A vehicle chassis diagnosis method, device, system and storage medium

Abstract

The application relates to vehicle diagnosis technology and discloses a vehicle chassis diagnosis method, device, system and storage medium, the method comprising the following steps: acquiring vehicle working condition data and fault code data through a diagnosis joint; wherein the vehicle working condition data comprises an engine rotating speed; based on the engine rotating speed, an electromagnetic metasurface sensor is controlled to switch to a corresponding electromagnetic scanning frequency band to perform electromagnetic scanning on a vehicle chassis; electromagnetic scanning data sent by the electromagnetic metasurface sensor is received, and an abnormal area is located based on the electromagnetic scanning data; electromagnetic characteristics of the abnormal area are matched with preset defect information templates to determine defect information; and based on the vehicle working condition data, the fault code data and the defect information, fault information of the vehicle chassis is determined. The application can realize non-contact high-precision detection of chassis structure abnormalities.

Description

Technical Field

[0001] This application relates to the field of vehicle diagnostic technology, and in particular to a vehicle chassis diagnostic method, apparatus, system and storage medium. Background Technology

[0002] Currently, vehicle fault diagnosis technology mainly relies on the On-Board Diagnostic (OBD) system interface, which reads preset parameters and fault code information from the Electronic Control Unit (ECU). While this method can reflect the operating status of the vehicle's electronic systems to some extent, it has significant limitations in detecting non-electronic latent faults (such as mechanical fatigue cracks and electrolyte leaks). Specifically, existing technologies mainly acquire data such as engine speed, coolant temperature, and fault codes based on the CAN bus and make judgments using preset thresholds. Therefore, it is difficult to identify physical structural defects that have not yet triggered fault codes, such as fatigue cracks in chassis metal components or weld cracks, resulting in potential safety hazards not being detected in time. While some high-end vehicles employ embedded temperature, pressure, or vibration sensors for auxiliary monitoring, these solutions suffer from issues such as complex installation, insufficient environmental adaptability, and limited coverage. Furthermore, although optical imaging and visual inspection technologies are highly sensitive to surface defects, they cannot penetrate metal structures to obtain changes in internal electromagnetic parameters, thus hindering the effective identification of hidden defects. In summary, existing diagnostic technologies have the following shortcomings: lack of non-contact structural inspection capabilities, low data fusion levels, and poor real-time performance, making it difficult to meet the high-precision dynamic detection requirements for sub-millimeter level defects. Summary of the Invention

[0003] In view of this, embodiments of this application provide a vehicle chassis diagnostic method, apparatus, system, and computer-readable storage medium.

[0004] In a first aspect, embodiments of this application provide a vehicle chassis diagnostic method, wherein the vehicle chassis is equipped with an electromagnetic metasurface sensor, the method comprising: Vehicle operating condition data and fault code data are obtained through a diagnostic connector; wherein, the vehicle operating condition data includes engine speed; Based on the engine speed, the electromagnetic metasurface sensor is controlled to switch to the corresponding electromagnetic scanning frequency band to perform electromagnetic scanning on the vehicle chassis. Receive electromagnetic scanning data sent by the electromagnetic metasurface sensor, and locate abnormal areas based on the electromagnetic scanning data; The electromagnetic characteristics of the abnormal region are matched with a preset defect information template to determine the defect information; Based on the vehicle operating condition data, fault code data, and defect information, the fault information of the vehicle chassis is determined.

[0005] In an optional implementation, controlling the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed includes: If the engine speed is within the first speed range, the electromagnetic metasurface sensor is controlled to scan in the first scanning frequency band to detect fatigue cracks in the chassis metal. If the engine speed is within the second speed range, the electromagnetic metasurface sensor is controlled to scan in the second scanning frequency band to detect defects in metallic and non-metallic components. If the engine speed is within the third speed range, the electromagnetic metasurface sensor is controlled to scan in the third scanning frequency band to detect abnormal electromagnetic parameters of the material. Wherein, the first rotational speed is less than the second rotational speed, and the second rotational speed is less than the third rotational speed; the electromagnetic wave frequency of the first scanning frequency band is less than the electromagnetic wave frequency of the second scanning frequency band, and the electromagnetic wave frequency of the second scanning frequency band is less than the electromagnetic wave frequency of the third frequency band.

[0006] In an optional implementation, locating the abnormal region based on the electromagnetic scanning data includes: The I / Q sampled data in the electromagnetic scanning data are subjected to Fourier transform processing, and electromagnetic characteristic parameters are calculated based on the transform results; the electromagnetic characteristic parameters include reflection coefficient, dielectric constant and conductivity; The electromagnetic characteristic parameters are compared with a preset electromagnetic characteristic database to determine the abnormal region.

[0007] In an optional implementation, matching the electromagnetic characteristics of the abnormal region with a preset defect information template to determine the defect information includes: Extract the peak reflection coefficient and resonant frequency offset of the abnormal region at each scanning frequency point; The peak reflection coefficient and the resonant frequency offset are pattern matched with a preset defect information data table to obtain the defect information; wherein, the defect information includes the size, shape and location information of the defect.

[0008] In an optional implementation, determining the fault information of the vehicle chassis based on the vehicle operating condition data, fault code data, and defect information includes: A fusion feature vector is constructed based on the vehicle operating condition data, fault code data, and defect information; The fused feature vector is input into a pre-trained diagnostic model to output vehicle chassis fault information; wherein, the vehicle chassis fault information includes defect type, risk level and repair recommendations.

