636 results about "Security assessment" patented technology

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 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 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.

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 relates to the technical field of the Internet of Things, and discloses a security assessment method and system based on the Internet of Vehicles, and the method comprises the steps: obtaining multi-dimensional original data, context data and a traffic data flow; attack event features are generated through data classification, cleaning, standardization and PCA feature extraction; constructing spatial and temporal distribution characteristics in combination with context data, determining a damaged object and a damage radius through a K-means clustering and morphological expansion algorithm, and outputting a damage area; analyzing abnormal behavior distribution based on the area and the traffic data flow, and generating a distribution diagram; analyzing the threat level according to the distribution map, the damaged object and the hazard radius, distributing resources by combining a greedy algorithm, and planning a time sequence to construct a dynamic defense strategy; optimizing a protection scheme according to the real-time feedback data; finally, behavior changes are continuously monitored, and a risk thermodynamic diagram and a safety gap list are generated. According to the method, the Internet of Vehicles safety assessment capability is improved.

The invention provides a network security assessment method and system based on threat intelligence, and the method comprises the steps: obtaining a threat intelligence data set and a to-be-assessed network data set, and carrying out the feature association extraction processing, thereby obtaining a target security feature set; the target security feature set covers threat and component association matching, threat and behavior influence conduction and network component protection capability features, calling a pre-training security assessment model to carry out risk situation analysis on the target security feature set, and generating a network security assessment result including a potential threat triggering probability and a threat influence propagation path, and determining a to-be-evaluated network risk level and key risk point distribution information according to the network security evaluation result, generating a network security evaluation report containing a risk positioning identifier, and outputting the network security evaluation report to the network management terminal to support security policy adjustment operation, so that the network security condition can be comprehensively and accurately evaluated, and the user experience is improved. And the pertinence and effectiveness of safety protection are improved.

The invention discloses a ship power grid transient electromagnetic interference grading evaluation method and system, relates to the technical field of ship power grid safety evaluation, realizes scientific grading evaluation of ship power grid transient peak interference, and provides a reliable basis for stable operation and equipment protection of a ship power grid. Comprising the following steps: measuring a transient peak sensitive threshold value of equipment by adopting a peak signal transduction sensitivity test; performing classification and importance coefficient assignment on the equipment according to the importance degree of the equipment; and calculating a weighted average sensitive threshold value of load equipment at the rear end of the distribution box by adopting a weighted average method. At the output end of a power distribution box of a ship power grid cabin, a transient peak conducted emission detection device is used for monitoring and collecting power grid transient peak conducted emission data in real time, and a power grid transient peak interference measured value is recognized from the power grid transient peak conducted emission data. Comparing the power grid transient peak interference measured value with the weighted average sensitive threshold value, and calculating a difference value between the two values; and according to a difference value result, carrying out ship power grid transient peak interference grading evaluation.

The embodiment of the invention provides an access request security assessment method and device, a resource server, a storage medium and a program product, and relates to the technical field of cloud native security. According to the method, entity attributes, access context, behavior features and environment features in an access request are obtained after identity verification is passed, and the features are input into a pre-trained long-short memory network model to dynamically generate trust scores; therefore, the trust evaluation of the access request is changed from the traditional static rule judgment to an intelligent scoring mechanism based on the multi-dimensional dynamic characteristics. After the trust score exceeds the score threshold and the target policy rule matched with the access request is found in the policy rule base, the access request is processed based on the target policy rule, and refined and dynamic security management of the access request is realized through combination of a multi-dimensional intelligent scoring mechanism and a policy rule matching mechanism; therefore, the security and the intelligent level of access control in the cloud native environment are improved.

