72 results about "Resource allocation algorithm" patented technology

The invention discloses an AI digital human real-time rendering method based on GPU acceleration, and relates to the technical field of digital human rendering, and the method comprises the steps: S1, constructing a multi-GPU hardware feature and load monitoring module, and collecting hardware feature parameters and current load states of each GPU participating in collaborative rendering in real time; according to the method, by constructing the multi-GPU hardware feature and load monitoring module and combining a dynamic task allocation algorithm, dynamic allocation of the rendering sub-tasks to the optimal GPU is achieved, the problems of GPU performance bottleneck and resource idleness caused by traditional fixed task allocation are solved, the multi-GPU collaborative rendering efficiency is improved, and the multi-GPU collaborative rendering efficiency is improved. A scene complexity analysis module and a self-adaptive resource allocation algorithm are built, the proportion of GPU resources between an AI driving module and a rendering module is dynamically adjusted according to scene complexity, the AI digital human interaction naturalness is improved in a simple scene, the rendering frame rate and the picture quality are guaranteed in a complex scene, and the method is suitable for being used in a large-scale scene. And finally, dual optimization of rendering efficiency and picture quality in AI digital human real-time rendering is realized.

The invention relates to a cloud edge multi-unmanned aerial vehicle cooperative resource optimization method assisted by a large language model, and belongs to the technical field of unmanned aerial vehicle communication, and the method comprises the following steps: S1, constructing an edge-cloud unmanned aerial vehicle cooperative reasoning system; s2, establishing a joint optimization model for discriminating gain maximization; s3, deploying a large language model at a cloud node, and generating a global strategy through a planner, a memory bank and an reflection evaluator; s4, deploying a deep reinforcement learning model at each edge node, and executing real-time optimization according to a global strategy and local observation data; s5, a collaborative feedback mechanism is established, the edge node feeds back an execution result to the cloud node, the cloud node updates a global strategy according to the feedback result, and the edge node adjusts real-time optimization parameters according to the updated global strategy; and S6, adopting an actor-commentator resource allocation algorithm for dynamic knowledge flow collaborative optimization, and realizing collaborative optimization through a distributed sensing and centralized decision framework.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

A multi-dimensional data processing system is disclosed for the automated ingestion, normalization, and computational mapping of project data across spatial, temporal, and regulatory dimensions. The system operates by ingesting diverse datasets—including two-dimensional schematics, three-dimensional data models, cost structures, and workforce logistics—and normalizing them within a unified schema. A dimensional calculations model enables the system to computationally align normalized data with spatial zones, project phases, and operational constraints, structuring it for further computational processing. By integrating this mapped data within a coordinated digital environment, the system applies task sequencing and resource allocation algorithms, generating optimized project plans that support complex, multi-dimensional data interoperability. The structured data is further configured for display through an interactive visualization interface, allowing computational resources to facilitate real-time analysis and adaptive data-driven project management.

The invention discloses an intelligent gateway control method based on edge computing, and the method comprises the steps: S1, collecting the operation state data of network nodes through a distributed sensor, carrying out the filtering, denoising and normalization processing, sorting according to a timestamp, and generating the preprocessing data containing network flow, node load and response time delay; s2, calculating the resource demand quantity of each node based on the preprocessed data, executing a dynamic resource allocation algorithm in combination with the available resource state of the node, and generating a resource allocation result containing a calculation resource quota and a task allocation relationship; s3, inputting a resource allocation result into a gateway control model, adjusting model parameters based on a real-time network state, and generating a control strategy; and S4, encoding the control strategy into an instruction sequence, encrypting the instruction sequence through a digital signature, transmitting the encrypted instruction sequence to an execution unit through a secure channel, and executing a control instruction after verifying integrity and signature validity. Therefore, dynamic resource allocation, real-time network state adjustment and security instruction transmission are realized.

