52 results about "Functional abnormality" patented technology

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

The invention discloses a cross-platform software compatibility testing and optimizing method, belongs to the technical field of software testing, and aims to solve the problem of existing cross-platform software compatibility testing. Firstly, software is installed on different test platforms, and function, interface and performance tests are carried out; the function test simulates user operation, calculates an average response delay time difference and a success rate, and marks functional abnormality key points; dividing detection points in interface testing, analyzing color, definition and coordinate values, and marking interface abnormal key points; the performance test monitors the number of flash quit times and the completion duration, and marks performance abnormity key points; then, the abnormal key points are sent to a positioning display module, different mark circles and colors are set in a problem display model according to abnormal types and severity, and priority ranking is carried out; maintenance personnel can optimize the software according to the data in the problem display model, so that the root of the problem is quickly positioned, the development efficiency is improved, and the quality and stability of the software on different platforms are ensured.

The invention provides a heart valve regurgitation detection method and system and a medium. The method comprises the steps that regurgitation area identification caused by heart valve structure or function abnormity is obtained; performing principal component analysis on the regurgitation area, and determining the main extension direction of the regurgitation of the corresponding heart valve; determining at least one morphological characteristic parameter of the reflux region according to the main extension direction; and determining the regurgitation state information of the heart valve according to the morphological characteristic parameters. According to the method, the spatial distribution of a reflux region is subjected to characteristic decomposition through principal component analysis, a complex irregular blood flow form is converted into a main extension direction with definite physical significance, and form characteristic parameters are calculated based on the main extension direction; random errors caused by subjective view angle differences or insufficient experience when valve reflux parameters are manually measured can be eliminated, and valve reflux evaluation results obtained by different operators under different detection conditions have high consistency and repeatability.

The invention relates to the technical field of health monitoring, in particular to a thyroid dysfunction detection system based on big data, which comprises a hormone data management module, a trend inflection point identification module, a section structure judgment module, a label dynamic updating module and a periodic evolution evaluation module. According to the method, continuous modeling is carried out on hormone concentration dynamic characteristics through multi-time-point data time sequence arrangement in hormone detection records, local extreme points of hormone values are recognized, key fluctuation inflection point positions are defined, an abnormal fluctuation range is recognized, and a reference interval ratio judgment mechanism is introduced to improve the abnormal recognition precision; the dynamic tracking of the function state change process is realized by recording the label evolution trajectory through the continuous state label comparison and updating process, the identification capability of potential function abnormality is enhanced by utilizing the quantitative evaluation of the individual state fluctuation stability, the identification efficiency and the monitoring timeliness of the key intervention individual are improved, and the monitoring accuracy is improved. And the continuity of thyroid function state change identification and the accuracy of intervention target identification are improved.

The invention provides a mood disorder assessment system based on multi-level feature fusion, and the system comprises a data collection unit which is used for collecting electroencephalogram signals of a plurality of brain regions of a to-be-assessed patient; the electroencephalogram feature extraction unit is used for extracting electroencephalogram features corresponding to the electroencephalogram signals of the brain regions; the multi-level feature extraction unit is constructed on the basis of the symptom features of the multiple testees and the corresponding electroencephalogram features, and is used for performing multi-level electroencephalogram feature latent variable extraction on the electroencephalogram features of the brain regions of the patient to be evaluated to obtain multi-level electroencephalogram feature latent variables; and the feature fusion and classification unit is used for carrying out classification prediction based on the electroencephalogram feature latent variables to obtain a mood disorder assessment result of the patient to be assessed. The method solves the problem that a mood disorder assessment system in the prior art adopts a single feature extraction and learning strategy and has no constraint of symptom information, so that the recognition capability of a model for mood disorders of different functional abnormality types is limited.

