982 results about "Security assessment" patented technology

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention relates to a graph-based network security event modeling method and system, and the method comprises the steps: obtaining topological data and security policy information of a network where network security equipment is located, and carrying out the hierarchical construction of a knowledge graph, and obtaining a multi-layer security graph; acquiring real-time monitoring data of the network security equipment, and performing graph adversarial learning association with the multilayer security graph to obtain a dynamic evolution graph sequence; obtaining alarm data of the network security device, and performing anomaly detection on the multilayer security map to obtain an anomaly propagation situation; performing security assessment construction on the dynamic evolution diagram sequence and the abnormal propagation situation to obtain an initial security assessment scheme; performing alarm identification and attack link prediction on the alarm data to obtain a link prediction result; and performing evaluation and prediction on the initial security evaluation scheme and the link prediction result to obtain a security situation evaluation strategy. According to the invention, the security condition of the current network can be evaluated more accurately.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention provides a telecommunication service fraud-related risk security assessment system based on a multi-modal fusion model, and the system comprises a multi-source data collection assembly which is responsible for obtaining original telecommunication service data in multiple ways, carrying out the preprocessing of the original telecommunication service data, and obtaining first telecommunication service data; performing feature extraction and behavior pattern analysis on the first telecommunication service data to obtain key features related to the telecommunication service; the assessment model construction component is responsible for constructing a fraud-related risk assessment model based on machine learning, inputting the key features into the fraud-related risk assessment model, outputting an intelligent assessment control matrix, classifying and rating fraud-related risks, and generating a risk assessment result; and the service collaborative linkage assembly is responsible for early warning the risk level of the telecommunication service system according to the risk assessment result, and taking prevention measures according to the risk level. According to the method, an objective evaluation standard is established, and the conversion of risk identification from experience judgment to data driving is realized.

The invention provides a small and medium-sized reservoir operation safety evaluation system based on sky-ground work, and belongs to the technical field of hydrological monitoring. The device comprises a space-time alignment layer, a feature weighting layer and a dynamic coupling layer which are connected in sequence, the space-time alignment layer is used for performing time alignment and space alignment on the input data of the sky-land water conservancy project; the feature weighting layer is used for quantifying contribution degrees of different monitoring features to safety assessment according to the data after time-space alignment; and the dynamic coupling layer adopts a dynamic weight distribution strategy for different modules through a multi-module coordination engine, fuses feature weighting to obtain a fusion result, and outputs a small and medium-sized reservoir operation safety index according to the fusion result. The evaluation result is more accurate, and the timeliness is better.

The invention discloses a power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

The present invention relates to the technical field of network security protection. Disclosed are a security protection method and apparatus for a customer service management system. The method comprises: acquiring an employee data set, and performing permission assignment, so as to generate an employee operation permission set; performing identity verification on an access user, and determining a data management operation permission range of the access user; acquiring a sensitive data set for encryption processing, and storing same in a customer service management system; accessing data of the customer service management system, implementing an access control policy, and performing risk assessment, so as to generate a system risk level; extracting detected anomalous data, synchronizing same to a security protection network, and outputting a protection control policy; and executing the protection control policy for security assessment, regularly updating the protection control policy, and performing intelligent security protection on the customer service management system. The present invention solves the technical problem in the prior art that customer service management systems have low security protection capacity, which leads to difficulty in preventing network attacks, thus achieving the technical effect of improving the security protection capacity of customer service management systems.

The invention provides a dry-type transformer turn-to-turn short circuit multi-physical field simulation analysis method considering insulation aging and harmonic influence, and relates to the technical field of simulation analysis, and the method comprises the following steps: constructing a physical simulation model of a to-be-tested dry-type transformer; setting physical parameters and multi-physical field operation conditions of the physical simulation model; calculating a corresponding physical field operation calculation result under each physical field operation condition; performing post-processing on the physical field operation calculation result; by establishing a detailed simulation model and accurately assigning physical property parameters of each component, physical property changes of the turn-to-turn insulating material at different aging stages and under different temperature and frequency conditions are emphatically considered; turn-to-turn short circuit fault modeling is further introduced, magnetic-thermal-flow coupling simulation calculation is carried out under multi-harmonic excitation, the multi-physical field distribution characteristics of the dry-type transformer during different turn-to-turn short circuit faults are explored, and a reliable basis is provided for design optimization and safety evaluation.

