755 results about "Risk quantification" patented technology

The invention relates to the technical field of post-disaster path planning, in particular to a post-disaster unmanned aerial vehicle path planning method and system based on double-population constraint multi-objective optimization. The method comprises the following steps: based on a post-disaster task scene model, establishing an unmanned aerial vehicle voyage multi-objective collaborative optimization objective function and constraint conditions, including constructing a multi-objective function system, setting system constraint conditions and establishing a constraint violation degree evaluation mechanism; performing path optimization by using a double-population constraint multi-objective evolutionary algorithm, including establishing a multi-unmanned aerial vehicle path coding mechanism and initializing a double-population architecture, implementing a double-population collaborative genetic reproduction operation, and determining a double-stage constraint processing strategy; environment selection based on elite perception sorting is implemented; the multi-target collaborative optimization model and the accurate risk quantification mechanism constructed by the invention effectively solve the key problems of single target and rough risk processing of the existing method.

The invention relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic affair atlas, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a four-dimensional space-time model comprising a time dimension, a geographic space dimension, a supply chain network space dimension and a risk influence space dimension, representing the supply chain event as four-dimensional spatio-temporal data; a supply chain entity is identified from the four-dimensional spatio-temporal data, risk events are extracted, a affair graph is constructed, and the affair graph takes the risk events as nodes and the evolution relation between the events as edges to calculate the relation weight between the events; calculating a probability quantized value of the risk conduction path based on the affair map, and obtaining a comprehensive risk score of the target entity; generating a risk mitigation strategy based on the comprehensive risk score; and monitoring the deviation between the actual risk occurrence condition and the prediction result, and updating the affair map and the risk mitigation strategy through adaptive parameter optimization and an incremental learning mechanism to form a self-evolutionary risk assessment system.

The invention discloses an artificial intelligence-driven supply chain risk early warning system, which relates to the technical field of supply chain risk early warning, and performs closed-loop operation according to five steps of cross-level data acquisition, semantic alignment, graph expansion causal prediction and scene synthesis. The method comprises the following steps: firstly, converging heterogeneous data in milliseconds by using an adapter and constructing a named initial graph; calling an industry ontology to complete node and edge standardization so as to generate a semantic unified graph; inferring implicit dependency by using a multi-scale threshold and revising an edge weight to obtain an implicit dependency enhanced graph; then, a causal mask and time sequence attention are applied to the enhanced graph, and a risk vector combining the node influence degree and the propagation probability is output; and finally, according to the service context and the resource constraint optimization matching strategy template, pushing a signature slow-release instruction and returning the signature slow-release instruction. The method has the advantages of data real-time consistency, explainable risk quantification and auditable instruction execution, and improves the toughness and compliance level of the supply chain.

The invention discloses a supply chain risk real-time early warning and response system, and relates to the technical field of supply chain early warning. A data acquisition module obtains a main supplier equipment state, a historical interruption record and standby supplier logistics route data in real time; the dynamic weight distribution module generates an alternative priority evaluation model based on the basic weight parameters and the sudden environment parameters; the substitution link pre-calculation module generates a multi-level substitution path according to the evaluation model and calculates a score; the risk quantification module calculates a main supplier risk value; when the risk value exceeds the threshold value, the instruction generation module calls the highest score to replace the path and sends a work order change instruction; the dynamic feedback module collects execution data, establishes a deviation relation and corrects an evaluation model weight rule; all the modules achieve a complete link from risk identification to coping strategy optimization through data interaction and calculation, and the real-time performance and accuracy of supply chain risk management are improved.

The invention discloses a project cost risk monitoring method and a project cost management platform, and the method comprises the steps: S1, carrying out the real-time collection and standardization processing of multi-source data: collecting dynamic data in real time through an Internet of Things device at a construction site, carrying out the butt joint of a design drawing, a financial system and the like, obtaining static data, and employing a data cleaning, conversion and integration technology; s2, carrying out risk factor identification and correlation analysis based on a knowledge graph; s3, implementing risk quantitative prediction and early warning driven by artificial intelligence; s4, real-time monitoring and automatic verification of contract performance of blockchain enabling are realized; s5, providing dynamic cost adjustment and optimization decision support; through real-time collection and standardization processing of multi-source data, data islands of all parties participating in a project are broken, and accuracy, integrity and timeliness of key data such as construction progress and cost expenditure are ensured. Based on big data analysis and artificial intelligence prediction, more accurate risk assessment and cost prediction are provided for project managers.

