178 results about "Differentiated service" patented technology

The invention discloses an intelligent flow arrangement method based on fusion expert network and deep reinforcement learning, which comprises the following steps: collecting network node and link state data in real time, and constructing a time sequence input vector and a topological graph structure; a time sequence neural network and a graph neural network are used for extracting traffic spatial-temporal features and node topological features respectively, future traffic is predicted through a classification network after fusion, and coarse-grained arrangement of network slices of different service levels is completed; modeling resource scheduling into a multi-agent Markov decision process, and designing a state space, an action space and a reward function; a deep reinforcement learning agent is initialized, and training is carried out through interaction experience; fusing a pre-trained expert strategy network, and constructing a total loss function to optimize network parameters; and finally generating an intelligent strategy capable of dynamically optimizing the flow path and resource allocation according to the real-time state. According to the invention, efficient resource scheduling under multi-service differentiation service quality requirements can be realized.

The invention relates to the technical field of wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The embodiment of the invention discloses a charging cost analysis method and system for a liquid cooling over-charging terminal, and belongs to the technical field of data analysis. According to the method, through multi-dimensional data acquisition and deep behavior analysis, accurate description of user charging behavior characteristics can be realized, the accuracy of charging demand prediction and resource scheduling is effectively improved, and the user behavior characteristics are converted into quantifiable parameters, so that the user experience is improved. According to the invention, the formulation of differentiated service strategies can be realized to enhance the personalized adaptation capability of the charging service. Besides, the generation and execution mechanism of the cross-terminal dynamic scheduling strategy can optimize the distribution efficiency of the power resources in the space-time dimension and relieve the local overload problem in the peak period, and the cooperative adjustment function of the real-time power supply parameters and the cost calculation logic can guarantee the charging efficiency and balance the operation stability of the power grid at the same time. In conclusion, the method can significantly improve the resource utilization efficiency of the charging facility.

The invention discloses a low earth orbit satellite network digital twin drive switching optimization method and system, and relates to the technical field of inter-satellite communication, and the method comprises the steps: building a digital twin system which is synchronously evolved with a physical LEO satellite network; predicting the link quality between each candidate satellite and the user terminal in a future time window based on a digital twin system; dividing a time window into a plurality of continuous time slices, constructing a directed weighted dynamic graph model which takes a satellite-time slice as a node and takes link comprehensive utility cost as an edge weight, and searching an optimal path in the directed weighted dynamic graph model by utilizing a shortest path algorithm so as to determine an optimal switching opportunity and a target satellite sequence; the optimal switching strategy is issued to a user terminal to be executed, and a link quality prediction result is corrected in real time through a feedback mechanism of the digital twin system to form closed-loop optimization; the method can effectively meet differentiated service quality requirements in a multi-service scene, and is suitable for a high-dynamic LEO satellite communication system.

A method is provided that is performed by a first node of a fat tree network for management of a plurality of traffic flows having differentiated service requirements. The method includes receiving, from a simulation model or a testbed environment, joint observations for the traffic flows. The traffic flows correspond to routing paths comprising different combinations of at least one leaf node, at least one spine node, and at least one super spine node of the fat tree network per routing path. The method further includes identifying, with a first reinforcement learning model, a first action to take to reduce or prevent congestion at the first node. The first action includes a reconfiguration of the first node for an identified routing path. The method further includes outputting, to a controller node, the reconfiguration of the first node.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

The present invention discloses a network slice access control method and system based on congestion control, including the following steps: S1, setting up an initial congestion control coefficient for any network slice, and calculating the slice throughput mean and congestion control threshold; S2, realizing differentiated service priority identification according to the service priority calculation of the network slice; S3, evaluating the severity of the current timeout of each service and realizing dynamic control of service timeout; S4, selecting a slice network to realize slice access of newly arrived services; S5, adjusting the congestion control coefficient by comparing the congestion rate according to the target slice applied for the new service access, realizing congestion window update, and thus realizing dynamic access control according to the slice status. The present invention realizes access control of different services by controlling the slice congestion degree under the premise of certain 5G network slice resources, effectively controlling the congestion rate within the slice network, and solving the collision problem of services within the slice.

