918 results about "Dynamic decision-making" patented technology

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

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 provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

The invention provides an agent-based large language model retrieval enhancement generation system and method, and the system comprises a planning layer which is used for receiving user query, carrying out the multi-round iterative decomposition of a complex task through a task planning agent, and generating an atomic query or a direct response; the execution layer is used for executing the atomic query generated by the planning layer in parallel, calling a search module to obtain external knowledge base data, and caching an intermediate result through a memory module; the answer detection module is used for performing multi-dimensional detection on the generated result, including preference, accuracy, integrity and logicality; the dynamic decision-making module is used for adaptively adjusting a subsequent retrieval strategy and a task planning process according to a detection result and user feedback; and a cross-layer interaction mechanism enables the planning layer and the execution layer to realize collaborative optimization through context sharing and iterative feedback.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

The invention relates to the technical field of AI interaction data processing, and discloses an AI interaction data processing system based on multi-modal perception and dynamic decision, comprising the following modules: a multi-modal perception module used for collecting environment data through a multi-source sensor; the data fusion module is used for generating fused feature data; the causal decision-making module is used for generating a decision-making action to cope with the change of the environment; the decision security module is used for identifying potential safety hazards in the high-risk scene and generating alternative decision or early warning information; the sensing calibration module is used for optimizing a sensing strategy in a changing environment; and the adaptive optimization module continuously optimizes the perception and decision strategy. According to the invention, the multi-modal sensing module is combined with a cross-modal consistency learning mechanism and a noise robustness enhancement technology, and the data fusion module introduces a context sensing attention mechanism and a multi-level feature alignment network, so that the sensing ability of the system to complex environment information and the comprehensiveness and accuracy of feature representation are effectively improved.

The invention discloses a subway ventilation demand dynamic decision-making method and system fused with people flow prediction, and belongs to the technical field of intelligent traffic and environment control, and the method comprises the steps: collecting real-time people flow data, historical passenger flow records, station layout information and external environment parameters of each region in a subway station; constructing a deep learning prediction model through multi-source data fusion, and generating short-term and medium-term people flow distribution prediction results; based on the prediction result, combining the ventilation equipment operation parameters, the air quality index and the energy consumption cost to construct a multi-objective optimization model; dynamic decision making is carried out on a ventilation system by utilizing the model, and an optimal ventilation strategy in different regions and different time periods is output; through a real-time feedback mechanism, prediction and control parameters are continuously corrected according to actual people flow changes, closed-loop regulation and control are formed, the response precision and the energy efficiency level of a subway ventilation system can be remarkably improved, and passenger comfort and environment safety are guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

The invention discloses a multi-agent collaborative reasoning system for intelligent teaching intervention, and relates to the technical field of artificial intelligence and education, and the system comprises a data collection and convergence module which collects data of learning behaviors, emotional states, academic scores and knowledge point mastering conditions of students by means of a classroom behavior analysis system, a camera, a microphone and a learning management system; according to the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the precision and individuation of teaching intervention are realized by constructing the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the system collects multi-dimensional data of students through the data acquisition and convergence module, and through the processing of the data conversion and cleaning module, the accuracy and availability of the data are ensured; the teaching strategy planning module generates a dynamic decision model by means of a large language model, a customized intervention strategy can be generated according to the learning condition and problem root of students, and the multi-agent collaborative reasoning module further improves the intelligent level of the system.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention provides an intelligent decision-based distribution network fault positioning method and system, and the method comprises the steps: obtaining a target state data set collected by a power distribution network global intelligent perception network, carrying out the time-space semantic fusion processing, generating a multi-dimensional feature map, carrying out the context perception discrimination of the multi-dimensional feature map through a dynamic decision inference engine, and carrying out the fault positioning of a distribution network. Generating a fault risk judgment result, performing causal link tracking on the target state data set based on the fault risk judgment result, generating a fault section positioning result, generating a fault positioning instruction according to the fault section positioning result, sending the fault positioning instruction to the distribution network intelligent operation and maintenance platform, and triggering a precise maintenance process. Therefore, the global state data of the power distribution network are comprehensively utilized, the accurate evaluation of the fault risk of the power distribution network, the rapid positioning of the fault section and the efficient triggering of the accurate maintenance process are realized, and the operation reliability and safety of the power distribution network are remarkably improved.

