292 results about "Strategy execution" patented technology

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The invention relates to the technical field of edge device security policy deployment, in particular to an edge device-oriented lightweight AI security policy adaptive deployment method. The method comprises the following steps: collecting operation state information of edge equipment, and constructing a current multi-dimensional environment vector and a sliding window feature vector; constructing a strategy candidate library, constructing a strategy adaptability scoring function based on the current multi-dimensional environment vector and a real-time perceived network threat event, and scoring and sorting all candidate strategy items to obtain a strategy execution candidate set; and performing scheduling optimization on the strategy execution candidate set by adopting a multi-objective optimization algorithm, and selecting a strategy combination with the highest deployment score as a deployment result. According to the method, a multi-dimensional strategy adaptability scoring function is constructed, candidate strategy items are screened according to current network threat events and system resource conditions, a strategy screening mechanism from fixed template type configuration to resource awareness and attack scene linkage is converted, and the pertinence and accuracy of strategy deployment are improved.

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 discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

The invention provides an intelligent e-commerce behavior event decision-making method and system fusing multi-source perception, and the method comprises the steps: obtaining a user behavior data set, a commodity attribute data set and an environment context data set through a preset data collection interface, and carrying out the intention recognition processing of multi-source data; generating an intention feature set containing preliminary intention features and refined intention features, then generating an interactive guidance strategy tree containing node branch weights and strategy execution priorities based on the intention feature set, and performing dynamic path adjustment processing on the interactive guidance strategy tree according to a real-time feedback data set to obtain an optimized strategy tree set; and finally, pushing the optimization strategy tree set to a target interaction interface to activate an interaction guide operation, thereby providing personalized and intelligent interaction guide for the user by fusing multi-source sensing data, deeply understanding the intention of the user and dynamically adjusting a decision strategy, and improving the operation efficiency of an e-commerce platform and the satisfaction of the user.

The invention relates to the technical field of network slice resource allocation, in particular to a 6G intelligent load balancing and fault self-healing method based on AI and network slices, which comprises the step of building an AI decision-making layer model architecture, a self-adaptive optimization mechanism, a resource dynamic scheduling mechanism and a strategy execution guarantee architecture. According to the method, the network load trend is predicted in real time through the AI technology, the resource allocation between the slices is dynamically adjusted, and the problem of low resource utilization rate caused by static allocation and periodic adjustment is solved. Meanwhile, an automatic fault detection and recovery mechanism is designed, when a node fault is detected, standby resource takeover can be quickly triggered, session continuity can be kept, the service interruption time is remarkably shortened, and the requirements of a 6G network for high reliability and low time delay are met.

The invention discloses an integrated information automatic supervision system and method for an edge device, and relates to the technical field of edge computing, an edge node management module of the system accesses the edge device, generates basic attributes and carries out containerized application deployment; the real-time monitoring and diagnosis module is used for collecting equipment operation data, identifying abnormity, positioning a fault type and triggering an alarm; the automatic strategy generation module is responsible for generating a customized repair strategy; the MESH communication and repair execution module constructs an edge device ad hoc network, realizes networking and offline strategy transmission and executes a repair instruction; the solution knowledge base module stores equipment fault types, historical repair cases and strategy templates, and updates strategy matching logic; the application deployment management module packages application programs through container mirror images, and distributes the application programs to edge nodes in batches according to scene templates; and the centralized management and control module visually displays the equipment state, the fault alarm and the strategy execution record, and provides manual intervention and global strategy configuration.

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

The invention discloses a multi-mode fusion driven multi-agent Transform collaborative power dispatching method and system, and the method comprises the steps: fusing three types of heterogeneous data, namely power grid topology, time sequence operation and equipment state, through a multi-mode projection network, and forming unified state representation; a Transform encoder is used for modeling a dependency and cooperation relationship between intelligent agents, and a mask decoder is used for generating a cooperation scheduling strategy in an autoregression mode; a strategy is evaluated and optimized by adopting a near-end strategy optimization algorithm and a joint reward function, and finally an instruction is converted into a control signal and a closed-loop learning mechanism is formed. The system comprises a multi-modal state perception and fusion module, a multi-agent collaborative representation module, a collaborative decision generation module, a joint strategy optimization and learning module and a scheduling strategy execution and feedback interface module. According to the method, the multi-source information utilization efficiency and the agent cooperation capability are effectively improved, and safe, stable and economical operation of the power grid under high-proportion new energy access is guaranteed.

