63 results about "Future risk" patented technology

The invention provides an operation risk early warning method and system based on a power grid information system, and relates to the technical field of power grid operation risk assessment. According to the method, equipment risk characterization and future risk score prediction are realized by constructing a risk perception graph fusing node and edge features; and identifying a high-risk region and a diffusion path by combining a clustering algorithm, and triggering a hierarchical response strategy based on a rule base. According to the invention, real-time early warning and intelligent management and control of the operation risk of the power grid are realized.

The invention relates to the technical field of electronic commerce dynamic management, and particularly discloses an electronic commerce management system and method based on big data, and the method comprises the steps: collecting the data of each commercial operation node of an electronic commerce platform, and carrying out the real-time collection and time serialization processing of the data; the transmission quantity, the flow direction and the interaction frequency of the collected data are analyzed, interaction modes among different operation nodes are monitored, and potential abnormal interaction nodes are identified; based on the abnormal interaction node, calculating a failure node coefficient for judging whether the node has a failure risk; for non-failure nodes, constructing a multi-dimensional risk assessment model, predicting future risks of the nodes by adopting a random forest algorithm, and calculating risk coefficients; through comprehensive analysis of the node abnormal coefficient, the failure node coefficient and the risk coefficient, priority early warning and emergency processing are performed on the high-risk node, so that the operation risk is effectively prevented, and the stability and the response capability of the platform are improved.

The invention discloses an engineering geologic body multi-parameter coupling risk assessment method, belongs to the technical field of geological safety monitoring, and can solve the problem that an existing geologic body risk assessment method cannot dynamically and automatically update water conservancy and hydropower engineering geologic body risk assessment along with multi-source real-time monitoring data and cannot predict a future risk trend. The method comprises the following steps: S1, generating initial risk distribution according to initial geological data of a target engineering area and a preset coupling rule; s2, determining a target parameter prediction value and a risk trend prediction value within a preset time length according to the real-time monitoring data flow, the geological updating data and the initial risk distribution; s3, generating a dynamic risk threshold according to the target parameter prediction value and the risk trend prediction value in combination with a preset engineering safety constraint condition; and S4, comparing the current measurement value of the real-time monitoring data flow with the dynamic risk threshold value, and determining the dynamic risk level of the target engineering area. The method is used for risk assessment of the water conservancy and hydropower engineering geologic body.

The invention discloses a drainage basin real-time flood control scheduling risk dynamic early warning and emergency scheme making method, which comprises the steps of obtaining a real-time total element information tensor and an engineering flood control design parameter set, and calculating to obtain a real-time hierarchical risk entropy time sequence front section based on safety margin distribution, and inputting real-time data and pre-constructed entropy flux Markov graph structure data into the trained entropy perception double-flow prediction model for reasoning to obtain a future risk prediction set. And a dynamic risk early warning result set is generated in combination with a preset entropy level threshold and a future failure chain probability, and an emergency scheduling scheme set is generated on the basis of meeting safety margin constraints and entropy reduction targets. According to the invention, dynamic early warning and decision making of strong physical-risk coupling are realized.

The invention discloses a lower-layer supply chain risk visual prediction method and system based on a visual technology, and belongs to the technical field of supply chain risk assessment, and the method specifically comprises the steps: obtaining related data of a lower-layer supply chain, carrying out the preprocessing, obtaining first monitoring data, constructing a target recognition model, and carrying out the recognition of the first monitoring data; identifying and extracting spatio-temporal features and time sequence features of the first monitoring data, constructing a multi-level risk assessment model, assessing a comprehensive risk score of a lower-layer supply chain, and predicting future risks; the key elements in the supply chain can be accurately positioned by performing feature extraction and recognition on the collected multi-modal data, and the potential risk factors and the comprehensive risk value can be effectively recognized and visually displayed by combining the risk assessment model.