The invention belongs to the technical field of electric energy dispatching, and particularly relates to a virtual power plant electric energy dispatching scheme optimization method based on intelligent decision analysis, which comprises the following steps: acquiring multi-source related parameter data; constructing a cascade prediction model based on the equipment basic data and the external environment data to obtain prediction results of all levels; constructing a risk assessment model based on prediction results of all levels in combination with equipment basic data and external environment data, generating a risk map in combination with a network vulnerability analysis method, and forming a schedulable resource pool; market risk data are acquired, a market risk comprehensive score is obtained, and a corresponding safety alternative scheme is triggered; and constructing a decision tree, determining a decision path in combination with the risk map, and generating a final optimization strategy. According to the method, through multi-dimensional means such as cascade prediction, risk quantitative evaluation, resource optimization screening and market risk response, high precision, high adaptability and intelligentization of virtual power plant electric energy scheduling are realized.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to a business contract intelligent review and risk quantification method and device, and the method comprises the steps: building and maintaining a business contract key term information base; obtaining and preprocessing a to-be-rechecked contract text; processing the text based on a bidirectional long-short term memory network and a conditional random field model, and extracting semantic representation; identifying key information through a multi-level attention mechanism; executing multi-label learning to classify and identify clause types and attributes; utilizing a dependency syntactic analysis technology to extract logical association and a responsibility chain among terms, and constructing a knowledge graph; identifying risk terms and generating risk prompts; business indexes are extracted, and risk open values are calculated; generating a rechecking report; the corresponding device comprises nine functional modules such as an information base management module, a text preprocessing module and a semantic representation extraction module, risk terms in a contract can be automatically recognized, a quantitative risk assessment result is provided, and contract auditing efficiency and accuracy are effectively improved.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention discloses an enterprise data security management system and method. The system comprises a data acquisition module which acquires a dynamic context signal of a user access request in real time; the risk calculation module receives the dynamic context signal and generates a real-time risk level signal through a preset risk evaluation model, and the risk evaluation model outputs a risk quantification result based on weighted calculation of a multi-dimensional context factor; the strategy execution module receives the real-time risk level signal and generates a dynamic access control signal; the unified strategy library module is respectively connected with the risk calculation module and the strategy execution module, and provides a strategy configuration signal of the risk assessment model for the risk calculation module; and the auditing tracking module is connected with the strategy execution module and the data acquisition module, receives the dynamic context signal and the dynamic access control signal, and generates an auditing log signal containing a risk decision chain. According to the enterprise data security management system and method, the problem of fine-grained access control failure caused by static strategy hysteresis can be solved.

The invention provides a mining area ecological restoration guidance system based on ecological big data. Relates to the technical field of environmental engineering and big data application, and comprises a data acquisition and fusion module used for acquiring remote sensing images, unmanned aerial vehicle aerial photography, ground sensor and historical monitoring data according to a unified space-time coordinate system and outputting fusion data; the prediction and risk quantification module is used for constructing a space-time deep learning model based on the fused data and outputting an ecological index prediction value and a corresponding risk probability; and the strategy optimization module is used for generating a restoration scheme according to the ecological index prediction value and the corresponding risk probability through a multi-target reinforcement learning model. According to the mining area ecological restoration guidance system based on ecological big data, ecological benefits, economic cost and residual risks can be considered synchronously, and a quantifiable and comparable optimal parameter combination is provided for a mining area restoration scheme.