The invention relates to the technical field of physiological parameter monitoring, in particular to a chronic wound healing real-time monitoring system which comprises a region division module, a cross trend recognition module, a linkage structure recognition module, a risk node positioning module and a response instruction module. According to the method, the mapping relation between the regional temperature extreme value and the acid-base change trend is constructed on the basis of continuous monitoring, accurate description of the dynamic state of the local microenvironment of the wound is achieved, the regional change sensitive section is captured through the cross combination mode of the trend direction pair, time sequence linkage recognition of inflammation development and tissue reaction is promoted, and the accuracy and accuracy of recognition are improved. A spatial correlation model is established according to a trend direction angle relation, functional abnormality blocks with significant trend departure are locked, the recognition granularity and response path classification capability of chronic wound abnormal development and intervention lag states is enhanced, the monitoring precision of the abnormal development stage is improved, and the pertinence of evaluation and the adaptability of intervention measures are recovered.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

The invention discloses an exception identification and calibration processing method and system based on a digestive tract endoscope image, and belongs to the technical field of exception identification and calibration processing. Extracting a focus area in the endoscopic image; endoscopic images and gastric acid value data are synchronously acquired in the same data acquisition period and are respectively recorded as periodic focus area and periodic gastric acid value data; calculating the change rate of the periodic focus area; calculating a fluctuation value of the periodic gastric acid value data; calculating comprehensive abnormal values in all data acquisition periods; calculating an abnormal difference value between different patients; presetting a threshold value, analyzing and performing alimentary canal abnormity identification calibration; according to the method, the change rate of the focus area and the fluctuation value of the gastric acid value are calculated, and comprehensive abnormal value analysis is combined, so that the structural and functional abnormalities of the digestive tract can be comprehensively evaluated, and a more accurate diagnosis basis is provided; by calculating the abnormal difference value between the patients, the group associated diseases can be effectively identified, automatic judgment and calibration are performed, and the accuracy and efficiency of diagnosis are improved.

The invention relates to the technical field of cognitive function assessment, and discloses a chronic pain patient early cognitive function abnormality assessment method and system, and the method comprises the steps: obtaining the MoCA scale score and electroencephalogram rsEEG signals of a target population; according to the MoCA scale score and the electroencephalogram rsEEG signal of the target population, obtaining MoCA score data and rsEEG data of chronic pain patients with abnormal cognition, chronic pain patients with normal cognition and healthy population, and constructing a chronic pain database; and comparing the basic information, the MoCA total score, the domain score and the brain region power spectrum ratio of the to-be-detected person with data of a chronic pain database to obtain an early cognitive function abnormality assessment result of the chronic pain patient. According to the method, MoCA scale scoring and the rsEEG technology are combined, quantitative objective markers for chronic pain cognitive dysfunction are determined, and doctors are effectively assisted in recognizing chronic pain patients with cognitive function impairment as early as possible and preventing and treating the chronic pain patients in time.

The invention belongs to the technical field of verification of an electric digital integrated module, and provides a verification method and system for a reusable storage module based on a UVM (Universal Verification Module), and the method comprises the steps: simulating a multi-host concurrent access scene through a UVM component, and generating and synchronously outputting an excitation signal to a to-be-verified storage module and a reference model; acquiring a reference operation result and an actual operation result generated by the to-be-tested storage module according to the excitation signal; according to the design function points of the to-be-verified storage module, the actual operation result and the reference operation result are compared and verified, and a verification result is obtained; if the verification result is that at least one design function point in the actual operation result and the reference operation result is inconsistent, it is judged that the function of the to-be-verified storage module is abnormal in the multi-host concurrent access scene, and key signal waveform data and key log data of the to-be-verified storage module are recorded and reported. According to the scheme, the comprehensiveness and the accuracy of verification are improved, and the function reliability and the verification efficiency of the storage module in a complex concurrent environment are improved.

The invention discloses a lung function evaluation method for a patient in a pneumonia rehabilitation period, and relates to the technical field of rehabilitation management. Extracting a lung function feature set based on the image data; analyzing based on multi-dimensional parameter association, and establishing a dynamic mapping relationship between the lung function feature set and associated physiological parameters; identifying abnormal associated parameters through a dynamic mapping relation between the lung function feature set and the associated physiological parameters, and when the deviation between the image feature recovery degree and the corresponding physiological parameter recovery degree exceeds a preset threshold value, judging that functional abnormal parameters exist; and matching a potential complication database based on the abnormal parameter type, analyzing the relevance between the abnormal parameter and a preset disease feature model, and outputting secondary disease risk early warning. The method has the advantages that through cross-modal fusion of the lung image features and the dynamic physiological parameters, the early recognition precision of the pneumonia convalescence complication is remarkably improved.