The invention relates to an individual future health state prediction method and device based on proteomics, and a medium. The method comprises the following steps: obtaining and preprocessing proteomics data; constructing a shared network: constructing an individual past and future common disease condition prediction neural network based on a twin network framework; backbone network construction: constructing a health specific outcome prediction neural network based on a multilayer perceptron method; and the health assessment network synthesizes the shared network and the backbone network architecture, carries out fusion and fine tuning on the extracted features in a potential space, and outputs the future risk assessment probability of various health specific outcomes through further training, updating and fine tuning of network parameters. Compared with the prior art, the method focuses on a proteomics data processing and modeling method, takes past and future health estimation as prior information, and realizes individualized health condition evaluation with multiple disease species and death as the outcome.

The invention provides a distribution network equipment operation risk assessment method and system based on digital twinning, and the method comprises the steps: calculating an overall potential risk assessment value of equipment according to a pre-established future risk analysis model based on meteorological prediction based on meteorological data and historical operation data of each piece of equipment, judging whether the overall equipment has potential risks according to the overall potential risk assessment value of the equipment; based on the meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, judging whether the whole power distribution network has a risk or not according to a pre-established combined assessment model of the influence of the weather on the grid risk; if the overall equipment has the potential risk and the overall power distribution network has no risk, adjusting a risk level threshold value of the equipment, otherwise, obtaining a risk assessment result of the equipment according to a pre-established environmental factor weighted health state assessment model based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data; and the accuracy and timeliness of risk assessment are improved.

The invention discloses an AGV full-life-cycle intelligent operation and maintenance method based on a twin map and reinforcement learning, and belongs to the technical field of equipment operation and maintenance management, and the method comprises the steps: employing an edge-level dynamic attenuation function and a node-level economic weight operator for aggregation, and obtaining a health vector containing a whole vehicle risk score and a part first-level risk identifier; the method comprises the following steps: training a reinforcement learning agent according to a three-section reward function comprising production line instant income, health improvement income and future risk deduction, and determining an operation and maintenance strategy weight file by adopting a profit-oriented gradient update mode optimization strategy with risk suppression factors; and carrying out equipment operation and maintenance management on the AGV in workshop operation according to the strategy weight file. According to the method, dynamic risk propagation and economic quantitative evaluation are adaptively fused under the real-time working condition of a workshop, and a comprehensive optimal decision of maintenance opportunity and production line income is realized under the condition of non-stop observation through a reinforcement learning strategy closed-loop driving AGV fleet preventive maintenance and scheduling method.

The present invention relates to the technical field of water conservancy project management, and discloses an intelligent monitoring system for water conservancy project seepage, comprising a multi-sensor perception module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision-making module, an adaptive monitoring and scheduling module, an automatic inspection and response module, a digital twin and simulation analysis module, and a communication module; the multi-sensor perception module is responsible for collecting multi-dimensional data related to seepage in real time. A machine learning algorithm is used to intelligently predict seepage trends, and risk assessment is performed in combination with real-time data. Compared with traditional monitoring methods based on preset thresholds, the present invention can automatically identify complex seepage patterns by learning historical data and real-time data, predict future risks in advance, and achieve forward-looking management of water conservancy project seepage risks, significantly improving the system's prediction accuracy and response speed.

The invention discloses a pipe jacking construction full-period and full-section soil pressure monitoring system and a method thereof, and relates to the technical field of soil pressure monitoring, the system comprises a distributed acquisition module, a multi-source information acquisition module, a fusion analysis module, a management decision module and an execution feedback module, and the evolution trend of soil pressure is predicted through a trend extrapolation model. The system can perform active early warning and regulation and control before a dangerous case occurs, post-remedy is changed into pre-prevention, meanwhile, a complete closed loop of perception-analysis-decision-execution-feedback is constructed, intelligent operation of the construction process is achieved, dependence on artificial experience is greatly reduced, and the construction efficiency is improved. The comprehensive risk index Q deeply fuses the current soil pressure, the operation coordination and the future risk, a comprehensive and accurate quantitative basis is provided for decision making, the system can automatically switch among four modes of economy, early warning, regulation and protection according to the Q value and the equipment health degree, the construction safety is ensured, and the construction efficiency can be dynamically optimized on the premise of safety.

