511 results about "Abnormality" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention discloses an abnormal behavior detection method based on multi-modal fusion and interpretability analysis, and the method comprises the steps: carrying out the data formatting, and converting unstructured behavior data into structured employee behavior data; thirdly, constructing a daily employee behavior sequence and extracting daily employee behavior characteristics; constructing a daily interaction graph and extracting an embedded representation of each employee node; statistical analysis is introduced to obtain daily employee statistical characteristics; splicing the three features to obtain a daily multi-modal feature fusion vector; and finally, identifying an abnormal vector set through an LOF algorithm, and carrying out importance analysis by adopting an SHAP value analysis method to output an abnormal reason. According to the method, multi-angle and multi-level feature representation is constructed, so that behavior diversity is captured more comprehensively, and perception and representation of complex user behaviors are improved; and meanwhile, fusion detection is carried out based on behavior feature importance analysis, so that abnormal employees can be locked, abnormal behavior reasons can be traced, and the interpretability of an abnormal detection result is enhanced.

The invention discloses a log abnormal behavior detection method and system based on periodic pattern mining and incremental learning, and relates to the technical field of system abnormality monitoring. The method comprises the steps of obtaining original log data and executing preprocessing operation; main periodic frequency components are extracted through time frequency analysis to form a periodic set, a periodic stable part sequence and a transient change part sequence are divided, and a periodic modeling mechanism and a transient modeling mechanism are used for modeling; a joint prediction model is constructed, Monte Carlo Dropout is introduced to estimate uncertainty, and Bayesian weighting is adopted to generate a prediction value; abnormity is judged through a periodic residual error, a transient residual error and an overall residual error, and an abnormal causal path is analyzed and identified in combination with transfer entropy; a memory sample driven playback and distillation mechanism is adopted to execute incremental training, and modeling structure parameters are dynamically adjusted. The method has the capabilities of periodic rule modeling, unsteady behavior expression, prediction fusion, abnormal causal identification and continuous model learning.

The invention is suitable for the technical field of fall detection and help calling, and provides a fall detection and help calling method for the elderly at home, and the method comprises the steps: collecting the multi-source health monitoring data of the elderly through a monitoring camera and a sensor, and carrying out the preprocessing based on the collected data; respectively extracting walking posture features, getting-up and sitting-down posture features, daily activity posture features, motion features, acceleration features, gyroscope features and heart rate features of the elderly; inputting the extracted features into a trained abnormal action detection model for action detection; when the old people have abnormal behavior actions, performing multi-dimensional analysis on the abnormal behavior actions of the old people by utilizing a clustering algorithm; and feeding back the analysis result of the abnormal behavior action of the old person in real time according to a preset help calling rule. According to the invention, the aims of comprehensively monitoring the health state of the elderly, timely discovering abnormity, deeply analyzing reasons and quickly responding to rescue can be fulfilled.

The invention relates to the technical field of network video monitoring, in particular to a terminal equipment collaborative video monitoring method and system, which comprises the following steps of: monitoring video data based on terminal equipment, extracting a motion vector, a pixel intensity value and a color histogram of each frame, carrying out space-time alignment, calculating an average value and a variance of each parameter, and calculating the average value and the variance of each parameter; time sequence characteristics are analyzed, parameter changes of key frames are extracted, and an initial behavior characteristic set is established; according to the method, the motion vector, the pixel intensity and the color histogram of each frame of video are analyzed, and the data are subjected to space-time alignment and statistical analysis, so that the understanding of a behavior mode is improved, and clustering analysis helps a system to efficiently identify abnormities, monitor in real time and calculate parameter deviation to ensure that abnormal behaviors are found in time; the adaptability and efficiency of the system to the monitoring environment are further improved through data exchange and collaborative analysis between the terminal devices, and the coverage range and the response speed of the monitoring system are remarkably enhanced through the highly collaborative processing mode.

