Vehicle diagnosis method based on thermal imaging detection and related device
By performing anomaly analysis on the vehicle operating parameter set and combining it with thermal imaging detection, the problems of low vehicle diagnostic efficiency and large errors in existing technologies have been solved, enabling precise location and accurate diagnosis of vehicle faults.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-27
- Publication Date
- 2026-04-03
AI Technical Summary
In existing technologies, data stream diagnostic instruments based on OBD interfaces cannot accurately locate faults caused by hidden overheating of components, while independent thermal imagers require manual operation and cannot be correlated with data streams in real time, resulting in low vehicle diagnostic efficiency and large errors. In particular, they are prone to misjudgment of high-voltage systems in new energy vehicles due to information disconnection.
By acquiring vehicle operating parameter sets for anomaly analysis and combining them with thermal imaging detection, dual verification of parameter anomaly characteristics and component temperature anomaly characteristics is achieved, generating accurate diagnostic reports.
It enables precise identification of the root cause and type of vehicle faults, improves the accuracy and efficiency of vehicle diagnosis, and reduces misdiagnosis.
Smart Images
Figure CN121783578A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle diagnostic technology, and in particular to a vehicle diagnostic method and related apparatus based on thermal imaging detection. Background Technology
[0002] With the increasing electrification and electronicization of automobiles, the complexity and accuracy requirements for vehicle fault diagnosis are rising. Currently, vehicle diagnosis can be performed using data stream diagnostic tools based on the OBD interface or independent thermal imagers. However, the former can only read vehicle parameter data but cannot display component temperature distribution, making it difficult to accurately locate faults caused by hidden overheating. The latter, while capable of detecting temperature anomalies, requires manual operation and cannot be correlated with the data stream in real time, necessitating manual comparison and analysis, resulting in low efficiency and large errors. This is especially true for complex components such as high-voltage systems in new energy vehicles, where information gaps can easily lead to misdiagnosis.
[0003] Therefore, improving the accuracy of vehicle diagnostics is an urgent issue that needs to be addressed. Summary of the Invention
[0004] This application provides a vehicle diagnostic method and related device based on thermal imaging detection. By linking and matching the abnormal analysis results of the vehicle operating parameter set with the thermal imaging temperature set of the corresponding abnormal location, dual verification of parameter abnormality characteristics and component temperature abnormality characteristics is achieved. This can accurately locate the root cause and type of fault, thereby improving the accuracy of vehicle diagnosis.
[0005] In a first aspect, embodiments of this application provide a vehicle diagnostic method based on thermal imaging detection, the method comprising: Obtain the set of 'a' vehicle operating parameters for the target vehicle; where 'a' is an integer greater than 1. Anomaly analysis is performed on the set of a vehicle operating parameters to obtain analysis results; the analysis results include whether the parameters are normal or abnormal. If the analysis result indicates that the parameters are normal, then a first diagnostic report is generated based on the set of a vehicle operating parameters. If the analysis result indicates that the parameter is abnormal, then obtain b abnormal parameter sets from the a vehicle operation parameter sets, and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. Thermal imaging detection is performed on the b abnormally associated locations to obtain b thermal imaging temperature sets; Based on the b sets of abnormal parameters and the b sets of thermal imaging temperatures, determine b target diagnostic results; A second diagnostic report is generated based on the diagnostic results of the b targets.
[0006] Secondly, embodiments of this application provide a vehicle diagnostic device based on thermal imaging detection. The device includes an acquisition module, an analysis module, a first generation module, a first determination module, a thermal imaging detection module, a second determination module, and a second generation module, wherein: The acquisition module is used to acquire a set of a vehicle operating parameters for the target vehicle; where a is an integer greater than 1. The analysis module is used to perform anomaly analysis on the a sets of vehicle operating parameters and obtain analysis results; the analysis results include whether the parameters are normal or abnormal. The first generation module is used to generate a first diagnostic report based on the set of a vehicle operating parameters if the analysis result indicates that the parameters are normal. The first determining module is used to, if the analysis result indicates that the parameter is abnormal, obtain b abnormal parameter sets from the a vehicle operation parameter set and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. The thermal imaging detection module is used to perform thermal imaging detection on the b abnormal associated locations to obtain b thermal imaging temperature sets; The second determining module is used to determine b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets; The second generation module is used to generate a second diagnostic report based on the b target diagnostic results.
[0007] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.
[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.
[0009] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.
[0010] By implementing the embodiments of this application, the abnormal analysis results of the vehicle operating parameter set can be linked and matched with the thermal imaging temperature set of the corresponding abnormal location, realizing dual verification of parameter abnormality characteristics and component temperature abnormality characteristics. This can accurately locate the root cause and type of fault, thereby improving the accuracy of vehicle diagnosis. Attached Figure Description
[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a system architecture diagram of a vehicle diagnostic system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 3 This is an application scenario diagram of a vehicle diagnostic system provided in an embodiment of this application; Figure 4 This is a schematic flowchart of a vehicle diagnostic method based on thermal imaging detection provided in an embodiment of this application; Figure 5 This is a flowchart illustrating an anomaly analysis provided in an embodiment of this application; Figure 6 This is a schematic diagram of a process for obtaining a set of b abnormal parameters according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a second diagnostic report provided in an embodiment of this application; Figure 8 This is a block diagram of the functional modules of a vehicle diagnostic device based on thermal imaging detection provided in an embodiment of this application. Detailed Implementation
[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0014] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0015] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.
