Control method and control system of 5G network equipment

Abstract

The invention relates to the technical field of communication transmission, in particular to a control method and a control system of 5G network equipment. The method comprises the following steps: acquiring 5G network equipment communication node data, and integrating the data to obtain a 5G network equipment communication architecture; performing feature extraction on the communication node data of the 5G network equipment to obtain flow data of the 5G network equipment, and performing transmission quantity calculation to obtain unstructured minimum transmission data; dividing the flow data of the 5G network equipment to obtain signal transmission grade range data; obtaining communication demand data of 5G network equipment, and carrying out traffic calculation to obtain optimized demand transmission traffic data; performing communication demand transmission grade division on the 5G network equipment communication demand data to obtain communication demand transmission grade data; and carrying out communication resource integration on the communication demand transmission level data and the optimized demand transmission flow data to obtain an equipment communication transmission strategy. The resource utilization rate can be improved.

Description

Technical Field

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

[0002] In the current rapidly developing field of communication technology, 5G network, as the fifth generation of mobile communication technology, marks the arrival of a new era of high speed, low latency, large capacity and multiple connections. In order to effectively manage and control 5G network equipment and provide stable and efficient services, researchers and engineers are constantly exploring new control methods and technologies. With the popularization of mobile Internet and the rapid growth of IoT devices, traditional network management methods face 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 Traditional control methods are no longer applicable to complex 5G network environments.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 equipment usually lack real-time response and dynamic adaptability. Faced with rapid changes in network traffic or sudden changes in application requirements, this method cannot quickly adjust and optimize the allocation of network resources, resulting in reduced service quality or waste of resources. Summary of the invention

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

[0004] To achieve the above object, a control method for a 5G network device comprises the following steps: 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.

[0005] The present invention can establish a comprehensive network topology by acquiring and analyzing the communication node data of 5G network devices. This is very important for understanding the connection mode of the entire network, the relationship between nodes and the communication path. This analysis helps to optimize the network layout and design and improve the stability and performance of the network. By extracting the traffic characteristics of the nodes and dividing the traffic, we can gain an in-depth understanding of the data transmission of each node. This is crucial for identifying high-load nodes, bottlenecks and potential resource waste points. By calculating the minimum data transmission amount, the data transmission efficiency can be optimized and unnecessary network load can be reduced. Through millimeter wave transmission simulation, the transmission effect and coverage of different signal frequency bands in the actual environment can be simulated and evaluated. This helps to determine the optimal signal transmission range level, thereby optimizing network coverage and communication quality, especially in scenarios with high-density device connections. By evaluating communication demand data and calculating the minimum transmission flow, network resources can be effectively managed and optimized. This method helps determine the actual resource consumption of communication needs and predict future needs, thereby avoiding the problem of over-allocation or under-allocation of resources and improving the availability and efficiency of the network. Through device communication transmission simulation data and demand transmission level data, an accurate communication resource integration strategy can be formulated. This strategy ensures that each device and node allocates resources according to its priority and needs, thereby maximizing network performance and service quality. The strategies transmitted to the communication control platform for execution can also be dynamically adjusted and managed to cope with real-time changing network conditions and requirements. Overall, these steps combine data analysis, simulation evaluation and optimization strategies to bring 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.

[0006] Optionally, step S1 specifically includes: 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.

[0007] The present invention helps to understand the distribution of devices in physical space by acquiring the communication node data of 5G network devices and extracting information such as the location and distance of the nodes. This is crucial for optimizing network coverage, avoiding signal interference, and reasonably placing devices. By analyzing the communication protocol of the node, it is possible to understand how different devices communicate and exchange data. This helps to optimize communication efficiency, ensure data transmission security, and adjust network settings according to communication needs. By integrating the connection relationship of the nodes, a physical connection diagram between various devices in the network can be established, which is very important for managing and monitoring the network status. By analyzing the star topology, the degree of centralization of the network and the importance of each node can be evaluated. This helps to optimize the data transmission path, improve network efficiency, and reduce the risk of single point failure. Clustering nodes with similar communication characteristics can identify different subnetworks or service areas in the network. This helps to optimize network resource allocation, improve service quality, and ensure network security. Analyzing the logical topology can reveal the flow pattern and path selection mechanism of data in the network. This is critical for optimizing routing algorithms, improving network response speed, and service quality. Mapping the physical and logical topologies helps to understand the data flow and communication paths in the network. This is of guiding significance for planning and optimizing network architecture and adjusting equipment layout. Combining the topological 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 communications.

[0008] Optionally, step S15 is specifically: 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: 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; 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.

