1407 results about "Intelligent Network" patented technology

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

The invention discloses a network card data local preprocessing system fused with edge computing, and relates to the technical field of edge computing and artificial intelligence collaborative optimization. Comprising an edge computing unit, a hierarchical collaborative architecture, a model hot switching and generative fragmentation module, an intention recognition and adaptive scheduling module, a delay energy consumption optimization scheduling module, a CXL zero-copy sharing module, an edge computing unit integrated processor, an FPGA or ASIC and a neuromorphic computing unit. According to the method, an FPGA, an ASIC and a neuromorphic computing unit are integrated in an intelligent network card, microsecond-level dynamic connection reconfiguration and adaptive generative model fragmentation execution are realized through a reconfigurable Mesh interconnection matrix, an attention layer and a feed-forward layer of a Transform class model are fragmented and allocated to different computing units for parallel execution, and cross-card streamlined processing is realized in cooperation with a zero-copy shared memory. And the intention recognition module is deeply coupled with the model hot switching module, so that dynamic model switching and fragmentation strategy optimization based on service priorities and system loads are realized.

The invention provides a factory energy safety dynamic monitoring method based on the Internet of Things and multi-mode perception, and belongs to the technical field of industrial Internet of Things. Comprising the following steps: acquiring electrical parameters, temperature data, combustible gas leakage concentration and vibration signals of equipment in real time; carrying out aggregation and protocol conversion on the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals through a multi-protocol intelligent gateway, and uploading the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals to a locally deployed edge computing node in a preset period; preprocessing the received data at the edge computing node, and generating a quantitative risk index based on a dynamic risk assessment model fusing the real-time state of the equipment, the historical aging trend and the environmental parameters; and carrying out risk grade judgment according to the quantitative risk index, and executing a corresponding grading response. Through fusion of multi-modal sensing data and edge intelligence, comprehensive sensing, real-time quantitative evaluation and hierarchical intelligent response of factory energy safety risks are realized.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

The invention discloses an intelligent network connection automobile active safety teaching control method based on digital twinning. The method comprises the steps that S1, multi-source sensor data are collected and preprocessed to generate a driving state data set; s2, inputting the state data into a digital twin model to drive a traffic scene and outputting a synchronous state; s3, based on Dueling-DDQN, executing strategy learning to generate an active safety control instruction; s4, the instruction response effect is simulated and verified in the virtual environment; s5, collecting driver operation behaviors, inputting the improved MHA-BiLSTM model to extract time sequence features, and outputting behavior features; and S6, comparing the driving behavior with a standard instruction item by item, calculating an operation deviation and a response difference, and generating a personalized active safety teaching task. According to the invention, efficient comparison and teaching feedback of the driving behavior and the active control strategy can be realized, and the intelligent level of driving training is improved.

The invention discloses an equipment health management and predictive maintenance method and system based on an industrial intelligent gateway, and relates to the technical field of industrial automation and predictive maintenance, and the method comprises the steps: collecting equipment data through a multi-source sensor, and carrying out the preprocessing of the equipment data through an intelligent gateway; mapping the high-quality data to a digital twinborn model for health state evaluation; based on an evaluation result, fault prediction is carried out by adopting knowledge-driven and data-driven fused intelligent diagnosis; generating and executing a personalized maintenance strategy; according to the system and the method, the problems of insufficient multi-source data fusion, insufficient health assessment accuracy and lack of individuation of a maintenance strategy are solved, and precision, self-adaption and resource optimization of equipment health management are realized.

