232 results about "Abnormality" patented technology

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 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 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 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 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.

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.

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 application provides a dynamic monitoring and optimization method and system of an artificial intelligence sandbox environment, relates to the technical field of artificial intelligence safety, and comprises the following steps: obtaining execution state data of a model, constructing a directed dependency graph for abnormality identification and propagation analysis, and generating an abnormal feature vector. Root cause information is extracted based on the vector, resource demand is predicted through historical record clustering, adjustment demand is generated, the demand is corrected through loop dependency analysis, and adjustment components are divided. Finally, the resource allocation ratio is solved by comprehensively analyzing multi-model information, and environment optimization instructions are generated. The application realizes accurate positioning, root cause analysis and dynamic optimization of abnormal behaviors in the sandbox, and improves monitoring efficiency and resource utilization.

This invention relates to the field of intelligent video surveillance and artificial intelligence analysis technology, specifically to a method for identifying abnormal behavior in gas stations by integrating cross-camera behavior chains. The method includes: collecting multiple video streams, historical trajectory data, a 3D spatial topology map containing blind spot nodes, and operating procedure text; extracting target appearance and movement features, combining topological connectivity probability and dynamic tolerance for cross-camera joint matching to generate an identity identifier; stitching trajectories according to timestamps, and inserting blind spot abnormal dwelling nodes in timed-out cross-node segments to form a spatiotemporal behavior path; inputting the path text and video stream slices along with the procedures into a visual language model for behavioral intent reasoning; determining the abnormality category, calculating a dynamic risk score, and triggering a graded alarm; this invention organizes fragmented actions and blind spot dwellings into a reasonable behavior chain, achieving dangerous logical behavior identification.

An under-car sensor (22) detects vibration of a floor surface of a car (10) of an elevator. A traction machine sensor (21) detects vibration of a traction machine (250) of the elevator. A server (100) infers the location of occurrence of an abnormality in the elevator on the basis of a raising / lowering condition of the car (10), the detection result from the under-car sensor (22), and the detection result from the traction machine sensor (21).

The invention provides a green accommodation resource management method and system based on real-time metering and abnormal behavior recognition, and relates to the technical field of energy metering. According to the invention, water consumption, electricity consumption and water temperature data of guest rooms are collected, cold and hot water flow is decomposed, heat energy consumption is calculated, and continuous water consumption abnormity identification and linkage valve control treatment are realized; accumulating energy consumption according to a statistical time period based on the check-in record, converting a guest room carbon emission index, generating guest reward information when an energy-saving standard is met, and writing the guest reward information into a check-in account during check-out settlement; and comparing the total energy consumption of the hotel with the energy consumption summary difference value of the guest room to output an energy consumption abnormity positioning result. According to the invention, room-level energy consumption real-time metering, abnormity identification and energy-saving evaluation are realized, and the performability and accounting performance of green accommodation resource management are improved.

The purpose is to appropriately manage the imbalance in the amount of signal processing. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of the normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the number of signals allocated to the opposing device 3 for each time period 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 the abnormal data that indicates the number of abnormal signals allocated to the opposing device 3 for each time period that deviates from the range of normal signal numbers, 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.

A technique is provided that uses AI processing to enable stable discrimination of various defect types for a defect occurring in a sheet product. Provided is an assistance device that assists a determination inspection for determining a type of an abnormality. The assistance device includes a classification processing unit and a learning discrimination unit. The learning discrimination unit discriminates an abnormality type for each of the one or more classes to be processed selected by the classification processing unit, based on an individual discrimination result from the learning discriminator trained with the discrimination criterion for each abnormality type.

An abnormality diagnosis device includes an abnormality determination unit configured to determine whether or not there is an abnormality with respect to a state quantity acquired from equipment and a cause estimation unit configured to estimate a cause of the abnormality in the equipment from a state quantity determined to be abnormal by the abnormality determination unit using a cause correspondence table in which a cause of an abnormal mode of the equipment identified in fault tree analysis is associated with the state quantity that is abnormal when the cause has occurred.