[0009] The present invention can obtain logical connection data and physical connection data between nodes by extracting logical connection features and physical connection features. It is helpful to understand the logical relationship between devices, such as which devices communicate or depend on each other in the network topology. It is helpful to obtain physical connection information between devices, such as the location, connection mode and transmission rate of the devices. Based on the logical connection data of the nodes, establishing a communication architecture diagram helps to show the communication relationship between devices, including central nodes, edge nodes and the connection mode 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 frequency of the nodes, it is possible to understand which devices communicate frequently and which devices are used less. Adjust network resource allocation according to the connection frequency data to ensure that high-frequency devices have sufficient bandwidth and processing power. Based on the connection frequency data, hierarchical division of nodes helps to identify core nodes and relatively marginal nodes that undertake important communication tasks in the network. It helps to allocate more resources to core nodes to ensure the stability and efficiency of the network. The network is layered according to functions and communication requirements, making management and maintenance more effective and targeted. Optimize the communication efficiency of core layer nodes, and provide more flexible services and coverage through edge layer nodes. Based on the core layer and edge layer node network, the hierarchical integration of the communication architecture can ensure the logical and physical coordination of the communication architecture of the entire 5G network equipment. Through hierarchical integration, the communication path and resource utilization are optimized, and the overall performance and efficiency of the network are improved.

[0010] Optionally, step S2 specifically includes: 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.

[0011] The present invention can analyze the data transmission mode and usage of the device by extracting node traffic characteristics. Knowing which nodes carry more data traffic is helpful for load balancing and avoiding network congestion and performance degradation. Identifying traffic peaks and off-peak periods helps optimize network resource configuration and management strategies. Different transmission strategies and optimization methods can be adopted for different types of traffic to maximize network resource utilization. Different quality of service (QoS) can be provided according to the traffic type, such as low-latency processing of video traffic and high-definition transmission of image traffic. Decoding the compressed transmission data to restore it to the original data ensures data integrity and accuracy. Extracting and analyzing the decompressed data for further processing and application, such as content analysis, machine learning, etc. Organizing the associated data for subsequent processing and analysis ensures that each type of data stream is managed and optimized according to its specific processing and transmission requirements. Calculating the minimum image transmission amount can determine the minimum bandwidth and resources required to transmit images and videos to avoid waste and improve efficiency. Calculating the minimum video transmission amount can ensure that image 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 and save network resources. At the same time, it can optimize the data transmission process, increase data transmission speed and response time, and improve user experience.

[0012] Optionally, step S25 is specifically: 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.

[0013] The present invention can accurately identify the boundary of an object or subject by detecting the edge of an image, which helps to more accurately separate the subject and the background in the subsequent steps. Retaining only the edge data can significantly reduce the amount of image data that needs 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, such as the subject area requires higher color and detail retention, while the background area can be compressed more. 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 minimizing the color depth 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 guaranteed. The color details of the subject area are adjusted to the minimum level required for minimum transmission, so as to reduce bandwidth occupation during the transmission process. The processed background area image and the subject area image are merged into a minimized transmission image. It is ensured that the integrated image data still maintains visual consistency and integrity. According to the amount of data of the minimized transmission image, the required minimum transmission bandwidth is accurately calculated. It is beneficial to ensure that the utilization rate of bandwidth and network resources is maximized when transmitting images, while ensuring the balance between image quality and transmission efficiency.

[0014] Optionally, step S26 is specifically: 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.

[0015] The present invention selects video frames to be transmitted, usually key frames and some secondary frames, according to video transmission flow data. Only key frames and optimized selected secondary frames are transmitted, which can reduce redundant data in transmission and improve transmission efficiency. Key frames and secondary frames are identified by calculating pixel differences between adjacent video frames. Only key frames are transmitted in detail, while secondary frames can be transmitted more efficiently through difference data, reducing bandwidth requirements. Color depth minimization processing is performed on secondary video frames to reduce the amount of data per frame. It is suitable for scenes with low network bandwidth, reduces the amount of data during transmission, and maintains the basic quality of the picture. Key video frames are divided into background and main body parts, and subsequent processing is performed respectively according to the separated background and main body images, such as color depth minimization of the background and color matching of the main body. Color depth minimization and matching processing are performed on the background image and the main body image of the key video frame respectively. By reducing the color details of each frame, the transmission bandwidth requirement is reduced while the visual quality of the main body image is maintained. The optimized main body image and background image are combined into minimized key video frames. Video encoding is performed on the minimized key video frames and secondary video frames to further optimize transmission efficiency. The minimum transmission bandwidth required is calculated based on the minimum amount of video data to be transmitted. This ensures that bandwidth and network resource utilization are maximized when transmitting videos, while maintaining a balance between video quality and transmission efficiency.

[0016] Optionally, step S3 specifically includes: 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.