The invention discloses a multi-dimensional space-time evidence chain analysis platform and method for sales data abnormal behavior recognition, and relates to the technical field of sales data processing. The analysis platform and method comprises the following steps: S1, collecting sales behavior data generated in the ordering process of a customer and the operation process of an employee through a sales order system, and preprocessing the sales behavior data; s2, constructing an employee continuous operation chain, extracting structured behavior characteristics, and quantifying the employee sales behavior deviation degree; s3, extracting a key field reconstruction operation chain of the employees with abnormal behaviors in the data source, and analyzing the overall risk intensity of behavior combination in the reconstruction operation chain; and S4, further evaluating consistency fluctuation of node behaviors in the abnormal operation chain, and generating a structured fund abnormality report and pushing the structured fund abnormality report to a financial platform interface. The problem that fund abnormity caused by the fact that salesmen make false sales through faking orders is difficult to recognize in a traditional detection method is solved.

The invention discloses a user entity behavior anomaly analysis and processing method and device, equipment and a medium, the method comprises the steps that a user access link is constructed based on user behavior data acquired from multiple sources, and the user behavior data comprises data acquired from a service log, a security log and network traffic; obtaining a sensitive information feature library and a sensitive information identification rule library which are obtained by performing traceability analysis on leakage events in the report case library; using the sensitive information identification rule base to convert the user access link into a multi-dimensional behavior portrait; calculating a corresponding behavior deviation degree based on the user access link and the multi-dimensional behavior portrait through an attention mechanism network model; and according to the behavior deviation degree and the sensitive information feature library, context-aware real-time filtering is carried out on data in transmission based on a multi-level sensitive factor dynamic filtering strategy. By monitoring the user behavior access link, the capability of responding to the abnormal behavior is improved.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

The invention discloses an elevator operation fault early warning method and system, relates to the technical field of intelligent elevator control, and aims to improve the operation safety and fault response capability of an elevator in a potential abnormal state. By constructing a load critical drift test working condition, a sudden non-vertical stress test working condition and a weak power supply fluctuation interference test working condition, abnormal behaviors which are difficult to appear but have potential safety hazards in the normal operation process of an elevator are induced; the method comprises the following steps: acquiring operation data under various working conditions, respectively calculating a drift overload response coefficient, a guide rail non-vertical interference coefficient and a power supply induced instability coefficient, respectively comparing the coefficients with preset thresholds, and judging whether operation faults such as brake response mismatching, asynchronous action of safety tongs and door lock control interference exist or not; and if the judgment result is abnormal, a targeted strategy is automatically generated and early warning is given out. According to the method, active recognition and classified response of elevator faults can be achieved, and the maintenance efficiency and the operation reliability are effectively improved.

The invention discloses a semantic analysis-based user abnormal intention recognition method and system, and belongs to the technical field of computer data processing, and the method comprises the steps of analyzing logic contradiction word combinations in a user session text to generate abnormal semantic marks, and collecting a user operation behavior sequence and environmental parameters to construct a spatio-temporal behavior trajectory map. And matching the abnormal semantic mark with a preset extensible abnormal mode library, if the generated primary abnormal confidence exceeds a threshold value, further comparing the behavior trajectory map to generate a behavior abnormal index, associating the abnormal semantic mark with the primary abnormal confidence to form a comprehensive abnormal feature vector, and calculating an environmental risk coefficient in combination with environmental parameters. And finally, judging by a triple decision arbiter, and if the abnormity is suspicious abnormity, activating a secondary verification process and updating an abnormal mode library. According to the method, semantic logic contradiction analysis, behavior trajectory map analysis and environmental risk assessment are fused, deep and camouflage abnormal intentions can be accurately recognized, and the method has adaptive learning ability.

The invention discloses an abnormal behavior detection method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring user behavior data; behavior feature vectors are determined in the user behavior data, and the behavior feature vectors comprise at least one of a mouse behavior feature vector, a keyboard behavior feature vector, a process behavior feature vector and a window interaction feature vector; and analyzing the behavior feature vector by using a machine learning model to obtain an anomaly score output by the machine learning model, the anomaly score being used for representing the degree of deviation of the behavior feature vector from a preset normal behavior feature vector. According to the method and the device, the technical problem that the accuracy of distinguishing the normal user activity from the remote control Trojan abnormal operation is insufficient due to the fact that a related abnormal behavior detection method lacks a refined behavior modeling technology and a dynamic behavior analysis mechanism is solved.