The invention relates to the technical field of medical image detection, in particular to a medical image analysis method and system based on image processing, and the method comprises the following steps: collecting a diffusion tensor image, carrying out the filtering preprocessing, normalizing the main diffusion direction, extracting an energy super-threshold voxel, calculating the node distance offset, and carrying out the correlation analysis to generate an atlas. And interpolating and aligning the brain return boundary to obtain a coupling graph, and outputting an abnormal probability distribution graph in a classified manner. According to the method, fiber details are ensured by adopting anisotropic diffusion filtering noise reduction of a diffusion tensor image sequence, three-dimensional normalization and wavelet energy analysis are coordinated, node detection specificity is enhanced by dynamic threshold screening, space-time coupling is quantified by combining displacement Euclidean distance and time sequence relevance, and an individualized coupling model is established by interpolating and fusing brain return boundaries; the template rigid constraint is broken through, the support vector machine recognizes an abnormal mode, structural distortion and functional abnormality cross-scale detection is achieved, and the early lesion image marker recognition efficiency is improved.

The invention discloses a multi-index fusion-based biotechnology product risk dynamic identification method and system, and relates to the technical field of biological safety management, and the method comprises the steps: S1, obtaining experimental data and environmental data from each stage of a transgenic crop life cycle, constructing a dynamic data set containing a molecular structure sequence, a function performance index and an ecological impact factor, and carrying out the construction of a dynamic data set; extracting the mutation frequency of the molecular structure sequence along with the time change, the abnormal fluctuation of the function expression index and the dynamic change trend of the ecological influence factor; s2, according to molecular structure sequence mutation frequency and functional performance index abnormal fluctuation, extracting a nucleotide replacement mode and a functional abnormality data set of the gene editing product in a research and development stage and an application stage, and determining a nucleotide replacement mode and a functional abnormality association mode; according to the biotechnology product risk dynamic identification method and system based on multi-index fusion, accurate prediction and dynamic management of ecological risks of transgenic crops are realized, and the environmental release safety is remarkably improved.

The invention discloses a neurology index prediction system and method. The method comprises the following steps: firstly, collecting case data of a neurology patient; processing the collected case data to form test data; meanwhile, constructing a neurology index prediction model; inputting test data into the neurology index prediction model for training; and finally, inputting the case data which does not participate in training into the trained neurology index prediction model, and generating a patient neurology disease prediction result from the case data which does not participate in training through the neurology index prediction model. The prediction comprehensiveness is improved through multi-modal data fusion, the detection sensitivity of early functional abnormalities is remarkably improved, the prediction model is more accurate and stable through high-dimensional nonlinear correlation mining and model generalization optimization, non-invasive low cost and clinical interpretability can be considered, invasive detection or high-cost image equipment is not needed, and the method is suitable for large-scale popularization and application. The method is suitable for large-scale screening of basic medical institutions.

Anomaly determination device and anomaly determination system. An anomaly determination device cooperates with a robot to determine whether an elevator is abnormal. The elevator includes an elevator control device that controls the operation of each part of the elevator, an appearance device that is provided in a car or a landing and includes at least one of an input unit and a display unit, and an appearance device control section that controls the operation of the appearance device based on first reception data. The robot has a function of detecting the operation state of the appearance device by a detection section and generating operation state data indicating the detected operation state, and a second function of acquiring first copy data through communication with the appearance device control section. The anomaly determination device includes a data collection section that collects the operation state data and the copy data, and a determination section that determines whether the appearance device is abnormal by judging the matching between the collected operation state data and the first copy data.

The invention provides a method for solving the abnormal condition of a network function NF. The method is performed by a network entity and comprises: receiving NF anomalous behavior / function analysis data from an operation, management and maintenance OAM; and performing an action based on the received NF abnormal behavior / function analysis data. This can improve the efficiency of handling NF abnormal behaviors / functions.

