Communication aggregation display method

Abstract

The present invention relates to the field of aggregate display technology, and in particular to a communication aggregate display method. The method comprises the following steps: extracting the wireless bandwidth of the communication node data of the cell and performing signal transmission frequency strength evaluation to obtain the node signal transmission frequency strength data; identifying the frequency interaction interference of the node signal transmission frequency strength data and performing frequency strength attenuation law identification to obtain the frequency strength attenuation law data; perceiving the multi-factor environmental state based on the frequency strength attenuation law data to obtain the frequency attenuation multi-factor environmental perception data; performing signal quality optimization processing based on the frequency attenuation multi-factor environmental perception data to obtain the signal quality optimization data; constructing a wireless communication quality knowledge graph based on the signal quality optimization data to obtain the wireless communication quality knowledge graph to perform wireless communication cell aggregate display. The present invention can make the aggregate display more perfect.

Description

Technical Field

[0001] The present invention relates to the field of aggregate display technology, and in particular to a communication aggregate display method. Background Art

[0002] The rapid development of wireless communication technology has greatly improved data transmission speeds and network capacity, but this has also led to increasingly severe spectrum resource shortages and network congestion. Carrier aggregation (CA), a core innovation of LTE-Advanced and 5G technologies, aims to increase user available bandwidth by simultaneously utilizing multiple carrier frequency bands, thereby improving data transmission rates and overall network performance. This technology not only significantly improves data throughput but also optimizes spectrum utilization and alleviates network congestion. With the application of CA, user terminals require real-time information about the status and information of the multiple cells they are currently connected to, which is crucial for the user experience. Existing wireless communication devices are unable to intuitively display cell aggregation status and related information, which not only limits users' understanding of network conditions but also hinders operators' ability to monitor and optimize network performance. Therefore, a communication aggregation display method is proposed. By intuitively and in real time displaying cell aggregation status and detailed information about its constituent cells on terminal devices, this method significantly enhances user perception and experience of the network and provides operators with more accurate network status feedback, thereby facilitating network optimization and resource management. The core of this method is to display cell-aggregated multi-carrier information in a user-friendly interface, enabling both users and operators to easily access and understand complex network conditions, thus supporting efficient wireless communications. However, traditional communication aggregation display methods suffer from blind spots that fail to accurately analyze and display wireless signals, as well as inaccurate analysis of environmental impacts. Summary of the Invention

[0003] Based on this, it is necessary to provide a communication aggregation display method to solve at least one of the above technical problems.

[0004] To achieve the above object, a communication aggregation display method is provided, the method comprising the following steps:

[0005] Step S1: Acquire cell communication node data; extract the communication node wireless bandwidth from the cell communication node data to obtain the communication node wireless bandwidth data; evaluate the signal transmission frequency strength based on the communication node wireless bandwidth data to obtain the node signal transmission frequency strength data;

[0006] Step S2: performing frequency interaction interference identification on the node signal transmission frequency strength data to obtain frequency interaction interference identification data; performing frequency intensity attenuation law identification based on the frequency interaction interference identification data to obtain frequency intensity attenuation law data;

[0007] Step S3: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency strength attenuation law data to obtain frequency attenuation coverage data; infer the distance influence of the frequency attenuation coverage data to obtain frequency attenuation distance influence data; and perform multi-factor environmental state perception based on the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

[0008] Step S4: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data; performing signal quality optimization processing on the signal coverage blind spot data according to the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data;

[0009] Step S5: Sort the signal coverage blind spot optimization difficulty level of the signal coverage blind spot data port according to the signal quality optimization data to obtain blind spot optimization difficulty level sorting data; construct a wireless communication quality knowledge graph according to the blind spot optimization difficulty level sorting data to obtain a wireless communication quality knowledge graph to perform wireless communication cell aggregation display.

