1569 results about "Risk forecasting" patented technology

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

The invention relates to a subway construction progress dynamic regulation and control method and system based on digital twinning. According to the method, a digital twin model is constructed by collecting multi-source heterogeneous data of a construction site, and a multi-dimensional construction progress characterization model is generated. Based on the model, progress deviation calculation and risk prediction are carried out to obtain a current progress deviation value. And once the value exceeds a preset threshold value, generating a regulation and control scheme set by using a multi-objective optimization algorithm. Then, the schemes are simulated and verified in a digital twinning environment, and a feasible regulation and control scheme set is formed to guide actual construction progress regulation and control. According to the method, by means of the digital twinborn technology and multi-source data fusion, the construction state is presented in real time, progress deviation and risks are rapidly recognized, an effective regulation and control strategy is formulated through an optimization algorithm, the construction progress management efficiency and accuracy are improved, the capability of responding to environment changes and emergencies is enhanced, construction delay and resource waste are reduced, and the construction progress management efficiency and accuracy are improved. And the subway construction management level and quality are optimized.

The invention relates to the technical field of risk analysis, in particular to a dangerous chemical transportation risk prediction system and method based on big data analysis, and the system comprises a multi-dimensional data acquisition module which is used for collecting human-machine-ring-pipe four-dimensional data in the liquid ammonia water transportation process, and carrying out the standardization processing and time-space synchronization, and obtaining a multi-source heterogeneous data set; the risk level acquisition module is used for constructing a man-machine-ring-management collaborative risk assessment model and analyzing the multi-source heterogeneous data set to obtain a risk assessment result; the human-machine-environment-management collaborative risk assessment model comprises a human-machine interaction key node identification layer, a management behavior-equipment response association analysis layer, an environment-material interaction dynamic risk assessment layer and a multi-dimensional factor risk cascade assessment layer; and the emergency disposal scheme acquisition module constructs an emergency disposal scheme intelligent recommendation model to perform grading and classification analysis on the risk assessment result, generates a multi-level risk early warning and emergency disposal scheme, and optimizes the emergency disposal scheme.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a railway tunnel portal geological disaster deformation early warning system based on an SAR radar, and the system comprises a digital twinborn body construction module which constructs a digital twinborn body with initial parameters based on basic data; the SAR deformation monitoring module is used for acquiring SAR deformation observation data; the digital twinborn body dynamic optimization module is used for inverting and updating parameters by using an optimization algorithm based on the SAR data, and generating an optimized twinborn body; a risk prediction and key area identification module which deduces a disaster scene based on the optimized twinborn body, predicts the risk and identifies a key risk area; and the intelligent early warning module is used for generating early warning information based on the prediction risk and the key risk area. According to the method, the geomechanical digital twins are dynamically optimized by adopting the SAR data, accurate prediction, key area identification and intelligent grading early warning of the geological disaster of the railway tunnel portal are realized, and the initiative and accuracy of risk cognition and early warning are remarkably improved.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention discloses a storage AGV dynamic path planning system based on multi-objective optimization, and relates to the technical field of storage logistics, and the system comprises a multi-source sensing and data collection module which is used for collecting the operation state, operation environment and external traffic information of an AGV and generating a standardized feature vector; and the cross-modal digital twinning and risk simulation module is used for constructing a virtual twinning body of a warehouse and external traffic and carrying out risk prediction and simulation under the driving of cross-modal sensing data. According to the invention, through the multi-source sensing and data acquisition module, the system can comprehensively acquire the AGV operation state, the operation environment and the external traffic information, and through combination with an advanced data fusion technology, a high-dimensional standardized feature vector is generated, so that an accurate and comprehensive data basis is provided for subsequent path planning and risk prediction; the cross-modal digital twinning and risk simulation module constructs a virtual twinning body of a warehouse and external traffic, and can reflect the dynamic change of the physical world in real time.

The invention provides an unmanned aerial vehicle flight authority management method and platform based on a block chain technology, and relates to the field of unmanned aerial vehicle flight management, and the method comprises the steps: constructing a block chain network, and encrypting and chaining flight authority; deploying the student model after knowledge distillation to an edge computing device for risk prediction before flight and abnormal flight identification in flight; before the unmanned aerial vehicle takes off, the flight legality is verified, pre-flight risk prediction is executed through a student model, and then a smart contract is called to perform matching verification of on-chain permission records; during execution of the flight task, an abnormal score multi-model combination mechanism is introduced to assist the student model to dynamically identify abnormal flight, and an intelligent contract execution response mechanism is triggered when an abnormal behavior is detected; after the unmanned aerial vehicle lands, the flight record is encrypted and stored in the off-chain storage system, and the flight record hash value, the off-chain storage index and the related access credential are linked. The whole-process authority management of the flight task of the unmanned aerial vehicle is realized, and the supervision efficiency is improved.

