Image recognition and analysis method and system for CAN bus data in the automotive field

Abstract

The present application discloses a method and system for image recognition and analysis of CAN bus data in the automotive field, relating to the field of image recognition technology. The method comprises: synchronously collecting a vehicle's CAN message data stream and a video data stream during vehicle operation during an acquisition cycle; splitting the CAN message data stream during the acquisition cycle into multiple signal data according to signal parameters, determining a comparison signal set based on all signal data during the acquisition cycle, and storing the comparison signal set in a comparison signal data source memory; dynamically identifying the video data stream during the acquisition cycle according to video parameters, and determining a reference signal based on the dynamically identified data during the acquisition cycle, and storing the reference signal in a computational database; analyzing the comparison signal set and the reference signal, and storing the comparison signals that meet set conditions in a signal database. The present application realizes the automatic acquisition and analysis of CAN bus data in the automotive field.

Description

Technical Field

[0001] The present application relates to the field of image recognition technology, and in particular to a method and system for image recognition and analysis of CAN bus data in the automotive field. Background Art

[0002] Controller Area Network (CAN) data analysis presents significant challenges due to the complexity of data collection and the large volume of data. Locating specific signals is difficult because it requires sifting through the vast amount of data. Even after finding the target signal, repeated calibration is required to ensure data accuracy.

[0003] During this complex data collection process, technicians need to deal with the following issues: (1) Large amounts of data: The amount of data transmitted by the CAN bus is huge and frequent, and technicians need to process a large number of data packets and filter out the key information they need. (2) Difficulty in signal positioning: Due to the complexity and diversity of the data, it is difficult for technicians to quickly locate specific signals directly from the massive amount of data, and a lot of time and effort is required for screening and analysis. (3) Cumbersome calibration process: Even if a specific signal is found, the data may be affected by factors such as noise and interference, so technicians still need to repeatedly calibrate and verify to ensure that the acquired signal is accurate and reliable.

[0004] During the CAN bus data analysis process, the data collection process is relatively complicated and the amount of data collected is complex. It is difficult for technicians to quickly locate the required specific signal from a large amount of data. After finding the specific signal in time, technicians still need to calibrate repeatedly. Summary of the Invention

[0005] The purpose of this application is to provide an image recognition and analysis method and system for CAN bus data in the automotive field, so as to solve the problem that CAN bus data in the automotive field cannot be automatically collected and analyzed.

[0006] To achieve the above objectives, this application provides the following solutions.

[0007] In a first aspect, the present application provides an image recognition and analysis method for CAN bus data in the automotive field, comprising:

[0008] During the acquisition cycle, the vehicle's CAN message data stream and the video data stream during vehicle operation are synchronously collected; the CAN message data stream includes: diagnostic data, broadcast data and diagnostic response data;

[0009] Splitting the CAN message data stream within the acquisition period into multiple signal data according to the signal parameters, determining a comparison signal set based on all the signal data within the acquisition period, and storing the comparison signal set in a comparison signal data source memory;

[0010] Dynamically identifying a video data stream within an acquisition period according to video parameters, determining a reference signal based on the dynamically identified data within the acquisition period, and storing the reference signal in a computational database;

[0011] The comparison signal set and the reference signal are analyzed, and the comparison signals in the comparison signal set that meet the set conditions are stored in a signal database.

[0012] Optionally, collect the vehicle's CAN message data stream, including:

[0013] Collecting diagnostic data and broadcast data through a CAN analyzer connected to the vehicle's OBD-II port;

[0014] Or use a CAN analyzer to capture the diagnostic request message sent by the diagnostic device to the OBD simulator, generate a diagnostic reply based on the captured diagnostic request message, simulate sending diagnostic reply data with different numbers of bytes, and collect the diagnostic reply data.

[0015] Optionally, the video data stream collected during vehicle operation includes:

[0016] The video data stream during vehicle operation is collected through video acquisition equipment.

[0017] Optionally, before collecting the video data stream of the vehicle during operation by the video collection device, the method further includes:

[0018] calibrating the video acquisition device;

[0019] Set video acquisition parameters; the video acquisition parameters include: acquisition quantity, acquisition interval, recognition area and recognition algorithm.

[0020] Optionally, the video data stream includes: vehicle status data, driving behavior data, instrument panel data and screen data of a diagnostic device.