[0010] By acquiring cell communication node data and extracting wireless bandwidth information therein, the present invention can help network administrators understand the bandwidth utilization of the entire communication network. By analyzing the wireless bandwidth, the bandwidth distribution between nodes can be determined, which helps to optimize network resource utilization and avoid congestion and bandwidth bottleneck problems. By evaluating the node signal transmission frequency intensity, the signal coverage and signal strength distribution between nodes can be understood, which helps to optimize network coverage and signal transmission quality, improve communication efficiency and stability, and perform frequency interaction interference identification on the node signal transmission frequency intensity data. Frequency interference problems in the network can be discovered and located in a timely manner, which helps to reduce the impact of interference on communication quality and improve network stability and reliability. Identifying the frequency intensity attenuation law helps to understand the attenuation of signals during propagation, thereby better planning network layout and optimizing signal transmission schemes, which helps to improve the coverage and signal transmission distance of the communication network and reduce the impact of signal attenuation on communication quality. By calculating the frequency attenuation coverage range based on the frequency intensity attenuation law data, the signal coverage range in space can be accurately predicted, which is helpful for rationally planning network layout and optimizing signal coverage. By inferring the distance impact of the frequency attenuation coverage range data, the degree of influence of different distances on signal strength can be evaluated, providing an important reference for network optimization. By analyzing the frequency attenuation multi-factor environmental perception data, the impact of various environmental factors on signal transmission can be comprehensively considered, such as terrain, buildings, and weather. This helps to improve the network's adaptability to environmental changes, thereby maintaining network stability and reliability. By identifying signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, coverage blind spot problems in the network can be discovered in a timely manner, so that corresponding optimization measures can be taken to optimize the signal quality of signal coverage blind spots. Network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve signal coverage and transmission quality in blind spot areas. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. Sorting the data according to the optimization difficulty level can provide network administrators with optimization strategy priorities so that they can solve network problems in a targeted manner. Based on the blind spot optimization difficulty level sorting data, we can construct a wireless communication quality knowledge graph, which integrates the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network management personnel with a clear guidance map to help them understand the problems and optimization direction in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, the signal quality, coverage and other information of each communication cell can be aggregated and displayed to form an intuitive network status display, which helps network management personnel quickly understand the operating status of the entire communication network, discover problems in a timely manner and take corresponding measures to ensure the normal operation of the network and the user's communication experience.Therefore, the present invention is an optimization process for the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that the blind spots of wireless signals cannot be accurately analyzed and displayed, and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze wireless signal blind spots, and improves the accuracy of environmental impact analysis.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Acquire cell communication node data;

[0013] Step S12: extracting the wireless bandwidth of the communication nodes in the cell to obtain the wireless bandwidth data of the communication nodes;

[0014] Step S13: performing an operating power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operating power difference data;

[0015] Step S14: performing signal transmission frequency strength evaluation on the node operation power difference data to obtain node signal transmission frequency strength data.

[0016] The present invention obtains cell communication node data, which is the basis for understanding the topology and node distribution of the communication network. This data includes information such as node location, connection method, and device type. By obtaining this data, network managers can fully understand the composition of the network, providing basic data support for subsequent optimization and management work. Extracting wireless bandwidth information from the communication node data can accurately understand the bandwidth resource allocation of each node, which helps optimize bandwidth utilization, rationally allocate resources, and avoid network congestion and bandwidth bottlenecks. By analyzing the operating power differences between nodes, the signal transmission capability and coverage range of different nodes can be evaluated, which helps to identify potential signal coverage issues, optimize node layout and parameter settings, and improve signal coverage quality. Evaluating the signal transmission frequency strength of the node based on the node operating power difference data can understand the transmission range and quality of the signal, which helps to determine the signal coverage area and signal quality distribution, providing a reference basis for network optimization.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: performing spatiotemporal intensity distribution feature analysis on the node signal transmission frequency intensity data to obtain signal frequency spatiotemporal intensity distribution data;

[0019] Step S22: performing non-uniform density calculation on the signal frequency-time-space intensity distribution data to obtain frequency-time-space intensity density data;

[0020] Step S23: performing frequency interaction interference identification on the node signal transmission frequency intensity data according to the frequency-time-space intensity density data to obtain frequency interaction interference identification data;

[0021] Step S24: identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the frequency interaction interference identification data to obtain frequency intensity attenuation law data.

[0022] The present invention analyzes the spatiotemporal intensity distribution characteristics of the node signal transmission frequency strength data to understand the distribution of the signal in the spatiotemporal domain, which helps to identify the strong and weak areas within the signal coverage range and find the dead angles or weak coverage areas of the signal coverage, thereby optimizing the node layout and parameter settings. The non-uniformity density calculation of the signal frequency spatiotemporal intensity distribution data can be performed to evaluate whether the spatial distribution of the signal strength is uniform, which helps to discover the intensity changes within the signal coverage area and provide a basis for further signal optimization. Frequency interaction interference identification is performed based on the frequency spatiotemporal intensity density data to discover the interference between signals of different frequencies, which helps to identify the frequency interference source and take corresponding measures to reduce or eliminate interference and improve communication quality. The frequency interaction interference identification data is used to identify the frequency intensity attenuation law of the node signal transmission frequency strength data to understand the attenuation law of the signal strength with distance, which helps to predict the signal coverage range, guide signal power adjustment and node layout, and improve network coverage.

[0023] Preferably, step S23 includes the following steps:

[0024] Step S231: performing a spatiotemporal power law distribution analysis on the frequency-spatiotemporal intensity density data to obtain frequency-spatiotemporal power law distribution data;

[0025] Step S232: performing intensity critical stability analysis on the frequency-space-time intensity density data according to the frequency-space-time power law distribution data to obtain frequency-intensity critical stability data;

[0026] Step S233: performing frequency intermittency identification on the frequency intensity critical stability data to obtain frequency intermittency data;

[0027] Step S234: performing interactive signal frequency correlation mining on the node signal transmission frequency strength data according to the frequency strength critical stability data and the frequency intermittency data to obtain interactive signal frequency correlation data;

[0028] Step S235: performing frequency interaction interference identification on the node signal transmission frequency strength data according to the interaction signal frequency correlation data, the frequency strength critical stability data, and the frequency intermittency data to obtain frequency interaction interference identification data.