[0017] The present invention extracts node resource configuration features, such as configuration information of resources such as processors and memories, by analyzing the communication node data of 5G network devices. This information can help optimize the resource utilization efficiency of nodes and improve system performance and response speed. Features are extracted from node signal transmission data, including signal strength, transmission rate, delay, etc. These features are used to evaluate and optimize signal transmission quality and ensure communication stability and efficiency between devices. According to the node resource configuration data, the resource allocation strategy and architecture of 5G network devices are formulated and implemented. This can ensure that each device can make the most effective use of its available resources during the communication process and improve the throughput and efficiency of the overall system. Based on the node signal transmission data, the signal transmission environment is integrated, and factors such as obstacles and multipath effects are considered to generate signal simulation data in a real environment. These data help evaluate communication performance in complex environments and optimize signal transmission strategies. Using signal simulation environment data and node resource allocation architecture, millimeter wave transmission simulation is performed on the traffic data of 5G network devices. Millimeter wave technology can improve data transmission rate and bandwidth utilization and adapt to high-density data transmission requirements. By calculating the attenuation of the device communication transmission simulation data, the attenuation of the signal during transmission is evaluated. This data is used to optimize signal transmission distance, power consumption and data quality to ensure stable communication connections. Different signal transmission range levels are divided based on communication transmission attenuation data. These levels guide the communication coverage and signal quality of devices under different environmental conditions, optimizing network coverage and service quality.

[0018] Optionally, step S4 is specifically: 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.

[0019] The present invention can accurately predict future communication transmission traffic by collecting and analyzing the communication demand data of 5G network equipment. This can help operators or network administrators to reasonably plan resources and avoid waste or shortage of resources. Through traffic estimation, network resources can be allocated in a targeted manner 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 of each device or user, thereby controlling the allocation and cost of network resources. This is particularly important for operators, as it can avoid service quality degradation or additional costs caused by overuse. By reasonably allocating traffic quotas, it can be ensured 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 communication 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 communication demand data can be immediately generated and uploaded to the communication control platform to ensure that the device transmits data on demand and avoid unnecessary traffic consumption. This helps to 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 transmission and processing efficiency of the data. The extracted feature data can provide a basis for subsequent decision-making, such as adjusting data transmission strategies or optimizing network configurations to cope with different communication needs 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 transmission traffic can also reduce operating costs, which is particularly important for large-scale data transmission and processing tasks.

[0020] Optionally, step S5 specifically includes: 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.

[0021] By extracting the communication demand location features, the system can accurately determine the location of the device or user, which is crucial for optimizing the signal coverage and service quality. These location data can be used for subsequent signal transmission level division and transmission environment matching. According to the signal transmission level range data, the communication demand location data is divided into different transmission levels. In this way, the transmission strength and priority of the signal can be adjusted according to the location and service requirements of the device or user, thereby 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 in which the device is located. This includes taking into account possible signal interference, multipath effects and other environmental factors in order to optimize the transmission and reception of the signal. Calculating the transmission attenuation of the communication demand transmission environment data and the optimization demand transmission flow data can help determine the signal loss that may be encountered during the signal transmission process. This provides key data support for subsequent resource allocation and transmission strategies. Based on the calculated transmission attenuation data, the system can perform minimum transmission attenuation 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 optimization demand transmission flow data to formulate the best transmission strategy. These strategies adjust the way signals are transmitted based on different communication needs and network conditions, thereby improving overall network efficiency and user experience.

[0022] Optionally, the present invention further provides a control system of a 5G network device, which is used to execute the control method of a 5G network device as described above, and the control system of the 5G network device includes: 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.

[0023] 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 to combine the operation and signal transmission medium between each module to complete the control method of the 5G network device. The internal modules of the system cooperate with each other to improve resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic diagram of the steps of the control method of the 5G network device of the present invention; Figure 2 Detailed step flow diagram of step S1 in the present invention; Figure 3 Detailed step flow diagram of step S2 in the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 The present invention provides a control method for a 5G network device, the method comprising the following steps: 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; In this embodiment, the 5G network equipment communication node data is obtained through the 5G network equipment communication control platform, and the data is analyzed in detail to reveal the connection mode and network topology between the devices. For example, by analyzing the physical location and network connection mode between the devices, the correlation between the devices and the data transmission path can be determined. These analysis results form the topology of the 5G network equipment. Next, according to the topology, the 5G communication architecture is integrated. This includes determining the role and function of each device in the network, such as the core network, edge computing nodes, and terminal devices. Through integration, the system can optimize data flow and service distribution to ensure efficient data transmission and network response speed. Network topology analysis tools and techniques, 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 diagram can be generated, and optimization algorithms can be further applied to adjust the network structure to meet specific communication needs and performance goals.

[0029] 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; In this embodiment, traffic feature extraction is performed on the communication node data of the 5G network device to obtain detailed traffic data. These data include not only 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, the minimum data transmission volume is calculated for unstructured data such as video and images. This calculation process takes into account the importance of the data and the real-time requirements of transmission to ensure that data loss and transmission delay are minimized during transmission. Deep learning technology or image analysis algorithms can be used to parse and classify video traffic and image traffic in real time. Based on the analysis of the data traffic pattern, the minimum transmission volume requirement for each type of data is determined. For example, video streaming may require a high-bandwidth and low-latency transmission channel, while image streaming may focus more on data integrity and real-time performance.

