4994 results about "Security management" patented technology

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention discloses an archive data security integration management system, which relates to the technical field of knowledge maps and comprises an archive data acquisition module, a knowledge intelligent analysis module, a knowledge map dynamic construction module, a security situation evaluation module and a security communication center module. The data acquisition module structurally acquires metadata and operation behaviors, the intelligent analysis module dynamically governs data quality, the map construction module constructs a three-dimensional security model, the situation evaluation module quantifies security influence and generates a strategy, and the communication center module realizes dynamic identity authentication and bandwidth allocation. Through the knowledge graph technology, comprehensive integration and safety management of the archive data are achieved, the data quality and utilization efficiency are improved, dynamic evaluation and coping with safety threats are achieved, and the safety and integrity of the archive data are ensured. Meanwhile, through dynamic identity authentication and bandwidth allocation, the safety and efficiency of cross-domain communication are improved.

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention relates to the technical field of airport safety management, in particular to an airport safety management method and system, and the method comprises the steps: obtaining various data of airport operation in real time through a multi-source heterogeneous data collection module, carrying out the feature fusion processing through a dynamic risk assessment model, generating a three-dimensional risk thermodynamic diagram, and outputting a risk level and a type identifier; triggering a hierarchical response mechanism, generating a resource configuration scheme through a resource scheduling optimization algorithm, and sending a scheduling instruction; in the emergency disposal stage, a multi-terminal collaborative platform is used for monitoring the disposal effect, when the risk diffusion coefficient exceeds a preset threshold value, a cross-department collaborative disposal protocol is started, and the resource allocation weight is dynamically adjusted. According to the method, the risk detection precision is improved through multi-source data fusion and space-time correlation analysis, multi-target optimization configuration is realized by adopting a dynamic resource scheduling matrix, a closed-loop mechanism of hierarchical response and cross-department co-processing is constructed, the traceability of the process is enhanced in combination with digital twin and block chain technologies, and the airport safety management level is effectively improved.

The invention provides a chemical industrial park illegal behavior intervention and tracing system based on edge calculation, and aims to solve the problems that an existing system is single in perception capability, insufficient in intelligent analysis, limited in edge collaboration, passive in intervention, weak in tracing capability and the like. According to the system, an improved ResNet-FPN and Transform architecture is adopted to realize multi-modal data fusion, and violation behavior analysis is carried out through a double-flow intention understanding framework and a graph neural network in deep combination with chemical process knowledge; a self-adaptive edge node networking and federated learning mechanism is adopted to realize dynamic scheduling of computing resources and knowledge migration; an intervention decision model based on risk entropy supports predictive intervention, and a multi-objective optimization algorithm ensures balanced safety and efficiency of an intervention strategy; a complete evidence chain is constructed by adopting a multi-modal trajectory reconstruction technology and a distributed evidence storage network, and credible tracing of violation behaviors is realized. The technical bottleneck of a traditional system in the aspects of professional scene understanding and real-time processing is broken through, and the intelligent level of safety management of the chemical industry park is remarkably improved.

The invention discloses an information security management method based on data processing, and particularly relates to the field of information security management, comprising multi-source heterogeneous data acquisition, multi-dimensional feature index calculation, comprehensive threat detection calculation and adaptive threat decision. According to the method, the covert communication channel is accurately identified by constructing the encrypted traffic multi-mode detection model and combining protocol fingerprint entropy and traffic self-similarity analysis; an improved entropy weight fusion algorithm is adopted, collaborative analysis of system process abnormity, network behavior offset and hardware degradation is achieved, and a cross-dimension attack chain is reconstructed; a storage medium physical feature-cryptographic module runtime joint monitoring mechanism is created for the first time, hardware layer physical attacks and cryptographic side channel vulnerabilities are synchronously defended through temperature-delay correlation evaluation and key derivation density analysis, a complete attack surface from a physical layer to an application layer is covered, and the security is improved. And the detection precision and the response real-time performance of the complex threats are obviously improved.

The invention relates to the technical field of electrical safety, in particular to an electrical operation risk assessment and early warning system based on big data analysis, which comprises a data acquisition module, a data fusion module, a feature extraction module, a risk assessment module, a real-time monitoring and early warning module, a historical data storage and analysis module and a visual monitoring module. Multi-source data are collected in real time by deploying multiple sensors, risk assessment is carried out through Kalman filtering and support vector regression algorithm fusion processing, feature variable extraction and combination of a support vector machine and a random forest algorithm, real-time monitoring and risk level judgment of an electrical working environment are achieved, long-term risk trend prediction is supported, and the risk assessment accuracy is improved. The visual monitoring module provides a basis for optimizing alarm rules and decision support, and displays an evaluation result, early warning information and an analysis result through a visual interface; the system has the advantages of being accurate in data processing, efficient in risk assessment, timely in early warning and the like, and can be widely applied to risk monitoring and safety management of electrical operation.