[0029] The present invention analyzes the frequency-space power law distribution of the frequency-space power law distribution data to understand the distribution law of the signal in the space-time domain, which helps to identify the changing trend of the signal strength at different space-time scales and provides a basis for understanding the signal propagation mechanism. The stability of the signal strength can be evaluated by performing the critical stability analysis of the intensity based on the frequency-space power law distribution data, which helps to determine the stability boundary of the signal strength and helps predict the performance of the signal under different environmental conditions. The frequency intermittent identification of the frequency intensity critical stability data can discover the intermittent change of the signal strength in time, which helps to identify the periodic change pattern of the signal emission and provide clues for subsequent frequency interference identification. The frequency intensity critical stability data and the frequency intermittent data are used to perform interactive signal frequency correlation mining on the node signal emission frequency intensity data to discover the correlation between different frequency signals, which helps to understand the interaction relationship between different frequency signals and provide deeper information for interference identification. The frequency interactive interference identification of the node signal emission frequency intensity data based on the interactive signal frequency correlation data, the frequency intensity critical stability data and the frequency intermittent data can identify the interference between different frequency signals, which helps to timely discover and resolve the interactive interference between frequencies and improve the stability and reliability of the communication network.

[0030] Preferably, step S24 includes the following steps:

[0031] Step S241: constructing a frequency interaction interference matrix for the frequency interaction interference identification data to obtain a frequency interaction interference matrix;

[0032] Step S242: performing diagonalization analysis based on the frequency interaction interference matrix to obtain frequency interaction interference diagonalization data;

[0033] Step S243: performing orthogonal basis vector identification on the frequency interaction interference matrix according to the frequency interaction interference diagonalization data to obtain frequency interference orthogonal basis vector data;

[0034] Step S244: performing basis vector distortion analysis on the frequency interference orthogonal basis vector data to obtain frequency interference basis vector distortion data;

[0035] Step S245: normalizing the frequency interaction interference matrix according to the frequency interference basis vector distortion data to obtain distortion rule normalized data;

[0036] Step S246: Identify the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the distortion law normalization data and the frequency interference basis vector distortion data to obtain frequency intensity attenuation law data.

[0037] The present invention constructs a frequency interaction interference matrix based on the frequency interaction interference identification data, and can clearly express the interference relationship between different frequencies in the form of a matrix, which helps system managers to quickly understand the degree of interference between different frequencies and provide a basis for subsequent interference processing. According to the diagonalization analysis of the frequency interaction interference matrix, the interference matrix can be converted into a diagonal matrix, so as to better understand the characteristics of inter-frequency interference, which helps to discover the main interference mode and optimize the interference processing strategy. According to the frequency interaction interference diagonalization data, the frequency interaction interference matrix is identified by orthogonal basis vectors, and the orthogonal basis vectors describing the frequency interference can be found, which helps to decompose the interference information into basic orthogonal modes, provide a basis for interference analysis and elimination, and orthogonal frequency interference. Basis vector distortion analysis of basis vector data can evaluate the accuracy and completeness of orthogonal basis vectors in describing interference, which helps to identify errors and deviations in the interference model and guide the improvement and optimization of the model. Normalizing the distortion law of the frequency interaction interference matrix based on the frequency interference basis vector distortion data can correct the distortion effect in the interference matrix and improve the accuracy of the interference model, which helps to more accurately describe the impact of interference and provide a more reliable basis for interference suppression and management. Identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data based on the distortion law normalized data and the frequency interference basis vector distortion data can analyze the change of signal strength with frequency, which helps to predict the attenuation trend of the signal at different frequencies and provide a reference for optimizing signal transmission parameters.