[0030] 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; In this embodiment, the 5G communication architecture is used to simulate the millimeter wave transmission of traffic data. Millimeter wave technology can provide higher data transmission rates and bandwidth, but is limited by the transmission distance and the influence of obstacles. According to the simulated data of device communication transmission, the range and efficiency of signal transmission are evaluated. This includes simulating and analyzing the transmission characteristics of millimeter wave signals under different environmental conditions, such as changes in urban environments and indoors and outdoors. The result will be data on the level of signal transmission range, which is used to optimize communication and resource allocation between devices. The collected traffic data can be simulated using RF simulation software or millimeter wave transmission models. Through simulation analysis, the system can determine the transmission path and transmission range of millimeter wave signals, thereby optimizing the signal strength and connection stability between devices.

[0031] 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; In this embodiment, the communication demand data of the 5G network equipment is obtained and evaluated through the 5G network equipment communication control platform, including the communication demand after taking into account various traffic data. By analyzing the actual traffic demand and current traffic data of the equipment, the 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 demand traffic evaluation data, the system calculates the optimized minimum transmission traffic. This process aims to maximize the utilization of network resources while ensuring that the communication needs of devices and users are met and 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 needs of the equipment. By analyzing data traffic patterns and usage patterns, the system can predict future traffic demands and optimize data transmission strategies and resource allocation accordingly.

[0032] 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.

[0033] In this embodiment, the communication demand data is divided according to the signal transmission level range data to determine the communication transmission level of each device. This division is based on the device location, service requirements and signal transmission priority to ensure the communication quality and stability under different network conditions. Next, the best device communication transmission strategy is formulated in combination with the device communication transmission simulation data and the optimization demand transmission traffic data. These strategies include optimizing data transmission paths, selecting appropriate transmission channels and adjusting transmission rates 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 demand data and transmission simulation data. Through algorithms and real-time monitoring, the communication transmission strategy of the device can be dynamically adjusted to adapt to the ever-changing network environment and user needs.

[0034] Optionally, step S1 specifically includes: 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; In this embodiment, obtaining the communication node data of 5G network equipment and extracting the node layout features and node communication protocol features is one of the key steps in 5G network design and optimization. The location coordinates, coverage, and communication frequency band of each node can be collected through the 5G network equipment communication control platform, network probes, or sensor devices to integrate the node layout data and node communication protocol data. For example, the global positioning system (GPS) is used to obtain the accurate geographic location information of the node, and a spectrum analyzer is used to analyze the signal frequency band and bandwidth usage of the node.

[0035] 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; In this embodiment, the collected node layout data is used to calculate the physical distance between nodes and analyze the signal strength attenuation. This information helps to determine the optimal node connection method to maximize coverage and signal quality. Algorithms in graph theory, such as the minimum spanning tree (MST) or the shortest path algorithm, are used to optimize the connection relationship between nodes. For example, the Prim algorithm can be used to construct a minimum spanning tree to form an efficient star topology.

[0036] 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; In this embodiment, the communication protocols (such as LTE, 5G NR, etc.) supported by each node are analyzed, and relevant protocol feature data such as protocol version, frequency band, and supported data rate are extracted. Clustering algorithms (such as k-means clustering) are used to group nodes with similar communication protocol features into clusters. For example, nodes that support the same protocol version and frequency band are clustered together to form a logical topology structure later. In a 5G network, different types of devices may use different communication protocols for data transmission. For example, the communication protocol used by a sensor node may be different from that of a main server or other nodes. By collecting and analyzing device communication data, clustering algorithms (such as K-means) can be used to classify devices so that devices in each category have similar communication behavior characteristics. For example, for sensor devices, nodes using the low power wide area network (LPWAN) protocol will be clustered; while for base stations 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. Logical topology analysis aims to understand the logical connection between devices, even if the devices are not physically connected directly, but they exchange data and collaborate 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 through Bluetooth or Wi-Fi, the logical connection between them can be shown in the logical topology diagram; or if the devices exchange data through a cloud server, this cloud-integrated structure can be reflected in the logical topology diagram.

[0037] Step S14: mapping the communication physical topology structure and the communication logical topology structure to obtain a 5G network device topology structure; In this embodiment, the communication physical topology and the communication logical topology are mapped to form the overall topology of the 5G network device. The node connection relationship at the physical level is combined with the communication protocol node clustering at the logical level to ensure the consistency and efficiency of the entire network. For example, the node location information in the physical topology is compared with the communication protocol mapping relationship in the logical topology to verify the actual operation of the network. The determined physical connection relationship and the generated logical cluster can be matched. For example, the node connections with the closest physical distance are assigned to the same logical cluster.

[0038] Step S15: Integrate the 5G communication architecture according to the 5G network device topology structure to obtain the 5G network device communication architecture.

