813 results about "Anomaly detection algorithm" patented technology

The invention discloses an unmanned aerial vehicle-based riverway hydrological sampling inspection method and system. The method comprises the steps of collecting unmanned aerial vehicle multi-parameter sensor data, determining a dynamic sampling rate through an enhanced difference rate adaptive sampling algorithm, performing multi-parameter cross validation and environment compensation, and finally reconstructing a data stream. A hydrological feature extraction transformation algorithm and a hydrological mode recognition algorithm are adopted to recognize a hydrological mode, analyze a state evolution process and predict a development trend; a multi-scale anomaly detection algorithm is adopted to identify anomalies, an anomaly source is positioned through a causal chain tracing algorithm, risk assessment and grading are carried out, an emergency decision scheme is generated, and an inspection strategy is optimized; evaluating system performance, optimizing resource configuration, dynamically adjusting parameters, and updating a prediction model. The intelligent level of river hydrological monitoring is improved, the sampling efficiency and the anomaly detection accuracy are improved, and the decision response time is shortened.

The invention provides a power cable fault detection method, which comprises the following steps of: obtaining a cable material, a laying environment and an insulation aging degree attribute parameter of each monitoring segment according to a pre-established power cable segment model, determining a correction coefficient of the segment model according to load current magnitude at different operating temperatures, and determining a power cable fault detection result according to the correction coefficient; the correction coefficient is used for adjusting the electrical parameter of each segment to adapt to the actual operation condition; in the fault positioning process, a time sequence analysis method is introduced, the dynamic change trend of current and voltage data before and after a fault occurs is considered, the fault occurrence moment is recognized through an anomaly detection algorithm, and the position coordinates of a fault point are corrected according to the difference between the cable material and the laying environment in combination with a judged fault area.

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention provides an asset data collection method based on the Internet of Things, and the method comprises the steps: constructing a directed acyclic graph model based on task decomposition and dependency for each node group, determining a task execution sequence and a data transmission mode between the nodes through employing a critical path analysis algorithm, and obtaining a task-oriented node cooperation process; for the data fusion result, dynamically updating a data model and a decision rule of the node by adopting an incremental learning algorithm, and timely discovering abnormal deviation of node data by adopting an anomaly detection algorithm to trigger dynamic adjustment of node marshalling; and constructing a node cooperation incentive mechanism based on the block chain, dynamically adjusting credit values and resource allocation of the nodes according to contribution degrees and task completion quality of the nodes, and constraining and arbitrating cooperation behaviors among the nodes by adopting an intelligent contract technology.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are improved.

The invention discloses a full-life-cycle auditing tracking system and method, and belongs to the technical field of data tracking backtracking, and the method specifically comprises the steps: carrying out the global unique identifier distribution of personnel configuration data during the first collection, embedding a timestamp, an operation identifier and a link identifier in each life cycle node of the data, constructing a real-time auditing model, and carrying out the real-time auditing of the data; through data change monitoring, behavior pattern recognition and an anomaly detection algorithm, each data operation is recorded in real time and compared with a preset auditing strategy, risk early warning is triggered, block chain evidence storage is established at a key operation node, a multi-block chain link relation network diagram is constructed based on multiple block chains, and when an abnormal node occurs, a data life chain is generated. Predecessor nodes of the abnormal nodes are backtracked, recursive check is carried out on each predecessor node, and an abnormal source and a responsibility subject are positioned; according to the method, the multi-step association risk can be found, the situation that tracking cannot be achieved during tracking and backtracking is prevented, the missed judgment rate is reduced, and the accuracy and efficiency of tracking and backtracking are improved.

The invention relates to the technical field of data processing. The invention particularly relates to an electric energy meter safe electricity utilization monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting power utilization monitoring data pairs of the electric energy meter; clustering data points of the collected power utilization monitoring data to obtain a cluster; obtaining the consistency of the data points in the clustering cluster and the outlier degree of each data point in the clustering cluster; the outlier degree of the data points is the degree that the index stronghold deviates from the cluster to which the index stronghold belongs, an LOF anomaly detection algorithm is used for obtaining the anomaly score of each collected data point, and when the anomaly scores are calculated, the optimal K value corresponding to the data points is calculated according to the outlier degree of the data points and the consistency of the data points in the cluster to which the data points belong; and according to the abnormal score of each data point, carrying out monitoring and early warning on the power consumption behavior. By adopting the method, the accuracy of the safe power utilization monitoring result of the power distribution network can be improved.