A vehicle dash cam may be configured to execute one or more neural networks (and / or other artificial intelligence), such as based on input from one or more of the cameras and / or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and / or to a safety manager.

The invention discloses an electronic signature security management method and system based on a block chain, and relates to the technical field of information security, and the method comprises the steps: generating a public key and a private key of a user through a quantum resistant encryption algorithm, binding the public key with the identity of the user, recording the public key and the identity of the user on the block chain, and storing the private key through HSM hardware encryption equipment; the method comprises the following steps: standardizing a to-be-signed document to generate a standard document, and calculating the standard document through a Hash algorithm to obtain a document Hash value; the signer signs the document hash value by using a private key and a CRYSTALS-Dillitium signature algorithm, generates an electronic signature and a signature proof, associates the electronic signature, the signature proof, the document hash value and a timestamp with a public key, and records the associated information on the block chain; and the standardized processing rule is written into the smart contract, and after a verifier submits a verification request, document hash value comparison, signature effective state check and signature proof verification are carried out through calling chain data by the smart contract.

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are improved.

The present invention discloses a safety risk analysis system and method based on multimodal data processing; it relates to the field of industrial automation and information technology; the purpose of the present invention is to specifically target high-risk scenarios of hazardous chemical enterprises, and provide a safety risk analysis system and method based on multimodal data processing, which is based on deep learning technology, comprehensively considers the changes in spatiotemporal characteristics, can monitor and evaluate the safety risks of hazardous chemical enterprises in real time, and automatically integrates multimodal data such as monitored videos, sensor data, and operation logs. The spatial features are extracted through convolutional neural networks, and the time series data is processed using recurrent neural networks and long short-term memory networks, and further feature extraction and fusion are performed. The key risk factors are also identified through the attention mechanism, thereby achieving a comprehensive and accurate safety risk assessment, thereby providing real-time warnings for safety managers to ensure that potential safety hazards are discovered in a timely manner.

Methods, systems, and computer storage media for providing generative artificial intelligence (AI) security management using a generative AI security engine in an item listing system. A generative AI security engine supports generative AI security management based on security analysis and detection operations for a plurality of generative-AI-supported applications and generative AI models. In operation, a request associated with prompt data is communicated from a generative AI client. Based on communicating the request, a response that is generated based on a redacted version of the prompt data is received at the generative AI client. The prompt data is analyzed using a plurality of security engine operations to cause generation of the redacted version of the prompt data. The redacted version of the prompt data is used to generate the response at a generative AI model. The response is caused to be generated at an interface associated with the generative AI client.

The invention provides a construction safety prompting method and system, and the method comprises the steps: carrying out the collection and fusion processing of multi-dimensional environment data of a construction site, and constructing a real-time updated environment digital twinborn model. And according to the model and individual physiological data of the workers, carrying out adaptive evaluation on the safety state of each worker to obtain an individual dynamic safety threshold. In combination with an environment digital twinborn model, an individualized dynamic safety threshold and real-time state data, a graph neural network reasoning technology is adopted to comprehensively evaluate the safety situation, and a potential risk trend is predicted. And according to a security situation assessment result and a risk prediction trend, generating adaptive early warning and intervention schemes for different risk levels by adopting a situation awareness grading processing mode, and providing accurate security prompts. According to the method, the safety state of the worker is monitored, predicted and early warned in real time according to the dynamic change of the construction site, the intelligent level and accuracy of construction safety management are improved, and the accident occurrence risk is reduced.

Methods, systems, and computer storage media for providing security posture management using an artificial intelligence security engine in a security management system. Security posture management supports security management of a computing environment based on contextual information associated with artificial-intelligence-supported applications. The security management system provides an artificial intelligence security graph associated with the artificial-intelligence-supported applications. The artificial intelligence engine uses the artificial intelligence security graph to correlate artificial intelligence attack monitoring data with operational data of the artificial-intelligence-supported applications. In operation, artificial intelligence attack monitoring data is accessed. An artificial intelligence security graph associated with a plurality of artificial-intelligence-supported applications is accessed. Based on the artificial intelligence attack monitoring data and the artificial intelligence security graph, operational data of an artificial-intelligence-supported application is accessed. The artificial intelligence attack monitoring data and the operational data are analyzed to identify an artificial intelligence security alert. The artificial intelligence security alert is communicated.