The invention discloses a vehicle hazard analysis and risk assessment method, device and equipment and a vehicle, and relates to the technical field of vehicles. The whole vehicle hazard analysis and risk assessment method comprises the following steps: obtaining function case information of a whole vehicle; performing security correlation analysis on the function case information to obtain function case security indication information; if the function case safety indication information indicates that the function case information is related to the safety of the whole vehicle, associating the function case information with a failure mode of the whole vehicle to obtain function abnormal information; then, performing security correlation analysis on the functional abnormality information to obtain functional abnormality security indication information; and if the functional abnormity safety indication information indicates that the functional abnormity information is related to the safety of the whole vehicle, performing hazard analysis and risk assessment on the whole vehicle based on the functional abnormity information. According to the embodiment of the invention, the analysis and evaluation efficiency of vehicle hazard analysis and risk evaluation can be improved.

The invention discloses a function application diagnosis method and device and a medium. The method comprises the following steps: receiving a notification event of function application execution failure; collecting multi-dimensional abnormal data information related to execution of the function application from an abnormal data collection platform; based on a predefined root cause analysis model, analyzing the multi-dimensional abnormal data information to determine a root cause causing execution failure of the function application; and according to the root cause, automatically creating a question list and distributing the question list to a corresponding responsible party. The method can automatically analyze and confirm the root cause responsible party of the execution failure of the function application, and greatly shortens the problem analysis and transmission path of the function abnormality.

To improve estimation accuracy of functional abnormality.SOLUTION: A medical diagnosis support method includes acquiring a medical image of a subject, acquiring variant information including an estimation result of variants of a plurality of genes of the subject by analyzing the medical image, classifying the plurality of genes into one or more co-occurrence groups based on co-occurrence information indicating co-occurrence of variants between genes, determining variants of one or more genes belonging to each of the co-occurrence groups based on the variant information, and estimating a functional abnormality of the subject based on a determination result of the variants of the genes.SELECTED DRAWING: Figure 1

The present invention discloses a design method and device for automotive communication middleware DDS, which relates to the field of automotive communication, including: determining the functional anomalies that occur in the DDS communication middleware during vehicle communication, and the hazardous events caused by the functional anomalies; conducting risk assessment on the hazardous events caused by the functional anomalies, and determining the highest risk level of the hazardous events caused by the functional anomalies; if the system needs to be analyzed according to the functional safety standard according to the highest risk level, the functional anomalies are classified according to the functional type to obtain various functional anomalies, and the top-level security requirements of various functional anomalies are determined; based on the top-level security requirements and various functional anomalies, a security mechanism is formulated, and the security state that the DDS communication middleware enters when various functional anomalies are detected is determined; based on the security mechanism and the security state, the design of the DDS communication middleware that meets the functional safety requirements is completed. By applying the technical solution of the present application, it can be ensured that the designed DDS communication middleware meets the functional safety requirements.

The application relates to the field of circuit fault simulation and technical training, and discloses an optoelectronic instrument circuit system simulation and training platform, which comprises a basic circuit simulation panel, a micro-environment physical stress simulator, a dynamic parameter adjustment network and a fault evolution and injection control unit. According to a preset fault cause-effect chain script, the fault evolution and injection control unit cooperatively controls the micro-environment physical stress simulator and the dynamic parameter adjustment network, so that a complete main fault cause-effect chain triggered by a physical inducement, continuously drifted by circuit parameters and finally manifested as functional abnormality is reproduced with high fidelity at a physical level. By combining the dynamic evolution of the fault with the interactive response of the diagnosis behavior, a training closed loop from phenomenon observation to operation consequence feedback is constructed, the authenticity and seriousness of the training are significantly enhanced, and the system analysis ability and standard operation consciousness of learners are effectively improved.