The invention discloses an underground pipe network intelligent sensing and safety evaluation system based on an urban lifeline, relates to the field of intelligent monitoring and management of urban infrastructures, and provides an intelligent sensing and safety evaluation system for an underground pipe network based on the urban lifeline by integrating advanced technologies such as an advanced sensor network, a deep learning algorithm, an Internet of Things technology, a digital twin model and meteorological data integration. Comprehensive real-time monitoring and accurate damage identification are carried out on the urban underground pipe network; a machine learning algorithm is combined with a physical model, a statistical method and application of a digital twinborn technology, so that the system can create a digital copy of a pipe network, synchronization of virtuality and reality is realized, and an integrated meteorological risk assessment tool and an early warning system can be combined with pipe network monitoring data and meteorological short-term and imminent forecast data; the potential influence of extreme weather on the pipe network is evaluated, and early warning is given out in time; the health monitoring, risk assessment and emergency response capabilities of the underground pipe network can be remarkably improved, and intelligent management of urban infrastructures is realized.

The invention provides an electric power infrastructure construction progress self-adaptive scheduling method based on digital twinning. The method comprises the steps that a construction progress digital twinning model is constructed according to preset historical data and construction data fed back in real time on a construction site; predicting a construction progress trend based on the construction progress digital twinborn model to obtain construction progress prediction data; performing construction safety assessment according to the construction progress prediction data in combination with uncertainty of human factors, a construction process and environmental conditions to obtain a construction safety assessment result; designing an adaptive scheduling method according to the construction safety evaluation result and the construction progress prediction data to optimize a construction progress scheduling scheme; performing iterative optimization on the optimized construction progress scheduling scheme by using an optimization algorithm to obtain an optimal scheduling scheme; and applying the optimal scheduling scheme to actual construction management. According to the method, the adaptive scheduling algorithm is combined with the digital twinborn model, so that intelligent progress management in power infrastructure construction can be realized.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention relates to the technical field of information technology consultation management, and discloses an information technology consultation management system based on big data, and the system comprises a data optimization module which is used for accessing multi-modal data, collecting federal data, expanding a data source, generating a dynamic cleaning rule, calculating the data quality, and carrying out the optimization processing; the security evaluation module is used for dynamically distributing privacy budget through multi-modal data, calculating a consultation risk value of an information technology in combination with a credit evaluation mechanism, performing block chain traceability and reinforcing compliance auditing, and analyzing a delay index to optimize an edge node model; the decision service module performs intelligent interaction based on compliance auditing, analyzes user characteristics, generates dynamic rules, performs personalized recommendation, and optimizes personalized recommendation in combination with reinforcement learning; and the feedback updating module is used for acquiring feedback data based on personalized recommendation, analyzing feedback reliability, updating compliance audit and adjusting privacy budget.

According to the offshore wind turbine single pile rigidity numerical simulation method considering long-term circulation and scouring, the accuracy and the calculation convenience are both considered in model building through the linear elastic steel pipe pile model and the Mohr-Coulomb soil body model. By calculating the horizontal ultimate bearing capacity of the single pile, combining the ultimate analysis method and the p-y curve calculation method, the influence of different scouring depths on the fan is considered, the horizontal load bearing capacity and inherent frequency evolution of the single pile are comprehensively measured, and a basis is provided for design and safety evaluation. A sandy soil rigidity attenuation model is established for secondary development of Abaqus and verified, and the rigidity change of sandy soil under long-term cyclic loading is considered, so that the simulation precision is improved. And the numerical model is tested and verified to ensure the reliability. And the accumulated lateral displacement of the steel pipe pile can be accurately predicted, the influence rule of cyclic load times, soil elasticity modulus and embedding depth factors on displacement development is disclosed, wind power engineering design optimization is assisted, and economical efficiency and safety are improved.

