112 results about "Resource allocation algorithm" patented technology

The invention discloses an unloading strategy and resource scheduling combined method based on the Internet of Vehicles. The method comprises the following steps: constructing a multi-server and multi-user vehicle edge computing system; constructing a communication model according to a vehicle edge computing system; constructing a calculation model; establishing a joint optimization problem model by taking balance of time delay and energy consumption and maximization of task success rate as targets; and solving the joint optimization problem model by using a grey wolf optimization algorithm based on chaotic mapping and differential mutation and a resource allocation algorithm based on delay balance to obtain a task unloading strategy set and a resource allocation strategy set. According to the unloading strategy and resource scheduling combined method based on the Internet of Vehicles, the problems that in the prior art, the task success rate is low due to uneven resource allocation in a multi-server environment, and single target optimization in a dynamic environment is difficult to meet the real-time performance and energy efficiency requirements at the same time are solved.

The invention belongs to the technical field of vehicle-mounted edge computing (VEC), and particularly relates to an unloading and resource allocation method for a DAG task in a vehicle-mounted edge computing environment. The method comprises the following steps: firstly, constructing a VEC unloading system in a two-way lane scene, and collecting task and equipment information and establishing an optimization model in combination with a vehicle moving model, a communication model and a calculation model; and secondly, aiming at a task in a directed acyclic graph (DAG), a task priority scheduling method based on a DAG topological structure is designed, so that a task scheduling strategy is optimized. On the basis, the problem is modeled as a Markov decision process by taking minimization of task completion time delay and system energy consumption as optimization objectives. The invention relates to the field of resource allocation, in particular to a task-dependent computing unloading and resource allocation algorithm based on a depth deterministic policy gradient (DDPG), and further provides a task-dependent computing unloading and resource allocation algorithm based on the depth deterministic policy gradient (DDPG) so as to solve the optimization problem.

The invention discloses an Internet of Vehicles communication resource allocation method based on multi-agent deep reinforcement learning. In order to solve the problems of dynamic channel interference, low resource allocation efficiency and multi-target collaborative optimization when a V2V link multiplexes V2I orthogonal spectrum resources in a cellular Internet of Vehicles scene, the method realizes joint optimization of discrete spectrum selection and continuous power control through agent autonomous decision and global collaboration, and improves the efficiency of resource allocation. The problem of quantization errors caused by an existing resource allocation algorithm with discrete action space is solved, the low delay and reliability requirements of the V2V link and the high-capacity communication requirement of the V2I link are effectively balanced, and the V2I link capacity is improved as much as possible while the transmission reliability of the V2V link is met.

The invention discloses a deep reinforcement learning resource allocation method for green mobile edge computing, which comprises the following steps of: establishing a communication network model, and initializing a communication environment, the number of base stations, the number of users and the number of subcarriers; determining an optimization target and a constraint condition; the optimization problem is converted into a Markov decision process, intelligent agents, a state space, an action space and a reward function are determined, a deep reinforcement learning algorithm is used for training, and an optimal strategy is distributed for each intelligent agent; the intelligent agent continuously interacts with the environment through PPO and D3QN algorithms, and network parameters are optimized and updated; an optimal resource allocation scheme is obtained; through a distributed multi-agent deep reinforcement learning-based resource allocation algorithm, the maximization of long-term average energy efficiency is realized, and strategy coordination among multiple agents is promoted, so that resource allocation is optimized, and an efficient and sustainable resource management solution is provided for the development of edge computing and communication networks in the future.

The invention discloses a resource allocation system and method based on an MLOps platform, and the system comprises an access controller which obtains a currently executed calculation task, verifies the calculation task, and carries out the access of the calculation task; the scheduler is used for acquiring resource use conditions corresponding to the cluster nodes in multiple dimensions and determining resource load conditions of the cluster nodes according to the resource use conditions; according to the resource load condition, distributing a corresponding cluster node for the calculation task; the control manager is used for managing the execution process of the calculation task; and the command line interface interacts with the user. Through a scheduler, according to a resource load condition, a resource request and limitation of a calculation task (MLOps task) are adjusted, a dynamic resource allocation algorithm is realized, college and optimal utilization of resources are ensured, and full utilization or excessive allocation of resource niches is prevented.

