1232 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 the technical field of railway data security, and provides a railway data asset security monitoring and risk early warning method and system, and the method comprises the steps: collecting multi-dimensional feature data of railway data assets, including equipment operation state data, network communication data, data access logs and environment perception data; performing heterogeneous data fusion preprocessing on the multi-dimensional feature data to generate a standardized feature vector; calculating security evaluation indexes of the standardized feature vector based on a dynamic weight distribution algorithm, wherein the security evaluation indexes comprise an integrity evaluation value, an availability evaluation value and a confidentiality evaluation value; generating a multi-level risk early warning signal according to the deviation degree of the safety evaluation index and the dynamic threshold value; and triggering dynamic control of a data asset protection strategy based on the risk early warning signal, wherein the dynamic control comprises access permission adjustment, data encryption strength grading and flow limitation. According to the invention, the safety and reliability of railway data assets can be improved, and data leakage and system fault risks are reduced.

The invention provides a power grid multi-agent large model safety evaluation index calculation method, and relates to the technical field of intelligent control. The method comprises the following steps: acquiring a voltage change rate and a frequency change rate, dividing four stages according to disturbance starting and extreme points, pairing curve trends, judging delay and synchronization characteristics, extracting a continuous change structure, and generating a disturbance subsection evolution structure; according to the method, through staged analysis of node voltage and frequency change trends and combination of trend consistency judgment and abnormal segment identification, dynamic structure reconstruction and trend mapping of a disturbance process are realized, and coherence and precision of index response state identification are improved through trend boundary collection and frequency classification; cooperative characteristic identification is enhanced through the coupling direction and delay analysis of inter-node multi-parameter change, the node adjustment priority is defined through regulation and control response sorting, an adjustment path under multi-dimensional linkage is constructed, and the refined management and control capacity of disturbance response and the pertinence of control decision are improved.

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 discloses a data security management method and system based on cloud computing, and relates to the technical field of data management, and the method comprises the following steps: S1, making a security policy; s2, performing security assessment on the cloud infrastructure; s3, performing identity authentication and access control; s4, data encryption and key management; s5, data classification and level-to-level management; s6, data leakage detection and defense; s7, performing auditing and compliance management; s8, abnormal behavior monitoring and threat response; and S9, an elastic backup and recovery mechanism. According to the data security governance method and system based on cloud computing, the elasticity and dynamic response capability of data security are remarkably improved by setting the distributed key management system and the intelligent monitoring model. The distributed key management system dispersedly stores the keys in different physical areas, so that the risk of single-point fault and attack is reduced; the AI-based intelligent behavior monitoring system analyzes user behaviors in real time, predicts abnormal operation and automatically responds.

The invention provides a method for diagnosing a structural service behavior based on a high dam accident mode and a failure path, and the method comprises the steps: constructing a high dam accident case database, and extracting accident feature parameters; by applying multi-scale layered adaptive corresponding analysis, through feature weight calculation, risk-out mode layered weighting, physical similarity index establishment and structural difference compensation, the problem of unbalanced feature weight is solved, and a weighted similar project sorting list is generated; constructing a multi-failure mode coupled time sequence adaptive network model, solving the problem that a failure path time sequence evolution mechanism is imperfect, and outputting a time sequence failure path prediction model; and in combination with the similar project sorting list and the time sequence failure path prediction model, performing project analogy analysis, and generating a high dam safety state evaluation report. The technical problems that the operation time of the ultra-high dam is short, the monitoring information is insufficient, the numerical analysis result is difficult to completely reflect the engineering practice and the like are solved, and a new technical path and a new solution are provided for high dam safety assessment and risk management.

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 discloses an unmanned inspection and intelligent fault judgment method. A security threat model is constructed through deep learning, potential risks are identified, a strategy is made, security analysis modeling based on a comparison technology is utilized, and system state abnormity is detected. In data transmission, confidentiality and integrity are guaranteed by integrating a plurality of security means, a multi-level protection framework is designed, and all-round defense is carried out from a network boundary, a host terminal and an application system. By means of anomaly detection monitoring index data, anomaly and risk are subjected to correlation analysis, and a basis is provided for decision making. And regularly carrying out security assessment and penetration testing, repairing vulnerabilities, and carrying out optimization and reinforcement. The method covers multiple aspects such as threat modeling, safety analysis, data protection, protection framework construction, anomaly detection and evaluation reinforcement, the safety and reliability of the unmanned inspection system are effectively improved, network attacks and illegal access are resisted, and stable operation of the system is guaranteed.

The invention discloses a prefabricated building integrated safety monitoring method based on BIM, and relates to the technical field of BIM, and the method comprises the steps: S1, building a BIM model and managing information of prefabricated parts; s2, safety assessment and simulation before construction; s3, real-time monitoring in the construction process; s4, performing quality safety evaluation after construction; the technical key points are as follows: in the construction hoisting process of the prefabricated part, the accurate control on the hoisting speed can be realized by monitoring the deviation angle of the prefabricated part in real time and dynamically adjusting the proportionality coefficient according to the deviation value by adopting a feedback control principle, so that the stability of the prefabricated part in the hoisting process is ensured; dynamic adaptability is achieved through proportion adjustment, the method is an effective means for guaranteeing hoisting safety, meanwhile, the problem that errors exist in the operation result of the structural analysis construction sub-model is solved, and the method can adapt to prefabricated parts in different environments.