The invention provides an intelligent resource dynamic sharing and task scheduling method based on a cloud desktop system. According to the method, a resource monitoring module is deployed on a local cloud desktop to monitor the utilization rate of key resources in real time, resources are requested from a cloud desktop system when the resources are insufficient, a cloud desktop resource management module selects a proper cloud desktop to provide the resources, and tasks are reasonably allocated by using a resource allocation algorithm. Meanwhile, efficient operation of the compute-intensive application in the cloud desktop system is realized through task scheduling and monitoring, data synchronization and a feedback adjustment mechanism. According to the method, the resource utilization rate is improved, the task execution time is shortened, the system stability is enhanced, the cost is reduced, and the problems that an existing cloud desktop system is unreasonable in resource allocation and low in task execution efficiency are effectively solved.

The invention discloses an enterprise service demand prediction and resource optimization configuration system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, the multi-source heterogeneous data is integrated through the multi-modal data fusion algorithm, the demand change rule is accurately captured through the time sequence prediction algorithm, the demand prediction precision is remarkably improved, and the problem that the demand perception ability is limited due to insufficient data utilization in a traditional method is solved; the resource optimization allocation algorithm is combined with the comprehensive utility function of the resource utilization rate and the customer satisfaction, the resource combination is dynamically adjusted, the resource allocation efficiency is greatly improved, the defect that a traditional resource allocation mode is static and lack of flexibility is overcome, and the user experience is improved by continuously monitoring customer interaction and service feedback data. Model parameters are dynamically updated, a resource allocation algorithm is optimized, continuous optimization of resource allocation is achieved, the problem that an existing method lacks an effective feedback mechanism is solved, long-term efficient operation of a system is ensured, and more competitive operation support is provided for enterprises.

The present invention discloses a blockchain-based edge intelligence system resource management method, comprising a system controller sensing the current user device artificial intelligence task information and the system's current wireless environment information; at the same time, the system controller senses the computing resource information of the user device, edge server and cloud server; uploading relevant information from the corresponding device to the system controller via a wireless connection; inputting the current artificial intelligence task information and the system's current wireless environment information into a trained optimization model deployed in the system controller, calculating the user device transmission power control, artificial intelligence task data volume control, user device artificial intelligence task offloading decision based on the current state, and the computing resource allocation to be used in the reasoning process and block generation process of the artificial intelligence task, and transmitting the calculation to each computing entity for execution; the present invention utilizes task offloading and computing resource allocation algorithms to improve system efficiency and user experience.

The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

The application discloses a kind of outbound line decision-making method and system based on incoming call display perception, the method includes: determining outbound task and obtaining the number attribute of outbound line, the number attribute and outbound task are modeled to obtain number feature vector and outbound service feature vector;Based on the willingness prediction network of pre-set, according to number feature vector and outbound service feature vector, the willingness score sequence corresponding to outbound line is obtained by prediction;For any outbound line, according to the constraint condition of willingness score and line cost, the target outbound line executed by outbound task is determined by calculating through the pre-set resource allocation algorithm;The feedback sample of target outbound line corresponding outbound task is obtained, and the willingness prediction network and resource allocation algorithm are continuously updated by hybrid learning mechanism.It can be seen that the application can realize the intelligent selection and dynamic optimization of outbound line, so as to effectively improve the reach rate of intelligent outbound system.

This invention provides a method for generating and distributing inspection tasks for coal mine inspection robots, comprising: constructing an inspection space mapping model; analyzing the correlation between inspection nodes; clustering inspection nodes using a multi-density clustering algorithm to form multiple inspection node clusters; optimizing the multiple inspection node clusters using a multi-objective ant colony algorithm to obtain multiple inspection path task combinations; calculating the task adaptation analysis results between the inspection robot and the inspection path task combinations; and assigning inspection path task combinations to the coal mine inspection robot using a convex resource allocation algorithm to generate coal mine inspection task distribution results. This invention uses a multi-density clustering algorithm to cluster inspection nodes and generate node clusters, which can accurately adapt to the scene characteristics of large differences in the density and uneven spatial distribution of inspection nodes in underground coal mines; and by using a convex resource allocation algorithm to assign the optimal inspection robot to each inspection path task combination, it can achieve precise optimization of inspection resources.

