50 results about "Global risk" patented technology

The invention discloses an Internet of Things intelligent risk processing method and related equipment thereof, belongs to the technical field of artificial intelligence, and is applied to intelligent risk control and transaction risk management of a financial system. According to the method, the device state, the time sequence data and the spatial topological relation are combined by obtaining the multi-dimensional parameters such as the device operation state, the network environment and the service scene and constructing the context feature vector. In a risk early warning analysis link, the layered intelligent agent accurately predicts local and global risks through a space-time association mechanism, and feeds back a prediction result to a state space to drive dynamic adjustment of control parameters. And in combination with the online optimization capability of the reinforcement learning algorithm, the control strategy can automatically adapt to the real-time change of the equipment state, and the collaborative effect of quick response of the equipment end, local optimization of the edge end and global regulation and control of the cloud end is realized. According to the invention, the timeliness and accuracy of risk early warning are improved, and the adaptive and intelligent management capability of the system in a complex network environment is enhanced.

The invention discloses a multi-parameter fusion dangerous rock mass instability precursor identification method, which comprises the following steps: S1, generating a thermodynamic diagram of dangerous rock mass crack distribution, dividing each sub-region and setting each risk level; constructing a monitoring network; s2, performing time alignment and space alignment on the monitoring data; s3, the GNN model calculates and outputs each instability probability of each monitoring node; and S4, generating a global risk thermodynamic diagram according to each instability probability, and performing graded early warning based on a preset probability threshold. According to the method, the unmanned aerial vehicle thermodynamic diagram is used for guiding sensor optimization deployment, multi-dimensional data such as deformation, osmotic pressure, COs and infrasonic waves are fused, the model for identifying the instability of the dangerous rock mass is constructed by using the GNN model, accurate calculation and graded early warning of the instability probability are achieved, and the identification accuracy and early warning real-time performance are remarkably improved.

The invention discloses an AIoT-based energy storage environment global risk intelligent early warning system and method, and the system employs an intelligent early warning system architecture of a perception layer, an edge calculation layer and an application layer, and extracts local anomaly, cluster-level cooperation, cabin-level space distribution and environment interference multi-level features based on multi-source data fusion and module-cluster-cabin layering. In combination with risk assessment and a positioning model, the early fault recognition sensitivity is remarkably improved, meanwhile, the whole system can realize AI model self-evolution based on specific setting of edge nodes and a cloud service platform, the performance can be effectively improved without manual intervention, the system reliability is guaranteed, and the operation and maintenance cost is reduced. The scheme of the invention is especially suitable for a large-scale distributed energy storage scene.

The invention relates to the technical field of risk management and control of a data processing center, in particular to a risk management and control system and method suitable for a container type data processing center, and the system and method precisely recognize a hierarchical conduction relationship and hidden association among multiple risk objects by judging whether a risk object connection combination is a crotch risk connection combination or not. According to the method, the problem of missed judgment caused by neglecting the chained influence among risk factors in traditional assessment is avoided, the co-belonging potential risk cabinets in the intersection of the multiple risk cabinet fields are captured, the collaborative amplification effect of multiple risk sources is accurately identified, the traditional assessment error area of simple superposition of multiple risks is broken through, and the risk assessment accuracy is improved. According to the design, the risk management and control index not only can reflect the general influence of a single risk, but also can highlight the high-priority hidden danger under the cross action of multiple risk sources, so that the global risk assessment is more suitable for the actual operation scene of the container.

The invention relates to the related technical field of cable monitoring, in particular to a cloud-side-end collaborative high-voltage cable intelligent monitoring and early warning method and system, and the method comprises the steps: carrying out the local preprocessing of a data collection result, building the local risk distribution of a cable, and transmitting the local risk distribution to an edge calculation unit; according to historical data and a data acquisition result, extreme state inversion and future risk fluidization prediction are carried out, collaborative risk assessment and global risk assessment are established, multi-scale game optimization is carried out on cable local risk distribution, collaborative risk assessment and global risk assessment, and a monitoring and early warning result is output. The technical problem that the local risk urgency and the global risk diffusion trend cannot be balanced because local risk judgment is carried out only through single node data is solved, local risk distribution, collaborative risk assessment and global risk assessment of the coupling cable are realized, an end-edge-cloud data closed loop is formed, and the reliability of the system is improved. The real-time performance, the accuracy and the global collaboration of high-voltage cable monitoring and early warning are comprehensively improved, and the technical effect of safe and stable operation of the high-voltage cable is guaranteed.

