1277 results about "Service layer" patented technology

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

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 relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention discloses an ocean holographic environment comprehensive digital twinning system, and relates to the technical field of ocean monitoring, the digital twinning system comprises a data layer, a model layer, an application layer, an environment layer, a governance layer and a service layer, multi-scale spatio-temporal data is used as a substrate, and a virtual-real mapping and intelligent simulation technology is used to simulate the ocean holographic environment comprehensive digital twinning system. A full-dimension virtual mirror image covering the seabed, the middle sea and the sea surface is constructed, and the system serves core scenes such as a supervisor, scientific research cooperation and ocean engineering. According to the digital twin system, a six-layer layered architecture is adopted, edge computing and cloud computing collaboration are combined, a data-model-service-governance-environment multi-dimensional collaboration system is formed, data-driven decision making and dynamic optimization are achieved, and the marine monitoring efficiency and accuracy are improved.

The invention discloses a manufacturing enterprise data center system and method based on a large model, and the system comprises a data collection layer which is used for constructing a multi-source heterogeneous data collection system; the data storage layer is used for constructing a data warehouse system comprising multiple layers and realizing data lake storage; the data processing layer and the service layer are used for deploying a model and integrating an anomaly detection module, a multi-modal fusion engine and a prediction model; the service layer is used for an interface group, integrating a Drive / ClickHouse query engine and a Redis cache, and supporting multi-condition combined query and millisecond response; deploying a visual service module and an RBAC / ABAC authority control system to realize data grading sharing; and the application layer is used for developing a production optimization application, a supply chain management application and a CRM application. Through a uniform data acquisition interface and an efficient data acquisition tool, mass data inside and outside an enterprise can be integrated, data islands are broken, centralized management and sharing of the data are realized, and availability and value of the data are improved.

The invention discloses a logistics transportation intelligent prediction scheduling method and system based on digital twinning. The method comprises the steps that transportation carrier data and cargo data are acquired in real time through Internet of Things sensing equipment; constructing a logistics transportation digital twinborn model, wherein the logistics transportation digital twinborn model comprises a physical entity layer, a data fusion layer, a virtual model layer and a decision service layer; simulating a logistics transportation process, and deducing and predicting resource demands in a future time window; the method comprises the following steps: dynamically adjusting marshalling configuration and loading and unloading operation time sequence of transportation carriers by constructing a digital twin-driven elastic resource scheduling mechanism; and training and optimizing the logistics transportation digital twin model based on feedback data and scheduling execution deviation data in the logistics transportation process to form a closed-loop optimization system. The method has the advantages that logistics transportation data are simulated through the digital twin technology, logistics carrier scheduling is predicted, resource demand prediction and elastic scheduling are achieved, and the transportation efficiency is optimized. And the logistics process is ensured to efficiently and flexibly cope with uncertain factors.

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 discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

The invention provides a cross-platform network speed measurement and adaptive path switching method and device and a storage medium. The method comprises the following steps: receiving a target domain name and a multi-source DNS server list; extracting historical network quality data corresponding to the environment identifier and each candidate IP address, inputting the historical network quality data into a link quality prediction model to obtain a prediction score of each candidate IP address, and screening according to a preset threshold to obtain a target IP subset; performing active detection of limited times on each IP address in the target IP subset to obtain a detection index; calculating the comprehensive score of each IP address based on the prediction score and the detection index, determining the optimal IP address with the highest comprehensive score, and recording the DNS server address of the optimal IP address; and establishing a network connection path according to the optimal IP address, and providing a DNS server address of the optimal IP address for a service layer. According to the method and the device, the cross-platform maintenance cost can be reduced, the real-time performance and the performance overhead are considered, the path is dynamically optimized, and rapid connection switching is realized.

The invention provides a multi-level service priority dynamic scheduling simulation method and device, a medium and a product. The method comprises the following steps: constructing a priority label system; establishing a dynamic binding relationship between the service flow and the priority label; when a service layer generates a data packet, embedding a priority label for the data packet based on the dynamic binding relationship; extracting a priority label carried by the data packet on a protocol stack MAC layer, and ensuring that the priority label is unvarnished to a lower layer protocol in a lossless manner; and based on the priority labels carried by the extracted data packets, classifying the data packets into logic queues with corresponding priorities, and executing a differentiated scheduling strategy. The method can adapt to diversified requirements of data services of different types and priorities for network resources, effectively improves the simulation model accuracy, improves the simulation experiment efficiency and simulation real-time performance and reliability in a multi-service priority 5G communication network simulation scene, more accurately reflects the performance of a real network, and improves the simulation experience of the 5G communication network. And meanwhile, the simulation platform has higher expansibility and flexibility.

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

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 provides an intelligent business resource scheduling method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: 1, capturing equipment layer sensor data, business layer order data and logistics layer vehicle track data in real time through distributed data collection nodes, and carrying out the real-time acquisition of the data; a dynamic time window division algorithm is adopted to carry out time sequence alignment processing on the heterogeneous data flow, a ternary state vector matrix with a unified timestamp is generated, the row dimension of the matrix represents a collection time sequence, and the column dimension of the matrix corresponds to equipment layer operation parameters, a service layer task queue state and logistics layer transportation node coordinates respectively. According to the invention, through multi-source data fusion, three-dimensional space analysis and an intelligent algorithm, resource gap accurate identification and cross-level scheduling optimization are realized, and the business processing efficiency and the resource utilization rate are improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a law and regulation question answering system and method based on an industry large model, and the system comprises a data layer, a model layer, a service layer and an application layer. The data layer is responsible for collecting, storing and managing law and regulation data; the model layer comprises an industry large model and an RAG module; the service layer provides question and answer generation, multi-round dialogue management and security control core services; the application layer provides diversified application scenes for different user groups; the method has the beneficial effects that the general large model is finely adjusted in a targeted manner by using massive professional data in the legal field, so that the model can deeply learn logic association between connotation and extension of legal concepts and provisions and judgment goals of cases. Through the fine adjustment, the model can accurately grasp the core of the question when facing a complex legal question, provides an answer meeting a legal professional standard, and ensures the accuracy and preciseness of the answer, thereby effectively solving the problem of understanding deviation of professional knowledge.

