13339 results about "Real-time data" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention relates to the technical field of intelligent decision making, in particular to a simulation system intelligent decision making method and system based on a knowledge graph and federated learning, and the method comprises the steps that each simulation node constructs a knowledge graph sub-graph based on local dynamic data, and multi-modal semantic representation is generated through space-time modeling and event chain reasoning; the federal center initializes a simulation decision model architecture and issues the simulation decision model architecture to each node; each node uses a local knowledge graph to train a time sequence diagram network, extracts an equipment degradation path and abnormal propagation characteristics, and uploads gradient parameters in combination with homomorphic encryption; the federation center fuses the multi-node model through a dynamic weight aggregation algorithm to generate a global decision model; and driving knowledge graph evolution based on real-time data, and performing causal reasoning and decision optimization on an event chain through a federal model. According to the method, the problem of insufficient dynamic decision adaptability in a multi-source data island and privacy sensitive scene in a simulation system is solved.

The invention relates to the technical field of battery thermal management, in particular to a battery thermal management control method and system, and the method comprises the steps: obtaining the basic operation parameter data of an automobile battery in real time, and carrying out the multi-dimensional data fusion, and obtaining a multi-dimensional data set; based on the thermodynamic model and the multi-dimensional data set, the temperature distribution condition in the battery is calculated in real time, and real-time temperature distribution information is obtained; based on the historical data and the operation state, the future temperature change trend of the battery is predicted through a recurrent neural network model; dynamically adjusting a control strategy of the battery thermal management system according to the real-time temperature distribution information and the prediction result; and analyzing the temperature difference condition of the battery monomers, detecting abnormal temperature rise, and performing temperature difference balance adjustment and triggering a multi-stage protection mechanism when the abnormal temperature rise exceeds a threshold value. According to the invention, the problems of inaccurate temperature prediction and response lag in the prior art are effectively solved, the control strategy is dynamically adjusted through real-time data fusion and recurrent neural network prediction, temperature difference balance and abnormal temperature rise detection are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

Systems and methods are provided for improving generative artificial intelligence (AI). Systems and methods can integrate more reliable data sources and enhance generative AI training and inference processes for complex tasks. The integration of real-time data and expert input can be included as crucial steps in aligning AI outputs with improved accuracy. Similarly, fine-tuning methodologies and augmentation algorithms can be used to focus on minimizing the occurrence of fabricated content, thereby significantly increasing the chances that the information generated is both current and credible.

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention provides a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, and relates to the technical field of logistics support. Comprising a data acquisition module, a data fusion and processing module, a scene prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, a self-adaptive network optimization module and a moral priority scheduling module, according to the logistics resource dynamic scheduling system, global efficient optimization of logistics resource dynamic scheduling and intelligent analysis and strategy optimization of real-time data are realized through advanced technologies such as a variable component optimization algorithm, fusion of quantum annealing and a genetic algorithm, deep reinforcement learning and combination of Transform and a genetic algorithm; and the rationality of resource allocation and the scheduling efficiency are greatly improved.

The invention relates to the technical field of engineering data monitoring, in particular to a hydraulic engineering data intelligent monitoring method and system based on digital twinning, and the method comprises the following steps: collecting real-time operation data through physical sensors disposed at different nodes of a hydraulic engineering; a self-adaptive digital twinborn model capable of being automatically adjusted is generated through a meta-learning method, and the self-adaptive digital twinborn model dynamically adjusts the internal structure of the self-adaptive digital twinborn model according to the change of real-time data; and on the basis of the adaptive digital twinborn model, constructing a simulation module of a plurality of target scenes, and performing decision simulation in a virtual environment in real time. According to the method, the accuracy and the stability of the digital twinborn model in a complex environment are improved, the model can quickly adapt to real-time changes in the operation process of a water conservancy project through the self-adaptive mechanism, and the prediction precision and the response of the model in a dynamic complex scene are improved.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention discloses a multi-agent cooperation system and method based on spatial calculation and multi-modal AI fusion, and the method comprises the steps: collecting all real-time data of a construction site through a multi-modal data collection and fusion unit, and carrying out the processing and integration of all collected data through a multi-modal AI algorithm, and generating a unified semantic association model; a three-dimensional virtual environment of a construction site is constructed through a space calculation unit, real-time synchronous mapping is formed for the construction site, and dynamic changes in the construction process are simulated and predicted; task allocation and behavior planning are carried out on multiple agents through an agent behavior management unit; and the intelligent agents are configured to perform real-time information sharing and task cooperation through a communication protocol through the multi-intelligent-agent cooperation unit. According to the scheme, the problems of difficulty in multi-source data integration, low cross-department cooperation efficiency, insufficient construction dynamic adjustment and the like in intelligent construction in the building industry can be solved.