The invention discloses a human-derived ampulla cancer cell line DPC-X2 and application thereof, and belongs to the field of microbial animal cell lines. The human-derived ampulla carcinoma cell line is named as the human-derived ampulla carcinoma cell line (homo sapiens) DPC-X2, and is preserved in the China Center for Type Culture Collection on October 30, 2025, and the preservation number is CCTCC NO: C202579. The human-derived ampulla cancer cell line DPC-X2 can be applied to establishment of a cell model for occurrence, development or metastasis of ampulla cancer. The human-derived ampulla cancer cell line DPC-X2 can be applied to cell models for researching differentiation mechanisms, cell morphology and dysfunction and tumor infiltration and metastasis mechanisms of ampulla cancer and guiding clinical comprehensive diagnosis and treatment. The human-derived ampulla cancer cell line DPC-X2 can be applied to research on the occurrence mechanism of the ampulla cancer and screening of drugs for preventing and treating the ampulla cancer. The human-derived ampulla cancer cell line DPC-X2 can be applied to establishment of an ampulla cancer animal model.

The invention relates to the technical field of database testing, in particular to a database testing method, system and equipment based on knowledge distillation and a medium, and the method comprises the following steps: constructing a multi-modal feature extractor, and performing multi-dimensional feature extraction on an input structured query statement and an execution context thereof; training the first network model based on a multi-modal feature sample generated by executing the historical test case in the reference database and execution behavior data corresponding to the multi-modal feature sample to obtain a teacher model; training the second network model according to the output probability distribution of the teacher model and the middle layer features to obtain a student model; and processing the structured query statement query of the to-be-tested database by the multi-modal feature extractor, respectively inputting the processed structured query statement query into the trained teacher model and student model, and judging whether functional abnormality exists in the current query or not according to the difference of output prediction results. The objective of the invention is to realize database function testing with high coverage rate, low manual intervention and interpretability.

The invention provides a domain controller exception handling method and system and a storage medium. The method comprises the steps that the working state of an SOC system is monitored based on a preset time period; wherein the working state at least comprises a GPIO state and SPI data; whether SPI communication abnormity exists or not is judged based on the SPI data, and if yes, a function abnormity type is obtained based on a preset time threshold level and the SPI data; otherwise, acquiring a function abnormality type based on the preset time threshold level and the current GPIO state; and executing corresponding exception handling work based on the function exception type. According to the method, the working state of the SOC system in the domain controller of the intelligent driver and the intelligent cabin is detected, and the abnormity is divided into different functional abnormity types based on different time threshold levels when the abnormity exists, so that the targeted abnormity processing is carried out, the requirements of the intelligent cabin and the intelligent driver on fault-tolerant time can be compatible, and the fault-tolerant efficiency of the intelligent driver and the intelligent cabin can be improved. And the safety and robustness of the system are ensured.

The invention discloses a diagnosis equipment abnormity positioning method and device, equipment and a storage medium, and relates to the technical field of vehicle diagnos.The method comprises the steps that if a function failure prompt of first diagnosis equipment is received, a diagnosis head interface on a preset trifurcate line is connected to a diagnosis head of a to-be-diagnosed vehicle; the preset trifurcate line further comprises a first extension line interface connected with the diagnosis head interface and a first socket serving as a diagnosis equipment interface. Establishing a data transmission link by connecting the first extension line interface with a second extension line interface on a preset extension line; the preset extension line further comprises a second socket serving as a diagnostic equipment interface; connecting the first diagnostic equipment to the first socket and connecting the second diagnostic equipment with normal functions to the second socket; and performing functional abnormality positioning on the first diagnosis equipment by comparing the log files recorded in the data acquisition operation process. Related workers can accurately position specific reasons of diagnosis failures, and the problem elimination speed is increased.

The present invention discloses a method and system for dynamically identifying risks of biotechnology products based on multi-indicator fusion, which relates to the field of biosafety management technology, including: S1, obtaining experimental data and environmental data from various stages of the life cycle of genetically modified crops, constructing a dynamic data set including molecular structure sequences, functional performance indicators and ecological impact factors, and extracting the mutation frequency of molecular structure sequences over time, abnormal fluctuations in functional performance indicators and dynamic change trends of ecological impact factors; S2, based on the mutation frequency of molecular structure sequences and abnormal fluctuations in functional performance indicators, extracting nucleotide substitution patterns and functional abnormality data sets of gene editing products in the research and development stage and the application stage, and determining the correlation pattern between nucleotide substitution patterns and functional abnormalities; this method and system for dynamically identifying risks of biotechnology products based on multi-indicator fusion realizes accurate prediction and dynamic management of ecological risks of genetically modified crops, and significantly improves the safety of environmental release.

