Control Method and Control System for 5G Network Equipment

Abstract

The present invention relates to the field of communication transmission technologies, and particularly to a control method and control system for 5G network devices. The method includes the following steps: obtaining communication node data of 5G network devices, and integrating them to obtain a communication architecture of 5G network devices; extracting features from the communication node data of 5G network devices to obtain traffic data of 5G network devices, and calculating the transmission volume to obtain unstructured minimum transmission data; dividing the traffic data of 5G network devices to obtain signal transmission level range data; obtaining communication requirement data of 5G network devices, and calculating the traffic to obtain optimized requirement transmission traffic data; dividing the communication requirement transmission level of the communication requirement data of 5G network devices to obtain communication requirement transmission level data; and integrating communication resources for the communication requirement transmission level data and the optimized requirement transmission traffic data to obtain a device communication transmission strategy. The present invention can improve resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the field of communication transmission technologies, and in particular, to a control method and a control system for 5G network devices. Background Art

[0002] In the current rapidly developing field of communication technologies, 5G networks, as the fifth-generation mobile communication technology, mark the arrival of a new era of high speed, low latency, large capacity, and multi-connection. In order to effectively manage and control 5G network devices and provide stable and efficient services, researchers and engineers have been continuously exploring new control methods and technologies. With the popularization of the mobile Internet and the rapid growth of Internet of Things devices, traditional network management methods are facing challenges such as large-scale connection management, resource allocation optimization, and network quality assurance. Traditional control methods are no longer applicable to complex 5G network environments. Traditional control methods for 5G network devices usually lack real-time response and dynamic adaptation capabilities. In the face of rapid changes in network traffic or sudden changes in application requirements, such methods cannot quickly adjust and optimize the allocation of network resources, resulting in a decline in service quality or resource waste. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a control method and a control system for 5G network devices to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for 5G network devices includes the following steps:

[0005] Step S1: Obtain 5G network device communication node data, and perform 5G communication topology structure analysis on the 5G network device communication node data to obtain the 5G network device topology structure; perform 5G communication architecture integration according to the 5G network device topology structure to obtain the 5G network device communication architecture;

[0006] Step S2: Extract node traffic characteristics from the 5G network device communication node data to obtain 5G network device traffic data, and perform unstructured traffic division on the 5G network device traffic data to obtain video traffic data and image traffic data; calculate the minimum data transmission volume for the video traffic data and the image traffic data to obtain the unstructured minimum transmission data;

[0007] Step S3: Perform millimeter-wave transmission simulation on the 5G network device traffic data through the 5G network device communication architecture to obtain device communication transmission simulation data; perform signal transmission range level division according to the device communication transmission simulation data to obtain signal transmission level range data;

[0008] Step S4: Obtain the communication requirement data of the 5G network device, and perform a traffic margin assessment on the communication requirement data of the 5G network device according to the 5G network device traffic data, so as to obtain the communication requirement traffic assessment data; calculate the minimum transmission traffic of the communication requirement according to the unstructured minimum transmission data and the communication requirement traffic assessment data, so as to obtain the optimized requirement transmission traffic data;

[0009] Step S5: Divide the communication requirement transmission level of the 5G network device communication requirement data according to the signal transmission level range data, so as to obtain the communication requirement transmission level data; integrate the communication resources of the 5G network device according to the device communication transmission simulation data for the communication requirement transmission level data and the optimized requirement transmission traffic data, so as to obtain the device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute the communication control task.

[0010] By obtaining and analyzing the communication node data of the 5G network device, the present invention can establish a comprehensive network topology structure. This is very important for understanding the connection mode of the entire network, the relationship between nodes, and the communication path. This kind of analysis helps to optimize the network layout and design, and improve the stability and performance of the network. Extracting the traffic characteristics of nodes and performing traffic division can deeply understand the data transmission situation of each node. This is crucial for identifying high-load nodes, bottlenecks, and potential resource waste points. By calculating the minimum data transmission volume, the data transmission efficiency can be optimized, and unnecessary network load can be reduced. Through millimeter-wave transmission simulation, the transmission effect and coverage range of different signal frequency bands in the actual environment can be simulated and evaluated. This helps to determine the optimal signal transmission range level, so as to optimize the network coverage and communication quality, especially in the scenario of high-density device connection. By evaluating the communication requirement data and calculating the minimum transmission traffic, the network resources can be effectively managed and optimized. This method helps to determine the actual resource consumption of the communication requirement and predict future requirements, so as to avoid the problems of over-allocation or insufficient resources, and improve the availability and efficiency of the network. Through the device communication transmission simulation data and the requirement transmission level data, an accurate communication resource integration strategy can be formulated. This strategy can ensure that each device and node are allocated resources according to their priorities and requirements, so as to maximize the network performance and service quality. The strategy transmitted to the communication control platform for execution can also achieve dynamic adjustment and management to cope with real-time changing network conditions and requirements. Generally speaking, these steps combine data analysis, simulation evaluation, and optimization strategies, bringing significant benefits to 5G network management and control. They not only improve the reliability and responsiveness of the network, but also effectively solve the challenges of resource allocation and quality management in complex network environments, and improve resource utilization.

[0011] Optionally, step S1 is specifically:

[0012] Step S11: Obtain the communication node data of 5G network devices, and extract the node layout features and node communication protocol features from the communication node data of 5G network devices, so as to obtain the node layout data and the node communication protocol data;

[0013] Step S12: Integrate the node connection relationships according to the node layout data, so as to obtain the communication node connection data, and perform star topology structure analysis according to the communication node connection data, so as to obtain the communication physical topology structure;

[0014] Step S13: Perform communication protocol node clustering according to the node communication protocol data, so as to obtain the communication protocol node clustering data, and perform logical topology structure analysis according to the communication protocol node clustering data, so as to obtain the communication logical topology structure;

[0015] Step S14: Perform communication topology structure mapping on the communication physical topology structure and the communication logical topology structure, so as to obtain the 5G network device topology structure;

[0016] Step S15: Integrate the 5G communication architecture according to the 5G network device topology structure, so as to obtain the 5G network device communication architecture.

[0017] The present invention helps to understand the distribution of devices in the physical space by obtaining the communication node data of 5G network devices and extracting information such as the position and distance of the nodes. This is crucial for optimizing the network coverage, avoiding signal interference, and reasonably arranging devices. By analyzing the communication protocols of the nodes, it is possible to understand how different devices communicate and exchange data. This helps to optimize the communication efficiency, ensure the security of data transmission, and adjust the network settings according to the communication requirements. Integrating the connection relationships of the nodes can establish a physical connection diagram of each device in the network, which is very important for managing and monitoring the network status. By analyzing the star topology structure, the centralization degree of the network and the importance of each node can be evaluated. This helps to optimize the data transmission path, improve the network efficiency, and reduce the risk of single-point failure. Clustering the nodes with similar communication characteristics can identify different sub-networks or service areas in the network. This helps to optimize the network resource allocation, improve the service quality, and ensure the network security. Analyzing the logical topology structure can reveal the data flow pattern and path selection mechanism in the network. This is crucial for optimizing the routing algorithm, improving the network response speed and service quality. Mapping the physical and logical topology structures helps to understand the data flow and communication path in the network. This has guiding significance for planning and optimizing the network architecture and adjusting the device layout. By synthesizing the topology structure information obtained in the previous steps, a more efficient, secure, and reliable 5G communication architecture can be designed and implemented. This is of great significance for supporting large-scale data transmission, multi-user access, and high-speed mobile communication.

[0018] Optionally, step S15 is specifically as follows:

[0019] Step S151: Extract the logical connection features and physical connection features of the 5G network device topology structure, so as to obtain node logical connection data and node physical connection data;

[0020] Step S152: Establish a communication architecture diagram based on the node logical connection data, so as to obtain a node communication architecture diagram;

[0021] Step S153: Statistically analyze the node connection frequency according to the node physical connection data, so as to obtain node connection frequency data;

[0022] Step S154: Divide the node physical connection data into node levels according to the node connection frequency data, so as to obtain core node connection data and edge node connection data;

[0023] Step S155: Construct a core layer node network based on the core node connection data; construct an edge layer node network based on the edge node connection data;

[0024] Step S156: Integrate the communication architecture levels of the node communication architecture diagram based on the core layer node network and the edge layer node network, so as to obtain the 5G network device communication architecture.

[0025] By extracting logical connection features and physical connection features, the present invention can obtain logical connection data and physical connection data between nodes. This is beneficial for understanding the logical relationships between devices, such as which devices communicate with or depend on each other in a network topology. It is also beneficial for obtaining physical connection information between devices, such as the location, connection method, and transmission rate of the devices. Based on the logical connection data of the nodes, establishing a communication architecture diagram helps to display the communication relationships between devices, including central nodes, edge nodes, and the connection methods between them. By analyzing the communication architecture, potential bottlenecks or optimization opportunities can be identified, thereby improving network performance and efficiency. By counting the connection frequencies of the nodes, it can be understood which devices communicate frequently and which devices are used less. Adjusting network resource allocation according to the connection frequency data ensures that devices with high-frequency usage have sufficient bandwidth and processing capabilities. Based on the connection frequency data, hierarchical partitioning of the nodes helps to identify core nodes that undertake important communication tasks and relatively marginal nodes in the network. This helps to allocate more resources to the core nodes to ensure the stability and efficiency of the network. Laying out the network in layers according to functions and communication requirements makes management and maintenance more effective and targeted. Optimizing the communication efficiency of the core layer nodes while providing more flexible services and coverage through the edge layer nodes. Based on the core layer and edge layer node networks, performing hierarchical integration of the communication architecture can ensure that the communication architectures of all 5G network devices are logically and physically coordinated. Optimizing communication paths and resource utilization through hierarchical integration enhances the overall performance and efficiency of the network.