[0038] The beneficial effects of the present invention are as follows: by obtaining cell communication node data and extracting wireless bandwidth information therein, network administrators can understand the bandwidth utilization of the entire communication network; by analyzing the wireless bandwidth, the bandwidth distribution between nodes can be determined, which helps to optimize network resource utilization and avoid congestion and bandwidth bottleneck problems; by evaluating the node signal transmission frequency strength, the signal coverage and signal strength distribution between nodes can be understood, which helps to optimize network coverage and signal transmission quality, improve communication efficiency and stability; by performing frequency interaction interference identification on the node signal transmission frequency strength data, frequency interference problems in the network can be discovered and located in a timely manner, which helps to reduce the impact of interference on communication quality and improve network stability and reliability; identifying the frequency strength attenuation law helps to understand the attenuation of signals during propagation, thereby better planning network layout and optimizing signal transmission schemes, which helps to improve the coverage and signal transmission distance of the communication network and reduce the impact of signal attenuation on communication quality. By calculating the frequency attenuation coverage range based on the frequency intensity attenuation law data, the signal coverage range in space can be accurately predicted, which is helpful for rationally planning network layout and optimizing signal coverage. By inferring the distance impact of the frequency attenuation coverage range data, the degree of influence of different distances on signal strength can be evaluated, providing an important reference for network optimization. By analyzing the frequency attenuation multi-factor environmental perception data, the impact of various environmental factors on signal transmission can be comprehensively considered, such as terrain, buildings, and weather. This helps to improve the network's adaptability to environmental changes, thereby maintaining network stability and reliability. By identifying signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, coverage blind spot problems in the network can be discovered in a timely manner, so that corresponding optimization measures can be taken to optimize the signal quality of signal coverage blind spots. Network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve signal coverage and transmission quality in blind spot areas. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. Sorting the data according to the optimization difficulty level can provide network administrators with optimization strategy priorities so that they can solve network problems in a targeted manner. Based on the blind spot optimization difficulty level sorting data, we can construct a wireless communication quality knowledge graph, which integrates the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network management personnel with a clear guidance map to help them understand the problems and optimization direction in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, the signal quality, coverage and other information of each communication cell can be aggregated and displayed to form an intuitive network status display, which helps network management personnel quickly understand the operating status of the entire communication network, discover problems in a timely manner and take corresponding measures to ensure the normal operation of the network and the user's communication experience.Therefore, the present invention is an optimization process for the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that the blind spots of wireless signals cannot be accurately analyzed and displayed, and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze wireless signal blind spots, and improves the accuracy of environmental impact analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic flow chart of the steps of a communication aggregation display method;

[0040] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0041] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0042] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0043] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described 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 those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0044] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0045] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0046] To achieve this, please refer to Figures 1 to 3 , a communication aggregation display method, the method comprising the following steps:

[0047] Step S1: Acquire cell communication node data; extract the communication node wireless bandwidth from the cell communication node data to obtain the communication node wireless bandwidth data; evaluate the signal transmission frequency strength based on the communication node wireless bandwidth data to obtain the node signal transmission frequency strength data;

[0048] Step S2: performing frequency interaction interference identification on the node signal transmission frequency strength data to obtain frequency interaction interference identification data; performing frequency intensity attenuation law identification based on the frequency interaction interference identification data to obtain frequency intensity attenuation law data;

[0049] Step S3: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency strength attenuation law data to obtain frequency attenuation coverage data; infer the distance influence of the frequency attenuation coverage data to obtain frequency attenuation distance influence data; and perform multi-factor environmental state perception based on the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

[0050] Step S4: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data; performing signal quality optimization processing on the signal coverage blind spot data according to the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data;

[0051] Step S5: Sort the signal coverage blind spot optimization difficulty level of the signal coverage blind spot data port according to the signal quality optimization data to obtain blind spot optimization difficulty level sorting data; construct a wireless communication quality knowledge graph according to the blind spot optimization difficulty level sorting data to obtain a wireless communication quality knowledge graph to perform wireless communication cell aggregation display.

[0052] In the embodiment of the present invention, reference Figure 1 The above is a flow chart of the steps of a communication aggregation display method of the present invention. In this example, the communication aggregation display method includes the following steps:

[0053] Step S1: Acquire cell communication node data; extract the communication node wireless bandwidth from the cell communication node data to obtain the communication node wireless bandwidth data; evaluate the signal transmission frequency strength based on the communication node wireless bandwidth data to obtain the node signal transmission frequency strength data;

[0054] In an embodiment of the present invention, cell communication node data is obtained: data of each communication node in the cell is collected, including location information, device type, and signal strength; wireless bandwidth information of each node is extracted from the collected communication node data; the wireless bandwidth information includes the total bandwidth, uplink and downlink bandwidth, and bandwidth utilization of each node; the extracted wireless bandwidth data is preprocessed, including data cleaning, outlier processing, and data format conversion; based on the processed wireless bandwidth data, the signal transmission frequency strength of each communication node is evaluated; the evaluation method can be calculated based on factors such as the node's power output, bandwidth usage, and user density; the calculation results include the signal transmission frequency and corresponding signal strength of each node.

[0055] Step S2: performing frequency interaction interference identification on the node signal transmission frequency strength data to obtain frequency interaction interference identification data; performing frequency intensity attenuation law identification based on the frequency interaction interference identification data to obtain frequency intensity attenuation law data;

[0056] In an embodiment of the present invention, the frequency intensity data of node signal transmission is used to identify the frequency interaction interference between each node. By analyzing the frequency distribution, transmission power, spatial position, etc. of each node, the possible frequency interference situation is detected. Common methods include spectrum analysis and cross-correlation analysis. The detected frequency interaction interference data is processed to remove false detection and noise. The processed data includes the interference source node, the interfered node, the interference frequency range, and the interference intensity. Based on the frequency interaction interference identification data, the frequency intensity attenuation law of each node is analyzed. Mathematical modeling and statistical analysis methods are used to identify the change law of signal strength with distance, obstacles, weather and other factors. The attenuation law data includes the change curve of signal strength with distance and the attenuation under different environments. The frequency interaction interference identification data and the frequency intensity attenuation law data are stored in a database for subsequent analysis and query.