[0021] Optionally, the signal parameters include: signal data type, signal length, signal position, coefficient and offset.

[0022] Optionally, determining a comparison signal set based on all signal data within an acquisition period includes:

[0023] Convert each signal data in the acquisition period into decimal, multiply by the coefficient and add the offset to obtain corresponding converted signal data;

[0024] The converted signal data within the acquisition period are formed into an array to obtain the comparison signal set.

[0025] Optionally, the video parameters include: target recognition parameters and the position, size and shape of the target in the video.

[0026] Optionally, analyzing the comparison signal set and the reference signal, and storing comparison signals in the comparison signal set that meet set conditions in a signal database, includes:

[0027] determining any comparison signal in the comparison signal set as a current comparison signal;

[0028] Draw a linear contrast image based on the current contrast signal and draw a linear reference image based on the reference signal;

[0029] Comparing and analyzing the linear contrast image and the linear reference image using a data normalization algorithm, a Pearson correlation coefficient algorithm, and a linear regression coefficient to obtain a Pearson correlation coefficient, a linear regression coefficient, and a linear regression offset corresponding to a current contrast signal;

[0030] sorting the comparison signals in ascending order of the Pearson correlation coefficient;

[0031] Update the current contrast signal to any contrast signal in the top 20% of the sorted contrast signals, and return "Use a data normalization algorithm, a Pearson correlation coefficient algorithm, and a linear regression coefficient to compare and analyze the linear contrast image and the linear reference image to obtain a Pearson correlation coefficient, a linear regression coefficient, and a linear regression offset corresponding to the current contrast signal";

[0032] The comparison signals, for which the difference between the linear regression coefficients and the linear regression offsets calculated twice are both within ±0.02, are stored in the signal database.

[0033] In a second aspect, the present application provides an image recognition and analysis system for CAN bus data in the automotive field, comprising:

[0034] A data acquisition unit is used to synchronously acquire the vehicle's CAN message data stream and the video data stream during vehicle operation during an acquisition cycle; the CAN message data stream includes: diagnostic data, broadcast data, and diagnostic response data;

[0035] a comparison signal set determination unit, configured to split the CAN message data stream within the acquisition period into a plurality of signal data according to the signal parameters, and determine a comparison signal set based on all the signal data within the acquisition period;

[0036] a reference signal determination unit, configured to dynamically identify a video data stream within an acquisition period according to video parameters, and determine a reference signal based on the dynamically identified data within the acquisition period;

[0037] The storage unit is used to analyze the comparison signal set and the reference signal, and store the comparison signals in the comparison signal set that meet the set conditions in a signal database.

[0038] According to the specific embodiments provided in the present application, the present application discloses the following technical effects: the present application discloses a method and system for image recognition and analysis of CAN bus data in the automotive field. First, within the acquisition period, the CAN message data stream of the vehicle and the video data stream during the vehicle operation are synchronously acquired; secondly, according to the signal parameters, the CAN message data stream within the acquisition period is split into multiple signal data, and a comparison signal set is determined based on all the signal data within the acquisition period, and the comparison signal set is stored in a comparison signal data source memory; then, according to the video parameters, the video data stream within the acquisition period is dynamically identified, and a reference signal is determined based on the dynamically identified data within the acquisition period, and the reference signal is stored in a calculation database; finally, the comparison signal set and the reference signal are analyzed, and the comparison signal set that meets the set conditions is stored in the signal database, thereby realizing the automatic acquisition and analysis of CAN bus data in the automotive field. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 A flowchart of an image recognition and analysis method for CAN bus data in the automotive field provided in one embodiment of the present application.

[0041] Figure 2 This is an architecture diagram of the image recognition and analysis method for CAN bus data in the automotive field.

[0042] Figure 3 This is a schematic diagram of the CAN message data flow acquisition process.

[0043] Figure 4 Schematic diagram of the video data stream acquisition process.

[0044] Figure 5 Schematic diagram of the signal data calculation and analysis process. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] The purpose of this application is to provide an image recognition and analysis method and system for CAN bus data in the automotive field, aiming to realize automatic collection and analysis of CAN bus data in the automotive field.

[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0048] In an exemplary embodiment, Figure 1 and Figure 2 As shown, the image recognition and analysis method of CAN bus data in the automotive field in this embodiment includes the following steps.

