920 results about "Service layer" patented technology

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention provides a knowledge graph-based security event automatic response method, which comprises the following steps of: acquiring multi-source security data, performing de-duplication and standardization processing, and generating a structured security data set; based on the network security ontology model, entities and relationships are extracted from the data set, and a security knowledge graph is constructed through entity alignment and conflict resolution; according to ATTamp; the CK framework divides network subnets, generates attack path diagrams of the subnets, and fuses the attack path diagrams into a global attack graph. Then, abnormal behavior nodes in the security knowledge graph are analyzed, and risk scores of the service layer, the host layer and the system layer are calculated in combination with the global attack graph; and matching a predefined response strategy library based on the risk scores, executing operations such as banning an IP (Internet Protocol), isolating a host or updating firewall rules and the like through an SOAR platform, generating a disposal report and updating a security knowledge graph. According to the invention, the network security event processing efficiency and accuracy can be improved, and the overall security protection capability of the system is enhanced.

The invention relates to a carbon emission checking system and method based on a block chain, and the system comprises a basic resource layer which collects enterprise energy consumption data in real time, employs cloud computing resources to provide data storage and computing capability, and employs a Hash algorithm to process the enterprise energy consumption data, and obtains a data abstract; the block chain network layer carries out distributed storage through an alliance chain architecture, carries out consensus evidence storage on the data abstract by adopting a PBFT consensus algorithm, and generates an encrypted data abstract; the intelligent contract layer receives the encrypted data abstract transmitted by the block chain network layer, and executes verification, calculation and supervision rules; and the application service layer calls the processing result of the intelligent contract layer, and respectively provides an enterprise carbon checking terminal service interface, an institution carbon checking terminal service interface and a competent department carbon supervision terminal service interface for the enterprise, a third-party checking institution and the competent department to check the carbon emission. According to the invention, high-efficiency check, high-credibility verification and whole-process traceable supervision of the carbon emission data are realized.

The invention discloses a surface mine transportation real-time intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that S1, an initial data loading module initializes the system; s2, the algorithm module verifies the vehicle state after receiving the scheduling request; s3, executing a path generation strategy according to states, wherein a normal vehicle generates all feasible routes based on the global path network; when a user appoints a starting point and a terminal point, a point-to-point optimal route is intelligently generated; s4, performing dynamic optimization scheduling based on the multi-source data; optimal task allocation is solved through a multi-objective optimization model, and a vehicle and route mapping relation is output; and S5, a scheduling result is issued to the vehicle-mounted terminal through the service layer, and a terminal interface displays a recommended route and operation guidance. According to the invention, a dynamic event-driven mechanism and a multi-source data collaborative optimization intelligent scheduling framework are fused, so that efficient distribution of vehicle tasks, real-time generation of paths and quick response to abnormal conditions are realized.

The invention relates to a full-life-cycle digital passport management system and method for electromechanical equipment, which take dynamic data in an operation and maintenance stage as a core and depend on linkage of an intelligent algorithm and full-cycle data. The problems of equipment parameter deviation, performance degradation and the like can be accurately identified based on five-dimensional full-period data integrated by a data passport construction module; and a fault influence range and associated equipment are positioned according to physical, logic and power dependency relationships presented by the equipment dependency graph construction module, so that a basis is provided for decision correction. Besides, through unique identification of a gene coding module, historical data and responsibility subjects of equipment can be traced, BIM model dynamic updating, feedback design and construction stage data verification optimization are driven, and a closed loop of operation and maintenance feedback-model correction-full-cycle data iteration is formed. The module solves the problems of traditional operation and maintenance data isolation and model static state, enhances the timeliness and accuracy of block chain evidence storage data, and supports applications such as intelligent service layer predictive maintenance and fault self-repairing.