The invention relates to the technical field of safety management and risk prediction, in particular to an intelligent safety management and risk prediction method and system based on cloud computing. The method comprises the following steps: dynamically accessing multi-source heterogeneous data through a cloud platform, and forming unified event representation through time alignment and credibility labeling; constructing a hierarchical mixed probability safety twin model, updating dynamic parameters by adopting credibility weighted online variational Bayesian, and outputting a state interface by combining structural adaptation, cross-object graph regularization and physical constraint projection; mapping the twinborn state into a causal feature, constructing an intervening causal graph, generating causal embedding by using a credibility weighted attention network, simulating an intervention operation in an embedding space, and quantifying a risk probability; and generating a multi-candidate security policy, evaluating and sorting through a multi-objective utility function, executing an optimal policy, collecting feedback data, and updating the model and the policy. According to the method, credibility regulation and control, probability twinning and causal intervention are fused, and real-time intelligent decision making of an industrial safety scene is supported.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to the technical field of food traceability, and discloses a food safety knowledge graph system, which comprises an acquisition module used for acquiring multi-modal data of food in stages to form traceability data; the risk portrait module is used for constructing a multi-dimensional risk portrait of the food; the feature fusion module is used for fusing the features of the traceability data to generate feature representation; the block chain evidence storage module is used for storing risk portraits and traceability data; the risk prediction module outputs a prediction result according to the risk prediction model; and the AI decision center module is used for optimizing a prediction result of the risk prediction model and outputting a food safety knowledge graph. According to the method, multi-modal data of links such as production, transportation and storage are collected, the feature fusion module fuses features of the traceability data based on a weighted multi-head attention mechanism, feature representation is generated, and analyzability of traceability information is enhanced. And multiple data sources are weighted and fused, so that the system can capture risks more accurately, and the omission ratio is reduced.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to the technical field of risk prediction, in particular to a wireless sensor-based high-altitude operation online monitoring and early warning system and a wireless sensor-based high-altitude operation online monitoring and early warning method. The method comprises the following steps: deploying multiple types of wireless sensor arrays for the aerial work platform to collect structural stress data, environment temperature and humidity and personnel state data, and constructing a multi-source standard data set; performing space-time alignment on the multi-source standard data set to generate a synchronized high-altitude data set; constructing a three-dimensional feature space by using the synchronized high-altitude data set, and generating a high-altitude operation fusion feature matrix; generating a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; therefore, through fusion of multi-source data, alignment of spatial-temporal characteristics, dynamic simulation of risk evolution and construction of a multi-level response mechanism, the risk prediction precision and real-time response capability in high-altitude operation are improved, and a comprehensive, systematic and efficient safety management and control system is finally formed.

The invention relates to a dynamic regulation and control method and system for mine ventilation. The method comprises the following steps: acquiring multi-source data in a mine, and preprocessing the multi-source data to obtain a standardized data set; calculating the risk of each region in the mine based on the standardized data set to obtain a risk prediction matrix; based on the standardized data set and the risk prediction matrix, the air volume demand of each area is calculated, and an air volume demand table is obtained; based on the air volume demand table, an optimization proposition corresponding to the air volume demand is constructed and solved, and a solution set of the optimization proposition is obtained; mapping the solution set based on a preset rule to obtain a feasible allocation scheme; and based on the feasible allocation scheme, generating an equipment cooperation instruction, and obtaining a security instruction set. According to the method, dynamic factors can be considered, the air volume is dynamically adjusted, multi-fan cooperation is achieved, and the effects of real-time sensing and autonomous dynamic decision making of mine ventilation are achieved.

The invention relates to the technical field of safety monitoring, in particular to a building construction safety monitoring method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data of a construction site, carrying out the distributed feature extraction of the multi-source heterogeneous data through employing a federal learning framework, and generating time-space correlated construction site state representation data; based on a preset dynamic risk prediction model, risk prediction is carried out by using the construction site state representation data, a multi-level risk prediction result is output, and the preset dynamic risk prediction model is constructed based on a construction safety knowledge graph and a space-time diagram neural network; and triggering an adaptive feedback mechanism according to the risk level corresponding to the prediction result, generating visual early warning information and an equipment control instruction, and linking a construction site control system to execute emergency response operation. The problems that a traditional monitoring method is tedious in data processing, insufficient in real-time performance, high in cost, lack of prediction capacity and the like are solved.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention provides a cigarette abnormal flow standard risk prediction method and system. The method specifically comprises the steps of collecting multi-source data to construct a causal feature set, generating a causal graph according to domain knowledge and algorithm mining, quantifying an average processing effect through a dual machine learning method, constructing a risk model to position a root cause, simulating risk change after intervention through anti-fact inference, and outputting a result. Through the dual machine learning and anti-fact inference technology, the causal effect is accurately quantified, the intervention effect is simulated, the confidence interval is dynamically adjusted, and the risk prediction and decision support capability can be remarkably improved.

