275 results about "Risk source" patented technology

The invention discloses an accounting data checking method and system based on artificial intelligence, and the method comprises the steps: extracting multi-modal accounting data from a distributed tax data source through a federated learning framework, carrying out the anonymization aggregation of the data through a differential privacy technology, and generating a privacy-protected joint feature vector; inputting the joint feature vector into a causal reasoning model, identifying an abnormal fluctuation mode in the accounting data through anti-fact analysis, and outputting an abnormal index set with causal association; performing traceability reasoning on the abnormal index set by using a dynamic time sequence knowledge graph, generating a cross-cycle risk conduction path, and positioning a risk source entity; and generating an explainable inspection decision tree based on the risk source entity, dynamically adjusting an early warning threshold through adaptive threshold optimization, and outputting a graded early warning signal and a targeted inspection scheme. According to the embodiment of the invention, the accuracy, interpretability and risk traceability of distributed tax inspection can be improved.

The invention discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

The invention relates to the technical field of safety management and risk assessment, and discloses a laboratory safety intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, building a three-dimensional digital model of a laboratory; s2, deploying a sensor network covering a laboratory; s3, monitoring an operation behavior in real time through a sensor network, and comparing the operation behavior with the operation logic chain; s4, when the sensor network detects that the physical environment is abnormal; s5, collaborating the logic abnormal state, the position and strength of the risk source and the risk type; and S6, performing evolution prediction based on the risk type and the risk source. According to the method, Bayesian fusion reasoning is performed on context information such as multi-dimensional sensor evidence and real-time operation regulations, so that high-precision and high-confidence identification of risk types is realized, and the problem that the risk types cannot be identified due to lack of cognition on field operation activities is solved. And the risk qualitative is fuzzy, the false alarm rate is high, and the real dangerous case and compliance operation interference cannot be distinguished.

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention belongs to the technical field of engineering progress tracking and early warning, and particularly discloses an AI-based engineering progress tracking and early warning method and system. According to the method, the problem of information islands in multi-modal data integration of a traditional method is solved by constructing the engineering space-time knowledge graph, and the comprehensiveness of construction site state sensing is improved; an intelligent mechanism of deviation analysis and trend deduction is established, and accurate prediction of the engineering state evolution trend is realized through multi-dimensional comprehensive evaluation of geometric morphology, construction process topology, resource in-place time sequence and quality compliance attributes; through structured early warning generation and multi-level risk conduction path modeling, the risk source and the influence range thereof are clarified, and a subsequent construction scheme is optimized in combination with a dynamic adjustment strategy, so that the scientificity and execution efficiency of project progress management are improved.

The invention discloses a power grid operation risk assessment method and system considering time period coupling characteristics, and relates to the technical field of power grid operation risk assessment, and the method comprises the following steps: obtaining operation data of a target power grid in a plurality of continuous time periods, and constructing a time-space state tensor based on the operation data; introducing a time period memory kernel function to obtain a cross-time period evolution memory curve of a node state; constructing a risk diffusion path diagram based on the evolutionary memory curve and the power grid power flow equation; establishing a critical energy accumulation function based on the risk diffusion path diagram, and triggering a node risk source mark when accumulation exceeds a preset threshold value; and generating a space-time risk evolution spectrum based on the risk source mark, and outputting a risk trajectory and a threshold breakthrough time point of each node in a continuous time period. According to the method, the cross-time evolution memory curve and the dynamic risk diffusion path diagram are constructed, so that high-risk nodes of continuous-time-period tail events are recognized in advance, and the problem of lack of cross-time-period accumulation risk quantification is solved.

The invention provides an evacuation strategy generation method for a hydropower station, and the method comprises the steps: obtaining the physical space data of the hydropower station, including the landform, building structure and equipment layout data; constructing a dynamic risk model based on the physical space data, and identifying a risk source type and a diffusion path; the personnel distribution data is collected through the positioning terminal, and an evacuation demand map is generated. The safe traffic capacity of the limited evacuation channel is calculated, and an initial evacuation path set is generated in combination with the evacuation demand map; performing coupling optimization on the initial evacuation path set based on three factors of path length, risk exposure duration and crowd density change rate; the state change of a risk source is dynamically monitored through a risk sensor network, strategy updating is triggered when the risk diffusion speed exceeds a preset threshold value, and finally an optimized evacuation strategy is output to terminal execution equipment. The method can achieve the real-time updating and efficient optimization of the evacuation strategy of the hydropower station, and improves the evacuation efficiency and the personnel safety guarantee capability.