The invention relates to the field of electric meter box monitoring, and discloses an anti-electricity-stealing method and system for an intelligent electric meter box, which is used for solving the problem of false alarm caused by harmonic interference when anti-electricity-stealing early warning is carried out, and comprises the following steps: collecting time domain signals of current and voltage in the intelligent electric meter box in a historical detection time period; converting the time domain signal into a frequency domain signal, extracting harmonic information of each harmonic, and establishing a normal harmonic feature model to obtain a target harmonic range; the method comprises the following steps: acquiring time domain signals of current and voltage in an intelligent electric meter box in real time, extracting harmonic information, performing filtering processing according to a target harmonic range, performing first abnormality judgment by using an abnormality detection algorithm, performing multi-parameter joint evaluation if abnormality is judged for the first time, and performing second abnormality judgment according to a multi-parameter joint evaluation result. And electricity larceny early warning is carried out according to the second abnormal judgment result, so that false alarm and missing alarm are effectively reduced, and the adaptability of the system to a complex electricity larceny mode is remarkably enhanced.

The invention discloses a data security dynamic protection method based on artificial intelligence, and relates to the field of network security, and the method comprises the steps: employing an anomaly detection algorithm to recognize malicious data streams and attack features according to a data stream log; according to the identified malicious data flow, a multi-agent reinforcement learning algorithm is adopted, and a protection strategy is adjusted through multiple agents according to the current environment state and attack characteristics; constructing a simulation environment, and training and evaluating an anomaly detection algorithm and a multi-agent reinforcement learning algorithm by generating a confrontation sample and a confrontation strategy; wherein the adversarial sample is generated by performing feature transformation on the malicious data stream and the normal data stream, and the adversarial strategy is generated by performing random disturbance on the existing attack strategy. In view of poor adaptability of a protection strategy for malicious data streams in the prior art, the malicious data streams and attack features are identified through an anomaly detection algorithm, and multi-agent reinforcement learning, collaborative optimization of flow control and a honeypot deployment strategy are adopted, so that the adaptability is improved.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention relates to the technical field of line monitoring, in particular to an electrical engineering high-voltage line fault monitoring system. The system comprises a data acquisition module used for acquiring current signals of monitoring nodes on a mining high-voltage line at all moments; the anomaly analysis module is used for constructing a harmonic anomaly characteristic value of each current signal according to current signal change and data mutation characteristics in an adjacent time period of each current signal; obtaining an abnormal score correction value of each current signal based on the probability density of each current signal and the harmonic abnormal characteristic value; the power transmission control module is used for acquiring all abnormal current signals based on the abnormal score correction value by using an abnormal detection algorithm; and obtaining a judgment result of an abnormal fault based on all the abnormal current signals. Therefore, the mining high-voltage line is monitored, the accuracy of monitoring the abnormal fault of the power supply of the mining high-voltage line is improved, and the stability of the power supply of the mining high-voltage line is effectively ensured.