The invention discloses a data center load prediction method and a refrigeration system intelligent regulation and control method. The intelligent regulation and control method comprises the following steps: obtaining an original load sequence including level, trend and seasonal characteristics according to an acquired IT load of a data center; obtaining initial load prediction information at a to-be-predicted moment according to the information; then, according to the load initial prediction information, dynamically determining a horizontal update feature, a trend update feature and a seasonal update feature corresponding to a to-be-predicted moment so as to obtain load target prediction information of the to-be-predicted moment, and finally, according to the uncertainty percentage corresponding to the to-be-predicted moment, determining a future risk level. And corresponding strategies are dynamically called to execute intelligent regulation and control on the system. According to the method, automatic intelligent balance regulation and control can be achieved, the future uncertainty is converted into an executable strategy, it is ensured that the refrigeration system always operates in the safest, most efficient and most energy-saving mode, and the manageability and operation efficiency of the data center are greatly improved.

The application provides a community child activity track and care tracking method and device based on an AI intelligent security system, relates to the technical field of track care tracking, performs multi-dimensional data collection and identity recognition on a child target entering a monitoring visual field, performs dynamic comparison and deviation analysis on current behavior and historical individual behavior baseline information of each child, performs risk level determination on a monitoring scene where the child is located and dynamic switching of a corresponding calculation accuracy mode, simultaneously calls a neighboring camera for multi-level cross verification on a high-risk event, performs graded early warning pushing on the confirmed high-risk event, and feeds back the result of the event into the individual behavior baseline information of the child for incremental updating to obtain future risk prediction data; the application can perform individualized care according to individual differences of children, adaptively allocate calculation resources according to risk scenes, filter false alarms through multi-source cross verification, and has a continuous learning evolution capability.

The invention relates to the technical field of reservoir safety monitoring, in particular to an AI (artificial intelligence)-based reservoir safety monitoring intelligent early warning system and method. The system monitors data of a reservoir dam body, periodically analyzes basic safety attributes of the reservoir dam body, assumes possible crack scenes of the reservoir dam body according to data expression of the reservoir dam body; according to the reservoir foundation safety evaluation method, the reservoir foundation safety is accurately depicted, the crack risk can be prospectively simulated, and the reservoir foundation safety evaluation method has the advantages that the reservoir foundation safety is accurately depicted through the mechanism of data-driven hypothesis, hypothesis combination with meteorology and multi-mode collaborative evaluation, so that the reservoir foundation safety evaluation efficiency is improved, and the reservoir foundation safety evaluation efficiency is improved. Safety evaluation is upgraded from historical data backtracking to future risk deduction, the method provides an intelligent solution of full-time monitoring, full-scene simulation and full-process closed loop for reservoir safety management, and the safety and management efficiency of water conservancy projects are remarkably improved.

The invention belongs to the technical field of oil depot risk management, and discloses a risk management system and device based on an oil depot, and the risk management system comprises the steps: constructing a management and control platform based on an industrial Internet technology, and obtaining oil depot production data collected by monitoring equipment in an operation range through the management and control platform; combining the oil depot production data with a partial least square method technology to construct a risk judgment model, and obtaining an abnormal data value in the working process of the oil depot by using the risk judgment model; judging an exceeded value of the abnormal data value by utilizing a risk standard threshold value, and analyzing a risk trend in the working process of the oil depot based on the exceeded value; and feeding back the risk trend to the management and control platform, and adjusting the working process of the oil depot by using the management and control platform. According to the method, the abnormal data value in the working process of the oil depot can be accurately identified, quantitative assessment of risks is realized, future risk changes can be predicted, and prospective guidance is provided for risk management.