The invention discloses an intelligent agricultural condition monitoring and early warning method and system, and the method comprises the steps: constructing a three-dimensional geographic model of a monitored farmland, and carrying out the grid division to generate a plurality of agricultural monitoring grids; obtaining multi-source agricultural condition data, predicting the agricultural condition risk level of each grid through a machine learning model, screening the grid with the high level as a target early warning area, and identifying a risk source and an initial monitoring point; establishing a multi-objective decision model according to the space coverage rate, the data acquisition precision and the deployment cost of the initial monitoring point, and solving the model by adopting a genetic algorithm to determine an optimized monitoring point; and simulating agricultural condition evolution of the optimized monitoring points in an extreme scene, screening the optimized monitoring points of which the stability indexes are higher than a preset value as final agricultural condition early warning nodes, and generating a dynamic monitoring network. According to the invention, problems of one-sided agricultural condition evaluation and inaccurate risk area positioning in the prior art are solved. And the agricultural condition change can be responded more timely and accurately, and the reliability and the stability of the monitoring system under complex and extreme conditions are remarkably enhanced.

The invention discloses a steam turbine maintenance decision optimization system and method based on fault cases, and belongs to the field of steam turbine fault maintenance, and the system comprises a data collection and processing module, a dynamic graph construction module, a multi-source data alignment module, a cross-domain fault simulation module, a fault causal deduction module, a maintenance strategy generation module, and a strategy adaptation migration module. According to the method, effective integration of cross-modal information can be realized, the expression ability of fault diagnosis and maintenance knowledge is enhanced, the adaptability and accuracy of the system are improved, the physical authenticity and reliability of fault prediction are improved, maintenance personnel are helped to understand a fault propagation mechanism and a risk source, and the transparency and reliability of decision making are improved; the limitation of a single scheme is avoided, it is guaranteed that a maintenance decision can respond to environment changes in time, the adaptability and efficiency of maintenance execution are improved, the maintenance effect can be rehearsed in advance, the field trial and error risk is reduced, scientific assessment and quantitative management of the risk are achieved, and the safety guarantee of the maintenance scheme is improved.

The invention provides a park environment risk traceability and grading early warning management and control method, and belongs to the technical field of risk early warning based on computer data processing. The method comprises the following steps: firstly, collecting video monitoring, environment and equipment state sensor and real-time meteorological heterogeneous data in a whole domain and a peripheral specific buffer zone of a park, and preprocessing the data to obtain standardized multi-source data; dividing grid units by taking a park boundary coordinate as a reference and distributing a unique standardized code, associating standardized multi-source data to a corresponding grid according to a rule, and performing fusion calculation on a grid-level risk characteristic value of each grid; then combining real-time meteorological data, tracking a risk diffusion path through a reverse derivation algorithm, positioning a risk source position and a core range, and outputting related parameters; and finally, grid risk levels are divided according to the grid risk characteristic values and a preset threshold interval, a corresponding early warning mechanism is triggered, and early warning information is output. According to the invention, the precision of environmental risk positioning, the reliability of traceability analysis and the refinement degree of an early warning result are improved.

The invention discloses a multi-risk-source tunnel construction early warning method, relates to the field of tunnel construction early warning, and provides a multi-risk-source tunnel construction early warning method which comprises tunnel vault and horizontal convergence deformation prediction based on a time sequence model and a tunnel risk prediction model based on the time sequence model. According to the method, tunnel face images and construction site monitoring data are expanded into important data sources, a corresponding database is established, and a tunnel face joint extraction model, a tunnel deformation prediction model and a tunnel deformation risk assessment model are established by using algorithms such as image recognition, a neural network and machine learning. And a tunnel construction risk assessment method is formed. The invention aims to provide scientific basis and technical support for safety construction and risk management of tunnel engineering by using an informatization technology.

The invention belongs to the technical field of data processing, and particularly relates to a ground pressure monitoring and early warning method and system based on multi-source data fusion, and the method comprises the steps: constructing a space-time distance improvement DBSCAN algorithm of micro-seismic events, carrying out the clustering of the micro-seismic events through the improved DBSCAN algorithm, and calculating the risk source index of each micro-seismic event cluster; in the three-dimensional grid space, acquiring risk potential energy of each grid unit according to the risk source index, the distance between the micro-seismic event cluster and the grid unit and the stress state of the grid unit; calculating a ground pressure risk index by combining the current value of the risk potential energy and the time change of the risk potential energy; and performing multi-level early warning according to the ground pressure risk index. Ground pressure early warning is carried out by fusing the microseismic space-time aggregation characteristics, the stress field state and the risk dynamic evolution trend, and the accuracy and timeliness of ground pressure disaster early warning are improved.