An embodiment of the present application provides a method and device for handling functional abnormalities, which relates to the technical field of vehicles. The method includes: obtaining the network connection status between the vehicle and the terminal device; if it is determined that the network connection status switches from the connected state to the abnormal disconnection state, obtaining the vehicle status of the vehicle, where the vehicle status is the driving state or the stopped state; when the vehicle status is the stopped state, outputting a prompt message; the abnormal disconnection state is the disconnection state when the distance between the vehicle and the terminal device is less than or equal to a first preset threshold, and the prompt message is used to prompt that the automatic locking function is abnormal, and the automatic locking function is used to control the vehicle to automatically lock when the distance between the terminal device and the vehicle is greater than or equal to a second preset threshold. Through the above method, the safety of the vehicle can be improved.

The application provides a mood disorder evaluation model construction method and equipment based on electroencephalogram and symptom-oriented fusion. The method comprises the following steps: obtaining multiple mood disorder related symptom characteristics of multiple subjects; collecting electroencephalogram characteristics of multiple brain regions of each subject to construct a first training sample set; based on the first training sample set, a multi-level symptom-oriented feature extraction module is constructed, and multiple levels of electroencephalogram characteristic latent variables oriented by symptoms are obtained; based on the electroencephalogram characteristic latent variables of each level, the corresponding mood disorder diagnosis label is labeled, and then the feature fusion module and the classification module are iteratively trained; based on the multi-level symptom-oriented feature extraction module, the feature fusion module and the classification module, a mood disorder evaluation model is obtained. The application solves the problem that in the prior art, a single feature extraction and learning strategy is adopted, and there is no constraint of symptom information, resulting in limited recognition ability of the model for mood disorders of different functional abnormal types.

The invention relates to the technical field of data analysis, in particular to a neural function feature analysis method and system based on multi-modal data, and the method comprises the following steps: obtaining multi-modal neural data, analyzing rhythm, reaction and motion features, calculating coupling and blocking parameters, and normalizing the coupling and blocking parameters into feature vectors to judge abnormality. According to the method, the cerebral cortex nerve potential and the limb movement track are aligned at high precision by constructing a unified time axis coordinate system, the signal phase is analyzed through Hilbert transform, and the nerve path block mapping parameter is calculated in combination with the reaction dispersion so as to quantify the conduction stability; determining a precursor nerve coupling parameter characterization motion preparation state aiming at a phase reverse drift phenomenon in a backtracking precursor potential segment at a motion fluctuation moment, and realizing automatic and accurate classification of neurological function abnormality categories based on comparison of a multi-dimensional feature vector and a standard baseline. The problems that a neural behavior coupling mechanism cannot be quantitatively analyzed, conduction block is difficult to distinguish and defect pain points are difficult to prepare are effectively solved.

The invention relates to the technical field of MCU fault early warning, in particular to an MCU fault early warning method and system based on predictive analysis. The method comprises the following steps: acquiring environmental parameters and operation characteristic parameters of an MCU, constructing a starting state characteristic vector group, and extracting a deviation index of the MCU; a state transition chain behavior graph of the MCU is constructed, a state transition chain consistency residual function is constructed, and a state transition chain consistency residual value is calculated; function response behaviors of the MCU are obtained, function response feature vectors are obtained through arrangement, a state function collaborative matching difference function is constructed, and whether the MCU is in a false operation state with abnormal functions or not is judged; and integrating MCU process faults and outputting fault early warning information. The invention provides an MCU fault early warning method based on starting state behavior modeling and functional response consistency comparison. A false operation state and an abnormal functional fault in the MCU starting process are recognized.