[0049] Step 1: During the acquisition cycle, synchronously collect the vehicle's CAN message data stream and the video data stream during vehicle operation; the CAN message data stream includes: diagnostic data, broadcast data, and diagnostic response data.

[0050] Specifically, such as Figure 2 As shown in the figure, by using an OBD simulator and a CAN analyzer to collect the vehicle's CAN message data stream, and combining it with visual data (video data stream) collected by an external video acquisition device, the visual data is converted into digital signals and dynamically analyzed with the CAN message data, thus realizing a new method for accurately finding target signals from CAN messages. This method not only improves the accuracy and efficiency of data analysis, but also opens up a new direction in the field of CAN data analysis.

[0051] During actual collection, the specific operation process includes the following steps.

[0052] Step 1: Message data collection.

[0053] In the process of message data collection, there are two methods: real vehicle data collection and data collection based on diagnostic equipment.

[0054] Real-vehicle data collection: In real-vehicle testing, by using a CAN analyzer device connected to the vehicle's OBD-II port, message data including diagnostic data and broadcast data can be collected in real time.

[0055] Data collection from diagnostic devices: An OBD simulator is required for data collection from diagnostic devices. First, the diagnostic device sends a diagnostic request to the OBD simulator, while a CAN analyzer captures the diagnostic request message. The CAN analyzer then generates a diagnostic response based on the received diagnostic request message, in accordance with the ISO 14229 standard. During the diagnostic response, the CAN analyzer simulates sending values (diagnostic response data) of varying byte lengths. The data generated by this series of operations constitutes the required message data.

[0056] Step 2: Video data collection.

[0057] Video data acquisition is the use of high-definition cameras or other image acquisition devices to capture real-time visual data during vehicle operation, such as vehicle status, driving behavior, dashboards, diagnostic equipment screens, etc.

[0058] Step 3: Parameter setting.

[0059] The parameter setting process is divided into two parts: signal parameters and video parameters. Signal parameters are used to analyze the collected message data, including indicators such as data signal length, signal position, coefficients, and offset. Video parameters are used to set up the subsequent video signal extraction process, covering parameters such as signal target identification (such as vehicles, human behavior, or screen devices) and the signal's position, size, and shape in the video.

[0060] Step 4: Start collecting (start state).

[0061] Start Acquisition marks the start of the synchronous acquisition of the CAN message data stream and the video data stream. This step ensures the time alignment of the data. Clicking the Start Acquisition button marks the official start of the synchronous acquisition of the CAN message data stream and the video data stream.

[0062] Step 5: Parsing, extraction, and calculation of CAN bus data.

[0063] The CAN message data stream is split into multiple signals based on the data signal length. These signals are converted to decimal, multiplied by a coefficient, and added with an offset. Subsequently, a new array is formed based on all the data values within the acquisition period. The values in this array will be used for the continued calculation in step 7.

[0064] Step 6: Identification and extraction of video data.

[0065] The data in the video data stream is dynamically identified according to the video parameters, and a new array is formed according to all the data values in the acquisition cycle. The values of the array will be used for the continued calculation in the seventh step.

[0066] Step 7: Data analysis, calculation and storage.

[0067] This step uses the array output from step 6 as the reference signal and the array output from step 5 as the comparison signal to compare the similarity between the two arrays. If the similarity meets the set value of the similarity parameter, the comparison signal information is stored in the signal database.

[0068] As an optional implementation, collecting the vehicle's CAN message data stream includes:

[0069] The diagnostic data and broadcast data are collected through a CAN analyzer connected to the vehicle's OBD-II port.

[0070] Or use a CAN analyzer to capture the diagnostic request message sent by the diagnostic device to the OBD simulator, and generate a diagnostic response based on the captured diagnostic request message, simulate sending diagnostic response data with different numbers of bytes, and collect the diagnostic response data.

[0071] As an optional implementation, collecting the video data stream during vehicle operation includes:

[0072] The video data stream during vehicle operation is collected through video acquisition equipment.

[0073] As an optional implementation manner, before collecting the video data stream of the vehicle during operation by the video acquisition device, the method further includes:

[0074] Calibrate the video capture device.

[0075] Set the video acquisition parameters; video acquisition parameters include: acquisition quantity, acquisition interval, recognition area and recognition algorithm.

[0076] As an optional implementation, the video data stream includes: vehicle status data, driving behavior data, instrument panel data, and screen data of a diagnostic device.