The invention discloses a data center operation and maintenance management method and system and electronic equipment, and belongs to the technical field of data management. The method comprises the following steps: acquiring real-time power and environment information of a data center; inputting the real-time power and environment information into an operation and maintenance decision model for real-time analysis to obtain real-time operation information and a real-time operation and maintenance strategy of the data center and predicted operation information and a predicted operation and maintenance strategy in a future preset time period; determining a risk event existing in the data center and a security risk corresponding to the risk event according to the real-time operation information, the predicted operation information and a preset security assessment strategy; when the safety risk is greater than a first preset threshold value, determining a target emergency strategy corresponding to the risk event according to a preset emergency strategy; executing operation and maintenance management on the data center according to the target emergency strategy; and executing operation and maintenance management on the data center according to the real-time operation and maintenance strategy and the prediction operation and maintenance strategy under the condition that the security risk is smaller than a first preset threshold value.

The invention relates to the technical field of data mining, in particular to a photovoltaic station safety assessment system and method based on big data analysis. The method comprises the following steps: obtaining inverter structure data; performing installation structure instability analysis based on the inverter structure data to obtain structure instability data; calculating a slip critical point according to the structural instability data to obtain slip critical point data; verifying the foundation bearing capacity according to the slip critical point data to obtain the foundation bearing capacity; carrying out settlement prediction according to the bearing capacity of the foundation to obtain foundation settlement data; performing structure inclination analysis according to the foundation settlement data to obtain structure inclination data; performing exhaust smoothness evaluation according to the structure inclination data to obtain exhaust smoothness data; and performing heat accumulation detection according to the air exhaust smoothness data to obtain heat accumulation data. According to the invention, based on the data mining technology, the real-time performance and accuracy of photovoltaic station structure and thermotechnical abnormality identification are improved.

The invention discloses a power transmission tower corrosion damage assessment operation and maintenance method and system combining digital twinning and AI, and relates to the technical field of power transmission and transformation project operation and maintenance safety assessment. Based on a parameterized digital twinning base model, a mechanical response and an environmental corrosion rate are obtained by combining environmental parameters, and then a training data set is constructed; training a multi-task machine learning model based on the training data set to obtain a trained multi-task machine learning model; performing feature extraction on the image data to obtain visual features, performing feature extraction on the point cloud data to obtain geometric features, and fusing the visual features and the geometric features by adopting a double-flow feature fusion network based on an attention mechanism to obtain fused features; and inputting the fusion features into a multi-task machine learning model for prediction to obtain a predicted mechanical response and an environment corrosion rate, and then evaluating the corrosion state and predicting the service life so as to realize rapid, accurate and intelligent evaluation and operation and maintenance decision support of the corrosion state of the power transmission tower.

The invention discloses a virtual simulation and security evaluation method and system based on a network security target range, and the method comprises the steps: constructing a network asset knowledge graph for describing a virtualized target range environment through information collection; secondly, based on an attack strategy grammar rule base, adopting a Monte Carlo Tree Search (MCTS) algorithm to carry out intelligent attack simulation on the knowledge graph so as to discover a nonlinear and multi-stage attack path; thirdly, constructing a Bayesian attack graph (BAG) based on the knowledge graph, and performing probabilistic and systematic quantitative evaluation on the security risk of each asset node in the network through Bayesian reasoning; and finally, integrating the attack path and the quantitative risk value, and generating a comprehensive assessment report containing a visual path, a risk sequence and a reinforcement suggestion. According to the target range simulation method and device, the problems that existing target range simulation is insufficient in confrontation authenticity, one-sided in safety evaluation and lack of predictability are solved by combining the strategy simulation of the MCTS and the global quantitative analysis of the BAG.

METHOD FOR DYNAMIC SECURITY ASSESSMENT OF STABILITY OF A POWER SYSTEM AND SYSTEM THEREOF The present disclosure relates to a computer-implemented method and system for providing a dynamic security assessment of stability of a power system comprising: receiving input data comprising an operational data record comprising OP-characterizing variables of the power system OP; applying the model to the OP-characterizing variables to obtain a stability classification. It is also disclosed a computer program product and a system configured to carry out the method herein disclosed.

