31 results about "Future risk" patented technology

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 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 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 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 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 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.

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 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.

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 invention relates to the field of data processing, and discloses an overwater pile foundation construction safety risk early warning system which comprises a data model updating module used for fusing real-time data to synchronize a digital data model; the risk prediction data generation module is used for performing simulation based on the synchronized model to generate a future risk index curve; and a management decision optimization module. When the risk index exceeds a preset threshold value, the module can automatically generate a candidate management instruction set, and the efficiency of each instruction is quantitatively evaluated through simulation deduction, so that a unique optimal management instruction is determined, and a negative management instruction is identified. According to the method, continuous quantitative prediction is carried out on future risks, and automatic deduction and optimization are carried out on the efficiency of multiple treatment measures, so that the problems that risk assessment lags behind, decision-making depends on artificial experience and optimality cannot be ensured in the prior art are solved, and automatic and prospective closed-loop management and control of the risks are realized.

The invention relates to the technical field of artificial intelligence and finance, and provides an insurance management method and device based on a deep sea net cage, equipment and a medium, and the method comprises the steps: obtaining the multi-dimensional environment information of a deep sea net cage region through a distributed sensing node network; performing preprocessing operation on the multi-dimensional environment information through an edge computing gateway to obtain preprocessed multi-dimensional environment information; constructing a risk factor matrix according to the preprocessed multi-dimensional environment information, and inputting the risk factor matrix into an LSTM model and a GNN model to carry out time sequence risk value prediction and spatial correlation risk value assessment so as to output a dynamic risk assessment result; and constructing a hybrid model according to the dynamic risk assessment result and a preset GLM model, and correcting the premium rate corresponding to the deep sea net cage in real time according to the hybrid model. Through the technical scheme, the future risk trend is accurately pre-judged, and the fairness and scientificity of insurance pricing are realized.

The embodiment of the invention discloses an environment safety monitoring method and device applied to a limited space. According to one specific embodiment, the method comprises the steps that a multi-source heterogeneous data set aiming at the limited space is collected, the multi-source heterogeneous data set is collected through a pre-deployed intelligent monitoring equipment set, and the multi-source heterogeneous data set comprises various data of different structures in the limited space; generating a key data situation map according to the multi-source heterogeneous data set; inputting the key data situation map into a pre-trained multi-modal risk prediction model to obtain a key data risk probability set; performing environmental safety monitoring on the simulation result set to generate an optimal monitoring result; and triggering an execution terminal in the limited space according to the optimal monitoring result. According to the embodiment, the prediction capability of future risks is improved, and the probability of missing the optimal opportunity of emergency disposal is reduced.

A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.

The present invention provides a program for estimating a risk that may occur in the future to a subject, on the basis of time-series information relating to the subject.　A risk estimation assistance program according to the present disclosure includes an information acquisition procedure, a risk calculation procedure, and a risk output procedure, and is for causing a computer to execute the procedures. The information acquisition procedure acquires time-series information indicating a feature of a subject, the risk calculation procedure calculates a risk that may occur in the future to the subject on the basis of the time-series information, and the risk output procedure outputs the risk.

The invention relates to the technical field of knowledge maps, in particular to a food safety risk early warning method and system, and the method comprises the following steps: inputting the product batch, storage unit, transportation unit and operator entity information, and the supplier geographic position, local weather early warning and financial news attribute information; according to the method, a dynamic logistics heterogeneous relation graph is constructed by deeply integrating internal operation entities such as product batches, storage and transportation units and the like with external environment attributes such as supplier positions, weather early warning, financial news and the like and defining a transfer and storage relation representing a logistics process and a common storage and common transportation relation representing risk coupling; by extracting the historical performance record from the map and combining the real-time updated logistics path, weather, public opinion and other multi-dimensional risk signals, the conversion from pure historical data statistics to future risk active prediction is realized, and the pre-judgment ability of the supply chain stability is enhanced.

A security countermeasure planning system includes a functional part which calculates one or more risk values of one or more resources included in a system to be diagnosed, a risk change estimation part which estimates one or more future risk estimation values of the one or more risk values of the one or more resources which the functional part calculates, and a countermeasure determination part which selects a countermeasure plan based on the one or more future risk estimation values estimated by the risk change estimation part.