The invention relates to a disturbance of consciousness disease course monitoring system. The multi-dimensional data acquisition module is used for acquiring electroencephalogram (EEG), heart rate variability (HRV) and skin conductance (EDA) physiological signal data of a patient in real time through a plurality of sensors arranged on the body of the patient, and preprocessing the acquired data through edge computing equipment; the self-adaptive disease course prediction model is based on the physiological signal data acquired by the multi-dimensional data acquisition module, combines the historical disease course data of the patient, trains a personalized disease course prediction model by utilizing a self-adaptive algorithm, and adaptively updates model parameters through a machine learning algorithm in the disease course development process of the patient, so that the disease course prediction accuracy is improved. The dynamic prediction of the progress of the disturbance of consciousness is realized; the continuous disease course monitoring module continuously obtains physiological signals of a patient through a high-frequency sampling technology, recognizes key events in the disease course in real time through an anomaly detection algorithm, the key events comprise sudden consciousness decline and epileptic seizure, generates early warning signals and triggers an emergency response mechanism.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention relates to the technical field of financial management, in particular to a financial management system based on a block chain, which comprises a data collection module, a data processing module, an intelligent reconciliation module and an intelligent adjustment module, the data processing module carries out data standardization and data verification on the transaction data, packages the transaction data passing the verification into blocks according to a time sequence, verifies the blocks, carries out verification by calculating a hash value and using a block consensus mechanism, and takes the successfully verified blocks as a block chain together; the intelligent reconciliation module determines an optimal reconciliation period through time sequence analysis, and identifies abnormal transactions by using an anomaly detection algorithm; the intelligent adjusting module adopts a three-time standard deviation method to set a difference threshold value, automatic adjustment or marking of manual assessment data is achieved, and the problems that in traditional financial management, data is prone to being tampered, the account checking efficiency is low, and abnormal recognition is inaccurate are solved.

The invention provides a water turbine anomaly detection and fault diagnosis method based on multi-sensor data fusion, and the method comprises the steps: collecting stress data, audio signals, vibration signals and working condition data during the operation of a water turbine, and carrying out the preprocessing of the collected data, including data cleaning, alignment and normalization; extracting time-domain, frequency-domain and time-frequency-domain features of the preprocessed stress data, audio signals and vibration signals, forming a high-dimensional feature vector in combination with the preprocessed working condition data, and performing dimensionality reduction on the high-dimensional feature vector by using principal component analysis; performing anomaly detection on the dimensionality-reduced high-dimensional feature vector according to a clustering algorithm, a local outlier factor algorithm and an anomaly detection algorithm constructed by an auto-encoder neural network, and generating a comprehensive anomaly score in combination with a preset weight to obtain an anomaly detection result; and when the water turbine is in an abnormal state, the fault type is diagnosed through the similarity between the dimensionality-reduced high-dimensional feature vector and the historical fault feature template.

The invention relates to the field of data processing, in particular to an operation state monitoring method and system for a connector production machine, and the method comprises the steps: obtaining operation state data of the connector production machine, carrying out the preprocessing, taking preset local reference data as a benchmark, determining the relative change degree of data of each dimension, and calculating the abnormal fluctuation degree. And meanwhile, performing first-order difference symbolization processing on the local reference data, and counting symbol continuous lengths to form a sequence so as to analyze the abnormal tendency. And combining the abnormal fluctuation degree and the tendency to construct a fault risk index, and calculating a local abnormal weighting factor at each moment. And finally, based on the factor, improving an LOF anomaly detection algorithm and calculating and correcting an anomaly score, thereby effectively monitoring the operation state of the connector production machine. According to the method, the LOF algorithm is improved, the defects of a traditional algorithm in time sequence and multi-type data anomaly detection are overcome, the monitoring accuracy and the fault early warning capacity are improved, and the production efficiency and reliability are improved.

The invention relates to the technical field of medicine sales inventory management, and discloses a medicine sales inventory management system based on artificial intelligence, which comprises a multi-modal anomaly detection module, a multi-modal anomaly detection framework is constructed, and medicine sales anomaly is detected by using an isolated forest algorithm, a depth auto-encoder and a sequence anomaly detection algorithm; the causal structure learning and attribution analysis module is used for constructing a drug sales causal structure learning and attribution analysis system and analyzing abnormal reasons by adopting a NOTEARS algorithm and an SHAP attribution algorithm; the intervention strategy generation and evaluation module is used for constructing an intervention strategy generation and evaluation system and simulating different strategy effects through a causal intervention formula; and the inventory strategy optimization and execution module is used for implementing inventory strategy optimization and execution and dynamically adjusting inventory management. According to the invention, automatic detection, cause attribution and intelligent correction of sales abnormity are realized, and a closed-loop management system is formed.