The invention relates to the technical field of network communication, and discloses an AI load-oriented network topology awareness and communication optimization method, an AI load-oriented network topology awareness and communication optimization device and a related system. The method comprises the following steps: acquiring gradient transmission delay data among computing nodes in a distributed AI training system, and constructing a gradient distribution table of gradient tensors and RDMA intelligent network card queue resources; performing aggregation operation on the gradient tensor in the gradient distribution table on a programmable data plane of the RDMA intelligent network card to obtain pre-aggregated gradient data; calculating a transmission efficiency index of the pre-polymerized gradient data and solving to obtain a bandwidth allocation coefficient; and adjusting an MTU frame parameter, a WQE queue depth and a credit window of the RDMA intelligent network card according to the bandwidth allocation coefficient, and generating a hybrid execution scheme. According to the method, intelligent distribution of the gradient tensor between hardware unloading and software processing is achieved, and the performance close to the optimal performance is obtained under the condition that resources are limited.

The invention discloses a real-time state synchronization method and system, electronic equipment and a storage medium, and belongs to the technical field of network communication.The method comprises the steps that in response to a connection request from a client side, persistent connection with the client side is established, and a state subscription relation is recorded; performing semantic analysis on the state change event from the business service layer to determine a push priority; a corresponding target intelligent gateway is found according to the target object identifier of the state change event, a pushing strategy is executed by fusing the client network condition maintained by the target intelligent gateway and the pushing priority, and the state is updated and synchronized to the related client; and monitoring the health state of the persistent connection, and when the connection is abnormal and recovered, performing state synchronization with the client by the intelligent gateway based on the stored incremental state information. According to the invention, the problems of unreliable connection, redundant pushing and poor expansibility of a traditional SSE scheme are solved, and high-reliability, high-efficiency and horizontally-expandable real-time state synchronization is realized.

The invention discloses an eSIM equipment intelligent network selection method based on environment perception and an AI strategy, and the method comprises the steps: collecting and preprocessing multi-source environment data, and generating a multi-mode original feature vector; constructing a three-dimensional situation tensor, generating a causal structure diagram, clustering to obtain a situation identifier, and embedding a situation; constructing a situation feature table, improving TabNet to perform feature selection, and outputting a high-dimensional situation representation vector; based on the situation characterization and the capability characteristics, performing chain type prediction on the performance and generating an anti-factual income score; constructing short-time domain and long-time domain preferences, generating a comprehensive preference value after fusion, and determining a target connection object; and an eSIM strategy configuration instruction is generated, intelligent network selection switching is carried out, and model parameters are incrementally updated. According to the method, active high-stability intelligent network selection of the eSIM equipment in a complex scene is realized through fusion of multi-source environment perception, causal inference and an AI intelligent strategy.

The invention discloses a smart park alarm method and system based on an intelligent gateway, and the method comprises the following steps: accessing a heterogeneous terminal through a multi-protocol southbound interface, generating a standardized event set, and building a spatial topology table; performing quality evaluation on the standardized event set to form a sequential cleaning event stream; target detection is carried out based on the sequential cleaning event flow, and a semantic event flow is output; performing space-time matching on the semantic event flow to obtain a fusion event set; according to a preset grading standard, a grading warning set is generated, and a linkage instruction set is generated; scheduling and executing the execution end according to the linkage instruction set to form a processing state feedback; integrating the grading warning set and the processing state feedback into an evidence packet, and reporting the evidence packet in parallel through the main and standby reporting links; and archiving and indexing the evidence packet and the reported result, generating an auditable record, and finishing synchronous updating with an upper platform. According to the invention, the smart park alarm method and system are realized.

The invention relates to the technical field of power grid monitoring, and discloses a power grid power equipment anomaly prediction method, system and device, and a storage medium, which are used for solving the problem that the existing single data source and threshold alarm prediction mode cannot adapt to advanced prevention and high-precision state detection. The method comprises the following steps: acquiring real-time electrical quantity time sequence data, image data and a monitoring report of each piece of power equipment in an equipment abnormal area in a power grid through an edge intelligent gateway; respectively carrying out feature extraction on the real-time electric quantity time sequence data, the image data and the monitoring report to obtain an electric quantity feature vector, a visual feature vector and a semantic feature vector; and carrying out weight fusion on the electrical quantity feature vector, the visual feature vector and the semantic feature vector, and carrying out abnormal state prediction based on the fused feature vector and a historical feature sequence.

