106 results about "Perception risk" patented technology

The application is suitable for the technical field of financial early warning, and provides a financial early warning system, comprising a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module and a self-adaptive early warning generation module. The system solves the technical problem that the existing financial early warning system has a single data dimension, is difficult to depict a risk correlation network, leads to one-sided early warning, has insufficient early warning capability for "contagious" crises caused by associated risks, and has a static and rigid early warning model, which lacks self-adaptation and evolution capability, leading to gradual degradation of long-term early warning performance, poor explainability of early warning results, limited decision support, and inability to develop accurate and effective risk mitigation measures, greatly weakening the actual decision support value of the early warning system. The application achieves the technical effects of accurate, explainable and self-adaptive early warning of associated and dynamic financial risks.

The application provides a forklift electronic coach practical training intelligent safety protection control system and method, the system comprises: a main control processing unit, a personnel safety identification module, a vehicle body state acquisition module, a fork operation monitoring module, an environment anti-collision detection module and a hierarchical safety execution module. Each module is used for collecting the state of the practical training personnel, the running state of the vehicle body, the fork operation working condition and the surrounding environment information, and sending to the main control processing unit. The main control processing unit judges the risk level based on the received information, and outputs differentiated safety control instructions to the hierarchical safety execution module according to the risk level, so as to execute early warning, operation permission flow limiting or emergency braking and other hierarchical intervention actions. The application also provides a corresponding method. Through multi-dimensional data acquisition and intelligent analysis, the application realizes active risk perception and closed-loop intervention of the forklift practical training process, and improves the practical training safety.

This invention relates to the field of big data analytics, specifically to a method and system for intelligent analysis and assessment of risk perception bias among residents in post-disaster reconstruction areas. The method involves fitting risk indicators using multi-source data and a collaborative model, and calculating risk assessment values ​​based on the fitting results. It obtains basic information data, subjective perception data, and resident behavior data through structured questionnaires. Initial correction coefficients are matched to the subjective perception data based on the basic information data, and error analysis is performed using resident behavior data to determine whether dynamic compensation of the initial correction coefficients is necessary. This significantly improves the accuracy of the final obtained true subjective perception data. By assessing residents' risk perception bias from two perspectives—true subjective perception data and risk assessment values—the method ensures the accuracy and comprehensiveness of the final results, achieving intelligent analysis and accurate assessment of risk perception bias among residents in post-disaster reconstruction areas.

This invention belongs to the field of intelligent transportation, specifically relating to a MaaS-IoT integrated method and system for crowd risk perception and intervention. The method first collects environmental data and travel orders for the target area through IoT devices and an online platform; then, it constructs a path decision model based on the Bandit algorithm; combining shared and unique feature vectors to achieve path recommendation. Next, it constructs a crowd risk event dataset based on historical data and generates an easily searchable vector database, and fine-tunes the LLM to obtain a crowd risk inference model. Then, it tokenizes the time-series data, and uses the crowd risk inference model to perform chain reasoning and Monte Carlo-Shapley attribution to generate corresponding results; finally, it generates intervention instructions based on the outputs of the two models, and issues them for execution after verification. This invention solves the problem that existing methods struggle to achieve timely risk perception and efficient intervention in public places where crowds gather.

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.

The present application relates to the technical field of cargo scheduling, in particular to a port piece bulk cargo handling digital resource scheduling and whole-process tracing system, which comprises a cargo digital unit, a cargo dynamic interaction unit, a scheduling optimization unit and a tracing verification unit. The present application takes the physical interaction characteristics of cargo and environment as the core driving element, captures the cargo state and environmental disturbance in real time through multi-modal sensor data collection, realizes the full-dimensional digital mapping from static attributes to dynamic behavior, and the physical feature closed-loop driving mechanism not only improves the accuracy of scheduling decision, but also builds a tracing system with chaotic characteristics through irreversible environmental disturbance fingerprint technology, effectively solving the problems of easy tampering and difficult reproduction of traditional tracing system. Secondly, a three-level risk perception scheduling system is innovatively designed, which deeply couples risk assessment and resource scheduling. This layered response strategy not only guarantees operation safety, but also avoids resource waste.

The application discloses a power dispatching operation risk linkage modeling method and system based on a knowledge graph, and belongs to the technical field of power system automation, which comprises the following steps: constructing an electric power business semantic field and a physical topology graph; generating a business semantic vector facing topology anchoring by using a neural network; identifying a risk event and encapsulating it as a risk logic cluster containing an influence chain and disposal knowledge; linking the risk logic to the topology graph by a bidirectional coupling engine to generate a risk linkage knowledge graph; and capturing artificial intervention decision differences based on visual deduction to realize double-space co-evolution closed loop. The application adopts semantic recognition and topology model deep fusion technology, can break through the digital linkage channel of risks and equipment and users, realizes dynamic risk perception and penetrating analysis, and improves the timeliness and accuracy of power dispatching in response to real-time operation risks.