The invention relates to the technical field of pet behavior data processing, and discloses a time-space sequence data processing method and system for pet abnormal behavior recognition, and the method comprises the steps: 1, obtaining multi-modal data, carrying out the time alignment, building a multi-scale scene semantic graph, and generating a grid occupancy frequency and a region transfer matrix; 2, constructing a normal behavior template library, and generating window-level spatio-temporal features; 3, establishing group normal behavior distribution by using a generative density model, calculating a residual error in combination with a time sequence prediction model, and obtaining individual model parameters; 4, performing statistics on historical rhythm distribution and calculating differences of the day to obtain rhythm deviations; 5, fusing multi-component anomalies to obtain a comprehensive anomaly score; step 6, introducing an Internet of Things event to generate a gating coefficient and adjusting an abnormal score; and 7, comparing the abnormal score after gating with a threshold value, and outputting an abnormal alarm. According to the invention, accurate identification and stable alarm of the abnormal behavior of the pet are realized.

The invention relates to the field of psychological disease early warning, and discloses a psychological disease early warning method and system based on artificial intelligence, and the method comprises the steps: carrying out the feature extraction and data fusion of collected and preprocessed physiological and behavior data, carrying out the emotion analysis of the emotional state of a user through the fused data, and carrying out the early warning of psychological diseases. The method comprises the following steps: preliminarily judging whether individualized emotion is abnormal or not, comparing physiological features and behavior features of abnormal emotion according to an analysis result of emotion abnormality, performing time sequence analysis on the physiological features and behavior features of known psychological disease symptoms, detecting whether a user has psychological diseases or not, and further evaluating and analyzing the severity of the diseases. According to the method and the system, the disease is subjected to graded early warning, so that the user is helped to realize own emotion change, the psychological disease is discovered and intervened as soon as possible, the analysis and detection of the psychological state are more accurate and reliable, the detection efficiency of the psychological disease is improved, and the disease risk is reduced.

The invention relates to the technical field of recharging abnormity monitoring, in particular to a big data and artificial intelligence-based recharging behavior abnormity monitoring system, which is characterized in that historical recharging behavior data and real-time user behavior data are acquired, a user static portrait and an equipment I P portrait are constructed, deviation measurement is performed on real-time behavior characteristics, and the difference between the real-time behavior characteristics and a historical baseline is quantified, so that the recharging behavior abnormity can be monitored. The method is used for identifying abnormal operation modes. A time sequence neural network model is trained based on a historical operation sequence, typical operation links and behavioral rhythms of a user are automatically learned, and time sequence structure abnormity is accurately identified. And the abnormity monitoring module fuses static offset and behavior offset results, adopts a reinforcement learning model, dynamically adjusts a response strategy according to a risk interception effect, a false alarm condition and user feedback, realizes adaptive optimization of risk control measures, and improves the real-time intelligent risk control capability of the system in a complex recharging scene.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The present invention discloses a method for detecting and analyzing mental health based on a large model, and relates to the technical field of electronic digital data processing. The method for detecting and analyzing mental health based on a large model comprises the following steps: extracting features; obtaining a data anomaly evaluation index; and performing anomaly level classification. The present invention obtains student data features by extracting features of student data corresponding to the students to be detected, and then obtains a data fluctuation coefficient by analyzing the data feature parameters of the historical time period, obtains a data anomaly evaluation index by combining the student data features and the data feature parameters obtained by inputting the student data features into a preset model, and finally obtains a data anomaly group by performing an anomaly level classification of the students to be detected in combination with a preset evaluation threshold, thereby improving the effectiveness of data visualization in mental health detection and analysis, and solving the problem of low effectiveness of mental health detection and analysis by data visualization in the prior art.