The invention discloses a petroleum industry personnel behavior safety video monitoring system based on artificial intelligence, and the system comprises a front-end equipment layer which is used for collecting physical environment data; the edge computing layer is deployed on a local server of a factory and is used for preprocessing and preliminarily analyzing the data acquired by the front-end equipment layer; the cloud analysis layer is used for carrying out deep analysis on the data uploaded by the edge calculation layer by utilizing deep learning and natural language processing technologies; the cloud analysis layer can integrate data from the front-end equipment layer, the edge computing layer and the cloud analysis layer; and the application service layer is used for presenting the result of the cloud analysis layer to a user in a user-friendly manner. According to the method, video spatio-temporal features are extracted through the CNN-BiLSTM fusion model, risk behaviors such as no helmet wearing and illegal operation are accurately identified, and the false alarm rate is reduced; the NLP analyzes the work order to construct the knowledge graph, the knowledge graph is matched with the video behavior event in a space-time manner, the risk score is dynamically evaluated, and the comprehensive early warning accuracy is improved.

The invention discloses a natural resource investigation monitoring system based on artificial intelligence, and the system comprises a data sensing layer which carries out the whole space-time coverage monitoring of mountain field water resources through a multi-source remote sensing device and an edge calculation node; automatically adjusting the weight of each data source by adopting a dynamic weight multi-modal fusion algorithm; according to the data fusion layer, original data, a processing flow and a model result are encrypted and stored through block chain nodes, data conflicts are detected through a graph neural network, and conflict marks are generated in combination with policy rules; verifying data consistency by a dynamic conflict detection algorithm; according to the intelligent analysis layer, natural resource, forest grass and water conservancy departments locally train exclusive models, and a knowledge-driven reasoning module generates analysis results in combination with geographic knowledge maps and policy rules. According to the application service layer, a three-dimensional space-time model integrates remote sensing data, an Internet of Things sensor and GIS information, the consistency of analysis results and policies is automatically verified through a rule engine, and law enforcement suggestions are generated.

The invention belongs to the technical field of network security, and relates to a terminal security access control method and device, computer equipment and a storage medium, the method comprises the following steps: deploying an infrastructure for terminal security access, the infrastructure comprising a client, a security system and a server component; the method comprises the following steps: performing initial registration on terminal equipment, and realizing unique binding of a terminal identity and loading of a basic security policy by acquiring an equipment fingerprint, issuing a certificate and configuring a policy; the user identity and the equipment credibility are verified in real time through a multi-factor dynamic authentication mechanism; through signature verification and encrypted transmission, application and network communication authentication is carried out, and application legality, communication channel security and operation environment credibility are ensured; and after the authentication is passed, trusted connection is established, fine-grained authorization is implemented, and security isolation of a service layer is realized through dynamic token and API management and control. The problems that in existing terminal access control, the authentication dimension is single, the authorization granularity is rough, and dynamic management and control are lacked are effectively solved.

The invention provides an authority management method based on MCP and RBAC models, and relates to the technical field of authority management, comprising: acquiring a tool description set, a system cue word and a user task; and outputting a task response corresponding to the user task by using the MCP service layer. The MCP service layer comprises an MCP Host, a plurality of MCP Clients, a plurality of MCP Servers and a large language model; the MCP Host is used for acquiring user input and sending a response result corresponding to the user input to the user; the MCP Client runs in the MCP Host and is responsible for establishing connection with the MCP Server and communicating with the MCP Server; the MCP Server is used for generating an available tool description and selecting and executing a called tool; the large language model is used for generating tool calling description and generating task responses corresponding to user tasks.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

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 intelligent RAG enhanced large model auxiliary writing system, and relates to the technical field of artificial intelligence auxiliary writing. In order to overcome the defects of an existing writing auxiliary tool, the adopted scheme comprises an interface and user layer for receiving a writing demand and redisplaying a generation result; the service layer is used for deploying three types of intelligent agents including writing, interactive writing enhancement and checking; the platform layer is used for carrying out industry adaptation analysis, mixed retrieval and RAG enhancement on writing demands and then sending the writing demands to the large model layer; three intelligent agents of writing, interactive writing enhancement and checking are called in sequence based on the output of the large model layer, and then a text is redisplayed to the interface and user layer after fact accuracy verification is completed through a tense knowledge graph; the data layer is used for providing a multi-type data storage system; the large model layer outputs a paragraph-level text and a reference index based on a writing large model, the paragraph-level text is returned to the platform layer, and reference index information is fed back to the interpretability and security layer for tracing; and the infrastructure layer is used for providing bottom layer resource support for each layer.