[0026] Optionally, step S2 is specifically as follows:

[0027] Step S21: Extract node traffic characteristics from the communication node data of 5G network devices to obtain 5G network device traffic data;

[0028] Step S22: Perform unstructured traffic partitioning on the 5G network device traffic data to obtain video traffic data and image traffic data;

[0029] Step S23: Obtain the transmission data of 5G network devices and perform unstructured data decompression on the transmission data of 5G network devices to obtain unstructured transmission data;

[0030] Step S24: Perform unstructured transmission association on the image traffic data and the video traffic data respectively according to the unstructured transmission data to obtain image transmission traffic data and video transmission traffic data;

[0031] Step S25: Calculate the minimum image transmission amount for the image transmission traffic data to obtain the minimum image transmission data;

[0032] Step S26: Calculate the minimum video transmission amount for the video transmission traffic data to obtain the minimum video transmission data;

[0033] Step S27: Integrate the unstructured data transmission volume of the minimum image transmission data and the minimum video transmission data to obtain the unstructured minimum transmission data.

[0034] By extracting the node traffic characteristics, the present invention can analyze the data transmission mode and usage of devices. Understanding which nodes carry more data traffic helps with load balancing, avoiding network congestion and performance degradation. Identifying peak and off-peak traffic periods helps optimize network resource allocation and management strategies. For different types of traffic, different transmission strategies and optimization methods can be adopted to maximize network resource utilization. According to the traffic type, different quality of service (QoS) can be provided, such as low-latency processing for video traffic and high-definition transmission for image traffic. Decode the compressed transmission data back into the original data to ensure data integrity and accuracy. Extract and analyze the decompressed data for further processing and applications, such as content analysis, machine learning, etc. Organize the related data for subsequent processing and analysis to ensure that each type of data stream is managed and optimized according to its specific processing and transmission requirements. Calculating the minimum image transmission volume can determine the minimum bandwidth and resources required to transmit images and videos, avoiding waste and improving efficiency. Calculating the minimum video transmission volume can ensure that images and video data can be transmitted and processed on demand even during peak network load periods. Integrating the minimum image transmission data and the minimum video transmission data can reduce the bandwidth required for data transmission, saving network resources. At the same time, it can optimize the data transmission process, improve the data transmission speed and response time, and enhance the user experience.

[0035] Optionally, step S25 is specifically as follows:

[0036] Step S251: Perform edge detection on the image transmission traffic data to obtain the transmission image edge data;

[0037] Step S252: Perform hierarchical division of the image transmission traffic data according to the transmission image edge data to obtain the main region image and the background region image;

[0038] Step S253: Minimize the color depth reduction of the background region image to obtain the minimized background region image;

[0039] Step S254: Perform approximate color depth matching of the main region image according to the minimized background region image to obtain the minimized main region image;

[0040] Step S255: Merge the minimized background region image and the minimized main region image to obtain the minimized transmission image;

[0041] Step S256: Calculate the minimum image transmission amount for the minimized transmission image based on the image transmission traffic data, so as to obtain the minimum image transmission data.

[0042] By detecting the edges of the image, the present invention can accurately identify the boundaries of objects or subjects, which helps to more precisely separate the subject and the background in subsequent steps. Retaining only the edge data can significantly reduce the amount of image data to be transmitted and save bandwidth resources. According to the edge data, the image is divided into a subject area and a background area. Different processing strategies are adopted for different areas. For example, the subject area requires higher color and detail retention, while the background area can be more data-compressed. Reducing the color depth of the background area can reduce the amount of data required for each pixel, thereby compressing the image file size. The background area image after color depth minimization is more suitable for transmission under low-bandwidth conditions while maintaining visual rationality. By matching the color depth of the subject area, the visual quality and realism of the subject area are ensured. Adjust the color details of the subject area to the lowest level required for minimum transmission to reduce bandwidth occupancy during transmission. Combine the processed background area image and the subject area image into a minimized transmission image. Ensure that the integrated image data still maintains visual consistency and integrity. Accurately calculate the required minimum transmission bandwidth according to the data volume of the minimized transmission image. This helps to ensure the maximization of bandwidth and network resource utilization when transmitting images, while ensuring the balance between image quality and transmission efficiency.

[0043] Optionally, step S26 is specifically as follows:

[0044] Step S261: Extract the transmission video frames according to the video transmission traffic data, so as to obtain the transmission video frames;

[0045] Step S262: Calculate the pixel differences between adjacent video frames of the transmission video frames to obtain the video frame pixel difference data, and divide the transmission video frames according to the video frame pixel difference data to obtain the key video frames and the secondary video frames;

[0046] Step S263: Minimize the color depth of the secondary video frames to obtain the minimized secondary video frames;

[0047] Step S264: Divide the key video frames hierarchically to obtain the key video frame background image and the key video frame subject image;

[0048] Step S265: Minimize the color depth of the key video frame background image to obtain the minimized video frame background image, and match the approximate color depth of the subject area of the key video frame subject image according to the minimized video frame image to obtain the minimized video frame subject image;

[0049] Step S266: Combine the minimized video frame main image and the minimized video frame background image to obtain the minimized key video frame, and perform video encoding on the minimized secondary video frame and the minimized key video frame to obtain the minimized transmission video;

[0050] Step S267: Calculate the minimum video transmission volume for the minimized transmission video according to the video transmission traffic data to obtain the minimum video transmission data.

[0051] The present invention selects the video frames to be transmitted according to the video transmission traffic data, usually key frames and some secondary frames. Only transmitting key frames and optimized secondary frames can reduce redundant data in transmission and improve transmission efficiency. By calculating the pixel differences between adjacent video frames, key frames and secondary frames are identified. Only detailed transmission is performed on key frames, while secondary frames can be transmitted more efficiently through difference data, reducing bandwidth requirements. Perform color depth minimization on secondary video frames to reduce the data volume per frame. Adapt to scenarios with low network bandwidth, reduce the data volume during transmission, and maintain the basic quality of the picture. Divide the key video frame into background and main parts, and perform subsequent processing respectively according to the separated background and main images, such as color depth minimization of the background and color matching of the main body. Perform color depth minimization and matching processing on the background image and main image of the key video frame respectively. By reducing the color details per frame, the transmission bandwidth requirement is reduced while maintaining the visual quality of the main image. Combine the optimized main image and background image into the minimized key video frame. Perform video encoding on the minimized key video frame and secondary video frame to further optimize the transmission efficiency. Calculate the required minimum transmission bandwidth according to the data volume of the minimized transmission video. Ensure the maximization of bandwidth and network resource utilization during video transmission while maintaining the balance between video quality and transmission efficiency.

[0052] Optionally, step S3 is specifically as follows:

[0053] Step S31: Extract node resource configuration characteristics and node signal transmission characteristics according to the 5G network device communication node data to obtain node resource configuration data and node signal transmission data;

[0054] Step S32: Perform node resource configuration mapping on the 5G network device communication architecture according to the node resource configuration data to obtain the node resource allocation architecture;

[0055] Step S33: Integrate the signal transmission environment based on the node signal transmission data to obtain the signal simulation environment data;

[0056] Step S34: Simulate the environment data based on the signal, and perform millimeter-wave transmission simulation on the traffic data of 5G network devices through the node resource allocation architecture, so as to obtain device communication transmission simulation data;

[0057] Step S35: Calculate the communication transmission attenuation amount of the device communication transmission simulation data, so as to obtain communication transmission attenuation amount data;

[0058] Step S36: Divide the signal transmission range levels according to the communication transmission attenuation amount data, so as to obtain signal transmission level range data.

[0059] The present invention analyzes the communication node data of 5G network devices, extracts node resource configuration characteristics, such as the configuration information of resources such as processors and memories. These information can help optimize the resource usage efficiency of nodes, improve system performance and response speed. Extract features from the node signal transmission data, including signal strength, transmission rate, delay, etc. These features are used to evaluate and optimize the signal transmission quality to ensure the communication stability and efficiency between devices. According to the node resource configuration data, formulate and implement the resource allocation strategy and architecture of 5G network devices. This can ensure that each device can most effectively utilize its available resources during the communication process, improving the overall system throughput and efficiency. Based on the node signal transmission data, integrate the signal transmission environment, consider factors such as obstacles and multipath effects, and generate signal simulation data in the real environment. These data help evaluate the communication performance in complex environments and optimize the signal transmission strategy. Utilize the signal simulation environment data and the node resource allocation architecture to perform millimeter-wave transmission simulation on the traffic data of 5G network devices. Millimeter-wave technology can improve the data transmission rate and bandwidth utilization rate to meet the high-density data transmission requirements. By calculating the attenuation amount of the device communication transmission simulation data, evaluate the attenuation of the signal during transmission. These data are used to optimize the signal transmission distance, power consumption and data quality to ensure a stable communication connection. Based on the communication transmission attenuation amount data, divide different signal transmission range levels. These levels guide the communication coverage range and signal quality of devices under different environmental conditions, and optimize the network coverage and service quality provision.

