990 results about "Diagnostic equipment" patented technology

The invention discloses an OBD tamper-proof two-way verification method, which comprises the steps of obtaining OBD equipment data and vehicle sensor data, registering to a cloud end based on the OBD equipment data, obtaining registration data, obtaining key data and non-key data according to the vehicle sensor data, symmetrically encrypting the key data by using a session key in the registration data, and verifying the key data and the non-key data. Performing approximate homomorphic encryption on the non-key data, obtaining a double-entropy source dynamic salt value according to the time entropy of the ciphertext and the OBD equipment operation entropy, reversely reconstructing the dynamic salt value by the cloud according to the timestamp and the registration data, verifying the ciphertext structure by using the dynamic salt value and generating a random challenge containing a salt value check code, and sending the random challenge to the cloud; the equipment verification result and the challenge value are signed and then transmitted back, the OBD equipment carries out reverse verification on the salt value according to the signature and the equipment verification result, and the cloud generates an authority token based on the reverse verification result. According to the method, the data authenticity and integrity of the diagnostic equipment are guaranteed through packet encryption, dynamic salt value generation and bidirectional identity verification of transmission data.

A biosensor cartridge can include a circuit board having a connection terminal configured to be electrically connected to an external diagnostic device, a biosensor chip configured to detect a target material from an analysis specimen, and transmit an electrical signal to the connection terminal of the circuit board based on the target material being present in the analysis specimen, the biosensor chip including a reactant configured to react specifically with the target material, and a housing enclosing the circuit board and the biosensor chip, and the connecting terminal being exposed outside of the housing. Also, the biosensor chip includes a plurality of channel areas separated from each other, and the reactant is attached to the plurality of channel areas.

The invention discloses a knowledge distillation-based electrical equipment state transition diagnosis method and related equipment, relates to the technical field of deep learning, and solves the problem of low cross-equipment diagnosis accuracy. According to the method, the finite element model of the power equipment is constructed to generate the simulation fault data, so that the dependence on real fault data can be reduced, and the problem of fault sample data scarcity is solved; then training a teacher network based on the simulation fault data, training a student network based on the measurement fault data, and realizing knowledge migration from the teacher network to the student network through KL divergence, thereby improving the generalization ability of the student network; and the parameters of the student network are adjusted according to the real-time working condition parameters of the to-be-diagnosed equipment, so that the to-be-diagnosed equipment can be diagnosed more accurately through the obtained working condition self-adaptive diagnosis model. By adopting the embodiment of the invention, cross-equipment high-precision state transition diagnosis can be realized.

The invention relates to a power equipment output abnormal defect diagnosis method and device based on transfer learning, computer equipment, a storage medium and a computer program product. The method comprises the steps of collecting source domain data and target domain data, and constructing a source domain pre-training model; obtaining a target domain adaptation model; performing abnormity judgment by calling the historical maintenance record of the to-be-diagnosed equipment to obtain a final diagnosis result; and updating the source domain pre-training model and the target domain adaptation model. By adopting the method, the problem of target domain data scarcity in power equipment fault diagnosis can be effectively solved by combining transfer learning with adversarial training and semi-supervised fine tuning, cross-equipment high-precision anomaly detection and defect classification are realized, meanwhile, a diagnosis result is optimized by utilizing historical maintenance records, and continuous iterative optimization of a model is supported.

A diagnostic device is configured to capture diagnostic information about a subject (e.g., the subject's ears). The diagnostic device may include a diagnostic extension housing diagnostic elements (e.g., imaging chip, light sources, or lenses) and an ear canal engagement section coupled to the diagnostic extension. The ear canal engagement section may be shaped to position the diagnostic extension superiorly within the ear canal. For example, one side of the ear canal engagement section may be longer than another, which may enable the ear canal engagement section to contact the inferior wall of the ear canal for a longer distance than it contacts the superior wall. This configuration may allow more movement of the diagnostic extension when the device is inserted into the ear, which further improves the alignment of diagnostic elements with a desired area of the ear for diagnoses (e.g., the eardrum).

