577 results about "Service resource" patented technology

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes in the fields of medical health, financial science and technology, intelligent interactive service and the like, and discloses a business service processing method based on dialogue recognition, which comprises the following steps: performing semantic analysis on current dialogue content, determining a key problem type, and obtaining a business processing record of a dialogue subject; under the condition that the service processing problem type is identified, obtaining a target operation record in combination with problem occurrence time and a current time interval, constructing a user operation behavior track based on an interface interaction event in the target operation record, and identifying a deviation node in the track; and generating a business processing exception report, matching the target service item in the service resource library according to the service demand keyword and the business processing record, and outputting operation guidance information. By fusing dialogue semantic analysis and user behavior trajectory modeling, automatic identification and accurate service recommendation of customer problems are realized, and the business processing efficiency and the service intelligence level are improved.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

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 discloses a service node deployment method, system and device, equipment and a storage medium, and can improve the deployment and debugging efficiency of service nodes. The method comprises the following steps: acquiring MCP Server configuration information and resource configuration information of service resources; generating deployment parameters used for operating the MCP Server and the service resources; sending the deployment parameters to a target server, so that the target server deploys an operation environment corresponding to the MCP Server based on the deployment parameters; in response to a debugging instruction for the service resources, performing debugging operation based on the running environment to obtain a debugging result; under the condition that the debugging result indicates that the service resource meets the preset operation condition, a service node construction request is sent to a target server, so that the target server constructs an MCP Server node in a to-be-called state, and node metadata of the MCP Server node is generated; and receiving the node metadata sent by the target server, and registering the node metadata in the MCP Server management platform.

The invention relates to the technical field of supply chain collaboration, in particular to a multi-level supply chain service ecological digital twin network collaborative optimization system which comprises a system and is used for building, optimizing and executing a supply chain digital twin network. The data acquisition module is used for acquiring physical entity data and service state data of a whole link of a supply chain in real time through an Internet of Things device and an information system interface; the data fusion and twinning construction layer is in communication connection with the physical perception layer and is used for carrying out fusion and standardization processing on the collected multi-source heterogeneous data and is based on a unified supply chain knowledge graph; according to the method, supply chain whole-process accurate mapping and real-time visualization are realized by constructing a multi-level digital twinborn body, automatic optimization is carried out among multiple targets such as cost aging carbon emission by applying an intelligent optimization algorithm, third-party service resources are dynamically scheduled based on a multi-attribute utility theory, cross-organization credible collaboration is realized by means of a block chain intelligent contract, and the service efficiency is improved. And a complete perception decision execution optimization intelligent closed loop is formed.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

The invention discloses a resource creation method, device and equipment for model service and a medium, and relates to the technical field of computers, and the method comprises the steps: converting a model service deployment demand input by a user into a standardized service template; analyzing the standardized service template to obtain a model service resource scheduling instruction, the model service resource scheduling instruction being used for indicating a target resource pool; and determining a target driver corresponding to a target resource pool indicated by the model service resource scheduling instruction, so as to create cloud resources of the model service in the target resource pool by using the target driver, thereby realizing automatic resource creation of the model service, greatly shortening the deployment period through full-process automatic operation, reducing the labor cost, and improving the deployment efficiency. The heterogeneous resource management process is effectively integrated, the delivery efficiency is remarkably improved, and an enterprise can flexibly and efficiently deploy various AI model services on a private cloud platform and deal with quickly changing business requirements leisurely.

The invention discloses an inference service resource allocation method, electronic equipment and a readable storage medium, and relates to the technical field of computers, the inference service resource allocation method comprises the following steps: obtaining a model file of an inference service, and analyzing the model file to determine a minimum storage resource demand required for running the inference service; determining a resource specification meeting the minimum storage resource requirement, wherein the resource specification is used for representing nodes required for running the reasoning service and storage resource configuration corresponding to the nodes; and according to the storage resource state of the node in the reasoning cluster, determining the resource configuration strategy matched with the resource specification, and configuring the storage resource in the reasoning cluster according to the resource configuration strategy to operate the reasoning service, thereby solving the technical problems of low automation degree and poor utilization rate of the storage resource when resource configuration is performed on the reasoning service, and improving the resource configuration efficiency. The error risk and cost of manual operation are reduced, and the utilization rate of storage resources is also improved.

The invention discloses a service-oriented distributed resource automatic extraction and packaging method and a server, and relates to the technical field of micro-services, and the method comprises the following steps: querying to obtain a service packaging task state according to a service identifier; determining whether a packing task being executed exists or not; if not, performing dynamic dependency analysis on the target service corresponding to the service identifier during operation, and constructing a service dependency graph; detecting cyclic dependency based on the service dependency graph, and if the cyclic dependency exists, determining a resource acquisition range; obtaining multi-dimensional service resources from the plurality of distributed data sources; carrying out resource aggregation processing on the multi-dimensional service resources to generate a service basic information file, and packaging the multi-dimensional service resources and the service basic information file to generate a service resource package; and uploading the service resource packet to a distributed service management platform for deployment, and creating an asynchronous task. According to the method, the dependency relationship dynamically generated during operation can be effectively captured, the error rate is reduced, and the efficiency is improved.