[0009] In an optional implementation, after outputting the vehicle chassis fault information, the method further includes: Determine the probability of vehicle malfunction based on confidence information; The confidence information includes the confidence level of vehicle operating condition data, the confidence level of fault code data, and the confidence level of defect information; The confidence level of the vehicle operating condition data is determined based on the matching degree between the engine speed fluctuation value and the first threshold, and the matching degree between the coolant temperature change rate and the second threshold. The confidence level of the fault code data is determined based on whether there are historical high-frequency fault codes. The confidence level of the defect information is determined based on the matching degree between the peak value of the reflection coefficient and the resonant frequency offset and the preset defect information data table.

[0010] In an optional implementation, it further includes: The two-dimensional coordinates of the abnormal region are determined based on the two-dimensional array coordinates of the electromagnetic metasurface sensor, and the two-dimensional coordinates are transformed into the three-dimensional coordinates of the vehicle. The three-dimensional coordinates are mapped onto a preset three-dimensional vehicle model; Based on the vehicle chassis fault information, anomalies are displayed in the vehicle's 3D model, and an alarm is triggered when a preset alarm threshold is reached.

[0011] Secondly, embodiments of this application provide a vehicle chassis diagnostic device, comprising: an electromagnetic metasurface sensor deployed on the vehicle chassis, the device comprising: The acquisition module is used to acquire vehicle operating condition data and fault code data through a diagnostic connector; wherein, the vehicle operating condition data includes engine speed; The control module is used to control the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed, so that the electromagnetic metasurface sensor can perform electromagnetic scanning on the vehicle chassis. The positioning module is used to receive electromagnetic scanning data sent by the electromagnetic metasurface sensor and locate abnormal areas based on the electromagnetic scanning data. The defect identification module is used to match the electromagnetic characteristics of the abnormal area with a preset defect information template to determine the defect information; The fault diagnosis module is used to determine the fault information of the vehicle chassis based on the vehicle operating condition data, fault code data and the defect information.

[0012] Thirdly, embodiments of this application provide a vehicle chassis diagnostic system, including a terminal device, a diagnostic connector, and an electromagnetic metasurface sensor for deployment on a vehicle chassis; The diagnostic connector is used to acquire vehicle operating condition data and fault code data, and send them to the terminal device; The electromagnetic metasurface sensor is used to perform electromagnetic scanning on the vehicle chassis according to the scanning command sent by the terminal device, and to feed back the scanning data to the terminal device. The terminal device is used to execute the vehicle chassis diagnostic method described in the foregoing embodiments.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed on a processor, implements the vehicle chassis diagnostic method described in the foregoing embodiments.

[0014] The embodiments of this application have the following beneficial effects: By deploying electromagnetic metasurface sensors on the vehicle chassis and dynamically adjusting the electromagnetic scanning frequency band in conjunction with engine speed, this application achieves non-contact, high-precision detection of chassis structural anomalies. This method utilizes electromagnetic scanning data to identify abnormal areas and combines vehicle operating conditions and fault code information to determine chassis fault information, avoiding the reliance on electronic fault codes in traditional diagnostic methods and improving the detection capability for latent structural defects such as metal fatigue cracks and electrolyte leakage. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This application illustrates the structure of a vehicle chassis diagnostic system according to an embodiment of the present application; Figure 2 This paper illustrates a first flowchart of a vehicle chassis diagnostic method according to an embodiment of this application. Figure 3 This paper illustrates a second flowchart of a vehicle chassis diagnostic method according to an embodiment of this application. Figure 4 A schematic diagram of the third process of the vehicle chassis diagnostic method according to an embodiment of this application is shown; Figure 5 The fourth flowchart of the vehicle chassis diagnostic method according to an embodiment of this application is shown; Figure 6 The fifth flowchart of the vehicle chassis diagnostic method according to an embodiment of this application is shown; Figure 7 A schematic diagram of a vehicle chassis diagnostic device according to an embodiment of this application is shown. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] The following is a combination of... Figure 1 The vehicle chassis diagnostic system of this application embodiment will be described.

[0023] like Figure 1 As shown, the vehicle chassis diagnostic system 10 includes a terminal device 11, a diagnostic connector 12, and an electromagnetic metasurface sensor 13 deployed under the vehicle chassis. The terminal device, serving as the system's main control and data processing unit, can be, but is not limited to, a portable electronic device with data processing and communication capabilities, such as a tablet computer, laptop computer, or smartphone.

[0024] After the terminal device 11 is started, the user inserts the diagnostic connector 12 into the vehicle's OBDII interface to establish a communication connection with the on-board electronic control unit (ECU) to obtain vehicle operating data (such as engine speed, coolant temperature, etc.) and fault code data (DTC). At the same time, the user deploys the electromagnetic metasurface sensor 13 directly under the vehicle chassis, and the array antennas of each electromagnetic metasurface sensor 13 are electrically connected through connecting cables to form a programmable scanning array.

[0025] The terminal device 11 establishes a communication connection (such as WiFi, Bluetooth, or wired connection) with the diagnostic connector and the electromagnetic metasurface sensor 13. Based on the acquired engine speed information, it dynamically controls the electromagnetic metasurface sensor 13 to switch to the corresponding scanning frequency band, enabling non-contact electromagnetic scanning of key chassis components. After completing the scan, the electromagnetic metasurface sensor 13 sends the scanned data to the terminal device 11. Subsequently, the terminal device 11 performs vehicle fault diagnosis based on the acquired vehicle operating condition data, fault code data, and scan data. The specific vehicle chassis diagnostic method is as follows.

