354 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 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 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 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 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 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.

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.

The invention relates to the technical field of artificial intelligence and network security, in particular to an abnormal behavior sample data generation method based on a generative adversarial network, which comprises the following steps: a potential space mapping step: obtaining normal behavior sequence data and mapping to generate potential feature vectors; a causal path filtering step of filtering and generating a to-be-evaluated abnormal sample by using a causal adjacency matrix based on the abnormal guide noise disturbance vector; an energy composite calculation step: receiving a sample and calculating a total energy value based on a weighting result of a distribution abnormity degree and a logic violation degree; a convergence judgment output step: outputting target abnormal behavior sample data when the total energy value meets a preset convergence condition; according to the method, the problem of impossible triangular deadlock is solved through an energy field constraint mechanism, the balance saddle point is searched at the manifold boundary, and it is ensured that the sample has concealment, aggressiveness and logic compliance at the same time.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention discloses a teaching question-answering method and device, a program product and a storage medium, and relates to the technical field of artificial intelligence education. The method comprises the following steps: acquiring multi-source interaction behavior data of a user; calculating interaction reference data of the user; calculating a behavior anomaly degree according to the multi-source interaction behavior data and the interaction reference data; determining a current cognitive state according to the behavior abnormality, and when the current cognitive state meets a preset trigger condition, generating an inquiry window containing inquiry content, the inquiry content being determined according to the current cognitive state and the content context of the current interaction; obtaining a first answer of the user based on the inquiry content, adjusting the inquiry content based on the first answer, and obtaining a second answer corresponding to the user based on the adjusted inquiry content; and calculating the inquiry acceptance rate of the user according to the first answer and the second answer, and adjusting the trigger condition according to the inquiry acceptance rate. By implementing the technical scheme provided by the invention, the effect of personalized teaching can be improved.

The invention provides a dynamic emotional state evaluation method based on employee multidimensional data analysis, which comprises the following steps: acquiring multidimensional data of an employee, including basic physiological data, working behavior data and environment associated data, and performing timestamp synchronization processing on the multidimensional data; extracting the fluctuation amplitude of the basic physiological data, the frequency anomaly of the working behavior data and the interference coefficient of the environment associated data, and performing weighted fusion to obtain an initial emotion feature vector; obtaining an employee historical emotion baseline database; wherein the employee historical emotion baseline database comprises emotion feature reference values of the employees in different working scenes; and performing difference calculation on the initial emotional feature vector and a historical emotional feature reference value in a corresponding working scene, and outputting a real-time emotional state evaluation result in combination with a preset emotional influence weight matrix. According to the invention, the defect that the emotion of the employee is difficult to accurately and comprehensively assess at present is overcome.

The object is to manage signal abnormalities even when there is little measurement data of the abnormal signal. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the intensity of each frequency component corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that shows abnormal intensities of frequency components that deviate from a normal intensity range, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

The invention relates to the technical field of data processing, and discloses a public resource transaction data monitoring and early warning and abnormal behavior recognition method and system. The method comprises the following steps: collecting historical data of a transaction subject to calculate a behavior baseline value, and marking a primary abnormal signal of which the deviation degree exceeds a threshold value; calculating a consistency coefficient through multi-dimensional cross validation, and screening secondary confirmation anomalies; constructing a three-layer anomaly propagation spectrum traceability search to calculate diffusivity and divide anomaly types; and the early warning priority is calculated by fusing the multiple score dimensions to generate an early warning instruction. According to the invention, the accuracy of abnormal behavior identification and the pertinence of early warning response are improved.

Provided is a data quantization method, comprising: running an artificial intelligence (AI) model, and obtaining data generated in the operation process of the AI model, wherein the data comprises a plurality of groups; determining an abnormality degree of data in a first group among the plurality of groups, wherein the abnormality degree indicates the difference degree of the size of the data in the first group; on the basis of the abnormality degree, determining parameters for performing abnormality suppression on the data in the first group; on the basis of the parameters for the abnormality suppression, suppressing the data in the first group; and quantizing the suppressed data in the first group. By respectively determining the abnormality degrees of different groups, and selecting for different groups appropriate parameters for abnormality suppression on the basis of the abnormality degrees so as to suppress abnormality values, adaptive suppression of data in different groups is achieved, thereby avoiding the situation of insufficient suppression or excessive suppression, and improving the quantization precision of the AI model.