[0016] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.
[0017] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.
[0018] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0019] The following is an explanation of the relevant terms used in this application: On-Board Diagnostics (OBD) interface: refers to the standardized diagnostic interface / system on a vehicle used to monitor the operating status of the powertrain, emission system, etc. Its core function is to monitor vehicle faults in real time and store fault codes.
[0020] Electronic Control Unit (ECU): This refers to the core control module of various electronic systems in a vehicle. It can receive signals from vehicle sensors (such as engine speed sensors and temperature sensors) and, in combination with preset control programs, precisely control components such as the engine, transmission, and braking system.
[0021] Vehicle Identification Number (VIN): A globally unique code consisting of 17 letters and numbers, equivalent to a vehicle's "ID number," possessing the characteristics of uniqueness, permanence, and standardization.
[0022] With the increasing electrification and electronicization of automobiles, the complexity and accuracy requirements for vehicle fault diagnosis are rising. Currently, vehicle diagnosis can be performed using data stream diagnostic tools based on the OBD interface or independent thermal imagers. However, the former can only read vehicle parameter data but cannot display component temperature distribution, making it difficult to accurately locate faults caused by hidden overheating. The latter, while capable of detecting temperature anomalies, requires manual operation and cannot be correlated with the data stream in real time, necessitating manual comparison and analysis, resulting in low efficiency and large errors. This is especially true for complex components such as high-voltage systems in new energy vehicles, where information gaps can easily lead to misdiagnosis.
[0023] Therefore, improving the accuracy of vehicle diagnostics is an urgent issue that needs to be addressed.
[0024] To address the aforementioned issues, this application provides a vehicle diagnostic method and related apparatus based on thermal imaging detection. The method involves acquiring *a* sets of vehicle operating parameters for a target vehicle, where *a* is an integer greater than 1; performing anomaly analysis on the *a* sets of vehicle operating parameters to obtain analysis results, where the analysis results include whether the parameters are normal or abnormal; if the analysis result indicates that the parameters are normal, generating a first diagnostic report based on the *a* sets of vehicle operating parameters; if the analysis result indicates that the parameters are abnormal, acquiring *b* sets of abnormal parameters from the *a* sets of vehicle operating parameters and determining *b* abnormal associated locations corresponding to the *b* sets of abnormal parameters, where *b* is a positive integer less than or equal to *a*; performing thermal imaging detection on the *b* abnormal associated locations to obtain *b* sets of thermal imaging temperatures; determining *b* target diagnostic results based on the *b* sets of abnormal parameters and the *b* sets of thermal imaging temperatures; and generating a second diagnostic report based on the *b* target diagnostic results. By linking and matching the abnormal analysis results of the vehicle operating parameter set with the thermal imaging temperature set of the corresponding abnormal location, dual verification of parameter abnormality characteristics and component temperature abnormality characteristics is achieved, which can accurately locate the root cause and type of fault, thereby improving the accuracy of vehicle diagnosis.
[0025] For easier understanding, please refer to Figure 1 , Figure 1This is a system architecture diagram of a vehicle diagnostic system provided in an embodiment of this application. The vehicle diagnostic system includes a data acquisition unit, an anomaly analysis unit, a fault location unit, a thermal imaging detection unit, and a fault diagnosis unit.
[0026] The data acquisition unit can communicate with the target vehicle's ECU through the OBD interface to collect various vehicle operating parameters such as the target vehicle's power battery, drive motor, engine, and braking system in real time. These parameters are then categorized and integrated into a set of vehicle operating parameters. In addition, the data acquisition unit can also collect basic information such as the vehicle's VIN code, model, and operating conditions (driving / idling / charging).
[0027] The anomaly analysis unit can pre-store a parameter standard library corresponding to the target vehicle (containing a normal parameter ranges). It receives a set of a vehicle operating parameters transmitted by the data acquisition unit, calculates the average value of each set of vehicle operating parameters, and compares it with the corresponding normal parameter range to determine whether there are any vehicle operating parameter sets with abnormal parameters in the a set of vehicle operating parameters. If all a set of vehicle operating parameters are normal, the a set of vehicle operating parameters is output to the fault diagnosis unit to generate a first diagnostic report; if there are vehicle operating parameter sets with abnormal parameters in the a set of vehicle operating parameters, b abnormal parameter sets are further selected and output to the fault location unit.
[0028] The thermal imaging detection unit integrates an infrared thermal imaging module, which can receive abnormal associated locations from the fault location unit, perform non-contact thermal imaging scanning on each abnormal associated location, collect temperature data and generate a thermal imaging temperature set; simultaneously calculate the average temperature of each thermal imaging temperature set, combine it with the normal temperature range corresponding to the vehicle model, filter out the abnormal average temperature and the corresponding abnormal associated location, and feed the thermal imaging temperature set back to the fault diagnosis unit.
[0029] The fault diagnosis unit can integrate b sets of abnormal parameters from the anomaly analysis unit and b sets of thermal imaging temperatures from the thermal imaging detection unit to generate b corresponding target diagnostic results. Simultaneously, the fault diagnosis unit can classify the fault severity level and repair priority corresponding to the b target diagnostic results, match them with a pre-set repair plan database to generate corresponding repair suggestions, and finally integrate all information to generate a standardized second diagnostic report, completing the entire vehicle fault diagnosis process.