The invention relates to the field of digital security assessment, and discloses a data security compliance risk assessment system, which comprises the steps of automatically retrieving data related to a target industry based on a preset network model, extracting a structured risk factor and an aging identifier of the data, and judging whether an index difference exists or not; performing semantic alignment and difference comparison on the output knowledge fragment and a local risk index, and judging whether the credible score of the risk adjustment suggestion exceeds a set threshold value or not; according to the generated updating suggestion, judging whether the updating result identifies a high-risk item or not; in a virtualized security event simulation environment, based on a multi-dimensional logic deduction algorithm, constructing an attack path corresponding to a high-risk item, simulating a multi-scene risk propagation process, calculating a comprehensive risk index and outputting a deduction result; and based on a deduction result, recording a risk propagation path, forming a verified risk knowledge fragment and feeding back the fragment to a safety compliance evaluation database. The method has the advantage of improving the accuracy of the evaluation result.

The invention discloses a fast lossless prediction method for the highest overfire temperature of a cable based on deep learning, and relates to the technical field of safety assessment after a bridge fire. According to the method, apparent image data of a cable anticorrosion-fireproof protection system at different temperatures are obtained through various fire tests, and a special data set is constructed after shunting preprocessing; a ConvNeXt-Tiny-based double-branch weight sharing prediction model is innovatively constructed, state features of a cable flame-retardant sealant layer and an anticorrosive layer are extracted through two parallel branches respectively, deep representation learning is performed through feature fusion and a shared backbone network, and finally high-precision parallel prediction of double temperature parameters is realized through two ordered classification prediction heads. A transfer learning strategy and a mixed loss function are used for training, so that the prediction precision and generalization ability are remarkably improved; finally, a visual window is developed, and cable fire temperature rapid nondestructive evaluation without dismounting the sensor is realized. The method provides reliable technical support for safety performance evaluation of the bridge cable after the fire disaster.

The invention relates to the technical field of intelligent safety management and control of a thermal power plant, and discloses a safety management and control application method and system based on big data acquisition and analysis. The method comprises the following steps: constructing an equipment operation situation map by fusing real-time data streams, and realizing dynamic panoramic perception of a system operation state; key nodes are identified based on the atlas, a real-time safety margin index is calculated by using a safety evaluation model, a dynamic instruction set with priority is generated, and the inspection robot is driven to carry out self-adaptive accurate data acquisition; and the central processing unit updates the atlas by comparing and analyzing real-time and historical data, so as to realize risk re-assessment and early warning. According to the method, equipment monitoring is improved from isolated parameter analysis to global association cognition, a fixed inspection mode is converted into predictive intervention based on real-time risks, and the state perception depth, the early warning timeliness and the resource allocation efficiency are improved.

A system for automated penetration testing using artificial intelligence (AI) is described, which aims to enhance cybersecurity by simulating attacks on software and computer systems in order to measure and identify vulnerability. The platform employs AI agents to perform tasks including script execution for vulnerability testing, social engineering simulations, comprehensive security assessments, and more, thereby reducing costs, time, potential risks associated with traditional penetration testing methods, and providing a more complete and available solution than humans can produce alone.

The invention relates to an ultra-deep well perforation test combined operation tubular column safety evaluation method. The method comprises the following steps that S1, a packer lower portion perforation detonation annulus pressure evolution finite element model and a dynamic negative pressure evolution numerical model after perforation detonation are established; s2, establishing a perforation instant tubular column coupling impact vibration evolution model, and determining main control factors; s3, establishing a dynamic evolution numerical model of the shock absorber at the moment of perforation, and obtaining a dynamic evolution model of the lower oil pipe of the packer, the shock absorber and the perforating gun at the moment of perforation; and S4, evaluating the safety of the pipe column at the lower part of the packer by taking the Mises yield criterion as an evaluation standard. The influence of perforation detonation transient wellbore pressure and dynamic negative pressure on a lower tubular column system of the packer is fully considered, the dynamic response rule of the tubular column is disclosed through numerical simulation and theoretical analysis, risk control measures are put forward, and the safety of the packer is improved. Theoretical basis and technical support are provided for optimal design of an ultra-deep well perforation test combined operation scheme and safety of an oil testing process.

