485 results about "Anomaly detection algorithm" patented technology

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 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 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 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, 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 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 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 discloses an AI-based database operating system risk prevention and control system and method, belongs to the technical field of deep learning, and aims to solve the technical problem of how to comprehensively identify, prevent and control database operation risks and improve data management efficiency. Comprising a data processing module for performing data preprocessing on historical operation logs; the risk identification module is used for carrying out grammar and semantic analysis on the SQL statements in the processed operation logs, constructing risk characteristics of the SQL statements, carrying out risk prediction through a trained risk identification model and outputting risk levels; the risk response module is used for carrying out risk warning level by level and carrying out classification and priority ranking on the risk warning based on the Bayesian network; and the user analysis module is used for monitoring user behaviors in real time based on the user portrait and an anomaly detection algorithm, forming alarm information based on a detection result and pushing the alarm information.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the cross technical field of computers and livestock breeding, and discloses an intelligent nutrition mode management method and system based on HAP multi-modal data, and the method comprises the steps: obtaining the multi-source heterogeneous HAP data of livestock, and carrying out the feature extraction and feature fusion of the multi-source heterogeneous HAP data; utilizing a controllable generative adversarial network to generate enhanced HAP feature data and an HAP feature data sequence simulating a physiological process; early abnormal signals are detected through an abnormal detection algorithm, and multi-modal attribution analysis is carried out; continuously optimizing the nutrition demand prediction model and the feeding strategy optimization model; according to the method, by effectively utilizing the neglected unstructured information in the past and combining the multi-modal fusion analysis, the early-stage discovery capability of the weak signal for indicating the physiological abnormality or nutritional imbalance of the livestock and poultry is improved.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to a computer network security detection system and method. The system comprises a data acquisition module, a threat identification module and an attack path analysis module, the data acquisition module is used for collecting network traffic, log files and user behavior data from a target network in real time, and performing preliminary filtering and preprocessing on the data; the threat identification module is used for identifying potential security threats by adopting a multi-dimensional feature analysis and anomaly detection algorithm based on the data content provided by the data acquisition module; and the attack path analysis module performs modeling and analysis on possible attack paths by utilizing a graph theory algorithm based on the detection result of the threat identification module so as to predict the development trend of attack behaviors. The invention further provides a computer network security detection method, real-time monitoring, path analysis and response to network security threats are achieved through cooperative work of all the modules, and therefore the accuracy and effectiveness of network security protection are improved.

The invention provides an automatic perception model method and system for illegal access in a network security isolation area, and belongs to the technical field of computer systems based on specific calculation models.The method comprises the steps that firstly, a network topological graph matrix of the security isolation area is constructed, an equipment asset list is established, and then distributed flow collection nodes are deployed to obtain real-time network data; a deep packet detection technology is used for extracting features to establish an equipment behavior baseline library, a multi-target risk assessment function is used for carrying out risk grade division on equipment, a multi-layer perceptron and a time sequence anomaly detection algorithm are used for identifying abnormal communication, and an equipment fingerprint identification mechanism based on physical layer characteristics is established to verify the legality of the identity of the equipment. A security isolation intelligent sensing network model is utilized to analyze network behaviors, a multi-dimensional abnormal scoring system is constructed to calculate risk scores, a response mechanism based on a rule engine is realized, a federal learning technology can be selectively adopted to optimize the model, and an all-dimensional and multi-level illegal access automatic sensing protection system is formed.

The invention discloses an ecological environment detection method and system based on multispectral remote sensing fusion, and relates to remote sensing image processing. The method comprises the following steps: collecting multispectral remote sensing image data of a target area; performing multiband joint atmospheric correction processing on the multispectral remote sensing image according to the scattering coefficient, the atmospheric light value and the transmissivity of each band; performing foreground and background analysis on the corrected multispectral remote sensing image; fusing the vegetation area and the non-vegetation area of each wave band image by adopting different weight strategies to generate a multispectral fusion image; and extracting spectral features of the multispectral fusion image, constructing a standard vegetation spectral feature library, and identifying regions deviating from a standard vegetation spectrum through an anomaly detection algorithm according to the extracted spectral features to obtain various vegetation coverage rates. In view of low vegetation identification precision caused by direct foreground and background division of a multispectral remote sensing image under an atmospheric interference condition, vegetation division is performed after a clear image is obtained, so that the detection precision is improved.