The invention discloses an elastic anti-interference distributed power resource allocation method with preset time convergence. The method comprises the following steps of: 1, describing a power resource allocation problem as a distributed optimization problem with total load constraint, and constructing a mathematical model of power grid power dispatching adopted by a preset time distributed power resource allocation method; 2, designing a preset time distributed resource allocation algorithm with elastic anti-interference capability under total load constraint; 3, solving a distributed optimization problem under the constraint of a total load in the absence of a network attack scene, and realizing convergence of preset time kd; and step 4, solving a distributed optimization problem with total load constraint in a scene with network attack based on the condition that false data injection attack is carried out on the system by the outside, and finally realizing convergence of the system state to an optimal value through preset time. The method can realize preset time convergence, and has elastic anti-interference capability when the outside carries out false data injection attack on the system.

The invention discloses a fluid pulsation accelerator architecture for reinforcement learning of a large language model, and belongs to the technical field of artificial intelligence hardware acceleration. The system aims at solving the problems that when a current universal processor executes an RLHF working load, an instruction driving normal form is not matched with data flow calculation, fixed parallel granularity cannot adapt to a dynamic load, and the efficiency is low due to the fact that a framework does not sense data statistical characteristics. The core of the system is a liquid systolic array calculation fabric capable of being dynamically reconstructed, execution is triggered through data flow, and kernel boundaries and instruction overhead are eliminated. According to the system, a global lookup table subsystem is fused, and nonlinear calculation is optimized through merging query and parallel lookup by using data distribution prior; and a dynamic scheduling unit is configured, and elastic parallelism is realized by adopting a resource allocation algorithm supporting work stealing. According to the method, the throughput and the energy efficiency of RLHF training and a large language model reasoning stage can be remarkably improved, and a new design normal form is provided for a next-generation AI special computing architecture.

A real-time resource allocation method for IEEE 802.11be WiFi based on deep deterministic policy gradient includes the following steps: 1) establishing an IEEE 802.11be WiFi network model; 2) determining the mobility model, path loss model, and interference model adopted by the network; 3) deriving the network throughput expression; 4) proposing an optimization problem with maximizing the minimum throughput as the objective function. This optimization problem aims to optimize the allocation of power, channels, and resource units in real time, thereby improving the network's minimum throughput; 5) designing a real-time resource allocation algorithm based on deep deterministic policy gradient to solve the optimization problem, realizing real-time resource allocation for IEEE 802.11be WiFi. This invention can effectively improve the network's minimum throughput.

The invention discloses a 6G air-space-ground integrated network resource allocation device based on an evolutionary game, and belongs to the technical field of calculation, reckoning or counting. According to the device, the problems of unbalanced resource allocation, task processing delay increase and the like caused by the fact that a plurality of tasks fight for limited computing resources at the same time in the 6G space-air-ground integrated network are considered, and the device for task unloading and resource allocation of the edge server is provided so as to meet the requirements of ground users for the tasks. According to the device, firstly, a 6G air-space-ground integrated calculation unloading network model is constructed; secondly, defining an optimization target and a constraint condition for minimizing task execution time delay; and finally, a task unloading and resource allocation problem is solved by adopting a combined task unloading and resource allocation algorithm. A simulation result shows that the algorithm has good network performance.

The invention discloses a double-agent-based resource allocation method in a space division multiplexing quantum optical network. The method is suitable for realizing cooperative transmission of classical optical signals and quantum key distribution signals in a multi-core optical network. The method mainly solves the problem that high-performance intelligent cooperative transmission is difficult to realize due to noise influence and resource competition in the common-fiber transmission process of classical signals and quantum signals in a multi-core optical network. A state representation method combining service requirements and environment information is constructed, a unified reward function considering classic signal transmission performance and quantum signal transmission performance at the same time is introduced, and a multi-agent near-end strategy optimization algorithm is adopted for joint training, so that a resource allocation algorithm based on double agents is realized; dynamic collaborative resource allocation of classical signals and quantum signals is realized, so that the network performance can be optimized, the network blocking rate is reduced, the transmission performance of the two signals is improved, and theoretical research and practical application of a space division multiplexing quantum optical network are promoted.