This application belongs to the field of flammable and explosive environment safety control technology, and particularly relates to a graded explosion suppression protection system and method. The system includes: a monitoring module for monitoring environmental information within the monitored area; a signal processing module for generating graded early warning commands based on the environmental information; an explosion suppression execution module for executing explosion suppression actions according to the graded early warning commands; and a reset module for generating reset information to reset the explosion suppression execution module. Based on the cooperation of the monitoring module, signal processing module, and explosion suppression execution module, corresponding explosion suppression actions can be automatically triggered without manual intervention, realizing a complete and rapid safety protection closed loop from risk perception to proactive suppression. This significantly improves the speed and reliability from risk detection to proactive intervention, greatly reducing the safety risk in the monitored area. Based on the cooperation of the reset module with the signal processing module and explosion suppression execution module, the system can quickly return to standby status after explosion suppression, simplifying maintenance steps and reducing operating costs.

This invention provides a dual-strategy robot safety motion control method and device based on risk perception, belonging to the field of robot motion control technology. The method includes: acquiring the current body perception vector and current environmental point cloud data of the target robot; acquiring a first state sequence based on the current body perception vector and current environmental point cloud data; inputting the first state sequence into a risk perception model to obtain a scalar risk value output by the risk perception model; comparing the scalar risk value with a preset safety threshold to obtain a comparison result; and determining the current execution strategy of the target robot based on the comparison result. The current execution strategy includes an agile strategy or a safety recovery strategy. The agile strategy instructs the target robot to autonomously plan a movement path, while the safety recovery strategy instructs the target robot to move based on avoidance commands. This allows the robot to improve safety and obstacle avoidance during movement while ensuring mobility.

The application relates to a supplier cooperation risk automatic identification method, system, device and medium. The method comprises the following steps: firstly, obtaining supplier decision maker historical text data and historical cooperation event logs, extracting decision maker personality characteristic keywords, sentiment tendency and dividing groups to obtain decision maker portrait labels; combining the portrait labels and the historical cooperation event logs, grouping and classifying delivery delays and quality dispute events to obtain correlation mode data; determining preliminary risk labels based on the correlation mode data, extracting quantitative indicators and obtaining a controlled state score through a weighted fusion algorithm; fusing the preliminary risk label quantitative value and the controlled state score to determine the overall risk level of the supplier; finally, obtaining supplier real-time emergency text, identifying keywords and evaluating the impact degree, and updating the overall risk level to obtain a dynamic risk perception result. The method can improve the risk identification accuracy and timeliness, avoid artificial subjective bias and reduce the control cost.

This invention relates to the field of security and control technology, specifically to an intelligent security and control system, comprising: a scenario determination unit for determining target scenarios requiring security and control; based on the target scenario, collecting corresponding environmental perception data, terminal perception data, and network perception data, and fusing them to obtain risk perception data corresponding to the target scenario; analyzing the risk perception data to obtain a comprehensive risk level; and a requirement determination unit for determining the control requirements of the target scenario based on the comprehensive risk level. This invention, by classifying and processing different types of risk perception information according to corresponding standards, comprehensively derives a comprehensive risk level, accurately quantifying the degree of risk, providing a scientific basis for subsequent control strategy formulation, matching control measures with risk levels, and improving the pertinence and effectiveness of control.

The application relates to the technical field of network security, and discloses a dynamic network security risk identification system and method based on multi-source intelligence and AI driving, which comprises a dynamic attack graph modeling module, an AI-driven attack simulation engine and a cooperation module.The modeling module fuses multi-source heterogeneous intelligence to construct a network attack graph and calculate node intelligence confidence; the AI engine takes the confidence into a state space, uses a dynamic entropy mechanism to real-time adjust the randomness of reinforcement learning exploration to accurately simulate an attack path; and the cooperation module links external attack surface management and a security operation center to execute closed-loop feedback verification of automatic defense response and path elimination.The application establishes a mapping relationship between intelligence confidence and exploration strategy, effectively solves the problems of uneven multi-source data quality and unknown path missing report, realizes an automatic closed loop from risk perception, simulation deduction to effect verification, and significantly improves risk identification accuracy and response timeliness.