The present specification provides a drilling risk determination method, device and electronic equipment based on federal learning, belonging to the technical field of drilling construction. Before implementation, multiple node terminals and a central node server deployed in multiple different oil and gas regions cooperate to perform a preset federal learning based on oil and gas region data privacy protection according to a preset joint training rule, on the premise that sample data does not leave the local oil and gas region, and obtain a global risk prediction model by performing a preset asynchronous update every interval preset time period combined with information freshness. In the specific implementation, the reference data of the drilled well in the first oil and gas region, the associated data of the target well, and the current global risk prediction model in the current time period are obtained; and the current global risk prediction model is matched and adjusted by using the reference data of the drilled well; and whether the target well currently exists a drilling risk is determined by processing the associated data of the target well by using the adjusted current first risk prediction model.

The invention belongs to the technical field of computer data processing and automatic control, and particularly discloses and provides a digital configuration and BIM (Building Information Modeling) mapping fabricated electromechanical facility linkage method, which comprises the following steps of: analyzing a building information model and digital configuration, and constructing an initial weighted mapping network; initial virtual pressure of each node is calculated, conduction and superposition of the pressure in the network are iteratively simulated, and a dynamic pressure distribution diagram representing global risks is generated; and when an anomaly is detected, reverse tracing is carried out along a path with the maximum pressure contribution degree gradient so as to locate a risk source. Furthermore, various candidate control strategies are deduced through simulation, scoring is carried out from multiple dimensions, and the optimal strategy is selected. In addition, by comparing simulation and actual execution effects, deviation data is utilized to adaptively optimize the weighted mapping network and simulation model parameters. According to the method, accurate prediction, rapid traceability, optimal decision and continuous self-learning of the risk of the complex facility system are realized, and the intelligence and reliability of operation and maintenance are improved.

The invention relates to a bank cash business-oriented public opinion risk insight system and method, and the system comprises an edge end which comprises a data collection module and an edge processing module, and a cloud end which comprises a cloud end analysis module; the data acquisition module acquires cash business data and local public opinion information in real time; the edge processing module performs feature extraction on the cash business data and the local public opinion information to obtain risk features; inputting the risk features into a preset multi-modal risk marking model for risk assessment to obtain a preliminary risk result; and the cloud analysis module analyzes according to the preliminary risk result to obtain a global risk report, and queries based on a preset dynamic knowledge graph to obtain a risk response strategy corresponding to the global risk report. According to the method, feature extraction and preliminary risk assessment are firstly carried out by utilizing the computing power of the edge end, and then global risk analysis is carried out and a risk response strategy is formulated through the cloud end, so that the computing pressure and processing time of the cloud end are reduced, the response speed of risk identification is improved, and public opinion risk prevention is facilitated.

The invention relates to the technical field of enterprise information security management, and discloses a multimodal early warning pushing method and system based on global risk real-time perception. Comprising the steps of detecting a sensitive operation link by using a pattern matching algorithm; monitoring a deviation condition between a business data input value and a system preset threshold value in real time, and generating a business data deviation mark when business data deviation conditions such as material inventory lower than a minimum inventory line, fund expenditure exceeding a maximum payment proportion and employment age exceeding a preset value are triggered; calculating a comprehensive deviation degree score by using a weighted summation algorithm; selecting an early warning push mode according to the risk level and receiver channel preference; and packaging a global risk early warning message and pushing the global risk early warning message through a multi-modal channel. According to the method, the hidden unauthorized risk generated by cross-system permission combination in the matrix management enterprise and the abnormal risk that the business data deviates from the preset value can be identified, and the early warning response rate is improved through multi-mode differentiated pushing.

The present application relates to the technical field of engineering technology research and development, in particular to a global disaster risk dynamic assessment method for vulnerability of hazard-affected bodies, comprising the following steps: obtaining structure attribute, state, disaster loss, danger, exposure and source data, generating a hazard-affected body state sequence, correcting dynamic vulnerability representation value, generating a spatial vulnerability correction amount according to spatial correlation, calculating risk score and determining risk level in combination with a reliability correction coefficient, and updating the hazard-affected body state sequence or dynamic vulnerability representation value based on evaluation write-back data. In the present application, the hazard-affected body state change, historical disaster loss, spatial correlation, data reliability and evaluation write-back are included in the same calculation link, so that the vulnerability is converted from a static index to an evaluation basis that is dynamically corrected with time and space, the transmission ability of local anomalies to global risk calculation is improved, the influence of quality difference of multi-source data on the stability of risk level is reduced, and a traceable feedback correction closed loop is formed.