[0039] In this embodiment, the communication architecture is integrated according to the topology of the 5G network equipment, and finally a complete 5G network equipment communication architecture is formed. According to the physical and logical topology, the functional role and configuration parameters of each node are designed. For example, the layout and configuration of the network equipment can be designed and deployed according to the mapping results of the physical topology and the logical topology. For example, the functional role of each node (such as base station, user equipment) is determined, and its configuration parameters (such as transmission power, frequency allocation) are adjusted to achieve efficient operation and coverage optimization of the overall network.

[0040] In the selected location, 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; In this embodiment, when extracting logical connection features and physical connection features of the 5G network device topology, it is first necessary to extract key information from the device configuration and network protocol. Logical connection features may include logical relationships between devices, such as virtual channels, mapping relationships between logical ports, etc. For example, by analyzing the device configuration file, the logical connection relationship between different devices is extracted to ensure the correct routing and transmission of data packets. The physical connection features involve the actual physical connection method between devices, including information such as cable connection type and port rate. For example, the physical connection relationship between each device is extracted from the physical interface information of the device to ensure the stability and performance of the network.

[0041] Step S152: Establishing a communication architecture diagram according to the node logical connection data, thereby obtaining a node communication architecture diagram; In this embodiment, a communication architecture diagram is established based on the node logical connection data, which is to visualize the logical relationship between devices in the network and build a network topology. For example, a network topology modeling tool 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.

[0042] Step S153: performing node connection frequency statistics according to the node physical connection data, thereby obtaining node connection frequency data; 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 the device port, the communication frequency distribution between different devices is obtained to provide data support for network load balancing and optimization. Statistical methods can also be used to count the node connection status of the node physical connection data to calculate the connection frequency distribution of each node.

[0043] 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; In this embodiment, the node physical connection data is divided into node hierarchies according to the node connection frequency data, in order to determine the core nodes and edge nodes in the network. For example, based on the communication frequency and traffic distribution between devices, the devices can be divided into core nodes (high communication frequency, large traffic) and edge nodes (low communication frequency, small traffic), so as to better optimize network resources and management strategies.

[0044] 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; In this embodiment, a core layer node network is constructed based on core node connection data, and an edge layer node network is constructed based on edge node connection data. For example, the core layer node network may include high-performance routers and switches for processing large amounts of data traffic and high-frequency communications; while the edge layer node network may include devices connected to end users, such as Wi-Fi access points or low-power devices, for providing access services and edge computing support.

[0045] 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.

[0046] In this embodiment, the node communication architecture diagram is integrated at the communication architecture level based on the core layer node network and the edge layer node network, in order to integrate and optimize the communication architecture of the entire 5G network device. For example, the overall communication architecture can be hierarchically integrated by using the high-speed transmission capability of the core layer nodes and the access service capability of the edge layer nodes, thereby optimizing network performance and effectively utilizing resources.

[0047] Optionally, step S2 specifically includes: Step S21: extracting node traffic features from 5G network device communication node data, thereby obtaining 5G network device traffic data; In this embodiment, node traffic feature extraction is performed to gain a deep understanding of the traffic patterns and data features of the device. This includes using techniques such as Deep Packet Inspection (DPI) to identify traffic types and features by analyzing data packets transmitted to and from the device. For example, deep learning-based traffic classification algorithms, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), can be used to identify different types of data streams, such as video streams, audio streams, and control streams. In this way, the traffic data of the device can be accurately extracted, providing a basis for further processing and analysis.

[0048] Step S22: performing unstructured traffic division on the 5G network device traffic data, thereby obtaining video traffic data and image traffic data; In this embodiment, the unstructured traffic partitioning of 5G network device traffic data is performed in order to separate specific video and image data from the overall traffic. This can be achieved by using pattern recognition techniques of traffic features, such as using machine learning algorithms to cluster data streams. 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 partitioning provides the basis for subsequent transmission and processing.

[0049] 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; In this embodiment, the 5G network device transmission data is obtained through the 5G network device communication control platform and the unstructured data is decompressed in order to process the data stream transmitted from the device to the network. In this step, a compression algorithm such as Gzip or Brotli can be used to decompress the compressed data stream 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 stream for subsequent data analysis and processing.

[0050] 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; In this embodiment, the image traffic and the video traffic are associated according to the unstructured transmission data in order to match and integrate the data obtained from different data sources (such as steps S22 and S23). This usually involves using metadata or timestamps to correspond the image stream and the 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, so as to perform more sophisticated analysis and processing in subsequent steps.

[0051] Step S25: Calculate the minimum image transmission amount for the image transmission flow data, thereby obtaining the minimum image transmission data; In this embodiment, the minimum image transmission amount calculation is performed on the image transmission flow data in order to determine the minimum amount of data required to transmit the 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 rate can be determined, thereby reducing the bandwidth and data volume required during the transmission process.