[0030] It is evident that by working together with the various functional units in the vehicle diagnostic system, intelligent vehicle fault diagnosis can be achieved throughout the entire process, from collecting vehicle operating parameters to screening for anomalies, locating faults, verifying them with thermal imaging, and generating diagnostic reports, thereby significantly improving the accuracy and practicality of the diagnosis.
[0031] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface, and one or more programs. The processor is connected to the memory and the communication interface via an internal communication bus.
[0032] The processor can be used for: Obtain the set of 'a' vehicle operating parameters for the target vehicle; where 'a' is an integer greater than 1. Anomaly analysis is performed on the set of a vehicle operating parameters to obtain analysis results; the analysis results include whether the parameters are normal or abnormal. If the analysis result indicates that the parameters are normal, then a first diagnostic report is generated based on the set of a vehicle operating parameters. If the analysis result indicates that the parameter is abnormal, then obtain b abnormal parameter sets from the a vehicle operation parameter sets, and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. Thermal imaging detection is performed on the b abnormally associated locations to obtain b thermal imaging temperature sets; Based on the b sets of abnormal parameters and the b sets of thermal imaging temperatures, determine b target diagnostic results; A second diagnostic report is generated based on the diagnostic results of the b targets.
[0033] The one or more programs are stored in the aforementioned memory and configured to be executed by the aforementioned processor, and the one or more programs include instructions for performing any step in the above method embodiments.
[0034] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.
[0035] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0036] It is understood that the electronic device may include more or fewer structural elements than those shown in the block diagram above, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is also understood that the electronic device may be a vehicle diagnostic device, and may also incorporate features such as… Figure 1 The system architecture described above.
[0037] For easier understanding, please refer to Figure 3 , Figure 3This is an application scenario diagram of a vehicle diagnostic system provided in an embodiment of this application. The target vehicle provides the vehicle diagnostic system with a set of vehicle operating parameters (i.e., operating data of various systems of the target vehicle, obtained by the data acquisition unit of the vehicle diagnostic system from the vehicle ECU via the OBD interface). The vehicle diagnostic system receives the a set of vehicle operating parameters from the target vehicle and performs analysis through internal anomaly analysis, fault location, thermal imaging detection, and fault diagnosis units to generate a corresponding first diagnostic report or second diagnostic report. If there are no parameter anomalies in the a set of vehicle operating parameters, the first diagnostic report is generated directly based on the a set of vehicle operating parameters. If there are parameter anomalies in the a set of vehicle operating parameters, b abnormal parameter sets are obtained from the a set of vehicle operating parameters, and b corresponding thermal imaging temperature sets are determined. Then, a second diagnostic report is generated based on the b abnormal parameter sets and the b thermal imaging temperature sets. Finally, the first or second diagnostic report output by the vehicle diagnostic system is sent to the target user.
[0038] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 4 This application describes a vehicle diagnostic method based on thermal imaging detection. Figure 4 This is a flowchart illustrating a vehicle diagnostic method based on thermal imaging detection provided in an embodiment of this application, specifically including the following steps: Step S401: Obtain a set of a vehicle operation parameters for the target vehicle.
[0039] Where 'a' is an integer greater than 1, the vehicle operating parameter set includes, but is not limited to: power battery parameter set, drive motor parameter set, engine parameter set, and braking system parameter set, without specific limitations. Multi-dimensional parameter data during the target vehicle's operation can be collected in real time through the vehicle's OBD interface or dedicated data acquisition equipment, and integrated into 'a' structured vehicle operating parameter sets according to system classification. Each parameter set is associated with the collection timestamp and the vehicle's current operating condition information.
[0040] Step S402: Perform anomaly analysis on the a sets of vehicle operating parameters to obtain the analysis results.
[0041] For easier understanding, please refer to Figure 5 , Figure 5 This is a flowchart illustrating an anomaly analysis provided in an embodiment of this application. The analysis result includes whether the parameters are normal or abnormal. The specific steps for performing anomaly analysis on the set of a vehicle operating parameters to obtain the analysis result include: A1. Obtain the parameter standard library corresponding to the target vehicle; the parameter standard library includes a normal ranges for parameters; A2. Calculate the average value of all parameters in each of the a vehicle operation parameter sets to obtain a parameter average values; each parameter average value corresponds to a normal range for a parameter. A3. If none of the a-values of the parameters exceeds the normal range of their corresponding parameters, then the analysis result is determined to be that the parameters are normal. A4. If any of the a-values exceeds the normal range of its corresponding parameter, then the analysis result is determined to be an abnormal parameter.
[0042] In a specific embodiment, firstly, a parameter standard library corresponding to the target vehicle is obtained. This parameter standard library includes a normal ranges of parameters that correspond one-to-one with a sets of vehicle operating parameters. Each normal range can be pre-calibrated based on the target vehicle's model, year, and power type (e.g., the normal voltage range for the power battery parameter set is 320V-360V, and the normal speed range for the drive motor parameter set is 0-12000rpm). Furthermore, each normal range is associated with a vehicle operating condition correction coefficient (e.g., the difference in parameter thresholds under idling, driving, and charging conditions).
[0043] Then, the average value of all parameters in each of the 'a' vehicle operating parameter sets is calculated to obtain 'a' parameter average values. Specifically, the arithmetic mean of continuously collected parameters within a single vehicle operating parameter set (e.g., 50 sets of power battery voltage data collected within 10 seconds) is calculated to obtain the parameter average value for that vehicle operating parameter set. Each parameter average value corresponds to the normal range of parameters in the same type of parameter set in the parameter standard library to ensure the matching of the average value with the normal range.