The invention discloses a hierarchical priority task orderly scheduling method based on a virtual thread, which comprises the following steps: S10, receiving task parameters transmitted by a business layer, performing task packaging setting, outputting packaged task objects, and transmitting the packaged task objects to a multi-dimensional calibrator; s20, performing task expiration verification, task fusing verification and dependency verification in the multi-dimensional verifier, returning a passing or failing identifier and a failing reason, and if the passing or failing identifier does not pass, triggering a monitoring alarm to record a discard log; s30, the verified tasks enter a two-dimensional queue manager to be subjected to hierarchical enqueue processing and capacity dynamic adjustment, and the capacity dynamic adjustment uses a dynamic resource allocator to achieve elastic allocation of queue capacity and virtual threads based on load awareness; and S40, performing task execution and task retry management on the tasks in the two-dimensional queue.

The invention discloses a simulation data management method based on a servitization integrated architecture, and relates to the technical field of combat simulation and deduction, and the method comprises the steps: building a servitization integrated data management architecture according to a data collection layer, a data storage layer, a data management layer, a data management layer and a data service layer; based on a service integrated data management architecture, data standardization is carried out, a data model is designed, data fusion preprocessing, data integration and data analysis are carried out on collected multi-source heterogeneous data according to a data standard and the data model, data fusion and warehousing management are completed, and meanwhile balance and unification of data security and sharing are guaranteed. According to the invention, a one-stop and all-around data management solution can be provided for simulation application.

The invention provides an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: determining an energy scheduling target node and a node interaction strategy through a block chain network according to the real-time operation data of energy storage equipment collected by an Internet of Things platform, and generating a decentralized scheduling instruction; docking a target node edge calculation unit according to the instruction, and outputting network configuration parameters; global state monitoring is carried out through an end-edge-cloud collaborative architecture, and data of charging and discharging abnormity of a physical layer and communication abnormity of a service layer are acquired; generating an exception handling strategy based on an adaptive deep reinforcement learning model, recalculating an energy scheduling target node, forming a closed-loop control instruction, and feeding back the closed-loop control instruction to the block chain network; according to the invention, processing delay easily caused by dependence on a master-slave block chain structure in a shared energy storage scene is avoided; the adaptability to the dynamic environment is enhanced, the sudden electricity price change or equipment abnormity is handled through automatic adjustment, and the effect of decision making in the complex power grid environment is improved.

The invention provides an automatic production station working hour statistical system and method based on near field communication, the system comprises five layers of architectures including an identity recognition layer, a data acquisition layer, an edge computing layer, a cloud service layer and a terminal interaction layer, and the method completes the steps of initialization, NFC interaction triggering, multi-mode mistaken touch prevention verification and the like by means of the system. NFC, a sensor and a face recognition technology are fused, core modules such as task intelligent matching, energy consumption-man-hour linkage accounting and cross-station collaborative man-hour splitting are designed, and the system further has the functions of offline intelligent caching and multi-dimensional man-hour analysis. According to the invention, automation and precision of man-hour statistics are realized, single-station standardization and multi-station collaborative production scenes are adapted, data are ensured to be reliable and free of loss, deep fusion of production data is realized, enterprise hardware and management costs are reduced, and the intelligent manufacturing fine management level is improved.

The invention discloses a material batching and conveying process monitoring system based on digital twinning, and belongs to the technical field of industrial automation and intelligent manufacturing. The system comprises a physical entity layer used for storing, proportioning and conveying materials; the data acquisition and sensing layer is used for acquiring multi-source heterogeneous data of materials and equipment in the physical entity layer in real time; the digital twinborn model layer is used for constructing and operating a digital twinborn model synchronously mapped with the physical entity layer; and the application service layer is internally provided with a multi-modal fusion fault diagnosis module, and the multi-modal fusion fault diagnosis module is used for diagnosing material batching and conveying faults. According to the method, the digital twinborn and multi-modal AI algorithms are deeply fused, the early warning and accurate root cause positioning of equipment faults are realized, the diagnosis confidence is improved through evidence fusion, the process decision is optimized in combination with a simulation sand table, the production continuity and quality stability are improved, the non-planned shutdown and maintenance cost is reduced, and the system is endowed with the continuous self-evolution capability.