The invention provides a content marketing effect evaluation and optimization method and system based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining historical data of content marketing; constructing a double-tower deep neural network model, processing the content data through a content tower to generate a content representation set, processing the scene data through a scene tower to generate a scene representation set, and calculating a matching score of the content representation set and the scene representation set through an interaction layer; the double-tower deep neural network model is trained through a comparative learning framework of hierarchical scene perception and difficult sample mining, and iterative optimization is carried out in combination with a content-scene cross attention collaborative characterization mechanism; and based on the matching score, designing a resource allocation algorithm of nonlinear mapping and progressive two-stage optimization, and generating a resource allocation optimization scheme.

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 information and energy cooperative transmission method, device and system, and belongs to the technical field of wireless communication, the information and energy cooperative transmission method dynamically adjusts the priority of information and energy transmission through a resource allocation algorithm combining deep learning with a priority queue, ensures that high-priority transactions are processed in time, and improves the efficiency of information and energy transmission. And the resource utilization and response speed of the system is optimized. The beam direction of the antenna array is dynamically adjusted, intelligent beam forming is carried out, and accurate coverage of multiple devices is achieved. According to the method, information data is sent in a Ti time period to realize communication transmission, radio frequency signals are sent in a Te time period to supply power, and the method provides supply electric energy for wireless terminal equipment while realizing communication transmission, so that the self-adaptive capability of the system is enhanced, and the transmission efficiency of information and energy is improved. On the whole, the energy efficiency of the system can be improved, resource allocation is optimized, and the information energy cooperative transmission requirement of the mobile intelligent terminal in a complex environment is met.

The invention belongs to the technical field of emergency processing, and discloses a reservoir dam system natural disaster damage emergency disposal decision-making method and system, and the method comprises the steps: carrying out the classification of preprocessed multi-source data through a clustering algorithm, and constructing a disaster information database based on the classification result; constructing a risk assessment model to assess the damage risk of natural disasters to the dam system, generating a plurality of emergency disposal strategies in combination with a multi-objective optimization algorithm, and screening out an optimal emergency disposal strategy by using a scenario simulation technology; a dynamic resource allocation algorithm is utilized to allocate resources, and a digital twinborn technology is utilized to simulate the dam system in real time to predict damage risks; and executing corresponding emergency disposal measures and recording emergency disposal effect data, evaluating the emergency disposal effect data by using a data envelope analysis algorithm, and optimizing the risk evaluation model based on an evaluation result. According to the invention, the data integration and processing capability is enhanced, and the accuracy and stability of the prediction result are improved.

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 relates to the technical field of fund management, in particular to an abnormal transaction management system based on multi-source data collaborative analysis. Comprising an information acquisition module, a fund flow analysis tracking module and an information optimization module. According to the method, the risk level of a target account is adjusted, abnormal transaction behaviors are limited, the target account is associated with a historical account, the path weight of a new transaction path is calculated, and the distribution condition of the path weights and the risk levels of the historical account and the target account can be visually seen by constructing a risk matrix, so that a data basis is provided for risk strategy adjustment. In addition, monitoring key points on the transaction path are divided based on the risk level of the transaction path, and efficient resource allocation and accurate risk prevention and control are ensured by formulating differentiated monitoring key points, so that the condition of imbalance of a resource allocation algorithm is reduced, and the resource allocation efficiency is improved. The resources are preferentially allocated to the fund flow with a great relationship, the key point is accurately investigated, and the tracking difficulty is reduced.