[0077] Step 2: Split the CAN message data stream within the acquisition period into multiple signal data according to the signal parameters, determine a comparison signal set based on all signal data within the acquisition period, and store the comparison signal set in the comparison signal data source memory.

[0078] As an optional implementation, the signal parameters include: signal data type, signal length, signal position, coefficient and offset.

[0079] As an optional implementation, determining a comparison signal set based on all signal data within an acquisition period includes:

[0080] Each signal data in the acquisition period is converted into decimal, multiplied by a coefficient and added with an offset to obtain the corresponding converted signal data.

[0081] The converted signal data within the acquisition period are formed into an array to obtain a comparison signal set.

[0082] Specifically, in actual operation, Figure 3 As shown in FIG, the acquisition process of the CAN message data stream specifically includes the following steps.

[0083] S11: Select a data source.

[0084] There are two ways to select data sources: real vehicle data collection and data collection based on diagnostic equipment.

[0085] Real vehicle data collection: During real vehicle testing, a CAN analyzer device is connected to the vehicle's OBD-II port to collect required message data, including diagnostic data and broadcast data, in real time.

[0086] Data collection from diagnostic devices: An OBD simulator is required for data collection from diagnostic devices. First, the diagnostic device sends a diagnostic request to the OBD simulator, while a CAN analyzer captures the diagnostic request message. The CAN analyzer then generates a diagnostic response based on the received diagnostic request message, in accordance with the ISO 14229 standard. During the diagnostic response, the CAN analyzer simulates sending values (diagnostic response data) of varying byte lengths. The data generated by this series of operations constitutes the required message data.

[0087] S12: (Message) data acquisition module.

[0088] The (Message) Data Acquisition Module applies the appropriate data processing and acquisition methods based on the selected data source. The (Message) Data Acquisition Module dynamically extracts the CAN message data provided by the CAN analyzer into the system memory.

[0089] S13: Set signal parameters.

[0090] Set the signal parameters required for the signal extraction process, including signal data type, signed / unsigned, coefficient, offset, extracted signal binary data bit length value (extraction judgment length value), etc., to facilitate use in subsequent steps.

[0091] S14: Generate a comparison data source.

[0092] In this step, the original message data collected by the (message) data acquisition module is first cut into a series of fixed-length raw signal data based on the extraction and judgment length value parameter. Then, these raw signal data are calculated according to other signal parameters and the resulting data is placed in the system memory where the comparison data source is located.

[0093] Step 3: Dynamically identify the video data stream within the acquisition period according to the video parameters, determine a reference signal based on the dynamically identified data within the acquisition period, and store the reference signal in a calculation database.

[0094] As an optional implementation, the video parameters include: target recognition parameters and the position, size and shape of the target in the video.

[0095] Specifically, in actual operation, Figure 4 As shown, the video data stream acquisition process specifically includes the following steps.

[0096] S21: Video capture device calibration.

[0097] Before starting to capture, it is necessary to calibrate the video capture equipment to ensure that the content to be captured is clear and included in the video range.

[0098] S22: Set video acquisition parameters.

[0099] Set the parameters required for video data stream processing. The parameter fields are described as follows.

[0100] Collection quantity: used to identify the number of data collection point information contained in the same data frame.

[0101] Interval: refers to the interval at which a data packet is processed during the subsequent dynamic calculation process.

[0102] Identification area: represents the area where a collection point is located, drawn as a square. The database stores the coordinate pixel values of the upper left corner and lower right corner of the video.

[0103] Recognition algorithm: used to process the recognition algorithm within the recognition area in the video data stream, including: digital recognition algorithm and state recognition algorithm.

[0104] S23: Determine whether it is a dynamic parameter.

[0105] Determine whether the data type field in the video capture parameters is dynamic data. If it is a fixed value, the video will process the first frame of video data and pass the processed data as a fixed value to the subsequent steps. If it is dynamic data, all data in the video data stream will be passed to the subsequent steps.

[0106] S24: Dynamic flow calculation.

[0107] This step is only executed when the data is dynamic. In this step, the continuously acquired video data stream is parsed frame by frame according to the video acquisition parameters. The corresponding recognition algorithm is called from each frame to derive a value. This data is then combined into a single data packet based on the interval parameters and stored in the computational database. The next data packet is then acquired.

[0108] S25: Reference signal data source.

[0109] The data source information after video signal processing is stored so that it can be used as a reference signal data source for subsequent calculations.

