73 results about "Global risk" patented technology

The invention discloses an inspection method and system for urban water supply and drainage pipelines, particularly relates to the field of urban pipeline inspection planning, and is used for solving the problems of low manual inspection efficiency, non-uniform scheduling distribution and operation conflict of multiple inspection units in the prior art. According to the method, a state space of multi-agent collaborative inspection scheduling is constructed by receiving a real-time state data stream of a pipeline network, and a pipeline node risk score is calculated based on a topology dependency coefficient and a state anomaly index; generating an action strategy sequence of each agent by using a pre-trained collaborative learning model, and outputting an inspection node priority and a path planning scheme; after the agents with resource conflicts are identified, strategy updating and optimization are executed according to the global risk score and the conflict cost; and actual inspection scheduling is carried out after secondary conflict verification is completed, so that efficient cooperation and dynamic optimization management of urban pipe network inspection is realized.

The invention relates to the technical field of block chains and data processing, in particular to an industrial digital real-time risk control and decision support system based on a block chain, which comprises a causal entropy chain construction module, a commercial plan simulation module, a multi-party credible cooperation module and a commercial credit assessment module. A causal entropy chain construction module is used for inferring a causal relationship of enterprise operation data by using a dynamic graph neural network, quantifying an entropy value of the enterprise operation data, packaging a result into a block, and recording the block on a block chain to form a causal relationship account book; based on the risk links with entropy values exceeding a preset threshold value in the causal relationship account book, the commercial plan simulation module performs adversarial risk simulation to generate a commercial decision plan; the multi-party credible cooperation module allows enterprises to cooperatively train a global risk prediction model through federated learning, zero-knowledge proof and other modes under the condition that private data is not exposed; and the commercial credit assessment module generates a dynamic credible two-dimensional code capable of representing the enterprise operation capability and the risk level, and the dynamic credible two-dimensional code is used for various commercial scenes.

The invention discloses an oil and gas pipe network global risk situation prediction method and device, equipment and a medium. The method is characterized by comprising the steps of performing data acquisition on a target oil and gas pipe network based on a preset acquisition period, and determining at least one piece of operation data of the target oil and gas pipe network; obtaining a standard operation data range corresponding to the operation data, identifying the operation data based on the standard operation data range, and determining an initial detection identifier of the operation data; if the initial detection identifier of the operation data is a problem identifier, determining the operation data as risk operation data; and performing risk identification on the target oil and gas pipe network according to the risk operation data, and determining a final risk prediction value of the target oil and gas pipe network. According to the method, the accuracy of risk prediction is improved, so that the occurrence of pipe network faults is effectively prevented, false alarm and missing alarm are reduced, and the risk prediction capability of complex working conditions and emergencies is improved.

The invention discloses a large model generation content risk identification and intervention method based on deep learning, and the method comprises the steps: collecting multi-modal data outputted by a content generation system, carrying out the preprocessing of the multi-modal data, and generating the preprocessed multi-modal input data; encoding and fusing the features of the multi-modal perception model to generate global risk feature representation data; inputting global risk features into a hunting optimization algorithm, and optimizing model structure parameters, risk thresholds and intervention strategy parameters; optimizing parameter-driven risk identification and hierarchical intervention, and outputting risk levels, categories and hierarchical intervention measures; an intervention effect and user feedback are collected, and continuous optimization and self-adaptive evolution of parameters are driven. According to the method, efficient risk identification and intelligent intervention on the large model generation content are realized, and the accuracy and the automation level of content security management are remarkably improved.

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 relates to the technical field of logistics scheduling, and discloses an intelligent cross-border logistics scheduling optimization system, and the system comprises the steps: S1, obtaining cross-border logistics full-chain parameters in real time through a multi-source heterogeneous data collection module, and generating a logistics basic data set; s2, performing real-time risk quantification processing on the logistics basic data set based on a dynamic risk modeling engine, and constructing a logistics risk factor matrix; by constructing a multi-source heterogeneous data acquisition module and integrating the real-time transportation situation, policy compliance and global risk data of a cross-border logistics full chain, the problems of data islands and information lag in a traditional system are solved, comprehensive perception and deep fusion of logistics basic data are realized, a solid data support is provided for subsequent intelligent scheduling, and the real-time transportation situation, policy compliance and global risk data of the cross-border logistics full chain are integrated. And the perception capability of the system to a complex cross-border environment is improved.