The invention relates to the technical field of computer deep learning, in particular to a deep learning task hybrid deployment method and system. The method comprises the steps that S1, a user submits a task through a Kubernetes native interface; s2, a task scheduling optimizer analyzes tasks according to resource requirements and service levels and allocates the tasks to proper nodes; s3, the SLO manager tracks real-time data of various performance indexes of the system and compares the real-time data with a preset service grade target; s4, the Koordlet component executes a specific resource allocation operation on the target node; and S5, the traffic safety monitor monitors the data streams among the nodes in real time. According to the method, a mixed deployment strategy for different types of resources is provided by analyzing the periodical rule of deep learning task resource use, dynamic resource sharing of online and offline tasks is achieved, and meanwhile it is ensured that the system can still run stably and efficiently under the high-load condition.

The invention relates to the technical field of digital factory production management, and discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: decomposing an enterprise order into a plurality of subtasks through a production task decomposition module, and extracting related information; the resource dynamic evaluation module collects data in real time to generate a resource state matrix; the multi-objective optimization algorithm module constructs a model and adopts an improved non-dominated sorting genetic algorithm to solve; the dynamic priority distribution module adjusts the priorities of the sub-tasks according to the real-time data; and the conflict resolution unit is used for solving resource allocation conflicts. The method can accurately decompose tasks, evaluate resources in real time, collaboratively optimize multiple targets, flexibly deal with abnormities, efficiently resolve conflicts, comprehensively improve the production efficiency and benefits of the digital factory, and effectively solve the complex problems in the production plan making and optimizing process of the digital factory.

The invention discloses a power equipment operation state prediction and anomaly simulation method based on digital twinning, and relates to the technical field of power grid operation. According to the method, through establishment of a digital twinborn model of the power equipment and real-time data analysis, prediction and anomaly simulation of the operation state of the power equipment are realized. The method comprises the following steps of: analyzing a physical structure, operation characteristics and load distribution of power equipment, constructing a digital twin model, performing multi-dimensional analysis on real-time data by utilizing edge calculation, extracting electrical characteristics and behavior patterns, generating an equipment state data set, and constructing an abnormal causal chain model based on the model and the data set. The method comprises the steps of identifying a fault mode caused by uneven load or overload, evaluating the influence of an abnormal state on the stability and efficiency of a power system through virtual simulation and fault propagation analysis, and obtaining an abnormal influence index of power equipment. The accuracy of power equipment fault prediction is improved, the stability and efficiency of a power system are optimized, and support is provided for intelligent operation and maintenance and fault early warning.

The invention relates to the field of frequency converter fault detection, and discloses a frequency converter fault prediction method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional real-time data in the operation process of a frequency converter; constructing a dynamic mapping relation to obtain a basic feature set; generating a time sequence feature vector capable of reflecting the state change of the equipment based on the basic feature set; comparing, analyzing and judging whether the equipment state deviates from a normal operation interval or not based on the historical operation data and the time sequence feature vector, and outputting a state deviation index; performing abnormal fluctuation judgment on the time sequence feature vector; extracting fluctuation amplitude and frequency characteristics of the key indexes to obtain quantitative description data of abnormal fluctuation; inputting the quantitative description data of the abnormal fluctuation into an abnormal prediction model; and generating a coping strategy and a triggering condition of the coping strategy based on the risk prediction result. The method has the advantages that the abnormal state of the frequency converter is recognized in time, and potential risks are predicted.

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

The invention discloses a freight logistics information intelligent tracking management method and system, and belongs to the technical field of freight management, and the method comprises the steps: obtaining unique identification information of a freight unit, building the association with order content, carrier information and a preset transportation path, constructing a freight entity relation graph, and carrying out the management of the freight logistics information; performing preprocessing and modal fusion on multi-source real-time data in a transportation process, and extracting a structured cargo state feature set; based on a dynamic space-time modeling mechanism, the current freight state is intelligently judged, and a risk prediction result set is generated; predictive reconstruction is carried out on the transportation path in combination with a risk result; automatically executing path reconstruction, vehicle scheduling or user notification after the abnormal event is identified; synchronizing the evolution state and the risk data to a cloud platform for subsequent task optimization; according to the invention, full-flow intelligent sensing, real-time scheduling and abnormal closed-loop response in the logistics process are realized, and the transparency, robustness and aging control capability of a transportation system are remarkably improved.

The invention provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.