The invention discloses a data transmission optimization method and system in hybrid deployment of a real-time system and a non-real-time system based on Jailhouse, belongs to the technical field of data transmission, and solves the problems of low data transmission efficiency, high copy overhead, lack of universality of interfaces and the like in an existing system. Comprising the steps that a hybrid deployment platform including a real-time system and a non-real-time system is built on a multi-core processor platform, and the hybrid deployment platform runs in an isolation environment of a Jailhouse partition manager; building an abstract transmission interface layer as a unified channel for accessing a shared memory by a real-time system and a non-real-time system; establishing a plurality of shared memory channels in the abstract transmission interface layer and scheduling according to the priority of tasks; establishing a data transmission path, and compressing a memory copy operation into one time by adopting a memory mapping multiplexing technology and a DMA (Direct Memory Access) collaboration mechanism; and a cache consistency maintenance technology is adopted, so that the data consistency is ensured, and the optimization of data transmission is completed. The method is suitable for application scenes such as industrial automation, intelligent connected automobiles and edge calculation.

The invention provides a river flow velocity intelligent monitoring method and system based on multi-scale optical tracking velocity measurement. A plurality of detectors with different characteristics are organically combined, and dynamic grid density adjustment based on flow velocity gradient is realized through adaptive grid adjustment; establishing a multi-dimensional track quality evaluation system through intelligent track evaluation; the real-time performance optimization realizes the performance optimization of multi-thread parallel processing, integrates an intelligent grid optical flow detection and dynamic density adjustment mechanism, and optimizes the detection precision and performance in real time according to the flow velocity gradient and the flow field complexity; a multi-track quality evaluation system is utilized, tracks are screened and fused from three dimensions of motion consistency, physical constraint and time stability, and the accuracy and reliability of data are remarkably improved. The method overcomes the problems that a traditional speed measurement method is complex in operation, limited in environment, insufficient in precision and the like, supports real-time processing, can be widely applied to the fields of water conservancy projects, environment monitoring, flood prevention early warning, scientific research and education and the like, and has remarkable advancement and practical value.

The invention discloses a multi-agent reinforcement learning multi-task agent decision-making method and system for a crossroad without signal lamps, and belongs to the technical field of intelligent network connection vehicle multi-task agent decision-making control. According to the method, vehicle-road cooperation is achieved through wireless communication, an automatic driving vehicle uploads state and task data to a road side unit, the road side unit adopts a multi-task mixed attention mechanism, data are processed through a self-attention module and a cross attention module, and decision features are generated and input into a strategy network. And finally generating a longitudinal control instruction by combining an offline and online mixed sampling strategy training model, and issuing and executing the longitudinal control instruction through wireless communication. According to the method, the sensing range can be expanded, the key target can be dynamically focused, the training efficiency and the decision-making stability are improved, and the collaborative decision-making capability and the traffic safety of the automatic driving vehicle in the mixed traffic environment are remarkably enhanced.

The invention belongs to the technical field of network scheduling, discloses an intelligent network scheduling method and system based on hybrid reinforcement learning, designs a multi-head attention strategy network based on Transform, can execute discrete protocol selection and continuous rate adjustment at the same time, and breaks through the limitation of a traditional scheduling model on action dimension division. Therefore, the scheduler can flexibly select the optimal strategy when facing a complex task, and the refinement and adaptability of the decision are remarkably improved. In order to overcome the problem of value over-estimation in reinforcement learning, a double-Q-Critic network structure is constructed, and double evaluation paths are introduced. In the training process, through minimizing the estimation value deviation of the two Critic values, the overhigh estimation is effectively inhibited, so that the convergence stability and the estimation value precision of the strategy are improved. In the reward mechanism, a structural causal model (SCM) is introduced to generate an anti-factual reward signal, and a real reward and an anti-factual reward are fused by constructing a causal intervention path for a specific action.