The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

The embodiment of the present application provides a node repair method based on erasure code and related equipment, which is used to solve the problems of slow node repair speed of erasure code and lack of scheduling framework. The method of the embodiment of the present application includes: obtaining the data to be repaired uploaded by the user; determining the strip to be repaired where the fault block to be repaired is located, and selecting the help block; determining the repair stripe, and selecting the node to be requested; constructing a repair directed acyclic graph, setting the vertices and repair edges of the repair directed acyclic graph; performing mapping on each vertex in the mapping set according to the dynamic weight mapping algorithm, and recording the help node and the node to be requested with the smallest load after mapping; all target help nodes determined in the repair directed acyclic graph are allocated based on the link resource allocation algorithm of slicing, and slicing the target help block according to the target repair link to obtain the slice help block, so as to complete the node repair of the fault block to be repaired according to the slice help block and the target repair link.

The application belongs to the technical field of optical communication task processing, and particularly relates to a task offloading and resource allocation method for Internet of Things equipment based on photon counting. The application scene comprises a base station, a mobile edge computing server and N Internet of Things equipment; the base station is equipped with N LDs and N PDs, the server is arranged on the base station, data communication is facilitated, the Internet of Things equipment all contain LDs and PDs as data transceiving pairs, and each device is internally provided with a computing power unit; the method comprises the following specific steps: constructing a data model of the mobile edge computing server and the equipment; constructing a task offloading system throughput optimization problem model of the mobile edge computing network; according to the data amount generated by each device at each moment and all queue backlog amounts, a task offloading and resource allocation algorithm based on MDPP-SCA is adopted to make a task offloading and resource allocation strategy. The application can maximize the system throughput and reduce the equipment power consumption, and is suitable for low-power-consumption and high real-time requirement Internet of Things application scenes.

The invention relates to the technical field of data processing, in particular to a high-speed mobile terminal routing addressing method and a communication device, and the method comprises the steps: collecting three-dimensional space coordinates, channel state indication information and adjacent node connection states, and constructing a network state data set with a timestamp; on the basis of node positions and connection states, a routing algorithm is combined with a topology prediction model to pre-judge a motion track, an optimal path containing a time effectiveness factor is calculated, and convergence delay is reduced through an increment updating mechanism; based on the path quality and the channel state, path stability and channel conditions are analyzed through a deep neural network, and a coding scheme is dynamically selected from a forward error correction coding library in combination with parallel simulation verification; evaluating energy efficiency of a resource allocation algorithm and a task scheduling algorithm in a digital twin simulation environment according to routing and coding requirements, and selecting a resource allocation strategy; through cross-layer optimization of an interface collaborative application path, a coding scheme and a resource strategy, a transmission index is monitored in real time and parameters are dynamically adjusted.

This invention provides a method and apparatus for multi-task scheduling in federated learning. The method involves acquiring a target federated learning task and its current events, wherein the target federated learning task includes multiple events, which are divided into stages based on their execution time and required resources. An event priority ratio is determined based on the attribute information of the target federated learning task and the type of the current event. A cumulative priority is determined based on the event priority ratio and the time the current event entered the scheduling queue. A predefined adaptive resource allocation algorithm is used to allocate computing nodes to the current event, and the current event is executed based on the computing nodes and according to the cumulative priority. This invention ensures fairness in task execution, reduces the overall task execution time of the system, and improves resource utilization.