The invention relates to the technical field of artificial intelligence model optimization, and discloses a multi-granularity dynamic pruning method and system for a generative AI model. The system comprises a model state acquisition module which acquires output of a middle layer in real time through a probe, and constructs a feature information set of neuron activation distribution, weight matrix norm and connection topology; the sparseness evaluation module outputs a sparseness risk value based on the feature information set, the initial sparseness parameter and the real-time computing resource state; a pruning planning collaborative analysis screening module generates a multi-granularity pruning scheme when the risk value exceeds a threshold value, calculates a collaborative interference value of precision recovery operation and screens an optimal scheme; and the pruning strategy execution feedback module updates model parameters, generates logs and feeds the logs back to the management terminal. The system realizes adaptive structure optimization and resource scheduling of the generative model in the reasoning process.

The invention relates to the technical field of computers, and discloses a multi-agent reinforcement learning fault diagnosis method based on edge-center hybrid optimization, and the method comprises the following steps: S1, collecting and preprocessing equipment data; s2, layered fault diagnosis: an intelligent diagnosis agent adopts a layered reinforcement learning structure and is composed of a high-layer strategy network and a low-layer strategy network; s3, edge-center hybrid optimization: designing a center strategy optimization agent, training a high-layer strategy network and a low-layer strategy network in stages, and performing compression and distillation on the trained strategy networks by adopting a teacher-student strategy structure; executing strategy fusion and global updating based on the received strategy execution information; and S4, strategy migration. According to the method, continuous learning and strategy updating are carried out in a scene in which fault samples are extremely scarce, diagnosis knowledge sharing, strategy synchronization and cross-device migration are realized by using a multi-agent cooperation mechanism, efficient deployment and operation at an edge device end are supported, and field state change is adapted in real time.

The invention provides a real-time command and control strategy optimization method and device based on multi-agent reinforcement learning, equipment and a storage medium, and the method comprises the steps: deploying a pre-trained cooperative intention network on edge equipment of each combat unit, and coding local observation data and a historical sequence thereof into a low-dimensional local cooperative intention vector; replacing original high-dimensional data as inter-unit communication content; whether broadcasting is carried out or not is dynamically determined according to observation uncertainty and a channel state by combining a self-adaptive intention broadcasting mechanism, and communication resources are distributed according to needs; after each unit receives an adjacent collaborative intention vector, a lightweight space-time attention module in a pre-trained local strategy execution network carries out space weighting on adjacent intentions and fuses time features of own historical intentions, and a collaborative and consistent real-time command and control instruction is generated under the condition that global information convergence is not needed.

The invention provides an industrial furnace temperature adaptive optimization control method and system based on a multivariable chaotic time sequence, and belongs to the technical field of industrial process control, and the method comprises the steps: collecting operation parameters to form a multivariable time sequence, and carrying out the processing verification of chaotic characteristics, and obtaining an analyzable sequence; extracting features through phase-space reconstruction and quantifying variable coupling strength to obtain multivariable chaotic features; a prediction model and an early warning model are constructed based on the above, and a temperature change trend and early warning information are obtained; and finally, setting and optimizing control parameters, generating strategy execution and combining feedback to form closed-loop control. According to the method, the multivariable chaotic characteristics of the industrial furnace are analyzed, the prediction and early warning model is constructed, and self-adaptive optimization control is carried out, so that accurate regulation and control of the temperature are realized, the product quality stability is improved, and the energy consumption and the production cost are reduced.

The invention provides a user behavior prediction method and system applied to an online marketing service platform, and the method comprises the steps: firstly obtaining a multi-scene user interaction record set which comprises browsing, searching, collecting and ordering interaction records in the online marketing service platform of a user; constructing a scene behavior association mapping table for recording user interaction behavior triggering association relationships in different marketing scenes, and performing dynamic tendency evolution modeling based on the scene behavior association mapping table to obtain a user behavior tendency evolution model; according to the method, a user behavior tendency evolution model is established, marketing scene behavior prediction adaptation is performed according to the user behavior tendency evolution model to obtain a user behavior prediction scheme, and finally the user behavior prediction scheme is transmitted to a platform marketing decision module to generate marketing strategy execution guidance, so that user behaviors can be comprehensively and accurately predicted, and the platform marketing accuracy is improved.