The invention discloses a multi-source operation exception association analysis and grading disposal method and system. The method comprises the steps of telemetry data collection and caching, non-blocking sending and self-adaptive readaptation, sliding time window and matrix construction, exception association clustering and comprehensive scoring, exception type judgment and grading alarm and self-healing disposal. The method comprises the following steps: continuously sensing a program execution state and an abnormal behavior, realizing data peak clipping storage by local cache, introducing a self-adaptive backoff retry mechanism, decentralizing transmission of abnormal data on the premise of not blocking a main service, and identifying and distinguishing anomalies in combination with abnormal propagation characteristics and an influence coupling relationship. And after quantitative judgment, automatically outputting a matched grading treatment strategy and an operation recovery measure. The closed-loop operation treatment mechanism of coverage sensing, transmission, aggregation, judgment and disposal constructed by the invention can realize rapid sensing, accurate attribution and adaptive response of the abnormity in a complex industrial load environment, and the stability and reliability of the operation of an industrial system are remarkably improved.

The invention discloses a dew-point meter measurement data processing method and equipment, relates to the technical field of industrial automatic measurement and control and fault diagnosis, and aims to construct a dynamic sensing window with a variable form to preprocess dew-point meter time sequence data, calculate fault probability entropy in real time and detect instantaneous abnormality. And a window form is decided in combination with the fault-window form mapping library, and adaptive switching is carried out to obtain an anomaly recognition result. And selecting a corresponding algorithm from the correction strategy library to correct the abnormity. According to the method, a dynamic sensing window with a variable form is constructed, and adaptive switching among panoramic, sniping and multi-focus forms is realized by taking the fault probability entropy calculated in real time as a drive. By means of a built-in fault-window form mapping library, abnormal symptoms and fault physical mechanisms are associated, multiple concurrent faults are separated, root causes are deduced, and decision space explosion is avoided. And on the basis of a diagnosis result, a correction strategy library targeted algorithm is called to correct abnormity, and an identification-diagnosis-correction intelligent closed loop is formed.

The invention discloses a public place abnormal behavior recognition method and system based on deep learning, and relates to the technical field of machine learning, and the method comprises the steps: connecting monitoring equipment to obtain and preprocess a video stream; identifying pedestrian targets through a lightweight target detection model and distributing IDs; tracking a pedestrian ID, constructing a motion track and identifying behavior action features; and training an abnormal behavior recognition model through an abnormal behavior sample library, importing a movement track and behavior action feature recognition abnormality, and generating abnormal feedback information according to the traceability level of a result and the like. The technical problems that in public place abnormal behavior detection through a traditional means, abnormal behaviors in a complex scene are difficult to accurately recognize, missing detection and false detection are prone to occurring, information is not timely and inaccurate, and the safety management and control requirement cannot be met are solved, and the purposes of accurately recognizing the public place abnormal behaviors and improving the safety of public place abnormal behavior detection are achieved. And the abnormal feedback information which is accurate and contains the traceability level, the risk level and the timeliness is output, and the technical effect of meeting the safety management and control requirements is achieved.

The invention discloses a video analysis-based early warning and monitoring method for children hyperactivity behavior and a storage medium, and the method comprises the steps: firstly, guaranteeing the comprehensive monitoring of children behaviors through employing a multi-channel video collection device, and providing a sufficient continuous data source for subsequent analysis; furthermore, the quality of monitoring data is ensured through the use of image noise reduction and segmentation technologies, and accurate motion features are effectively extracted through the combination of an optical flow algorithm and a background subtraction algorithm; further, the movement behaviors of the children are comprehensively analyzed by calculating behavior parameters such as movement amplitude, movement direction change and continuous movement time, and potential behavior abnormity is revealed; the multi-dimensional behavior feature vector obtained through behavior parameter analysis is compared with the standard template, the behavior type of the child is automatically recognized, early-stage symptoms of abnormal behaviors such as hyperactivity and the like are found in time, and a basis for early diagnosis and intervention is provided for behavior problems such as hyperactivity and the like of the child.

The present invention relates to the field of industrial equipment anomaly detection, and in particular to a slaughter line anomaly identification method and system based on digital twins. The method comprises: collecting operating data and health reference data of a splitting saw in a slaughter line through a data acquisition unit and a digital twin interface unit; obtaining a preliminary abnormality judgment result of the operating state of the splitting saw by performing digital twin reference correction and cumulative control chart anomaly detection processing on the instantaneous current data of the driving unit; obtaining an abnormality source tendency discriminant factor by fusing and evaluating the physical feature data and health reference data of the splitting saw; obtaining an abnormality identification result of the current processing cycle of the splitting saw by performing a comprehensive analysis of the preliminary abnormality judgment result and the abnormality source tendency discriminant factor, thereby improving the accuracy of abnormality identification of equipment in the slaughter line.