The invention relates to the technical field of mobile terminal safety monitoring, and discloses a risk signal screening monitoring method and system for a mobile terminal. The method comprises the following steps: collecting intensity change sequences of various risk signals of the mobile terminal, calculating cosine similarity and propagation momentum factors of deviation rate vectors, counting time sequence mutual information of the risk signals to determine a causal propagation relationship, constructing a directed propagation graph to predict a future risk evolution value, and calculating the risk evolution value of the mobile terminal. And calculating a weighted distance, dynamically adjusting a judgment threshold and judging a multi-step progressive attack. The technical problems that in the prior art, staged progressive attacks cannot be recognized, risk evolution cannot be pre-judged in advance, the detection sensitivity cannot be adaptively adjusted, and the response strategy is extensive and single are solved. According to the method and the device, the recognition accuracy and the response timeliness of the multi-step progressive Trojan horse attack are improved.

The application discloses an improved risk field-based multi-target trajectory optimization method for an unmanned vehicle, and belongs to the technical field of intelligent traffic and automatic driving.The method comprises the following steps: three risk fields are respectively constructed, i.e., a driving environment risk field which is combined with road shape, static obstacles and environmental conditions; a vehicle risk field which considers vehicle size, position, driving direction and simultaneously considers the motion interaction between the vehicle and obstacles; and a predicted trajectory risk field which evaluates future risks in advance according to the possible driving trajectory of surrounding vehicles, combines trajectory credibility and uncertainty, and integrates the three risk fields to form a comprehensive risk field, constructs a three-dimensional space-time model based on the risk field, and finally outputs an executable trajectory by considering driving safety, efficiency, comfort and economy through reasonable safety strategies and multi-target optimization rules.The application has high risk evaluation accuracy in a dynamic traffic environment and good trajectory planning effect.

The invention relates to the field of risk assessment, in particular to a water conservancy project quality safety supervision risk assessment system. The method comprises the specific implementation steps that firstly, nonlinear interaction of risk factors in multi-source heterogeneous data is converted into quantitative risk numerical values through a risk quantification model, and the quantitative risk numerical values and context factors are packaged into risk state vectors; then, weights are distributed for the risk state vectors of all the completed engineering stages before, risk fusion vectors are constructed by fusing the weighted risk state vectors and heterogeneous data of the current engineering stage, and then the future risk evolution trend of the water conservancy project structure is predicted. When the prediction trend deviates from the safe trajectory, the system triggers early warning in advance and automatically backtracks the model to find out the main risk factor. In order to ensure the reliability of early warning, the system generates supervision robustness at the same time, and when the supervision robustness is lower than a preset threshold value, secondary assessment of the risk is automatically initiated according to the main risk factors and the robustness.

The invention discloses a multi-modal tunnel construction risk assessment method and system, and belongs to the field of construction risk assessment, and the method comprises the steps: carrying out the preprocessing of collected tunnel construction multi-modal data, and integrating the multi-modal data into a cloud multi-modal database; performing data fusion processing on the data of the multi-modal database through a gating multi-modal fusion method to obtain a fusion feature containing a plurality of physical parameters; mapping the fusion features into specific physical parameters through a physical parameter decoder, and performing risk assessment through a multi-environmental factor risk assessment model in combination with the physical parameters; and importing a risk assessment result into the BIM model, and displaying current and future risk areas, risk levels and potential risk types in the tunnel construction process. According to the method, the multi-physical field control equation set, the inter-field coupling mechanism and the risk prediction model are combined, and deep mining and intelligent analysis of multi-modal data are achieved.

The present invention relates to systems and methods for intelligently optimizing supply chain is provided. In particular, the systems and methods provide the capability to configure supply chain systems so as to: balance between cost and service is optimised and profitability maximised; configure system parameters to respond to both current and future risks; ensure that variability is built into plans enabling maximised efficiency; and human error and bias are eliminated from the planning process such that pro-active rather than reactive behaviour becomes the norm.