[0026] The following describes the vehicle chassis diagnostic method using specific examples.

[0027] Figure 2 A schematic flowchart of a vehicle chassis diagnostic method according to an embodiment of this application is shown. Exemplarily, the vehicle chassis diagnostic method includes steps S210-S250: Step S210: Obtain vehicle operating condition data and fault code data through the diagnostic connector.

[0028] As an example, the terminal device obtains the vehicle's operating condition data and fault code information through a diagnostic connector connected to the vehicle's OBDII interface. Specifically, the terminal device calls a pre-built diagnostic software package matched to the vehicle model under test (e.g., "2023 Tesla Model 3 Diagnostic Package") and sends standardized diagnostic command frames to the diagnostic connector. The command format follows vehicle communication protocols such as ISO 14230 or ISO 15765. A typical frame structure can be composed of an ID field and a data field group (e.g., 07 DF (ID field) 02 0902... (data field)).

[0029] For example, by sending an identification command (such as Req:08 FB E0 02 09 02...), the terminal device can receive a response frame (such as Ans:08 FD 00 10 14 49...) returned by the vehicle's ECU and extract the Vehicle Identifier (VIN), such as VRXXXXXXXX. After obtaining the vehicle's VIN, the terminal device can automatically match the corresponding diagnostic software package and electromagnetic characteristic database, and can call the corresponding 3D CAD model of the vehicle model, thereby providing basic information for subsequent vehicle fault diagnosis.

[0030] In addition, the terminal device can also send function code 0C to obtain the real-time engine speed (accuracy ±10 rpm); and send function code 05 to obtain the coolant temperature (accuracy ±1℃), which can be used as the basis for subsequent electromagnetic scanning frequency band selection.

[0031] At the same time, the terminal device sends a DTC read command (function code 03) to obtain the currently stored fault code (such as P0301) and historical fault records, including the fault occurrence time and corresponding mileage, for subsequent multi-source fusion analysis with electromagnetic scanning data.

[0032] Step S220: Based on the engine speed, control the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band to perform electromagnetic scanning on the vehicle chassis.

[0033] Engine speed has varying effects on the dynamic stress of a vehicle chassis; for example, the higher the engine speed, the greater the dynamic stress on the chassis. At different engine speeds, the mechanical fatigue state, crack propagation trend, and stress concentration areas of the vehicle chassis will differ. Therefore, this embodiment determines the electromagnetic scanning strategy of the electromagnetic metasurface sensor based on the engine speed, thereby enabling more targeted detection of structural anomalies under different stress states.

[0034] Specifically, when determining the electromagnetic scanning frequency band of the electromagnetic metasurface sensor, if the engine speed is within a first speed range, the electromagnetic metasurface sensor is controlled to scan in the first scanning frequency band to detect fatigue cracks in the chassis metal; if the engine speed is within a second speed range, the electromagnetic metasurface sensor is controlled to scan in the second scanning frequency band to detect defects in metallic and non-metallic components; if the engine speed is within a third speed range, the electromagnetic metasurface sensor is controlled to scan in the third scanning frequency band to detect abnormal electromagnetic parameters of the material; wherein, the first speed is less than the second speed, the second speed is less than the third speed; the electromagnetic wave frequency of the first scanning frequency band is less than the electromagnetic wave frequency of the second scanning frequency band, and the electromagnetic wave frequency of the second scanning frequency band is less than the electromagnetic wave frequency of the third scanning frequency band.

[0035] Typically, the first speed range is the idle speed range, which can be less than or equal to 800 rpm; the second speed range is the medium speed range, which can be greater than 800 rpm and less than or equal to 3000 rpm; and the third speed range is the high speed range, which can be greater than 3000 rpm.

[0036] Because the reflection, transmission, and absorption characteristics of electromagnetic waves when interacting with materials are closely related to frequency, electromagnetic waves in different frequency bands have different sensitivities to material defects. The low-frequency band (first scanning band, 0.1-2 GHz) is sensitive to structural defects. At this frequency, the electromagnetic waves have longer wavelengths and greater penetration depth, making it suitable for detecting macroscopic defects in metal structures (such as cracks and holes). It also has a good response to metal fatigue caused by low-frequency vibrations, making it more suitable for detecting fatigue cracks in chassis metals at idle speeds. The mid-frequency band (second scanning band, 2-6 GHz) can be used to detect minute defects in metal parts and also has some response to anomalies in non-metallic structures such as composite materials, plastics, and battery casings. At medium speeds, it can take into account both structural and material state changes, serving as a transitional scanning band that can be used to detect defects in both metallic and non-metallic parts. The short wavelength in the high-frequency band (third scanning band, 6-10GHz) is very sensitive to changes in the dielectric constant of materials, making it suitable for detecting problems such as electrolyte leakage, abnormal battery electrolyte state, and aging of insulating materials. At high speeds, vehicles are in a dynamic state, and high-frequency scanning helps to capture dielectric anomalies under dynamic conditions, so it can be used to detect abnormal electromagnetic parameters of materials.

[0037] In this embodiment, by linking the scanning frequency band with engine speed, the system can dynamically select the optimal scanning strategy, avoiding the limitation of a fixed frequency band being unable to cover different defect types, thereby improving the targeting and accuracy of detection. Furthermore, engine speed, as a key parameter easily obtained from the CAN bus, is closely related to the vehicle's operating state, providing a basis for time synchronization and state matching for the fusion analysis of electromagnetic scanning data and on-board electronic data, thus further improving the diagnostic accuracy of the diagnostic system.