[0052] Step S26: Calculate the minimum video transmission volume for the video transmission flow data, thereby obtaining the minimum video transmission data; In this embodiment, the minimum video transmission volume calculation is performed on the video transmission traffic data in order to determine the minimum amount of data required to transmit the video while maintaining high quality. This includes the use of video codecs (such as H.264, H.265) and video analysis technology. For example, by analyzing the resolution, frame rate, and content complexity of the video, the optimal encoding parameters and compression rate can be determined, thereby minimizing the bandwidth and data volume required for transmission.

[0053] 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.

[0054] In this embodiment, the minimum image transmission data and the minimum video transmission data are integrated as unstructured data transmission amounts according to timestamps, and the unstructured transmission data to be compressed is compressed in order to integrate data from different sources and finally prepare for transmission.

[0055] Optionally, step S25 is specifically: Step S251: performing transmission image edge detection on the image transmission flow data, thereby obtaining transmission image edge data; In this embodiment, when the image transmission flow data is subjected to transmission image edge detection to obtain transmission image edge data, an edge detection method based on a Sobel operator may be used. This method identifies edges by calculating the gradient amplitude and direction of each pixel in the image. For example, for an image with a resolution of 1920x1080, edge detection may be performed in the following manner: first, the image is grayed, and then a convolution operation is performed on each pixel using a Sobel operator to obtain horizontal and vertical gradients, and finally edge pixels are determined based on the gradient amplitude.

[0056] 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; In this embodiment, the transmission image is hierarchically divided according to the edge data of the transmission image, and a region segmentation method based on edge detection results can be used. For example, the pixels obtained by edge detection can be divided into a main region and a background region. For an edge image obtained after edge detection, the image can be divided into a main region with a strong edge and a background region with a weak or no edge by connected region analysis or a threshold-based segmentation method.

[0057] Step S253: Minimizing and reducing the color depth of the background area image, thereby obtaining a minimized background area image; In this embodiment, the color depth of the background area image is minimized and reduced by using color quantization or color depth reduction technology. For example, the color space of the pixels in the background area is converted, and the pixels are quantized using a clustering algorithm to reduce the color depth to reduce the amount of data and transmission costs.

[0058] 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; In this embodiment, the subject area image is subjected to approximate color depth matching of the subject area according to the minimized background area image, and a color space mapping and matching algorithm may be used. For example, the color information of the background area is used as a reference to perform approximate color matching on the pixels of the subject area, so as to ensure that the color authenticity and clarity of the subject area are maintained as much as possible while reducing the color depth.

[0059] Step S255: merging the minimized background area image and the minimized main body area image to obtain a minimized transmission image; In this embodiment, the image merging of the minimized background region image and the minimized main body region image may adopt image fusion and synthesis technology. For example, the background region image that has been processed by color quantization and the main body region image that has been processed by color matching are mixed at the pixel level, so that the synthesized image has reduced data volume in terms of color and details while maintaining the overall quality of the image.

[0060] 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.

[0061] In this embodiment, the minimum image transmission amount of the minimized transmission image is calculated based on the image transmission flow data, and a compression coding algorithm can be used. A compression algorithm is selected based on the image transmission flow data, and further data optimization is performed on the minimized transmission image to obtain the final minimum image transmission amount. For example, lossless compression technology such as Huffman coding or Lempel-Ziv-Welch (LZW) algorithm can be used to reduce the data size without causing perceptible loss of image quality. These algorithms reduce the amount of transmitted data by effectively encoding recurring image blocks or pixel patterns. For pixels in the main area and background area, spatial correlation and local pixel similarity can be used to further optimize coding efficiency to ensure the minimization of the amount of transmitted data. In addition, predictive coding technology, such as differential image-based coding methods, can also be considered to further reduce the amount of data by predicting pixel values ​​and transmission errors. For example, predictive coding is performed on the main area, the value of the current pixel is predicted based on the values ​​of adjacent pixels, and the transmission error is reduced to reduce the amount of data. Finally, the minimum image transmission amount is calculated using the pixel information in the image transmission flow data and the transmission amount required for the color depth. Optionally, step S26 is specifically: Step S261: extracting transmission video frames according to the video transmission flow data, thereby obtaining transmission video frames; In this embodiment, the transmission video frame is extracted according to the video transmission flow data, so as to obtain the transmission video frame; the frame rate and bit rate information in the video flow data can be used to extract the video frame frame by frame by parsing the video stream. For example, each frame image can be extracted from the video file using tools such as FFmpeg, or the video frame can be parsed and obtained through the video codec in the real-time video stream.

[0062] 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; In this embodiment, the transmission video frame is subjected to adjacent video frame pixel difference calculation to obtain video frame pixel difference data, and the transmission video frame is divided into key video frames according to the video frame pixel difference data to obtain key video frames and secondary video frames; 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 changes) or secondary (small changes). The key frame is where the pixel difference between two adjacent frames is small.

[0063] Step S263: Minimizing the video frame color depth of the secondary video frame to obtain a minimized secondary video frame; In this embodiment, the color depth of the secondary video frame is minimized to obtain a minimized secondary video frame. In this step, the color information of the secondary frame can be reduced by using a 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 frame to a smaller level, while maintaining a non-significant decrease in visual quality.

