44 results about "Risk avoidance" patented technology

The invention discloses an AI-driven digital economic supply chain optimization method. According to the invention, a novel supply chain system with active pre-judgment and dynamic adaptation is constructed. The core breakthrough of the method is that passive risk management is converted into a opportunity creation mechanism, and traditional supply chain decision logic is remodeled by relying on a virtual-real symbiotic intelligent deduction system. The generative adversarial network simulates compound crisis scenes such as geographical conflicts and digital asset transactions in real time, and a quantum optimization algorithm and Monte Carlo tree search are combined, so that the system not only can predict a conduction path of global chip shortage to a production line, but also can generate breakthrough solutions such as cross-chain encryption settlement and redundant node resource securitization. The pre-judgment type deduction mechanism, block chain dynamic scheduling and metabolic resource configuration form a closed loop, and in response to encrypted currency flash collapse, a decentralized payment channel can be automatically switched to avoid risks, a digital asset arbitrage window can be captured through an intelligent contract, and co-evolution of risk resolution and value increment is realized.

The invention relates to air route quantitative evaluation, in particular to an unmanned aerial vehicle air route dynamic quantitative evaluation method based on comprehensive risk avoidance, and the method comprises the steps: constructing a multi-dimensional air route risk quantitative evaluation system, and enabling each dimension to comprise a plurality of specific and quantifiable risk evaluation factors; mapping the observation value of each risk assessment factor into a standardized risk score; determining the weight of each risk assessment factor according to a dynamic weight adjustment strategy, and calculating a route comprehensive risk score by combining the standardized risk score of each risk assessment factor; executing the evaluation process on all the candidate paths, calculating an air route comprehensive risk score for each candidate path, preferentially recommending the candidate path with the highest air route comprehensive risk score, and generating an air route risk evaluation report at the same time; according to the technical scheme provided by the invention, the defect that accurate dynamic quantitative evaluation of the route of the unmanned aerial vehicle is difficult in the prior art can be effectively overcome.

The application provides a tower crane construction safety monitoring method and system, and belongs to the technical field of engineering construction safety monitoring. The method comprises the following steps: acquiring multi-source data of a target tower crane, wherein the multi-source data comprises sensor data, video monitoring data and environmental data; determining a plurality of operating characteristics of the target tower crane based on the multi-source data; the plurality of operating characteristics comprise operating state characteristics, importance characteristics and environmental risk characteristics; determining a monitoring strategy of the target tower crane based on the plurality of operating characteristics; monitoring safety parameters of the target tower crane based on the monitoring strategy to obtain safety monitoring results; performing risk assessment based on the safety monitoring results and generating a risk avoidance strategy; wherein the target tower crane is one of a plurality of tower cranes in a construction site. The application improves the accuracy of tower crane construction safety monitoring, discovers risks in a timely manner and generates a risk avoidance strategy, thereby ensuring the safety of tower crane construction.

The invention relates to the technical field of engineering management, in particular to an engineering scene-oriented salary performance risk identification and control method and system. According to the technical scheme, the method comprises the steps of obtaining a risk tag type, a risk tag standard value and risk tag data; and judging whether the risk tag data corresponding to the risk tag type is empty or not. And constructing salary performance weights in one-to-one correspondence with the risk tag types. And calculating the coincidence coefficient of the contractor. And calculating the project capability goodness of fit, the project risk adaptation value and the risk avoidance degree of each contractor. And carrying out sorting recommendation on the contractor according to the risk avoidance degree from large to small. The higher the project capability goodness of fit and the higher the project risk adaptation value are, the higher the contractor is matched with the project, so that manual matching of related workers is avoided, and the management burden is reduced. Calculation is carried out in two aspects of project capability and project risk adaptation, calculation is more comprehensive, and the obtained recommended contractor better meets the requirement.