[0038] Step S230: Receive electromagnetic scanning data sent by the electromagnetic metasurface sensor and locate abnormal areas based on the electromagnetic scanning data.

[0039] In this step, the terminal device receives electromagnetic scanning data returned by the electromagnetic metasurface sensor and identifies abnormal areas in the vehicle chassis based on this data. This process includes signal processing of the electromagnetic scanning data to obtain electromagnetic characteristic parameters, and comparing and analyzing these parameters with a pre-set electromagnetic characteristic database to achieve precise location of vehicle structural anomalies.

[0040] In some implementations, such as Figure 3As shown, locating abnormal areas based on electromagnetic scanning data includes steps S310-S320: Step S310: Perform Fourier transform processing on the I / Q sampled data in the electromagnetic scanning data, and calculate the electromagnetic characteristic parameters based on the transform results.

[0041] Among them, electromagnetic characteristic parameters include reflection coefficient, dielectric constant and conductivity.

[0042] As an example, after receiving electromagnetic scan data from the electromagnetic metasurface sensor, the end device first processes the I / Q sampled data therein. This electromagnetic scan data is typically represented in complex form, containing in-phase (I) and quadrature (Q) components, with multiple sampling points (e.g., 1024 sampling points) at each frequency point.

[0043] The terminal device performs a Fast Fourier Transform (FFT) on the I / Q data, converting the time-domain signal into a frequency-domain signal to extract the electromagnetic response characteristics at each frequency point. Based on the transformed frequency-domain data, the terminal device further calculates electromagnetic parameters reflecting material properties, including reflection coefficient, dielectric constant, and conductivity. These parameters can reflect changes in the physical state inside the material, such as cracks, corrosion, and electrolyte leakage.

[0044] The reflection coefficient can be calculated by extracting the frequency domain signal using a fast Fourier transform. It characterizes the intensity of electromagnetic wave reflection on the material surface and can be used to determine whether there are defects on or near the chassis surface. The dielectric constant and conductivity can be estimated based on an S-parameter inversion algorithm combined with an electromagnetic propagation model. The dielectric constant reflects the material's response to an electric field and is sensitive to defects such as electrolyte leakage and insulation aging. Conductivity reflects the material's electrical conductivity and is suitable for detecting defects such as metal fatigue and cracks that reduce conductivity.

[0045] Step S320: Compare the electromagnetic characteristic parameters with a preset electromagnetic characteristic database to determine abnormal areas.

[0046] As an example, after extracting the electromagnetic characteristic parameters, the terminal device compares the electromagnetic characteristic parameter values ​​at each scanning position with a preset electromagnetic characteristic database. This database stores the range of electromagnetic characteristic parameters (such as reflection coefficient range, dielectric constant distribution, conductivity reference value, etc.) for different vehicle models and components under normal conditions, and can automatically match the parameter model of the corresponding vehicle model based on the VIN code.

[0047] If the electromagnetic characteristic parameters of a certain area differ significantly from the standard values ​​of the corresponding location in the database (e.g., a sudden increase of 20dB in reflection coefficient, a decrease of 15% in conductivity, etc.), then the area is determined to be an abnormal area. For example, if the reflection coefficient of a certain chassis part increases abnormally at a frequency of 2GHz, accompanied by a decrease in conductivity, the system can initially identify it as a suspected crack defect.

[0048] Step S240: Match the electromagnetic characteristics of the abnormal area with the preset defect information template to determine the defect information.

[0049] In this step, the terminal device further extracts the electromagnetic response characteristics (including the peak reflection coefficient and resonant frequency offset at each scanning frequency point) of the abnormal area identified in step S300 above, and performs pattern matching with a preset defect information data table to identify the defect type, size, shape, and location information corresponding to the abnormal area. It can be understood that this process mainly includes two stages: feature extraction and template matching, aiming to achieve accurate identification and classification of defects in the vehicle chassis structure.

[0050] Exemplary, such as Figure 4 As shown, step S240 includes sub-steps S410-S420: Step S410: Extract the peak reflection coefficient and resonant frequency offset of the abnormal region at each scanning frequency point.

[0051] As an example, after completing the electromagnetic scan and identifying the abnormal region, the terminal device performs an in-depth analysis of the electromagnetic response characteristics of the abnormal region at multiple scanning frequency points. These electromagnetic response characteristics include, but are not limited to, the peak reflection coefficient and the resonant frequency offset. In this embodiment, the defect information is primarily determined through the peak reflection coefficient and the resonant frequency offset.

[0052] The peak reflection coefficient refers to the maximum reflection coefficient of an anomalous region selected among multiple scanning frequency points. This value reflects the material's ability to reflect electromagnetic waves of that frequency and is closely related to the material's surface integrity and conductivity. The resonant frequency shift is the change in resonant frequency identified by comparing the current scan result with the standard electromagnetic response curve of that region in a preset database. This resonant frequency shift is an important indicator parameter of changes in the material's internal structure (such as cracks, voids, and stress concentration).

[0053] For example, in an abnormal region, if the system detects that the reflection coefficient at the 2GHz frequency point is 20dB higher than the normal value, and the resonant frequency shifts to a lower frequency by 5%, it can be preliminarily determined that there is an obvious structural defect in the region.