The present invention discloses a vehicle-side collaborative task offloading and resource allocation method based on mobile and parked vehicle assistance. The network controller perceives all current device task information and the current wireless environment information of the system, inputs the current task information and the system wireless environment information into a trained optimization model deployed in the network controller, and calculates the device task offloading decision and resource allocation situation based on the current state; according to the obtained device task offloading decision information, it is decided whether the device task is to be executed on the edge server of the base station or on the mobile or parked vehicle under the corresponding base station; the present invention comprehensively considers the offloading decision of the device task, allocates the transmission power of the device, and allocates the computing resources of the edge server and the computing resources of the vehicle under the constraint of maximum cost to minimize the weighted sum of all task processing delays and device energy consumption, and utilizes the task offloading and resource allocation algorithm to improve system efficiency and user experience.

Disclosed in the present invention is a feedback-free transmission and dynamic resource allocation method for a fully-decoupled space-air-ground integrated network, which provides a solution for resource management of space-air-ground multi-node cooperation. The method comprises: on the basis of a deep learning-based channel state information prediction model, replacing a traditional feedback signal by means of user geographic location information, so as to implement multiple-input multiple-output transmission; and designing a many-to-one matching model for enabling adaptive allocation of spectrum resource blocks between space-air-ground heterogeneous nodes, so as to ensure matching stability and have the characteristics of low complexity and fast convergence. For cooperative transmission of multiple nodes and resource allocation between the heterogeneous nodes, the spectrum efficiency of a system is improved by means of feedback-free channel state information prediction and a resource allocation mechanism. Compared with a single-connection mode and polling-based resource scheduling in a traditional network, the flexible resource allocation algorithm of the present invention significantly increases the network capacity and improves user communication quality.

The invention provides a heterogeneous resource pooling management-oriented equipment virtualization orchestration method, and relates to the technical field of equipment virtualization orchestration, and the method comprises the steps: carrying out the equipment virtualization of to-be-orchestrated equipment, unifying the to-be-orchestrated equipment into a same resource pool, obtaining a production resource formula of each production plan, and obtaining a production resource formula of each production plan; establishing a resource control atlas according to a historical production resource formula, obtaining a production resource formula of a future production plan as a demand resource, and arranging resources in a resource pool through a resource allocation algorithm according to the real-time digital label and the demand resource of each virtual device type, the method comprises the steps of arranging virtual devices, outputting a logic connection relation and arranged virtual device resources through a resource control atlas, marking timestamps on the arranged virtual device resources, obtaining the usage amount of resource types of each device type through the same time interval, and calculating a historical resource allocation equilibrium coefficient as an optimization target. And optimizing the single arrangement ratio, the weight adjustment coefficient and the fitting coefficient.

The invention provides a meta-task management method and device, and the method comprises the steps: S10, receiving a task list, extracting task information, and obtaining real-time available resources; s20, filtering real-time available resources, screening out all our roles and opposite roles for the current task list, and allocating the our roles and the opposite roles to each task by using a resource allocation algorithm in combination with the extracted task information to form a meta-task list; and S30, executing the meta-task list, and monitoring the execution condition at the same time. According to the method, real-time response to the dynamic environment is realized by fusing the global state and the historical execution information, local optimal traps caused by neglecting global state change and inter-task collaboration are avoided, and it is ensured that meta-task execution is consistent with a global target. By monitoring the execution condition of the meta-task, the monitoring result can be incorporated into a reference system for subsequent distribution, the strategy adjustment period is shortened, and the timeliness and accuracy of decision making are improved.

The invention relates to the technical field of cognitive Internet of Vehicles communication, in particular to a spectrum resource allocation algorithm considering reliable transmission. According to the scheme, firstly, channel historical transmission data including reliability indexes such as bandwidth, packet loss rate, bit error rate and transmission delay are collected, and a multi-dimensional channel reliability feature sequence is constructed; then, a lightweight Transform prediction model is constructed to predict the channel reliability states of multiple time slots of the channel in the future, modeling is carried out on channel time sequence correlation through a time sensing multi-layer perceptron, and a future channel reliability index prediction result is obtained through a dimension separable output structure; further performing normalization processing and index fusion on the prediction result, and constructing a channel reliability index; on the basis, a self-adaptive spectrum access mechanism based on reinforcement learning is introduced in combination with a network load state, and a self-adaptive spectrum access decision of the vehicle terminal is realized by dynamically selecting an overlay access mode or an underlay access mode. According to the method, collaborative optimization of channel reliability prediction and a spectrum access strategy can be realized in a high-mobility Internet of Vehicles environment, the system throughput and the spectrum utilization rate are improved, and the packet loss rate is reduced, so that the reliable transmission performance of Internet of Vehicles communication is improved.