The invention discloses a traceability management method and system for an industrial product, and relates to the technical field of industrial product production management, a comprehensive product traceability database is established, and detailed records of product production, accessory production links and assembly procedures are covered; performing deep analysis on historical product abnormal conditions by using a semantic recognition model, recognizing abnormal state types and parameters, constructing a historical abnormal state parameter group, and establishing association with abnormal accessories to form an abnormal state-abnormal accessory association group; performing classification collection and preferential selection on the association groups, and constructing an abnormal state-abnormal accessory corresponding library; for the current abnormal condition of the product, the semantic recognition model is used again for recognition and analysis, and the abnormal part and the production link thereof are quickly locked by matching the abnormal state-abnormal part corresponding library, so that efficient tracing is realized; according to the method, the tracing efficiency and accuracy are remarkably improved, the tracing cost is reduced, and powerful support is provided for quality control of industrial products.

The invention discloses a ship network abnormal behavior detection system based on multi-protocol deep analysis, and belongs to the technical field of ship network security. According to the ship network abnormal behavior detection system integrating multi-protocol semantic analysis, communication chain modeling, state awareness and behavior scoring, a unified semantic intermediate expression structure is constructed, fields of various heterogeneous protocols are abstracted into standard semantic units, behavior portraits are established in combination with a communication topological graph and an equipment state sequence, and the behavior portraits are subjected to state awareness and behavior scoring. And multi-dimensional anomaly recognition and hierarchical response are realized based on a weighted scoring mechanism, so that the blank in the aspects of cross-protocol fusion recognition and behavior modeling closed-loop detection in the prior art is filled, and the security risk recognition requirements of complex data and frequent switching in a ship scene are met.

The invention provides a method for generating a personalized nerve regulation and control stimulation scheme, and aims to generate a precise nerve regulation and control stimulation scheme for an individual through an electroencephalogram evaluation result. The method comprises the following steps: firstly, constructing a scheme library containing a plurality of transcranial electrical stimulation basic schemes; secondly, collecting resting-state electroencephalogram signals of a user, extracting electroencephalogram characteristic indexes related to emotion, cognition and sleep, and comparing the electroencephalogram characteristic indexes with a norm database to judge cognition risks; generating a primary stimulation scheme according to an evaluation result, and if a cognitive risk exists, further detecting that a feature index is abnormal and generating a targeted correction scheme; all the schemes are subjected to priority ranking, and the sequence is determined according to the abnormal severity degree and the clinical weight; and finally, outputting a personalized treatment scheme sequence of one week. According to the method, the accuracy and effectiveness of treatment are improved, high individuation, systematicness, practicability and dynamic optimization potential are achieved, and powerful support is provided for nerve regulation and control treatment.

The invention discloses a data quantification method, which comprises the steps of operating an artificial intelligence AI model, acquiring data generated in the operation process of the AI model, the data comprising a plurality of groups, determining the abnormal degree of the data in the first group in the plurality of groups, the abnormal degree indicating the difference degree of the size of the data in the first group, and determining the difference degree of the size of the data in the second group in the plurality of groups. And determining parameters for performing exception suppression on the data in the first group according to the exception degree, suppressing the data in the first group according to the parameters for exception suppression, and quantifying the suppressed data in the first group. According to the method, the abnormal degrees of different groups are determined respectively, and the appropriate abnormal suppression parameters are selected for the different groups based on the abnormal degrees to perform abnormal value suppression, so that adaptive suppression of data in the different groups is realized, the situation of insufficient suppression or excessive suppression can be avoided, and the quantization precision of the AI model is improved.

The invention relates to a treatment method for realizing TMS treatment for mood disorder and anxiety disorder based on emotion induction. The treatment method comprises the following steps: performing clinical assessment on mental and psychological states of a patient before treatment; performing brain magnetic resonance examination processing on the patient based on the emotion induction state; performing priority ranking processing on the selected individualized target spots according to the clinical evaluation of the patient and the abnormal degree of the brain function activity under emotion induction; a TMS treatment scheme under the emotion induction scene is formulated according to the target priority arrangement sequence; performing treatment evaluation in a short-term time period, and performing long-term prediction of a future residual treatment trajectory by using the curative effect prediction model; after treatment is finished, a treatment track is fed back to the local database, and clinical evaluation after treatment is carried out. The invention further relates to a corresponding device, a processor and a storage medium. By adopting the method, the device, the processor and the storage medium, the contrast difference of the patient in different situations can be effectively displayed through the situational stimulation library and the situational brain function image.