[0060] Optionally, step S4 is specifically:

[0061] Step S41: Obtain the communication demand data of 5G network devices, and estimate the transmission traffic of the communication demand data of 5G network devices, so as to obtain estimated communication transmission traffic data;

[0062] Step S42: Calculate the balance of the traffic quota for the traffic data of 5G network devices, so as to obtain traffic quota balance data;

[0063] Step S43: Perform traffic margin classification calculation on the estimated communication transmission traffic data based on the traffic quota balance data. If the estimated communication transmission traffic data is less than the traffic quota balance data, generate the to-be-communicated demand data and upload it to the 5G network device communication control platform to execute the communication control task; if the estimated communication transmission traffic data is greater than or equal to the traffic quota balance data, generate the communication demand traffic evaluation data;

[0064] Step S44: Extract the unstructured data transmission traffic characteristics from the communication demand traffic evaluation data to obtain the unstructured to-be-transmitted traffic data;

[0065] Step S45: Perform unstructured minimum transmission traffic calculation on the unstructured to-be-transmitted traffic data according to the unstructured minimum transmission data to obtain the optimized demand transmission traffic data.

[0066] By collecting and analyzing the communication demand data of 5G network devices, the present invention can accurately predict future communication transmission traffic. Doing so can help operators or network administrators reasonably plan resources and avoid resource waste or insufficiency. Through traffic estimation, network resources can be allocated targeted to ensure that the network can still provide efficient services during peak periods and save resources during off-peak periods. The calculation of the traffic quota balance can help determine the usage limit for each device or user, thereby controlling the allocation of network resources and costs. This is particularly important for operators and can avoid a decline in service quality or additional costs caused by overuse. By reasonably allocating traffic quotas, it can ensure that all users can enjoy stable and high-quality network services, enhancing user satisfaction and loyalty. According to the comparison between the estimated communication transmission traffic and the traffic quota balance, the to-be-communicated demand or traffic evaluation data can be generated in real time for subsequent communication control and adjustment. When the communication demand is less than the traffic quota balance, the to-be-communicated demand data can be immediately generated and uploaded to the communication control platform to ensure that the device transmits data as needed and avoid unnecessary traffic consumption. This helps improve the overall efficiency and service quality of the network. For unstructured transmission traffic data, feature extraction can help identify important information and patterns in the data, thereby optimizing the data transmission and processing efficiency. The extracted feature data can provide a basis for subsequent decision-making, such as adjusting the data transmission strategy or optimizing the network configuration to meet different communication demands and scenarios. By calculating the minimum transmission traffic of unstructured data, the network resource consumption during data transmission can be minimized, improving the overall efficiency and performance of the network. Reducing the transmission traffic can also reduce operating costs, which is particularly important for large-scale data transmission and processing tasks.

[0067] Optionally, step S5 is specifically as follows:

[0068] Step S51: Extract communication demand location features from the communication demand data of 5G network devices to obtain communication demand location data;

[0069] Step S52: Classify the communication demand location data according to the signal transmission level range data to obtain communication demand transmission level data;

[0070] Step S53: Match the communication demand transmission level data with the device communication simulation data to obtain communication demand transmission environment data;

[0071] Step S54: Calculate the communication transmission attenuation amount for the communication demand transmission environment data and the optimized demand transmission flow data according to the device communication simulation data to obtain communication demand transmission attenuation amount data;

[0072] Step S55: Perform minimum transmission attenuation amount resource allocation on the communication demand transmission attenuation amount data through the node resource allocation architecture to obtain communication demand resource allocation data;

[0073] Step S56: Integrate the communication demand resource allocation data and the optimized demand transmission flow data to obtain the device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute communication control tasks.

[0074] Through the extraction of communication demand location features, the system can accurately determine the location of devices or users, which is crucial for optimizing signal coverage and service quality. These location data can be used for subsequent signal transmission level classification and transmission environment matching. According to the signal transmission level range data, the communication demand location data is classified into different transmission levels. This can adjust the transmission intensity and priority of the signal according to the location and service requirements of the device or user, thus ensuring the efficiency and service quality of the network. By matching with the device communication simulation data, the system can more accurately predict the communication environment where the device is located. This includes considering possible signal interference, multipath effects, and other environmental factors to optimize signal transmission and reception. Calculating the transmission attenuation amount of the communication demand transmission environment data and the optimized demand transmission flow data can help determine the signal loss situation that may be encountered during signal transmission. This provides key data support for subsequent resource allocation and transmission strategies. Based on the calculated transmission attenuation amount data, the system can perform minimum transmission attenuation amount resource allocation. This means that resources can be more effectively allocated to each device or user to minimize energy loss and data loss during signal transmission. Integrate the communication demand resource allocation data and the optimized demand transmission flow data to formulate the best transmission strategy. These strategies will adjust the signal transmission method according to different communication demands and network conditions, thus improving the overall network efficiency and user experience.

[0075] Optionally, the present invention further provides a control system for a 5G network device, which is used to execute a control method for a 5G network device as described above. The control system for the 5G network device includes:

[0076] A communication topology analysis module, which is used to obtain 5G network device communication node data, perform 5G communication topology structure analysis on the 5G network device communication node data, so as to obtain the 5G network device topology structure; perform 5G communication architecture integration according to the 5G network device topology structure, so as to obtain the 5G network device communication architecture;

[0077] An unstructured traffic calculation module, which is used to extract node traffic characteristics from the 5G network device communication node data, so as to obtain 5G network device traffic data, and perform unstructured traffic division on the 5G network device traffic data, so as to obtain video traffic data and image traffic data; perform minimum data transmission volume calculation on the video traffic data and the image traffic data, so as to obtain unstructured minimum transmission data;

[0078] A signal transmission range division module, which is used to perform millimeter wave transmission simulation on the 5G network device traffic data through the 5G network device communication architecture, so as to obtain device communication transmission simulation data; perform signal transmission range level division according to the device communication transmission simulation data, so as to obtain signal transmission level range data;

[0079] A communication demand traffic evaluation module, which is used to obtain 5G network device communication demand data, and perform traffic margin evaluation on the 5G network device communication demand data according to the 5G network device traffic data, so as to obtain communication demand traffic evaluation data; perform communication demand minimum transmission traffic calculation according to the unstructured minimum transmission data and the communication demand traffic evaluation data, so as to obtain optimized demand transmission traffic data;

[0080] A communication resource integration module, which is used to perform communication demand transmission level division on the 5G network device communication demand data according to the signal transmission level range data, so as to obtain communication demand transmission level data; perform 5G network device communication resource integration on the communication demand transmission level data and the optimized demand transmission traffic data according to the device communication transmission simulation data, so as to obtain a device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute communication control tasks.

[0081] The control system of the 5G network device of the present invention can implement any control method of the 5G network device of the present invention, and is used as a medium for coordinating the operations and signal transmissions between various modules to complete the control method of the 5G network device. The internal modules of the system cooperate with each other, thereby improving resource utilization rate. Brief Description of the Drawings

[0082] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0083] Figure 1 It is a schematic flowchart of the steps of the control method for the 5G network device of the present invention;

[0084] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0085] Figure 3 It is a detailed schematic flowchart of step S2 in the present invention;

[0086] The realization of the object of the present invention, functional features, and advantages will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed Embodiments

[0087] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0088] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0089] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0090] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a control method for a 5G network device, and the method includes the following steps:

[0091] Step S1: Obtain the communication node data of 5G network devices, and perform 5G communication topology analysis on the communication node data of 5G network devices to obtain the 5G network device topology structure; perform 5G communication architecture integration according to the 5G network device topology structure to obtain the 5G network device communication architecture;

[0092] In this embodiment, the communication node data of 5G network devices is obtained through the 5G network device communication control platform, and these data are analyzed in detail to reveal the connection methods and network topology structures between devices. For example, by analyzing the physical locations and network connection methods between devices, the relevance and data transmission paths between devices can be determined. These analysis results form the topology structure of 5G network devices. Next, according to the topology structure, the 5G communication architecture is integrated. This includes determining the roles and functions of each device in the network, such as the core network, edge computing nodes, and terminal devices, etc. Through integration, the system can optimize data flow and service distribution, ensuring efficient data transmission and network response speed. Network topology analysis tools and technologies, such as network simulation software or automated network analysis algorithms, can be used to process and analyze the collected 5G communication node data. Through algorithms and models, a visual network topology structure diagram can be generated, and further optimization algorithms can be applied to adjust the network structure to meet specific communication requirements and performance goals.

[0093] Step S2: Extract the node traffic characteristics from the communication node data of 5G network devices to obtain the 5G network device traffic data, and perform unstructured traffic division on the 5G network device traffic data to obtain video traffic data and image traffic data; calculate the minimum data transmission volume for the video traffic data and the image traffic data to obtain the unstructured minimum transmission data;

[0094] In this embodiment, the traffic characteristics are extracted from the communication node data of 5G network devices to obtain detailed traffic data. These data include not only the traditional data transmission volume but also unstructured data such as video and image traffic. By analyzing the node data, the traffic characteristics of each device are extracted, such as data transmission rate, frequency, and data type. Subsequently, for unstructured data such as video and image, the minimum data transmission volume is calculated. This calculation process takes into account the importance of the data and the real-time requirements of transmission, ensuring the minimization of data loss and transmission delay during transmission. Deep learning technologies or image analysis algorithms can be used to perform real-time parsing and classification of video traffic and image traffic. Based on the analysis of the data traffic patterns, the minimum transmission volume requirements for each type of data are determined. For example, video streams may require high-bandwidth and low-latency transmission channels, while image streams may place more emphasis on data integrity and real-time performance.