The invention provides a fault-recoverable mobile edge computing-oriented robust dependent task scheduling method, which comprises the following steps of: acquiring mobile edge computing system parameters which comprise a dependent task set, an edge network, the number of available CPU (Central Processing Unit) kernels of a server, kernel processing capability and link bandwidth; calling an adaptive resource allocation algorithm based on the system parameters to dynamically determine the scheduling sequence of the sub-tasks, and allocating system resources required for execution to the sub-tasks to obtain a resource allocation matrix; executing the subtasks according to the resource allocation matrix; when a server fault occurs, calling a server fault rescheduling algorithm to update the resource allocation matrix; when a communication link fault occurs, calling a link fault rescheduling algorithm to update the resource allocation matrix; executing the subtasks according to the updated resource allocation matrix until all the outlet subtasks are executed; obtaining execution completion time frames of all the dependent tasks; according to the invention, the robustness and effectiveness of the mobile edge computing system can be improved.

The invention relates to the technical field of data transmission, in particular to an industrial internet data integration processing method and system, and the method comprises the steps: obtaining data needing to be transmitted by each link node of an industrial internet in real time; the method comprises the following steps: presetting a time window, calculating a comprehensive influence index and a joint priority index for the bandwidth of each link node through a delay effect possibly generated by congestion in combination with a transmission condition in a link, and carrying out bandwidth allocation by synthesizing the joint priority indexes of all link nodes and using a resource allocation algorithm, and bandwidth allocation is carried out on each type of data flow in the link nodes, and integrated processing is carried out on the transmitted industrial internet data. The invention aims to improve the stability and the real-time performance in a data transmission process so as to ensure the reliability and the accuracy of subsequent integration.

Embodiments of the present application provide an attribute selection method and device, electronic equipment and storage medium. The method comprises: initializing a plurality of populations, each population comprising a plurality of simulation nodes; determining the fitness of each simulation node according to the values of delay, bandwidth, throughput, computing resources, storage resources, network resources and load balancing indicators contained in each simulation node, and a preset fitness function; determining the simulation node with the maximum fitness from the plurality of populations based on the fitness of each simulation node; taking the key attribute corresponding to the population to which the simulation node with the maximum fitness belongs as a target attribute, the target attribute being an input of a resource allocation algorithm in an actual application process, and obtaining a resource allocation result. The key attribute of the population to which the simulation node with the maximum fitness belongs is taken as an input of a resource allocation algorithm in an actual application, so that resource allocation is performed in the actual application through the key attribute and the resource allocation algorithm, and resource utilization efficiency is improved.

The invention belongs to the technical field of wireless communication, and particularly relates to an active reconfigurable intelligent surface enabled satellite-ground RSMA network resource allocation device and algorithm. The algorithm comprises the following steps: establishing a satellite-ground RSMA system model containing nonlinear energy collection; jointly optimizing satellite active beam forming, an ARIS reflection coefficient matrix and a user power distribution ratio, and constructing a system spectrum efficiency maximization optimization problem; the method comprises the following steps: aiming at satellite beam forming and power distribution ratio optimization sub-problems, fixing an ARIS reflection coefficient matrix, and solving through a convex optimization technology to obtain an optimal solution; and aiming at an ARIS reflection coefficient optimization sub-problem, satellite beam forming and a power distribution ratio are fixed, and a feasible solution is obtained through iterative solution by a positive semi-definite relaxation and penalty function method. A simulation result shows that the algorithm is obviously superior to a traditional multiple access technology and a passive RIS auxiliary scheme in the aspect of spectrum efficiency, and the effectiveness and superiority of the algorithm are verified.