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 invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

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 relates to the field of network security in the industrial field, and discloses an industrial field network security online monitoring system, which comprises an asset identification module, a topology analysis module, a risk assessment module, a dynamic updating module, a decision support module and a visualization module, the system obtains equipment information in the network through a stateless scanning technology, constructs an equipment topological graph, and evaluates equipment risks based on a graph convolutional network; by monitoring the equipment state and the topological structure in real time, the system can dynamically update the risk score and provide an accurate safety assessment result; and according to an evaluation result, the system generates a network security management decision suggestion, and helps a network administrator to effectively manage and cope with the network security risk through multi-dimensional visual display of an equipment state, a risk score, vulnerability information and the like. The method has the advantages of comprehensive equipment identification, dynamic risk assessment, intelligent decision support, visual display and the like, and the efficiency and the automation level of network security management are improved.

The invention relates to the field of enterprise safety assessment, in particular to an enterprise data safety capability assessment method and system. The method comprises the following steps of: obtaining an enterprise historical security log based on an enterprise information system, performing real-time data flow analysis and dynamic positioning of a storage position on the enterprise historical security log, and constructing an enterprise data flow mapping model; performing abnormal intrusion attack simulation on the enterprise data flow mapping model, performing enterprise security risk assessment, and marking potential risk nodes; performing historical normalized behavior analysis on the dynamic enterprise data flow model, and performing access behavior deviation detection on potential risk nodes to obtain abnormal intrusion behavior data of the enterprise system; and performing multi-time-window intrusion trajectory backstepping on abnormal intrusion behavior data of the enterprise system, and performing deep mining of abnormal attack features to obtain intrusion attack features. According to the invention, efficient and accurate enterprise data security assessment is realized, so that the enterprise is helped to make a targeted security protection decision.

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 provides a linkage early warning method based on security situation assessment and application thereof. The linkage early warning method comprises the following steps: data preprocessing and feature extraction; an abnormal evaluation model and a normal evaluation model are constructed, and training and parameter adjustment are carried out; evaluating the data acquired in real time by using a safety evaluation function, judging the current safety state of the hydraulic power plant, and outputting a safety parameter; constructing a decision function, and determining whether the current state is abnormal or not based on the safety parameters and environmental factors EnvFactors; dynamically selecting and determining to select an abnormal evaluation model and a normal evaluation model for real-time evaluation, and judging whether the current state needs early warning or not; when early warning is needed, an emergency command system and early warning information are linked to carry out early warning and emergency response, and possible safety risks of the hydraulic power plant are prompted. According to the invention, by constructing the security evaluation function and the decision function and combining the neural network model, the problem that the existing system lacks enough flexibility and adaptability and cannot effectively cope with variable network security threats is solved.

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 discloses a power monitoring regulation and control system and method used in the operation process of a ship lock miter gate, and relates to the technical field of ship locks. The method comprises the following steps: firstly, monitoring the vibration state of each structural member of the ship lock miter gate, calculating a rigidity evaluation value of the ship lock miter gate, adjusting an electric control signal of a proportioning pump according to the rigidity evaluation value, and optimizing the non-deformability of a gate body structure; secondly, the dynamic stress of the door body is monitored, the dynamic stress safety evaluation value of the ship lock miter gate is obtained, the output frequency of the motor is adjusted through the frequency converter, and structural overload can be avoided; and finally, the back pull rod prestress monitoring and adjusting module adjusts the prestress of the back pull rod in combination with the vibration state, the dynamic stress and other characteristic data, so that the stability and the structural safety of the door body are kept. Structural damage caused by excessive vibration or stress overload can be avoided, the service life of equipment is prolonged, and stable operation of a ship lock system is ensured.

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 belongs to the technical field of bridge engineering, and particularly discloses a bridge safety early warning method and system based on fusion of a data model and a physical model. Comprising the following steps: in a first stage, on the basis of a neural network monitoring site excitation-response mapping relation model established by an initial state full-bridge finite element model, calculating response information corresponding to field excitation, and comparing response monitoring signals to determine whether a structure is damaged from a full-bridge angle; in the second stage, through data expansion from a monitoring point position to a non-monitoring point position, a set threshold value is compared to determine a damage position and a damage grade; and in the third stage, adjusting parameters of the model are calculated according to excitation-response signals of a monitoring point position sensor, the finite element structure model is updated, the residual bearing capacity of the structure is calculated by combining state information borne by the structure model, and the service life of the component is determined. The invention provides a safety assessment method fusing monitoring data and a physical model for structural health monitoring, and the method is clear in physical significance, high in real-time performance and reliable in result.