The application relates to the technical field of construction safety management, and discloses a building engineering safety risk intelligent early warning and management and control method, which comprises the following steps: firstly, a three-dimensional digital scene containing static background and dynamic entity and a cause-effect graph representing a risk cause-effect relationship are constructed; secondly, a risk chain activated is identified by using entity state prediction, and risk saliency is calculated; thirdly, when the risk exceeds a threshold value, an alternative intervention instruction set is generated, and parallel counterfactual simulation is performed on each instruction to calculate global risk after intervention; fourthly, an integrated utility score is calculated by combining intervention cost and global risk, and an optimal management and control instruction is selected and issued for execution; and finally, factual data after instruction execution is collected to calculate prediction deviation. The application quantitatively evaluates management and control effect through counterfactual deduction, continuously optimizes model parameters based on execution feedback, effectively solves the problems of risk coupling difficulty in prediction and poor model adaptability in a dynamic construction environment, and balances safety and efficiency.

The application discloses a kind of dangerous house wisdom supervision systems, it is related to dangerous waste intelligent supervision technical field, including, acquisition text data in the process of dangerous waste supervision, time series monitoring data and image data, generate standardization multimodal feature matrix, and pass through entity identification and relationship extraction, construct dynamic knowledge graph;Based on dynamic knowledge graph, extract the dynamic node features of each entity node, and identify high-risk entities and colonies, while calculating dynamic risk weight, generating risk quantification list;The dynamic risk weight in risk quantification list is used as risk cost, constructs multi-objective logistics path optimization function that fuses economic cost, time cost and risk cost, and generates global optimal transport plan.The application realizes the upgrade from discrete risk point monitoring to global risk network situation awareness, and can actively warn potential high-risk colonies.

The invention discloses a water resource collaborative robust scheduling method considering supply and demand dual uncertainty, which comprises the following steps of: evaluating risk exposure contribution rates of incoming water and demand water to system risks, and constructing an asymmetric incoming water and demand water robust uncertainty set according to the risk exposure contribution rates; a supply and demand risk coupling amplification index is calculated, the joint uncertainty space is screened and reconstructed based on the index, and a joint scene tree is generated; inputting the scene tree into a water resource hierarchical collaborative scheduling model, performing cross-period optimization by using a global resource allocation layer, and determining a staged risk quantification parameter representing the risk constraint strength of each stage; and a local scheduling decision-making layer is utilized to take the parameters as cross-layer risk constraints, and a dynamic scheduling strategy meeting global risk control requirements is generated. Through risk scene construction and cross-layer risk explicit conduction driven by a physical mechanism, the problems of excessive conservative and extreme risk defense failure in the scheduling process are effectively solved.

The application discloses a kind of based on knowledge graph modeling's energy risk index system construction method, system, equipment and medium, belong to energy system risk management technical field, including constructing based on risk propagation operator dynamic knowledge graph ontology;Equivalent impedance of risk transmission path is calculated and key path is identified based on the dynamic knowledge graph, risk potential field dynamic simulation is carried out, and global risk index is generated;Receiving user instruction is contextualized to prune global graph, and focus subgraph is generated;Receive intervention instruction on focus subgraph, simulate intervention measure and deduce new index after the change of risk field;Based on risk index and key path data, generate explainable risk cause knowledge card and carry out visual presentation.The application realizes the whole chain ability from risk transmission quantization, global situation simulation, man-machine collaborative pruning, intervention deduction to explainable analysis, improves the fine, intelligent and credibility level of energy system risk management.

The invention discloses an operation and maintenance script execution processing method and device and electronic equipment. The method comprises the following steps: performing semantic analysis on a plurality of operation and maintenance scripts of a target server to obtain initial risk tags corresponding to the plurality of operation and maintenance scripts; based on the initial risk labels corresponding to the operation and maintenance scripts, an attack chain atlas is constructed, the attack chain atlas is used for indicating the dependency relationship between operation entities corresponding to the operation and maintenance scripts and environment states and potential attack paths, and the operation entities represent operation objects corresponding to the operation instructions; obtaining global risk values of the plurality of operation and maintenance scripts based on the initial risk labels corresponding to the plurality of operation and maintenance scripts and the attack chain atlas; and determining execution time periods and execution strategies of the plurality of operation and maintenance scripts according to the operation indexes, the business indexes and the global risk values of the target server. The technical problem of security risks in the execution process of the operation and maintenance script is solved.