[0057] Step S3: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency strength attenuation law data to obtain frequency attenuation coverage data; infer the distance influence of the frequency attenuation coverage data to obtain frequency attenuation distance influence data; and perform multi-factor environmental state perception based on the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

[0058] In an embodiment of the present invention, the frequency intensity attenuation law data obtained from step S2 is used, and an attenuation model (such as a free space path loss model and an indoor propagation model) is applied to calculate the frequency attenuation coverage range of each node. Factors such as the node's transmit power, antenna gain, and frequency attenuation law are taken into consideration to calculate the effective coverage range of each node, generate a coverage map, and display the signal coverage of each node under different conditions. Frequency attenuation coverage data is obtained, including the signal coverage area of each node in different environments. The frequency attenuation coverage data is used to analyze the frequency attenuation coverage data and infer the intensity change of the signal at different distances. Through simulation and experiment, the specific impact of distance on signal strength is analyzed, and a distance impact curve is generated to display the law of signal strength change with distance. Frequency attenuation distance influence data is obtained, including detailed information on the change of signal strength with distance. The frequency attenuation distance influence data is used to consider various environmental factors (such as buildings, trees, weather conditions, etc.) to perceive the comprehensive impact on signal attenuation. Sensor data, environmental monitoring data, etc. are used to obtain the current environmental state. Machine learning and statistical analysis methods are applied to identify the specific impact of environmental factors on signal attenuation, and frequency attenuation multi-factor environmental perception data is obtained, including signal attenuation under different environmental states.

[0059] Step S4: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data; performing signal quality optimization processing on the signal coverage blind spot data according to the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data;

[0060] In an embodiment of the present invention, frequency attenuation multi-factor environmental perception data and frequency attenuation coverage data are used to analyze coverage data and environmental perception data to identify signal coverage blind spot areas. Blind spot identification includes areas with no signal and areas with signal strength below a threshold. A blind spot distribution map is generated to display all identified signal coverage blind spot locations. Signal coverage blind spot data is obtained, including the specific location and area information of all blind spots. Signal coverage blind spot data and frequency attenuation multi-factor environmental perception data are used to improve signal coverage and quality by adjusting the transmission power, optimizing the antenna direction, and adding relay equipment. Network optimization algorithms (such as power control algorithms and frequency allocation algorithms) are used for global optimization. Key blind spot areas are optimized to ensure that signal coverage and quality meet expected standards, and signal quality optimization data is obtained, including optimized signal coverage and quality indicators.

[0061] Step S5: Sort the signal coverage blind spot optimization difficulty level of the signal coverage blind spot data port according to the signal quality optimization data to obtain blind spot optimization difficulty level sorting data; construct a wireless communication quality knowledge graph according to the blind spot optimization difficulty level sorting data to obtain a wireless communication quality knowledge graph to perform wireless communication cell aggregation display.

[0062] In an embodiment of the present invention, the signal quality optimization data and signal coverage blind spot data obtained in step S4 are used to analyze the geographical location, environmental complexity, existing infrastructure conditions and other factors of each signal coverage blind spot, and evaluate the optimization difficulty. The evaluation indicators of optimization difficulty include construction difficulty, cost estimation, and optimization effect expectation. The optimization difficulty score of each blind spot is calculated, and the weights of various factors are comprehensively considered. According to the optimization difficulty score, all signal coverage blind spots are sorted and the priority is determined. A sorting algorithm (such as a weight sorting method or a priority queue) is used to generate blind spot optimization difficulty level sorting data, and the blind spot optimization difficulty level sorting data is obtained, which includes the optimization difficulty level and sorting results of each signal coverage blind spot. The main nodes and relationships of the knowledge graph are determined, including signal coverage blind spots, optimization difficulty levels, optimization strategies, and optimization effects. The design and modeling are carried out using libraries (such as Neo4j) or knowledge graph construction tools (such as RDF, OWL), and the optimization difficulty level ranking data is associated and integrated with other related data (such as geographic information, environmental data, network topology, etc.). The data is imported into the knowledge graph using data integration tools and ETL (Extract, Transform, Load) processes. According to the designed graph structure, a wireless communication quality knowledge graph is constructed, including the creation of nodes, the definition of relationships, and the filling of data to ensure the structural integrity of the graph and the accuracy of the data. The constructed knowledge graph is optimized and adjusted to improve the query efficiency and display effect. The performance of the graph is optimized using indexing, caching and other technologies to obtain the wireless communication quality knowledge graph, which contains information such as signal coverage blind spots, optimization difficulty levels, optimization strategies and effects.