The invention provides a power grid security protection system and method based on a large language model. The system comprises a multi-source data acquisition module, a large language model enhancement analysis module, a security evaluation module and an interaction and output module, wherein the large language model enhancement analysis module generates a multi-stage attack chain; determining a corresponding risk level according to the importance index of the power grid equipment related to the generated attack chain and the real-time alarm data, and generating a dynamic defense response strategy according to the risk level; the security evaluation module calculates a power grid equipment security risk index based on the dynamic security scoring model according to the power field big language model, and determines a vulnerability repair strategy corresponding to the power grid equipment according to the power grid equipment security risk index; the interaction and output module generates a visual security report according to the multi-stage attack chain; and the dynamic defense response strategy and the vulnerability repair strategy are pushed to the dispatching center in real time, so that the reliability of power grid security protection is effectively improved.

The invention provides an intelligent power supply management system based on multi-energy complementation, which relates to the technical field of intelligent power supply management and comprises a multi-energy supply module, a data acquisition module, a multi-source data fusion module, a scheduling optimization module, a demand prediction and response module, a supply safety evaluation module, an energy storage and load balancing module and an energy scheduling module. The multi-energy supply module comprises a solar energy supply sub-module, a wind energy supply sub-module, an energy storage system and a traditional power grid access sub-module, and by combining multi-source data and an advanced algorithm, the precision of power load prediction is remarkably improved. Compared with a traditional prediction method based on historical data, the method comprehensively considers the influence of various external factors such as climate change, holidays and festivals, and can more accurately predict the power demand fluctuation. The system can dynamically adjust the load prediction model in real time, improves the reliability of power supply, and can effectively cope with challenges caused by demand changes, environment changes and emergencies.

The invention discloses a network security supervision system based on a network technology, and particularly relates to the technical field of data security, the network security supervision system comprises an intelligent detection terminal, an edge cooperation terminal, a cloud platform, a strategy management terminal, a management control terminal and a system database, the intelligent detection terminal is used for collecting network security data in real time and performing data preprocessing; the edge collaboration terminal is used for carrying out preliminary analysis on the integrated data and identifying common security threats; the cloud platform is used for carrying out multi-source data fusion, carrying out deep analysis and evaluating a network security situation; the strategy management terminal is used for formulating differentiated security strategies and dynamically adjusting the security strategies; the management control terminal is used for providing a visual operation interface for system management and decision making; and the system database is used for storing data for a long time and supporting a real-time retrieval function. By adopting a multi-layer distributed architecture and multi-source data fusion, the accuracy of security assessment is enhanced, and the efficiency of threat monitoring is improved.

The invention discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

The invention relates to the technical field of safety assessment of building construction, and discloses an online simulation analysis method for safety assessment of a foundation pit, which comprises the following steps: S1, data acquisition, cleaning and preprocessing: acquiring soil pressure, underground water level, structural deformation, temperature and humidity information around the foundation pit by deploying a plurality of sensors, the invention further provides an online simulation analysis system for foundation pit safety assessment, online simulation analysis equipment for foundation pit safety assessment and a computer readable storage medium. The deep reinforcement learning algorithm, the intelligent early warning mechanism and the distributed storage and encryption technology are introduced, real-time monitoring, accurate early warning and data safety guarantee of foundation pit safety assessment are achieved, and the safety, efficiency and reliability of the foundation pit construction process are improved.

The invention discloses a dynamic monitoring method and system for settlement and inclination of a tower drum of a wind generating set, and relates to the technical field of safety monitoring of wind power generation infrastructure, the method comprises the following steps: synchronously collecting inclination data and positioning data of each monitoring point, and carrying out space-time alignment to obtain a fusion data set; decoupling the dynamic elastic response of the tower drum and the steady-state deformation of the foundation from the fused data through frequency domain analysis, and further calculating the overall inclination rate, the settlement amount and the non-uniform settlement rate of the tower drum; performing safety assessment based on the calculated parameters and a preset safety threshold, and determining a basic safety state and a risk mode; and finally, generating graded early warning information and decision information including risk positioning and trend prediction according to an evaluation result. Through the above mode, the method achieves the precise separation and monitoring of the dynamic deformation characteristics of the tower drum, improves the accuracy of safety evaluation and the timeliness of early warning, and provides an effective data support for the operation and maintenance decision of a wind generating set.