This application discloses a method for evaluating the impact of multimodal connected vehicle information on driver workload and perceived risk. In controlled driving experiments or real-world driving scenarios, multi-source data related to connected vehicle information prompts are collected simultaneously. The multi-source data is preprocessed, and then event-level samples are constructed. Finally, the AutoML method is used to select the most suitable machine learning model for estimation. m ( X )and e ( X The residuals required for DML are constructed. Then, based on the event-level sample data after the biased residuals are removed, the average treatment effect ATE under different treatment definitions is estimated, and the conditional treatment effect CATE is obtained based on the causal forest model. The average treatment effect ATE is used to evaluate the overall effect strength of a single factor, and the conditional treatment effect CATE is used to characterize the heterogeneous response pattern under different combinations of driving behavior states and physiological states.

The application relates to the technical field of software-defined wide area network and discloses a multi-link global routing method and system based on dynamic policy linkage, which is applied to an SD-WAN controller and used for solving the technical limitation problem that in a traditional SD-WAN routing method, decision basis is limited to a current state and policy rules are statically fixed. The method generates a predicted value and a confidence evaluation of future performance of a link through a time series prediction model; based on the confidence evaluation, historical, current and future state information is dynamically fused to form a link quality trend value; and the confidence evaluation is used to dynamically modulate the validity degree of a multi-dimensional service policy weight in evaluation; finally, path selection probability distribution is obtained through iterative optimization, and an optimal path is selected according to a service type for forwarding, so that routing decision is upgraded from static rule response to dynamic optimization based on risk perception, and the foresight, intelligence, stability and adaptive robustness of network routing decision in a complex dynamic environment are significantly improved.

This invention discloses a renewable energy dispatching method, device, terminal, and medium based on risk perception. The method predicts photovoltaic (PV) power generation using confidence level, environmental data, and a PV power generation prediction model; constructs a two-layer optimization model to coordinate aggregator costs and building user costs during demand response; and obtains building flexible participation capacity and demand response incentive price by solving the two-layer optimization model. Based on confidence level, PV power generation, building flexible participation capacity, and demand response incentive price, it constructs an aggregator day-ahead bidding profit optimization model and supply and demand constraints, respectively. Based on the aggregator day-ahead bidding profit optimization model and supply and demand constraints, it optimizes the energy storage system's charging and discharging strategy and energy trading strategy. This solves the problem of existing technologies failing to consider the uncertainty of power generation, user participation costs in demand response, and willingness to respond, making it difficult to effectively balance the risks of power shortages and trading benefits, and achieving power dispatch optimization under uncertain conditions.

This invention discloses a rule engine-based intelligent approval method for financial shared expense reimbursement, specifically in the field of intelligent expense reimbursement approval. It addresses the challenges of unifying the handling of differences and conflicts in approval rules among participating parties in multi-party collaborative expense reimbursement scenarios, as well as the lack of risk identification capabilities in the approval process. The method identifies the business activity type of collaborative expense reimbursement requests, constructs a collaborative rule knowledge graph based on each participant's local rule engine, performs conflict detection and resolution on expense approval rule units, forming a conflict-free joint execution rule set, and generates sub-approval tasks with globally unified event identifiers. After completing local compliance verification for each participant, it further constructs cross-party execution trajectories based on the approval results and rule trigger sequences, identifies risks in the collaborative expense reimbursement execution mode, and generates a final approval decision result with risk warnings, achieving unified coordination and risk perception in complex collaborative expense reimbursement processes.

This invention discloses a multi-dimensional information security risk dynamic assessment system and method for detection platforms, relating to the field of information security technology. It collects multi-dimensional operational parameters such as assets, vulnerabilities, and attacks, converting them into risk factors. An initial comprehensive risk index is calculated using a radial basis function algorithm. Real-time business load is sensed to calculate weight offsets, and risk weights are redistributed and normalized, updating the risk index. A sliding assessment cycle is used to clean the sequence, and quantile regression is applied to determine a basic threshold. This basic threshold is then adjusted using historical statistical data of the load status to generate a dynamic risk threshold. When the risk index exceeds the dynamic threshold, a risk is identified and a response is triggered. This achieves accurate risk fusion and adaptive judgment, reduces response lag, and improves assessment accuracy, aiming to solve the problems of insufficient risk perception and high false alarm rates in complex environments.

This invention discloses an intelligent scheduling system for maritime logistics, comprising a data acquisition module, a data fusion module, a state perception module, a scheduling decision module, and an execution feedback module. The data acquisition module acquires engineering, crew, and maritime data and maps them to a spatiotemporal index grid. The data fusion module extracts feature vectors and calculates dynamic attention weight coefficients based on a comprehensive risk index to generate a comprehensive state vector. The state perception module quantifies the probabilities of faults, errors, and accidents. The scheduling decision module uses the comprehensive state vector as input and generates the optimal scheduling scheme through reinforcement learning based on a reward function with coupled penalty terms. The execution feedback module issues the scheme and returns the execution results. The system achieves multi-source data fusion and dynamic risk perception, improving the safety and intelligence level of maritime scheduling.