[0063] By acquiring cell communication node data and extracting wireless bandwidth information therein, the present invention can help network administrators understand the bandwidth utilization of the entire communication network. By analyzing the wireless bandwidth, the bandwidth distribution between nodes can be determined, which helps to optimize network resource utilization and avoid congestion and bandwidth bottleneck problems. By evaluating the node signal transmission frequency intensity, the signal coverage and signal strength distribution between nodes can be understood, which helps to optimize network coverage and signal transmission quality, improve communication efficiency and stability, and perform frequency interaction interference identification on the node signal transmission frequency intensity data. Frequency interference problems in the network can be discovered and located in a timely manner, which helps to reduce the impact of interference on communication quality and improve network stability and reliability. Identifying the frequency intensity attenuation law helps to understand the attenuation of signals during propagation, thereby better planning network layout and optimizing signal transmission schemes, which helps to improve the coverage and signal transmission distance of the communication network and reduce the impact of signal attenuation on communication quality. By calculating the frequency attenuation coverage range based on the frequency intensity attenuation law data, the signal coverage range in space can be accurately predicted, which is helpful for rationally planning network layout and optimizing signal coverage. By inferring the distance impact of the frequency attenuation coverage range data, the degree of influence of different distances on signal strength can be evaluated, providing an important reference for network optimization. By analyzing the frequency attenuation multi-factor environmental perception data, the impact of various environmental factors on signal transmission can be comprehensively considered, such as terrain, buildings, and weather. This helps to improve the network's adaptability to environmental changes, thereby maintaining network stability and reliability. By identifying signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, coverage blind spot problems in the network can be discovered in a timely manner, so that corresponding optimization measures can be taken to optimize the signal quality of signal coverage blind spots. Network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve signal coverage and transmission quality in blind spot areas. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. Sorting the data according to the optimization difficulty level can provide network administrators with optimization strategy priorities so that they can solve network problems in a targeted manner. Based on the blind spot optimization difficulty level sorting data, we can construct a wireless communication quality knowledge graph, which integrates the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network management personnel with a clear guidance map to help them understand the problems and optimization direction in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, the signal quality, coverage and other information of each communication cell can be aggregated and displayed to form an intuitive network status display, which helps network management personnel quickly understand the operating status of the entire communication network, discover problems in a timely manner and take corresponding measures to ensure the normal operation of the network and the user's communication experience.Therefore, the present invention is an optimization process for the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that the blind spots of wireless signals cannot be accurately analyzed and displayed, and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze wireless signal blind spots, and improves the accuracy of environmental impact analysis.

[0064] Preferably, step S1 includes the following steps:

[0065] Step S11: Acquire cell communication node data;

[0066] Step S12: extracting the wireless bandwidth of the communication nodes in the cell to obtain the wireless bandwidth data of the communication nodes;

[0067] Step S13: performing an operating power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operating power difference data;

[0068] Step S14: performing signal transmission frequency strength evaluation on the node operation power difference data to obtain node signal transmission frequency strength data.

[0069] In an embodiment of the present invention, a network monitoring tool or management system is used to collect data from each communication node (such as a base station, router, or hotspot) within a cell. The data includes basic information such as node location, node type, node status, number of connected users, and operating frequency band. The collected communication node data is integrated, deduplicated, cleaned, and normalized to ensure data integrity and consistency, and detailed node information required for subsequent analysis is prepared. Wireless bandwidth information for each node is extracted from the integrated communication node data. The wireless bandwidth information includes the total bandwidth, uplink and downlink bandwidth, and bandwidth utilization of each node. Operating power data for each communication node is collected, and the power output of the node under different time periods and loads is recorded. Based on the wireless bandwidth data and operating power data, the power output differences of each node under different load conditions are analyzed, power difference values are calculated, and nodes with unstable or abnormal power output are identified. The power difference analysis results are processed to generate node operating power difference data. The data includes statistical information such as the average power output, standard deviation, maximum value, and minimum value of each node. Signal transmission frequency data for each communication node is collected, and the operating frequency band and transmission frequency information of the node are recorded. The signal transmission frequency strength of each node is evaluated based on the node operating power difference data and signal transmission frequency data. The evaluation method can be calculated based on factors such as node power output, bandwidth usage, and user density. The calculation results include the signal transmission frequency and corresponding signal strength of each node. The evaluation results are processed to generate node signal transmission frequency and strength data.

[0070] The present invention obtains cell communication node data, which is the basis for understanding the topology and node distribution of the communication network. This data includes information such as node location, connection method, and device type. By obtaining this data, network managers can fully understand the composition of the network, providing basic data support for subsequent optimization and management work. Extracting wireless bandwidth information from the communication node data can accurately understand the bandwidth resource allocation of each node, which helps optimize bandwidth utilization, rationally allocate resources, and avoid network congestion and bandwidth bottlenecks. By analyzing the operating power differences between nodes, the signal transmission capability and coverage range of different nodes can be evaluated, which helps to identify potential signal coverage issues, optimize node layout and parameter settings, and improve signal coverage quality. Evaluating the signal transmission frequency strength of the node based on the node operating power difference data can understand the transmission range and quality of the signal, which helps to determine the signal coverage area and signal quality distribution, providing a reference basis for network optimization.