[0054] Step S420: Perform pattern matching between the peak reflection coefficient and the resonant frequency offset and a preset defect information data table to obtain defect information.

[0055] The defect information includes the size, shape, and location of the defect.

[0056] Exemplary, after extracting the aforementioned electromagnetic features, the terminal device performs pattern matching with a preset defect information data table to identify the specific defect type. This defect information data table stores feature parameter templates for various typical defects (such as cracks, holes, corrosion, electrolyte leakage, etc.) at different frequency bands. It includes not only the range of reflection coefficient variations and resonant frequency offsets, but also electromagnetic parameter distribution patterns, the physical size, shape, and typical location information of the defect. By constructing a multi-dimensional feature space, this embodiment can compare the extracted electromagnetic features with the data table using a lookup table method to obtain the best-matching defect type and its feature information.

[0057] For example, if the reflection coefficient of a certain area suddenly increases by 20dB at the 2GHz frequency and the resonant frequency offset is -5%, the system compares it with the defect information data table and matches the "0.1mm crack" template. Then, it is determined that there is a suspected crack defect in the area, and the system outputs the size (0.1mm), shape (linear), and location information (a certain coordinate of the chassis) of the defect.

[0058] Step S250: Based on vehicle operating condition data, fault code data, and defect information, determine the fault information of the vehicle chassis.

[0059] This step mainly involves constructing a multi-source fusion feature vector based on the vehicle's operating condition data, fault code data, and defect information identified through electromagnetic scanning. This vector is then input into a pre-trained diagnostic model, ultimately outputting the vehicle's fault type, risk level, and repair recommendations, thereby achieving vehicle diagnosis.

[0060] In some implementations, such as Figure 5 As shown, step S250 includes steps S510-S520: Step S510: Construct a fused feature vector based on vehicle operating condition data, fault code data, and defect information.

[0061] Exemplary, in this embodiment, a condition feature vector can be constructed based on the vehicle's real-time operating data (such as engine speed, coolant temperature, etc.) to describe the vehicle's current operating state. For example, engine speed fluctuation values ​​are used to determine whether the vehicle is in an abnormal vibration or unstable state; coolant temperature change rate is used to assess the impact of thermal stress on the material state, etc. For example, the condition feature vector can be represented as [X,Y], where X represents the engine speed fluctuation value and Y represents the coolant temperature change rate.

[0062] A fault feature vector is generated based on the fault code data obtained from the diagnostic connector. The fault feature vector is a binary feature vector or embedding vector that converts the fault code (such as P0301) into a fault code, indicating whether the vehicle currently has a historical high-frequency fault code. For example, the feature vector is [0, 0, 0, …] when there is no fault code, and [1, 0, 0, …] when there is a "cylinder 1 misfire" fault code.

[0063] A defect information vector is constructed based on defect information to describe the physical characteristics of structural anomalies, including: defect size (e.g., a 0.1mm crack), defect shape (e.g., "linear", "point-like", "area-like"), and defect location (e.g., "left front suspension link," which can be converted into coordinates or coded representation). For example, a defect information vector can be represented as [A, B, C], where A represents the defect size, B represents the defect shape, and C represents the defect location.

[0064] After obtaining the above three types of features, the defect information is concatenated or weighted with the working condition feature vector and the fault feature vector to construct a multi-dimensional fused feature vector.

[0065] Step S520: Input the fused feature vector into the pre-trained diagnostic model to output vehicle chassis fault information.

[0066] As an example, after constructing the fused feature vector, the terminal device inputs it into a pre-trained diagnostic model for fault identification and assessment. This diagnostic model can be implemented based on any one or more combinations of the following: machine learning models (such as random forests, support vector machines, K-nearest neighbors, etc.), deep learning models (such as fully connected neural networks, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), etc.), and rule-machine learning hybrid models (combining expert rules (such as association rules between fault codes and defect locations) with machine learning model outputs to improve the interpretability and accuracy of the diagnosis).

[0067] During the training phase, the diagnostic model can use a large amount of historical vehicle chassis diagnostic data, including but not limited to electromagnetic scan data and operating condition information of normal vehicles, vehicle data with known defect types (such as cracks, leaks, and corrosion), and corresponding maintenance records and risk assessment labels.

[0068] After training, the model can classify and regress the input fused feature vectors, outputting vehicle chassis fault information. This fault information includes defect type (such as metal fatigue cracks, electrolyte leakage, weld breakage, etc.), risk level (divided into low, medium, and high risk levels; for example, a crack depth ≥0.5mm located on a critical load-bearing component is marked as high risk), and repair recommendations (outputting repair strategies based on the diagnostic results, such as "recommend replacing the left front suspension link" or "recommend further checking the battery pack seal," etc.).

[0069] It should be noted that before obtaining vehicle chassis fault information through the pre-trained diagnostic model, time alignment processing needs to be performed on the acquired vehicle operating condition data, fault codes, and scan data collected by the electromagnetic metasurface sensor to ensure the consistency of multi-source data in the time dimension, thereby improving the accuracy and reliability of the diagnostic model.

[0070] Specifically, the terminal device adds a uniform timestamp (1ms accuracy) to each type of data, which is generated synchronously by the system clock. For example, CAN bus data (such as engine speed and coolant temperature) is collected every 100ms; timestamps are recorded synchronously when electromagnetic scan data at each frequency point is collected; and timestamps are also recorded when fault code data is read.