[0095] Step S3: Perform millimeter-wave transmission simulation on the 5G network device traffic data through the 5G network device communication architecture to obtain device communication transmission simulation data; perform signal transmission range level division based on the device communication transmission simulation data to obtain signal transmission level range data;

[0096] In this embodiment, millimeter-wave transmission simulation is performed on the traffic data using the 5G communication architecture. Millimeter-wave technology can provide higher data transmission rates and bandwidths, but is limited by transmission distance and obstacles. Based on the device communication transmission simulation data, the range and efficiency of signal transmission are evaluated. This includes analyzing the transmission characteristics of millimeter-wave signals under different environmental conditions through simulation, such as changes in urban environments and indoor / outdoor settings. The result will be data on the signal transmission range level for optimizing communication and resource allocation between devices. Radio frequency simulation software or millimeter-wave transmission models can be used to simulate the collected traffic data. Through simulation analysis, the system can determine the transmission path and range of millimeter-wave signals, and then optimize the signal strength and connection stability between devices.

[0097] Step S4: Obtain the 5G network device communication requirement data, and perform traffic margin evaluation on the 5G network device communication requirement data based on the 5G network device traffic data to obtain communication requirement traffic evaluation data; perform communication requirement minimum transmission traffic calculation based on the unstructured minimum transmission data and the communication requirement traffic evaluation data to obtain optimized requirement transmission traffic data;

[0098] In this embodiment, the 5G network device communication control platform is used to obtain and evaluate the communication requirement data of 5G network devices, including the communication requirements considering various traffic data. By analyzing the actual traffic requirements of the devices and the current traffic data, traffic margin evaluation can be performed to ensure the effective allocation and utilization of network resources. Based on the unstructured minimum transmission data and the communication requirement traffic evaluation data, the system calculates the optimized minimum transmission traffic. This process aims to maximize network resource utilization while ensuring that the communication requirements of devices and users are met, improving the overall network performance. Data mining and machine learning algorithms can be used to analyze historical data and real-time data to predict and evaluate the communication requirements of devices. By analyzing data traffic patterns and usage patterns, the system can predict future traffic requirements and optimize data transmission strategies and resource allocation accordingly.

[0099] Step S5: Perform communication requirement transmission level division on the 5G network device communication requirement data based on the signal transmission level range data to obtain communication requirement transmission level data; perform 5G network device communication resource integration on the communication requirement transmission level data and the optimized requirement transmission traffic data based on the device communication transmission simulation data to obtain a device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute communication control tasks.

[0100] In this embodiment, the communication requirement data is divided according to the signal transmission level range data to determine the communication transmission levels of each device. This division is based on the device location, service requirements, and signal transmission priorities to ensure communication quality and stability under different network conditions. Then, by combining the device communication transmission simulation data and the optimized demand transmission traffic data, the best device communication transmission strategies are formulated. These strategies include optimizing the data transmission path, selecting appropriate transmission channels, and adjusting the transmission rate to improve the efficiency of data transmission and the response speed of the network. Network optimization tools and automated policy management systems can be used to integrate and analyze the collected communication requirement data and transmission simulation data. Through algorithms and real-time monitoring, the device communication transmission strategies can be dynamically adjusted to adapt to the changing network environment and user requirements.

[0101] Optionally, step S1 is specifically as follows:

[0102] Step S11: Obtain the 5G network device communication node data, and perform node layout feature extraction and node communication protocol feature extraction on the 5G network device communication node data, so as to obtain node layout data and node communication protocol data;

[0103] In this embodiment, obtaining the 5G network device communication node data and performing node layout feature extraction and node communication protocol feature extraction on it are one of the key steps in 5G network design and optimization. The position coordinates, coverage range, communication frequency band, etc. of each node can be collected through a 5G network device communication control platform, network probe, or sensor device, so as to integrate the node layout data and node communication protocol data. For example, the global positioning system (GPS) can be used to obtain the accurate geographical location information of the node, and a spectrum analyzer can be used to analyze the signal frequency band and bandwidth usage of the node.

[0104] Step S12: Integrate the node connection relationships according to the node layout data to obtain communication node connection data, and perform star topology structure analysis according to the communication node connection data to obtain the communication physical topology structure;

[0105] In this embodiment, using the collected node layout data, calculate the physical distance between nodes and analyze the signal strength attenuation situation. This information helps to determine the optimal node connection method to maximize the coverage range and signal quality. Algorithms in graph theory, such as the minimum spanning tree (MST) or shortest path algorithm, are used to optimize the connection relationships between nodes. For example, the Prim algorithm can be used to construct a minimum spanning tree to form an efficient star topology structure.

[0106] Step S13: Cluster communication protocol nodes based on node communication protocol data to obtain communication protocol node clustering data, and perform logical topology structure analysis based on the communication protocol node clustering data to obtain a communication logical topology structure;

[0107] In this embodiment, analyze the communication protocols supported by each node (such as LTE, 5G NR, etc.), and extract relevant protocol feature data, such as protocol version, frequency band, and supported data rate. Use a clustering algorithm (such as k-means clustering) to group nodes with similar communication protocol features into clusters. For example, cluster nodes that support the same protocol version and frequency band together for subsequent formation of a logical topology structure. In a 5G network, different types of devices may use different communication protocols for data transmission. For example, the communication protocol used by sensor nodes may be different from that of the main server or other nodes. By collecting and analyzing device communication data, a clustering algorithm (such as K-means) can be used to classify the devices so that devices within each category have similar communication behavior characteristics. For example, for sensor devices, nodes using low-power wide-area network (LPWAN) protocols will be clustered; while for base station or server devices, nodes using TCP / IP or UDP protocols may be clustered. These clustering results can help understand and optimize the communication efficiency and data transmission mode between devices. The logical topology structure analysis aims to understand the logical connection method between devices. Even if the devices are not physically directly connected, they exchange data and cooperate through communication protocols. By analyzing the clustered devices, a logical topology diagram can be constructed to show the logical relationship between the devices. For example, if the devices in a cluster are mainly connected to each other via Bluetooth or Wi-Fi, their logical connection can be shown in the logical topology diagram; or if the devices exchange data through a cloud server, this cloud integration structure can be reflected in the logical topology diagram.

[0108] Step S14: Perform communication topology structure mapping on the communication physical topology structure and the communication logical topology structure to obtain a 5G network device topology structure;

[0109] In this embodiment, map the communication physical topology structure and the communication logical topology structure to form the overall topology structure of 5G network devices. Combine the node connection relationship at the physical level with the communication protocol node clustering at the logical level to ensure the consistency and efficiency of the entire network. For example, by comparing the node location information in the physical topology structure with the communication protocol mapping relationship in the logical topology structure to verify the actual operation of the network. The determined physical connection relationship and the generated logical clusters can be corresponded. For example, assign the node connections with the closest physical distance to the same logical cluster.

[0110] Step S15: Integrate the 5G communication architecture based on the 5G network device topology structure to obtain the 5G network device communication architecture.

[0111] In this embodiment, the communication architecture is integrated according to the 5G network device topology structure, and finally a complete 5G network device communication architecture is formed. According to the physical and logical topology structures, the functional roles and configuration parameters of each node are designed. For example, according to the mapping results of the physical topology structure and the logical topology structure, the layout and configuration of network devices can be designed and deployed. For example, determine the functional roles of each node (such as base stations, user equipment), and adjust their configuration parameters (such as transmission power, frequency allocation) to achieve the efficient operation and coverage optimization of the overall network.

[0112] Optionally, step S15 is specifically as follows:

[0113] Step S151: Extract the logical connection features and physical connection features of the 5G network device topology structure to obtain node logical connection data and node physical connection data;

[0114] In this embodiment, when extracting the logical connection features and physical connection features of the 5G network device topology structure, key information needs to be extracted from device configurations and network protocols first. The logical connection features may include the logical relationships between devices, such as the mapping relationships of virtual channels and logical ports. For example, by analyzing device configuration files, the logical connection relationships between different devices are extracted to ensure the correct routing and transmission of data packets. The physical connection features involve the actual physical connection methods between devices, including cable connection types, port rates, etc. For example, the physical connection relationships between various devices are extracted from the physical interface information of the devices to ensure the stability and performance of the network.

[0115] Step S152: Establish a communication architecture diagram based on the node logical connection data to obtain a node communication architecture diagram;

[0116] In this embodiment, a communication architecture diagram is established according to the node logical connection data to visualize the logical relationships between devices in the network and construct the network topology. For example, network topology modeling tools such as Graphviz or professional network management software can be used to convert the node logical connection data into a visual communication architecture diagram, thereby helping network administrators understand and manage the logical structure of the network.

[0117] Step S153: Statistically analyze the node connection frequencies according to the node physical connection data to obtain node connection frequency data;

[0118] In this embodiment, node connection frequency statistics are performed based on node physical connection data, which involves analyzing the actual communication frequency and data traffic between devices. For example, by monitoring the traffic statistics information of device ports, the communication frequency distribution between different devices can be obtained, providing data support for network load balancing and optimization. Statistical methods can also be used to count the node connection situations of node physical connection data, and the connection frequency distribution of each node can be counted.

[0119] Step S154: Perform node hierarchy division on the node physical connection data according to the node connection frequency data, so as to obtain core node connection data and edge node connection data;

[0120] In this embodiment, node hierarchy division is performed on the node physical connection data according to the node connection frequency data, which is to determine the core nodes and edge nodes in the network. For example, based on the communication frequency and traffic distribution between devices, devices can be divided into core nodes (high communication frequency and large traffic) and edge nodes (low communication frequency and small traffic), so as to better optimize network resources and management strategies.