[0110] Step 4: Analyze the comparison signal set and the reference signal, and store the comparison signals in the comparison signal set that meet the set conditions into the signal database.

[0111] As an optional implementation, step 4 includes:

[0112] Step 41: Determine any comparison signal in the comparison signal set as the current comparison signal.

[0113] Step 42: Draw a linear contrast image based on the current contrast signal, and draw a linear reference image based on the reference signal.

[0114] Step 43: Compare and analyze the linear contrast image and the linear reference image using a data normalization algorithm, a Pearson correlation coefficient algorithm, and a linear regression coefficient to obtain the Pearson correlation coefficient, the linear regression coefficient, and the linear regression offset corresponding to the current contrast signal.

[0115] Step 44: Sort the comparison signals in ascending order of the Pearson correlation coefficient.

[0116] Step 45 : Update the current comparison signal to any comparison signal in the top 20% of the comparison signals after sorting, and return to step 43 .

[0117] Step 44: storing the comparison signal in which the difference between the linear regression coefficients and the linear regression offset calculated twice are both within ±0.02 in the signal database.

[0118] Specifically, in actual operation, Figure 5 As shown, the process of signal data calculation and analysis specifically includes the following steps.

[0119] S31: Data collection.

[0120] During data analysis, the data acquisition function dynamically extracts data from both the comparison signal source and the reference signal source. Since both data are dynamic, the amount of data increases over time.

[0121] During the initial data collection, all data from the start time to the end time of the reference signal data is extracted and output to the interface to form two comparison curves, facilitating better analysis and comparison. After outputting the dynamic curve, the extracted comparison signal data and reference signal data are output together for subsequent calculation steps.

[0122] During subsequent data acquisition, the end time of the previous acquisition is used as the start time for this acquisition. All data from this start time to the maximum end time of the reference signal source is collected and output to the interface and appended to the interface ratio curve to facilitate better analysis and comparison. After outputting the dynamic curve, the extracted comparison signal data and reference signal data are output again to the subsequent calculation steps, forming a continuous acquisition process.

[0123] S32: Data comparison.

[0124] After obtaining the reference signal and the comparison signal set, a comparison analysis is performed between the reference signal and the comparison signal.

[0125] First, a comparison signal is extracted from the comparison signal set. Its first value is determined to be time-matched with the first value of the reference signal. If not, the start time is determined based on the time of the last occurrence. The end time is then determined to be consistent. If so, the end time is determined based on the time of the first occurrence. Next, the data between the start and end times is plotted as linear graphs. Finally, the two image data are compared and analyzed using a data normalization algorithm, a Pearson correlation coefficient algorithm, and a linear regression coefficient to obtain similarity, coefficient, and offset parameter values. A general description of this analysis method is provided below.

[0126] Data normalization algorithm: for dataset , the standardized dataset It can be calculated by the following formula:

[0127] .

[0128] in, Dataset Dataset The mean of For the dataset The standard deviation of .

[0129] Pearson Correlation Coefficient Algorithm: Dataset and datasets Pearson correlation coefficient between It can be calculated by the following formula:

[0130] .

[0131] in, For the dataset or dataset The amount of data in For the dataset The i-th data in; For the dataset The mean of For the dataset The i-th data in; For the dataset The mean of .

[0132] Linear regression coefficients: The general form of the linear regression model is , in this algorithm, the linear regression coefficient and the linear regression offset The calculation is based on the standardized data, and the specific formula is as follows:

[0133] .

[0134] .

[0135] in, is the dependent variable; is the independent variable; For the dataset The standard deviation of For the dataset The standard deviation of For the dataset The mean of For the dataset The mean of .

[0136] S33: Dynamic calculation.

[0137] According to the comparison signal The value of the comparison signal is used to perform a reverse sorting operation on all the comparison signals in the comparison signal set. Then, the 20% comparison signals at the front of the sort are extracted and compared with the reference signals extracted from the video data stream for a second data comparison calculation. If the data obtained after the second calculation is the same as the data obtained in the first calculation, and If the difference is kept within ±0.02, then these data are the final signal data required and are stored in the signal database.