This application relates to a vehicle risk avoidance method and electronic device, belonging to the field of vehicle technology. The vehicle risk avoidance method includes: when the vehicle is parked and an animal recognition event is detected, acquiring animal status information and personnel location information, wherein the animal recognition event indicates the presence of a dangerous animal target within a preset area around the vehicle, and the animal status information is determined based on the state of the dangerous animal target; the personnel location information is used to represent the relative position between the personnel associated with the vehicle and the vehicle; determining a target risk avoidance strategy based on the animal status information and personnel location information; and controlling the vehicle to execute the target risk avoidance strategy. This application establishes a complete risk avoidance process from risk perception to targeted risk avoidance strategy formulation, and then to automatic execution of the risk avoidance strategy, enabling vehicles to provide users with proactive warnings and risk avoidance assistance in outdoor scenarios such as camping, thereby reducing the safety risks posed by dangerous animals in outdoor environments.

This invention discloses a construction supervision decision-making method and system based on game optimization and multi-agent reinforcement learning, relating to the field of construction supervision decision-making. This invention constructs a digital twin environment based on a Stackelberg master-slave game architecture, combines graph attention networks to extract features from the dynamic spatiotemporal interaction graph of the construction site, applies continuously differentiable hard safety constraints to the game process using a safety risk potential energy field based on physical field theory, and employs a multi-agent reinforcement learning algorithm based on Lagrange duality optimization to solve the Nash equilibrium strategy. This enables the supervision agent and the construction / resource agent to achieve an automatic balance between risk avoidance and schedule optimization in complex dynamic games, realizing intelligent collaborative decision-making that balances safety compliance and construction efficiency.

The application relates to a danger judgment and active guidance method and system for power construction safety supervision, and belongs to the technical field of power construction safety. The method comprises the following steps: collecting multi-modal data of a construction scene through intelligent glasses; analyzing the data at an edge computing end to obtain a scene semantic category and an interactive behavior of a worker; according to the scene semantic category, dynamically scheduling and hot loading a matched risk identification model set from a model library to identify a risk factor and a three-dimensional position thereof; acquiring a three-dimensional voxelized static danger level base map; mapping the risk factor into a dynamic risk observation point cloud in a voxel space; taking the static base map as a priori and taking the dynamic point cloud as observation evidence, performing recursive Bayesian filtering through a dynamic Bayesian network to generate a dynamic danger level three-dimensional distribution; generating and rendering graded AR early warning information; when a violation trend occurs, generating and rendering a three-dimensional safety path of a risk avoidance area. The method realizes adaptive, accurate and active construction safety supervision.

The application provides a business process intelligent optimization method and system based on multi-source data fusion, relates to the technical field of data processing, and comprises the following steps: obtaining a to-be-done business process request of an enterprise network; performing multi-node safety accident prediction on to-be-done business data; performing safety configuration adjustment on the to-be-done business process request according to a business process accident graph; performing multi-level evaluation and optimization on a business process safety adjustment group through a multi-level safety risk evaluation model; performing multi-party evolution and global optimization on the business process safety adjustment group according to a multi-level safety adjustment benchmark; and obtaining a safety adjustment strategy to perform business process safety optimization management. The application solves the technical problem that, in the prior art, due to a static and passive protection mechanism in a traditional business process, it is difficult to avoid risks before execution, thereby affecting the safety of the business process, and improves the safety of the business process of the enterprise through active risk prevention and control and optimized safety configuration.

The invention discloses an intelligent community management system based on artificial intelligence, and relates to the field of intelligent community management, and the intelligent community management system comprises a scene perception pre-judgment module, a resource dynamic scheduling module, a man-machine cooperation interaction module and a full-period data closed-loop module, the scene perception pre-judgment module is a core data input end, and the full-period data closed-loop module is a core data output end. The information acquisition module is responsible for acquiring community multi-dimensional information and pre-judging a potential scene and providing a decision basis for subsequent modules; and the resource dynamic scheduling module is used for undertaking a result of the scene perception pre-judgment module and realizing adaptive allocation of human and material resources of the community. According to the intelligent community management system based on artificial intelligence, active risk avoidance and demand response can be realized, through the multi-modal fusion acquisition and behavior intention pre-judgment sub-module of the scene perception pre-judgment module, the existing passive alarm limitation is broken through, the problem can be recognized in advance, sufficient time is reserved for disposal, and the risk avoidance and demand response can be realized. The community safety risk and the resident life inconvenience are reduced.