[0044] Next, determine if any of the a parameter averages exceeds their corresponding normal range. If none of the a parameter averages exceed their corresponding normal range, the analysis result is considered normal. Simultaneously, the deviation of each parameter average from its corresponding normal range can be recorded for parameter status description in the first diagnostic report. If any of the a parameter averages exceeds their corresponding normal range, the analysis result is considered abnormal. Then, count the number of parameter averages exceeding the normal range and the names of the corresponding vehicle operating parameter sets, as the basis for subsequently obtaining b abnormal parameter sets.
[0045] It is evident that by performing anomaly analysis on multiple vehicle operating parameter sets, we can achieve standardized and quantitative detection of vehicle operating parameters, quickly and accurately determine the overall abnormal state of the parameters, avoid misjudgments caused by fluctuations in single sample parameters, and improve the reliability and efficiency of anomaly analysis.
[0046] Step S403: If the analysis result indicates that the parameters are normal, then generate a first diagnostic report based on the set of a vehicle operating parameters.
[0047] Specifically, the first diagnostic report can record the target vehicle's VIN code, model, year, powertrain type, diagnostic time, diagnostic equipment number, and the data collection conditions (such as driving speed, load status, and ambient temperature) for a set of vehicle operating parameters, without specific limitations. Then, it lists the name of each set of vehicle operating parameters, the average value of the parameters, and the corresponding normal range, indicating that all average values are within the normal range, and calculates the deviation rate of each parameter set from the normal range. Based on the normal status of each set of vehicle operating parameters, it then determines the health level (e.g., "Excellent / Good") of the corresponding vehicle systems (such as the power battery system, drive motor system, and braking system) and provides routine maintenance recommendations (e.g., "Regularly check battery pack sealing" and "Replace brake fluid according to mileage").
[0048] Step S404: If the analysis result indicates that the parameter is abnormal, then obtain b abnormal parameter sets from the a vehicle operation parameter sets, and determine the b abnormal correlation positions corresponding to the b abnormal parameter sets.
[0049] Where b is a positive integer less than or equal to a.
[0050] For easier understanding, please refer to Figure 6 , Figure 6 This is a flowchart illustrating a method for obtaining b sets of abnormal parameters according to an embodiment of this application. The specific steps for obtaining the b sets of abnormal parameters from the a sets of vehicle operating parameters include: B1. Obtain the average value of the parameters that exceed the normal range of their corresponding parameters from the a average values, and obtain b abnormal average values; B2. Determine the set of b vehicle operating parameters corresponding to the b abnormal average values in the set of a vehicle operating parameters as the set of b abnormal parameters.
[0051] In a specific embodiment, firstly, the average value of the parameters that exceed the normal range of their corresponding parameters among the a average values are obtained, resulting in b abnormal average values. Simultaneously, the specific value of each abnormal average value, the name of the corresponding vehicle operating parameter set, and the magnitude of the deviation from the normal range can also be recorded as auxiliary information.
[0052] Then, based on the one-to-one correspondence between the abnormal average values and the vehicle operating parameter sets, b sets of vehicle operating parameters corresponding to b abnormal average values are selected from a sets of vehicle operating parameters and determined as b abnormal parameter sets.
[0053] It is evident that by accurately filtering out abnormal average values that exceed the normal range and matching them with the corresponding vehicle operating parameter sets, specific abnormal parameter sets can be quickly located, the source of parameter abnormalities can be clarified, and accurate basis can be provided for subsequent abnormal correlation location matching, avoiding interference from invalid data and improving the pertinence of diagnosis.
[0054] The specific steps for determining the b abnormal association positions corresponding to the b abnormal parameter sets include: C1. Obtain the vehicle model information of the target vehicle; C2. Determine the component location information based on the vehicle model information; C3. Obtain the abnormal parameter type corresponding to each of the b abnormal parameter sets to obtain b abnormal parameter types; C4. Based on the preset mapping relationship between abnormal parameter types and reference associated location sets, determine the first reference associated location set corresponding to the first abnormal parameter type; the first abnormal parameter type is any one of the b abnormal parameter types. C5. Filter the first reference associated position set according to the component position information to obtain the first reference associated position; C6. Determine the first reference association position as the abnormal association position corresponding to the first abnormal parameter type among the b abnormal association positions.
[0055] In a specific embodiment, firstly, communication can be established with the vehicle ECU through vehicle diagnostic equipment to automatically read the VIN code of the target vehicle and parse it to obtain vehicle information including brand, model, year, power type (gasoline / pure electric / hybrid), and core configuration. If communication fails, the information is supplemented by manual input. Then, based on the vehicle information, the standardized vehicle component database built into the vehicle diagnostic equipment is retrieved to match the specific component location information of the vehicle model, including the precise installation coordinates of core components of each system, their subsystem, physical connection relationship, and relative position to the whole vehicle, forming a structured component location list.
[0056] Next, feature analysis is performed on each of the b sets of abnormal parameters to extract the core vehicle system anomaly types (such as "abnormal power battery voltage", "abnormal drive motor winding temperature", and "abnormal engine fuel pressure"), ultimately obtaining b abnormal parameter types that correspond one-to-one with the b sets of abnormal parameters. Then, based on the preset mapping relationship between abnormal parameter types and reference associated location sets, the first reference associated location set corresponding to the first abnormal parameter type is determined. This first abnormal parameter type can be any one of the b abnormal parameter types. This mapping relationship is pre-built and stored in the vehicle diagnostic equipment, established based on historical vehicle fault cases and technical specifications, clearly defining the locations of all potential faulty components corresponding to each abnormal parameter type.