[0121] Step S155: Construct a core layer node network according to the core node connection data; construct an edge layer node network according to the edge node connection data;

[0122] In this embodiment, a core layer node network is constructed according to the core node connection data, and an edge layer node network is constructed according to the edge node connection data. For example, the core layer node network can include high-performance routers and switches for processing a large amount of data traffic and high-frequency communication; while the edge layer node network can include devices connecting to end users, such as Wi-Fi access points or low-power devices, for providing access services and edge computing support.

[0123] Step S156: Perform communication architecture level integration on the node communication architecture diagram based on the core layer node network and the edge layer node network, so as to obtain the 5G network device communication architecture.

[0124] In this embodiment, communication architecture level integration is performed on the node communication architecture diagram based on the core layer node network and the edge layer node network, which is to integrate and optimize the communication architecture of the entire 5G network device. For example, architecture methods such as microservice architecture and RESTful API can be used to hierarchically integrate the overall communication architecture through the high-speed transmission ability of the core layer nodes and the access service ability of the edge layer nodes, so as to achieve the optimization of network performance and the effective utilization of resources.

[0125] Optionally, step S2 is specifically as follows:

[0126] Step S21: Extract node traffic characteristics from the communication node data of 5G network devices to obtain 5G network device traffic data;

[0127] In this embodiment, extracting node traffic characteristics is to deeply understand the traffic patterns and data characteristics of the devices. This includes using techniques such as Deep Packet Inspection (DPI) to identify traffic types and characteristics by analyzing the packets transmitted to and from the devices. For example, traffic classification algorithms based on deep learning, such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), can be used to identify different types of data flows, such as video streams, audio streams, and control flows. Through this method, the traffic data of the devices can be accurately extracted, providing a basis for further processing and analysis.

[0128] Step S22: Perform unstructured traffic division on the 5G network device traffic data to obtain video traffic data and image traffic data;

[0129] In this embodiment, performing unstructured traffic division on the 5G network device traffic data is to separate specific video and image data from the overall traffic. This can be achieved by using pattern recognition techniques of traffic characteristics, such as using machine learning algorithms for data flow clustering. Specifically, clustering algorithms such as K-means or DBSCAN can be used to identify and separate video streams and image streams. For example, for video streams, continuous high-bandwidth streams can be identified, while for image streams, they may appear as intermittent lower-bandwidth streams. This division provides a basis for subsequent transmission and processing.

[0130] Step S23: Obtain the 5G network device transmission data and perform unstructured data decompression on the 5G network device transmission data to obtain unstructured transmission data;

[0131] In this embodiment, obtaining the 5G network device transmission data and decompressing the unstructured data through the 5G network device communication control platform is to process the data flow transmitted from the device to the network. In this step, compression algorithms such as Gzip or Brotli can be used to decompress the compressed data flow for further processing. For example, when compressed image or video data is received from the 5G network device communication control platform, the corresponding decompression algorithm can be used to restore it to the original unstructured data flow for subsequent data analysis and processing.

[0132] Step S24: Perform unstructured transmission association on the image traffic data and the video traffic data respectively according to the unstructured transmission data to obtain image transmission traffic data and video transmission traffic data;

[0133] In this embodiment, correlating the image traffic and video traffic based on unstructured transmission data is to match and integrate the data obtained from different data sources (such as steps S22 and S23). This generally involves using metadata or timestamps to correspond the image stream and video stream. For example, the timestamp information of the data stream can be used to match a specific image data stream with the corresponding video data stream for more refined analysis and processing in subsequent steps.

[0134] Step S25: Calculate the minimum image transmission amount for the image transmission traffic data to obtain the minimum image transmission data;

[0135] In this embodiment, calculating the minimum image transmission amount for the image transmission traffic data is to determine the minimum amount of data required to transmit an image while maintaining good quality. This can be achieved by using compression algorithms (such as JPEG, PNG) and image analysis techniques (such as structured grayscale analysis). For example, by analyzing the complexity and content of the image, an appropriate compression ratio can be determined to reduce the bandwidth and data volume required during transmission.

[0136] Step S26: Calculate the minimum video transmission amount for the video transmission traffic data to obtain the minimum video transmission data;

[0137] In this embodiment, calculating the minimum video transmission amount for the video transmission traffic data is to determine the minimum amount of data required to transmit a video while maintaining high quality. This includes using video codecs (such as H.264, H.265) and video analysis techniques to achieve. For example, by analyzing the resolution, frame rate, and content complexity of the video, the optimal encoding parameters and compression ratio can be determined to minimize the bandwidth and data volume required during transmission.

[0138] Step S27: Integrate the minimum image transmission data and the minimum video transmission data for the unstructured data transmission amount to obtain the unstructured minimum transmission data.

[0139] In this embodiment, integrating the minimum image transmission data and the minimum video transmission data according to the timestamp for the unstructured data transmission amount, and compressing the uncompressed unstructured transmission data is to integrate the data from different sources and finally prepare for transmission.

[0140] Optionally, step S25 is specifically:

[0141] Step S251: Perform edge detection on the transmitted image for the image transmission traffic data to obtain the transmitted image edge data;

[0142] In this embodiment, when performing edge detection on the image transmission traffic data to obtain the edge data of the transmitted image, an edge detection method based on the Sobel operator can be used. This method identifies edges by calculating the gradient magnitude and direction of each pixel in the image. For example, for an image with a resolution of 1920x1080, edge detection can be performed as follows: First, the image is grayscaled, and then the Sobel operator is used to perform a convolution operation on each pixel to obtain the horizontal and vertical gradients. Finally, the edge pixels are determined based on the gradient magnitude.

[0143] Step S252: Perform hierarchical partitioning of the image transmission traffic data according to the edge data of the transmitted image to obtain the main region image and the background region image;

[0144] In this embodiment, when performing hierarchical partitioning of the transmitted image according to the edge data of the transmitted image, a region segmentation method based on the edge detection result can be used. For example, the pixel points obtained by edge detection can be divided into the main region and the background region. For an edge image obtained through edge detection, the image can be divided into the main region with strong edges and the background region with weak or no edges through connected region analysis or threshold-based segmentation methods.

[0145] Step S253: Minimize and reduce the color depth of the background region image to obtain the minimized background region image;

[0146] In this embodiment, when minimizing and reducing the color depth of the background region image, color quantization or color depth reduction techniques can be used. For example, perform color space conversion on the pixels in the background region and use a clustering algorithm to perform quantization processing on the pixels to reduce the color depth to reduce the data volume and transmission cost.

[0147] Step S254: Perform approximate color depth matching of the main region image according to the minimized background region image to obtain the minimized main region image;

[0148] In this embodiment, when performing approximate color depth matching of the main region image according to the minimized background region image, color space mapping and matching algorithms can be used. For example, use the color information of the background region as a reference to perform approximate color matching on the pixels in the main region to ensure that while reducing the color depth, the color authenticity and clarity of the main region are maintained as much as possible.

[0149] Step S255: Merge the minimized background region image and the minimized main region image to obtain the minimized transmitted image;

[0150] In this embodiment, to merge the minimized background region image and the minimized subject region image, image fusion and synthesis techniques can be adopted. For example, the background region image after color quantization processing is mixed with the subject region image after color matching processing at the pixel level, so that the synthesized image reduces the data volume in terms of color and details while maintaining the overall quality of the image.

[0151] Step S256: Calculate the minimum image transmission amount for the minimized transmission image based on the image transmission traffic data, so as to obtain the minimum image transmission data.

[0152] In this embodiment, to calculate the minimum image transmission amount for the minimized transmission image based on the image transmission traffic data, a compression coding algorithm can be adopted. Select a compression algorithm according to the image transmission traffic data, and perform further data optimization on the minimized transmission image to obtain the final minimum image transmission amount. For example, lossless compression techniques such as Huffman coding or Lempel-Ziv-Welch (LZW) algorithm can be used to reduce the data size without causing a perceptible loss of image quality. These algorithms reduce the transmission data volume by effectively encoding recurring image blocks or pixel patterns. For the pixels in the subject region and the background region, the spatial correlation and local pixel similarity can be utilized to further optimize the coding efficiency and ensure the minimization of the transmission data volume. In addition, predictive coding techniques can also be considered, such as the coding method based on differential images, to further reduce the data volume by predicting pixel values and transmission errors. For example, perform predictive coding on the subject region, predict the value of the current pixel based on the values of adjacent pixels, and transmit the error to reduce the data volume. Finally, calculate the minimum image transmission amount using the pixel information and the required transmission amount of the color depth in the image transmission traffic data.

[0153] Optionally, step S26 is specifically as follows:

[0154] Step S261: Extract the transmitted video frames according to the video transmission traffic data, so as to obtain the transmitted video frames;

[0155] In this embodiment, to extract the transmitted video frames according to the video transmission traffic data, so as to obtain the transmitted video frames; the frame rate and bit rate information in the video traffic data can be utilized to extract video frames frame by frame by parsing the video stream. For example, tools such as FFmpeg can be used to extract each frame image from a video file, or the video frames can be parsed and obtained through a video codec in a real-time video stream.