The method for power collaborative distribution and risk avoidance of fracturing vehicle fleet relates to the technical field of fracturing vehicles, and is executed by a central collaborative controller which is in communication connection with local controllers corresponding to the fracturing vehicles; the method comprises the following steps: collecting running state parameters of each fracturing vehicle and receiving a total target of operation; constructing a dynamic collaborative state model of the vehicle fleet based on the running state parameters; establishing a multi-target collaborative optimization model; performing real-time solution on the multi-target collaborative optimization model to calculate target speed setting values; judging whether any target speed setting value is close to a preset safe working limit; judging whether there is a potential risk; selecting to adjust the target speed setting value according to the judgment result; and distributing final control instructions to the local controllers of the fracturing vehicles. The application can realize a global optimization control method with high robustness for power dynamic collaborative distribution of fracturing vehicle fleet, avoidance of running risks and the like.

The invention belongs to the field of tourism recommendation, and provides a tourism recommendation method and system based on multi-modal data, and the method comprises the steps: constructing a multi-modal tourism causality cognitive map; natural interaction information input by a user is collected, and tourism intention variables and implicit preference features of the user are extracted through an intention recognition module; performing forward causal reasoning in the causal cognitive map based on the tourism intention variable, determining a target scenic spot sequence meeting the intention of the user, and performing path feasibility evaluation; according to the target scenic spot sequence, executing an anti-fact reasoning operation in a cognitive map, identifying adverse factors and reasons thereof in a potential path, and performing interpretation enhancement and risk avoidance correction on a recommended path; and a recommendation result is output, the result comprises a recommendation sequence of multiple paths, and each path is attached with user intention interpretation, a causal path diagram, a risk analysis diagram and an accessibility weight score.

This technology provides the ability to further improve the safety of those being monitored. [Solution] A danger avoidance support system, server device, information processing method, program, and a monitored terminal are provided, comprising a monitored terminal carried by the person being monitored and a server. The server acquires the current location information of the monitored terminal, acquires danger information associated with area and time, and transmits predetermined warning information to the monitored terminal when the current location meets notification conditions based on the risk level and reliability of the acquired danger information. The reliability level is based on information source attributes, etc., and the risk level can be corrected by environmental factors. The server updates through relearning, and the terminal outputs vibrations, etc., according to the danger level.

This invention discloses an end-to-end autonomous driving method based on a physical information fusion model, comprising: S1, collecting environmental observations, vehicle control actions, and calculated ground truth values ​​of physical features, and storing them in an experience replay pool; S2, training a world model that fuses physical information, which jointly learns latent state evolution and physical indicator prediction through an encoder, a state transition network, and a physical prediction head, and optimizes by minimizing the joint loss incorporating physical prediction errors; S3, based on the trained world model, performing policy learning in the latent space that considers physical indicator constraints to achieve preventative risk avoidance and smooth control; S4, deploying the trained policy to the vehicle controller for online control. This invention significantly improves the safety, stability, and physical consistency of autonomous driving systems in complex scenarios by explicitly modeling and predicting physical indicators in the world model and using them as forward-looking constraints in policy learning.

The application provides a financial abnormal behavior pattern recognition method and system, the method comprising: converting behavior anchor information into a high-dimensional feature vector, calculating the projection value of each high-dimensional feature vector on different behavior intention axes, generating a preliminary intention vector, optimizing the intention vector accuracy, and generating a final behavior intention vector; embedding the behavior intention vector into a preset behavior configuration manifold, identifying the atypical pattern of the behavior intention vector, and labeling the high-risk nodes in the atypical pattern; judging the spatial distance of each behavior intention vector to the labeled high-risk nodes in the behavior configuration manifold, introducing a high-risk weight based on the spatial distance and the basic disturbance density, obtaining a weighted disturbance density, dynamically adjusting the future deduction depth, and forming a prediction result with probability distribution; performing abnormal scoring on the prediction result and triggering a warning action, the application is converted from post-recognition to pre-prediction, when the intention structure deviates, it can still be identified in the configuration manifold, and the risk of deliberately avoiding identification of financial abnormal behavior is reduced.