The invention discloses a visual identification method and system, and the method comprises the steps: obtaining a visible light image, infrared thermal imaging and depth point cloud data of a target scene, and generating a time-space consistent multi-modal heterogeneous feature tensor; inputting the multi-modal heterogeneous feature tensor into a spatial frequency sensing optimizer to generate a detail-enhanced optimized feature matrix; on the basis of the optimized feature matrix, adopting an adversarial generative network to synthesize a multi-scale shielding sample, and generating an identification feature vector with enhanced adversarial robustness; inputting the identification feature vector into a self-adaptive decision engine to generate an environment self-adaptive dynamic decision parameter; and constructing a multi-scale verification pyramid according to the dynamic decision parameters, fusing the confidence score of each level through a self-correction module, and outputting a final recognition result. According to the embodiment of the invention, high-robustness and high-accuracy visual identification can be realized.

The invention provides an air water production intelligent prediction and adjustment method and system based on meteorological big data, and relates to the technical field of air water production, and the method comprises the steps: collecting data through a meteorological sensor, constructing an enhanced feature space, fusing multi-domain knowledge through a migration cross-domain learning network, and predicting the water production through combining with a deep neural network. A control strategy is generated based on a deep reinforcement learning model, optimal control parameters are selected through multi-target Bayesian optimization and a dynamic decision algorithm, the air water production amount can be accurately predicted, intelligent adjustment is achieved, the water production efficiency is improved, energy consumption is reduced, and high environmental adaptability and robustness are achieved.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention discloses an edge computing assisted wireless communication system, which relates to the technical field of edge computing, and comprises an edge computing node cluster which is deployed at a base station and an access point and provides distributed computing and storage capability; the resource sensing module is used for monitoring computing resources, communication resources and task queue states of the edge nodes in real time; the dynamic scheduling controller is used for dynamically deciding a task unloading path based on reinforcement learning and game theory algorithms, wherein the task unloading path comprises local processing, edge node cooperative processing, cloud unloading and global time delay and energy consumption optimization; the cross-layer communication interface supports multi-protocol fusion of 5G NR and Wi-Fi 6, and realizes low-delay data interaction between edge nodes; and the security verification module is used for ensuring credibility and integrity of data between edge nodes through a lightweight block chain and a TEE (Trusted Execution Environment). According to the invention, through a hybrid decision framework of reinforcement learning and game theory, the problem that a single algorithm in a traditional scheme cannot give consideration to dynamic environment adaptability and multi-node benefit coordination is solved.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

The invention provides a highway tunnel intelligent illumination control method and system, and the method comprises the steps: collecting a real-time perception data set of a plurality of monitoring nodes in a target tunnel, covering a light source operation parameter, an illumination intensity parameter and an environment state parameter sequence, carrying out the feature extraction of the real-time perception data set, and carrying out the feature extraction of the real-time perception data set; generating a global feature set including light source operation stability, regional illumination coordination and environmental coupling influence features, and then inputting the global feature set into a preset dynamic decision model for strategy decision to obtain a multi-stage illumination regulation and control strategy set covering different regions, including target brightness, dimming time sequence and equipment cooperation parameters; and finally, generating an executable instruction set according to the multi-stage illumination regulation strategy set, issuing the executable instruction set to each partition illumination control terminal, and triggering adaptive dimming of the illumination equipment, thereby realizing intelligent management and control of tunnel illumination, improving an energy-saving effect and illumination quality, and ensuring driving safety.

The invention provides a low-altitude airspace dynamic decision-making system based on a multi-modal large model, and relates to the technical field of low-altitude airspace traffic management.The low-altitude airspace dynamic decision-making system is characterized in that a first thermal disturbance coverage map, a second shear wind high-risk area map, a third turbulence risk drainage basin map and a fourth sudden obstacle prediction area map are generated by a map construction module; the aircraft can judge whether the aircraft enters the dangerous area in real time and assess the safety of the flight path according to the superposed risk value. When the superposition risk value # imgabs0 # of the flight path section passing through the jth area in the first original flight path of the ith aircraft is in a middle risk section, the system generates a first-level avoidance strategy, and the height, the speed and the course of the aircraft are adjusted; and when the superposed risk value # imgabs1 # is a high-risk section, the system generates a secondary avoidance strategy, and bypasses a high-risk area by remarkably increasing the flight height and reducing the speed.