The invention belongs to the technical field of building construction automation and intelligent engineering management, and discloses a BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation system, which comprises the steps of generating a three-dimensional point cloud model based on point cloud data and UWB positioning data, further constructing a four-dimensional space-time model, synchronously generating composite conflict quantification data, and performing BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation. Correlation analysis is carried out in combination with construction specifications, and a space-time coupling semantic model is generated; fusing the penetrating scanning data of the millimeter wave radar and the UWB positioning data, and constructing a two-dimensional conflict thermodynamic diagram of a space margin-time window; generating a risk level conflict list in combination with a historical case library; generating a dynamic avoidance strategy list, refining the dynamic avoidance strategy list into an avoidance path execution scheme, verifying a strategy execution effect, judging a conflict elimination state, and generating an avoidance strategy execution state report; and evaluating the effectiveness of the avoidance strategy, dynamically adjusting the priority of the strategy, synchronously updating the dynamic BIM semantic model and the two-dimensional conflict thermodynamic diagram, and forming a strategy iteration closed loop.

The invention discloses a data processing system and method based on a federated architecture interface and related equipment, and relates to the technical field of computer software, in particular to a federated architecture distributed system formed based on a central coordination node and a plurality of regional autonomous nodes. The central coordination node is used for performing global strategy making, metadata block chain evidence storage, conflict detection and federated monitoring and self-healing to realize global management and control, and the regional autonomous node adapts to a physical machine heterogeneous environment to perform local strategy execution, heterogeneous protocol conversion and resource autonomous regulation and control so as to realize flexible regulation of a regional strategy. According to the scheme, through a federated and intelligent treatment system, inherent contradictions between centralized management and control and department autonomy, standardization and rapid iteration and global safety and local flexibility are solved, technical support is provided for an enterprise to construct a standardized, high-reliability and easy-to-expand API ecological system, digital transformation is assisted, and the enterprise safety is improved. And global controllability and local autonomy of enterprise-level API ecology are realized.

The invention discloses a central control system of a digital multimedia exhibition hall, and relates to the technical field of central control systems, a time-space correlation analysis module constructs a fault propagation probability graph of exhibition hall equipment and audience behaviors, and a coupling degree analysis module verifies a cross-layer causal relationship by using a directional disturbance injection method based on the fault propagation probability graph; a coupling degree index is fitted by analyzing an incidence matrix of a resource scheduling operation and a fault propagation chain, a dynamic priority decision module fits a fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into a priority function, and a collaborative priority of a short-term suppression action and a long-term eradication action is calculated. And the strategy execution module calls the execution sequences of the two types of actions through the interface of the central control platform, and executes corresponding strategy actions. The central control system is not only high in dynamic adaptive capacity, but also capable of responding to audience demands and equipment state changes in real time, improving exhibition management efficiency and enhancing immersive experience of audiences.

The invention relates to the field of retail management, and discloses a retail chain response type system. The system comprises a user behavior perception module, an inventory dynamic mapping module, a cross-store collaborative scheduling module, a price response optimization module and a strategy execution feedback module. A commodity popularity model is dynamically constructed by sensing customer behavior data and combining real-time inventory information, and cross-store inventory collaborative allocation and price strategy response are realized. The system also continuously optimizes the overall response effect through a closed-loop feedback mechanism, and improves the sales conversion rate and the inventory turnover efficiency. According to the method, a multi-dimensional popularity scoring model and a dynamic pricing formula are particularly introduced, and responsive and high-adaptability retail operation management is realized.

