339 results about "Risk source" patented technology

The invention discloses a food risk prevention and control intelligent remote early warning method and system, and the method comprises the steps: collecting environment and operation data in real time through an edge calculation terminal, screening low-entropy data based on a data conflict entropy value, writing the low-entropy data into a block feature chain, and carrying out the encryption transmission; fusing the block feature chain data and the historical behavior game data of the participants by using a space-time diagram convolutional network to generate a hybrid risk index; constructing a digital twinborn body, calculating a cross-modal propagation coefficient through transfer learning, dynamically simulating a risk diffusion path, and generating a prevention and control strategy candidate set; quantizing an early warning level confidence interval by adopting a quantum random number algorithm, and triggering a differentiated encryption early warning instruction through an intelligent contract; and finally, aggregating early warning response data by using federal learning to reversely deduce a risk source, and synchronously correcting the block chain weight and the digital twinborn parameters. According to the invention, full-cycle risk tracing, dynamic simulation and collaborative early warning of the food supply chain are realized, and the risk identification precision and the prevention and control response efficiency are effectively improved.

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

In order to overcome the defects in the prior art, the invention provides an urban gas pipeline risk assessment method based on a knowledge graph, and the method comprises the following steps: 1, collecting historical information of gas pipeline accidents, including detailed information of accident causes, types and consequences, and collecting images, videos and text reports of accident sites, needed information is identified and extracted, then multi-modal fusion is carried out to obtain a comprehensive feature vector, and an urban gas pipeline-risk source knowledge graph is established. And step 2, constructing an urban gas pipeline digital model. And step 3, acquiring related information of the running state of the urban gas pipeline in real time, and inputting the information into the urban gas pipeline-risk source knowledge graph for risk assessment. And for risk points which may form risks, marking and prompting the risk points on the urban gas pipeline digital model, and / or forming alarm information. Through the advanced knowledge graph technology, early warning and real-time response to gas pipeline accidents are achieved, and the overall emergency management efficiency and scientificity are improved.

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

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

The invention discloses a reservoir-lake-river system extreme flood simulation and flood control risk assessment method, system and device and a storage medium, and the method comprises the steps: firstly recognizing a risk source factor of a historical extreme flood event, depicting the spatial distribution uncertainty of the risk source factor through a Vine Copula function, and generating a risk source factor sample through Latin hypercube sampling; then, the samples are reduced based on a Neural Gas algorithm, and a risk source factor scene tree is constructed; generating an extreme flood hygrograph scene by combining historical flood data and adopting a same-multiple-ratio scaling method; and then a joint flood control optimization scheduling model is constructed, flood control risks of the reservoir and the lake are evaluated, and the evaluation effect is analyzed. The system is simple in structure and accurate and stable in risk assessment, fine risk assessment of flood disaster high-incidence areas is facilitated, and theoretical support of extreme flood risk assessment can be provided for areas lacking data.

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

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.

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

The invention discloses a biosafety laboratory airflow risk monitoring method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring airflow sensor data and facility state data, calculating a restraint integrity index according to the two types of data, performing dynamic time warping matching on a real-time change curve of the index and a preset baseline database, outputting a matching degree, and when the matching degree is lower than a preset threshold value, predicting that an airflow restraint barrier has a fracture risk, positioning a risk source according to the data source causing the abnormal matching degree, taking the source as a release point of a virtual aerosol pollutant, executing fluid dynamic simulation in combination with real-time environment data, generating a three-dimensional space-time diffusion trajectory of the pollutant in a laboratory, and extracting a risk quantitative index; and accurate laboratory airflow risk early warning information is generated based on the quantitative indexes. According to the invention, early warning can be carried out before the risk occurs, and the problem of safety hysteresis in airflow risk monitoring of the biosafety laboratory is relieved.