The invention relates to the field of mobile communication network operation and maintenance, in particular to a cross-domain fault positioning method and device of a VoLTE (Voice over Long Term Evolution) network and a medium, mainly comprising the following steps: collecting multi-source data of a physical layer, a virtualization layer, a bearing layer and a service layer in a VoLTE service chain; based on the network architecture hierarchy and the physical link information of the multi-source data, generating a dynamic end-to-end topological graph through association; when a plurality of network element warnings of a service layer are detected, checking an equipment state of a bearing layer associated with the service layer; if the devices of the bearing layer give an alarm at the same time, judging that the fault of the bearing layer is a root cause; if it is not detected that the devices of the bearing layer give an alarm at the same time, calculating a root cause probability based on a preset influence factor, and taking the node with the maximum probability as a root cause; and marking a node corresponding to the root cause and an associated link of the root cause in the dynamic end-to-end topological graph. According to the invention, the fault in the VoLTE network can be efficiently and accurately analyzed and positioned.

The invention provides an SDN (Software Defined Network) dynamic control system based on a cloud native architecture, and belongs to the technical field of SDN and cloud native. The system adopts a three-layer decoupling architecture of a service layer, a control layer and a data layer, wherein the service layer consists of a core network, a cloud platform and edge nodes and is used for issuing service requirements and executing a joint resource scheduling strategy; the control layer comprises four core modules, namely a micro-servitization SDN control module, a cross-domain collaborative engine module, an end-to-end service quality guarantee module and an edge collaborative optimization module, and is responsible for unified analysis, decision and scheduling to realize dynamic updating of a network strategy and multi-cluster collaborative optimization; the data layer is composed of an SDN switch and a router, and is used for executing a flow table rule and collecting network situation data. Through deep fusion of the SDN and the cloud native technology, the problems of upgrade interruption, low cross-domain collaboration efficiency and edge resource limitation of a traditional network architecture are solved, and the method can be widely applied to hybrid cloud management, industrial Internet of Things and intelligent city network scenes.

The invention relates to the technical field of power system security, and particularly discloses a ground wire management operation system based on ground wire cabinet monitoring, which comprises a physical sensing layer, an edge computing layer, a block chain consensus layer and an application service layer. The physical sensing layer collects the identity, the in-place state and the electrical parameters of the grounding wire; the edge calculation layer carries out local data processing and event judgment; the block chain consensus layer realizes data credible evidence storage and cross-domain sharing through an intelligent contract and a distributed account book; and the application service layer provides asset panoramic display, cooperative verification and risk early warning services. Through the above architecture, unified management and safety collaboration of data of the whole life cycle of the grounding wire are realized, and the safety operation and maintenance level of a power grid is improved.

The invention belongs to the technical field of aviation airborne system design, and provides a helicopter airborne system-oriented novel software architecture and a construction method thereof, and the architecture comprises a hardware abstraction layer, a framework resource layer, an intelligent platform service layer and an application software layer from bottom to top. And the hardware abstraction layer realizes decoupling of hardware and basic software of an operating system. The framework resource layer supports operation of a hybrid key operating system, supports and isolates tasks and application programs with different key levels, software bases and computing power requirements, and provides basic computing communication resources and AI computing power resources. The intelligent platform service layer comprises middleware, a servitization function unit and a servitization management unit, and the servitization function unit comprises atomic services and enhanced services. And the application layer interacts with the intelligent platform service layer and is decoupled from a bottom software and hardware environment. The framework is suitable for unmanned / manned helicopters, and generalization of platform support and maximum reuse of software assets are achieved.