The invention relates to the technical field of laboratory safety management, and discloses an AI model fused laboratory safety management risk intelligent pre-judgment method and system. The method comprises the steps of collecting original monitoring data of a laboratory monitoring area, and extracting risk signals and abnormal signals corresponding to abnormal events from the original monitoring data; obtaining relevance characteristics between the risk signals and adjustment characteristics of the risk signals by abnormal events; risk features representing risk differences are generated according to the features, and target risk probability distribution is obtained based on the risk features; and performing interference suppression processing on the original monitoring data according to the target risk probability distribution to separate a prediction risk waveform of the target risk source. According to the method, through deep mining of the relevance of risk signals and the adjustment effect of abnormal events, the accuracy of risk pre-judgment is improved, potential risks can be effectively captured, and a more intelligent and more reliable risk pre-judgment means is provided for laboratory safety management.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention relates to the technical field of national asset risk early warning, and discloses a national asset risk intelligent early warning system based on multi-source data fusion. The system comprises a risk stage identification module which obtains enterprise operation parameters through a national capital supervision data interface, divides the life cycle of a national capital enterprise into an operation stable period, an investment expansion period and an asset recombination period, and outputs corresponding risk stage identifiers; a multi-source data fusion module performs staged weight distribution on financial data, public opinion data and compliance data according to identifiers to obtain a differential fusion data set and construct a dynamic risk twinborn model; the risk feature extraction module calculates the deviation between the model and real-time monitoring data, and generates a multi-dimensional risk feature vector containing quantitative indexes of financial abnormality, public opinion popularity and compliance deviation; and the intelligent early warning output module evaluates the vector by using a staged risk propagation tree algorithm, generates a risk level signal and an associated risk source positioning result, and assists national asset risk early warning and supervision.

The invention discloses a thermal power plant operation risk intelligent early warning method based on deep learning. The method comprises the following steps: S1, collecting operation data to construct a multi-modal time sequence; s2, respectively inputting modal sub-networks after standardization processing; s3, setting a reversible residual structure for each sub-network and introducing a dynamic scaling mechanism; s4, introducing a time gating mechanism to adjust residual response; s5, fusing the modal features to form fusion representation; s6, performing forward transformation, combining with intermediate state reverse reconstruction, and calculating a residual error; s7, a sliding window aggregates residual errors to generate a scoring sequence, and risk levels are output; and S8, generating a key factor sequence based on residual attribution analysis features. According to the invention, operation abnormity in-advance identification and risk source accurate positioning under complex working conditions are realized.

The invention discloses a low-altitude flight risk assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an initial weight corresponding to a multi-level index in a multi-level risk index system, and the multi-level index comprises a comprehensive risk index, a multi-dimensional risk index and a basic risk source index; based on the current flight state information, adjusting an initial weight corresponding to the multi-level index to obtain a combined weight adaptive to the current flight environment; determining a target index from the multi-level indexes, and obtaining an original value associated with the target index; and performing risk assessment based on the original value and the combined weight to obtain a risk assessment result. According to the method, the risk index weight can be dynamically adjusted according to the current flight environment, and the real-time performance and accuracy of risk assessment are improved.

The invention discloses a thermal power plant limited space operation safety and environment multi-parameter monitoring system, and relates to the technical field of operation safety monitoring. A structure parameter model is constructed based on a space volume structure, a gas exchange path, a risk type and an operation position, and multi-parameter sensing nodes are configured; extracting abnormal fluctuation characteristics of the environmental parameters through edge computing nodes; a gas flow model and a heat distribution model are combined to carry out space-time inversion, a risk source is positioned, a space risk heat map is dynamically generated, the position and action state of an operator can be obtained in real time, the exposure probability is calculated, and automatic identification and graded early warning of a high-risk area are realized; the system has high-precision, low-delay and full-coverage monitoring capability, and the intrinsic safety of limited space operation of the thermal power plant is improved.