[0156] Step S262: Calculate the pixel difference between adjacent video frames for the transmitted video frames, so as to obtain the video frame pixel difference data, and divide the transmitted video frames into key video frames and secondary video frames according to the video frame pixel difference data;

[0157] In this embodiment, the adjacent video frame pixel differences of the transmitted video frames are calculated to obtain video frame pixel difference data, and the transmitted video frames are divided into key video frames and secondary video frames according to the video frame pixel difference data; the pixel-level difference calculation technology can be used to determine the changes between video frames, and then identify key frames and secondary frames. For example, the difference pixels between two frames are calculated and a threshold is set to determine which frames are key (large change) or secondary (small change). The key frames are those with relatively small pixel differences between adjacent frames.

[0158] Step S263: Minimize the video frame color depth of the secondary video frames to obtain minimized secondary video frames;

[0159] In this embodiment, the video frame color depth of the secondary video frames is minimized to obtain minimized secondary video frames; in this step, the color information of the secondary frames can be reduced through color depth reduction technology, thereby saving transmission bandwidth and storage space. For example, algorithms such as color quantization or tone mapping are used to reduce the number of colors in the secondary frames to a relatively small level while keeping the visual quality not significantly reduced.

[0160] Step S264: Perform video frame hierarchical division on the key video frames to obtain the key video frame background image and the key video frame main image;

[0161] In this embodiment, the video frame hierarchical division is performed on the key video frames to obtain the key video frame background image and the key video frame main image; in this step, segmentation algorithms such as the watershed algorithm or threshold segmentation based on pixel values can be used to distinguish the background and the main body. For example, according to the changes in pixel brightness or color, the image is divided into the background and the main body parts, and the image information of these two parts is extracted respectively.

[0162] Step S265: Minimize the video frame color depth of the key video frame background image to obtain the minimized video frame background image, and perform approximate color depth matching on the main body area of the key video frame main image according to the minimized video frame image to obtain the minimized video frame main image;

[0163] In this embodiment, a color depth reduction algorithm, such as color quantization or a method based on a compression algorithm, is used to reduce the color precision of the background image from 24 bits per pixel of the original image to 16 bits or lower. This can be achieved by methods such as clustering of pixel values or discrete cosine transform (DCT). For example, for a video frame with a resolution of 1920x1080, if 16-bit color depth is used, the RGB value of each pixel can be reduced from 24 bits to 16 bits, thus significantly reducing the data volume. For the main image, especially moving objects or people in a dynamic scene, an approximate color depth matching method is adopted. This can be achieved by detecting the main region in the image and making color depth adaptive adjustments according to its characteristics to retain necessary details while reducing the data volume. For example, for a video frame containing a person, the background may be relatively static, and the color depth and details of the person region can be preferentially retained, while a lower color depth is used for the static background to achieve a compression effect.

[0164] Step S266: Combine the minimized video frame main image and the minimized video frame background image to obtain a minimized key video frame, and perform video encoding on the minimized secondary video frame and the minimized key video frame to obtain a minimized transmission video;

[0165] In this embodiment, the minimized video frame main image and the minimized video frame background image are combined to obtain a minimized key video frame, and the minimized secondary video frame and the minimized key video frame are subjected to video encoding to obtain a minimized transmission video; the main image and the background image that have undergone color depth minimization are combined into a minimized key video frame. This may include overlaying or appropriately synthesizing the two parts of the image to ensure that the final image maintains good visual quality. For example, for a video frame, the background image that has undergone color depth reduction is synthesized with the main image that has undergone color depth matching, retaining the details of the main body while effectively compressing the data of the background. The processed key frames and secondary frames are combined into the final transmission video frame according to certain rules. For example, video coding standards such as H.264 or H.265 are used to compress and encode each frame to minimize the transmission bandwidth requirement while maintaining the video quality.

[0166] Step S267: Calculate the minimum video transmission amount for the minimized transmission video according to the video transmission traffic data to obtain the minimum video transmission data.

[0167] In this embodiment, the minimum video transmission amount of the minimized transmission video is calculated according to the video transmission traffic data, so as to obtain the minimum video transmission data; in the last step, the actual transmission amount of the minimized transmission video can be calculated according to the video frame information and the encoded data amount processed previously. For example, by combining the compression ratio and frame rate information of the video frames, the actual required bandwidth or storage space is calculated to ensure the best effect and cost - effectiveness during the transmission process.

[0168] Optionally, step S3 is specifically as follows:

[0169] Step S31: Extract the node resource configuration characteristics and node signal transmission characteristics according to the communication node data of the 5G network device, so as to obtain the node resource configuration data and the node signal transmission data;

[0170] In this embodiment, according to the communication node data of the 5G network device, the node resource configuration characteristics are first extracted. This includes extracting detailed information on key resources such as processor capabilities, storage capacity, and network interfaces from the hardware configuration of the node. For example, for a base station node, its processor model and frequency, memory size, and the number of Ethernet ports will be extracted. At the same time, the node signal transmission characteristics, that is, data such as the communication signal strength and transmission rate between nodes, will also be extracted. These characteristic data are crucial for the subsequent node resource configuration mapping and the generation of signal simulation environment data.

[0171] Step S32: Perform node resource configuration mapping on the 5G network device communication architecture according to the node resource configuration data, so as to obtain the node resource allocation architecture;

[0172] In this embodiment, performing node resource configuration mapping on the 5G network device communication architecture according to the node resource configuration data aims to optimize the network's resource allocation and efficiency. For example, by analyzing the resource utilization rate and location information of each node in the node resource configuration data, the resource utilization situation of each node is mapped to the 5G network device communication architecture through spatial relationships. This mapping can be achieved through intelligent algorithms or network planning tools to ensure that the network can operate stably under different load conditions.

[0173] Step S33: Integrate the signal transmission environment based on the node signal transmission data, so as to obtain the signal simulation environment data;

[0174] In this embodiment, integrating the signal transmission environment based on the node signal transmission data is to simulate the signal propagation situation in the real world according to the node signal transmission situation. For example, by combining terrain, buildings, and meteorological conditions, through computer simulation or on - site measurement, the signal strength and transmission characteristics of each node in a specific environment are obtained. These data are crucial for predicting and optimizing the signal coverage range, especially in deployments in urban or complex terrains.

[0175] Step S34: Simulate the environmental data based on the signal, and perform millimeter-wave transmission simulation on the traffic data of 5G network devices through the node resource allocation architecture, so as to obtain device communication transmission simulation data;

[0176] In this embodiment, simulating the millimeter-wave transmission of the traffic of 5G devices through the node resource allocation architecture according to the signal simulation environmental data is to evaluate the transmission effect and performance of the network in the high-frequency band. For example, professional simulation software such as Keysight PathWave and Ansys HFSS is used to simulate the transmission rate and signal quality between devices in different frequency bands, so as to optimize the resource utilization rate and transmission efficiency between devices, and ensure the stable operation of the 5G network when multiple devices are connected simultaneously.

[0177] Step S35: Calculate the communication transmission attenuation amount of the device communication transmission simulation data, so as to obtain communication transmission attenuation amount data;

[0178] In this embodiment, calculating the communication transmission attenuation amount of the device communication transmission simulation data is to quantify the attenuation degree of the signal during transmission. For example, through the transmission model and experimental data, calculate the attenuation of the signal at different frequencies and distances, and then predict the communication quality and reliability between devices. This data is crucial for network planning and optimization, and can help engineers determine the appropriate device layout and enhance the signal coverage ability.

[0179] Step S36: Divide the signal transmission range levels according to the communication transmission attenuation amount data, so as to obtain signal transmission level range data.

[0180] In this embodiment, dividing the signal transmission range levels according to the communication transmission attenuation amount data is to classify the signal coverage quality in different areas of the network. For example, define the signal coverage range of each node under specific signal-to-noise ratio or bit error rate conditions, and divide it into excellent, good, medium, and poor levels. These level data are crucial for service providers and network managers, and can help them optimize network coverage and improve the user experience.

[0181] Optionally, step S4 is specifically as follows:

[0182] Step S41: Obtain the communication requirement data of 5G network devices, and estimate the transmission traffic of the 5G network device communication requirement data, so as to obtain estimated communication transmission traffic data;

[0183] In this embodiment, communication requirement data of each device is obtained through the 5G network device management system, which includes information such as the expected transmission data volume, transmission frequency, and priority of the device. This data is usually collected from the device through network management protocols (such as SNMP or REST API), and undergoes data cleaning and preprocessing to ensure accuracy and integrity. For example, scripts can be written in the Python programming language to automate the data collection and processing process, using libraries such as PySNMP to implement SNMP data acquisition, or using the Requests library to handle REST API calls. Then, historical data and prediction algorithms (such as time series analysis or machine learning models) are used to estimate the traffic of the communication requirement data, so as to obtain the estimated transmission traffic data required for device communication in a future period. This estimation can be based not only on past usage patterns, but also on future growth trends and the impact of specific events, such as increased communication requirements during holidays or special events.

[0184] Step S42: Calculate the traffic quota balance for the 5G network device traffic data to obtain the traffic quota balance data;

[0185] In this embodiment, the traffic quota balance is calculated based on the traffic data of the 5G network device. This involves monitoring and recording the traffic used by each device, and calculating the remaining available traffic quota according to the set quota policy (such as monthly or daily limit). For example, a database can be used to store the traffic usage of the device, and SQL queries can be written to calculate the current remaining traffic quota of each device. These calculations can be performed in real time through network traffic monitoring tools, or processed in batches regularly. The accurate calculation of the traffic quota balance can effectively manage the communication resources of the device, ensure that the network traffic is within the control range, and avoid problems such as overuse and service interruption.

