310 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 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 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 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.

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.

A method, computer program product, and computer system of artificial intelligence (AI) intervention to detect and mitigate an abnormality associated with an object moving during performance of a process. A trained recurrent neural network (RNN) determines, from sensor data, a probability (Pr1) that the abnormality existed at a first time, where Pr1 exceeds a threshold T1 and in response, an alternative generative adversarial network (AGAN) determines a probability (Pa1) that the abnormality existed at the first time. A score S1, which is computed as a function of Pr1 and Pa1, exceeds T1 and in response the RNN and the AGAN determines, from the senso data, a probability (Pr2) and a probability (Pa2), respectively, that the abnormality existed at a second time. A score S2, which is computed as a function of at least Pr2, Pa2, and (S1-T1), exceeds a threshold (T2) and in response, the abnormality is mitigated.

The present disclosure provides an industrial process-oriented abnormality automatic recovery and event-driven linkage method, comprising: classifying and recording abnormality occurred in an industrial process, scanning unprocessed abnormality records in a preset period, the abnormality at least including one of device offline, device failure, no available device, call failure and production line alarm; for each abnormality record scanned, querying the current state of the corresponding device according to the abnormality type, and judging whether it meets the preset recovery condition corresponding to the abnormality type; in response to meeting the preset recovery condition, generating a recovery instruction based on the abnormality record, finding an execution instance waiting for the abnormality and sending an abnormality recovery signal; in response to listening to the abnormality recovery signal, executing the execution instance. The present disclosure automatically completes abnormality recovery by including each abnormality in life cycle management, associates abnormality recovery and process continuation with event signals, and realizes abnormality traceability and recovery automation.

The invention is suitable for the technical field of industrial network security protection and intelligent risk assessment, and provides a network security reinforcement strategy generation method and system based on AI analysis, and the method comprises the steps: obtaining a current to-be-assessed target instruction, and an initial security risk score value generated for the target instruction, meanwhile, historical control instruction data of equipment corresponding to the target instruction is obtained; the method can identify recessive intrusion behaviors which conform to the issuing rule but have operation sequence abnormity, parameter floating abnormity or response time sequence abnormity, is especially suitable for early discovery and trend perception of long-term latent attacks, breaks through the limitation that protocol legal instruction behavior abnormity is difficult to identify in the prior art, and improves the detection accuracy. The problem of misjudgment of a scoring system in a critical region due to context deficiency is further relieved, the stability, interpretability and dynamic adaptability of network risk assessment are remarkably improved, and the method has good engineering practicability and popularization value.

Embodiments of the present specification provide a test processing method and device, wherein the test processing method comprises: determining an abnormality prevention and control index of a first test terminal by means of object features and relationship features of an abnormality occurrence terminal and aggregated object features in a model training process of a graph embedding model through federated learning; determining a relationship state between objects in an object set of a second test terminal corresponding to an abnormality occurrence server by means of object features of the second test terminal, and determining an abnormality prevention and control index of the second test terminal according to the relationship state; and sending the abnormality prevention and control index of the first test terminal and the abnormality prevention and control index of the second test terminal to a test evaluation platform.

The present invention appropriately handles an abnormality occurring in an abnormality determination target object.　An abnormality handling device 10 is provided with: a determination unit 11 that acquires information on an image obtained by imaging an abnormality determination target object, and determines an abnormality occurring in the abnormality determination target object on the basis of the acquired information on the image; a decision unit 12 that decides a handling policy for the abnormality occurring in the abnormality determination target object on the basis of the determination by the determination unit 11; and a generation unit 13 that generates a prompt to cause a generative AI model 21 to generate a handling policy explanation text for explaining the handling policy decided by the decision unit 12 to an abnormality handler.

It can sometimes be difficult to properly identify the data that contributed to the anomaly. [Solution] The calculation device includes: a calculation result acquisition unit that acquires the calculation result of the degree of abnormality using time-series log data consisting of log data over a predetermined time width and time-series numerical data representing measured values ​​over a predetermined time width; an edited abnormality acquisition unit that acquires the edited abnormality, which is the degree of abnormality at the time of editing, using time-series numerical data at the time the degree of abnormality was calculated and edited log data obtained by editing a part of the log data included in the time-series log data; and a contribution calculation unit that calculates a contribution that indicates the degree to which the event corresponding to the edited log data contributed to the abnormality using the degree of abnormality and the edited abnormality.

The invention discloses an old people abnormal behavior identification method and system based on multi-modal fusion, and the method comprises the steps: an edge end synchronously collects visual, inertial and environmental heterogeneous data, and extracts each modal feature after time alignment and preprocessing; vision and inertia feature weights are dynamically corrected through a cross-modal attention adapter, and modal unbalance is relieved; constructing a multi-modal fusion graph, executing a graph attention mechanism (HLGAtt) in a hyperbolic Lorentz space, and depicting a behavior hierarchical relationship; and detecting anomaly based on the graph attention network, positioning a root cause node through random walk, and outputting an anomaly category and root cause interpretation. Compared with the prior art, the method has the advantages that the anomaly identification F1 score is increased by more than 6%, the false alarm rate is lower than 0.7 times per day, and the method has the characteristics of high reliability and interpretability, can be widely applied to nursing homes, communities and family scenes, and provides technical support for intelligent nursing for the aged.

The application relates to an industry classification and abnormality identification method, device, electronic equipment and storage medium. The industry classification and abnormality identification method comprises the following steps: S1, sample screening, screening registered telephone numbers of a plurality of enterprises in an industry; S2, continuously learning algorithm calculation, applying a machine learning algorithm to extract communication information records of sample objects, continuously tracking and training industry / enterprise sample communication characteristics; S3, learning result correction, selecting to-be-audited correction number objects in a random sampling manner, adopting multi-point correction, obtaining industry information and enterprise information of the number objects, judging and identifying the industry to which the number objects belong, and correcting inconsistent cases based on the judgment result; S4, abnormal fluctuation detection, timely finding and detecting abnormal fluctuations of different normal behaviors of enterprise / number objects, and being used for management and early warning. According to the industry classification and abnormality identification method, abnormal fluctuations of different normal behaviors of enterprise / number objects can be timely identified and found in advance.

The invention provides an automatic testing method and device and a storage medium. According to the embodiment of the invention, the case log generated by the test case is acquired and analyzed in real time in the process of executing the test task, the type of the exception occurring in the test environment can be identified in time, and the exception can be repaired based on the preset repair strategy and the generated exception context information containing the structured data. And dynamically executing a repair operation aiming at the abnormal type so as to recover the execution process of the influenced test case after successful repair. According to the embodiment of the invention, continuous monitoring and accurate diagnosis of the test environment are realized, and the test environment is dynamically repaired during the operation of the test task, so that test interruption caused by the abnormality of the test environment is reduced, and the continuity of the test process and the reliability of the test result are enhanced.

The invention discloses an odorant for relieving fear emotion. The active ingredients of the odorant are linalyl acetate and 1, 8-cineole. According to the present invention, it is found that the mice can smells linalyl acetate and 1, 8-cineole every day so as to effectively improve the behavioral abnormality caused by fear emotion, the effect is evaluated through scene fear, open field and elevated cross maze experiments, and the experimental result shows that the combined use of the two odorants can significantly improve the fear emotion of the mice. The discovery provides a new thought for research and development of products related to prevention and treatment of fear emotion.