This application provides a non-contact health monitoring device, method, equipment, and medium, belonging to the field of health monitoring technology. It collects Wi-Fi amplitude data and calculates Shannon entropy to form an instantaneous vitality index sequence. The instantaneous vitality index sequence is processed through a sliding time window to determine behavioral feature vectors and activity intensity indices. Normal behavior boundaries are defined using behavioral feature vectors from historical normal periods, and a baseline lower limit for activity intensity is determined based on the activity intensity indices from historical normal periods. If the behavioral feature vector of the current window is outside the normal behavior boundary, or the activity intensity index of the current window is lower than the baseline lower limit, it is determined to be an abnormal window. When both conditions are met simultaneously, it is determined to be a high-risk rhythmic abnormality window. This allows for graded early warning monitoring of the activities of elderly people living alone. Using the scheme of this application, it is possible to effectively identify environmental steady-state channel jitter and transient channel disturbances caused by human behavior.

An abnormality diagnosis device according to the present invention includes: an operational signal collecting unit that collects a plurality of sensor signals; an in-operational-segment operational signal extracting unit that extracts a sensor signal for each predetermined operational segment of equipment from the plurality of sensor signals collected by the operational signal collecting unit; a signal level acquiring unit that acquires a signal level for each predetermined frequency band for each operational segment with respect to the sensor signal for each operational segment extracted by the in-operational-segment operational signal extracting unit; a degree-of-deviation calculating unit that calculates the degree of deviation of the signal level for each operational segment and each frequency band acquired by the signal level acquiring unit from the signal level of the same equipment, the same operational segment, and the same frequency band during normal operation; and an abnormal part specifying unit that specifies an abnormal part of the equipment on the basis of the degree of deviation calculated by the degree-of-deviation calculating unit.

The application discloses a smelting plant personnel behavior abnormality monitoring method and system, relates to the smelting plant monitoring technical field, constructs a three-dimensional analysis model, sets an abnormal sensitivity coefficient in combination with a work area risk type, provides a quantitative basis for abnormal detection, configures a virtual human body model, captures personnel behavior trajectories and actions by using visual analysis technology, determines basic abnormality parameters, uses a simulation simulation identification library to deeply analyze real-time behaviors of the virtual human body model, dynamically determines observation abnormality parameters, comprehensively calculates abnormality degrees based on the basic abnormality parameters, the observation abnormality parameters and the abnormal sensitivity coefficient, and realizes accurate alarm. The application solves the problems that traditional manual monitoring is easy to be missed and is not accurate, significantly improves the efficiency and reliability of smelting plant safety monitoring by means of automatic and intelligent monitoring means, and provides strong technical support for safety production.

The invention relates to the field of neurodevelopmental disorders, and discloses an electro-oculogram signal analysis and diagnosis system based on neurodevelopmental disorders, which extracts signal features from pre-processed electro-oculogram signals, and analyzes inter-group differences between signal features of a child patient group and signal features of a healthy child group. The method comprises the following steps: preliminarily screening out signal features with significant statistical difference between two groups, then carrying out severity correlation analysis on the screened signal features, screening and forming a feature list highly related to ADHD severity, and then comparing eye movement features of a child patient group under natural behaviors with eye movement features of a healthy child group, so as to obtain a feature list related to ADHD severity. The method comprises the following steps of: identifying and forming an abnormal signal feature list, fusing the feature list highly related to the ADHD severity and the abnormal signal feature list, calculating a comprehensive abnormal score, drawing an ROC curve according to the calculated comprehensive abnormal score, and carrying out grading evaluation on the ADHD severity of a child patient, so that accurate diagnosis and severity judgment on the ADHD disease are realized.

To provide an abnormality determination device and the like capable of easily and accurately determining abnormalities in component supply equipment for component mounting.SOLUTION: An abnormality determination device 110 is an abnormality determination device 110 capable of determining an abnormality of a feeder 7 that sequentially supplies multiple components P to a component mounting device M3 by sending multiple pockets 14a in which multiple components P are individually stored. The abnormality determination device includes: an acquisition unit 111 that acquires position information regarding a supply position of the relevant components P for each of the multiple components P sequentially supplied by the feeder 7 for component mounting with component mounting device M3; an estimating unit 112 that estimates a stop position of one pocket 14a in which one component P out of the multiple components P is stored by performing predetermined statistical processing on the basis of, the acquired multiple position information; and a determination unit 113 that determines the abnormality of the feeder 7 on the basis of the estimated stop position.SELECTED DRAWING: Figure 9