The invention discloses an energy storage battery thermal diffusion modeling method based on P2D-solid heat transfer coupling, and relates to the technical field of energy storage battery thermal management. The method comprises the following steps: constructing a pseudo two-dimensional (P2D) electrochemical model and a three-dimensional heat conduction model of a single battery, and calculating lithium ion concentration, potential and corresponding heat source distribution; mapping the obtained heat source as a volume heat source to a thermal model and realizing bidirectional feedback; setting a thermal runaway triggering condition to simulate temperature rise abrupt change; battery SOH state modeling is introduced, and the aging process is dynamically corrected and reflected through material parameters; finally, time domain simulation is carried out, and temperature field evolution and safety behavior prediction in the whole life cycle are obtained. According to the method, the coupling process of the microelectrochemical reaction and the macroscopic temperature change of the battery can be captured at the same time, the thermal diffusion characteristics of different decline stages are accurately described, and visual modeling of thermal runaway behaviors is supported. The method has the remarkable advantages of high precision, high applicability, perfect safety assessment capability and the like.

The invention discloses an electric power safety tool management method and system, and relates to the technical field of electric power safety tool management.The method comprises the steps that when a tool using list of a user is received, tool images and life index parameters corresponding to all electric power safety tools are obtained; and performing correlation evaluation according to the appliance image and the service life index parameter corresponding to each electric power safety appliance to obtain a corresponding correlation sequence, screening each electric power safety appliance according to the correlation sequence and the used appliance list to obtain a corresponding target appliance, and when a return request of a user is received, returning the corresponding target appliance to the corresponding electric power safety appliance. And obtaining a return time point of the user, and generating a corresponding return record according to the return time point and the appliance number of each target appliance. The technical problems that in the prior art, the electric power safety tools and instruments are managed mainly based on RFID, but safety evaluation is not conducted on the electric power safety tools and instruments in the using process, and the management reliability of the electric power safety tools and instruments is reduced are solved.

The invention belongs to the field of network security, and relates to a method and a device for constructing a network security operating system, electronic equipment and a storage medium, and the method comprises the following steps: constructing an autonomously controllable improved microkernel infrastructure; based on the microkernel infrastructure, constructing a full-stack layered security control computing architecture base; constructing intelligent agent components, and deploying a multi-intelligent agent collaborative protection component system; integrating trusted computing and an integrity measurement verification system; performing dynamic adaptation and execution of multiple security policies; and a standardized safety evaluation and adaptive optimization closed loop is established. A trusted computing base is cut from a design source, so that the probability of occurrence of high-risk vulnerabilities is reduced; the real-time defense that the threat is changed and the strategy is changed is realized, and the blind area of the static strategy in resisting the unknown threat is made up; the malicious codes can be blocked before running, and the post passive situation that traditional security software only depends on a feature library for searching and killing is broken; and the contradiction between security capability solidification and threat dynamic evolution is fundamentally solved.

The invention provides an artificial intelligence-based engineering safety assessment method and system, and belongs to the technical field of engineering structure health monitoring. The method comprises the following steps: acquiring physical parameter time sequence data of an engineering structure through a distributed sensor network, synchronously acquiring surface visual data, and processing the data; inputting the processed data into a multi-modal artificial intelligence analysis model; processing time sequence data by an improved Mogrimer LSTM network through four-round iterative optimization and a sensor health sensing mechanism, and realizing damage positioning, classification and crack width quantification by an improved YOLOv8 network through a coordinate attention module and a decoupling detection head; fusing the time sequence and the visual features through a cross-modal attention mechanism, and generating a risk part feature vector; and calculating a safety risk index based on the risk part feature vector, and outputting a three-level risk level and a structured report. According to the method, multi-source data are fused, and the precision and real-time performance of engineering safety assessment are remarkably improved.