (t i ) are the timestamp and the front wheel angle measurement value, respectively, n is the number of data points, SSE is the sum of the squares of the difference between the predicted value and the true value (i.e., the residual) of each data point, and the optimal fitting parameters are obtained by minimizing the sum of the squares of the residuals; Calculate the process of minimizing the residual sum of squares SSE, use Apache Commons Math library in Java to perform least squares fitting, and obtain the optimal fitting parameters and optimal fitting curve; Step S4: After obtaining the optimal fitting curve, determine the average angle before the front wheel steering starts as a reference line, find the time point where the front wheel fitting curve intersects the reference line, and at the same time determine the average angle before the rear wheel steering starts as a reference line, find the time point where the rear wheel fitting curve intersects the reference line, and make a difference between the two time points to calculate the rear wheel following delay time; f Fangle (t F )-reference_angle_f=0; f Rangle (t R )-reference_angle_r=0; t delay =t R -t F ; In the formula, f Fangle (t) and f Rangle (t) are the third-order polynomial functions of the front wheel angle and the rear wheel angle, reference_angle_f and reference_angle_r are the average angles of the front and rear wheels before they start turning, t F is the time point when the third-order polynomial function curve of the front wheel angle intersects with its reference line, t R is the time point when the third-order polynomial function curve of the rear wheel angle intersects with its reference line, t delay It is the delay time for the rear wheel to follow; Step S5: When the obtained rear wheel following delay time is within the qualified range, the test is qualified.

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