[0057] Then, each reference associated position in the first set of reference associated positions is compared one by one with the component position information. Positions of components not installed on the target vehicle and positions not directly related to the abnormal parameter type are eliminated. Only the component positions that actually exist on the target vehicle and are highly related to the first abnormal parameter type are retained and determined as the first reference associated positions. Finally, the first reference associated position is determined as the abnormal associated position corresponding to the first abnormal parameter type among the b abnormal associated positions. Steps C4 to C6 can be repeated to process the remaining b-1 abnormal parameter types sequentially, ultimately completing the matching of the b abnormal parameter sets with their corresponding abnormal associated positions, resulting in b abnormal associated positions.
[0058] As can be seen, by using multi-layered filtering to accurately match the unique abnormal associated location corresponding to the abnormal parameter type, the positioning deviation caused by differences in parts of different vehicle models can be avoided, thus achieving accurate and efficient locking of abnormal associated locations.
[0059] The first set of reference associated locations includes multiple reference associated locations, each corresponding to an association priority. The step of filtering the first set of reference associated locations based on the component location information to obtain the first reference associated location includes: D1. Obtain at least one reference associated position among the plurality of reference associated positions that matches the component position information; D2. Determine at least one association priority corresponding to the at least one reference association position; D3. Determine the highest priority among the at least one association priority as the first association priority; D4. Determine the reference association position corresponding to the first association priority among the at least one reference association position as the first reference association position.
[0060] In a specific embodiment, the first set of reference associated locations includes multiple reference associated locations, and each reference associated location corresponds to an associated priority. The associated priority is pre-defined based on the probability of fault occurrence, parameter influence weight, and maintenance experience; a higher priority indicates a higher degree of association. First, each reference associated location in the first set of reference associated locations is matched one by one with the actual assembled components and location information of the target vehicle recorded in the component location information, filtering out at least one reference associated location that actually exists in the target vehicle, forming a candidate associated location list. Then, a preset associated priority mapping table is retrieved, and the associated priority corresponding to each reference associated location (i.e., at least one reference associated location) in the candidate associated location list is matched to obtain at least one associated priority.
[0061] Next, the highest priority among at least one association priority is selected as the first association priority, and the reference association position corresponding to the first association priority among at least one reference association position is selected as the first reference association position.
[0062] It is evident that by matching component location information to filter effective reference associated locations and combining the associated priority to lock the first reference associated location with the highest degree of association, the core abnormal associated location can be accurately located from multiple potential abnormal associated locations, avoiding redundant detection and improving the accuracy and efficiency of fault location.
[0063] Step S405: Perform thermal imaging detection on the b abnormal associated locations to obtain b thermal imaging temperature sets.
[0064] Specifically, the vehicle diagnostic equipment can perform continuous thermal imaging scans on b anomaly-related locations, collecting temperature data (including maximum, minimum, and average temperatures, and temperature distribution coordinates) within a preset time window, and generating corresponding thermal images (marked with temperature color levels and anomaly areas). The vehicle diagnostic equipment is equipped with a thermal imaging integration module. After identifying the b anomaly-related locations, this module automatically calls preset detection parameters (such as focal length and temperature measurement range for different anomaly-related locations) and guides the thermal imaging lens to accurately align with the anomaly-related locations based on the vehicle component location database built into the diagnostic equipment. Simultaneously, it receives real-time vehicle operating information (such as idling / driving status and battery SOC value) and dynamically adjusts the temperature measurement sensitivity. Then, the temperature data measured at each anomaly-related location is integrated with the thermal image to form a thermal imaging temperature set corresponding to each anomaly-related location, resulting in b thermal imaging temperature sets.
[0065] Step S406: Determine b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets.
[0066] The specific steps for determining the b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets include: E1. Determine the b normal temperature ranges corresponding to the b abnormal associated locations based on the vehicle model information; E2. Calculate the average value of all temperatures in each of the b thermal imaging temperature sets to obtain b average temperature values. E3. Obtain the average temperature value that exceeds the normal temperature range of the b average temperatures to obtain c average temperatures; c is a natural number less than or equal to b. E4. Obtain the set of abnormal parameters corresponding to the c average temperatures in the set of b abnormal parameters, and obtain c abnormal parameter sets; E5. Obtain bc abnormal parameter sets from the b abnormal parameter sets excluding the c abnormal parameter sets; E6. Determine bc target diagnostic results based on the bc abnormal parameter sets; E7. Determine c target diagnostic results based on the c abnormal parameter sets and the c average temperature values.
[0067] In a specific embodiment, firstly, based on the vehicle model information, the component temperature standard library built into the vehicle diagnostic equipment is retrieved, and the normal operating temperature range corresponding to the component type at each abnormal location is matched (e.g., the normal temperature range of the power battery terminal is -20℃ to 60℃, and the normal temperature range of the drive motor bearing is 0℃ to 80℃), resulting in b normal temperature ranges corresponding one-to-one with b abnormal locations. Then, the average value of all temperatures in each of the b thermal imaging temperature sets is calculated to obtain b average temperature values, where each average temperature value corresponds to the normal temperature range of one abnormal location.