A diagnostic device, a battery manufacturing system, a battery pack, an electric vehicle, and a diagnostic method are provided. The diagnostic device according to the present invention is a diagnostic device for diagnosing the states of first to n-th active materials (n is a natural number of 2 or more) included in a battery electrode (the electrode is a positive electrode or a negative electrode), and includes: a profile acquisition unit configured to acquire a target electrode profile indicating a correspondence relationship between the capacity and voltage of the electrode; and a diagnostic unit configured to generate first to m-th simulation electrode profiles (m is a natural number of 2 or more) from predetermined first to n-th reference active material profiles, individually compare the first to m-th simulation electrode profiles with the target electrode profile, and diagnose the states of the first to n-th active materials based on the comparison results.

The invention relates to the technical field of fault detection, and discloses a big data-based machine room equipment fault diagnosis method and system, and the method comprises the steps: collecting load information data streams generated by all equipment in a machine room in real time, and carrying out the filtering, and obtaining a filtered information sequence; performing grouping processing on the filtered information sequence by using a K-means clustering algorithm to obtain a dynamic change mode group, calculating a key statistical index, and obtaining an abnormal feature set; if the abnormal feature set exceeds a preset variance threshold, classifying the abnormal feature set and determining a fault mode to obtain a fault mode category set; matching the fault mode category set with a preset fault mode database to obtain a fault mode matching result, and generating a corresponding risk assessment report to obtain a risk assessment report; and adjusting the operation parameters of the machine room equipment to obtain the equipment operation parameters. According to the method, equipment faults can be diagnosed in time.

A portable, multimodal dental diagnostic device for assessing the vitality of the dental pulp, consisting of: a detachable diagnostic component comprising a variety of probes, including the following: a heat probe configured to deliver controlled heat stimuli to a tooth surface and measure the heat response of the dental pulp; an electrical probe configured to deliver low-voltage electrical pulses to the tooth surface and measure the electrical impedance of the dental pulp; and an optical probe configured to assess blood oxygen saturation and microcirculation in the dental pulp using near-infrared light; a microcontroller unit configured to process sensor data from the thermal probe, the electrical probe and the optical probe, to calibrate measurements based on the oral base temperature and the ambient conditions, to aggregate multimodal sensor data and to control the operation of the thermal probe, the electrical probe and the optical probe; a cloud platform configured to receive and store aggregated sensor data, run artificial intelligence algorithms to analyze multimodal sensor data and generate diagnostic insights that include vital signs and diagnostic probabilities; a user application that is communicatively connected to the cloud platform and configured to wirelessly receive aggregated sensor data, display detailed diagnostic reports with vital signs and trends to the user, integrate with electronic health data systems, and issue alerts in case of abnormal readings; a wireless communication module connected to the microcontroller unit, configured to transmit the aggregated sensor data to the cloud platform; a display interface configured to show real-time feedback from the probes, including vitality assessments and sensor readings; and a main body containing the microcontroller unit, a rechargeable power supply and the display interface, wherein the rechargeable power supply can be charged via the current provided through a charging port on the main body, and wherein the main body is also equipped with on / off and start / stop switches; the device is configured to provide a non-invasive, quantitative real-time analysis of dental pulp health by combining physiological responses from thermal, electrical, and optical measurements.

The utility model relates to the technical field of in-vitro diagnosis automation equipment, and discloses a reaction container feeding device and diagnosis equipment, and the reaction container feeding device comprises a hopper, a partition plate, a driving assembly, a material pushing part and a feeding assembly; a feeding hole is formed in the top of the hopper, and an opening is formed in the bottom; the partition plate is fixedly arranged in the hopper and divides the opening into a first sub-opening and a second sub-opening; the driving assembly is arranged at the lower end of the hopper; the material pushing part and the feeding assembly are connected with the driving assembly, the driving assembly drives the material pushing part to move up and down and penetrate through the first sub-opening so as to jack up the reaction container, and the driving assembly drives the feeding assembly to move up and down and penetrate through the second sub-opening so as to receive the reaction container jacked up by the material pushing part. The driving assembly is adopted to drive the material pushing piece to jack the reaction container to the feeding assembly and drive the feeding assembly to move upwards to receive the reaction container, so that the feeding structure of the reaction container is simplified, the occupied space is small, and the reliability is high.