The invention discloses a wearable safety early warning system and method based on perception and dynamic risk avoiding guidance. The wearable safety early warning system comprises a wide area perception module, an edge calculation module, a multi-level alarm module, a risk avoiding path planning module and a voice guidance module. According to the invention, comprehensive monitoring of pedestrian limb movements, metal articles and emotional fluctuations in a 30-meter range is realized through the wide-area multi-modal sensing module, an accurate dynamic risk thermodynamic diagram is constructed through synchronous acquisition and fusion of multi-source data, and a reliable data basis is provided for risk assessment. The'unstable value 'calculation of the edge calculation module adopts a dynamic weight adjustment mechanism, and the weight of each index is flexibly adjusted according to the threat source distance, so that the accuracy and pertinence of risk level judgment are improved, and the system can identify potential threats more accurately.

The invention discloses a virtual power plant risk avoidance optimal energy and standby scheduling method considering demand response. The technology aims at solving the defects of an existing virtual power plant scheduling method in the aspects of coping with uncertain factors such as renewable energy power generation, load demand changes and market price fluctuations, mining demand response potential, refining risk management and the like. By constructing a multi-stage stochastic optimization model covering a demand response plan and integrating a self-elastic and cross-elastic response type load model, the initiative of customers is accurately described, and conditional value-at-risk (CVaR) is adopted as a risk management tool, so that flexible adjustment and economic operation benefit maximization of a virtual power plant in an uncertain environment are realized. Through verification, the method can significantly improve the expected profit of the virtual power plant, effectively reduces the risk, and especially has a good effect when customers participate in load reduction and transfer at the same time.

The application discloses an AR intelligent safety helmet auxiliary system for a high-risk operation environment, and relates to the technical field of construction safety.The system uses a Bayesian network to combine historical data to perform probability reasoning, uses an LSTM network to mine time sequence characteristics, and further uses D-S evidence theory fusion, so that the object falling trend can be more accurately recognized, early warning can be given, and valuable risk avoidance time can be obtained for operation personnel.Meanwhile, the dynamic decision mechanism of the system makes the triggering of the buffer mechanism more reasonable, reduces the occurrence rate of safety accidents, reduces unnecessary resource waste, and provides a scientific and efficient solution for high-risk operation safety protection.

The application provides an emergency rescue path planning method and system with adaptive risk avoidance, comprising: establishing an emergency rescue road network structure; before an actual natural disaster occurs, performing a risk entropy driven adaptive Monte Carlo simulation analysis, performing path information statistical analysis on the Monte Carlo simulation result, and establishing a pre-planned path library; when the actual natural disaster occurs, establishing a prior risk distribution of each planned path in the pre-planned path library; using a sequential Bayesian inference to dynamically correct the prior risk distribution of each planned path in the emergency rescue process; based on the path posterior risk mean of each planned path after the sequential Bayesian inference, performing dynamic planning of the emergency rescue path, selecting an optimal rescue path, and performing an emergency rescue task until the emergency rescue activity ends. Through the path dynamic adjustment strategy, the dispersed transportation strategy, the path pre-planning strategy, and the network communication free strategy, the application ensures that the rescue resources efficiently and safely reach the target location under the natural disaster scene of flood, typhoon, and the like.

This invention discloses a disaster avoidance planning method based on dynamic spatiotemporal evolution, relating to the field of building safety technology. The method includes: real-time collection of environmental status data and personnel location data through a multi-source sensor network and positioning system deployed within the building; based on a pre-set disaster dynamics model, using the environmental status data and personnel location data as initial conditions, extrapolating the disaster spatiotemporal evolution set within a specific future time window; abstracting the building space into a graph structure composed of multiple disaster avoidance planning units based on a BIM model, using the disaster spatiotemporal evolution set as input to the evacuation route planning units, and providing a set of parallel planning routes for evacuation groups in different zones within the building; continuously monitoring the personnel density on each route and the real-time updated disaster spatiotemporal evolution set, and automatically triggering a route redistribution mechanism based on the monitoring results. This invention can significantly improve the safety and efficiency of personnel evacuation during building emergencies and reduce the risk of secondary disasters.