The invention discloses an electricity customer credit rating management method and system based on a differentiated service strategy, particularly relates to the field of energy service, and solves the problem of cold start user credit evaluation deviation and power service resource dynamic adaptation optimization. Seamless migration of credit scoring parameters and smooth transition of user portraits are realized by introducing multi-dimensional feature similarity analysis of virtual data and real data and a time-driven weight attenuation strategy, and continuity and adaptive optimization capability of an evaluation system are ensured; meanwhile, an intelligent service matching network is constructed based on the credit rating updated in real time, and a closed-loop service management and control system driven by data feedback is formed by dynamically adjusting a prepayment mode threshold value and an electric charge overdraft elastic interval strategy, so that the credit value of a user is deeply associated with the power resource configuration efficiency, and the user experience is improved. The operation risk and the service cost of a power grid enterprise are reduced, and finally collaborative optimization ecology considering energy safety management and user behavior guidance is established.

A method for facilitating paging policy differentiation, performed by a Session Management Function (SMF) is provided. The method includes determining that a paging policy differentiation feature is supported by a User Plane Function (UPF) (325). The method includes sending, toward the UPF (325), a message instructing the UPF (325) to insert a Paging Policy Indicator (PPI) value (110) into downlink packets based on a Differentiated Services Code Point (DSCP) value (106). The message includes DSCP-to-PPI control information.

The invention discloses a financial user behavior analysis method and system based on a multi-feature large model, and belongs to the field of financial user data analysis and processing. According to the method, financial feature vectors of a user are constructed by collecting historical financial data (including transaction unit price, quantity, financial product total price, transaction rate and the like) of the user, and the cosine similarity of any two feature vectors is calculated to perform mean clustering, so that an initial financial category is obtained. Furthermore, through a multi-threshold segmentation and factor analysis method, the initial category is optimized, independent factors and information contents thereof are extracted, a separation necessity value and a separation value are calculated, and finally a plurality of user categories are divided and financial user portraits are constructed. According to the method, a multi-dimensional data source and various data modeling technologies can be effectively fused, the refined description capability of user behaviors is improved, and accurate recommendation and differentiated services in the financial field are supported. The system comprises a data acquisition module, a clustering analysis module, a clustering optimization module, a category subdivision module, a factor analysis module, a category division module and a user portrait construction module, and is suitable for financial user behavior analysis, marketing and personalized recommendation.

The invention discloses an AI dynamic game virtualization resource scheduling system and method for a 5G low-delay service, and aims to solve the problems of insufficient QoS guarantee and low resource utilization rate caused by insufficient static rule adaptability, lack of service differentiation scheduling and low fusion degree of a game model and AI. A service feature extraction module is used for distinguishing a low-delay service (T = 0) and a common service (T = 1), constructing an AI dynamic game model taking the two types of services as participants, defining a payment function comprising a QoS satisfaction function and a resource consumption cost function, solving Nash equilibrium by adopting a deep reinforcement learning algorithm, outputting an optimal resource demand strategy, and realizing the optimal resource demand strategy. And the bandwidth and CPU allocation are dynamically adjusted through the virtualized resource management module. According to the invention, the delay compliance rate of the low-delay service can be improved by 20%-30%, the resource utilization rate of the common service can be improved by 15%-25%, the strategy iteration period is shortened to millisecond level, and differential fine scheduling and dynamic optimization in a 5G mixed service scene are realized.

The invention provides an end-network cooperative transmission system and method for a multi-mode network. The system comprises a terminal service demand layer which is used for identifying the service type of a terminal in a multi-mode network, generating structured service demand information and transmitting the service demand information to a network collaborative decision-making layer; the network cooperation decision-making layer is used for fusing the service demand information and the link state information, generating a transmission optimization strategy including path selection, priority mapping, bandwidth allocation and rate control, and transmitting the transmission optimization strategy to the transmission optimization strategy execution layer; and the transmission optimization strategy execution layer is used for executing a transmission optimization strategy, collecting link state information in the multi-mode network in real time through INT, and transmitting the link state information to the network cooperation decision layer. According to the method, efficient scheduling of heterogeneous network resources and high-reliability transmission guarantee of differentiated services are achieved, and the method is suitable for key service scenes sensitive to time delay and reliability, such as Internet of Vehicles and ultra-high-definition video transmission.