The invention provides an equipment fault diagnosis method and device based on an AI large model, equipment and a medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the vibration data of the equipment, and constructing a training data set; performing multi-scale adaptive normalization processing on the vibration data in the training data set to obtain normalized data; performing multi-scale frequency domain feature mapping on the normalized data to obtain multi-scale feature mapping output; based on a target training data set formed by multi-scale feature mapping output, training the deep neural network model to obtain a target deep neural network model; inputting the target vibration data of the to-be-diagnosed equipment into the target deep neural network model for fault diagnosis, and outputting a fault diagnosis result; and inputting the fault diagnosis result and pre-acquired equipment operation data into the AI large model, and generating an intelligent operation and maintenance suggestion. According to the method, the feature distinction degree is improved, the conditions of inter-layer scale drift and gradient instability are avoided, and the fault feature recognizability is improved.

The invention provides a vehicle fault after-sales service method and device, a storage medium and electronic equipment, and is applied to the technical field of after-sales services, and the method comprises the steps: obtaining a fault after-sales request sent by diagnosis equipment; when the fault after-sales request is a tire pressure monitoring system fault diagnosis request, vehicle diagnosis data of a vehicle is obtained through diagnosis equipment, and a target after-sales service person is determined in all after-sales service persons through a recommendation algorithm; and pushing the fault after-sales request and the vehicle diagnosis data to a target after-sales service worker, so that the after-sales service worker uses the vehicle diagnosis data to remotely assist a customer in performing tire pressure detection system fault diagnosis on the vehicle. The fault after-sales request is sent through the diagnosis equipment, the after-sales service system quickly responds to the fault after-sales request, the response time of after-sales service is shortened, vehicle diagnosis data are provided at the first time through the diagnosis equipment, accurate data are provided for after-sales service personnel, the diagnosis work difficulty of the after-sales service personnel is reduced, and the diagnosis accuracy is improved.

The invention relates to the technical field of intelligent automobile diagnosis and maintenance, and discloses an automobile diagnosis data processing method and device, computer equipment and a storage medium. The method comprises the following steps: firstly, carrying out frequency statistics on automobile fault codes in a historical record of diagnosis equipment, generating a to-be-processed fault code data set, inputting the to-be-processed fault code data set into a maintenance diagnosis model to generate a preliminary maintenance suggestion, and forming a preliminary maintenance suggestion data set; then, maintenance reference information is obtained according to fault codes in the data set, fusion preprocessing is carried out on the maintenance reference information and the initial maintenance suggestion data set, and a diagnosis fusion data set is generated; thirdly, performing quality correction on the fusion data set to obtain a corrected fusion diagnosis data set; and finally, integrating the three data sets according to a priority rule to generate a standard automobile diagnosis data set. According to the method, through multi-stage data processing and optimization, the accuracy and reliability of automobile diagnosis data are remarkably improved, high-quality data support is provided for intelligent automobile diagnosis, and the maintenance efficiency and precision are improved.

The invention relates to a vehicle diagnosis system, method and device, which are used for solving the unsafe influence caused by using a long-term secret key to carry out vehicle diagnosis. The vehicle diagnosis system comprises a key management system and a to-be-diagnosed unit, the key management system receives a key authorization request sent by a diagnosis device, generates a temporary key according to the key authorization request and sends a key authorization response to the diagnosis device, the temporary key is carried in the key authorization response, and the to-be-diagnosed unit sends the temporary key to the diagnosis device. And the key management system configures the temporary key to the to-be-diagnosed unit, so that the diagnosis device and the to-be-diagnosed unit complete diagnosis based on the temporary key and obtain a diagnosis result. Wherein the temporary key is independent of the long-term key in the vehicle. The vehicle diagnosis is completed by configuring the temporary secret key, and the diagnosis equipment can be prevented from being in contact with the long-term secret key in the vehicle as much as possible, so that the privacy data of a vehicle owner can be protected, the possibility that illegal personnel control the operation of the vehicle by using the long-term secret key can be reduced, and the driving safety of the vehicle owner is improved.

The utility model relates to the technical field of in vitro diagnosis automation equipment, and discloses a feeding device cup arranging structure, a feeding device and diagnosis equipment, and the feeding device cup arranging structure comprises a containing channel, a moving assembly, a base and a driving part; a feeding hole is formed in the top of the accommodating channel; the moving assembly is arranged in the containing channel, can move in the vertical direction of the containing channel and is used for bearing the reaction cup; the base is arranged at the lower end of the containing channel, the driving piece is arranged on the base, the moving assembly is connected with the driving piece through the connecting piece, and the driving piece is used for driving the moving assembly to move up and down along the containing channel. The moving assembly is arranged in the containing channel, the reaction cups collide with the inner wall of the containing channel in the falling process, under the action of gravity, the opening ends of the reaction cups are upwards arranged at the moving assembly, the cup clamping and cup transverse phenomena are avoided, and the reliability is high.