The invention discloses an access control equipment data management method and system based on multi-source fusion. The method comprises the following steps: collecting a multi-source access control data stream in real time; based on a preset feature extraction rule set, extracting a multi-modal biological feature vector, a voucher legality identifier, an abnormal behavior probability value and an equipment health degree index, and inputting the multi-modal biological feature vector, the voucher legality identifier, the abnormal behavior probability value and the equipment health degree index into a dynamic security assessment matrix generation model to generate a real-time security assessment matrix; matching the dimension safety score of the real-time safety evaluation matrix with a preset threshold strategy library, and dynamically generating an access control strategy instruction set; and issuing the access control strategy instruction set to the target access control equipment execution terminal. The method has the following advantages and effects: the fault tolerance bottleneck of a single-dimensional decision chain is broken through, and the system misjudgment rate is reduced by at least one order of magnitude on the premise of ensuring the security by establishing a dynamic coupling mechanism of the multi-source data stream.

The invention provides a vehicle network integration cooperative regulation and control method and system. The method comprises the following steps that 1, the system collects access parameters of multiple types of charging facilities; 2, constructing a power distribution network safety evaluation model based on the data collected in the step 1; 3, determining a charging label of the accessed electric vehicle according to the product of the real-time SOC and the SOH; 4, comparing the three-phase voltage deviation, the current unbalance degree and the load growth rate of the power distribution network with the history in the same period to obtain an overload risk rate, and further calculating a power grid safety margin coefficient; 5, calculating a final safety evaluation result to truly reflect the vulnerability level of the current power grid; and step 6, constructing a digital twinborn model of the target area, performing simulation verification on the initial instruction, and collecting equipment state, vehicle response and power grid operation data after regulation and control. By applying the technical scheme, an efficient and collaborative vehicle network interaction system can be realized, and deep fusion and collaborative development of the energy and traffic fields can be promoted.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention relates to the technical field of machine learning and nuclear power safety assessment, and discloses a nuclear power safety assessment method and system based on machine learning, and the method comprises the steps: collecting and preprocessing the multi-source heterogeneous data of a nuclear power station; constructing a time convolution network model to extract multi-scale time sequence features of the multi-modal data and realize modal alignment; constructing a contrast learning model to optimize multi-modal feature representation; constructing a multi-granularity prototype learning model in the optimized feature representation space; constructing an adaptive attention fusion model to dynamically integrate multi-modal information, and continuously optimizing an evaluation model by a continuous learning technology; through multi-modal fusion and multi-scale time sequence representation learning, comprehensive assessment of the safety state of the nuclear power station is realized, and the comprehensiveness of safety assessment is improved.

The invention discloses an electric vehicle charger safety waiting management method and system based on an improved sequential network. The method comprises the following steps: S1, recording vehicle remaining capacity, accessory power consumption, a thermal risk tag and service pressure; s2, dividing active units according to a queuing order and service pressure, constructing an energy consumption and heat dissipation double-layer topology, and updating a weight in real time; s3, writing the double-layer topology and the historical state into a time sequence buffer area, traversing edge weight iteration vehicle electric quantity and battery temperature, and calculating a safety window; s4, calculating the waiting time of each vehicle according to the safety window and the queuing data, and generating a risk sequence; s5, the vehicles of the same unit are locally rearranged preferentially according to the urgency degree, and a correction queue is formed after fairness and connectivity verification; and S6, intercepting the correction queue according to idle stations, completing vehicle and station mapping, and issuing a charging instruction after reachability and power verification. The method has great superiority in microscopic safety evaluation, dynamic topology increment maintenance and real-time rearrangement giving consideration to safety and fairness.

The invention relates to the technical field of construction safety assessment, and discloses a construction safety assessment method based on multi-model fusion. The method comprises the following steps: acquiring a multi-source safety monitoring data set of a construction site, wherein the multi-source safety monitoring data set covers image sequence data, sensor time sequence data and operation log text data; performing parallel feature extraction processing on the multi-source data to generate an image feature vector, a sensor feature vector and a text feature vector; calling a multi-model fusion framework to carry out cross-modal fusion processing on the three feature vectors to obtain a fusion security feature set; carrying out security risk assessment analysis based on the fused security feature set, and generating a security risk prediction value and a high-risk region identifier; and finally, generating a security policy adjustment instruction according to the risk prediction value and the high-risk area identifier, and feeding back the security policy adjustment instruction to the field control system to trigger real-time intervention operation. According to the method, a high-risk area can be recognized in time, intervention is triggered, and intelligent support is provided for safety management of a construction site.