[0186] Step S43: Perform a traffic margin classification calculation on the estimated communication transmission traffic data according to the traffic quota balance data. If the estimated communication transmission traffic data is less than the traffic quota balance data, generate the to-be-communicated requirement data and upload it to the 5G network device communication control platform to execute the communication control task; if the estimated communication transmission traffic data is greater than or equal to the traffic quota balance data, generate the communication requirement traffic assessment data;

[0187] In this embodiment, it is necessary to perform a traffic margin classification calculation on the estimated communication transmission traffic data based on the already calculated traffic quota balance data. The main purpose of this step is to determine whether the current traffic usage of the device is within a safe range or whether it is necessary to execute a communication control policy. For example, a threshold can be set to determine whether the estimated transmission traffic of the device is less than, equal to, or greater than the traffic quota balance data. If the estimated traffic is less than the balance data, the system marks the corresponding 5G network device communication demand data as pending communication demand data and uploads it to the 5G network device communication control platform to execute communication control tasks such as speed limiting or delaying transmission. On the contrary, if the estimated traffic is greater than or equal to the balance data, the corresponding 5G network device communication demand data is marked as communication demand traffic evaluation data for subsequent optimization and adjustment of the traffic quota policy.

[0188] Step S44: Extract the traffic characteristics of unstructured data transmission from the communication demand traffic evaluation data to obtain unstructured traffic data to be transmitted;

[0189] In this embodiment, the generated communication demand traffic evaluation data is further processed to extract its unstructured data transmission traffic characteristics. This usually involves using data mining and machine learning techniques such as feature engineering and data preprocessing to identify and extract key features in the communication demand data. For example, the Pandas and Scikit-learn libraries in Python can be used to perform data cleaning, feature selection, and transformation operations. The goal of feature extraction is to convert the communication demand data into a structured data format that can be used for subsequent optimization, facilitating the establishment of a prediction model or more in-depth data analysis.

[0190] Step S45: Calculate the unstructured minimum transmission traffic for the unstructured traffic data to be transmitted based on the unstructured minimum transmission data to obtain optimized demand transmission traffic data.

[0191] In this embodiment, based on the already extracted unstructured transmission data characteristics, it is necessary to calculate the unstructured minimum transmission traffic. This step aims to determine the minimum transmission requirements for each data transmission task to ensure a balance between network resource utilization efficiency and communication quality. For example, algorithms based on factors such as data size, transmission distance, and transmission priority can be used to calculate the minimum transmission traffic. These calculations usually involve prioritizing transmission tasks and resource scheduling to maximize network throughput and transmission efficiency. For example, greedy algorithms or dynamic programming methods can be used to implement the calculation of the minimum transmission traffic to meet the real-time requirements and resource constraints of communication between devices. For unstructured data, similarity comparisons can also be made through pre-completed unstructured minimization operations. Then, for data with high similarity, methods of unstructured data minimization are adopted to reduce the data transmission volume.

[0192] Optionally, step S5 is specifically as follows:

[0193] Step S51: Extract communication demand location features from the communication demand data of 5G network devices to obtain communication demand location data;

[0194] In this embodiment, in 5G network devices, communication demand location feature extraction is to obtain the precise location information of the user equipment through positioning technologies (such as GPS, base station positioning). These location data are crucial for subsequent communication optimization. For example, in an urban environment, through GPS positioning, the location of the user in a high-rise building-intensive area can be accurately obtained, which helps to optimize the signal coverage range and transmission efficiency.

[0195] Step S52: Classify the communication demand location data according to the signal transmission level range data to obtain communication demand transmission level data;

[0196] In this embodiment, using the pre-defined signal transmission level range data, the transmission level is classified according to the location information of the issued communication demand to obtain the signal transmission levels at different locations.

[0197] Step S53: Match the communication demand transmission level data according to the device communication simulation data to obtain communication demand transmission environment data;

[0198] In this embodiment, the matching of the communication demand transmission environment is to match the classified transmission level data with the actual communication environment according to the device communication simulation data. For example, in a city, by simulating and considering factors such as buildings, weather conditions, and other wireless signal interferences, the optimal transmission mode and frequency band configuration are determined to improve network coverage and data transmission rate.

[0199] Step S54: Calculate the communication transmission attenuation amount for the communication demand transmission environment data and the optimized demand transmission flow data according to the device communication simulation data to obtain communication demand transmission attenuation amount data;

[0200] In this embodiment, the calculation of the communication demand transmission attenuation amount is to calculate the attenuation amount of the signal during transmission according to the communication environment data and the optimized transmission flow demand. For example, through path loss models and power attenuation calculations, the attenuation of the signal during long-distance transmission or in high-rise building clusters is evaluated, and then the transmission power and spectrum usage are adjusted to minimize signal loss to the greatest extent. It is also possible to match the simulation data similar to the communication demand transmission environment data and the optimized demand transmission flow data according to the simulated environment scenarios in the device communication simulation data to achieve the purpose of quickly calculating the communication transmission attenuation amount.

[0201] Step S55: Perform minimum transmission attenuation resource allocation on the communication demand transmission attenuation data through the node resource allocation architecture, so as to obtain communication demand resource allocation data;

[0202] In this embodiment, minimum transmission attenuation resource allocation is performed on the transmission attenuation data through the node resource allocation architecture to ensure stable transmission quality in various communication environments. For example, based on the resource allocation algorithm and dynamic power control technology, the frequency band allocation and power distribution of each base station are adjusted to ensure the balance of network coverage and the stability of communication quality.

[0203] Step S56: Integrate the communication demand resource allocation data and the optimized demand transmission traffic data to obtain the device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute communication control tasks.

[0204] In this embodiment, the communication demand resource allocation data and the optimized demand transmission traffic data are integrated to obtain the device communication transmission strategy, and transmit it to the 5G network device communication control platform to execute communication control tasks. For example, according to the analyzed resource allocation and real-time network load conditions, the specific transmission strategy of each device is determined, such as dynamically allocating spectrum resources or adjusting QoS parameters, to optimize network performance and user experience.

[0205] Optionally, the present invention further provides a control system for a 5G network device, which is used to execute a control method for a 5G network device as described above. The control system for the 5G network device includes:

[0206] A communication topology structure analysis module, which is used to obtain 5G network device communication node data, perform 5G communication topology structure analysis on the 5G network device communication node data, so as to obtain the 5G network device topology structure; perform 5G communication architecture integration according to the 5G network device topology structure, so as to obtain the 5G network device communication architecture;

[0207] An unstructured traffic calculation module, which is used to extract node traffic characteristics from the 5G network device communication node data, so as to obtain 5G network device traffic data, perform unstructured traffic division on the 5G network device traffic data, so as to obtain video traffic data and image traffic data; perform minimum data transmission volume calculation on the video traffic data and the image traffic data, so as to obtain unstructured minimum transmission data;

[0208] A signal transmission range division module, which is used to perform millimeter wave transmission simulation on the 5G network device traffic data through the 5G network device communication architecture, so as to obtain device communication transmission simulation data; perform signal transmission range level division according to the device communication transmission simulation data, so as to obtain signal transmission level range data;

[0209] A communication demand traffic evaluation module, which is used to obtain the communication demand data of 5G network devices, and perform traffic margin evaluation on the communication demand data of 5G network devices according to the traffic data of 5G network devices, so as to obtain communication demand traffic evaluation data; calculate the minimum transmission traffic of communication demand according to the unstructured minimum transmission data and the communication demand traffic evaluation data, so as to obtain optimized demand transmission traffic data;

[0210] A communication resource integration module, which is used to divide the communication demand transmission levels of the communication demand data of 5G network devices according to the signal transmission level range data, so as to obtain communication demand transmission level data; integrate the communication resources of 5G network devices for the communication demand transmission level data and the optimized demand transmission traffic data according to the device communication transmission simulation data, so as to obtain a device communication transmission strategy and transmit it to the 5G network device communication control platform to execute communication control tasks.

[0211] The control system of the 5G network device of the present invention can implement any control method of the 5G network device of the present invention, and is used as a medium for coordinating the operations and signal transmissions between various modules to complete the control method of the 5G network device. The internal modules of the system cooperate with each other, thereby improving resource utilization.

[0212] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0213] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for 5G network equipment, characterized in that: The following steps are involved: Step S1: Acquire 5G network device communication node data, and perform 5G communication topology structure analysis on the 5G network device communication node data, thereby obtaining the 5G network device topology structure; integrate the 5G communication architecture according to the 5G network device topology structure, thereby obtaining the 5G network device communication architecture; Step S2: extracting node traffic features from 5G network device communication node data to obtain 5G network device traffic data, and performing unstructured traffic division on the 5G network device traffic data to obtain video traffic data and image traffic data; calculating the minimum data transmission amount on the video traffic data and the image traffic data to obtain unstructured minimum transmission data; Step S3: Performing millimeter wave transmission simulation on 5G network device traffic data through the 5G network device communication architecture, thereby obtaining device communication transmission simulation data; performing signal transmission range level classification according to the device communication transmission simulation data, thereby obtaining signal transmission level range data; Step S4: Acquire the communication demand data of the 5G network equipment, and perform flow margin evaluation on the communication demand data of the 5G network equipment according to the flow data of the 5G network equipment, so as to obtain the communication demand flow evaluation data; calculate the minimum transmission flow of the communication demand according to the unstructured minimum transmission data and the communication demand flow evaluation data, so as to obtain the optimized demand transmission flow data; Step S5: Classify the communication demand data of the 5G network equipment into communication demand transmission levels according to the signal transmission level range data, so as to obtain the communication demand transmission level data; integrate the communication demand transmission level data and the optimized demand transmission flow data according to the equipment communication transmission simulation data, so as to obtain the equipment communication transmission strategy, and transmit it to the 5G network equipment communication control platform to execute the communication control task.