The invention discloses an industrial internet attack and defense situation and risk early warning and sensing method, and particularly relates to the field of internet risk early warning and sensing, which comprises the following steps of: acquiring multi-source heterogeneous data, constructing a basic data set covering an attack, service and equipment ternary space, including attack characteristics, service influence and equipment control vectors, and solving the problems of data heterogeneity and dispersion; constructing a triple function based on the data set, respectively quantifying the attack comprehensive threat degree, the influence degree of the attack on the service and the malicious control risk of the equipment, and retaining the characteristics of each dimension; a dynamic network topology model is constructed, nodes, edges and edge weights are defined, an attack propagation path and influence intensity are described, and node state dynamic updating is achieved; and finally, a risk prediction model is constructed by fusing quantitative indexes and topological information, a global risk value is calculated, graded early warning is realized through triple dimensions, a corresponding response mechanism is matched, and the timeliness and effectiveness of industrial internet security protection are improved.

The invention discloses a transnational trust modeling method fusing quantum state mapping and language phase difference, comprising the following steps: 1) estimating the site of each country based on the voting data of the united countries, and calculating the trust weight between countries through a weighting function in combination with the political ideal point distance and voting consistency of each country to obtain the initial quantized value of transnational trust; 2) mapping the initial trust value to a quantum state on a Bloch ball, applying phase perturbation according to semantic difference reported by a core country media, and simulating trust interaction between countries under discourse difference in combination with a quantum game mechanism so as to obtain a trust value mapped back to a classical space; and 3) carrying out iterative updating on the trust value by adopting a social influence model, dynamically describing an evolution process of the trust relationship between the countries, and outputting a time sequence change result of the trust network between the countries. According to the method, by fusing the international voting behavior and the transnational utterance difference, dynamic and quantifiable modeling of the inter-country trust relationship is realized, and an innovative methodological support is provided for international relationship research, transnational strategic game analysis and global risk early warning.

The invention discloses an energy consumption risk index system construction method, system and device based on knowledge graph modeling and a medium, and belongs to the technical field of energy system risk management, and the method comprises the steps: constructing a dynamic knowledge graph ontology based on a risk propagation operator; based on the dynamic knowledge graph, calculating equivalent impedance of a risk conduction path and identifying a key path, performing risk potential energy field dynamic simulation, and generating a global risk index; receiving a user instruction to perform situational pruning on the global atlas, and generating a focus sub-atlas; receiving an intervention instruction on the focus subgraph, simulating an intervention measure and deducing a new index after the change of the risk field; and based on the risk index and the key path data, generating an interpretable risk cause knowledge card and performing visual presentation. According to the method, the full-chain capability from risk conduction quantification, global situation simulation, man-machine collaborative pruning and intervention deduction to interpretable analysis is realized, and the refinement, intelligence and credibility level of energy system risk management is 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 discloses a risk grading early warning method, device and equipment for a dam and a storage medium, and relates to the technical field of dam risk early warning, and the risk grading early warning method for the dam comprises the steps: receiving multi-source monitoring data of the dam, and obtaining a time-space incidence matrix according to the multi-source monitoring data; performing feature extraction on the space-time incidence matrix to obtain local risk features; acquiring environment time sequence data, and generating global risk semantic representation according to the local risk features and the environment time sequence data; and realizing risk grading early warning of the dam according to the global risk semantic representation. The accuracy of dam risk early warning can be improved.