The system according to this embodiment aims to predict future risks based on historical data and propose specific countermeasures. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects historical data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future risks based on the data analyzed by the analysis unit. The proposal unit proposes specific countermeasures based on the risks predicted by the prediction unit.

The invention relates to the technical field of economic early warning, prevention and control, and particularly discloses a hospital economic risk early warning, prevention and control system, which comprises a data acquisition and preprocessing module, an intelligent risk prediction module, a digital twinborn simulation decision module, a decision fusion module and a user interaction module, according to the method, the long-short-term memory network is adopted for time sequence modeling, the long-term dependency relationship in the data can be captured, the accuracy of future risk prediction is improved, and the system can continuously optimize the prediction model, adapt to data changes and ensure the timeliness and reliability of the prediction result; the digital twinborn simulation decision-making module can construct a digital twinborn model of hospital economic operation based on the risk prediction value, simulate system states under different management measures and risk scenes and provide visual decision-making support for managers, and the decision-making fusion module carries out fusion processing on the risk prediction value and a scene analysis result to generate a comprehensive decision-making index. The hospital economic risk condition is comprehensively reflected, and a scientific decision basis is provided for managers.

Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.

The invention belongs to the technical field of power system operation and control, and particularly relates to a power system dynamic simulation and comprehensive risk assessment method and system fusing source, network, load and storage multi-element collaborative interaction characteristics, and the method comprises the steps: constructing a multi-agent-based source, network, load and storage collaborative simulation model; setting a simulation scene including initial disturbance and uncertainty, and operating the co-simulation model; aiming at a system risk state identified in a simulation result, based on a time-varying system interaction directed graph, adopting a path backtracking and contribution degree analysis algorithm to deconstruct a key propagation path causing the risk state, and quantifying the contribution degree of each agent and an interaction event in risk formation; based on a contribution degree deconstruction result, taking minimization of current risk and future risk propagation uncertainty as a target, and generating a cooperative control strategy packet involving a plurality of links including a source link, a network link, a load link and a storage link; and taking the cooperative control strategy packet as a new control input, and feeding back the new control input to the cooperative simulation model for verification and effect evaluation to complete a closed loop.

The application relates to an old-age hidden emergency early warning method based on circadian physiological rhythm deviation, relates to the technical field of medical early warning, and comprises the following steps: acquiring multi-modal physiological data and behavior activity data of an old patient and carrying out pretreatment; based on the multi-modal physiological data, a virtual digital twin corresponding to the old patient is constructed and initialized; real-time parallel processing: physiological data is analyzed to obtain a first evaluation result; behavior activity data and time axis information are input into the virtual digital twin to deduce a physiological state change track; the first evaluation result is subjected to consistency checking with a simulation state, and model parameters of the virtual digital twin are dynamically adjusted according to the result; a current health risk level and a future risk prediction report are generated; and the current health risk level and the future risk prediction report are synchronously pushed to a remote monitoring terminal for display and early warning; the application is used to solve the problems that a traditional method cannot find future disease risk of a patient and is inaccurate in judging disease of the patient.

The application provides a reinforcement learning strategy training method and device and a storage medium. The method comprises: generating a plurality of action data in response to a control instruction of a user; updating an intervention experience pool and a full experience pool according to the action data, or updating the full experience pool; updating network parameters of an intervention value network and network parameters of a value network according to the intervention experience pool and the full experience pool, and updating network parameters of a first strategy network based on the updated intervention value network and the value network; and updating network parameters of a second strategy network according to the network parameters of the first strategy network in a preset interaction period. The application can predict future risks through intervention signals themselves without directly imitating intervention actions, so that the strategy can identify hidden dangerous states that seem safe at present but will inevitably lead to future intervention, and the performance of the trained strategy is improved.