The invention relates to the technical field of digital intelligent supervision, in particular to a supervision method based on industrial chain digital collaboration, which comprises the steps of realizing efficient and credible collection and fusion of industrial chain heterogeneous data through a block chain and federated learning technology, ensuring data privacy and constructing a unified data basis; the graph neural network is used for dynamically learning an industry chain entity relationship, a key path and a vulnerability node are accurately identified, a hidden risk structure is revealed, and the risk positioning capability is improved; by fusing policies, behaviors and topological data, the risk entropy value is calculated in real time, early warning is generated, multi-dimensional risk quantification and dynamic monitoring are achieved, and the early warning accuracy is enhanced; a supervision instruction matched with the risk is automatically triggered based on the intelligent contract, so that timeliness and accuracy of supervision measures are ensured, and human intervention errors are reduced; map and risk model parameters are dynamically adjusted through a reinforcement learning mechanism, and continuous optimization of a supervision strategy and minimization of service interference are realized.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention discloses a fire alarm linkage system for fire engineering based on the Internet of Things. The operation process of the system specifically comprises the following steps: generating a dynamic risk probability distribution diagram based on a building three-dimensional point cloud model and multi-source data, and deploying a differential sensor array; environment multi-physical field parameters are collected in real time through a differential sensor array, and a multi-mode fire behavior sensing signal set is generated; building a fire situation credibility map: calling a multi-criterion decision module to calculate a linkage equipment weight distribution matrix based on the fire situation credibility map, and generating a multi-stage linkage control instruction set in combination with the building topological structure model and the personnel distribution thermodynamic diagram; and performing multi-cross-system cooperative execution and closed-loop optimization. The method has the following advantages and effects that the inherent limitation of a traditional system can be broken through by constructing an intelligent prevention and control framework combining risk quantification, situation deduction and decision closed loop, the personnel evacuation efficiency is effectively improved, and damage caused by major accidents such as detonation is reduced.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention relates to the technical field of credit risk assessment, in particular to a credit risk dynamic feature extraction method based on hierarchical reinforcement learning. The layered reinforcement learning-based credit risk dynamic feature extraction method comprises the following steps of S1, obtaining credit risk data, and constructing a knowledge graph; s2, extracting candidate feature attributes, constructing an initial risk tag, and generating first-time multi-dimensional vector sample data; s3, performing dynamic feature engineering processing, and performing re-calibration and iterative updating on the initial risk label of the first-time multi-dimensional vector sample data; s4, performing spatio-temporal feature fusion processing, performing risk dynamic assessment, and calculating a risk prediction value corresponding to the time sequence feature; and S5, constructing a layered DQN framework, and dynamically outputting an interpretable risk feature set in combination with SHAP value quantification contribution verification. According to the method, high-precision and high-interpretability credit risk assessment is realized through multi-source data deep integration, self-attention driven feature engineering and time-space fusion risk quantification.

The invention relates to the technical field of information security, in particular to a user information protection method and device based on a dynamic desensitization strategy, and the method comprises the steps: determining sensitive data and a sensitivity score thereof through a named entity recognition model and a sensitivity scoring algorithm, extracting scene parameters, and determining a scene risk score through a risk quantification algorithm; and then a dynamic desensitization strategy generation algorithm is called to generate a target desensitization strategy, and finally, the strategy is used for desensitization and a reversible desensitization data identifier is generated. Through the series of steps, the desensitization strategy is dynamically generated according to different use scenes, the problem of scene adaptability deficiency caused by static desensitization strategy stiffness is solved, meanwhile, the reversible desensitization reduces the security risk caused by reverse cracking of rules, excessive desensitization is avoided, and the data availability is improved.

The invention relates to the technical field of communication networks, in particular to a resource allocation system and method in a satellite-ground fusion network, and the system comprises a satellite-ground topology and channel evolution predictor which is used for obtaining satellite ephemeris, user GNSS positioning and moving vectors, ground base station topology and a real-time CQI / RSRP measurement report; generating future link state information; the business QoE impact and switching cost modeling device is used for calculating transient interruption time of a switching action; generating switching cost information; the continuity risk quantification engine is used for generating continuity risk information containing the link maintaining risk probability, the instantaneous impact collapse probability and the link switching risk probability; the risk evasion type resource decision-making device is used for calculating maintenance link comprehensive cost and switching link comprehensive cost; calculating a dynamic switching damping threshold value; generating risk avoidance decision information; according to the invention, the high-frequency ping-pong switching near the edge state point is effectively inhibited, and the signaling overhead caused by invalid switching is reduced.

