63 results about "Risk avoidance" patented technology

The invention discloses an insurance intelligent decision engine system based on a multi-modal user portrait, and relates to the technical field of insurance industry, the system comprises a semantic representation space construction unit used for performing feature decoupling on collected multi-modal data by using variational modal decomposition, extracting modal features, and constructing a semantic representation space based on each modal feature; a user portrait generation unit; a user classification result acquisition unit; and the insurance decision-making unit is used for analyzing the multi-source health data in the user portrait by using a cross-modal alignment technology, constructing a layered health risk assessment model, and generating an insurance recommendation strategy and a risk avoidance scheme in combination with a user classification result. According to the method, resource waste and efficiency loss caused by scattered storage and repeated development of data are avoided through multi-modal data fusion and construction of a unified semantic representation space; and in combination with a hierarchical label system, the user portrait can be automatically generated, and the intelligent level of insurance business is enhanced.

The invention discloses a debt risk real-time analysis system and method based on big data and artificial intelligence. The system comprises a data acquisition and preprocessing module, a credit risk analysis module, a market risk analysis module, a mobility risk analysis module, a comprehensive risk scoring and early warning module and a risk optimization and decision support module. Each module of the system can perform real-time data analysis and prediction by using machine learning and deep learning algorithms, can generate a credit risk analysis result, a market risk analysis result and a mobility risk analysis result respectively, finally performs weighted summation on each result, and can realize dynamic monitoring and early warning based on the summation result. And enterprises, financial institutions and investors are helped to effectively avoid risks in commercial transactions. Therefore, by adopting the embodiment of the invention, the default risk in the commercial transaction can be obviously reduced, and the decision-making efficiency and the robustness of the financial market can be improved.

The invention discloses a lifting stage structure control system and method, and relates to the technical field of stage control, and the lifting stage structure control system and method comprises the steps: S1, collecting the multi-source state data of stage equipment in real time, carrying out the data preprocessing, and constructing a multi-source state database; s2, constructing a motion state space model, predicting a time sequence motion sequence at a future moment, judging whether stage equipment is overlapped or not, predicting a collision risk, and taking risk avoiding measures; s3, the obstacle avoidance priority of the stage equipment is judged, a stage equipment movement scheme is generated, and a control instruction is issued; s4, motion operation is executed, the health degree of motion execution of the stage equipment is evaluated in real time, and graded early warning measures are taken; s5, displaying the stage situation and risk based on the collision risk and health degree evaluation result, and generating an analysis report; the problem that potential collision risks cannot be avoided in time due to dynamic path intersection caused by cooperative operation of multiple pieces of stage equipment in a complex stage scene is solved.

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 discloses a financial digital intelligent management system, and relates to the technical field of computers, and the system comprises an atomic power unit ACU, a side effect metadata management module which is used for decomposing a financial process into ACU and defining the structured side effect metadata of the ACU, and the structured side effect metadata comprises an influence object, a type, a condition, a range, a preset risk level and the importance of an associated data object; the dynamic process arrangement engine module is used for generating candidate execution paths according to the process definition and the ACU side effect metadata, and selecting a final execution plan by taking the score minimization as an optimization target; and the execution control and risk relief module selectively performs influence domain sandbox rehearsal before actual execution of the ACU according to a risk assessment result, and executes conditional rollback or compensation when execution fails or unacceptable side effects are generated. According to the invention, through prospective side effect management and intelligent risk avoidance and control, the stability, reliability, auditing performance and combinable innovation capability of a financial automation process are significantly improved.

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 invention discloses a dynamic planning method for a risk avoiding path of a high-risk dammed lake. The method comprises the following steps: S1, establishing a dammed lake downstream risk area range calculation model coupling a dam break flood outer envelope line and a secondary disaster range; s2, establishing a segmented risk avoiding window time calculation model; s3, establishing a crowd-based path planning model based on dynamic toughness weight and multi-objective collaborative optimization; and S4, performing dynamic rolling updating based on the flood routing process. According to the method, the downstream risk avoiding range of the barrier lake can be accurately defined, and the accuracy of the downstream risk avoiding range of the barrier lake is improved through superposition of the outsourcing line and the secondary disaster range through multi-method flood inrush simulation; a crowd-based path planning method based on dynamic toughness weight and multi-objective collaborative optimization is established, the limitation that only the shortest path is considered in traditional path planning is broken through, and differential risk avoiding path dynamic generation is realized in combination with crowd characteristics, real-time road conditions and secondary disaster risks.

The invention relates to the technical field of intelligent driving, and discloses an intelligent risk avoiding method and system for a vehicle, and the method comprises the steps: obtaining disaster early warning information from a natural disaster early warning platform; acquiring sensing data of the surrounding environment of the vehicle; according to the disaster early warning information and the sensing data, determining that the vehicle has a risk avoiding demand; and controlling the vehicle to execute risk avoiding operation according to the type of the disaster. In combination with disaster early warning information of the natural disaster early warning platform and sensing data of the vehicle to the surrounding environment, risk avoiding control is carried out on the vehicle, and the driving safety is improved.