The invention provides a cable laying management system for an offshore wind plant, relates to the technical field of cable laying management, and realizes refinement and dynamic risk assessment by accurately dividing sections, deeply mining historical operation data and carrying out correlation analysis. According to the method, the fault probability of each section can be accurately estimated, a global risk map is generated to strengthen risk early warning, key nodes can be screened according to risk levels and association characteristics, monitoring resources are flexibly deployed, a scientific priority list is formed to optimize operation and maintenance resource configuration, and the response speed is increased; meanwhile, a full-chain safety guarantee system is constructed by visually identifying a risk area and setting an alarm threshold value, intelligent support is provided for operation and maintenance decisions by means of a data-driven prediction model and continuously optimized analysis logic, and finally the collaborative targets of controllable safety risk, efficient resource utilization, reduction of operation and maintenance cost and stable operation of the system are achieved.

The invention discloses a multi-party risk control assessment system and method for data asset transaction, and relates to the technical field of data asset transaction, and the method comprises the steps: extracting a risk feature set based on a federated learning framework, deploying a local model training module in a distributed environment, and extracting a global risk feature set; performing noise addition processing on the global risk feature set to generate an intermediate feature data set; integrating the intermediate feature data set by using a global aggregation server, and performing weighted fusion on the intermediate features of all parties to obtain global features; constructing a risk assessment model based on a gradient boosting decision tree, calculating the risk probability of global features in a distributed environment, and calculating key indexes of systematic risks; and if the key index of the systematic risk is greater than the risk threshold, risk early warning is distributed to each participant through the global aggregation server, so that the intelligent level and cooperative efficiency of risk control are improved.

The invention discloses an intelligent risk early warning and prevention and control platform based on enterprise management and a construction method thereof, and relates to the technical field of enterprise risk management. The enterprise relationship graph construction module is utilized, multi-source data are integrated, complex association between enterprises is accurately described, a basis is provided for risk propagation path modeling, dynamic features of risk propagation are captured through the risk propagation time sequence feature extraction module, the early warning accuracy is improved, and the risk propagation time sequence feature extraction module is used for extracting the risk propagation time sequence feature from the cross-domain knowledge migration module with the help of a federated learning framework. On the premise that privacy is protected, efficient migration of risk knowledge of different industries is achieved, the model generalization ability is enhanced, a security calculation and model optimization module ensures data security in multi-party cooperation, a global risk early warning model is optimized, the cooperation efficiency is improved, and finally, the security calculation and model optimization module is used for realizing multi-party cooperation. The real-time risk early warning module and the risk prevention and control strategy optimization module realize real-time monitoring of risks and dynamic prevention and control strategy adjustment, and ensure the timeliness and adaptability of the risk management system.

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 application discloses a kind of based on sensor's mixing station scene loader real-time safety monitoring method and system, method includes: complete construction environment and equipment state perception network.In edge deployment computing platform and delay communication module, combined lightweight deep neural network and dynamic brake strategy realize hierarchical braking;Cloud end relies on distributed computing resources, through graph convolution network constructs space-time correlation model, combined with adaptive weight strategy realizes multi-target task scheduling optimization;Path planning link through reinforcement learning framework, fusion including pose, material state, conflict risk in parameter, by environment modeling, conflict evaluation and trajectory smoothing generates optimal path;Feedforward neural network real-time prediction collision risk, combined hierarchical response mechanism forms " prediction-decision-execution " closed loop;Establish multi-level early warning trigger mechanism.Let safety monitoring consider real-time, also have global risk insight ability.

The invention provides a smart breeding early warning method and system based on the Internet of Things, and relates to the technical field of smart breeding management, and the method comprises the steps: determining N key breeding nodes, and deploying an Internet of Things device; working data streams of N breeding units are collected, a breeding anomaly recognition channel library is called to carry out channel matching and anomaly recognition, an anomaly feature set is determined, risk assessment is carried out, and global risk parameters are obtained; and constructing a grading early warning mechanism, generating an early warning signal based on the global risk parameters, and performing cultivation early warning management and control on the target cultivation environment space through the early warning signal and the global risk parameters. According to the method and the device, the technical problems of inaccurate early warning and untimely response caused by single anomaly detection rule and lack of global perception capability are solved, the breeding anomaly identification channel library is utilized, and a risk assessment and grading early warning mechanism is introduced, so that comprehensive monitoring and early warning of the breeding environment are realized, and the accuracy of the breeding environment is improved. And the accuracy and timeliness of intelligent breeding early warning are improved.