The embodiment of the invention discloses a management method and system for MCP server instances. The method comprises the following steps of receiving configuration changes of a plurality of MCP server instances or a plurality of MCP tools through an MCP server registration management page; registering a management end through the MCP server, storing configuration data corresponding to the configuration change in a storage medium, and issuing a configuration change event message; a plurality of MCP server instances are operated in an isolated mode through the MCP server process according to the configuration data, and each MCP server instance achieves safe isolated operation under cooperative support of an HTTP interface service layer, a session manager, a tool manager and an Apikey permission checker provided by the MCP server process. According to the embodiment of the invention, the multiple MCP server instances and the multiple MCP tools are dynamically configured through the MCP server registration management page, the multiple MCP server instances can be quickly published according to the service scene, and the development and maintenance cost is reduced.

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 relates to a home safety monitoring system, which comprises a hardware layer, a data processing layer, an analysis decision-making layer and an application service layer, the hardware layer comprises a set top box, a multi-angle camera, a high-sensitivity microphone, an environment sensor and wearable equipment, and is used for collecting multi-source sensing data; the data processing layer is connected with the hardware layer and is used for preprocessing the multi-source sensing data to generate a standardized data stream; the analysis decision-making layer is connected with the data processing layer and comprises a dynamic weighted scoring model, and the dynamic weighted scoring model dynamically adjusts weight parameters according to real-time data and historical behaviors and generates a comprehensive risk score in combination with a cross-modal verification correction coefficient and a historical behavior correction term; and the application service layer is connected with the analysis decision-making layer and is used for triggering early warning according to a risk scoring result and linking emergency resources to respond.

The invention discloses an electric power AI digital virtual business system, which relates to the technical field of electric power business systems and comprises a data layer, an application service layer and a safety guarantee layer. The data layer comprises a data acquisition module and a data processing module, the data acquisition module is used for collecting customer data, power business data and external data in real time and providing basic data for subsequent data processing and analysis, and the data processing module is used for sorting, storing and preliminarily analyzing the collected original data. According to the invention, the virtual business hall is constructed by means of VR / AR technology, real-time guidance of a virtual guider is cooperated, and natural dialogue interaction is realized in combination with the intelligent interaction module, so that the business handling process is more intuitive, the sense of participation of customers is improved, the customers do not need to go to an entity business hall and handle businesses and consult questions anytime and anywhere through various terminals, and the business handling efficiency is improved. For example, a one-key electricity handling module can be used to complete a complex electricity handling process at home, so that time and energy are greatly saved, and service accessibility is improved.

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 relates to the technical field of data processing, in particular to a main data management method and system based on enterprise data governance, and the method comprises the steps: defining a basic model structure based on an enterprise general main data object, and storing the basic model structure in a metadata form; obtaining a business system change event and a manual expansion demand and converting the event and the demand into a structured description; zero-coding extension configuration of attributes, classification and association relationships is completed through a visual interface; executing interface adaptation, data mapping adaptation and rule conflict detection; and synchronizing the effective extension model to a service layer, a cleaning layer and a monitoring layer. The system comprises a demand awareness module, a dynamic modeling module, an extension configuration module, an adaptation engine module and a life cycle management module. According to the method, full-process management of the master data model from demand capture to business landing is realized, and the model expansion efficiency and the business adaptation capability are improved.

The invention relates to an AI-based service anomaly detection method, and belongs to the technical field of data processing. Comprising the following steps: acquiring runtime data of a production environment, including service layer data, hardware layer data, network layer data and link layer data; supplementing the collected data, carrying out classified marking on data information, and defining a hierarchical relationship among the data; sending the data to an AI model, carrying out correlation analysis through a time dimension and a space dimension, and carrying out link analysis; the AI large model updates the knowledge graph and generates the model according to each operation and maintenance; and displaying the fault knowledge graph by using a graphical front end. According to the AI-based service anomaly detection method provided by the invention, multi-dimensional data are comprehensively collected and correlation analysis is carried out, so that rapid positioning of a fault is realized. The fault discovery time is greatly shortened through the real-time detection capability, and the fault processing efficiency is further improved through incremental updating and intelligent optimization of the knowledge graph.

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 relates to the technical field of big data, and discloses a massive small file reading optimization method, system and device and a medium, and the method comprises the following steps: scanning small files in a distributed file system by using a Spark calculation engine to obtain metadata information of the small files; based on the metadata information, small files are classified, and the small files with the same type and data relevance are classified into one group; the resource states of cluster nodes are monitored in real time, the resource states comprise the processor utilization rate, the memory occupancy rate and the I / O load, the load score of each node is calculated, and small file task quotas are dynamically allocated; executing small file merging operation in parallel by utilizing a Spark memory calculation engine to generate a merged file containing a plurality of original small files; creating and maintaining index information in an external database, and recording the position and attribute of each original small file in the combined file; and providing a read-write interface through the special service layer, and positioning and accessing specific small file contents in the combined file.

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.