The invention belongs to the technical field of financial science and technology, and discloses a multi-agent collaborative document-driven quantitative research and transaction self-evolution system and method. The system adopts a layered architecture, comprises a data and factor service layer, an execution engine layer, an agent layer, an interface service layer and a front-end control layer, and aims to solve the core problems that research and engineering implementation are separated in quantitative strategy research and development, code generation lacks interface constraints and strategies lack self-evolution ability. According to the system, through cooperative work of a research agent, a factor agent and a coding agent, a strategy idea described by a natural language is automatically converted into a standardized document, configuration and executable code; through a closed loop formed by self-monitoring, self-diagnosis and self-improvement intelligent agents, automatic diagnosis and optimization of problems of strategy overfitting, factor attenuation and the like are realized, continuous self-evolution of the strategy is driven, and full-life-cycle automatic management from strategy conception, factor design, automatic coding and real disk back-testing to feedback autonomous optimization is realized.

The invention relates to a digital twin-driven distributed warehousing system and method, and belongs to the technical field of intelligent logistics. The system comprises a sensing execution layer, a network transmission layer, an edge computing layer, a cloud platform layer and an application service layer. The method comprises the following steps: acquiring storage physical entity data through a perception execution layer; the data is transmitted to the edge computing layer and the cloud platform layer through the network transmission layer; in the cloud platform layer, a high-fidelity virtual warehouse is constructed and synchronized through a digital twin engine; using a multi-agent reinforcement learning algorithm to perform centralized training in a digital twin environment to generate a distributed cooperative scheduling strategy; and issuing the strategy to an edge computing layer and a sensing execution layer, and driving an autonomous mobile robot cluster to execute a cooperative task. According to the invention, step-type improvement of the warehousing system in flexibility, intelligence and cooperative capability is realized, and the problems of rigid bottleneck, non-optimal decision and low cooperative efficiency of a traditional system are solved.

The invention discloses a social group discovery system based on fused semantic information. The system comprises a presentation layer, a presentation layer, a service layer and a data layer. The presentation layer supports checking of a group division result; the display layer displays a user structure network, group dynamic division and group characteristic indexes through a visual tool; the service layer is composed of a semantic tag generation module, a user structure analysis module, a social group division module and a social group analysis module, semantic and structural features are fused through a graph attention network, and accurate group division is realized in combination with an adaptive embedded graph aggregation algorithm; and the data layer stores the original data set of the social platform and the processed data. The system inputs user texts into a large language model to generate semantic tags, node embedding is generated by calculating node association weights, group division is completed by combining an adaptive embedding graph aggregation algorithm, and finally group characteristics are quantitatively analyzed from the aspects of cohesion, conductivity and the like. The system is suitable for scenes such as vertical domain community mining.

One or more embodiments of the invention provide a file system, method and device applied to a modern browser. The file system comprises a storage layer and a service layer, and the storage layer comprises a running memory and a source private file system; the service layer comprises a joint file system, and the joint file system is obtained by combining a temporary file system instance and a persistent file system instance; wherein a first file directory which is realized by relying on a running memory is created under the temporary file system instance, a second file directory which is realized by relying on a source private file system is created under the persistent file system instance, and access paths of the first file directory and the second file directory are the same. The access priority of the persistent file system instance is higher than that of the temporary file system instance.

The invention discloses an intelligent document generation system and method based on generative artificial intelligence, and relates to the technical field of generative artificial intelligence and document processing, the system comprises an intelligent document processing service layer and a data resource layer, when the intelligent document processing service layer executes a task, required data is read from the data resource layer; the intelligent document processing service layer comprises a content generation module, a verification module and a coordination management module, and the content generation module is used for generating preliminary content; the verification module is used for verifying the preliminary content, generating structured feedback information when the verification is not passed, and driving the content generation module to perform iterative correction according to the feedback information; and the coordination management module is used for receiving tasks and performing overall scheduling of the system. According to the method, the AI system is endowed with self-checking and self-correcting capabilities, the problems of reliability and structure maintenance of content generation can be systematically solved, and the quality and credibility of automatic document generation are remarkably improved.