This invention discloses an attention-integrated multimodal federated learning method and system for risk monitoring. On the client side, an attention-integrated hybrid early fusion module dynamically injects multimodal features into globally shared latent variables through attention mechanisms and entropy-based gating mechanisms. On the server side, a plug-and-play dual-track federated aggregation strategy decouples state aggregation from model aggregation. The state track employs robust filtering based on the geometric median, while the model track supports algorithm-independent parameter updates. Between communication rounds, a neighborhood-weighted personalized reference mechanism constructs a gradient-based semantic topology to balance global consistency and local task preferences. This invention simultaneously addresses the technical challenges of modality incompleteness, data statistical heterogeneity, and Byzantine adversarial robustness in multimodal federated learning, thus providing an efficient and robust collaborative learning solution for disaster risk perception under privacy-preserving conditions.

This invention discloses an adaptive carbon footprint data processing method and system based on the coupling of risk perception feedback and large model inference constraints. The method includes: parsing the metadata of the original data object and calculating a data risk vector containing time deviation and physical conservation deviation; constructing an initial computational topology and transforming data requirements into a constraint satisfaction problem; if no solution is found, triggering topology adaptive reconstruction based on semantic reasoning; dynamically adjusting the inference hyperparameters of the generative model based on the data risk vector, and updating the mapping weights between risk and hyperparameters online using the logical deviation scores generated by subsequent source tracing audits. This invention solves the technical problem of inference illusion caused by high-uncertainty data by establishing a closed-loop coupled control mechanism between data risk and model behavior; and solves the technical problem of static workflows being unable to cope with process interruptions caused by missing data through runtime semantic reconstruction of the computational topology.

This invention discloses an intelligent optimization method for energy management based on a swarm algorithm, comprising the following steps: S1, collecting multi-source time-series data and adjustable equipment parameters from the energy management system to generate a prediction sequence, constituting a scheduling space; S2, calculating a risk index sequence to form a risk map; S3, constructing a mapping structure between scheduling schemes and gap fields; S4, constructing a coupled coding structure between population individuals and gap fields to form an initial population cluster; S5, forming an updated population cluster using an improved HHO algorithm; S6, performing selection, replication, and elimination operations to form a new generation population cluster; S7, performing convergence determination, updating the risk map and prediction sequence, and completing the optimization closed loop. This invention enables risk perception, dynamic adjustment, and global optimization of complex energy systems across multiple time scales, improving the economy, energy efficiency, and operational safety of the energy management process.

The application discloses a content security protection method, an electronic device and a computer readable storage medium, relates to the technical field of artificial intelligence security, and comprises the following steps: dividing to-be-protected content into at least one slice of binding metadata, solving the problem of extensive pollution positioning, and realizing accurate positioning at the slice level; determining the comprehensive judgment result of the slice through multi-factor quantitative scoring and a multiple injection detection mechanism, solving the problem of high detection missing rate, and realizing quantifiable and accurate detection; performing reservation, marking or isolation processing on the slice according to the comprehensive judgment result, extracting factual elements from the isolated slice to construct a safe context, solving the problems of detection interruption and information loss, and realizing the isolation of pollution without interrupting the task; while continuing to execute the task under the safe context, implementing gating on tool calling, output content and memory writing, solving the problems of pollution cross-round diffusion and tool chain risk amplification, and realizing adaptive protection of risk perception.

The application provides a multi-modal data-driven intelligent claim settlement risk control system, relates to the technical field of insurance services, and comprises a multi-modal data acquisition module, a multi-modal data fusion and correlation analysis module, a historical claim analysis module and a decision output module; the multi-modal data acquisition module acquires multi-modal data related to a vehicle accident claim settlement event; the multi-modal data fusion and correlation analysis module constructs a multi-modal fusion model and calculates a correlation index; the historical claim analysis module acquires metadata of a claim settlement event and calculates a claim settlement similarity index based on the metadata; the decision output module pre-sets a correlation threshold and a claim settlement similarity threshold, establishes an inspection mechanism, and determines whether to trigger a manual inspection process. The application realizes multi-dimensional evidence consistency verification and space-time context risk perception capability that cannot be achieved by a conventional system, locks high-risk cases through a double-threshold joint decision mechanism, and automatically triggers manual inspection, effectively solving the limitations of traditional risk control systems, such as dependence on a single data source, difficulty in identifying complex fraud patterns, and lack of dynamic adaptability.