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 invention provides a space-air-ground integrated Internet of Vehicles resource allocation method, device and equipment, and belongs to the field of resource allocation, and the method comprises the steps: constructing an Internet of Vehicles system model comprising satellites, unmanned aerial vehicles and vehicles; the unmanned aerial vehicle broadcasts resource condition inquiry messages regularly and collects resource conditions in the coverage range of the unmanned aerial vehicle to form a resource aggregation directory; after receiving the resource request message, the unmanned aerial vehicle searches a corresponding resource in a resource aggregation directory according to the request type, executes a preset resource allocation algorithm under the condition that the corresponding resource exists, generates a task request, and sends the task request to the service vehicle or the adjacent unmanned aerial vehicle; returning a resource mismatching message under the condition that the corresponding resource does not exist; and after receiving the result feedback of the service vehicle or the unmanned aerial vehicle, the unmanned aerial vehicle returns the result to the vehicle sending the resource request message. Therefore, limited resources can be utilized more effectively, and the utilization rate of the resources is improved under the condition that the resources are rare or the communication is real.

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 present invention aims to solve the high latency problem in 5G ultra-dense networks caused by users with insufficient computing power processing low-latency, high-reliability applications, and implements a joint optimization strategy for offloading strategy and resource allocation under the condition of limited computing resources and channel resources. First, a system model of MEC and local computing in a 5G ultra-dense network scenario is constructed, and a mixed integer nonlinear optimization problem that minimizes task completion time is constructed. Then, a joint optimization strategy for task offloading decision and resource allocation is proposed for the optimization problem. This strategy first uses variable substitution to simplify the problem, and then solves it by decomposing the sub-problems. The original problem is decomposed into two sub-problems: computing resource allocation and channel resource allocation. The Lagrange multiplier method is first used to obtain the optimal solution for computing resources, and then a channel resource allocation algorithm based on the idea of differential evolution is used to perform channel resource allocation under the condition of determining the optimal solution for each computing resource allocation.

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.

The invention discloses a data processing resource dynamic allocation method for a data-in-data station, and relates to the technical field of power generation enterprise data processing. The method comprises the following steps: processing real-time data of a data middle station through a Kafka system, and generating a real-time data processing result; processing off-line data of the data middle station through a Hadoop system and generating an off-line data processing result; predicting the data volume of the real-time data processing result and the offline data processing result in unit time in the future, and outputting a real-time data volume predicted value and an offline data volume predicted value; carrying out calculation resource quantity distribution on the real-time data volume predicted value and the offline data volume predicted value by adopting a dynamic weight resource distribution algorithm; according to the method, resource requirements in unit time in the future can be matched more accurately, excessive allocation or insufficiency of resources is avoided, and the utilization rate of the resources is greatly improved.

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 provides a mobile cell resource minimization slice resource configuration method and system, and belongs to the technical field of train communication, and the method comprises the steps: building a train mobile network model based on a relay system, and constructing a resource slice framework; establishing a communication and service quality assurance model for a passenger on-demand service and a beyond-visual-range service by considering a downlink bearing demand of a train intelligent service; establishing an optimization problem taking bandwidth allocation and terminal preemptive grouping as target variables; simplifying an original problem by using a convex optimization theory, and decomposing the original problem into a resource block allocation sub-problem and a mini time slot preemption sub-problem; and iteratively solving the two sub-problems by using a resource block allocation and preemption efficient algorithm based on block coordinate descent to obtain an optimal resource allocation strategy. Under the condition of non-ideal train-ground link transmission caused by the high-speed movement characteristic of a train, the deterministic transmission requirement of heterogeneous services is ensured, and the joint design of a preemption-based slice deployment strategy and a resource allocation algorithm is realized.

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 present invention discloses a wireless communication network resource allocation algorithm for dynamic adjustment on demand, which specifically includes the following steps: Step 1: Quantitatively describe the characteristics of tasks in the wireless communication network and represent them as input vectors of fixed dimensions #imgabs0# Step 2: Construct a high-reliability and low-latency resource allocation model based on a dynamic neural network with variable widths; Step 3: For the said model, give an optimization objective and a knowledge-driven solution; Step 4: Establish a knowledge base regarding the deployment environment and the optimal inference model; Step 5: Obtain the optimal model from the knowledge base to get the resource allocation scheme. The technical effect of the invention is to divide the network resource allocation problem into two stages: First, determine the optimal width of the decision network according to the task characteristics and the user's computing power; then, input the task characteristics into the optimal decision network to obtain the optimal resource allocation scheme.

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.