The present invention relates to a method and system for predicting regulatory environment risks based on spatiotemporal correlation, which belongs to the field of artificial intelligence and includes acquiring data to obtain regulatory environment data information including time and space dimensions; data preprocessing; using the STGCN spatiotemporal correlation network to regularly train and learn online through the data information model acquired in real time, and continuously correcting and optimizing the model by updating the model weight parameters to reduce learning errors and improve accuracy; for prediction results exceeding the risk threshold, it is considered to be a risk area, and the system automatically issues a risk warning signal. The present invention captures the sequence information of the time dimension and the space dimension by establishing a spatiotemporal relationship model, and based on spatiotemporal correlation analysis, it can quickly and efficiently accurately predict the future risks of the regulatory environment, and intuitively display the risks through visualization technology to assist police in the safety management and risk prevention of the regulatory environment, and ensure the long-term stability of the regulatory environment.

This application provides a reinforcement learning policy training method, device, and storage medium. The method includes: generating multiple action data in response to user control commands; updating an intervention experience pool and a full experience pool, or updating the full experience pool, based on each action data; updating the network parameters of an intervention value network and a value network based on the intervention experience pool and the full experience pool, and updating the network parameters of a first policy network based on the updated intervention value network and value network; and updating the network parameters of a second policy network according to a preset interaction period based on the network parameters of the first policy network. This application can predict future risks through the intervention signal itself without directly imitating intervention actions, enabling the policy to identify seemingly safe but potentially dangerous states that will inevitably lead to future intervention, thereby improving the performance of the trained policy.

This application relates to the field of medical data processing, proposing a medical data processing method, related apparatus, and storage medium. The method includes: acquiring electronic health records, genomic data, family medical history, lifestyle and behavioral data, and environmental exposure data of a target user within a preset time window; determining the target user's temporal characteristics, static baseline characteristics, and interaction characteristics; predicting the risk value of the target user for a target disease in multiple future time periods; determining the risk gradient value of the target user for the target disease in each future time period; and determining a health screening strategy for the target disease based on the risk value and risk gradient value of the target disease in multiple future time periods. This application predicts the future risk value and risk gradient value of the target disease using multimodal data of the target user, and formulates targeted health screening strategies, fully considering individual differences. The formulated health screening strategies are more tailored to each individual's actual situation.

The application relates to the technical field of network security, and discloses a software supply chain situation awareness method and device, an electronic device and a storage medium. The method comprises the following steps: extracting risk features of multi-source heterogeneous data in a software supply chain to be evaluated; determining direct risk factors existing in the software supply chain to be evaluated based on the risk features; calculating a probability of a top event being a supply chain risk event according to a risk evolution link of "direct risk factor-intermediate event-top event", wherein one or more direct risk factors lead to one type of intermediate event, and each intermediate event jointly determines the probability of the top event being the supply chain risk event; and calculating a prediction probability of a risk event occurring in the software supply chain to be evaluated in the future according to periodic changes of the probability of the supply chain risk event. The application actively warns future risk trends based on historical situations, provides forward-looking guidance for security decisions, and improves the accuracy, timeliness and initiative of situation awareness.

The present invention discloses a method, system, device and storage medium for achieving host security protection and early warning, belonging to the field of network security technology, and solving the technical problem of how to actively, efficiently and accurately implement risk protection measures and predict potential security threats. The method is as follows: based on modular RAG+fine-tuning Fine-Tuning technology, a hybrid enhancement strategy is implemented, structured prompt words are retrieved and generated, a standardized instruction set is output, invalid information branches are avoided, and data is updated in the security knowledge base to enhance the quality and accuracy of AI-Agent execution; an AI-Agent host security monitoring model is constructed, data on security vulnerabilities, user operations and network activities are analyzed and trained to obtain a mature risk monitoring model; the target's operating feature data is collected in real time, and data analysis and feature point extraction are performed; risk features are correlated and analyzed to generate a repair strategy that meets the instruction set trigger conditions, and future risk trends are predicted. Multi-dimensional and multi-level host security protection can be achieved.