The invention relates to the technical field of large language models, in particular to a high-concurrency large language model high-speed reasoning deployment method, which comprises the following steps of: extracting a weight coefficient corresponding to a user identity identifier, an emergency degree numerical value mapped by a request type and a resource occupation value converted by an estimated calculation amount according to the user identity, the request type and the estimated calculation amount; according to the method, the dynamic priority is generated by performing comprehensive operation on the user identity, the request type and the estimated calculation amount, so that differentiated services for reasoning tasks are realized, interactive requests with high urgency degree can bypass batch processing tasks with long time consumption, the response delay and delay jitter of key services are reduced, and the user experience is improved. Meanwhile, according to the complexity of the request text and the geographic coordinates of the user, the task is intelligently routed to the model node with the most suitable scale in the distributed network, so that the wide area network transmission delay is greatly reduced through edge processing, and the computing power waste caused by using a super-large-scale model to process the simple task is also avoided.

The invention relates to the technical field of information processing, in particular to an elastic capacity expansion and contraction method, device and equipment for a container arrangement platform and a storage medium, and solves the problem of insufficient service perception of a traditional HPA by collecting resource indexes and service indexes of containers in real time and performing joint modeling on the service indexes and system indexes. The multi-index joint prediction model is used for predicting resource requirements in a certain time in the future, differentiated service priority weights are distributed for different service services, the number of Deployment copies is dynamically adjusted based on the resource requirements and the service priority weights, capacity expansion and shrinkage adjustment information is generated based on changes of the number of the Deployment copies, and the capacity expansion and shrinkage adjustment information is used for adjusting the capacity expansion and shrinkage of the different service services. According to the method, the response effect on burst flow is improved, the operation state of the capacity expansion and contraction adjustment information is monitored in real time, when the monitoring information is abnormal, the rollback strategy is executed, the abnormal feedback information is generated, response is rapid, and the capacity expansion and contraction requirement of the delay sensitive application container is met.

The invention discloses a dynamic optimization method and system for a service function chain of a 5G core network, and relates to the technical field related to mobile communication, and the method comprises the steps: collecting network service data in real time through a three-dimensional perception module; decomposing a service function chain optimization problem into a multi-stage decision model; decomposing into a long-term UPF deployment main problem and a short-term traffic scheduling sub-problem, and solving to obtain a global optimal solution; dynamically determining UPF deployment positions and number; dynamically selecting a traffic forwarding path meeting the service level; and constructing a dual-node deployment framework at the core layer and the edge layer, and executing a differentiated service function link control strategy. The technical problems of low resource utilization rate, high energy consumption and poor service adaptability caused by lack of dynamic collaborative optimization and service function chain static optimization of UPF deployment and traffic scheduling in the prior art are solved, and the technical effects of improving the resource utilization rate of the edge server, reducing the network energy consumption, optimizing the service quality and guaranteeing the user experience are achieved.

The invention discloses a multi-tenant resource demand prediction method based on an LSTM-XGBoost fusion model. The multi-tenant resource demand prediction method comprises the following steps: constructing multi-dimensional input characteristics including a tenant historical resource usage amount, a task priority, an SLA constraint coefficient and a time period load mode; respectively inputting the input features into an LSTM (Long Short Term Memory) time sequence channel and an XGBoost attribute channel for feature extraction; splicing the feature vectors output by the two channels, and inputting the spliced feature vectors to a fusion layer for joint modeling; and outputting the resource demand prediction vector in the future time period through a regression device. By introducing a dual-channel modeling structure, structured information such as historical resource usage sequences and task priorities of tenants, SLA constraint coefficients and load modes is processed respectively, and accurate prediction of multi-dimensional virtual resource demands is realized. By combining a fusion layer and a regression output mechanism, the prediction precision before resource scheduling is improved, efficient utilization and fair distribution of platform resources are realized, and differentiated service guarantee requirements in a multi-tenant environment are met.

The invention provides a data transmission method and device, and relates to the technical field of communication. According to the embodiment of the invention, the network slice information corresponding to the application program can be matched according to the application information of the application program operated by the terminal equipment, so that differentiated service requirements can be met, and the utilization rate of network resources is improved. The method comprises the following steps: when a first application program runs, determining first network slice information corresponding to the first application program by utilizing a second application program; and using a protocol data unit PDU session activated by the first network slice information to transmit a data stream generated by the operation of the first application program.