[0071] When performing time alignment, timestamp-based data matching can be used. This involves matching data from different sources according to their timestamps, ensuring that the feature vectors input to the diagnostic model correspond to the vehicle's state at the same moment. This time alignment method ensures that the engine speed, coolant temperature, fault codes, and electromagnetic scan data in the fused feature vectors input to the diagnostic model all come from the same moment or a similar time window, thus avoiding misjudgments caused by timing misalignments.

[0072] In some implementations, after outputting vehicle chassis fault information, the method further includes: determining the probability of vehicle fault based on confidence information.

[0073] As an example, after outputting vehicle chassis fault information, the terminal device further quantifies the reliability of the diagnostic results based on multi-dimensional confidence information, thereby determining the probability of vehicle failure.

[0074] The confidence information includes the confidence level of vehicle operating data, the confidence level of fault code data, and the confidence level of defect information.

[0075] Vehicle operating condition data confidence level is used to assess the stability and reliability of collected data such as engine speed and coolant temperature. The confidence level is determined by comparing engine speed fluctuations with a first threshold. If the fluctuation is less than the first threshold (e.g., ±50 rpm), it indicates stable engine operation and high data confidence; otherwise, the confidence level decreases. Similarly, the coolant temperature change rate is compared with a second threshold. If the change rate is less than the second threshold (e.g., ±1℃ / s), it indicates stable thermal conditions and high data confidence; otherwise, it is considered abnormal fluctuation, and the confidence level is lowered. Ultimately, the vehicle operating condition data confidence level can be derived using a weighted average or logical judgment method based on the matching degree of the above two parameters.

[0076] Fault code data confidence is used to assess how well the fault code information supports the current diagnostic results. The confidence level is determined based on the presence of historically high-frequency fault codes. Specifically, if the current fault code is a historically high-frequency fault code (e.g., its frequency in the last 100 diagnoses exceeds a set threshold), then the fault code is considered to have high reference value and a high confidence level; if the current fault code is an intermittent fault code or appears for the first time, the confidence level is low; if there is no fault code or the fault code is invalid (e.g., verification failed), the fault code data confidence level is the lowest value (e.g., 0).

[0077] The defect information confidence level is used to evaluate the degree of match between defect information identified by electromagnetic scanning and a preset defect information data table. The defect information confidence level can be determined based on the matching degree between the peak reflection coefficient and the resonant frequency offset and the preset defect information data table. Specifically, the identified peak reflection coefficient is compared with typical values ​​for this type of defect in the database; the resonant frequency offset is matched with the offset range in the database; if both are within the preset range, the defect information confidence level is high; if there is a large deviation, the confidence level is appropriately reduced.

[0078] After obtaining the above three types of confidence levels, the terminal device performs weighted fusion to calculate the probability of vehicle malfunction. For example: In the formula, F represents the probability of vehicle malfunction. , and These are the weighted coefficients for the confidence levels of vehicle operating condition data, fault code data, and defect information, which can be obtained through training based on historical data or set based on experience. , and These are the confidence levels of vehicle operating condition data, fault code data, and defect information, respectively.

[0079] After determining the probability of vehicle failure, the risk level can be adjusted based on the probability of vehicle failure. For example, if the probability of failure is high and the confidence level is high, the initial risk level is maintained or increased; if the probability of failure is low or the confidence level is low, the risk level is reduced or marked as pending confirmation; finally, the final risk level after fusing the confidence level is output as the final presentation of the diagnostic results.

[0080] In some implementations, such as Figure 6 As shown, the vehicle chassis diagnostic method also includes steps S610-S630: Step S610: Determine the two-dimensional coordinates of the abnormal region based on the two-dimensional array coordinates of the electromagnetic metasurface sensor, and transform the two-dimensional coordinates to the three-dimensional coordinates of the vehicle.

[0081] Exemplary, the electromagnetic metasurface sensor consists of a two-dimensional array of multiple antenna elements, each acquiring its corresponding electromagnetic scan data during scanning. The terminal device identifies anomalous sensor elements based on electromagnetic response intensity and spatial distribution characteristics, and determines the two-dimensional array coordinates (e.g., row and column numbers) of the anomalous area based on its array position. Subsequently, the system converts these two-dimensional array coordinates into three-dimensional spatial coordinates (X / Y / Z) of the vehicle chassis using a pre-defined coordinate mapping model. This mapping model is established based on the sensor deployment location, vehicle chassis geometry, and scanning angle, ensuring spatial positioning accuracy within ±2mm. For example, if the system identifies an anomalous area located in the 3rd row and 5th column of the electromagnetic metasurface array, and combines the sensor installation height with the vehicle chassis structural parameters, it converts this into three-dimensional coordinates (X=1200mm, Y=300mm, Z=50mm), indicating that the anomalous area is located near the left front suspension link of the chassis.

[0082] Step S620: Map the three-dimensional coordinates to the preset three-dimensional vehicle model.

[0083] As an example, after calculating the 3D coordinates of the abnormal area, the terminal device calls a preset 3D CAD model of the vehicle that matches the current vehicle's VIN code and maps the coordinates to the corresponding physical location in the model. This 3D model contains at least key structural information about the vehicle chassis, such as the suspension system, battery pack, crossbeams, and welding points. After locating the specific location of the abnormal area in the model, the system can further perform auxiliary diagnostic analysis by combining the structural attributes of that location (such as whether it is a load-bearing component or a fatigue-prone area). For example, in the 3D model of a certain SUV model, the system identifies an abnormal area located at the chassis crossbeam, a critical load-bearing structure, which can be used to improve the risk level assessment of the defect.