The invention discloses a health management service system and method based on AI optimization. The method comprises the following steps: S1, collecting multi-source health data of a user and constructing a structured health data set; s2, constructing a health state graph containing node attributes, edge connection weights and time indexes; s3, inputting the health state atlas into a linear graph neural network to generate a health state embedded vector; s4, collecting context information of a user, and fusing to generate personalized state perception representation and a health target embedding vector; s5, inputting the improved general value layering model to generate a structured intervention action candidate set; s6, screening and outputting a personalized health intervention scheme based on the matching score; s7, constructing a state evolution sequence, and inputting the state evolution sequence into a semi-supervised anomaly detection algorithm for monitoring and recognition; and S8, when the risk threshold is exceeded, triggering early warning and dynamically adjusting the intervention scheme. According to the method, collaborative optimization of personalized modeling and intelligent intervention strategies is realized, and the method is suitable for health management service scenes.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

The invention relates to the technical field of cross-border logistics, and discloses a cross-border e-commerce commodity traceability method and system based on a block chain, and the method comprises the following steps: generating a unique traceability code for each cross-border e-commerce commodity, extracting an original information field of the cross-border e-commerce commodity, and calculating an anti-counterfeiting hash value; performing structured packaging on the original information field and the anti-counterfeiting hash value according to a preset template, confirming a transaction through a consensus mechanism, writing the transaction into a block chain, and generating a block hash value; operation information of all logistics nodes is collected, abnormity is recognized through an abnormity detection algorithm, and data fingerprints obtained after abnormity recognition are encrypted and stored; the encrypted and signed operation information is submitted to a block chain network, and a block chain node verifies the validity of a digital signature through a public key of a producer and verifies the legality through a consensus mechanism; and proving an aggregation verification result by using zero knowledge. According to the invention, a complete traceability path is displayed, and full-chain transparent management from production to consumption is realized.

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.

The invention discloses a construction method of a multi-dimensional time sequence anomaly detection system. The method comprises the following steps: acquiring multi-dimensional time sequence data; preprocessing the obtained multi-dimensional time sequence data; constructing a depth anomaly detection algorithm, constructing an association graph of the multi-dimensional time series data by using cosine similarity and a TopK strategy, learning the association graph through a graph neural network, extracting space and time features of the multi-dimensional time series data, and calculating an anomaly score of each data point; and constructing an anomaly detection system suitable for real-time multi-dimensional time series data based on Apache Flink. Partitioning and efficient detection of multi-dimensional time series data are completed through a real-time stream processing module, and an anomaly positioning map and an anomaly propagation map are generated. The method has the advantages of high efficiency, accuracy, real-time performance and the like on the anomaly detection of the multi-dimensional time series data, can effectively deal with a large amount of multi-dimensional time series data generated in the operation process of a complex system, and timely discovers and locates the anomaly condition so as to support quick response of system managers.

The invention discloses a network asset detection method and device, equipment and a storage medium, and relates to the field of network security governance, and the method comprises the steps: determining the flow data, text data and topological data of a target network asset, carrying out the preprocessing of the flow data, the text data and the topological data, and obtaining a target network asset detection result; obtaining processed flow data, processed text data and processed topological data; defining an abnormal situation of the physical equipment, and determining an anomaly detection algorithm based on the time convolutional network, a preset large model and a preset asset knowledge graph; and performing anomaly detection on the target network assets by using the processed flow data, the processed text data, the processed topological data, the abnormal situation and the anomaly detection algorithm to determine abnormal assets in the target network assets, and performing preset repair operation on the abnormal assets. Therefore, high-accuracy, low-delay and interpretable intelligent asset detection can be realized.

The invention relates to the technical field of safety monitoring, and discloses a multistage safety early warning and linkage processing system for an electricity-hydrogen complementary energy station, and the system comprises a monitoring collection module which is used for deploying a multi-source sensor network in the electricity-hydrogen complementary energy station, collecting key parameter data in real time, and generating a real-time data set comprising a timestamp, a sensor ID and a parameter value; the anomaly detection module is used for performing multi-dimensional analysis by adopting an anomaly detection algorithm according to the real-time data set, identifying an anomaly mode and outputting a graded early warning signal; the evaluation grading module is used for carrying out dynamic risk evaluation in combination with the early warning signal and the system state, and outputting a quantitative risk grade and a disposal suggestion; the linkage triggering module is used for automatically matching and triggering a corresponding plan according to the risk level and outputting a specific linkage control instruction; an execution feedback module; according to the invention, the emergency response speed is improved, the false alarm rate caused by normal working condition fluctuation is reduced, and fundamental conversion from passive response to active early warning is realized.

The invention discloses a sewage plant inflow water quality abnormity early warning method, comprising the following steps: analyzing an inflow water high-frequency time sequence data fluctuation rule of a sewage plant through an algorithm, constructing an inflow water quality prediction model, obtaining a prediction residual sequence on the basis of water quality prediction, and eliminating an inflow water normal fluctuation rule of the sewage plant; and substituting the residual error sequence into an isolated forest algorithm to realize water quality abnormity monitoring and water inflow early warning for water quality abnormity. A water quality anomaly detection method based on time sequence prediction is constructed, and the requirement of inflow water quality prediction of a sewage treatment plant can be met; the problem that water quality abnormity is not obvious in water quality indexes is solved by excavating the water quality fluctuation rule of sewage treatment inlet water and combining with an unsupervised abnormity detection algorithm to realize abnormity judgment.