The invention belongs to the technical field of wireless network communication, and discloses a dual-time-scale optimization method for multi-unmanned aerial vehicle cooperative auxiliary task unloading and caching, which comprises the following steps: setting an updating time slot of an unmanned aerial vehicle service caching decision as a first time slot, updating time slots of the unmanned aerial vehicle flight path decision, the computing resource allocation decision and the user unloading decision are second time slots; wherein the first time slot is composed of a plurality of second time slots; constructing a first optimization objective function and a second optimization objective function; in each first time slot, obtaining requirements of a user task for various service resources, and updating a service cache decision of the unmanned aerial vehicle according to the first optimization objective function; and in each second time slot, obtaining an unmanned aerial vehicle position, a user task and an unmanned aerial vehicle service cache, and updating an unmanned aerial vehicle flight path decision, a computing resource allocation decision and a user unloading decision according to the second optimization objective function.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention provides a kernel function trajectory-based deep learning inference service performance analysis method, which comprises the following steps of: based on service indexes and hardware theoretical computing power acquired from a production cluster, defining floating point operation times per request (FPR) index to quantify service resource efficiency, and identifying high FPR hotspot services; positioning a reasoning iteration candidate boundary based on a GPU kernel function trajectory, verifying iteration integrity through fingerprint matching and chi-square test, and calculating a second reasoning iteration number IIPS and a model reasoning efficiency MIE; aiming at calculation-intensive operators on the key path, combining a dynamic Roofline model to estimate an operator theoretical performance upper limit, and based on actual execution time, calculating efficiency and a BottleScore index to identify a key bottleneck operator; and outputting targeted optimization suggestions according to analysis results of service efficiency analysis, model efficiency analysis and operator efficiency analysis. According to the method, the inference behavior pattern can be automatically identified from massive kernel trajectories, and the efficiency loss of each level is quantified.

The invention relates to the field of power supply work order service management, in particular to a power supply service management method and system based on multi-source data. The method comprises the following steps: collecting a current fault repair work order based on a power supply service platform, carrying out deep work order text semantic analysis and standard fault instance modeling, and constructing a global fault instance map; recognizing a client real-time repair call audio stream, carrying out time window voice analysis one by one, and carrying out intelligent fault work order filling, thereby obtaining a real-time audio intelligent fault work order; performing fault demand decomposition on the real-time audio intelligent fault work order and the global fault instance map, performing power supply service resource dynamic configuration regulation and control, and constructing a service resource regulation and control engine; and performing global fault work order real-time processing based on the service resource regulation and control engine, performing overdue work order early warning, and generating a service timeliness early warning strategy. Through dynamic resource allocation and preventive work order creation, the power supply service efficiency, the power grid reliability and the intelligent level are improved.

The invention discloses a service-based killer chain analysis construction method, which belongs to the technical field of cooperative combat, and comprises the following steps: constructing a service resource pool, and setting a service discovery mechanism; on the basis of the fusion situation set, a situation switching event is recognized so as to perform local updating, and a three-level situation view is constructed; constructing a directed hyperedge, generating a directed hypergraph in combination with function category constraints, generating a dimensionality reduction state space based on multi-dimensional constraints, and generating an initial combat candidate set; configuring a management node, sending task invitation information to the initial combat candidate set in combination with the subtasks, generating a response scheme, performing adaptive screening, generating an initial killing chain, and performing failure verification; constructing a servitization twinborn body, deducing the initial killing chain, and executing delay at a calculation end so as to perform elimination recombination or delay optimization; a service combination template is constructed, an efficiency feedback mechanism is established, the superposition influence of frequent situation switching and state space catastrophe is cooperatively solved, and the real-time performance of generation of the killing chain is remarkably improved.