[0084] Step S630: Display the anomaly in the vehicle 3D model based on the vehicle chassis fault information, and trigger an alarm prompt when the preset alarm threshold is reached.

[0085] As an example, after the coordinate mapping is completed, the system visualizes the abnormal area in the 3D model. For example, different displays can be made based on the risk level. For instance, low-risk defects are highlighted in yellow; medium-risk defects are highlighted in flashing red; and high-risk defects are highlighted in continuous red with the text message "Immediate repair recommended".

[0086] Meanwhile, the system also features an alarm threshold mechanism, which can be dynamically adjusted based on the importance of the component. For example, for critical load-bearing components such as chassis beams, an alarm is triggered if the defect depth is ≥0.1mm; for decorative components, an alarm is triggered only if the defect depth is ≥1mm. When defects (cracks, holes, etc.) in abnormal areas reach or exceed this threshold, the system automatically triggers alarm prompts, such as screen pop-ups, buzzer alarms, and push notifications to the maintenance system, reminding users or maintenance personnel to handle the issue promptly.

[0087] Furthermore, after completing the vehicle chassis diagnostic process, the terminal equipment can also generate a structured diagnostic report based on the diagnostic results, which can be used to record the diagnostic process, output diagnostic conclusions, and provide a basis for subsequent maintenance, management, or filing.

[0088] Specifically, the diagnostic report includes, but is not limited to, basic vehicle information (including vehicle unique identifier (VIN), mileage, diagnosis time, model information, etc.), system status information (CAN bus data parsing results, such as engine speed, coolant temperature, fault code information, etc.), electromagnetic metasurface sensor detection results (abnormal area images, defect information (size, shape, location), electromagnetic parameters (reflection coefficient, resonant frequency shift), risk level, etc.), repair suggestion information, and confidence assessment results.

[0089] In addition, the diagnostic report may also include 3D model screenshots of abnormal areas, marking the abnormal locations in the vehicle's 3D model; diagnostic process logs (including data acquisition timestamps, scanning frequency bands, diagnostic model versions, etc.).

[0090] Diagnostic reports can be generated and stored locally on the terminal device, or uploaded to a cloud server or sent to the maintenance management system via a wireless communication module (such as WiFi, 4G / 5G), enabling electronic management and remote access to diagnostic data.

[0091] This embodiment achieves non-contact, high-precision detection of chassis structural defects by deploying an electromagnetic metasurface sensor in the vehicle chassis and dynamically adjusting the electromagnetic scanning frequency band in conjunction with engine speed information. The electromagnetic metasurface sensor switches to the corresponding scanning frequency band at different engine speeds, effectively identifying various defect types such as metal fatigue cracks and electrolyte leaks. By analyzing the electromagnetic scanning data and fusing it with vehicle operating condition information, this embodiment can accurately locate abnormal areas and identify defect information, thereby improving the accuracy and real-time performance of chassis structural anomaly detection.

[0092] Figure 7 A schematic diagram of a vehicle chassis diagnostic device according to an embodiment of this application is shown. Exemplarily, the vehicle chassis diagnostic device includes: The acquisition module 100 is used to acquire vehicle operating condition data and fault code data through the diagnostic connector; wherein, the vehicle operating condition data includes engine speed.

[0093] The control module 200 is used to control the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed, so that the electromagnetic metasurface sensor can perform electromagnetic scanning on the vehicle chassis.

[0094] The positioning module 300 is used to receive electromagnetic scanning data sent by the electromagnetic metasurface sensor and locate abnormal areas based on the electromagnetic scanning data.

[0095] The defect identification module 400 is used to match the electromagnetic characteristics of the abnormal area with a preset defect information template to determine the defect information.

[0096] The fault diagnosis module 500 is used to determine the fault information of the vehicle chassis based on vehicle operating condition data, fault code data and defect information.

[0097] It is understood that the device in this embodiment corresponds to the vehicle chassis diagnostic method in the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0098] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0100] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0101] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A vehicle chassis diagnostic method, characterized in that, The vehicle chassis is equipped with an electromagnetic metasurface sensor, and the method includes: Vehicle operating condition data and fault code data are obtained through a diagnostic connector; wherein, the vehicle operating condition data includes engine speed; Based on the engine speed, the electromagnetic metasurface sensor is controlled to switch to the corresponding electromagnetic scanning frequency band to perform electromagnetic scanning on the vehicle chassis; the electromagnetic metasurface sensor is composed of a two-dimensional array of multiple antenna units, and each unit can acquire corresponding electromagnetic scanning data during the scanning process; Receive electromagnetic scanning data sent by the electromagnetic metasurface sensor, and locate abnormal areas based on the electromagnetic scanning data; The electromagnetic characteristics of the abnormal region are matched with a preset defect information template to determine the defect information; Based on the vehicle operating condition data, fault code data, and defect information, the fault information of the vehicle chassis is determined; The step of controlling the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed includes: If the engine speed is within the first speed range, the electromagnetic metasurface sensor is controlled to scan in the first scanning frequency band to detect fatigue cracks in the chassis metal. If the engine speed is within the second speed range, the electromagnetic metasurface sensor is controlled to scan in the second scanning frequency band to detect defects in metallic and non-metallic components. If the engine speed is within the third speed range, the electromagnetic metasurface sensor is controlled to scan in the third scanning frequency band to detect abnormal electromagnetic parameters of the material. Wherein, the first rotational speed is less than the second rotational speed, and the second rotational speed is less than the third rotational speed; the electromagnetic wave frequency of the first scanning frequency band is less than the electromagnetic wave frequency of the second scanning frequency band, and the electromagnetic wave frequency of the second scanning frequency band is less than the electromagnetic wave frequency of the third frequency band.