[0064] 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; In this embodiment, the key video frame is divided into video frame hierarchies to obtain a key video frame background image and a key video frame subject image; in this step, a segmentation algorithm such as a watershed algorithm or a threshold segmentation based on pixel values ​​can be used to distinguish the background from the subject. For example, according to the change of pixel brightness or color, the image is divided into the background and the subject, and the image information of the two parts is extracted respectively.

[0065] Step S265: performing video frame color depth minimization on 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; 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 accuracy of the background image from 24 bits per pixel of the original image to 16 bits or lower bits. 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 a 16-bit color depth is used, the RGB value of each pixel can be reduced from 24 bits to 16 bits, thereby significantly reducing the amount of data. For the main image, especially the moving objects or characters in the dynamic scene, an approximate color depth matching method is adopted. This can be achieved by detecting the main area in the image and adaptively adjusting the color depth according to its characteristics to retain the necessary details while reducing the amount of data. For example, for a video frame containing a person, the background may be relatively static, and the color depth and details of the person area can be retained first, while using a lower color depth for the static background to achieve a compression effect.

[0066] 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; 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 video encoded to obtain a minimized transmission video; the main image and the background image that have been processed with minimized color depth are combined into a minimized key video frame. This may include superimposing the two parts of the image or performing appropriate synthesis processing to ensure that the final image maintains good visual quality. For example, for a video frame, the background image that has been processed with color depth reduction is synthesized with the main image that has been processed with color depth matching, retaining the details of the main body while effectively compressing the background data. The processed key frame and the secondary frame are combined into the final transmission video frame according to certain rules. For example, a video coding standard such as H.264 or H.265 is used to compress and encode each frame to minimize the transmission bandwidth requirement while maintaining the video quality.

[0067] 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.

[0068] In this embodiment, the minimum video transmission amount of the minimized transmission video is calculated based on the video transmission flow data, thereby obtaining the minimum video transmission data; in the last step, the actual transmission amount of the minimized transmission video can be calculated based on the previously processed video frame information and the amount of encoded data. For example, the actual required bandwidth or storage space is calculated in combination with the compression rate and frame rate information of the video frame to ensure the best effect and cost-effectiveness during the transmission process.

[0069] Optionally, step S3 specifically includes: 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; In this embodiment, based on the communication node data of the 5G network equipment, the node resource configuration feature extraction is first performed. This includes extracting detailed information on key resources such as processor capability, storage capacity, network interface, etc. from the hardware configuration of the node. For example, for a base station node, its processor model and frequency, memory size, number of Ethernet ports, etc. will be extracted. At the same time, the node signal transmission characteristics, that is, the communication signal strength, transmission rate and other data between nodes will also be extracted. These feature data are crucial to the generation of node resource configuration mapping and signal simulation environment data in subsequent steps.

[0070] 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; In this embodiment, node resource configuration mapping is performed on the 5G network device communication architecture according to the node resource configuration data, aiming to optimize the resource allocation and efficiency of the network. For example, by analyzing the resource utilization and location information of each node in the node resource configuration data, the resource utilization 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.

[0071] Step S33: integrating the signal transmission environment based on the node signal transmission data, thereby obtaining signal simulation environment data; In this embodiment, the signal transmission environment integration 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, the signal strength and transmission characteristics of each node in a specific environment are obtained through computer simulation or field measurement in combination with terrain, buildings and meteorological conditions. These data are crucial for predicting and optimizing signal coverage, especially in deployments in cities or complex terrains.

[0072] 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; In this embodiment, according to the signal simulation environment data, the millimeter wave transmission simulation of the traffic of 5G devices is performed through the node resource allocation architecture 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 are used to simulate the transmission rate and signal quality between devices in different frequency bands, so as to optimize the resource utilization and transmission efficiency between devices and ensure that the 5G network can run stably when multiple devices are connected at the same time.

[0073] Step S35: calculating the communication transmission attenuation of the device communication transmission simulation data, thereby obtaining communication transmission attenuation data; In this embodiment, the communication transmission attenuation is calculated for the device communication transmission simulation data in order to quantify the attenuation of the signal during the transmission process. For example, the attenuation of the signal at different frequency bands and distances is calculated through the transmission model and experimental data, and the communication quality and reliability between devices are predicted. This kind of data is crucial for network planning and optimization, and can help engineers decide on the appropriate device layout and enhance signal coverage.

[0074] Step S36: Classify the signal transmission range according to the communication transmission attenuation data, so as to obtain signal transmission level range data.

[0075] In this embodiment, the signal transmission range is graded according to the communication transmission attenuation data in order to grade the signal coverage quality in different areas of the network. For example, the signal coverage range of each node is defined under a specific signal-to-noise ratio or bit error rate condition, and is divided into good, medium, and poor grades. These grade data are crucial for service providers and network managers, as they can help them optimize network coverage and improve user experience.