The application provides a construction early warning method and device based on multi-source data space-time correlation, belongs to the technical field of data processing, and aims at the problems of insufficient instantaneity and effectiveness of current construction prediction. First construction state data of a target construction area is acquired, space-time correlation of the first construction state data is performed based on a first generation timestamp and a first generation space position of the first construction state data, a plurality of first construction space-time data groups are obtained, a key construction risk source of the target construction area is identified based on the first construction space-time data groups, a first risk identification result is obtained, and in the case that the first risk identification result is that there is a risk, an alarm prompt information is generated and the alarm prompt information is displayed in the target construction area. Construction risk identification can be performed according to the space-time correlation of multi-source heterogeneous data of the construction site, prediction of future risks is realized, and the instantaneity, reliability and foresight of construction early warning are significantly improved.

The application discloses a dynamic risk assessment and scheduling system based on multi-source perception and uncertainty optimization, relates to the technical field of dynamic risk assessment and intelligent scheduling, and solves the technical problem that in the prior art, the uncertainty types of multi-source perception data are not fully covered, the quantification methods are not unified, and the accuracy and reliability of risk assessment are insufficient; the system comprises a multi-source perception module, a ternary uncertainty unified representation module, a risk-perception collaborative optimization module, a forward risk prediction module and a scheduling decision module which are sequentially connected and form a closed-loop feedback link; the application can realize decoupling processing and unified quantification representation of three types of uncertainty, dynamically optimizes the configuration of perception resources based on real-time risk situation, completes active prediction and adaptive scheduling decision of future risks, and is widely applicable to dynamic risk management and control requirements in multiple scenes such as industrial safety production, intelligent transportation and energy management.

We provide the system. [Solution] A means for collecting and pre-processing data from a monitoring device, A means for analyzing collected data to predict the amount of materials used and the timing of necessary replenishment, A means for automatically generating a material ordering plan based on the analyzed results, A means for generating work instructions for on-site workers and notifying them via a display device, A means of assessing future risks and notifying the site supervisor of proposed countermeasures, A system that includes this.

The invention relates to the technical field of dam engineering safety management, in particular to a dam life body equivalent circuit self-diagnosis and mutation prediction method, which comprises the following steps of: firstly, analogy a dam into a life body containing a structure system, a circulation system and the like, then constructing an equivalent circuit model, converting core signs such as seepage and deformation into equivalent resistance with physical significance, and carrying out equivalent diagnosis and mutation prediction on the equivalent resistance. Establishing a series-parallel connection topology according to a stress-seepage coupling mechanism; the comprehensive health index current I (h) is calculated through the Ohm's law to represent the health state, the risk is predicted with the fact that the first-order derivative of I (h) to the water level is zero as the sudden change critical point criterion, the future risk can be predicted through forward and reverse rehearsal, and the safe water level interval is delimited. The method realizes scientific and explainable diagnosis, accurate identification of early damage and advanced early warning, depends on existing monitoring equipment, can be popularized to infrastructures such as side slopes and the like, and has economic and practical values.

The present disclosure provides a ship berthing path optimization method and system based on adaptive risk assessment, relating to the technical field of intelligent navigation in port area, comprising: initializing a global berthing reference path; performing dynamic obstacle prediction based on dynamic obstacle observation information; performing future risk assessment based on the global berthing reference path from the current period, constructing a collision risk function, taking the observation information of the dynamic obstacle as the dynamic environmental change intensity, considering the influence of the port water environment on the tug execution error and the collision avoidance safety margin, and adaptively calculating the trigger threshold; comprehensively considering the collision risk function, the adaptive trigger threshold and the dynamic environmental change intensity, constructing a trigger variable to determine whether to trigger local re-planning; when the local re-planning is not triggered, the historical solution is reused, and when the local re-planning is triggered, only the affected period is locally corrected and the path optimization result is spliced and output. The present disclosure realizes online optimization with safety, feasibility, low computational complexity and smooth trajectory.

The invention discloses a dynamic emergency resource scheduling method based on multi-dimensional data analysis, and belongs to the technical field of emergency management and resource scheduling, and the method comprises the following steps: S1, collecting and fusing multi-dimensional data, and constructing a multi-source heterogeneous database; s2, disaster chain network construction: constructing a disaster chain evolution network model by applying a complex network theory based on the data in the step S1, calculating node degree, betweenness centrality and vulnerability, and identifying key risk nodes and evolution paths; s3, performing dynamic risk assessment, quantifying a current risk level and predicting a future risk situation; s4, performing an emergency resource optimization scheduling decision, and preferentially blocking a high-risk chained propagation path according to a risk assessment result in the step S3; and S5, scheduling and implementing feedback regulation and control. According to the method, multi-dimensional dynamic information such as real-time weather, infrastructure states and social disaster-bearing bodies is effectively fused, dynamic simulation and risk assessment are actively carried out on a disaster chain type evolution law, disaster spreading is effectively controlled, and overall loss is reduced.

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.