The invention discloses a switch service quality optimization method and device based on intelligent prediction and adaptive scheduling, equipment and a storage medium, and relates to the technical field of network communication, and the method comprises the steps: selecting a multi-dimensional feature model based on real-time network flow data; obtaining a flow demand prediction result based on the multi-dimensional feature model; adjusting a service priority based on the traffic demand prediction result and a real-time network load; and setting a differential service code point value based on the adjusted service priority, and realizing the differential service of the network flow. According to the invention, modeling is carried out on the traffic through multiple dimensions, deep analysis and prediction of traffic behaviors are realized by using the model, the priority and resource allocation are intelligently adjusted according to the real-time change of the network load and the traffic prediction result, and a finer and more intelligent traffic scheduling decision is provided.

The invention provides a communication method and a communication device. The method comprises the following steps: receiving a first task; one or more attributes corresponding to the first task are determined, any two attributes in the one or more attributes correspond to certificates of the same communication resource or certificates of different communication resources, and the certificate of one communication resource corresponds to at least one attribute in the one or more attributes; the certificates of the communication resources are in one-to-one correspondence with the communication resources; and obtaining certificates of one or more communication resources corresponding to the one or more attributes. According to the method provided by the invention, the network side can realize differentiated services for different tasks, so that the user experience is improved.

The invention relates to the technical field of information, and provides a high-value power customer oriented service optimization method and equipment, and the method comprises the steps: judging a power customer feature correlation coefficient through a correlation coefficient matrix, and removing redundant features; marking high-value power customers according to the power customer feature set after dimension reduction; obtaining dynamic behavior data of the high-value power customers from the high-value customer group to obtain dynamic feature vectors of behaviors of the high-value power customers; aiming at the dynamic feature vector, the long-short-term memory network model outputs a behavior prediction result; complaint records and interaction logs of high-value power customers are obtained, and complex appeals are marked; determining a priority service object by adopting a weighted scoring method; and adopting a recommendation algorithm to generate a personalized service strategy for the priority service object. According to the method, accurate identification, behavior prediction and service optimization of high-value power customers are realized, the service quality and satisfaction of the power customers are effectively improved, and technical support is provided for fine operation and differentiated services of power enterprises.

The invention provides a differentiated service classification and multi-path traffic scheduling method and system, and belongs to the technical field of network communication, the quintuple, packet size and timestamp characteristics of each data packet are captured in real time, a timestamp is mapped to an image horizontal axis, the packet size is mapped to a longitudinal axis after standardization, and discrete data is converted into a continuous visual mode; classifying the traffic in the network by using a pre-trained classification model, and distributing corresponding service requirements and priorities for each traffic type; distributing an INT data packet for each path, and detecting and monitoring the time delay, throughput and packet loss rate of the path in real time; and selecting an optimal path by adopting a CMAB algorithm in combination with the priority of the flow, the service demand, the real-time performance of the path and historical scheduling feedback. According to the invention, comprehensive path quality evaluation is realized; the high-priority service is ensured to obtain stable and high-quality transmission quality; and the accuracy and robustness of path selection are improved.

The invention relates to the technical field of computers, and discloses a process scheduling method based on a tenant isolation strategy, and the method comprises the steps: receiving a process scheduling request of a tenant, and carrying out the standardization processing and legality verification; querying a predefined tenant mapping table, and determining an internal unified tenant identifier corresponding to the flow scheduling request and associated single-tenant concurrent quota, tenant resource weight, isolation level and strategy rule; obtaining a single-tenant concurrent quota and a tenant resource weight, generating a tenant isolation strategy in combination with the calculated rate limiting parameter and the tenant correction priority, and performing admission judgment; and based on the judgment result, distributing the flow scheduling request to the corresponding tenant scheduling unit according to the local priority, selecting a target tenant scheduling unit according to the scheduling qualification value and the global scheduling priority, generating an execution instance and triggering the execution instance. Through differentiated services, resource monopoly is prevented, task hunger is eliminated, system overload is avoided, multi-dimensional mutual restriction is carried out, and isolation between tenants is achieved.