[0068] Next, each temperature average is compared with the normal temperature range of its corresponding anomaly-related location. Temperature averages exceeding the normal temperature range are filtered out, resulting in c temperature averages, where c is a natural number less than or equal to b. Then, the abnormal parameter sets corresponding to the c temperature averages from the b abnormal parameter sets are obtained, resulting in c abnormal parameter sets. From the b abnormal parameter sets, bc abnormal parameter sets (excluding the c abnormal parameter sets) are obtained. These bc abnormal parameter sets consist only of parameters with abnormalities, but whose corresponding anomaly-related locations have normal temperatures.
[0069] Then, for each of the bc abnormal parameter sets, based on its parameter abnormality type (e.g., high voltage, abnormal speed) and the corresponding system fault rule base, its fault type (e.g., "power battery management system sampling error," "drive motor sensor fault") is determined, forming bc target diagnostic results corresponding to the bc abnormal parameter sets. For each of the c abnormal parameter sets and its corresponding temperature average, the parameter abnormality characteristics and temperature abnormality characteristics are jointly analyzed (e.g., "power battery voltage abnormality + high terminal temperature"), matched with a preset "parameter-temperature" joint fault diagnosis model, to determine the fault type (e.g., "power battery terminal poor contact leading to voltage abnormality and high temperature"), forming c target diagnostic results corresponding to the c abnormal parameter sets and c temperature averages.
[0070] As can be seen, by matching the corresponding normal temperature range according to the target vehicle's model information, and combining the anomaly screening of the average temperature, different scenarios of parameter anomalies accompanied by temperature anomalies and parameter anomalies alone can be distinguished and diagnosed separately. This enables the linkage analysis of parameter and temperature data, accurately locates the root cause and type of fault, avoids the limitations of diagnosis based on a single data dimension, and improves the accuracy and comprehensiveness of the diagnostic results.
[0071] Step S407: Generate a second diagnostic report based on the b target diagnostic results.
[0072] The specific steps for generating the second diagnostic report based on the b target diagnostic results include: F1. Determine the b severity levels corresponding to the b target diagnostic results; F2. Determine b maintenance priorities based on the aforementioned b severity levels; F3. Determine b maintenance suggestions corresponding to the b target diagnostic results based on the preset maintenance plan database; F4. Generate the second diagnostic report based on the b target diagnostic results, the b maintenance priorities, and the b maintenance recommendations.
[0073] In a specific embodiment, firstly, based on a preset fault severity determination rule (which covers dimensions such as the impact of the fault on vehicle driving safety, damage to the lifespan of core components, and the risk of fault propagation), each of the b target diagnostic results is classified into b severity levels. These severity levels include high severity, medium severity, and low severity.
[0074] Then, the maintenance priorities are mapped from highest to lowest severity level to determine the maintenance priority for each of the b severity levels, resulting in b maintenance priorities. These maintenance priorities include high priority, medium priority, and low priority; the higher the severity level, the higher the maintenance priority.
[0075] Next, the repair solution database built into the vehicle diagnostic equipment is retrieved, and repair suggestions are matched for each of the b target diagnostic results to obtain b repair suggestions. These repair suggestions must clearly specify the troubleshooting steps, required tools / parts, safety guidelines, and estimated labor hours. This repair solution database includes standard repair procedures, parts replacement recommendations, and operational precautions for various types of fault diagnostic results.
[0076] Finally, a second diagnostic report is generated based on b target diagnostic results, b repair priorities, and b repair recommendations. This second diagnostic report includes a basic information section, a fault details section, a repair plan section (listing repair recommendations for each target diagnostic result), and a risk warning section (explaining the potential consequences of not repairing in a timely manner). The basic information section includes, but is not limited to, the vehicle's VIN code, diagnostic time, and number of faults. The fault details section lists each target diagnostic result, its corresponding severity level, and repair priority.
[0077] As can be seen, by classifying the severity levels of the target diagnostic results and matching them with maintenance priorities, and by generating targeted maintenance suggestions in conjunction with the maintenance solution database, a structured second diagnostic report is finally generated. This not only clearly presents the urgency and priority of the fault, but also provides clear guidance for maintenance operations, improves the efficiency and standardization of maintenance decisions, and allows target users to intuitively grasp the full picture of the fault and maintenance solutions.
[0078] For easier understanding, please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of a second diagnostic report provided in an embodiment of this application. The second diagnostic report includes a basic information section, a fault details section, a repair plan section, and a risk warning section. For example, the basic information section includes: "Vehicle VIN code, model, diagnosis time, current operating condition, etc."; the fault details section includes: "Poor contact of the high-voltage terminal of the battery pack", "Charging and discharging current fluctuation ±15A, voltage loss 0.8V", "Positive terminal temperature 68℃"; the repair plan section includes: "Grind the oxide layer of the terminal and re-tighten it"; the risk warning section includes: "This fault will cause a decrease in charging and discharging efficiency. Continued use may cause the terminal to overheat and the battery pack power supply to be interrupted. It is recommended to stop using it immediately and complete the repair operation as soon as possible."
[0079] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0080] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.