The invention belongs to the technical field of image recognition and power grid capital construction sites, and particularly relates to a power grid capital construction dynamic risk recognition method based on a multi-modal image and LLM deduction, and the method comprises the steps: limiting the relation between an inspection object and a design based on a stress chain and an equipotential chain, and generating a constraint framework; performing multi-channel sampling on objects in the constraint framework; after the channels are aligned, a state evidence packet is generated, and the design relation and the state evidence packet are aligned; a stress chain and an equipotential chain are assembled, and a local topological graph is obtained through cutting and minimum necessary path selection; based on the local topological graph, mapping mechanism elements of the nodes and the edges into a fixed semantic slot position and action mode template, and generating mechanism description; and outputting a dynamic risk source list based on the mechanism description and the local topological graph. According to the method, the multi-modal image is subjected to constraint mapping to form the mechanism elements and the minimum necessary topology, and deduction is carried out on the stress chain and the equipotential chain, so that the dynamic risk source is accurately identified, and evidence-level traceable decision output is realized.

The invention provides a network security analysis method and system based on system log auditing analysis, and belongs to the technical field of network security. Behavior chain knitting processing is executed on a streaming log sequence by capturing the streaming log sequence generated by network nodes of a power monitoring system in real time, and the network security is obtained. Associating continuous operation record entries of the same operation main body to form a dynamic behavior chain, performing mode matching on the dynamic behavior chain based on a preset security rule base, identifying candidate behavior fragments, performing reverse traceability analysis on the candidate behavior fragments, constructing a risk diffusion diagram, and performing risk analysis on the risk diffusion diagram. And finally, generating a security analysis report containing a risk source node, a diffusion path and an affected resource list, and pushing the security analysis report to a security management and control center, so that the real-time performance, accuracy and comprehensiveness of network security analysis of the power monitoring system can be effectively improved, and safe and stable operation of the power system is ensured.

A system and method for detecting a cybersecurity issue in a software container layer and mitigating the same is presented. The method includes: detecting a software container including a plurality of layers; associating a first layer of the plurality of layers with a first image of the software container, and associating a second layer of the plurality of layers with a second image of the software container; inspecting each of the plurality of layers for a cybersecurity issue; detecting a cybersecurity object on the first layer, wherein the cybersecurity object indicates the cybersecurity issue; initiating a remediation action on the first image, in response to detecting the cybersecurity object on the first layer.

The invention discloses an intelligent land use monitoring system and method based on natural resources, and relates to the technical field of intelligent land use monitoring. The system comprises a multi-source data integration module, a risk source identification module, a risk quantification and early warning module and a dynamic monitoring and slow release module. The multi-source data integration module constructs a space-time reference grid, collects four types of data and establishes a four-dimensional incidence matrix through grid ID anchoring; the risk source identification module generates a violation inducement characteristic spectrum based on the matrix and determines a risk source type; the risk quantification and early warning module calculates an initial risk value and a diffusion probability, and generates a dynamic early warning map and a monitoring priority sequence; and the dynamic monitoring and slow-release module updates data and risk parameters in real time and outputs a slow-release prompt, so that accurate pre-judgment and dynamic supervision of land use risks are realized.

The invention discloses a quantitative early warning method for coupling risks of an urban lifeline system, and the method comprises the steps: collecting the real-time data of each system, and constructing a multi-level network which reflects the physical connection and function dependence relation between the systems; determining risk source nodes in the network, and traversing all communicated potential risk transmission paths along a dependency direction; for each path, a path cascade failure probability is calculated by fusing a node historical fault probability and a real-time state; quantitatively evaluating the risk value of each path in combination with the key level of the affected node; and finally, according to the risk value sorting, outputting the path in the front of the sorting and the key node thereof as early warning information. According to the method, global identification and quantitative evaluation of the cross-system risk conduction chain are realized, weak links of the network can be accurately positioned, and a scientific decision basis is provided for targeted risk prevention and control.

The invention discloses a fault-tolerant multi-source fusion navigation positioning method based on multi-solution separation, and belongs to the technical field of integrity monitoring. According to the method, for multiple risk sources faced by a multi-source fusion navigation system in a complex environment, rigorous monitoring and elimination of different sensor risk sources are realized through the steps of constructing a risk fault tree model, dividing a fault mode set, carrying out parallel filtering positioning, carrying out space-time synchronization, carrying out robust fusion, carrying out protection level calculation and the like. And finally obtaining a fault-tolerant positioning solution. According to the method, integrity prior information and dynamic risk monitoring capability are fused, a navigation positioning framework with high robustness and flexible adaptability is constructed, the integrity of a multi-source fusion system can be guaranteed, and the requirements of high-precision and high-safety scenes such as intelligent transportation and unmanned systems are met.