A system and methodology for automated security assessment of microservices includes a microservice composition model, data gathering, security assessment, and labeling components. It treats microservices as separate projects, collecting source code, dependencies, and runtime information. The security assessment employs tools to analyze code, track vulnerabilities, and identify risks. Predefined rules categorize microservices and assign security state labels. A hidden Markov model predicts security states based on historical data, enabling proactive security management and risk mitigation.

The invention discloses a video analysis-based electric power operation personnel behavior identification and early warning system and method, which realize accurate identification and real-time early warning of electric power operation personnel behaviors by combining a video analysis technology with multi-modal data fusion, and effectively improve the safety management level of an operation site. Compared with a traditional safety supervision mode, the method employs a mode of combining deep learning target detection and time sequence behavior analysis, improves the recognition accuracy of operators and safety equipment, fuses the data of equipment worn by the operators with video data, improves the detection precision, and improves the safety supervision accuracy. And misjudgment caused by illumination change, shielding or complex environment is reduced. Besides, high-risk violation behaviors such as no safety helmet wearing, no safety belt fastening, violation climbing, high-altitude object throwing and the like are accurately recognized through the violation detection module, and different levels of alarm measures are adopted according to the severity of the violation behaviors in combination with an early warning feedback mechanism, so that the pertinence and response efficiency of early warning are improved.

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention belongs to the technical field of contract security management, and discloses a supplier contract security management system and method based on a block chain. Encryption fragmentation storage and multi-node verification are carried out on the supplier contract to ensure that data cannot be tampered; based on this, supplier multi-dimensional qualification evaluation and credit point recording are carried out; contract term coding and trigger condition setting are realized, and a self-execution contract control chain is formed; contract execution state consensus is achieved through cross-organization cooperation and distributed consensus; the privacy security is guaranteed by adopting data hierarchical encryption and authority fine control; generating a risk situation map in combination with compliance requirement mapping and real-time risk assessment; constructing a contract performance traceability chain based on historical data indexes and association analysis; performing abnormal behavior detection and security event classification processing; and finally, an optimization suggestion and a performance evaluation report are generated. According to the method, the safety, efficiency and transparency of supplier contract management are remarkably improved.

The invention discloses a network security intelligent management and control system based on big data, and relates to the field of network security management. Comprising a data acquisition processing module, an intelligence fusion analysis module, a threat dynamic detection module, an attack deduction prediction module, a virtual mapping simulation module, an edge collaborative defense module, a defense strategy optimization module, an automatic decision execution module and a threat intelligence sharing module. According to the method, the detection capability of complex network attacks is improved, the attack path in the network environment can be visually presented, the security operation and maintenance efficiency is improved, the attack path is accurately blocked, and the attack success rate is reduced; comprehensive security event priority ranking can be realized, the scientificity of defense decision is improved, meanwhile, the system can adapt to different attack scenes, the defense efficiency is improved, it is ensured that a defense strategy always adapts to the current security situation, resource waste is avoided, and the defense cost-benefit ratio is improved.

The invention discloses a chemical industry park emergency processing method based on digital twinning. High-precision digital modeling is carried out on the physical environment of the chemical industry park, and dynamic changes in the park are sensed and reflected in real time; the Internet of Things equipment model is fused with the twin scene, the Internet of Things equipment model is matched with equipment information of an actual park enterprise, and collaborative linkage of the model and monitoring equipment is achieved; emergency scene simulation of various chemical industrial parks is provided, and different scenes are selected for drilling according to actual requirements; a chemical industrial park accident emergency resource scheduling model is constructed, a digital twinborn simulation technology and a hybrid intelligent optimization algorithm are fused, and resource allocation in a dynamic and complex environment is realized; and a collision-free safe path from the starting point to the ending point is found by comprehensively considering the necessary points and the bypassing areas. According to the invention, through integration of a high-precision digital twinning technology, a comprehensive emergency simulation drilling system and method are provided for the chemical industry park, so that the emergency response capability and the safety management level of the park are comprehensively optimized and improved.