The invention provides a medical resource utilization behavior assessment method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-assessed behavior sequence; dividing the behavior sequence into a plurality of windows with the same time length, and converting each behavior point in each window into a corresponding behavior label; generating a sample sequence corresponding to the behavior sequence based on a plurality of windows of the behavior sequence and a plurality of behavior tags in each window; calculating an abnormal score of the behavior sequence based on the sample sequence; capturing a plurality of forward dependencies and a plurality of backward dependencies of the behavior sequence; splicing the plurality of forward dependencies and the plurality of backward dependencies to generate a feature vector; reconstructing the feature vector to obtain a reconstructed behavior sequence; calculating a reconstruction error value between the reconstruction behavior sequence and the behavior sequence; and judging whether the behavior sequence is credible or not based on the abnormal score and the reconstruction error value of the behavior sequence to be evaluated. According to the invention, whether the medical resource utilization behavior is credible can be accurately evaluated.

The invention relates to an information management method and device for a drug clinical test, a medium and equipment, and relates to the technical field of information management. Determining an abnormal reason of the abnormal clinical test information according to the target abnormal dimension and the corresponding target fluctuation dimension in which the clinical test information is easy to be abnormal; when the abnormal reason is that an error occurs when the abnormal clinical test information is input, a suitable input form corresponding to the abnormal clinical test information is determined, and the clinical test information of the test abnormal dimension is obtained again based on the suitable input form; and when the abnormal reason is that the body of the target subject is abnormal in the drug clinical test, sending an abnormal early warning to a terminal of a clinical test worker. The method has the effect of improving the accuracy of the collected clinical test information.

A method of diagnosing whether there is abnormality includes obtaining data having a quantity of state of one or more assessment items from the facility, classifying, for each operation state of the facility, data obtained from the facility, assessing sufficiency of the number of pieces of data for each classified data group, calculating multiple parameter values configuring a trained model in accordance with the sufficiency and holding the parameter values in association with the operation state, obtaining the parameter values configuring the trained model without overlaps for each parameter, in accordance with the operation state of data to be diagnosed and a status of holding of the parameter values associated with each operation state, creating the trained model to diagnose data, with the parameter values, calculating a degree of abnormality for determination as to abnormality based on the trained model, and determining whether or not there is the abnormality.

The invention discloses a livestock and poultry behavior pattern abnormity identification method based on a distribution estimation algorithm and a residual network. The method comprises the following steps: S1, obtaining a livestock and poultry behavior uniform structure data set with a consistent structure; s2, constructing a livestock and poultry behavior probability distribution model and storing model parameters; s3, generating a behavior probability difference scoring matrix; s4, constructing a residual neural network model, completing model parameter initialization, and executing multi-layer residual operation to obtain a depth anomaly feature vector; and S5, marking a normal behavior mark or an abnormal behavior mark for each piece of livestock and poultry behavior data, generating a behavior track aggregation result in combination with the behavior abnormality judgment result and the corresponding timestamp information, and completing abnormal grade division on the behavior abnormality judgment result and the behavior track aggregation result according to a preset rule. According to the method, the collaborative change rule among the multi-modal data can be captured, so that the model can detect the significant probability deviation at the initial stage of stress or the early stage of sudden abnormality, and the method has higher prospective abnormality early warning capability.