2. The vehicle chassis diagnostic method according to claim 1, characterized in that, The method of locating abnormal areas based on the electromagnetic scanning data includes: The I / Q sampled data in the electromagnetic scanning data are subjected to Fourier transform processing, and electromagnetic characteristic parameters are calculated based on the transform results; the electromagnetic characteristic parameters include reflection coefficient, dielectric constant and conductivity; The electromagnetic characteristic parameters are compared with a preset electromagnetic characteristic database to determine the abnormal region.

3. The vehicle chassis diagnostic method according to claim 1, characterized in that, The step of matching the electromagnetic characteristics of the abnormal region with a preset defect information template to determine the defect information includes: Extract the peak reflection coefficient and resonant frequency offset of the abnormal region at each scanning frequency point; The peak reflection coefficient and the resonant frequency offset are pattern matched with a preset defect information data table to obtain the defect information; wherein, the defect information includes the size, shape and location information of the defect.

4. The vehicle chassis diagnostic method according to claim 3, characterized in that, The step of determining the fault information of the vehicle chassis based on the vehicle operating condition data, fault code data, and defect information includes: A fusion feature vector is constructed based on the vehicle operating condition data, fault code data, and defect information; The fused feature vector is input into a pre-trained diagnostic model to output vehicle chassis fault information; wherein, the vehicle chassis fault information includes defect type, risk level and repair recommendations.

5. The vehicle chassis diagnostic method according to claim 4, characterized in that, After outputting the vehicle chassis fault information, the following is also included: Determine the probability of vehicle malfunction based on confidence information; The confidence information includes the confidence level of vehicle operating condition data, the confidence level of fault code data, and the confidence level of defect information; The confidence level of the vehicle operating condition data is determined based on the matching degree between the engine speed fluctuation value and the first threshold, and the matching degree between the coolant temperature change rate and the second threshold. The confidence level of the fault code data is determined based on whether there are historical high-frequency fault codes. The confidence level of the defect information is determined based on the matching degree between the peak value of the reflection coefficient and the resonant frequency offset and the preset defect information data table.

6. The vehicle chassis diagnostic method according to claim 1, characterized in that, Also includes: The two-dimensional coordinates of the abnormal region are determined based on the two-dimensional array coordinates of the electromagnetic metasurface sensor, and the two-dimensional coordinates are transformed into the three-dimensional coordinates of the vehicle. The three-dimensional coordinates are mapped onto a preset three-dimensional vehicle model; Based on the fault information of the vehicle chassis, the abnormality is displayed in the vehicle's three-dimensional model, and an alarm prompt is triggered when the preset alarm threshold is reached.

7. A vehicle chassis diagnostic device, characterized in that, include: An electromagnetic metasurface sensor is deployed on the vehicle chassis, the device comprising: The acquisition module is used to acquire vehicle operating condition data and fault code data through a diagnostic connector; wherein, the vehicle operating condition data includes engine speed; The control module is used to control the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed, so that the electromagnetic metasurface sensor can perform electromagnetic scanning on the vehicle chassis; the electromagnetic metasurface sensor is composed of a two-dimensional array of multiple antenna units, and each unit can acquire corresponding electromagnetic scanning data during the scanning process; The positioning module is used to receive electromagnetic scanning data sent by the electromagnetic metasurface sensor and locate abnormal areas based on the electromagnetic scanning data. The defect identification module is used to match the electromagnetic characteristics of the abnormal area with a preset defect information template to determine the defect information; The fault diagnosis module is used to determine the fault information of the vehicle chassis based on the vehicle operating condition data, fault code data and the defect information. The step of controlling the electromagnetic metasurface sensor to switch to the corresponding electromagnetic scanning frequency band based on the engine speed includes: If the engine speed is within the first speed range, the electromagnetic metasurface sensor is controlled to scan in the first scanning frequency band to detect fatigue cracks in the chassis metal. If the engine speed is within the second speed range, the electromagnetic metasurface sensor is controlled to scan in the second scanning frequency band to detect defects in metallic and non-metallic components. If the engine speed is within the third speed range, the electromagnetic metasurface sensor is controlled to scan in the third scanning frequency band to detect abnormal electromagnetic parameters of the material. Wherein, the first rotational speed is less than the second rotational speed, and the second rotational speed is less than the third rotational speed; the electromagnetic wave frequency of the first scanning frequency band is less than the electromagnetic wave frequency of the second scanning frequency band, and the electromagnetic wave frequency of the second scanning frequency band is less than the electromagnetic wave frequency of the third frequency band.

8. A vehicle chassis diagnostic system, characterized in that, This includes terminal equipment, diagnostic connectors, and electromagnetic metasurface sensors for deployment on vehicle chassis; The diagnostic connector is used to acquire vehicle operating condition data and fault code data, and send them to the terminal device; The electromagnetic metasurface sensor is used to perform electromagnetic scanning on the vehicle chassis according to the scanning command sent by the terminal device, and to feed back the scanning data to the terminal device. The terminal device is used to execute the vehicle chassis diagnostic method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the vehicle chassis diagnostic method according to any one of claims 1-6.

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