The invention discloses a service-oriented dynamic resource management method and system. According to the method, indexes such as a CPU and a memory on a physical node are directly collected, monitoring errors of a virtualization layer are avoided, it is ensured that resource data truly reflects the hardware occupancy condition, physical resource occupancy is attributed to specific services through service labels, abstract conversion from hardware resources to service requirements is achieved, and a foundation is laid for differentiated management; the threshold value of the resource utilization rate of each service is calculated hierarchically according to the type of the service, the core service is guaranteed preferentially, if the resource utilization rate exceeds the corresponding threshold value, manual intervention is not needed, a standardized control interface is automatically called to execute the resource expansion operation, and the accuracy, high efficiency and adaptability of resource allocation are achieved.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The embodiment of the invention discloses a micro-service intrusion tolerance scheduling method and system for a cloud edge collaborative network. A specific embodiment of the method comprises the following steps: constructing a Markov decision process model of a resource and security joint state space according to collected operation information, and analyzing a scheduling demand and a security risk of a micro-service instance; calculating a resource allocation strategy of each node according to the total resource amount, the minimum demand of the micro-service resource in the cloud edge collaborative network and the bargaining weight so as to perform resource allocation; a multi-agent reinforcement learning algorithm is adopted, and redundant deployment, migration paths and cleaning strategies of the micro-service instances are dynamically adjusted; issuing the optimized scheduling scheme to a cloud data center and an edge node; and monitoring the running state and the safety condition of the micro-service instance in real time, and dynamically adjusting the scheduling strategy. According to the embodiment, the security of the micro-service in the cloud edge collaborative network can be effectively improved, the risk that the system is attacked is reduced, and the resource utilization efficiency and the service performance can be optimized.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

The invention provides a space-air-ground three-dimensional communication cross-network cooperative reliable transmission method, device and system. The system comprises a generalized service layer, a resource adaptation layer and a fusion network layer, the generalized service layer receives a service request initiated by a user, analyzes the service request to obtain a service attribute, and sends the service attribute to the resource adaptation layer; the resource adaptation layer performs service resource adaptation mapping by using a reinforcement learning algorithm according to the service attribute and the current network state information, generates a service deployment strategy and issues the service deployment strategy to the fusion network layer; and the fusion network layer schedules a corresponding network component according to the received service deployment strategy, and completes path selection, forwarding scheduling and coding strategy application tasks corresponding to the service request. The system has multi-protocol identification and unified packaging capabilities, and can receive multiple data formats and carry out standardized processing. According to the method, multi-path flexible scheduling, network coding and covert channel methods are integrated, and the limitation of an existing communication system in the aspects of protocol compatibility, transmission efficiency and reliability is broken through.

The invention belongs to the technical field of scenic spot management, and particularly relates to a scenic spot global perception and service system based on agent cooperation. The system can collect and analyze tourist touring information in real time, the tourist touring information comprises real-time positions, moving tracks and regional staying time of tourists, so that tourist bearing pressure values of all touring sub-regions in a future time period can be accurately predicted, and in specific implementation, a trend analysis module recognizes time change characteristics of tourist distribution based on a global people flow thermodynamic diagram, so that the tourist bearing pressure values can be accurately predicted. And the resource coordination module generates a resource scheduling instruction according to the regional bearing pressure value, dynamically coordinates the service resources in each touring sub-region to ensure the balance of the service capability of each region, and collects tourist feedback information and evaluates the resource coordination effect through the feedback optimization module. And on the basis of the resource scheduling correction instruction, resource scheduling is optimized and adjusted, so that the service resource configuration efficiency and the tourist satisfaction degree are continuously improved.

The invention relates to the field of information security, discloses a service resource access method and device, computer equipment, a computer readable storage medium and a computer program product, and is applied to a server. The method comprises the following steps: in response to a login verification request sent by a client, obtaining a to-be-verified verification code returned by the client; under the condition that the verification code to be verified passes verification, generating a target encryption token according to token element information of the client, and storing the target encryption token to a target database; in response to a resource access request sent by a client, acquiring authentication information; decrypting the encrypted token to be verified according to the access channel information, and acquiring a stored token under the condition that a plaintext token obtained by decryption passes verification; and under the condition that the stored token is matched with the to-be-verified encryption token, sending a target service resource requested to be accessed by the resource access request to the client. By adopting the method, the technical effect of improving the flexibility of a token verification mechanism can be realized.

The invention relates to the technical field of public service networks, in particular to an adaptive network evolution public service radius measuring and calculating method, which comprises the following steps that: according to service contents provided by a public service, the public service is divided into a plurality of functional units, and each functional unit comprises at least one service point; constructing a public service network matched with the public service; setting a network evolution mechanism for the public service network to realize self-adaptive evolution of capacity, connection and modular growth of the public service network; setting a dynamic adjustment strategy to realize resource flexible scheduling of the public service and optimization of a service coverage range; obtaining a service coverage area, obtaining a service radius of each service point in the service coverage area based on a multilayer network model, and forming an optimal service radius vector set in combination with the number of demand points covered by each service point and resource consumption under each service radius; and determining the optimal coverage radius and the optimal service radius of each service point.