[0081] When dividing each function into modules according to its corresponding function. Figure 8 This is a functional block diagram of a vehicle diagnostic device based on thermal imaging detection provided in an embodiment of this application. The vehicle diagnostic device 800 based on thermal imaging detection includes an acquisition module 810, an analysis module 820, a first generation module 830, a first determination module 840, a thermal imaging detection module 850, a second determination module 860, and a second generation module 870, wherein: The acquisition module 810 is used to acquire a set of a vehicle operation parameters of the target vehicle; where a is an integer greater than 1. The analysis module 820 is used to perform anomaly analysis on the a sets of vehicle operating parameters and obtain analysis results; the analysis results include whether the parameters are normal or abnormal. The first generation module 830 is used to generate a first diagnostic report based on the a sets of vehicle operating parameters if the analysis result indicates that the parameters are normal. The first determining module 840 is used to, if the analysis result indicates that the parameter is abnormal, obtain b abnormal parameter sets from the a vehicle operation parameter set and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. The thermal imaging detection module 850 is used to perform thermal imaging detection on the b abnormal associated locations to obtain b thermal imaging temperature sets. The second determining module 860 is used to determine b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets; The second generation module 870 is used to generate a second diagnostic report based on the b target diagnostic results.
[0082] Optionally, in the process of performing anomaly analysis on the a sets of vehicle operating parameters and obtaining analysis results, the analysis module 820 is specifically used for: Obtain the parameter standard library corresponding to the target vehicle; the parameter standard library includes a normal ranges for parameters. Calculate the average value of all parameters in each of the a vehicle operation parameter sets to obtain a parameter average values; each parameter average value corresponds to a normal range for a parameter. If none of the a-values exceeds the normal range of its corresponding parameter, then the analysis result is determined to be that the parameter is normal. If any of the a-values exceeds the normal range of its corresponding parameter, then the analysis result is determined to be an abnormal parameter.
[0083] Optionally, in obtaining the set of b abnormal parameters from the set of a vehicle operating parameters, the first determining module 840 is specifically used for: Obtain the average value of the parameters that exceed the normal range of their corresponding parameters from the a average values, and obtain b abnormal average values; The set of b vehicle operating parameters corresponding to the b abnormal average values in the set of a vehicle operating parameters is defined as the set of b abnormal parameters.
[0084] Optionally, in determining the b abnormal correlation positions corresponding to the b abnormal parameter sets, the first determining module 840 is further specifically used for: Obtain the vehicle model information of the target vehicle; The component location information is determined based on the vehicle model information; Obtain the abnormal parameter type corresponding to each of the b abnormal parameter sets to obtain b abnormal parameter types; Based on the preset mapping relationship between abnormal parameter types and reference associated location sets, the first reference associated location set corresponding to the first abnormal parameter type is determined; the first abnormal parameter type is any one of the b abnormal parameter types. The first reference associated position set is filtered according to the component position information to obtain the first reference associated position; The first reference associated position is determined to be the abnormal associated position corresponding to the first abnormal parameter type among the b abnormal associated positions.
[0085] Optionally, the first set of reference associated locations includes multiple reference associated locations, each corresponding to an association priority. In the step of filtering the first set of reference associated locations based on the component location information to obtain the first reference associated location, the first determining module 840 is further specifically used for: Obtain at least one reference associated position from the plurality of reference associated positions that matches the component position information; Determine at least one association priority corresponding to the at least one reference association position; The highest priority among the at least one association priority is determined as the first association priority; The reference associated position corresponding to the first association priority among the at least one reference associated positions is determined as the first reference associated position.
[0086] Optionally, in determining the b target diagnostic results based on the b sets of abnormal parameters and the b sets of thermal imaging temperatures, the second determining module 860 is specifically used for: Based on the vehicle model information, determine the b normal temperature ranges corresponding to the b abnormal associated locations; Calculate the average value of all temperatures in each of the b thermal imaging temperature sets to obtain b average temperature values; Obtain the average temperature value that exceeds the normal range of its corresponding temperature from the b average temperature values to obtain c average temperature values; c is a natural number less than or equal to b. Obtain the set of abnormal parameters corresponding to the c average temperatures in the set of b abnormal parameters, and thus obtain the set of c abnormal parameters; Obtain bc abnormal parameter sets from the b abnormal parameter sets excluding the c abnormal parameter sets; Based on the bc sets of abnormal parameters, determine bc target diagnostic results; Based on the c sets of abnormal parameters and the c average temperatures, determine c target diagnostic results.
[0087] Optionally, in generating the second diagnostic report based on the b target diagnostic results, the second generation module 870 is specifically used for: Determine the severity levels corresponding to the b target diagnostic results; Based on the aforementioned b severity levels, determine b maintenance priorities; Based on a pre-set maintenance plan database, determine b maintenance recommendations corresponding to the b target diagnostic results; The second diagnostic report is generated based on the b target diagnostic results, the b maintenance priorities, and the b maintenance recommendations.
[0088] It is evident that by linking and matching the abnormal analysis results of the vehicle operating parameter set with the thermal imaging temperature set of the corresponding abnormal location, dual verification of parameter abnormality characteristics and component temperature abnormality characteristics is achieved, which can accurately locate the root cause and type of fault, thereby improving the accuracy of vehicle diagnosis.
[0089] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiments shown above. The vehicle diagnostic device 800 based on thermal imaging detection can be used to execute the method embodiments of this application, and will not be described again here.
[0090] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.
[0091] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.
[0092] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.
[0093] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0094] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.
[0095] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.
[0096] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).
[0097] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.
[0098] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.