This invention discloses a method and system for assessing open-source software supply chain security risks based on multimodal feature fusion, solving the technical problem that existing open-source software supply chain security risk assessment technologies result in unsatisfactory assessment effects. The method includes acquiring code semantic data, community governance time-series data, and license compliance data, and inputting them into a multimodal security risk assessment model comprising a multimodal feature encoding module, a cross-modal contrastive learning module, a cross-attention and gating fusion module, and a multi-task risk assessment module. The model sequentially completes multimodal feature encoding, cross-modal contrastive learning alignment, global risk vector extraction, and risk index assessment, outputting a comprehensive security risk index. Finally, it uses preset multi-level risk thresholds for risk level matching, outputting the open-source software supply chain security risk level assessment result.

The invention provides an intelligent research and judgment method for chemical dangerousness, a medium and a display robot, and belongs to the technical field of chemical dangerousness research and judgment. Inputting the attribute vector into a risk assessment model combining a spiral progressive network structure and a dual learning framework, and expanding a receptive field layer by layer through five spiral expansion layers to realize collaborative extraction of local features and a global mode; reversely deducing safety attributes from a prediction result by using a dual task, performing difference verification with original input to form closed-loop optimization, calling historical cases to perform similarity comparison to correct the prediction result when a danger level exceeds a safety threshold, and finally outputting a research and judgment conclusion through multi-dimensional cross verification. The problem that the misjudgment rate of the mixture and the disguise chemicals is high due to the fact that local features and a global risk mode are difficult to cooperatively recognize in the chemical risk study and judgment process is solved.

The invention discloses a federated learning-based risk assessment method, device and equipment and a storage medium, and is applied to the technical field of artificial intelligence, and the method comprises the steps: obtaining current training sample data, and carrying out the model updating of a local risk assessment model deployed under the own equipment based on the current training sample data, obtaining the current model parameter update quantity in the current model update time period; obtaining a historical model parameter update quantity, and determining a data quality score according to the current model parameter update quantity, the historical model parameter update quantity and the current training sample data; encrypting and transmitting the current model parameter update quantity, the data quality score and the node performance data of the equipment to a central server, performing model updating on a global risk assessment model deployed in the server, and encrypting and transmitting the updated global model parameter update quantity to each client; and updating the local risk assessment model under the own equipment based on the global model parameter update quantity.

The application discloses an inter-agent prompt injection protection method and system based on semantic auditing and trust perception reasoning verification, and the method comprises the following steps: acquiring the output content of external tools or data sources received by an agent in a task execution process; performing semantic auditing on the output content, identifying potential risk semantics by calculating the deviation between the semantics of the output content and the original task intention of a user; performing trust perception reasoning verification on the output content, calculating the trust score of the content source based on the source characteristics of the output content; performing injection mode detection on the output content, identifying whether the output content contains a predefined prompt injection style; performing behavior drift analysis on the output content, calculating the behavior drift degree by comparing the reasoning track of the agent under the output content with a reference track; aggregating the foregoing results to generate a global risk score; and triggering corresponding protection actions when the global risk score exceeds a preset threshold.

The invention relates to the technical field of risk distribution calculation methods, and discloses a method for calculating ship collision risk distribution in a port channel water area, and the method comprises the following steps: 1, defining a channel ecological space unit and a ship ecological unit, the channel ecological space unit is used for delimiting and associating a fixed feature library based on port channel function partitions, geographic features and traffic flow, and the ship ecological unit comprises basic attribute dimensions, motion attribute dimensions and intention attribute dimensions; and step 2, sensing and complementing multi-dimensional collaborative data. Through adoption of an edge-cloud collaborative architecture and edge nodes, local risks are processed in real time, pre-judgment information is transmitted in a cross-node linkage manner, a cloud integrates global data to generate a thermodynamic diagram and a risk transmission path, and through layered calculation and non-full data interaction, local risk millisecond-level response is realized, global risks are ensured not to be missed, and the risk transmission efficiency is improved. And the port channel dynamic management and control requirements are met.