The invention provides an end-to-end reliable retransmission method, a computing node and a system for an AI intelligent computing network. The method comprises the following steps: a source end and a destination end establish synchronization through a handshake mechanism; the source end sends a data packet to the destination end, and locally caches the sent data packet; the destination end receives the data packet, compares the serial number of the data packet with the expected serial number of local maintenance, and returns an acknowledgement message or a retransmission request to the source end according to a comparison result, and the acknowledgement message carries the current expected serial number; in response to the confirmation message, the source end releases all the data packets of which the serial numbers are smaller than the expected serial number carried by the confirmation message in the local cache; or, in response to the retransmission request or transmission timeout, acquiring the corresponding data packet from the local cache for retransmission; and after the source end sends the last data packet, if the confirmation message is not received within the preset time, actively sending a state query request to the destination end to obtain the receiving state of the last data packet.

The invention provides an intelligent network connection device remote cluster control method and system based on vehicle-road cooperation, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining vehicle end state information and road side sensing information, carrying out the cross-domain fusion based on a vehicle-road space-time alignment rule to obtain a global view, grouping vehicle end devices according to the road segment attribution and task association degree, and carrying out the clustering of the vehicle end devices; and generating a hierarchical control instruction according to the grouping structure and the global view, executing remote control, and adaptively adjusting an alignment rule by using feedback information. According to the invention, the accurate control of the equipment cluster in the vehicle-road cooperation environment is realized, and the operation efficiency and safety of the vehicle-road cooperation system are improved.

The invention discloses a method and a system for realizing remote operation and maintenance of equipment through cooperation of an industrial intelligent gateway and a cloud platform, and relates to the technical field of remote operation and maintenance of industrial Internet of Things, and the method comprises the following steps: the gateway collects multi-modal data of the equipment and locally preprocesses the multi-modal data to generate a structured data packet; lightweight Transform model forward propagation is executed, and an encrypted feature vector is extracted and uploaded to a cloud platform; the cloud platform adopts federated learning to update model parameters based on the feature vectors, issues the model parameters to the gateway, and synchronously constructs equipment digital twin bodies; analyzing the natural language operation and maintenance instruction through the digital twin to generate a control command; an equipment association graph is constructed, a fault propagation path is analyzed by using a graph convolutional network (GCN), and an early warning is generated; and after executing the control instruction, the gateway feeds back state data to the cloud platform, updates the digital twin and model input, and forms closed-loop control. According to the invention, collaborative optimization of data privacy protection, cross-device fault prediction and intelligent decision is realized, and the operation and maintenance efficiency and security are improved.

The invention relates to the technical field of intelligent gateways, in particular to a method and system for automatically expanding intelligent router plug-ins through LLM. The method comprises the steps that a user describes a plug-in function requirement of the intelligent router in a natural language through voice or text input equipment; and the cloud preprocessing service carries out character conversion processing on the voice data, carries out contradiction check and invalid information filtering on natural language requirements, extracts structured requirement information and inputs the structured requirement information to the cloud big language model. According to the intelligent router plug-in development method and system, the problems that in traditional intelligent router plug-in development, the manual intervention degree is high, the process is long and compatibility is insufficient are effectively solved by constructing an automatic system with the cloud large language model and the multi-service module cooperating. Through cooperation of multiple technical modules, a full-automatic process covering demand input, code generation, engineering integration, test verification and terminal deployment is constructed, and the intelligent router plug-in development efficiency and quality are remarkably improved.