The invention relates to the technical field of data analysis, and discloses a trajectory prediction and abnormal behavior detection system and method, and the method comprises the steps: obtaining a target monitoring image; constructing a target track segment of the moving target based on the target monitoring image; performing prediction completion on the target trajectory segment to obtain a target trajectory of the moving target; calculating the compression degree and the expected speed deviation of the target trajectory; identifying a potential anomaly of the target trajectory based on the degree of compression and an expected velocity deviation; if the target trajectory has potential abnormality, predicting a target expected trajectory based on the target monitoring image; and calculating a matching degree between a target trajectory and the target expected trajectory, and identifying an abnormal target trajectory based on the matching degree. According to the invention, the abnormal track of the moving target can be accurately identified, and the efficiency and the foresight of abnormal track detection are improved.

To provide an abnormality diagnostic system of an object structure capable of accurately and stably performing an abnormality diagnosis of the object structure.SOLUTION: An abnormality diagnostic system performs eigenvalue analysis with respect to a shape model obtained by modeling a structure of an object structure 200, and constructs a degradation model based on a result of the eigenvalue analysis. The system includes: an estimation section 420 for estimating, based on the degradation model and an acceleration measurement value 11, an acceleration estimation value and a stress estimation value at at least one evaluation object point being a part of the object structure 200 and also estimating an estimation value of a natural vibration number and an estimation value of an attenuation ratio which are a vibration parameter of the object structure 200; and an abnormality determination section 430 for determining a normal / abnormal state of the object structure 200 by machine learning based on the acceleration estimation value, the stress estimation value, the estimation value of the natural vibration number, and the estimation value of the attenuation ratio.SELECTED DRAWING: Figure 1

The invention relates to the technical field of machine learning training fault tolerance, and discloses a LoRA training fault tolerance method based on state awareness. The method comprises the steps that training indexes are collected through a sensor network, a time sequence knowledge base is constructed, and sampling frequency is adjusted in a self-adaptive mode; historical data is utilized to generate a state change trend baseline, and abnormity is preliminarily identified through deviation degree comparison. And for the abnormal behavior, performing verification and risk assessment by fusing the gradient frequency domain characteristics and the loss curve form of the abnormal behavior. Encoding the anomalies into a high-dimensional point set, analyzing and identifying a continuous homologous structure of the high-dimensional point set by adopting topological data, and judging a systematic gradient anomaly mode according to topological characteristics; and deducing a compensation coefficient according to geometric attributes of the topological characteristics, calibrating a risk estimation value, and dynamically reconstructing a monitoring strategy. According to the method, systematic training faults can be accurately identified from a data structure level, adaptive intelligent fault-tolerant control is realized, and the reliability and efficiency of a training process are improved.

The invention provides an edge device behavior security detection method for a swan gap system, and the method comprises the steps: extracting behavior expected data of each component from a source code or an intermediate representation, and constructing an expected behavior graph; collecting behavior data of each component, and constructing an actual behavior graph; comparing the actual behavior map with an expected behavior map, calculating the structural similarity between the actual behavior map and the expected behavior map, and marking a node or a behavior path deviating from the expectation; and comparing the capacity, authority and component calling relationship between the nodes or behavior paths deviating from the expectation and the expectation behavior map to obtain an abnormal behavior source, taking response measures of different levels according to the type, severity and influence range of the behavior paths deviating from the expectation, and generating an abnormal report. According to the method, deviation between behavior maps is identified through a structured comparison method, and abnormal behaviors such as unauthorized capability combination calling, implicit permission extension and service path hijacking are effectively discovered.

The invention relates to the technical field of health monitoring, in particular to a health abnormity early warning method and system based on behavior data analysis. The method comprises the following steps: collecting behavior data and physiological data of a user; constructing a behavioral physiological knowledge graph by using a graph structure causal attention modeling method, introducing a hysteresis loop operator to identify a hysteresis influence path of behavioral data on physiological data, and calculating a behavioral physiological potential causal pathway by using a behavioral physiological causal path identification method based on a hysteresis loop embedded graph structure; sequentially constructing a time sequence health hyperbola function and a three-layer dynamic health threshold envelope structure, and calculating a trend offset integral index of the time sequence health hyperbola function; and constructing a group reference health orbit manifold structure, and identifying and early warning the health deviation condition of the user. The invention provides a technical scheme for traceable interpretation of health abnormality causes and non-sudden risk health abnormality early warning.