The invention relates to the technical field of 5G communication, in particular to an optimization method of a high-performance 5G communication module. Comprising the following steps: acquiring key operation parameters in a multi-carrier concurrent state, and constructing a characteristic graph model reflecting a parameter evolution relationship; mining a state clustering mode through a graph polarization spectral clustering algorithm, and identifying key paths and core parameter nodes which influence communication performance in combination with a multi-hop path optimization algorithm of a graph attention mechanism; taking the key path strategy graph as input, combining target communication scene features, and adopting a graph structure alignment meta-learning algorithm to generate an optimal parameter configuration strategy; the strategy is applied to an actual module, a self-adaptive control model fusing fuzzy reasoning and reinforcement learning is constructed, and dynamic evaluation of the strategy execution effect and fine parameter tuning are achieved. According to the invention, the performance scheduling efficiency of the 5G communication module under the complex network condition is improved, and the intelligence, generalization ability and scene adaptability of the system are obviously enhanced.

The invention relates to a dynamic polymer visual multi-level load collaborative management method. Firstly, power system operation, user energy consumption behaviors and environment parameters are obtained and subjected to structured processing to obtain a data set; performing hierarchical division according to resource characteristics and spatial distribution characteristics in the data set to generate a dynamic polymer; extracting time domain and frequency domain characteristic parameters of each level of load, and constructing a layered load behavior model; then, based on the dynamic polymer topology, the hierarchical load behavior model and the constraint conditions, a multi-objective optimization function is constructed to generate a collaborative management strategy; and finally, a strategy is optimized by means of real-time feedback, virtual verification and a long-term learning mechanism, a layered visual mapping rule is constructed, and the dynamic aggregate, the layered load behavior model and a strategy execution result are graphically output. Compared with the prior art, the method has the advantages of being good in data integration performance, high in dynamic adaptability, capable of achieving multi-target tradeoff and the like.

The invention discloses a production operation benchmarking system and method based on multi-source data analysis, and belongs to the technical field of production operation benchmarking, and the system comprises a multi-source confrontation fusion module, a production situation awareness map module, a self-adaptive diagnosis analysis module, a cross-domain collaborative deduction strategy module, a hidden variable benchmarking analysis module and a cross-scene migration module. According to the method, enterprise resources are quantized into a matrix, cross-domain resource allocation is optimized in combination with a collaborative gain function, collaborative gains are measured through a nuclear norm and a Frobenius norm, multi-target weighting is combined, priorities of different fields are balanced, the global optimality of a collaborative strategy is improved, the enterprise resource state is synchronized to a digital twinborn model, and the collaborative resource allocation efficiency is improved. A strategy execution process is simulated, burst interference is injected, strategy stability is verified, a cross-domain cooperation efficiency comprehensive value is calculated through a four-flow state, a single-domain independent optimization mean value is compared, and cooperation gain is quantified.

The invention relates to the technical field of enterprise data processing, and discloses an intelligent analysis system for enterprise operation data based on artificial intelligence. The system comprises an operation data acquisition module, an operation state analysis module, a decision tree generation module, a data compensation module and an execution parameter generation module. The operation data acquisition module acquires enterprise finance, supply chain logistics and market dynamic data in real time, integrates and cleans multi-source heterogeneous data, and generates an operation state data set; the operation state analysis module identifies a deviation degree between the key business index and a preset threshold value, and calculates a multi-dimensional abnormal value by means of a dynamic weighting strategy to obtain an abnormal feature set; a decision-making tree generation module constructs a multi-layer decision-making path according to the multi-layer decision-making path, matches an optimal branch in combination with a historical case library, and generates a visual decision-making tree; the data compensation module analyzes the influence of missing data and automatically supplements an external data source to generate a compensation parameter set; and the execution parameter generation module analyzes node priorities, quantifies resource configuration and time window parameters, and generates a strategy execution parameter packet.

The invention relates to the technical field of network situation awareness, in particular to a network situation intelligent awareness security system based on multi-source data analysis, which accurately acquires a perception threat confidence vector, a service interruption risk vector, an attack chain complete vector and a user behavior deviation vector based on multi-source data fusion and targeted analysis. According to the method, network situation key information can be comprehensively and accurately captured, a reliable basis is provided for subsequent decision making, the comprehensiveness and real-time performance of threat perception are improved, dynamic balance of safety protection, service efficiency and operation and maintenance cost is achieved by relying on a multi-target game mechanism and a network space digital twinning technology, and the safety and reliability of threat perception are improved. A simulation deduction deviation value is introduced as a core index of strategy evaluation, a real network situation is simulated through a digital twin environment, the difference between a strategy execution effect and an expected target is quantified, and in combination with reproduction of an attack scene by the twin environment, deviation value analysis is ensured to be more fit with the real network environment.