Claims
1. A vehicle diagnostic method based on thermal imaging detection, characterized in that, The method includes: Obtain the set of 'a' vehicle operating parameters for the target vehicle; where 'a' is an integer greater than 1. Anomaly analysis is performed on the set of a vehicle operating parameters to obtain analysis results; the analysis results include whether the parameters are normal or abnormal. If the analysis result indicates that the parameters are normal, then a first diagnostic report is generated based on the set of a vehicle operating parameters. If the analysis result indicates that the parameter is abnormal, then obtain b abnormal parameter sets from the a vehicle operation parameter sets, and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. Thermal imaging detection is performed on the b abnormally associated locations to obtain b thermal imaging temperature sets; Based on the b sets of abnormal parameters and the b sets of thermal imaging temperatures, determine b target diagnostic results; A second diagnostic report is generated based on the diagnostic results of the b targets.
2. The method as described in claim 1, characterized in that, The anomaly analysis of the a sets of vehicle operating parameters yields the following results: Obtain the parameter standard library corresponding to the target vehicle; the parameter standard library includes a normal ranges for parameters. Calculate the average value of all parameters in each of the a vehicle operation parameter sets to obtain a parameter average values; each parameter average value corresponds to a normal range for a parameter. If none of the a-values exceeds the normal range of its corresponding parameter, then the analysis result is determined to be that the parameter is normal. If any of the a-values exceeds the normal range of its corresponding parameter, then the analysis result is determined to be an abnormal parameter.
3. The method as described in claim 2, characterized in that, The step of obtaining b sets of abnormal parameters from the set of a vehicle operating parameters includes: Obtain the average value of the parameters that exceed the normal range of their corresponding parameters from the a average values, and obtain b abnormal average values; The set of b vehicle operating parameters corresponding to the b abnormal average values in the set of a vehicle operating parameters is defined as the set of b abnormal parameters.
4. The method as described in claim 1, characterized in that, Determining the b abnormal association positions corresponding to the b abnormal parameter sets includes: Obtain the vehicle model information of the target vehicle; The component location information is determined based on the vehicle model information; Obtain the abnormal parameter type corresponding to each of the b abnormal parameter sets to obtain b abnormal parameter types; Based on the preset mapping relationship between abnormal parameter types and reference associated location sets, the first reference associated location set corresponding to the first abnormal parameter type is determined; the first abnormal parameter type is any one of the b abnormal parameter types. The first reference associated position set is filtered according to the component position information to obtain the first reference associated position; The first reference associated position is determined to be the abnormal associated position corresponding to the first abnormal parameter type among the b abnormal associated positions.
5. The method as described in claim 4, characterized in that, The first set of reference associated locations includes multiple reference associated locations, each corresponding to an association priority. The step of filtering the first set of reference associated locations based on the component location information to obtain the first reference associated location includes: Obtain at least one reference associated position from the plurality of reference associated positions that matches the component position information; Determine at least one association priority corresponding to the at least one reference association position; The highest priority among the at least one association priority is determined as the first association priority; The reference associated position corresponding to the first association priority among the at least one reference associated positions is determined as the first reference associated position.
6. The method as described in claim 5, characterized in that, The step of determining b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets includes: Based on the vehicle model information, determine the b normal temperature ranges corresponding to the b abnormal associated locations; Calculate the average value of all temperatures in each of the b thermal imaging temperature sets to obtain b average temperature values; Obtain the average temperature value that exceeds the normal range of its corresponding temperature from the b average temperature values to obtain c average temperature values; c is a natural number less than or equal to b. Obtain the set of abnormal parameters corresponding to the c average temperatures in the set of b abnormal parameters, and thus obtain the set of c abnormal parameters; Obtain bc abnormal parameter sets from the b abnormal parameter sets excluding the c abnormal parameter sets; Based on the bc sets of abnormal parameters, determine bc target diagnostic results; Based on the c sets of abnormal parameters and the c average temperatures, determine c target diagnostic results.
7. The method according to any one of claims 1-6, characterized in that, The process of generating a second diagnostic report based on the b target diagnostic results includes: Determine the severity levels corresponding to the b target diagnostic results; Based on the aforementioned b severity levels, determine b maintenance priorities; Based on a pre-set maintenance plan database, determine b maintenance recommendations corresponding to the b target diagnostic results; The second diagnostic report is generated based on the b target diagnostic results, the b maintenance priorities, and the b maintenance recommendations.
8. A vehicle diagnostic device based on thermal imaging detection, characterized in that, The device includes an acquisition module, an analysis module, a first generation module, a first determination module, a thermal imaging detection module, a second determination module, and a second generation module, wherein: The acquisition module is used to acquire a set of a vehicle operating parameters for the target vehicle; where a is an integer greater than 1. The analysis module is used to perform anomaly analysis on the a sets of vehicle operating parameters and obtain analysis results; the analysis results include whether the parameters are normal or abnormal. The first generation module is used to generate a first diagnostic report based on the set of a vehicle operating parameters if the analysis result indicates that the parameters are normal. The first determining module is used to, if the analysis result indicates that the parameter is abnormal, obtain b abnormal parameter sets from the a vehicle operation parameter set and determine b abnormal correlation positions corresponding to the b abnormal parameter sets; b is a positive integer less than or equal to a. The thermal imaging detection module is used to perform thermal imaging detection on the b abnormal associated locations to obtain b thermal imaging temperature sets; The second determining module is used to determine b target diagnostic results based on the b abnormal parameter sets and the b thermal imaging temperature sets; The second generation module is used to generate a second diagnostic report based on the b target diagnostic results.
9. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-7.