[0071] Preferably, step S2 includes the following steps:

[0072] Step S21: performing spatiotemporal intensity distribution feature analysis on the node signal transmission frequency intensity data to obtain signal frequency spatiotemporal intensity distribution data;

[0073] Step S22: performing non-uniform density calculation on the signal frequency-time-space intensity distribution data to obtain frequency-time-space intensity density data;

[0074] Step S23: performing frequency interaction interference identification on the node signal transmission frequency intensity data according to the frequency-time-space intensity density data to obtain frequency interaction interference identification data;

[0075] Step S24: identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the frequency interaction interference identification data to obtain frequency intensity attenuation law data.

[0076] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0077] Step S21: performing spatiotemporal intensity distribution feature analysis on the node signal transmission frequency intensity data to obtain signal frequency spatiotemporal intensity distribution data;

[0078] In an embodiment of the present invention, the node signal transmission frequency strength data obtained in step S14 is used to ensure that the data contains information such as timestamp, node location, transmission frequency, signal strength, etc., and the data is sorted and sorted by time and space (geographic location), creating a time series and a spatial grid to facilitate subsequent analysis, analyzing the changes in signal strength in different time periods, identifying peak periods and trough periods, generating a signal strength time distribution graph, showing the trend of signal strength changes over time, analyzing the distribution of signal strength in different geographic locations, identifying areas with high and low signal strength. Generate a signal strength spatial distribution graph to show the distribution characteristics of signal strength in space, combine time and space dimensions, and analyze the spatiotemporal distribution characteristics of signal strength. Generate a signal strength spatiotemporal distribution graph to show the joint distribution law of signal strength in time and space, and obtain signal frequency spatiotemporal strength distribution data, including the distribution characteristics of signal strength in different time and space.

[0079] Step S22: performing non-uniform density calculation on the signal frequency-time-space intensity distribution data to obtain frequency-time-space intensity density data;

[0080] In an embodiment of the present invention, the signal frequency spatiotemporal intensity distribution data obtained in step S21 is used to ensure that the data contains information such as time, spatial position, and signal strength. The study area is divided into several small spatiotemporal grid units (such as time periods and geographical cells), and density calculation indicators such as the mean, variance, maximum, and minimum values of the signal strength are defined; time dimension: the mean and variance of the signal strength in each time period are calculated; spatial dimension: the mean and variance of the signal strength in each spatial grid are calculated, and the mean and variance of the signal strength in each spatiotemporal grid unit are calculated to generate a non-uniform density distribution map to display the non-uniform distribution characteristics of the signal strength in time and space, analyze the non-uniformity of the signal strength in time and space, identify high-density and low-density areas of signal strength, and use statistical methods (such as coefficient of variation, entropy value, etc.) to quantify the degree of non-uniformity to obtain frequency spatiotemporal intensity density data, including the non-uniform density distribution and statistical results of the signal strength in time and space.

[0081] Step S23: performing frequency interaction interference identification on the node signal transmission frequency intensity data according to the frequency-time-space intensity density data to obtain frequency interaction interference identification data;

[0082] In the embodiment of the present invention, the frequency spatiotemporal intensity density data obtained in step S22 and the node signal transmission frequency intensity data in step S14 are used to define interference types (such as co-channel interference, adjacent frequency interference, intermodulation interference, etc.), identification is at the same frequency, the signal strength distribution of different nodes, the signal strength superposition between the co-frequency nodes is analyzed, the co-frequency interference area is determined, identification is at adjacent frequencies, the signal strength distribution of different nodes, the signal strength between the adjacent frequency nodes is analyzed, the adjacent frequency interference area is determined, the signal interaction situation between multiple nodes is analyzed, possible intermodulation interference is identified, intermodulation interference signal strength is calculated, intermodulation interference area is determined, the interference data identified is processed, and frequency interaction interference identification data is generated.

[0083] Step S24: identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the frequency interaction interference identification data to obtain frequency intensity attenuation law data.

[0084] In an embodiment of the present invention, the frequency interaction interference identification data obtained in step S23 and the node signal transmission frequency strength data in step S14 are used to define signal attenuation models (such as a free space model, a logarithmic distance model, a segmented model, etc.) in different environments. Model parameters are set according to different environmental factors (such as buildings, trees, weather, etc.). The signal strength and distance data are combined to calculate the basic attenuation law of the signal strength using the attenuation model. A basic attenuation curve of signal strength varying with distance is generated. According to the frequency interaction interference identification data, the basic attenuation model is corrected, the influence of interference on signal strength is considered, the attenuation law of signal strength is recalculated, and a corrected attenuation curve is generated. The attenuation model is further corrected in combination with multi-factor environmental data, the influence of environmental factors on signal attenuation is considered, and finally frequency strength attenuation law data is generated to reflect the signal attenuation in the actual environment. The identified attenuation law data is processed to generate frequency strength attenuation law data. The data includes information such as the attenuation model, attenuation parameters, and attenuation curve. Frequency strength attenuation law data is obtained, including the attenuation law of signal strength under different environments and interference conditions.