2. The control method of the 5G network device according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire 5G network device communication node data, and perform node layout feature extraction and node communication protocol feature extraction on the 5G network device communication node data, thereby obtaining node layout data and node communication protocol data; Step S12: integrating node connection relationships according to the node layout data to obtain communication node connection data, and performing star topology analysis according to the communication node connection data to obtain a communication physical topology structure; Step S13: clustering the communication protocol nodes according to the node communication protocol data, thereby obtaining the communication protocol node clustering data, and performing logical topology structure analysis according to the communication protocol node clustering data, thereby obtaining the communication logical topology structure; Step S14: mapping the communication physical topology structure and the communication logical topology structure to obtain a 5G network device topology structure; Step S15: Integrate the 5G communication architecture according to the 5G network device topology structure to obtain the 5G network device communication architecture.

3. The control method of the 5G network device according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: extracting logical connection features and physical connection features from the topological structure of the 5G network device, thereby obtaining node logical connection data and node physical connection data; Step S152: Establishing a communication architecture diagram according to the node logical connection data, thereby obtaining a node communication architecture diagram; Step S153: performing node connection frequency statistics according to the node physical connection data, thereby obtaining node connection frequency data; Step S154: dividing the node physical connection data into node hierarchies according to the node connection frequency data, thereby obtaining core node connection data and edge node connection data; Step S155: constructing a core layer node network according to the core node connection data; Build an edge layer node network based on edge node connection data; Step S156: Based on the core layer node network and the edge layer node network, the node communication architecture diagram is integrated into the communication architecture hierarchy to obtain the 5G network equipment communication architecture.

4. The control method of the 5G network device according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting node traffic features from 5G network device communication node data, thereby obtaining 5G network device traffic data; Step S22: performing unstructured traffic division on the 5G network device traffic data, thereby obtaining video traffic data and image traffic data; Step S23: acquiring 5G network device transmission data, and performing unstructured data decompression on the 5G network device transmission data, thereby obtaining unstructured transmission data; Step S24: performing unstructured transmission association on the image traffic data and the video traffic data respectively according to the unstructured transmission data, thereby obtaining the image transmission traffic data and the video transmission traffic data; Step S25: Calculate the minimum image transmission amount for the image transmission flow data, thereby obtaining the minimum image transmission data; Step S26: Calculate the minimum video transmission volume for the video transmission flow data, thereby obtaining the minimum video transmission data; Step S27: integrating the minimum image transmission data and the minimum video transmission data into unstructured data transmission amounts, thereby obtaining unstructured minimum transmission data.

5. The control method of the 5G network device according to claim 4, characterized in that: Step S25 is specifically as follows: Step S251: performing transmission image edge detection on the image transmission flow data, thereby obtaining transmission image edge data; Step S252: dividing the image transmission flow data into transmission image layers according to the transmission image edge data, thereby obtaining a main area image and a background area image; Step S253: Minimizing and reducing the color depth of the background area image, thereby obtaining a minimized background area image; Step S254: performing approximate color depth matching of the main body region image on the main body region image according to the minimized background region image, thereby obtaining the minimized main body region image; Step S255: merging the minimized background area image and the minimized main body area image to obtain a minimized transmission image; Step S256: Calculate the minimum image transmission amount for the minimized transmission image according to the image transmission flow data, so as to obtain the minimum image transmission data.

6. The control method of the 5G network device according to claim 4, characterized in that: Step S26 is specifically as follows: Step S261: extracting transmission video frames according to the video transmission flow data, thereby obtaining transmission video frames; Step S262: performing pixel difference calculation of adjacent video frames on the transmission video frame, thereby obtaining video frame pixel difference data, and dividing the transmission video frame into key video frames according to the video frame pixel difference data, thereby obtaining key video frames and secondary video frames; Step S263: Minimizing the video frame color depth of the secondary video frame to obtain a minimized secondary video frame; Step S264: dividing the key video frame into video frame layers, thereby obtaining a key video frame background image and a key video frame main image; Step S265: Minimizing the video frame color depth of the key video frame background image to obtain a minimized video frame background image, and performing approximate color depth matching of the main area of ​​the key video frame main image according to the minimized video frame image to obtain a minimized video frame main image; Step S266: Combining the minimized video frame main image and the minimized video frame background image to obtain a minimized key video frame, and performing video encoding on the minimized secondary video frame and the minimized key video frame to obtain a minimized transmission video; Step S267: Calculate the minimum video transmission amount for the minimized transmission video according to the video transmission flow data, so as to obtain the minimum video transmission data.

7. The control method of the 5G network device according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting node resource configuration features and node signal transmission features according to the 5G network device communication node data, thereby obtaining node resource configuration data and node signal transmission data; Step S32: mapping the node resource configuration of the 5G network device communication architecture according to the node resource configuration data, thereby obtaining a node resource allocation architecture; Step S33: integrating the signal transmission environment based on the node signal transmission data, thereby obtaining signal simulation environment data; Step S34: According to the signal simulation environment data, the millimeter wave transmission simulation is performed on the 5G network device traffic data through the node resource allocation architecture, so as to obtain the device communication transmission simulation data; Step S35: calculating the communication transmission attenuation of the device communication transmission simulation data, thereby obtaining communication transmission attenuation data; Step S36: Classify the signal transmission range according to the communication transmission attenuation data, so as to obtain signal transmission level range data.

8. The control method of the 5G network device according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Acquire 5G network equipment communication demand data, and estimate the transmission flow of the 5G network equipment communication demand data, thereby obtaining estimated communication transmission flow data; Step S42: Calculate the traffic quota balance of the 5G network device traffic data to obtain traffic quota balance data; Step S43: performing flow margin classification calculation on the estimated communication transmission flow data according to the flow quota balance data; if the estimated communication transmission flow data is less than the flow quota balance data, generating communication demand data to be uploaded to the 5G network equipment communication control platform to execute the communication control task; if the estimated communication transmission flow data is greater than or equal to the flow quota balance data, generating communication demand flow assessment data; Step S44: extracting unstructured data transmission flow characteristics from the communication demand flow evaluation data, thereby obtaining unstructured flow data to be transmitted; Step S45: performing unstructured minimum transmission flow calculation on the unstructured flow data to be transmitted according to the unstructured minimum transmission data, so as to obtain optimized required transmission flow data.

9. The control method of the 5G network device according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: extracting communication demand location features from the 5G network device communication demand data, thereby obtaining communication demand location data; Step S52: classifying the communication demand location data into communication demand transmission levels according to the signal transmission level range data, thereby obtaining communication demand transmission level data; Step S53: matching the communication transmission environment with the communication requirement transmission level data according to the device communication simulation data, thereby obtaining the communication requirement transmission environment data; Step S54: calculating the communication transmission attenuation of the communication demand transmission environment data and the optimization demand transmission flow data according to the device communication simulation data, thereby obtaining the communication demand transmission attenuation data; Step S55: performing minimum transmission attenuation resource allocation on the communication demand transmission attenuation data through the node resource allocation framework, thereby obtaining communication demand resource allocation data; Step S56: Integrate the communication demand resource allocation data and the optimization demand transmission traffic data into a transmission strategy to obtain the device communication transmission strategy, and transmit it to the 5G network device communication control platform to perform communication control tasks.

10. A control system for 5G network equipment, characterized in that: Used to execute a control method for a 5G network device as claimed in claim 1, the control system of the 5G network device comprises: The communication topology structure analysis module is used to obtain the communication node data of the 5G network equipment, and perform 5G communication topology structure analysis on the communication node data of the 5G network equipment, so as to obtain the topology structure of the 5G network equipment; and integrate the 5G communication architecture according to the topology structure of the 5G network equipment, so as to obtain the communication architecture of the 5G network equipment; The unstructured traffic calculation module is used to extract node traffic features from the communication node data of the 5G network equipment, thereby obtaining the 5G network equipment traffic data, and to perform unstructured traffic division on the 5G network equipment traffic data, thereby obtaining video traffic data and image traffic data; and to perform minimum data transmission calculation on the video traffic data and the image traffic data, thereby obtaining unstructured minimum transmission data; The signal transmission range division module is used to perform millimeter wave transmission simulation on 5G network device traffic data through the 5G network device communication architecture, thereby obtaining device communication transmission simulation data; and perform signal transmission range level division according to the device communication transmission simulation data, thereby obtaining signal transmission level range data; The communication demand flow evaluation module is used to obtain the communication demand data of the 5G network equipment, and to evaluate the flow margin of the communication demand data of the 5G network equipment according to the flow data of the 5G network equipment, so as to obtain the communication demand flow evaluation data; to calculate the minimum transmission flow of the communication demand according to the unstructured minimum transmission data and the communication demand flow evaluation data, so as to obtain the optimized transmission flow data of the demand; The communication resource integration module is used to classify the communication demand data of 5G network equipment into communication demand transmission levels according to the signal transmission level range data, so as to obtain the communication demand transmission level data; integrate the communication demand transmission level data and the optimized demand transmission flow data of 5G network equipment communication resources according to the equipment communication transmission simulation data, so as to obtain the equipment communication transmission strategy, and transmit it to the 5G network equipment communication control platform to perform the communication control task.

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