The invention provides a financial risk intelligent early warning method based on multi-source data fusion, and the method comprises the steps: separating the periodic characteristics of income data from the abnormal transaction influence through a time series decomposition technology if the transaction volume fluctuation ratio in an income stability index exceeds a preset threshold value; a weighted moving average algorithm is adopted to smooth records with low data alignment precision, and a smoothed income fluctuation sequence is generated; according to the node level risk set, modeling is carried out on the periodic characteristics of the dynamic risk contribution degree, the change trend of an income fluctuation sequence in a future time period is predicted through a long and short-term memory network, and a dynamic risk prediction sequence containing abnormal transaction influence is generated; and if the risk value of a certain time point in the dynamic risk prediction sequence exceeds a preset warning threshold value, identifying a high risk point driven by the abnormal transaction influence through an isolated forest algorithm, and generating an early warning signal set containing a timestamp and a regional market difference. The financial risk management capability of an enterprise can be effectively improved.

The invention relates to the technical field of community security, in particular to an intelligent community security management system and method, and the method comprises the steps: collecting equipment data, verifying the daily operation of equipment, collecting personnel and event data, and carrying out the data association and data integration; when an abnormal event is identified, determining a basic level according to the integrated data, determining an associated risk probability through a Bayesian network model, inputting the basic level and the associated risk probability into a dynamic weight adaptive grading model, and determining a final level of the event; inputting the obtained event final grade into a trend prediction model to obtain a risk prediction value, determining a comprehensive risk value of each region according to the risk prediction value, and generating a real-time risk thermodynamic diagram; and according to the final grade of the event and the associated data, screening the processing personnel meeting the conditions to perform task assignment, and performing event processing by the processing personnel. According to the scheme, by constructing the equipment, event and personnel association chain, dynamic verification and intelligent grading are realized, and the management efficiency is improved.

The invention relates to the technical field of financial risk prediction, in particular to a financial risk prediction method and device, a storage medium and equipment. The method comprises the following steps: acquiring financial data, and extracting multi-modal features; based on the multi-modal features, generating a causal graph by adopting a constraint-based causal discovery algorithm, and quantifying causal intensity by adopting a structural equation model; adopting the quantized causal graph as a constraint of an anti-fact generative adversarial network, generating an anti-fact scene and analyzing a risk conduction path; training a causal graph neural network model based on the multi-modal features, the causal graph and the risk conduction path; and interpretability analysis and risk monitoring are carried out. According to the technical scheme, financial risk causal association can be accurately constructed, an anti-fact scene clear conduction path can be generated, and through interpretable analysis and monitoring, the risk prediction accuracy and interpretability are improved, financial risks are helped to be prevented and controlled in time, and the financial system stability and the risk response capacity are enhanced.

The invention discloses an intelligent platform system for modern industrial system construction, and the system specifically comprises a heterogeneous data fusion center which is used for collecting and standardizing the multi-source heterogeneous data of enterprise equipment and environment in an industrial chain in real time; the knowledge graph construction module is used for generating a real-time operation situation graph according to the multi-source heterogeneous data; the AI decision engine is used for executing risk prediction and resource optimization path planning according to the real-time operation situation map; the intelligent dispatching center is used for dynamically reconfiguring idle equipment, talents and funds of an industrial chain according to resource optimization path planning; and the ecological value contract module is used for calling a block chain to convert the data assets into on-chain verifiable collaborative benefits according to the reconfiguration result of the intelligent dispatching center. According to the invention, industrial chain data fusion, situation awareness, intelligent decision, resource dynamic configuration and data asset collaborative income conversion are realized, and the overall operation efficiency and collaborative value of an industrial system are improved.

The invention discloses a river water level dynamic monitoring and flood overflow risk prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source hydrological data, and constructing a time series data set; s2, performing interpolation, denoising and normalization processing on the data to generate a unified time sequence format; s3, constructing a water level prediction model comprising a bidirectional long short-term memory network and an attention mechanism; s4, inputting the preprocessed data into the water level prediction model, and outputting a multi-time-step predicted water level sequence; s5, a dynamic threshold value is set according to the historical extreme value and the real-time hydrological condition, and the flood overflow risk is judged; s6, generating and caching a risk tag, and recording an error; s7, outputting a prediction result and risk information through a communication interface; and S8, periodically updating the input data in a rolling manner, and repeatedly executing the prediction and monitoring process. According to the invention, depth prediction and a dynamic threshold control mechanism are fused, and water level monitoring and flood overflow risk intelligent early warning are realized.

The invention relates to the technical field of micro-service operation and maintenance, and discloses a full-link intelligent fault simulation and assessment defense method in a micro-service scene. The method comprises the following steps: constructing a dynamic dependency graph based on a service registration center and real-time communication traffic; performing matching backtracking on historical faults according to the atlas, and generating a fault injection point list and a propagation path set sensed by the atlas; in the isolated environment, driving the programmable agent to perform multi-dimensional fault injection, and synchronously acquiring full-amount system response signals; performing multi-dimensional deviation calculation on the signal and the base line to form an observation record containing deviation intensity and propagation rate; and training a graph neural network model with time sequence dependence understanding capability based on the record, and carrying out fault mode identification and evolution prediction on an online real-time link, and outputting a risk assessment report. According to the method, the authenticity of fault simulation and the initiative of risk prediction are improved.