The embodiment of the invention provides a global risk intelligent early warning and disposal method and system based on a power system, and belongs to the technical field of risk early warning and disposal. The risk intelligent early warning and disposal method comprises the steps that various feature data, including material purchasing feature data, marketing service feature data and engineering project feature data, of a power system are acquired; preprocessing each kind of feature data to construct a training set of each kind of feature data; respectively constructing an early warning model of each kind of feature data; training a corresponding early warning model by adopting the training set of each kind of feature data; according to the method, the early warning models of various feature data are adopted to perform various risk identification and diagnosis on the power system, so that the adaptability and precision of early warning of the power system are effectively improved, and the reliability and efficiency of risk disposal are improved.

The application discloses a social major event risk assessment report compiling and editing method based on artificial intelligence, relates to the technical field of natural language processing, and comprises the following steps: S1, generating a risk assessment report draft; S2, generating a real cause and effect value; S3, performing logical judgment on the real cause and effect value; S4, generating a global risk difference degree; S5, performing effectiveness determination on the global risk difference degree; S6, constructing a lesion feature tensor by improving a Longformer model, and fusing attention hidden states by calculating output based on non-lesion polarization overflow noise tending to zero masking and diagonal strip attention; S7, performing lesion targeting revision and grammar correction determination, and outputting a risk assessment report compiling and editing final draft. The application overcomes the limitations of weak cause and effect representation, single logic and ignoring pathological noise isolation in traditional methods, and provides an efficient solution for intelligent correction of social major event risk assessment reports.

The application relates to a kind of wisdom water affairs real-time monitoring early warning method and system based on Internet of Things, solve the problem that abnormal identification, mechanism simulation and decision link are disconnected in the prior art, leading to traceability difficulty, early warning delay, its method includes: through edge node real-time collection analysis water quality data and identify abnormality, aggregate data after uploading to cloud and simulate to generate global risk atlas reflecting abnormal distribution and diffusion trend.Accordingly, dynamically enhance the monitoring of key areas, lock the suspected source in combination with topological information, and finally comprehensively evaluate the hazard level and generate a graded early warning push.The application has the following effects: through edge-cloud collaboration and mechanism data fusion, a closed-loop system from abnormal perception, dynamic tracing to intelligent decision-making is constructed, and the ability of water affairs risk from passive alarm to active prediction is upgraded.

The application discloses a kind of water resource collaborative robust scheduling method considering supply and demand double uncertainty, comprising: the risk exposure contribution rate of risk of system risk of inflow and water demand is evaluated, and asymmetric inflow and water demand robust uncertainty set is constructed accordingly;Supply and demand risk coupling amplification index is calculated, based on the index, the joint uncertainty space is screened and reconstructed, and a joint scenario tree is generated;Scenario tree is input into water resource hierarchical collaborative scheduling model, inter-period optimization is carried out using global resource allocation layer, and stage risk quantification parameter representing risk constraint strength of each stage is determined;The parameter is used as cross-layer risk constraint by using local scheduling decision layer, and a dynamic scheduling strategy that meets the requirements of global risk control is generated.The application effectively solves the problems of over-conservatism and extreme risk defense failure in the scheduling process through risk scenario construction driven by physical mechanism and cross-layer risk explicit conduction.

The invention discloses a shipyard steel plate storage yard dynamic optimization method based on space-time coupling risk propagation. The method aims at solving the problem that the plate turnover operation efficiency is low due to the fact that a traditional steel plate storage yard management method of a shipyard cannot dynamically adapt to production plan changes. The core innovation of the invention lies in that a dynamic'plate turnover risk field 'model is provided, and a global risk field of a steel plate storage yard is constructed to guide optimization by quantifying the potential plate turnover risk of each stacked steel plate on a vacant site below the steel plate in the future based on the warehouse-out time of the steel plate and considering the exponential attenuation effect of the risk along with the layer distance. On the basis, the method combines a self-adaptive greedy randomization construction strategy and a path reconnection enhanced local search strategy, the self-adaptive greedy randomization construction strategy generates a high-quality initial solution through dynamic balance greedy and randomness, the path reconnection enhanced local search strategy evolves to a historical elite solution through guiding search to achieve deep optimization, and local optimum is effectively avoided. According to the method, the plate turning frequency can be remarkably reduced by 30%-50%, the strong real-time dynamic response capability is achieved, the optimal stacking scheme can be rapidly regenerated when the warehouse-in and warehouse-out plan is changed, and the operation efficiency, the intelligent level and the production flexibility of the shipyard steel plate storage yard are greatly improved.