[0138] The method of the present application ensures the integrity and stability of the CAN signal data source and the video acquisition data source by storing them in different data source libraries, which helps to maintain and manage different types of data; at the same time, it connects the OBD simulator and the CAN analyzer to realize the collection of multi-source vehicle data, which can provide a richer data source; the video acquisition module does not limit the collection object and can obtain vehicle screen data and instrument panel data, etc. This flexible design can meet different data requirements; data signal search and comparison are performed based on the data template extracted from the video data stream. Once the required signal comparison is completed, the system will start the continuous optimization and automatic verification function, and perform continuous verification according to the preset verification parameters, which increases system performance and improves the accuracy and efficiency of data analysis; once the verification is met, the verified data will be directly stored in the signal library, which improves the efficiency and reliability of data management and facilitates subsequent data processing, decision-making and system development; before performing signal comparison, the real-time data signal source and the real-time video signal source are specially collected, and the comparison signal source and the reference signal source are generated through processing. This meticulous processing improves the accuracy and reliability of the data; in the signal comparison process, with the reference signal as the benchmark, the comparison signal source is used to find the change curve data set similar to the reference signal, and it is extracted to effectively screen out the relevant data and improve the efficiency and accuracy of signal matching.

[0139] In an exemplary embodiment, a system for image recognition and analysis of CAN bus data in the automotive field is provided, comprising:

[0140] The data acquisition unit is used to synchronously collect the vehicle's CAN message data stream and the video data stream during vehicle operation during the acquisition cycle; the CAN message data stream includes: diagnostic data, broadcast data and diagnostic response data.

[0141] The comparison signal set determination unit is used to split the CAN message data stream in the acquisition period into multiple signal data according to the signal parameters, and determine the comparison signal set based on all the signal data in the acquisition period.

[0142] The reference signal determination unit is used to dynamically identify the video data stream within the acquisition period according to the video parameters, and determine the reference signal based on the dynamically identified data within the acquisition period.

[0143] The storage unit is used to analyze the comparison signal set and the reference signal, and store the comparison signals in the comparison signal set that meet the set conditions in the signal database.

[0144] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for image recognition and analysis of CAN bus data in the automotive field, characterized in that: The image recognition and analysis method of CAN bus data in the automotive field includes: During the acquisition cycle, the vehicle's CAN message data stream and the video data stream during vehicle operation are synchronously collected; the CAN message data stream includes: diagnostic data, broadcast data and diagnostic response data; Splitting the CAN message data stream within the acquisition period into multiple signal data according to the signal parameters, determining a comparison signal set based on all the signal data within the acquisition period, and storing the comparison signal set in a comparison signal data source memory; Dynamically identifying a video data stream within an acquisition period according to video parameters, determining a reference signal based on the dynamically identified data within the acquisition period, and storing the reference signal in a computational database; Analyzing the comparison signal set and the reference signal, and storing the comparison signals in the comparison signal set that meet the set conditions in a signal database; Collect the vehicle's CAN message data stream, including: Collect diagnostic data and broadcast data through a CAN analyzer; the CAN analyzer is connected to the vehicle's OBD-II port; Or use a CAN analyzer to capture the diagnostic request message sent by the diagnostic device to the OBD simulator, generate a diagnostic response based on the captured diagnostic request message, simulate sending diagnostic response data with different byte numbers, and collect the diagnostic response data; The video data stream includes: vehicle status data, driving behavior data, instrument panel data, and screen data of diagnostic equipment; Analyzing the comparison signal set and the reference signal, and storing the comparison signals in the comparison signal set that meet the set conditions in a signal database, includes: determining any comparison signal in the comparison signal set as a current comparison signal; Drawing a linear contrast image based on the current contrast signal and drawing a linear reference image based on the reference signal; specifically including: Determine whether the first value of the current comparison signal is consistent with the first value of the reference signal; if not, the time of whichever occurs last is used as the starting time; Determine whether the end time is consistent. If consistent, the end time is determined by the one whose end time is earliest. The data of the current comparison signal and the data of the reference signal between the start time and the end time are plotted into linear images respectively; The linear contrast image and the linear reference image are compared and analyzed using the data normalization algorithm, the Pearson correlation coefficient algorithm, and the linear regression coefficient to obtain the Pearson correlation coefficient, the linear regression coefficient, and the linear regression offset corresponding to the current contrast signal; Sort the contrast signals in ascending order of the Pearson correlation coefficient; Update the current contrast signal to any of the top 20% of the sorted contrast signals and return "Use the data normalization algorithm, Pearson correlation coefficient algorithm, and linear regression coefficient to compare and analyze the linear contrast image and the linear reference image to obtain the Pearson correlation coefficient, linear regression coefficient, and linear regression offset corresponding to the current contrast signal." The comparison signals for which the difference between the linear regression coefficients and the linear regression offset calculated twice are both within ±0.02 are stored in the signal database.