The application discloses a chemical industrial park safety risk intelligent management and control system and method, belongs to the technical field of risk management and control, and solves the problems of incomplete tank area risk perception, data transmission delay and insufficient dynamic path risk avoidance. The system comprises a sensing device, a 5G communication module and an industrial Ethernet, a data transmission network is formed by the 5G communication module and the industrial Ethernet, and data is transmitted to a cloud platform; a time sequence database is used by the cloud platform to establish a device code index, a rule engine executes leakage judgment, and when methane is continuously 3-5 cycles > 25% LEL and the pressure drop rate > 0.5 MPa / min, a warning is triggered; a risk analysis module calls three-dimensional geographic information data to generate a 500-meter-2000-meter radius evacuation path through a Dijkstra algorithm, avoids the upwind area of the real-time wind direction and preferentially connects a fire station; and an application terminal receives a warning instruction containing a leakage device code and a dynamic path. The system is used for rapid warning and evacuation planning of chemical safety accidents.

The invention relates to a food detection scheduling method, system and device based on reinforcement learning and a medium. The method comprises the following steps: generating a system state containing a real-time risk assessment vector based on dynamic technical feature vectors of all samples to be detected and state codes of detection equipment; inputting the system state into a pre-constructed strategy network to obtain an output scheduling decision; on the basis of a dynamic weight generation network, calculating according to the real-time risk assessment vector to obtain an instant reward for executing a scheduling decision, monitoring whether a preset high-risk event occurs in the detection equipment system, and if the high-risk event occurs, generating an event-driven reward; and when an event-driven reward is generated, backtracking a historical decision information sequence corresponding to the high-risk event according to the historical decision tracking list, and calculating to obtain a tracing reward of the historical decision information sequence based on the event-driven reward and the historical decision information sequence. By adopting the method, scheduling risk avoidance and dynamic balance and collaborative optimization of the overall operation efficiency can be realized.

The invention discloses a power grid emergency material dispatching double-layer optimization method and system under an extreme weather. The method comprises the following steps: firstly, acquiring real-time meteorological monitoring data and vulnerability characteristics of power grid equipment, calculating a dynamic time sensitivity coefficient, and generating a material demand priority parameter in combination with a disaster point load level; secondly, constructing a double-layer material scheduling model, and introducing constraints to realize degraded supply under resource limitation by taking the maximization of a key load recovery index as a target at the upper layer; the lower layer achieves dynamic risk avoiding and time-space synchronization of people and goods through constraints with the purpose of minimizing the comprehensive scheduling cost; and finally, a double-layer iteration solving strategy based on interlayer interaction is adopted, an upper-layer topology construction mask is utilized to execute dimension reduction locking and dynamic truncation on lower-layer flow, and real-time weather and road condition data are accessed in the solving process to execute online rolling correction. According to the method, the problem that traditional scheduling lacks environment perception and supply and demand flexibility is solved, and the real-time adaptability, decision robustness and cooperation efficiency of power grid emergency repair under the extreme weather are improved.

The present application belongs to the field of renewable energy grid connection technology, and discloses a risk avoidance scheduling method and system of a geographically distributed energy alliance. The method constructs an energy alliance self-balancing scheduling deterministic model with the aim of maximizing internal income by obtaining equipment parameters, meteorological parameters, load parameters and economic parameters. The energy alliance self-balancing scheduling deterministic model can provide a deterministic solution under accurate prediction value parameters based on actual data, thereby effectively guiding energy scheduling. The risk scheduling model established in combination with the information gap decision theory can cope with the uncertainty problem of line transmission congestion in the power transmission network, and provides an optimal risk avoidance scheduling strategy for the energy alliance. The use of the scheduling method significantly enhances the economy and robustness of the energy alliance when facing transmission congestion risks, and provides a scientific and effective solution for the energy management of third-party entities.

The invention provides a financial abnormal behavior pattern recognition method and system. The method comprises the steps that behavior anchoring information is converted into high-dimensional feature vectors, the projection value of each high-dimensional feature vector on different behavior intention axes is calculated, a preliminary intention vector is generated, intention vector precision is optimized, and a final behavior intention vector is generated. Embedding the behavior intention vector into a preset behavior configuration manifold, identifying an atypical mode of the behavior intention vector, and labeling a high-risk node in the atypical mode; judging the spatial distance of each behavior intention vector marked with a high-risk node in the behavior configuration manifold, introducing a high-risk weight based on the spatial distance and the basic disturbance density, obtaining the weighted disturbance density, dynamically adjusting the future deduction depth, and forming a prediction result with probability distribution; according to the financial abnormal behavior identification method and device, post-event identification is converted into pre-event prediction, when the intention structure deviates, the intention structure can still be identified in the configuration manifold, and the deliberate avoidance risk of financial abnormal behavior identification is reduced.