The invention belongs to the technical field of vehicle state detection and early warning, and particularly relates to a vehicle state monitoring and early warning method and system.The monitoring and early warning method comprises the steps that a sensor obtains real-time data, an anomaly detection algorithm is applied, and an abnormal event is marked; calculating an information priority according to the abnormal severity and the driving scene, and distributing the information priority to a high-priority queue; multi-mode early warning is generated, and high-frequency sound and vibration are used during high-speed driving; extracting a voice prompt, and generating voice waveform data through a voice synthesis module; voice input of a driver is recognized, intention is analyzed, and abnormal information feedback is provided; according to the scene and the abnormal state, interactive output is optimized, and detailed information is displayed during low-speed congestion; dynamically adjusting the interface of the instrument panel, and amplifying the key area in case of abnormal severity; and integrating the image data and the voice data, generating a multi-mode signal, and transmitting the multi-mode signal after rendering processing. The vehicle abnormal information can be timely and accurately transmitted to a driver, and the driving safety is effectively improved.

The invention discloses a fry culture dynamic monitoring and feeding optimization method based on deep learning. The method comprises the following steps: S1, installing a water quality sensor and a camera to collect water quality data and fry image data; s2, performing feature extraction on the fry image data by using an improved convolutional neural network; s3, analyzing a growth trend and an activity mode of the fry by using a long-short-term memory network to obtain a comprehensive score; s4, obtaining a feeding strategy based on a multi-target adaptive PPO algorithm, and forming a personalized feeding plan; s5, performing detection and early warning on abnormal data through an anomaly detection algorithm, and adjusting a breeding environment; and S6, continuously optimizing the fry culture dynamic monitoring model. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring and feeding of fry culture, and remarkable technical value and economic benefits are brought to practical application.

The invention discloses an intelligent network traffic anomaly detection and automatic isolation system, which comprises a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module and a visual monitoring and alarm platform, by introducing a deep learning algorithm and a multi-dimensional anomaly detection algorithm, deep mining and comprehensive analysis of network traffic can be realized, feature information is automatically learned and extracted from mass data, an accurate traffic behavior model is constructed, and abnormal behaviors in the traffic are detected in real time. And full-process automatic processing from data acquisition, preprocessing, intelligent analysis to automatic isolation is realized. When abnormal traffic is detected, the system can automatically execute isolation measures and monitor the isolation purpose in real time, and compared with a traditional method based on a rule base or threshold judgment, the method is higher in detection accuracy and real-time performance.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security scheduling management method based on deep learning, which comprises the following steps: acquiring video monitoring data and access control record data of a plurality of communities, cleaning the data, unifying the format and content of the data by adopting a data standardization technology, and storing the unified data into a database; the method comprises the steps of obtaining standardized community security and protection data, mining a personnel flow mode and a vehicle track mode among different communities by adopting a graph algorithm analysis technology on the basis of a community relation network to obtain a cross-community personnel flow mode and a vehicle track mode, and obtaining a cross-community security and protection result according to analysis results of the cross-community personnel flow mode and the vehicle track mode. And an anomaly detection algorithm is adopted to identify potential safety hazards, and if an abnormal condition is detected, early warning information is generated. According to the invention, deep correlation analysis of cross-community security data is realized, and the intelligent level of community security management is effectively improved.

The invention provides an internal network threat intelligent analysis and prediction method and system based on a large language model, and relates to the technical field of network technology and security. According to the method, the long-range dependency processing capacity of the LLMs is utilized, global and long-range dependency analysis of the user behavior sequence is achieved, and the limitation of a traditional fixed session unit is broken through. Rich semantic features and context information are extracted from original log data through the deep semantic analysis ability of LLMs, and the feature expression ability is greatly improved. In combination with the reasoning capability of LLMs and a traditional anomaly detection algorithm, context-based deep semantic analysis is realized, and the recognition capability of complex threats is improved. In the aspects of real-time monitoring and response, the LLMs are introduced to assist in complex event processing and dynamic rule generation, and the intelligent level and efficiency are improved. Aiming at challenges of data management and analysis, an LLMs is utilized to enhance a data management strategy, a storage structure is optimized, and an intelligent data life cycle management and query interface is provided.