The invention provides an intelligent Internet of Things safety monitoring method and system, and the method comprises the steps: obtaining an equipment operation record and a behavior operation record, carrying out the event serialization coding of the equipment operation record, obtaining a sensor event sequence vector, carrying out the behavior mode vectorization processing of the behavior operation record, obtaining a behavior event sequence vector, and carrying out the monitoring of the behavior event sequence vector. Inputting the sensor event sequence vector and the behavior event sequence vector into a pre-trained association analysis model, carrying out cross-sequence causal relationship mining processing, generating an abnormal event association graph, and based on event node attributes and association edge weight values in the abnormal event association graph, executing risk conduction path analysis to obtain a risk conduction path; and obtaining a key conduction path set and a risk accumulation intensity value of the house safety risk, and generating a safety monitoring result containing the risk level identifier and the risk source positioning information according to the key conduction path set and the risk accumulation intensity value. According to the invention, the accuracy and comprehensiveness of intelligent internet-of-things safety monitoring are effectively improved.

The invention discloses an unmanned ship path optimization method based on visual detection, and belongs to the technical field of unmanned ships, and the method comprises the steps: receiving first image information collected by a visual sensor carried by an unmanned ship and navigation parameter information from the unmanned ship, and constructing an environment perception model; performing motion trend prediction and unmanned ship collision risk comprehensive evaluation on the dynamic target; constructing a multi-dimensional threat assessment function, carrying out quantitative sorting on all known collision risk sources, defining a threat assessment index for each risk source, and optimizing a multi-target conflict logic; and dynamically adjusting the course and speed of the unmanned ship based on the output threat assessment result. In the implementation process of the technical scheme, the surrounding environment is perceived in real time through visual detection, parameters such as obstacle types, distances, relative speeds and movement directions are input into a multi-dimensional threat assessment function, and dynamic weighted sorting of different risk sources is realized, so that navigation path optimization is performed on the unmanned ship.

The invention relates to the technical field of industrial Internet of Things and edge computing, in particular to an intelligent risk identification method and system for an energy increasing station. The method comprises the following steps: deploying a sensor network at an energy adding station and synchronously acquiring signals, extracting multi-scale time-frequency and physical response characteristics, mapping heterogeneous characteristics to energy flow fingerprints through a hybrid projection model, and performing self-calibration enhancement by using a differential baseline; energy flow fingerprints are decoded and mapped into semantic features, candidate abnormal modes are recognized through short-term trend analysis and multi-source cross correlation, and local abnormal scores are calculated; constructing a physical semantic constrained local causal network based on candidate anomalies, deducing an anomaly propagation path, quantifying node risk contribution, grading and positioning a key risk source; and packaging the risk information into a token matching strategy library, executing a transactional action chain according to a hierarchical intervention principle, monitoring an effect in real time and supporting rollback upgrading. According to the invention, real-time identification, causal deduction and intelligent dynamic response of the energy station risk are realized.

The invention relates to the technical field of big data analysis and application operation and maintenance, and discloses a multi-data fusion-based application operation and maintenance planning method, which comprises the following steps of: combining multi-source sensor data into a multi-dimensional state vector representing a local space-time state, and comparing the multi-dimensional state vector with a baseline vector representing a normal operation mode to generate a drift vector; according to the method, the stability of the correlation mode in the multi-dimensional data is analyzed, and then the stability of the modulus length and the direction of the drift vector in the time sequence is analyzed, so that potential risks which cannot be perceived by a traditional threshold value mode can be recognized. The focus point of risk recognition is transferred from isolated value out-of-limit to analysis of the stability of the correlation mode in the multi-dimensional data; the early gradual change risk formed by the synergistic effect of a plurality of physical quantities can be effectively identified, and the risk source can be traced based on the analysis process, so that the predictability and decision reliability of operation and maintenance planning are remarkably improved.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The invention discloses a flexible operation and maintenance early warning device based on artificial intelligence. The flexible operation and maintenance early warning device comprises a multi-source data acquisition module, a data preprocessing and fusion module, an AI state recognition module, a risk reasoning and knowledge graph module, a flexible strategy engine module and an early warning output module. The device realizes cleaning, noise reduction, time alignment and feature fusion of multi-source data by collecting equipment operation indexes, log behaviors, security events and environmental parameters. The AI state recognition module is used for recognizing an operation state, a behavior mode and potential abnormity, and the risk reasoning and knowledge graph module completes risk source positioning and reason verification based on an entity relationship and a semantic link. And the flexible strategy engine dynamically generates an early warning strategy according to a reasoning result, and pushes early warning information in a multi-channel manner through an early warning output module. The device can realize high-accuracy, interpretable and self-adaptive operation and maintenance early warning capability, and is suitable for intelligent operation and maintenance management of hospital machine rooms and key business systems.