2. The image recognition and analysis method for CAN bus data in the automotive field according to claim 1 is characterized in that: Collect video data streams during vehicle operation, including: The video data stream during vehicle operation is collected through video acquisition equipment.

3. The image recognition and analysis method for CAN bus data in the automotive field according to claim 1, characterized in that: Before the video data stream of the vehicle operation process is collected by the video acquisition device, the following is also included: calibrating the video acquisition device; Set video acquisition parameters; the video acquisition parameters include: acquisition quantity, acquisition interval, recognition area and recognition algorithm.

4. The image recognition and analysis method for CAN bus data in the automotive field according to claim 1, characterized in that: The signal parameters include: signal data type, signal length, signal position, coefficient and offset.

5. The image recognition and analysis method for CAN bus data in the automotive field according to claim 4 is characterized in that: The comparison signal set is determined based on all signal data within the acquisition period, including: Convert each signal data in the acquisition period into decimal, multiply by the coefficient and add the offset to obtain corresponding converted signal data; The converted signal data within the acquisition period are formed into an array to obtain the comparison signal set.

6. The image recognition and analysis method for CAN bus data in the automotive field according to claim 1, characterized in that: The video parameters include target recognition parameters and the position, size and shape of the target in the video.

7. An image recognition and analysis system for CAN bus data in the automotive field, characterized in that: The image recognition and analysis system for CAN bus data in the automotive field includes: A data acquisition unit is used to synchronously acquire the vehicle's CAN message data stream and the video data stream during vehicle operation during an acquisition cycle; the CAN message data stream includes: diagnostic data, broadcast data, and diagnostic response data; a comparison signal set determination unit, configured to split the CAN message data stream within the acquisition period into a plurality of signal data according to the signal parameters, and determine a comparison signal set based on all the signal data within the acquisition period; a reference signal determination unit, configured to dynamically identify a video data stream within an acquisition period according to video parameters, and determine a reference signal based on the dynamically identified data within the acquisition period; a storage unit, configured to analyze the comparison signal set and the reference signal, and store the comparison signals in the comparison signal set that meet set conditions in a signal database; Collect the vehicle's CAN message data stream, including: Collect diagnostic data and broadcast data through a CAN analyzer; the CAN analyzer is connected to the vehicle's OBD-II port; Or use a CAN analyzer to capture the diagnostic request message sent by the diagnostic device to the OBD simulator, generate a diagnostic response based on the captured diagnostic request message, simulate sending diagnostic response data with different byte numbers, and collect the diagnostic response data; The video data stream includes: vehicle status data, driving behavior data, instrument panel data, and screen data of diagnostic equipment; Analyzing the comparison signal set and the reference signal, and storing the comparison signals in the comparison signal set that meet the set conditions in a signal database, includes: determining any comparison signal in the comparison signal set as a current comparison signal; Drawing a linear contrast image based on the current contrast signal and drawing a linear reference image based on the reference signal; specifically including: Determine whether the first value of the current comparison signal is consistent with the first value of the reference signal; if not, the time of whichever occurs last is used as the starting time; Determine whether the end time is consistent. If consistent, the end time is determined by the one whose end time is earliest. The data of the current comparison signal and the data of the reference signal between the start time and the end time are plotted into linear images respectively; The linear contrast image and the linear reference image are compared and analyzed using the data normalization algorithm, the Pearson correlation coefficient algorithm, and the linear regression coefficient to obtain the Pearson correlation coefficient, the linear regression coefficient, and the linear regression offset corresponding to the current contrast signal; Sort the contrast signals in ascending order of the Pearson correlation coefficient; Update the current contrast signal to any of the top 20% of the sorted contrast signals and return "Use the data normalization algorithm, Pearson correlation coefficient algorithm, and linear regression coefficient to compare and analyze the linear contrast image and the linear reference image to obtain the Pearson correlation coefficient, linear regression coefficient, and linear regression offset corresponding to the current contrast signal." The comparison signals for which the difference between the linear regression coefficients and the linear regression offset calculated twice are both within ±0.02 are stored in the signal database.

Images