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

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

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

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

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

The invention discloses a risk assessment and obstacle avoidance method for state uncertainty of a low-altitude aircraft, and relates to the technical field of aircraft control, and the method comprises the steps: constructing surrounding Gaussian distribution for the state uncertainty of the aircraft according to the state of the aircraft and the historical deviation data of the state; generating field intensity distribution of each risk source in the space according to a Gaussian function; constructing a risk probability field according to the surrounding Gaussian distribution and the field intensity distribution corresponding to each risk source; the expected field intensity of the aircraft in the risk probability field is determined, and then the stress of the aircraft is determined; and adjusting the track of the aircraft according to the stress so as to avoid the obstacle. According to the method, a dynamic aircraft risk probability field suitable for a low-altitude aircraft is designed on the basis of an offset model surrounding Gaussian distribution, field intensity distribution of each risk source in space and an aircraft state probability distribution and dynamic risk field model constructed through fusion, decision support is provided for obstacle avoidance of the aircraft, and the risk probability of the aircraft is improved. And the safety of low-altitude flight is effectively improved.

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

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

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

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

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

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

The invention belongs to the technical field of geological disaster early-stage analysis, and particularly relates to a geological disaster early-stage analysis method and system based on multi-source data, and the method comprises the steps: identifying a risk source, determining an analysis range, and identifying a risk region and a region property in the analysis range; disaster formation mode analysis: analyzing geological disaster hidden dangers to carry out disaster pregnancy conditions and disaster formation processes, and establishing geological disaster geological identification marks and indexes; risk factor analysis: on the basis of a geological disaster forming mode, analyzing geological disaster movement and accumulation characteristics by adopting a mathematical model of rock-soil body movement and fluid-solid coupling movement, and estimating dynamic reserves of the accumulation characteristics; potential disaster-bearing body vulnerability analysis, including social vulnerability analysis, material vulnerability analysis and environmental vulnerability analysis; through cooperation of the units, the line can be prevented from being selected below a slope where debris flow easily occurs or near a channel according to an estimation result, and the risk that a project is damaged by the debris flow is reduced.

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

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

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

The invention discloses an intelligent real estate registration method and system, relates to the technical field of real estate registration, and solves the technical problems that real-time monitoring and effective management of key indexes of a real estate registration business process are lacked, and risks are difficult to effectively prevent. Key indexes such as business handling time limit, error rate and illegal behavior occurrence rate are monitored in real time, risk sources are analyzed from multiple dimensions based on a large amount of historical data, a risk identification model is constructed by adopting advanced modeling methods such as a neural network, potential risks in real estate registration business can be accurately identified, and the risk identification accuracy is improved. The risk level can be accurately evaluated by combining comprehensive risk score calculation and a risk level division standard, corresponding emergency plans are made for different risk levels, hierarchical management and control of risks are realized, timeliness and effectiveness of emergency response are improved, loss caused by the risks is reduced, and safe and stable development of real estate registration business is guaranteed.

The invention provides a Ti65 pipe external corrosion quantitative risk comprehensive evaluation method and system, and relates to the technical field of pipe detection, and the method comprises the following steps: carrying out data collection and preprocessing, carrying out comprehensive geometric model construction, carrying out comprehensive risk index calculation and grade division, and carrying out risk early warning and decision output. And finally carrying out dynamic optimization and model verification. The temperature, chloride ion concentration, microorganism density and stress data are synchronously collected through a heterogeneous sensor combination, multi-field coupling analysis of internal and external corrosion factors is achieved, the risk source of the whole life cycle of the pipeline is covered, in-situ real-time processing is achieved through a high-frequency sensor and edge calculation, physical invasion of the pipeline is not needed, delay is greatly reduced, and the detection accuracy is improved. And the dynamic monitoring requirement is met.

The invention discloses a mountain area double-lane road driving dynamic risk assessment method based on an artificial potential field theory. The method comprises the following steps: S1, extracting trajectory data with potential conflicts in vehicle natural driving data; s2, constructing an obstacle risk field, and quantifying driving risks caused by dynamic and static obstacles on a road; s3, constructing a road line risk field, and quantifying potential risks caused by driving of the vehicle deviating from the center line; s4, constructing a road boundary risk field, and quantifying the risk generated when the vehicle approaches the road boundary; s5, integrating the risk field models of the three driving risk sources to construct a comprehensive driving risk field model; and S6, visualizing the driving risk of the specific target vehicle in the form of a risk map. According to the method, a mountain area double-lane road driving dynamic risk assessment model based on the artificial potential field theory is constructed, and the mountain area double-lane road driving dynamic risk is scientifically, reasonably and accurately described and discriminated from the angle of accident prevention.

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