The invention relates to the technical field of intelligent automobile test and evaluation, in particular to an intelligent networked automobile automatic driving performance evaluation method based on simulation test, which comprises the following steps: S1, generating a reference steady-state traffic flow in a simulation platform; s2, injecting traffic flow disturbance parameters into the reference steady-state traffic flow, and recording whole-course operation data of the reference steady-state traffic flow; s3, extracting performance indexes of the tested vehicle; s4, calculating a deviation degree, and quantifying the deviation degree as a performance attenuation rate; s5, performing weighted fusion to obtain a comprehensive disturbance tolerance index; and S6, outputting an evaluation conclusion of the automatic driving performance of the tested vehicle according to the comprehensive disturbance tolerance index. According to the method, the reference traffic flow is constructed, the disturbance is injected, the performance attenuation rate is quantified, and then the disturbance tolerance index is generated through weighting, so that objective, comparable and interpretable comprehensive evaluation of the automatic driving performance of the intelligent networked automobile is realized.

The invention discloses an operation system, method and equipment of a live working robot with an intelligent distribution network, and a storage medium, and relates to the technical field of intelligent distribution networks of robots, and the system comprises a first man-machine interaction module which supports bidirectional interaction with an operator through voice, characters, images and the like; the second multi-mode sensing module senses the equipment state, the obstacle and the environment change in the working environment in real time through a visual sensor, an auditory sensor, a force sensor and the like; the big language model processes and analyzes the instruction through a natural language and generates a task execution step, and adjusts a task strategy according to the perception data; the decision planning module formulates a task path and resource scheduling in combination with a multi-objective optimization algorithm; the safety monitoring module monitors the safety state in the operation process in real time and triggers early warning; the anomaly detection and processing module receives the early warning information and starts an autonomous navigation and path planning technology; according to the system, autonomous and safe operation of the robot is achieved, and task execution efficiency, adaptability and flexibility are improved.

The invention discloses an equipment logic interlocking control method supporting edge collaboration and an intelligent gateway, and belongs to the field of industrial control and industrial Internet of Things. Reading a configuration center rule and generating a verification entry list; reading a local key value cache, sending a remote state query request and analyzing a state response message; obtaining an interlocking judgment result based on aging screening, numerical value comparison and Boolean operation of the collection timestamps; and executing physical bus message issuing or failure response output according to an interlocking judgment result. According to the method, a data access mode of combining configuration center rule management and local cache is adopted, so that the state of the dependent object can be uniformly verified, and the interlocking judgment traceability is improved.

The invention discloses an intelligent network security risk assessment method and system, and belongs to the technical field of network security, and the method comprises the steps of network security related data collection, network security related data processing, network security risk assessment model construction and intelligent assessment. According to the scheme, basic features and time sequence features are combined and expanded into polynomial feature vectors, fluctuation verification sampling and weighted error marking are combined, normal data are dynamically screened, abnormal data are removed through a self-adaptive residual error threshold value, and high-quality data support is provided for network security risk assessment; the method comprises the steps of generating confrontation features and comprehensive physical risk features, obtaining multi-source fusion features based on decision coefficient weighted fusion, constructing a physical topological graph and a dynamic association graph, and carrying out spatial-temporal feature coding and risk prediction decoding through a time multi-head attention mechanism, double-view graph convolution and attention fusion. And the evaluation efficiency and accuracy of the complex network security risk are comprehensively improved.

The invention discloses a method and system for adapting an industrial intelligent gateway to multiple industrial protocols, and relates to the technical field of industrial Internet of Things edge computing, and the method comprises the steps: monitoring industrial network traffic self-learning, and constructing a protocol fingerprint database; dynamically constructing a virtual equipment twinborn body based on the fingerprint database to form a simulation environment; automatically verifying protocol configuration in a simulation environment; the application verification configuration is connected with the entity device to collect data; performing in-stream calculation processing on the data stream in real time to generate an advanced event and feature data; according to the method, automatic identification and compatibility of unknown protocols are realized, production accidents are avoided through virtual simulation verification, data value extraction is synchronously completed in the data acquisition process, and the security, reliability and data processing efficiency of protocol adaptation are effectively improved.