The invention is suitable for the technical field of vehicle diagnosis, and provides a vehicle diagnosis system and method, and the system comprises a processor which is connected with a plurality of electronic control units (ECUs) of a vehicle and a vehicle diagnosis device, and is used for analyzing a diagnosis configuration file in a preset folder under the condition that the vehicle diagnosis device establishes communication connection with the plurality of ECUs, and sending the diagnosis configuration file to the vehicle diagnosis device; and obtaining diagnosis configuration data of the plurality of ECUs. According to diagnosis business process parameters in the diagnosis configuration data, creating a corresponding software channel for each ECU, and packaging all configuration parameters of each software channel in corresponding straight-through structure examples; and managing straight-through structure instances of all software channels through a pointer array and a thread channel mapping table in the global management structure body. And the server is also used for receiving the global channel configuration parameters through the universal thread pool and matching a corresponding working thread for each software channel. According to the system, synchronous diagnosis of a plurality of ECU systems is realized by establishing a parallel diagnosis architecture, and the efficiency of vehicle diagnosis is improved.

The invention discloses a fault uploading system and method for a domain controller in the technical field of automobiles, electronic equipment and a medium. The system comprises a diagnostic event management module, a diagnostic event monitoring module and a communication module, the diagnosis event management module is used for acquiring a real-time fault list; the diagnosis event monitoring module is used for acquiring diagnosis fault codes in the fault list according to a set period, and storing the diagnosis fault codes into corresponding data sending cache regions respectively according to the codes of the sub-control units; and the communication module is used for calling a CANFD communication channel bound with the sub-control unit code, and sending the diagnosis fault code to a CANFD bus based on the set data structure body in the data sending cache region. By adopting the method, the delay and complex operation caused by the traditional method of obtaining the fault information by depending on external diagnosis equipment can be avoided, and the fault information of the plurality of sub-control units can be uploaded independently, sectionally and parallelly.

The present invention discloses a system and method for healthcare diagnostics using foundation models with uncertainty triage, designed to deliver reliable, explainable, and safety-assured diagnostic outcomes across multimodal clinical data. The invention integrates a foundation model processor pretrained on diverse medical datasets with an uncertainty estimation processor configured to quantify epistemic and aleatoric uncertainties in diagnostic predictions. A triage control unit dynamically classifies cases into high, medium, and low-confidence categories based on computed uncertainty indices, ensuring that only high-confidence cases are automatically finalized, while uncertain or ambiguous cases are routed for clinician review. The system further incorporates a feedback adaptation processor that recalibrates model parameters and uncertainty thresholds based on expert feedback, maintaining alignment with clinical reliability standards over time. Implemented as a hardware-integrated diagnostic device, the invention supports real-time inference, secure data handling, and interpretability visualization through uncertainty heatmaps and attention overlays.

The present invention discloses a function control method, comprising: obtaining regional information about the target diagnostic device; obtaining permission information for a target function corresponding to at least one vehicle model based on the regional information; and controlling the use permission of the target function based on the permission information. The present invention also discloses a function control server, device, and storage medium. The present invention enables the server to promptly control the use permission of the relevant functions of the vehicle diagnostic device based on changes in the business scenario, thereby avoiding non-compliance of the vehicle diagnostic device and improving the flexibility of function control.

The embodiment of the invention discloses a vehicle maintenance suggestion generation method based on AI Copilot and a related device. The method comprises the steps that a fault code and vehicle information of a target vehicle are acquired through diagnosis equipment; according to the fault code and the vehicle information, a first maintenance suggestions of the target vehicle are generated through an AI Copilot model; obtaining historical user feedback information of each first maintenance suggestion in the a first maintenance suggestions, and obtaining a pieces of historical user feedback information; b second maintenance suggestions are determined from the a first maintenance suggestions according to the a historical user feedback information; determining a display parameter of each second maintenance suggestion in the b second maintenance suggestions to obtain b display parameters; according to the b display parameters, the b second maintenance suggestions are displayed through a display screen of the diagnosis equipment. According to the embodiment of the invention, when the vehicle maintenance suggestion of the target vehicle is generated, the accuracy of the vehicle maintenance suggestion can be improved, so that the maintenance efficiency is improved.