The invention relates to the technical field of project cost progress management control methods, in particular to a project cost progress management control method and system. Comprising a multi-dimensional data integration and simulation deduction module, an intelligent early warning and dynamic decision module and a block chain collaboration and knowledge evolution module. Through the unique coding rule, the engineering quantity list, the material price, the labor cost and the model component are dynamically associated, the construction progress plan is embedded in the BIM model, the 4D construction animation is automatically generated, the resource demand peak value of each time period is counted, the resource conflict is identified in advance, and the component coding integrity and the cost parameter logicality are automatically verified through the attribute checking tool. And the manual auditing time is reduced.

The invention relates to the technical field of robot control, and particularly discloses a robot indoor moving path planning method and system. An environment sensing module collects environment data through a multi-mode sensor array and constructs a three-dimensional semantic map; the dynamic decision-making module generates a candidate path set based on the three-dimensional semantic map data output by the environment perception module, and transmits a decision-making result to the path optimization module through a priority arbitration mechanism; the path optimization module performs multi-objective optimization according to candidate paths of the robot kinematics constraint and dynamic decision module; the execution control module is used for converting an optimized path into a bottom layer driving instruction through kinematics calculation and feeding back an execution state to the dynamic decision-making module in real time to form closed-loop control. A dynamic environment modeling mechanism is adopted, through variable resolution perception and semantic map construction, a layered decision-making framework and multi-target collaborative optimization are adopted, and the dynamic environment modeling mechanism is optimized; and the adaptive control system is selected to significantly improve the environmental adaptability and reliability of the system.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention relates to the technical field of grinding machining, and discloses a blade grinding wheel abrasion loss online compensation method which comprises the steps that a dynamic hexagonal detection grid is constructed based on the rotating phase of a grinding wheel, non-repeated high-density sampling is achieved through a Fibonacci spiral expansion path, a detection area is dynamically adjusted to reduce the overlapping rate, and the blade grinding wheel abrasion loss is obtained. Micron-sized wear transition is accurately captured; dispersing the abrasion loss into minimum compensation units, constructing a dynamic state equation in combination with grinding force, dressing displacement and workpiece errors, introducing an attenuation coefficient related to the service life of a dressing wheel, and automatically correcting a model drift error; a grading triggering strategy of residual uncompensated quantity is adopted, dynamic decision making of a processing period is combined, and through integral multiple compensation and a margin temporary storage mechanism, it is ensured that long-term accumulative errors are restrained at the submicron level while overmodulation oscillation is avoided. Through the synergistic effect of space-time coupling detection, discrete-continuous double-domain modeling and intelligent compensation decision, the grinding wheel abrasion compensation precision and the system stability are remarkably improved.

The invention discloses an edge cloud collaborative federated digital twin model construction method based on a dynamic hierarchical game, and aims to construct an edge cloud collaborative federated model generation framework based on distributed sensing data to solve the problems of data islands, high time and energy overhead, poor model performance and the like existing in central dynamic digital twin model construction. And an online decision optimization problem is modeled by taking maximization of long-term model quality and reduction of long-term resource overhead as targets, and dynamic decision optimization is realized. In a federated digital twinborn model generation framework, a bottom sensor collects feature data of a physical entity and transmits the feature data to an edge server to construct a local digital twinborn model, and a cloud server collects all the local digital twinborn models and integrates the local digital twinborn models into a global digital twinborn model. According to the method, the technical problems of model heterogeneous dynamic evolution, sensor resource overlapping and sharing and computing communication resource joint optimization in federated digital twinborn construction are effectively solved, the quality of the digital twinborn model is remarkably improved, and the system energy consumption and configuration cost are reduced.