[0076] Optionally, step S4 is specifically: 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; In this embodiment, the communication demand 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. These data are usually collected from the device through a network management protocol (such as SNMP or REST API), and are cleaned and preprocessed to ensure accuracy and completeness. 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 process REST API calls. Then, the communication demand data is used to estimate the traffic flow using historical data and prediction algorithms (such as time series analysis or machine learning models) to obtain the estimated transmission traffic data required for device communication in the future. This estimate can not only be based on past usage patterns, but also take into account future growth trends and the impact of specific events, such as increased communication demand during holidays or special events.

[0077] Step S42: Calculate the traffic quota balance of the 5G network device traffic data to obtain traffic quota balance data; 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 based on the set quota policy (such as monthly or daily limits). 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 in batches on a regular basis. Accurate calculation of the traffic quota balance can effectively manage the communication resources of the device, ensure that the network traffic is under control, and avoid problems such as overuse and service interruption.

[0078] 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; In this embodiment, it is necessary to classify and calculate the traffic surplus of the estimated communication transmission traffic data based on the 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 a communication control strategy needs to be executed. 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 perform communication control tasks, such as speed limiting or delayed 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 strategy.

[0079] Step S44: extracting unstructured data transmission flow characteristics from the communication demand flow evaluation data, thereby obtaining unstructured flow data to be transmitted; In this embodiment, the generated communication demand flow assessment data is further processed to extract its unstructured data transmission flow characteristics. This usually involves the use of 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, 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, so as to facilitate the establishment of a prediction model or more in-depth data analysis.

[0080] 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.

[0081] In this embodiment, according to the extracted unstructured transmission data features, it is necessary to calculate the unstructured minimum transmission flow. This step is intended to determine the minimum transmission requirement of each data transmission task to ensure a balance between network resource utilization efficiency and communication quality. For example, an algorithm based on factors such as data size, transmission distance, and transmission priority can be used to calculate the minimum transmission flow. These calculations usually involve prioritizing and scheduling resources for transmission tasks to maximize network throughput and transmission efficiency. For example, a greedy algorithm or a dynamic programming method can be used to calculate the minimum transmission flow to meet the real-time requirements and resource constraints of inter-device communication. For unstructured data, similarity comparison can also be performed through unstructured minimization operations completed in advance. Then, for data with high similarity, an unstructured data minimization method is adopted to reduce the amount of data transmission.

[0082] Optionally, step S5 specifically includes: Step S51: extracting communication demand location features from the 5G network device communication demand data, thereby obtaining communication demand location data; In this embodiment, in 5G network equipment, communication demand location feature extraction is to obtain the precise location information of the user equipment through positioning technology (such as GPS, base station positioning). These location data are crucial for subsequent communication optimization. For example, in an urban environment, GPS positioning can accurately obtain the location of the user in a high-rise building-dense area, which helps to optimize signal coverage and transmission efficiency.

[0083] 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; In this embodiment, the pre-defined signal transmission level range data is used to divide the transmission levels according to the location information of the communication demand, so as to obtain the signal transmission levels of different locations.

[0084] Step S53: matching the communication demand transmission level data with the communication transmission environment according to the device communication simulation data, thereby obtaining the communication demand transmission environment data; In this embodiment, the communication demand transmission environment matching is to match the divided transmission level data with the actual communication environment according to the device communication simulation data. For example, in a city, by simulating buildings, weather conditions and other wireless signal interference factors, the best transmission mode and frequency band configuration are determined to improve network coverage and data transmission rate.

[0085] 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; In this embodiment, the communication demand transmission attenuation calculation is to calculate the attenuation of the signal during the transmission process based on the communication environment data and the optimized transmission flow demand. For example, through the path loss model and power attenuation calculation, the attenuation of the signal in long-distance transmission or high-rise building complexes is evaluated, so as to adjust the transmission power and spectrum usage to minimize signal loss. 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 scenario in the device communication simulation data, so as to achieve the purpose of quickly calculating the communication transmission attenuation.

[0086] 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; In this embodiment, the transmission attenuation data is allocated with the minimum transmission attenuation resource 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.

[0087] 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.

[0088] In this embodiment, the communication demand resource allocation data and the optimized demand transmission flow data are integrated to obtain the device communication transmission strategy, and transmitted to the 5G network device communication control platform to perform communication control tasks. For example, based on the analyzed resource allocation and real-time network load, 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.

[0089] Optionally, the present invention further provides a control system of a 5G network device, which is used to execute the control method of a 5G network device as described above, and the control system of the 5G network device includes: 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.

[0090] 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 to combine the operation and signal transmission medium between each module to complete the control method of the 5G network device. The internal modules of the system cooperate with each other to improve resource utilization.

[0091] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0092] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should 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 5G network equipment communication demand data 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 5G network equipment communication resources according to 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 demand transmission level data with the communication transmission environment according to the device communication simulation data, thereby obtaining the communication demand 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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