The invention discloses an intelligent safety management method and device for engineering construction and a medium. The method comprises the following steps: acquiring a monitoring video stream; identifying a plurality of to-be-detected targets contained in the monitoring video stream according to predefined detection target features in the intelligent camera, and extracting a motion trail sequence of the plurality of to-be-detected targets in real time; performing cooperative relationship identification on the to-be-detected target to determine a construction cooperative group included in the monitoring video stream; identifying an abnormal behavior node corresponding to each constructor in the construction cooperation group to obtain an estimated risk event corresponding to the abnormal behavior node; simulating a risk conduction path of the pre-estimated risk event to obtain constructors affected by the abnormal behavior nodes; and calculating a dangerous area corresponding to the risk conduction path to determine a risk avoiding instruction of the constructor. Through the method, the technical problem that in the prior art, a long-distance engineering construction safety management method cannot predict safety risks caused by individual abnormity in a construction group is solved.

This disclosure relates to an industrial equipment control method and apparatus for early warning anomaly detection and risk avoidance. The method includes: acquiring the controllable state and statistical state of preset parameters of the equipment to be controlled at the current moment; inputting the controllable state and statistical state into a control action decision model, and determining the selection probability of each preset control action by using the objective function and constraints of the control action decision model with the goal of maximizing benefits; determining the target control action based on the preset action selection strategy and the selection probability of each preset control action; and controlling the equipment to be controlled according to the target control action. Through the above technical solution, the controllable state and statistical state of preset parameters at the current moment can be input into the control action decision model to generate control actions in real time and control the equipment. This allows for timely implementation of corresponding control actions to handle anomalies when abnormal characteristics are detected, reducing losses caused by equipment anomalies.

The invention discloses a micro-grid day-ahead risk avoidance bidding method and a related device. The method comprises the following steps: obtaining target multi-dimensional operation data of a target electricity market transaction network; constructing a first leader model and a target follower model according to the target multi-dimensional operation data; determining a deterministic constraint corresponding to the target uncertainty factor; acquiring a real-time price scene set; according to the real-time price scene set and a CVaR risk criterion, optimizing the first leader model to obtain a CvaR-based wholesale electricity market risk avoidance model; determining a first optimal solution according to the target follower model, the deterministic constraint and a wholesale electricity market risk avoidance model based on CvaR; determining a target potential loss expected value corresponding to the MGO; and adjusting the first bidding scheme according to the target potential loss expected value to obtain a second bidding scheme. By adopting the method and the device, the operation risk of the MGO is reduced.

A system and method for managing road transportation of sensitive materials using structured representations of perceived risk. The system includes a memory storing road-segment risk-perception information comprising risk labels, exposure durations, and user-defined risk tolerances. A routing interface receives a route-related request, and a data processing apparatus generates routing instructions based on comparisons of exposure durations, avoidance durations, or risk acceptability thresholds. Risk-feature maps are generated from geographic data using spatial data processing techniques, and risk labels are assigned to road segments based on intersections with risk features. The system enables user input via a graphical interface to adjust tolerances or avoidance values for distinct risk types, with visual feedback provided on aggregate temporal avoidance. The approach permits integration of subject-matter expert perception data into routing decisions by expressing risk avoidance as a temporal cost, allowing risk-informed route evaluation using conventional time-based routing algorithms.

The invention discloses a visual management method for on-site construction of a construction project perceived by the Internet of Things, and belongs to the technical field of construction engineering management. Comprising the following steps: constructing a digital twin base model; deploying a multi-mode Internet of Things sensing network and performing data semantic mapping; creating and driving a dynamic digital twinborn body in real time; constructing and solving a construction element coupling risk potential field model; a transmissive visual interaction is provided. Dynamic elements of a construction site are abstracted into'potential points' carrying instantaneous risk potential, and interaction force among the potential points and composite risk potential of any point in space are calculated in real time through a mathematical formula defining risk gravitation and safety repulsive force. By solving the model, the method can quantify and visualize a dynamic composite risk generated by superposition of a plurality of risk sources, realizes predictive early warning from an event result to a risk cause, and can generate a navigation type optimal risk avoiding path instruction.