The invention discloses a chemical industry 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 field risk perception, data transmission delay and insufficient dynamic path risk avoidance. The system comprises a sensing device; the 5G communication module and the industrial Ethernet form a dual-mode transmission network to upload data to a cloud platform; the cloud platform uses a time sequence database to establish an equipment code index, and a rule engine executes leakage judgment of methane for continuous 3-5 cycles gt; 25% LEL and a pressure drop rate gt; early warning is triggered at the speed of 0.5 MPa / min, a risk analysis module calls three-dimensional geographic information data to generate an evacuation path with the radius of 500-2000 m through a Dijkstra algorithm, an upwind area of the real-time wind direction is avoided, and the fire station is preferentially connected; and the application terminal receives an early warning instruction containing the leakage equipment code and the dynamic path. The system is used for rapid early warning and evacuation planning of chemical safety accidents.

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 present invention relates to a driving assistance system. A first risk potential field used for risk avoidance control is the sum of a risk potential field in which a valley of risk value extends in the longitudinal direction of the lane and a risk potential field in which the risk value is maximum at the location of an object and decreases with distance from the object. Steering control is performed to bring the vehicle closer to a first minimum point, found within a first search range and serving as the minimum point of the first risk potential field. If multiple first minimum point candidates exist within the first search range, the first minimum point candidate located in a direction away from the nearest preceding object when viewed from the previous minimum point and closest to the previous minimum point is selected as the current first minimum point.

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.

The invention belongs to the technical field of enterprise project management, and particularly relates to an enterprise project full-process intelligent management system, which comprises a project starting module, a project planning module, a project execution module, a project monitoring module and a project ending module, and is characterized in that the project monitoring module comprises a progress monitoring unit, and the progress monitoring unit is connected with a performance monitoring unit; the performance monitoring unit is connected with a risk monitoring unit, the risk monitoring unit is connected with a problem monitoring unit, and the risk monitoring unit comprises a risk early warning unit and a risk management unit. Through data mining and natural language processing technologies, a project management team can be helped to discover and analyze potential risks and problems; through simulation and prediction, a team is helped to make strategies for coping with risks and problems, various aspects of enterprise operation can be monitored in real time, potential risk points can be identified, early warning can be given out in time, and the enterprise is helped to avoid risks in advance.

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 embodiment of the invention belongs to the technical field of artificial intelligence, is applied to insurance application and claim business processing scenes, and relates to a data processing method and device, equipment and a storage medium thereof. Inputting into a pre-trained case investigation and distribution model; determining a target investigation model corresponding to the target case data; sending the target case data to a corresponding investigation model for case investigation; and obtaining a case investigation feedback result, and determining a risk type of the target case data based on the feedback result. The data processing method is applied to an insurance service scene, risk identification is carried out on service application data uploaded by a service user, so that service fraudulent behaviors are identified, risk behaviors are avoided, losses of a service party and a service user party are reasonably reduced, and meanwhile, in combination with difference of investigation models, the risk of the service party is effectively reduced. And case allocation investigation can be carried out more efficiently and more reasonably when the target case data is processed.

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 present application discloses a method for high-risk scenario recognition and passive safety collaboration based on a critical risk avoidance state, including: obtaining current target vehicle state data; determining the critical risk avoidance time based on the current target vehicle state data T cep ; calculating the successfully avoidable time according to the current target vehicle state data and the critical risk avoidance time T cep ; using the successfully avoidable time for high-risk scenario recognition. When no high-risk scenario is recognized, the current vehicle distance is re-obtained. When a high-risk scenario is recognized, passive safety constraints are carried out in stages using a passive safety collaboration strategy; the present application uses the critical risk avoidance time to characterize the spatio-temporal proximity of the vehicle, improves the high-risk scenario recognition ability of the target vehicle, can initiate a timely passive safety collaboration response in high-risk scenarios, improves the safety factor of the target vehicle, and reduces the incidence of traffic accidents.

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.

The present invention relates to a driving assistance system. A risk potential field used for risk avoidance control (deceleration control) includes an obstacle potential field. The obstacle potential field is a risk potential field in which the risk value is maximum at the location of an object and decreases as the distance from the object increases. The search range is the range between the location of the object and a location that is a predetermined gap away from the object. A minimum point (valley) of the risk potential field is searched within the search range. Deceleration control is performed based on the positional relationship between the minimum point and the object. If multiple minimum points exist in the search range, the minimum point closest to the object is selected.

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 present invention belongs to the field of road vehicle control and relates to a dynamic driving risk warning and avoidance method and system enabled by a multi-dimensional risk field. The method evaluates driving risks, issues warnings based on driving risks, and implements risk avoidance strategies. During driving risk assessment, driving environment information is acquired, a possible state domain is constructed, and the accident discrimination domain of a single accident is calculated based on the state vectors in the possible state domain, thereby determining the possible accident domain of the single accident and the severity of the single accident. Subsequently, the risk value of a single accident and the risk value of a single state domain at the current time step as well as the real-time risk value of a single accident and the real-time risk value of multiple accidents are calculated. When the real-time risk value exceeds the real-time risk threshold, a dynamic time series of the risk field is generated. This method can not only optimize the calibration complexity and accuracy of the risk field model, but also provide an effective risk avoidance strategy to ensure driving safety.