The invention discloses an agent-driven global risk early warning and monitoring method and system, and relates to the technical field of global risk early warning and monitoring, and the method comprises the steps: building a lightweight digital twinborn body of a corresponding subsidiary company based on the registration information of a transnational enterprise overseas subsidiary company, and the attributes of the country and the industry of the transnational enterprise overseas subsidiary company; deploying a plurality of AI agents, docking a multi-language text data source, independently executing named entity recognition, event extraction and risk strength scoring, and outputting a structured risk description and a risk score of a corresponding dimension; for the same target sub-company, acquiring a risk score output by the AI intelligent agent, calculating a difference between a maximum value and a minimum value, and performing normalization processing on the difference by combining a historical risk coupling coefficient of a country where the target sub-company is located to obtain risk judgment dispersion; the method is based on the early warning accuracy of an AI intelligent agent to risk events of the same kind in a past preset time window, the geopolitical sensitivity level of a current event occurrence place and whether the current event belongs to a sudden sparse event type or not.

The invention relates to the technical field of water conservancy project risk management, and discloses a water conservancy project risk intelligent regulation and control method based on expert privilege. The method comprises the steps of constructing a dynamic global risk network, abstracting facilities and environments into nodes and connections, and defining basic risk units. The unit characteristics are obtained through scanning, and the real-time risk field intensity of the unit characteristics under the current network topology is calculated. When the field intensity exceeds a threshold value, an expert equal weight decision is triggered, risk contribution analysis is carried out on related units, an equal weight regulation and control coefficient is generated accordingly, and the equal weight regulation and control coefficient is mapped into a specific regulation and control instruction. Before execution, regulation conflicts are evaluated through virtual deduction, and final regulation is completed according to a result. According to the method, the dynamic propagation and aggregation effect of the risk in the network can be accurately quantified, collaborative regulation and control based on contribution degree traceability are realized, and the overall coordination of risk response and the rationality of measure allocation are improved.

The application relates to the technical field of decision support, and discloses a bidding whole-process automation and decision support system based on multi-agent cooperation. First, the updating intensity sequence and global risk score data of the demand extraction side and the review and early warning side are collected, and the instantaneous phase and effective frequency are extracted through signal analysis. Then, phase relationship and frequency ratio analysis are carried out, the frequency mismatch and coupling strength are calculated, and whether the system is in a locked state is determined. When the locking effect is detected, the real risk signal is extracted through the unlocking residual error module to eliminate the false fluctuations caused by the model rhythm difference. Then, the residual error signal quality is evaluated by using a signal-to-noise ratio calculation module, and a document-level risk score is comprehensively output in combination with a mapping function, so that accurate identification and intelligent decision support of the bidding document risk are realized, and the accuracy and stability of risk prediction are significantly improved.

An electronic trading system (ETS) implements risk mitigation methods for orders and quotes associated with a market participant on the ETS. The methods determine a measure of risk associated with one or more trading positions. One of the methods globally counts the number of breaches of risk thresholds associated with a trading symbol and market participant across all matching engines on the ETS over a rolling time period, and if this global risk counter exceeds a maximum, disables all further trades by the market participant on the ETS. Another method limits the number of automatic re-enablements that a market participant can request in response to prior breaches of risk thresholds that resulted in disabling any further trading by the market participant on the ETS.

This application relates to an online hallucination processing method, apparatus, device, and readable storage medium for language models. The method includes: in the process of text generation by the language model, performing assertion-level atomization parsing on the incremental output in real time to obtain assertion atoms; for each assertion atom, retrieving data from evidence services and tool receipt storage services to determine the binding relationship between the assertion atom and the candidate evidence set and tool receipt set; determining a multi-perspective hallucination risk score for each assertion atom based on the binding relationship, the internal state of the language model, and external environmental signals, and determining a global risk score based on the multi-perspective hallucination risk score of each assertion atom; matching the intervention action corresponding to the risk level from a preset intervention strategy graph according to the risk level of the global risk score, and executing the intervention action. This enables accurate, interpretable, and low-latency unified suppression of multiple types of hallucinations without relying on model retraining.