The invention provides a bullying recognition notification method based on equipment cloud collaboration and AI keywords, and relates to the field of campus security and artificial intelligence, and the method comprises the steps: an equipment end collects environment audio in real time, detects bullying keywords in real time through a local AI model, and triggers data uploading; the cloud receives the audio and associates the audio with the scene video, performs secondary identification by using a multi-modal fusion model, and generates a behavior emergency index UI and a scene risk coefficient SRF; calculating a comprehensive risk value based on the UI and the SRF, and judging a bullying event in combination with a dynamic threshold value; and sending alarm information to a guardian, a teacher or a security platform according to the comprehensive risk value grade. According to the method, the recognition accuracy is remarkably improved through equipment cloud cooperative processing and multi-modal fusion analysis; a dynamically quantized UI / SRF evaluation system is introduced to realize scientific risk grading; an anti-disassembly mechanism, a local cache, breakpoint resume and an edge computing disaster recovery strategy are built in to guarantee the robustness of the system; the problems of inaccurate identification, risk quantification deficiency, response stiffness and easy equipment failure are solved.

The invention discloses a deep and far sea wind power foundation platform and load integrated system under extreme environment conditions, and belongs to the technical field of offshore wind power platforms. The system comprises a basic platform body, a multi-dimensional monitoring subsystem, an environment-load coupling analysis subsystem, an intelligent regulation and control execution subsystem, a safety early warning decision subsystem and a shore-based collaborative management subsystem. Through synchronous acquisition of extreme marine environment, basic platform structure load and operation state data, distributed multi-type sensor layout and high-frequency sampling, data acquisition comprehensiveness and timeliness are realized, monitoring data are fused, a coupling model is constructed, 24-hour load change is predicted, and the real-time performance of the system is improved. According to the method, the attitude of the platform is dynamically adjusted, bearing configuration is optimized, emergency protection is started, the limitation of traditional separation analysis and fixed regulation and control is broken through, and the risk disposal and operation and maintenance efficiency is improved through risk quantification and grading early warning, remote monitoring and intelligent scheduling of a shore-based collaborative management subsystem and four-level early warning accurate matching coping strategies.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

The invention relates to the technical field of safety production management, and discloses a safety production hidden danger closed-loop management and control supervision system and a risk assessment early warning method, and the system comprises an environment modeling module which is used for building a digital model of a production unit and calculating a systematic instability coefficient based on historical data; the risk assessment module is used for quantifying the initial risk potential energy in combination with the coefficient and the hidden danger inherent attribute; the risk evolution module is used for dynamically updating risk potential energy according to a time and space coupling rule; the closed-loop verification module is used for judging the actual effectiveness of the intervention measure by comparing the management entropy before and after the intervention after the intervention measure is implemented; and the self-adaptive feedback module is used for automatically updating the systematic instability coefficient in the environment model according to the judgment result. According to the method, risk quantification, dynamic early warning, effect verification and model self-optimization are integrated, a complete closed-loop management process is formed, and the scientificity and foresight of safety production management are remarkably improved.

The invention relates to the technical field of fishing port safety monitoring, and discloses a fishing port fishing boat berthing safety monitoring system. The system comprises a fishing boat dynamic sensing module which collects dynamic position and attitude data of each fishing boat in a fishing port in real time; the berth state analysis module generates berth occupation state distribution information based on the data; the environmental parameter acquisition module acquires fishing port hydro-meteorological parameters in real time; the safety constraint initialization module outputs an initial safety monitoring constraint including an allowable yaw threshold value and a maximum allowable collision risk value in combination with a berth occupancy state and hydro-meteorological parameters; the parking risk assessment module executes risk quantitative analysis and generates risk level identification data by taking the initial safety monitoring constraint as a condition; a scheduling parameter optimization module optimizes berth resource scheduling according to the risk level identification data to obtain safety scheduling control parameters; and the alarm signal generation module converts an alarm dimension according to the safety scheduling control parameter and the dynamic position data updated in real time, and outputs a multi-stage acoustic alarm signal.

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

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.