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 provides an emergency disaster dynamic decision-making method and system based on a multi-modal AI large model, and the method comprises the steps: firstly obtaining a real-time disaster monitoring data set of a target disaster area, covering disaster image collection, environment sensing, historical response records and geographical distribution topological data, carrying out the feature extraction of the real-time disaster monitoring data set, and carrying out the feature extraction of the real-time disaster monitoring data set; then calling a pre-trained multi-modal dynamic decision-making model, analyzing the disaster scene matching degree of the multi-modal disaster feature set, generating a matching degree score and disaster response optimization strategy set, and screening a target optimization strategy based on the matching degree score, and generating an emergency decision instruction set and sending the emergency decision instruction set to a disaster response terminal, finally obtaining decision execution feedback data of the disaster response terminal, and performing incremental parameter optimization processing on the multi-modal dynamic decision model to realize more accurate and effective emergency disaster dynamic decision.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention provides an anomaly detection multi-classification method based on multi-source operation and maintenance data fusion. Comprising a data input layer, a parallel coding layer realized through dissimilatory multi-modal coding and a hierarchical multi-modal fusion architecture, a space-time feature fusion layer realized through a space-time perception dynamic gating attention enhancement mechanism, and a dynamic decision optimization layer realized through a gradient perception dynamic smooth loss function. The spatio-temporal feature fusion generates a feature representation and weight matrix with a dynamic attention weight through a spatio-temporal perception dynamic gating attention enhancement mechanism, and outputs the feature representation and weight matrix to the dynamic decision optimization layer; and the dynamic decision optimization layer realizes anomaly detection through a classifier taking a gradient perception dynamic smooth loss function as feedback, so that key problems such as multi-source heterogeneous data fusion, time sequence dynamic modeling and data label imbalance are solved, the anomaly detection accuracy and robustness of a training cluster are effectively improved, and the anomaly detection accuracy and robustness of the training cluster are improved. And a reliable technical support is provided for intelligent operation and maintenance of a complex training cluster.

The invention discloses an intelligent war game deduction method based on reinforcement learning, and the method comprises the steps: constructing a dynamic battlefield model: constructing an adjustable battlefield simulation platform, and defining the landform, resources and army distribution elements in a battlefield; a reinforcement learning strategy is generated and optimized, an intelligent strategy generation algorithm based on deep reinforcement learning is designed, and an efficient combat strategy is generated in multiple rounds of training by constructing a state space and an action space and combining a situation reward function; a double-agent chess playing training mechanism is introduced, black parties and white parties are modeled into reinforcement learning agents, red parties and blue parties are modeled into reinforcement learning agents, and a real battlefield game is simulated through multiple rounds of alternate training; result visualization deduction: a dynamic decision visualization function is provided, and battlefield situation, troop dynamics and a strategy execution process can be displayed in real time; according to the method, the problems of rule solidification, limited strategy generation capability, insufficient antagonism and the like in the prior art are solved.

The invention discloses a user-noninductive cold and hot sensing remote memory exchange system, which belongs to the field of computer storage, and is characterized in that cluster nodes are divided into extensible remote memory service nodes and computing client nodes according to roles; the remote memory node comprises a remote memory exchange server and a memory resource registration module; the computing client node comprises a FrontSwap-based remote memory exchange client, and a remote memory exchange server side is registered as a memory resource which is non-inductive to a user; furthermore, popularity statistics based on a popularity histogram is realized in a kernel mode; the memory exchange behavior is guided through the dynamic decision-making module based on online reinforcement learning, the memory management efficiency and the system performance are remarkably improved, and efficient and non-inductive memory expansion service is provided for user application.

The invention discloses a network communication dynamic optimization method based on multi-module collaboration, and relates to the technical field of network communication, a dual-mode communication module is deployed at each network node, and the network communication dynamic optimization method comprises the following steps: each network node broadcasts own existence information and power line channel characteristics through an HPLC (High Performance Liquid Chromatography) channel of the dual-mode communication module; each network node scans surrounding wireless networks through an HRF channel of the dual-mode communication module and reports own wireless channel quality information to the gateway; the global state sensing module collects all information and constructs a global network view containing physical topology and a channel quality map. A dual-mode cooperation mechanism, a machine learning prediction model and a dynamic decision strategy can adapt to complex dynamic environments such as power line noise fluctuation and wireless interference change, and communication parameters can be autonomously optimized without manual intervention; and meanwhile, the modular design is convenient to expand to a multi-mode communication scene, and has a wide application prospect.