The invention discloses an efficiency self-adaptive optimization method and system for data retrieval, relates to the technical field of data management, and discloses an efficiency self-adaptive optimization system for data retrieval. Comprising a mode acquisition module, a task evaluation module, a cost prediction module, a strategy selection module, a consensus retrieval module, an execution decision module, a cache management module and a model updating module. Risk perception support is provided for strategy selection and execution modes; according to the method, resource consumption, execution time delay and path complexity under a plurality of standard retrieval strategies are predicted by using a query cost prediction model, and a confidence score is output, so that prospective evaluation of a strategy execution effect is realized.

The invention discloses an airline dynamic price adjustment collaboration method and system, and the method is executed under the collaboration of a coordination server and a plurality of airline local servers, and comprises the steps: obtaining the historical flight ticket selling data of each airline local server through the airline local server, and carrying out the privacy protection processing of the data locally; each airline local server trains a global pricing model issued from the coordination server by using the local data subjected to privacy protection processing to obtain local model parameters and uploads the local model parameters to the coordination server; and the coordination server aggregates all the received local model parameters, updates the global pricing model, and issues the updated global pricing model parameters to the local server of each airline. According to the method, joint modeling and collaborative pricing are realized on the premise that the data are not out of the local, vicious price competition is avoided, the overall income of multiple airlines is improved, and the stability and fairness of strategy execution are guaranteed.

The invention relates to the technical field of data distributed storage, in particular to a data distributed secure storage system and method based on computer science and technology. The system comprises a data fragmentation and strategy binding module, a strategy declaration evidence storage and state tracking module, a strategy execution voucher generation module and a voucher verification and data assembly module. The system carries out fragmentation processing on original service data through a dynamic fragmentation algorithm to generate encrypted data fragments and a strategy binding statement; storing the strategy binding declaration to a distributed account book and maintaining a strategy state table; generating a policy execution voucher including an access qualification proof, a digital signature response, a policy hash value and a timestamp based on the system real-time state and the access request; and the distributed storage node implements decentralized data access control by verifying the policy execution credential. According to the method, the single-point fault and strategy tampering risk of centralized authority management are effectively solved, and a reliable data security sharing scheme is provided for a supply chain financial service scene.

The invention discloses an edge computing and distributed zero-trust architecture system and a data processing method. The system comprises an edge computing device cluster, a distributed zero-trust control center, an edge security gateway and a block chain identity infrastructure. The edge computing node collects hardware state, vulnerability and behavior data through the terminal security agent module; the block chain identity infrastructure provides distributed identity identification and verifiable credential service; the edge security gateway executes data packet filtering and single packet authentication; and the distributed zero-trust control center calculates a real-time security coefficient and generates a differentiation strategy, and the log is synchronized to the alliance chain for evidence storage. According to the data processing method, security access is realized through access request initiation, single packet authentication, data acquisition, trust evaluation, strategy execution and log evidence storage. According to the method, the edge device state and distributed zero trust are deeply fused, the problems of incomplete identity authentication and static permission control in an edge scene are solved, and the security and traceability of an edge computing network are improved.

The invention discloses a target-driven energy supply and demand intelligent optimization system and method. The system comprises an input module, a multi-target quantification module, a decision module, an execution module and a feedback module. The input module receives an input high-level target instruction; the multi-target quantification module receives a target instruction to form a target quantification signal; the decision module is connected with the dynamic multi-target quantization module and is used for receiving the target quantization signal and outputting a strategy execution signal; the execution module is connected with the reinforcement learning intelligent decision-making module and is used for converting the strategy execution signal into a control instruction of the energy equipment; the feedback module collects energy system state data and external environment data in real time, and drives target weight adaptive adjustment and strategy iteration updating. According to the target-driven energy supply and demand intelligent optimization system and method, the problems that an existing energy optimization system is difficult to dynamically convert an abstract high-level target into a quantization parameter and cannot adapt to environmental changes to adjust the priority of the target can be solved.