The invention discloses an urban public facility layout optimization method and system based on multi-source space-time big data, and relates to the technical field of urban planning. Comprising the following steps: firstly, acquiring and fusing multi-source space-time big data such as mobile equipment signaling data and point-of-interest data; identifying and delimiting an urban public service three-level center system based on the fusion data through a spatial clustering and network analysis algorithm, and determining each center level and a spatial influence range; constructing a differentiated service demand quantification model for each level of service center; constructing a multi-stage collaborative layout optimization model, and completing facility hierarchical assignment and site selection optimization; evaluating the layout scheme in the simulation environment and outputting a grading evaluation report; through dynamic analysis and multi-level collaborative optimization driven by multi-source space-time big data, accurate matching of public facility layout and service requirements is achieved, and meanwhile the service coverage rate and cross-level connection efficiency are improved.

The invention discloses a large-scale constellation general calculation collaborative resource allocation method facing non-uniform service requirements, and mainly solves the problems of time delay and high energy consumption overhead when task unloading is carried out by utilizing a satellite network under the condition that ground network resources are limited in the prior art. According to the implementation scheme, a large-scale constellation network is constructed; the method comprises the following steps: dividing the ground into a plurality of position areas, taking one area as a reference, jointly optimizing task scheduling by adopting an improved DRL-DAO (Deep Reinforcement Learning Differentiated Task Offload) algorithm, realizing efficient dynamic resource allocation to complete calculation of an offload task, and calculating the offload task between adjacent areas according to channel space-time correlation and task correlation. And performing fine adjustment on the trained model through transfer learning to complete task scheduling and resource allocation of all regions. According to the method, differentiated services can be provided for different types of tasks, resources are reasonably utilized to improve the user service quality, the high task completion rate and the minimum time delay and energy consumption weighted sum are achieved, and the method can be used for scheduling processing of various types of tasks of a user terminal.

The invention discloses a beam forming optimization method and device for a communication perception integrated system. The method aims at a scene of differentiated demands of communication and sensing services, and aims to meet the communication rate demand of a ground user and improve the sensing performance. The beam forming optimization method for the integrated system is designed by adopting theories of continuous convex approximation, positive semi-definite relaxation, first-order Taylor extension, sequential rank-one constraint relaxation and the like. According to the method provided by the invention, the sensing gain can be maximized, the communication rate requirement of the ground user can be ensured, a resource management scheme meeting the differentiated service requirement of the communication and sensing integrated system can be obtained, and the network transmission performance can be obviously improved.

The embodiment of the invention provides an intelligent customer service response method and device, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for financial scenes and medical scenes. The method comprises the following steps: performing service period prediction by combining user portrait features and behavior features extracted from service interaction data to obtain a user service period state; performing product demand prediction according to dialogue features, behavior features, user portrait features and user service period states extracted from the service interaction data to obtain a product service demand degree; and sending a target service response scheme generated based on the product service demand degree and the user service cycle state to the user. According to the embodiment of the invention, the target service response scheme is generated by combining the product service demand degree and the user service period state generated by the dialogue features, the behavior features and the portrait features of the user, differentiated service strategies can be adopted for clients with different demand degrees in a proper service stage, and the intelligent customer service response effect is improved.

The invention provides a smart campus task collaboration method and system oriented to multi-group service optimization, and relates to the technical field of campus service management. According to the method, historical service logs are called, and storage nodes are distributed according to a service process to generate data sets before and after collaboration; determining a group cooperation state based on the data set, and constructing an exclusive task measurement set and a group behavior feature set; quantifying a cross-group task cooperation priority through a double-evaluation mode mark; generating a group-task collaborative set in combination with iterative operation, and screening and optimizing by using a segmentation coefficient (cohesion + separation degree); the system correspondingly comprises a data acquisition and storage module, a task and feature construction module, a collaborative priority quantification module and a collaborative adaptation analysis and optimization module. According to the method, the problems of lack of precise adaptation, fuzzy priority quantization and the like in multi-group coordination in the prior art are solved, directional optimal configuration of service resources is realized, task coordination efficiency and group satisfaction are improved, and an efficient solution is provided for multi-group differentiated services of the smart campus.