According to the fluid-structure interaction-based dynamic response analysis method for the floating type photovoltaic structure, the anchoring system action of the floating type photovoltaic structure is equivalent to concentrated force, and a simplified floating type photovoltaic structure numerical model is established; and establishing a numerical analysis method for safety evaluation of the floating photovoltaic structure under a given wind wave and flow condition based on fluid-structure interaction. By adopting the method disclosed by the invention, the instantaneous stress-strain field of each component of the floating photovoltaic structure with relatively high precision can be obtained, so that a basis can be provided for the design of each component of the floating photovoltaic structure, and a reference basis can be provided for the safety assessment of the floating photovoltaic structure.

This invention relates to a computer-implemented method and system for performing security evaluation on a machine learning (ML) model. The method includes determining a taxonomy of the ML model and of the environment in which the machine learning model is implemented at one or more stages in the model's lifecycle. The method additionally includes generating, based on the determined taxonomy, a set of assumptions about the ML model and the environment. An adversarial test attack is performed on the ML model at a stage in its lifecycle, based at least in part on the set of assumptions, and one or more failure modes in the ML model are identified based on the result of the first adversarial attack. The system may include a threat modelling component, an assessment component, a reporting component, and a risk mitigation component.

The invention relates to the technical field of bridge engineering and structure health monitoring, in particular to a safety assessment method and system based on a bridge digital twin model. The method comprises the following steps: performing three-dimensional reconstruction according to geometric information of a bridge to obtain a BIM model of the bridge; based on the BIM model, fusing multi-dimensional special parameters to construct a simulation digital twinborn model of the bridge; correcting the simulation digital twinborn model by using historical monitoring data; and in combination with real-time monitoring data and the corrected simulation digital twinborn model, carrying out safety assessment on the bridge. According to the method, the bridge digital twinborn model is constructed by fusing multi-dimensional special parameters, then the bridge digital twinborn model is dynamically corrected based on historical monitoring data, and finally, the actual state and performance change of the bridge are accurately mapped in real time through the corrected bridge digital twinborn model. The problem of comprehensive, accurate and real-time assessment of bridge structure safety is solved.

The invention discloses a system safety assessment method and system for an energy storage power station containing a network-building type energy storage converter, and the method comprises the following steps: S1, collecting converter control parameters, power grid dynamic data and lithium battery mechanism data through multi-source heterogeneous data fusion, and carrying out the preprocessing through noise elimination and a normalization algorithm; s2, constructing a network construction characteristic quantitative evaluation system including indexes such as dynamic stability and voltage supporting capability; s3, establishing a three-layer evaluation system of an equipment layer, a control layer and a system layer, and calculating a total risk value of the system by using a weight distribution algorithm; s4, verifying system adaptability and optimizing parameters through time domain simulation in scenes such as a strong power grid and a weak power grid; and S5, generating an evaluation report containing parameter optimization suggestions, triggering graded early warning based on an index threshold value, and automatically generating a closed-loop control instruction. According to the invention, multi-dimensional safety assessment of the energy storage power station is realized, and systematic technical support is provided for safe operation of the network-forming type energy storage power station.

The invention discloses an identity authentication method and system based on a block chain, and particularly relates to the technical field of identity authentication security. Comprising the steps of S01, user login request verification, S02, user identity detection enhancement verification, S03, user identity authentication abnormity control, S04, user access security assessment data acquisition, S05, user access security analysis and S06, user access security assessment. The face recognition confidence, the iris matching degree and the user face video stream are collected on the user login page, the user identity compliance is verified, the safety of identity verification is improved, the authentication accuracy is improved, the safety evaluation data of the authentication server in the block chain network are collected, the user authentication access risk evaluation coefficient is calculated, and the user authentication access risk evaluation efficiency is improved. And the server authentication access risk is monitored, so that the change of the server authentication access risk can be monitored in real time, the occurrence of security events is effectively prevented, and the user information management efficiency is improved.