The invention discloses a functional navigation path determination method, computer equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: receiving a target diagnostic software identifier, a target version number and a target function identifier sent by diagnostic equipment; querying a first navigation path from a preset navigation path set corresponding to the target diagnostic software identifier according to the target version number and the target function identifier; under the condition that the first navigation path is not queried, acquiring user operation data corresponding to the target function identifier from a user operation data set corresponding to the target diagnosis software identifier and the target version number; acquiring a second navigation path as a target navigation path according to user operation data corresponding to the target function identifier; and sending the target navigation path to the diagnostic equipment. The use convenience of the user can be improved, the user can be helped to quickly know how to enter the function interface of the target function, and the diagnosis experience of the user is increased. Therefore, the diagnosis efficiency and the user experience can be improved.

The invention discloses a diagnostic device comprising a housing defining an inner space; a tube holder positioned on a top surface of the housing and configured to receive a sample tube, comprising a sample, to be applied to a running buffer, therein; a puncturing element extending vertically from a bottom of the tube holder and located inside the tube holder; the puncturing element comprising at least one side opening configured to allow passage of all the sample and located at a base of the puncturing element; a collection pocket located within the inner space and configured to receive the at least one sample; a plurality of immunochromatographic strips having a proximal end and a distal end; the proximal end positioned within the collection pocket and the distal end positioned away from the collection pocket and within the inner space.

A diagnostic device (10) for guiding a doctor in performing diagnostic procedures on a patient's body comprises a main body (12) having a top surface (13) and a bottom surface (14), with one or more user output modules (22) positioned on the top surface (13) and a set of sensors comprising peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14). A processor (302) executes instructions stored in a memory (306) for receiving and processing preliminary inputs about the patient through a preliminary data acquisition module (307), determining a target body region and starting point, and performing scans to determine boundaries and examine specific organs. The device employs an artificial intelligence / machine learning (AI / ML) engine (310) to generate real-time audio, visual, and haptic instructions for guiding the operator, process multiple data streams, identify abnormalities, and provide diagnostic recommendations.

There are provided an ultrasound diagnostic apparatus and a control method of the ultrasound diagnostic apparatus which allows an examiner to easily understand a depth reached by an insertion object in a case of inserting the insertion object into a blood vessel while capturing an ultrasound image representing a cross section of the blood vessel of the subject.An ultrasound diagnostic apparatus includes an ultrasound probe; a monitor; an image acquisition unit that acquires ultrasound images of a plurality of frames in which a cross section of a blood vessel of a subject is consecutively imaged while the ultrasound probe is moved; an insertion object detection unit that detects an insertion object inserted into the subject, from each of the ultrasound images of the plurality of frames; and a superimposed display unit that superimposes at least one insertion object detected from the ultrasound images of the plurality of frames by the insertion object detection unit, on the ultrasound image of a latest frame among the ultrasound images of the plurality of frames, and displays the insertion object on the monitor.

The invention discloses a fence piece production line monitoring device and monitoring system, and relates to the technical field of production line monitoring. Comprising a data acquisition device, an edge computing device, a feedback control device, a whole-process quality tracing module and a cloud platform data analysis center. The data acquisition device acquires multi-station multi-mode real-time data, the edge computing device performs data fusion, preprocessing and preliminary abnormality diagnosis, the cloud platform develops deep analysis and model learning, the feedback control device generates an instruction to adjust production line parameters, and the whole-process quality tracing module realizes production whole-link data binding tracing. According to the method, accurate diagnosis of production abnormity, predictive maintenance of equipment, dynamic optimization of the process and closed-loop control of quality are realized, the production efficiency and the product quality are greatly improved, and the failure shutdown loss is reduced.