The invention relates to the technical field of intelligent operation and maintenance, and discloses an operation and maintenance visual data management platform, which comprises a relation graph construction module used for constructing a relation graph based on a physical deployment relation and a logic calling relation between a physical server and a logic micro-service, and calculating betweenness centrality characteristics and a global influence value of each node; the service resource coupling module is used for receiving the service and resource index data, calculating a normalized ratio based on a preset sliding time window, and generating a resource load matching degree feature; the anomaly detection module is used for constructing an anomaly detection model and outputting an anomaly score; and the self-adaptive alarm decision module is used for dynamically adjusting the feature weight by adopting a reinforcement learning algorithm according to the service scene label, and generating an early warning measure according to the abnormal score. According to the invention, fusion perception and intelligent early warning of the system structure and the resource state are realized, and the accuracy of anomaly detection and the intelligent level of operation and maintenance response are improved.

The invention belongs to the technical field of cloud native and automatic operation and maintenance, and discloses a micro-service resource automatic telescoping system and method based on multi-dimensional state evaluation. Comprising a data collector responsible for acquiring service indexes in real time, a decision engine for performing multi-dimensional state evaluation to generate a resource adjustment strategy, and a decision executor for executing an adjustment decision. In order to realize high intelligence and adaptivity, the core control loop also works cooperatively with three key auxiliary modules: a dynamic threshold management module responsible for dynamically learning and resetting a threshold, an event detection engine used for identifying a load mode, and a genetic algorithm optimizer capable of dynamically optimizing the weight of a decision model. The system disclosed by the invention is a lightweight and responsive micro-service real-time automatic telescopic system with high self-adaptive capability.

The invention discloses an enterprise different-stage data matching service method based on AI artificial intelligence. The method comprises the following steps: S1, collecting operation data of an enterprise; s2, preprocessing the operation data; s3, extracting a time characteristic index sequence, and constructing an enterprise development stage evolution path; s4, constructing an enterprise development stage identification model, and generating an enterprise development stage label and a matching trend vector; s5, taking the enterprise development stage label and the matching trend vector as query conditions, and retrieving candidate service resource items; and S6, calculating a basic matching score by using the service resource matching model, performing directional adjustment, and outputting a recommended service resource list. According to the method, AI modeling and a comparative learning mechanism are fused, enterprise stage identification and trend-driven recommendation are realized, and the method has the advantages of high intelligence, accurate matching and high adaptability.

The invention discloses a tire fault real-time positioning and maintenance service matching method. An information acquisition end, a fault judgment end, a report generation end, a service screening end and a service matching end are included. Tire state parameters and current accurate geographical location information of a vehicle are collected in real time, when it is monitored that tire parameters exceed a preset normal threshold range, it is judged that a tire breaks down, fault information and positioning information are sent to a cloud server together, a fault degree evaluation model is started, and an evaluation report is generated. A maintenance service resource database is established, a cloud server automatically screens maintenance service providers according to tire fault positioning information, an available maintenance service resource list is formed, and maintenance service matching is performed by using an intelligent matching algorithm; according to the invention, fault top-speed early warning is realized, the response time is shortened, the on-site processing time consumption is reduced, the resource waste is avoided, the fuel oil and labor cost is reduced, excessive maintenance or insufficient maintenance is avoided, and the service is transparent and traceable.

The invention relates to the technical field of intelligent travel terminals, and discloses a multi-protocol compatible intelligent travel terminal AI service self-adaption method. The method comprises the steps of firstly collecting travel service request data in a multi-protocol environment, then constructing a multi-protocol adaptation model through dynamic protocol conversion middleware, and unifying heterogeneous protocol requests; and dynamically allocating service resources by adopting a reinforcement learning framework, predicting user travel demands based on a hidden Markov model, and adjusting service recommendation priorities. And constructing a multi-modal interaction adaptation engine to fuse the multi-modal features to generate a unified service response. In addition, the system is also provided with a protocol compatibility verification module, a service priority dynamic scheduling algorithm, a federated learning driven user preference updating model and a fault-tolerant protocol adaptation engine. According to the invention, the AI service adaptation capability of the intelligent travel terminal in a multi-protocol environment is improved, the resource allocation is optimized, the user demand prediction accuracy and the interaction experience are improved, and the reliability and the fault tolerance of the system are enhanced.

The invention discloses a kano-based internet nursing service demand classification system, and relates to the technical field of internet medical treatment and artificial intelligence, and the system comprises a local Kano model training module, a safety parameter aggregation module, a global model updating and distribution module, a local model updating and federated evaluation module, and a privacy enhancement module. According to the method, a federated learning framework and a multi-layer encryption protocol are adopted, a cross-mechanism data collaboration mechanism is constructed, user data of all participating mechanisms are subjected to local closed-loop processing, model iteration is completed only through encryption parameters, the cross-domain transmission risk of original data is avoided, fusion extraction of multi-source data features is also achieved, and the user experience is improved. The global model integrates nursing demand laws of different regions and different types of institutions to form a demand classification standard covering a whole scene, provides a cross-region and cross-crowd unified demand judgment standard for an Internet nursing platform, and supports cross-domain scheduling and configuration of service resources.