The invention discloses an unknown attack detection method and system for intelligent network security situation awareness, and the method comprises the steps: obtaining multi-dimensional data of a power grid monitoring system, carrying out the data preprocessing, carrying out the network attack preliminary detection of the multi-dimensional data through a pre-built network security situation awareness framework, and outputting suspected attack data, the method comprises the steps of performing data training on historical attack data through an improved OCN open set classification network, identifying attack features of each known attack type, performing semantic similarity calculation on the attack features and suspected attack data, performing attack feature mapping and clustering on the suspected attack data based on semantic similarity, and obtaining an unknown attack feature clustering result. And according to the unknown attack clustering result, carrying out attack type classification on unknown attacks in the suspected attack data to obtain an unknown attack detection result. The method has the effects of detecting unknown attack means in time, effectively reducing the risk that the power grid system suffers from network attacks and guaranteeing safe and stable operation of the power grid system.

The invention provides a new energy vehicle battery remaining life prediction and reverse logistics process dynamic optimization method and system based on intelligent network connection. The method comprises the following steps that vehicle operation information and battery state information are obtained in real time through a vehicle-mounted terminal; constructing a lithium ion battery residual life prediction model based on BiLSTM, and optimizing parameters in the lithium ion battery residual life prediction model by using an improved whale optimization algorithm; according to the capacity data and the residual life of the lithium ion battery, the battery scrapping number and time distribution of each area are obtained, the logistics quantity among the nodes in the reverse logistics network is distributed through an improved particle swarm optimization algorithm, the optimal recycling and scrapping path is obtained, and on the premise that the recycling requirement and the transportation constraint are met, the recycling efficiency of the lithium ion battery is improved. And dual optimization of regional overall logistics cost and carbon emission cost is realized. According to the invention, the logistics quantity configuration scheme among the recovery nodes can be dynamically optimized, and the overall operation efficiency of the recovery network is improved.

The invention relates to the technical field of network communication, in particular to an intelligent network card data forwarding method and system based on an FPGA, and the method comprises the steps: after the FPGA receives a data stream, firstly extracting a time interval standard deviation of N data packets as a time feature, executing parallel hash operation on a source IP address and a destination IP address, and mapping the source IP address and the destination IP address into three-dimensional coordinates; splicing the time features and the space coordinates into a two-dimensional matrix; a time vector T and a space vector S are separated from the two-dimensional matrix, the time feature similarity is calculated through cosine similarity, the space similarity is calculated through an Euclidean distance normalization algorithm, and affinity is obtained after weighted fusion; the condition that the affinity exceeds a first preset threshold value is used as a triggering condition for micro-flow slice splitting, and a data label is added to the slice; the FPGA intelligent network card implements heterogeneous channel binding based on slice affinity, and the global crossbar switch realizes non-blocking channel switching according to priority. And the slice splitting is triggered according to the affinity threshold, so that the high-speed transmission efficiency is improved.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein. A server system for managing communication is configured to receive from a user at a healthcare practice, an electronic request to obtain a medical prior authorization (medPA) for a specialty drug to administer for a patient of the healthcare practice; select, using a rules service, a communication pathway to submit the medPA to a healthcare payer from a plurality of communication pathways; and initiate a connection with the healthcare payer using the selected communication pathway.

The invention discloses a network security policy intelligent optimization system based on an improved SPF algorithm, and belongs to the technical field of network security. According to the system, by constructing a network security topological graph, network assets, services and users are defined as nodes of the graph, and potential access relations are defined as edges. A multi-dimensional and dynamically quantized'security risk index 'is provided as a weight of an edge, and the index integrates multiple dimensions. An improved SPF algorithm is adopted, and a lowest risk path from any source node to a target node is calculated. And according to the calculated optimal path, a refined access control strategy is automatically generated and issued, and redundancy analysis and optimization suggestion are carried out on the existing strategy. According to the method, the problems that a traditional security policy is complex in configuration, prone to errors and incapable of adapting to dynamic risk changes are solved, conversion from static and passive access control to dynamic and active risk path optimization is achieved, and the intelligent level and overall security of network security defense are remarkably improved.