The invention belongs to the technical field of industrial operation and maintenance, and particularly relates to an industrial equipment intelligent operation and maintenance system based on multi-source information fusion, and the system comprises a multi-source information sensing module which is used for collecting physical quantity data and technological parameters of related equipment of a petrochemical engineering site in real time; the local data processing module is used for preprocessing the multi-source data acquired by the multi-source information sensing module and uploading the processed multi-source data; wherein the preprocessing operation comprises missing data completion, data normalization and abnormal data detection; the cloud server is provided with a data fusion module and a fault diagnosis module; the data fusion module is used for carrying out space-time synchronization on all data so as to associate data with consistent time features and / or space features, carrying out weighted summation on the associated data, and fusing to generate a high-dimensional feature vector; and the fault diagnosis module is used for diagnosing the fault type of the equipment according to the high-dimensional feature vector, and triggering a corresponding alarm strategy according to a diagnosis result and an anti-explosion rule of a petrochemical engineering site.

The invention belongs to the technical field of automation equipment management, and discloses an industrial internet automation equipment fault self-detection method comprising the following steps: S1, data acquisition; s2, data preprocessing; s3, feature extraction; s4, constructing a fault diagnosis model; s5, performing fault diagnosis; according to the method, key links of data acquisition, preprocessing, feature extraction, fault diagnosis model construction, fault diagnosis, model optimization and the like are integrated to form a set of complete automatic fault detection and diagnosis system, equipment operation state data are acquired, feature parameters capable of reflecting equipment faults are extracted after preprocessing, and the fault diagnosis model is optimized. According to the method, the fault diagnosis model is constructed, the occurrence time, the category, the position and the maintenance suggestion of the equipment fault can be accurately diagnosed, meanwhile, the diagnosis precision and efficiency are continuously improved through a continuous optimization mechanism, the operation stability and reliability of the automation equipment in the industrial internet environment are effectively improved, and the maintenance cost is reduced.

The invention discloses a pipetting amount testing method and system of in-vitro diagnostic equipment and a storage medium, and particularly relates to the technical field of in-vitro diagnostic equipment, and the pipetting amount testing method comprises the steps of S1, multi-modal sensing data acquisition and preprocessing, S2, data fusion processing, S3, adaptive calibration, and S4, dynamic monitoring and report generation. According to the invention, the flow velocity and volume change of the liquid can be accurately calculated, bubble interference is effectively eliminated, adaptive calibration is realized, real-time monitoring and abnormity positioning are realized, a calibration report is generated and safely stored and traced, meanwhile, the optimal system performance is guaranteed, and the accuracy, reliability, intelligent degree and data safety of the pipetting amount test of the in-vitro diagnostic equipment are improved.

The present invention relates to the technical field of motor diagnosis, and specifically to a motor diagnosis method and apparatus, a motor diagnosis device, and a motor diagnosis system. The motor diagnosis method is applied to the motor diagnosis device in the motor diagnosis system; the motor diagnosis system comprises a motor device, the motor diagnosis device, and an electronic device; the motor device is communicatively connected to the motor diagnosis device; the motor diagnosis device is communicatively connected to the electronic device. The method comprises: receiving operation data and an initial fault identifier that correspond to a motor device and are sent by the motor device; analyzing the initial fault identifier to determine target fault description information corresponding to the motor device; and transmitting the target fault description information and the operation data to an electronic device, so that the electronic device displays the target fault description information and the operation data. The present invention ensures the accuracy of the determined target fault description information corresponding to the motor device, and implements diagnosis of a motor. The present invention solves the problems of high cost and low efficiency of returning a motor device to the factory for maintenance, as well as inability to perform after-sales service.

The invention relates to the technical field of vehicle detection, and discloses a vehicle paint diagnosis method and device, diagnosis equipment and a readable storage medium, and the vehicle paint diagnosis method comprises the steps: carrying out the signal collection of a vehicle paint surface, obtaining original optical image information and original light intensity information, carrying out the feature extraction of the original optical image information and the original light intensity information, and carrying out the feature extraction of the original optical image information and the original light intensity information; the method comprises the steps of obtaining optical image feature information and light intensity feature information, inputting the optical image feature information into a convolutional neural network model to obtain vehicle paint surface defect information, comparing the light intensity feature information with a spectrum parameter database, obtaining vehicle paint surface coating thickness distribution information based on a frequency domain inversion algorithm, and obtaining operation information of a target vehicle. According to the vehicle paint diagnosis method, the vehicle paint state can be comprehensively evaluated, the structured vehicle paint diagnosis report is generated, and the accuracy, efficiency and intelligent level of vehicle paint diagnosis are improved.