This invention relates to a multi-dimensional fire early warning method, system, and product for cable trenches based on edge computing, within the field of cable safety protection technology. The multi-dimensional fire early warning method for cable trenches includes: collecting multi-dimensional sensor signals; automatically matching scenarios based on the multi-dimensional sensor signals; assigning corresponding weights to sub-signals based on the matched scenarios; and obtaining a fire development index through a trend prediction model after weighted fusion. Alarm thresholds are dynamically adjusted, and comparison results between sub-signals and corresponding alarm thresholds are obtained. A fire risk level is output for early warning based on the fire development index and comparison results. This invention, through the collection of multi-dimensional sensor signals, achieves full-dimensional, full-lifecycle risk perception of cable trench electrical safety, environmental conditions, structural stability, and fire characteristics. Simultaneously, based on multi-dimensional sensor signals, it predicts fire hazards and can detect early warnings of fire sources and precursory hazards such as cable insulation aging, partial discharge, water immersion, and dust accumulation.

This invention discloses a method and system for handling emergency events displayed on a head-up display (HUD). The method uses multi-source data fusion to predictively assess driving risks and driver intentions, and calculates an urgency coefficient. It employs a dual-core MCU hardware architecture and a resource preemptive scheduling strategy. When an emergency event is determined, the high-performance main core, dedicated to emergency tasks, immediately preempts the regular display resources managed by the auxiliary core through inter-core communication, ensuring that emergency alerts are rendered with a deterministic delay of microseconds. Simultaneously, the solution introduces dynamic visual enhancement technology, enabling warning icons to move dynamically according to the direction of danger, change their shape and flashing frequency according to the risk level, and adapt their brightness according to the driver's attention level, thereby actively guiding the driver's gaze and enhancing risk perception. This invention upgrades the HUD from a passive information display to an active safety assistant, significantly improving driving safety.

The present application relates to the technical field of monitoring high-risk operations, and particularly relates to a high-risk operation intelligent monitoring management system based on multi-element linkage. The present application constructs the mutual linkage of key elements of high-risk operations to achieve the technical standard of risk perception and control in the operation process at the safety level. At the same time, through an electronic fence automatic drawing algorithm and an electronic fence automatic validation algorithm, the operation risk state is determined in a limited space. Through micro-network data acquisition, deep learning and continuous training, an operation risk high-accuracy AI model is obtained to maximize the elimination of safety hazards in the work of operation personnel on the fan platform working surface and the fan tower working surface. The working state of the crane is determined by the proportion of time nodes of the crane outside the electronic fence within the preset time length, and corresponding adjustment is made when the working state is unqualified, thereby improving the working accuracy of the crane. The present application effectively monitors the operation risk and improves the work efficiency.

This invention discloses an artificial intelligence-based smart wearable security alert method and system, relating to the field of smart wearable security technology. The invention includes: risk perception, collaborative verification and evaluation, and tiered alerts and protection. Risk perception constructs a dynamic personal digital behavior profile by collecting multi-dimensional data from textile-generated smart wearable devices. Based on the identified abnormal activity types and combined with fingerprint authentication, it accurately identifies information leakage threat events, transforms these events into collaborative analysis requests, and assesses confidence levels through collaborative evaluation of similar devices. It then reversely determines the type of information leakage threat event and triggers tiered alerts based on the type and confidence level of the event. This supports proactive protection, countermeasures, and sharing of information leakage threat event type characteristics, solving the problems of traditional passive protection, limitations of single-device perception, and high false alarm rates, thereby improving the accuracy and proactivity of security protection for textile-generated smart wearable devices.

The application provides a water conservancy hub operation risk real-time control system and method, and belongs to the technical field of water power and water conservancy, and the system comprises: a risk monitoring subsystem, which is used for constructing a risk transmission link, laying a risk monitoring network, and monitoring the change of an external environment of a hydropower station and the operation of an internal unit in real time through terminal monitoring equipment and manual inspection; a risk analysis subsystem, which is used for analyzing the risk transmission link, determining a risk occurrence point, formulating and screening an optimal risk cutting scheme according to the risk occurrence point, feeding back the cutting situation of the risk to the risk analysis subsystem through a risk control subsystem, and synchronously analyzing the propagation position of the risk by using a parallel simulation method; and the risk control subsystem, which is used for cutting the risk and feeding back the cutting situation of the risk to the risk analysis subsystem. The application realizes multi-dimensional risk perception and control of the hydropower station and improves the risk perception and risk response capability of the hydropower station.