The invention relates to the technical field of channel safety management, and more specifically relates to a channel multi-source data fusion digital twinborn risk early warning system comprising a multi-source data acquisition module used for acquiring channel specific data types, comprising AIS data, radar data, video monitoring data, hydro meteorological data, channel surveying and mapping data, ship report data, shore-based sensor data and unmanned aerial vehicle patrol data, and comprehensive data support is provided for channel management through space-time alignment, dynamic weight adjustment and conflict resolution; a high-fidelity digital twin is constructed, fluid dynamics and a ship behavior model are fused, and a real channel scene is restored; intelligent risk early warning and traceability are realized by using deep learning, and decision scientificity is improved; efficient virtual-real interaction is realized through multi-terminal pushing and sand table deduction, and a management closed loop is formed; a cloud-edge-end framework and the like are adopted to optimize system performance, and real-time performance and safety in a large-scale scene are guaranteed.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

This invention relates to the field of security encryption management technology, and more particularly to a computer software security encryption management system and method. The method includes the following steps: acquiring software data and extracting privacy files; then performing interleaved layering transformations on the privacy files to generate basic encryption units; the basic encryption units are mapped through grid encoding to form an encrypted grid encoding matrix; a virtual identity index is generated based on this matrix; a fake retrieval channel is simulated based on the virtual identity index and the basic encryption units to monitor computer access to data; simultaneously, illegal attacks are induced and visitor behavior patterns are recorded; potential internal threats are deduced using the collected behavior patterns and fake retrieval channels; and the basic encryption units are progressively encrypted and solidified to improve the security and effectiveness of data protection, thus constructing a comprehensive security management system. This invention achieves a more flexible and secure computer software security encryption management method.

The invention discloses a block chain-based digital right encryption management method and system, and relates to the technical field of digital right security management. According to the method, a unique identifier is generated for digital asset information through a Hash algorithm, data privacy is ensured by using asymmetric encryption, and asset uniqueness identification and information encryption protection are realized; according to the method, encrypted asset information is subjected to uplink evidence storage through an intelligent contract, node verification is carried out by adopting a consensus algorithm, tampering resistance, authenticity and transparency of the asset information are analyzed, authenticity of digital assets and safety and reliability of storage are guaranteed, legality of ownership transfer is verified through a consensus mechanism when the digital assets are transacted or transferred, and security and reliability of the digital assets are guaranteed. It is ensured that the transaction meets the preset compliance standard, the asset state on the block chain is updated in real time, and the security and compliance of the transaction are improved; the asset ownership history and the transaction path are tracked and recorded through the traceability mechanism of the block chain, and the traceability and transparency of the asset circulation process are realized.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention discloses a safety management risk dynamic monitoring system for safety production, and belongs to the technical field of industrial safety management. The system collects monitoring parameter data in an enterprise in real time by integrating toxic gas, temperature, pressure and other sensors and video monitoring equipment, dynamically displays risk distribution of major hazard sources and key areas by combining a three-dimensional map labeling function, identifies high-risk areas and potential hazards through risk grading evaluation and hidden danger correlation analysis, and improves the safety of the enterprise. A response mechanism is dynamically adjusted according to the risk level, an emergency plan and a remote equipment control function are combined, major risks are handled in a rapid linkage mode, the possibility of accident expansion is remarkably reduced, an intelligent inspection module achieves efficient and accurate inspection tasks, inspection data are collected and analyzed in real time, and an inspection report containing the task completion rate and abnormal conditions is generated. Through closed-loop management, hidden danger rectification and continuous optimization are realized, real-time monitoring of safety risks is realized, and the occurrence rate of safety production accidents is effectively reduced.

The invention discloses an electric power Internet of Things industrial control equipment safety assessment method based on a multi-modal large model. The method mainly comprises the steps of data collection and preprocessing, multi-modal large model processing, safety risk assessment and result display and decision support. The method comprises the following steps: firstly, collecting multi-modal data of the power Internet of Things industrial control equipment, and preprocessing to ensure the consistency and accuracy of the data; secondly, performing deep fusion and feature extraction on the multi-source heterogeneous data by adopting a multi-modal large model technology and combining an orthogonal sequential fusion (OSF) strategy; then, establishing and training a security risk assessment model, quantifying the fault probability, the risk level and the potential influence, and providing more accurate security assessment in combination with background information provided by the knowledge graph; and finally, displaying an evaluation result through a visual interface. According to the invention, the automation level and accuracy of safety risk assessment of the power Internet of Things industrial control equipment are effectively improved, and stable operation and safety management of a power system are ensured.