The present disclosure relates to the technical field of anomaly detection, and provides an anomaly detection method and apparatus, and a computer readable storage medium. The method comprises: acquiring first customs declaration data to be detected, wherein said first customs declaration data comprises a plurality of fields associated with corresponding items and addresses, the plurality of items corresponding to the plurality of fields can form a plurality of item sets, and each item set comprises two items among the plurality of items; calculating an evaluation distance between two addresses associated with the two items in each item set; performing association rule mining on a plurality of pieces of historical second customs declaration data to obtain an evaluation index representing an association relationship between the two items in each item set; using an anomaly detection algorithm to process the evaluation distance and the evaluation index so as to obtain a first result representing whether there is a risk between the two items in each item set or not; and on the basis of the first result, determining whether there is an anomaly in the first customs declaration data or not.

The invention belongs to the technical field of equipment state monitoring, and particularly relates to a boiler wall temperature anomaly detection and early warning method based on approximate entropy calculation, which comprises the following steps: dividing a large coal-fired power plant boiler wall temperature time sequence into continuously overlapped sliding windows; calculating three statistical parameters in each sliding window; the three statistical parameters comprise standard deviation, range and approximate entropy; forming a three-dimensional time sequence matrix formed by n groups of parameters based on the three statistical parameters; and performing anomaly detection and early warning on the three-dimensional time sequence matrix by using an isolated forest anomaly detection algorithm. Abnormalities are accurately recognized from different dimensions, and high-dimensional data are efficiently dealt with. The method has the real-time performance and the dynamic performance, online calculation and real-time response can be achieved, the model is flexibly updated along with new data inrush, and the anomaly detection requirement of the dynamic data environment is met.

The invention discloses a real-time monitoring management method and system for water conservancy and hydropower engineering, and relates to the field of water conservancy and hydropower. The method comprises the steps that environment data X and effect data Y are collected, and a correlation coefficient matrix between the environment data X and the effect data Y is constructed; constructing an anomaly detection algorithm library, and constructing an evaluation index matrix according to the correlation coefficient matrix and the anomaly detection algorithm library; according to the evaluation index matrix, selecting an optimal anomaly detection algorithm for each type of monitoring data; carrying out abnormal value processing on the collected monitoring data according to an optimal abnormal detection algorithm; grouping the monitoring data subjected to abnormal value processing according to seasons and working conditions, calculating a mean value and a standard deviation of each group of data through statistics in combination with a correlation coefficient matrix, and obtaining a mean value + / -2 sigma as a first-level early warning threshold value and a mean value + / -3 sigma as a second-level early warning threshold value; and performing monitoring management according to the first-level early warning threshold and the second-level early warning threshold. In view of low monitoring precision caused by lack of flexibility in hydraulic engineering threshold setting in the prior art, the monitoring precision is improved.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

The invention provides a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and the method comprises the steps: firstly obtaining an original data set which is generated by cross-system call and comprises fragmented call data, peripheral business transaction data and log data, and carrying out the breakpoint supplement processing to generate a complete data set; then, transverse anomaly diagnosis is carried out on the complete data set, a decision tree algorithm is used for positioning a fault propagation path and an abnormal object, then longitudinal anomaly detection is carried out on the abnormal object, operation logs, error stacks and infrastructure indexes are analyzed based on a multi-dimensional anomaly detection algorithm, and an anomaly reason analysis result is generated; and finally, determining a fault source, a fault object and an influence range according to an analysis result, and outputting a cross-system fault diagnosis report, thereby comprehensively utilizing multi-source data, realizing accurate positioning and deep analysis of cross-system faults, improving fault diagnosis efficiency and accuracy, and ensuring stable operation of the system.

The invention relates to the technical field of battery monitoring, and discloses a battery global temperature and pressure distribution monitoring system and method, and the system comprises a distributed sensing unit, a signal processing unit, a data analysis unit, an intelligent decision-making unit, a wireless communication module, and a man-machine interaction terminal. Battery temperature and pressure data are acquired through a high-density flexible sensor array, fine sensing is realized by combining multi-dimensional feature extraction and an anomaly detection algorithm, and a response strategy is generated by using a rule engine; the battery state can be comprehensively monitored, local abnormity can be captured in time, safety and reliability are improved, the method is suitable for the field of power batteries and energy storage systems, the problem that in the prior art, due to insufficient sensor arrangement density, monitoring of the internal state of the battery is not comprehensive is solved, and safety is improved. And the real-time monitoring of the global temperature and pressure distribution of the battery and the intelligent response of the abnormal state are realized.