[0085] The present invention analyzes the spatiotemporal intensity distribution characteristics of the node signal transmission frequency strength data to understand the distribution of the signal in the spatiotemporal domain, which helps to identify the strong and weak areas within the signal coverage range and find the dead angles or weak coverage areas of the signal coverage, thereby optimizing the node layout and parameter settings. The non-uniformity density calculation of the signal frequency spatiotemporal intensity distribution data can be performed to evaluate whether the spatial distribution of the signal strength is uniform, which helps to discover the intensity changes within the signal coverage area and provide a basis for further signal optimization. Frequency interaction interference identification is performed based on the frequency spatiotemporal intensity density data to discover the interference between signals of different frequencies, which helps to identify the frequency interference source and take corresponding measures to reduce or eliminate interference and improve communication quality. The frequency interaction interference identification data is used to identify the frequency intensity attenuation law of the node signal transmission frequency strength data to understand the attenuation law of the signal strength with distance, which helps to predict the signal coverage range, guide signal power adjustment and node layout, and improve network coverage.

[0086] Preferably, step S23 includes the following steps:

[0087] Step S231: performing a spatiotemporal power law distribution analysis on the frequency-spatiotemporal intensity density data to obtain frequency-spatiotemporal power law distribution data;

[0088] Step S232: performing intensity critical stability analysis on the frequency-space-time intensity density data according to the frequency-space-time power law distribution data to obtain frequency-intensity critical stability data;

[0089] Step S233: performing frequency intermittency identification on the frequency intensity critical stability data to obtain frequency intermittency data;

[0090] Step S234: performing interactive signal frequency correlation mining on the node signal transmission frequency strength data according to the frequency strength critical stability data and the frequency intermittency data to obtain interactive signal frequency correlation data;

[0091] Step S235: performing frequency interaction interference identification on the node signal transmission frequency strength data according to the interaction signal frequency correlation data, the frequency strength critical stability data, and the frequency intermittency data to obtain frequency interaction interference identification data.

[0092] In an embodiment of the present invention, the frequency-space-time intensity density data is analyzed for space-time power-law distribution. This generally involves the following steps: data preprocessing: removing outliers, data smoothing, etc. Fitting distribution: selecting an appropriate power-law distribution model and fitting it using statistical tools. Parameter estimation: estimating the parameters of the power-law distribution model, such as the power-law exponent, scale parameter, etc. During the analysis process, relevant data of the frequency-space-time power-law distribution, including the power-law exponent and the fitting curve, are obtained; ensuring that relevant data of the frequency-space-time power-law distribution, including the power-law exponent and the fitting curve, have been obtained, and performing frequency-intensity critical stability analysis based on the frequency-space-time power-law distribution data. This includes the following steps: defining critical stability indicators: determining indicators for evaluating frequency intensity stability, such as signal change rate, frequency interference degree, etc. Calculating stability indicators: calculating the stability indicators of each region or node based on the frequency-space-time power-law distribution data. Analysis Results: Stability indicators are analyzed to identify potential weaknesses or areas of low stability, and improvement recommendations are proposed. During the analysis, relevant data on frequency intensity critical stability is obtained, which can be used to guide network optimization or interference management strategy development. Frequency intermittency is identified using the frequency intensity critical stability data. This includes the following steps: Defining the Definition and Indicators of Frequency Intermittency: Determining characteristics or indicators for identifying frequency intermittency, such as the amplitude of frequency fluctuations or the frequency of frequency fluctuations. Data Processing: Preprocessing the frequency intensity critical stability data, such as noise removal and data smoothing. Identifying Frequency Intermittency: Applying appropriate algorithms or techniques, such as time series analysis or spectrum analysis, to identify patterns or characteristics of frequency intermittency. Ensure that frequency intensity critical stability data and frequency intermittency data are prepared. These data will be used to mine interactive signal frequency correlations of node signal transmission frequencies. Based on the frequency intensity critical stability data and frequency intermittency data, interactive signal frequency correlations are mined for node signal transmission frequency intensity data. Specific steps include: Data Correlation: Correlating the frequency intensity critical stability data and frequency intermittency data with the node signal transmission frequency intensity data. Association analysis: Apply association analysis algorithms (such as association rule mining, sequence pattern mining, etc.) to mine the association rules or patterns between node signal transmission frequencies. Data integration: Integrate the interactive signal frequency association data obtained through mining to facilitate the subsequent identification of frequency interaction interference. During the analysis process, data reflecting the association relationship between node signal transmission frequencies are obtained. These data can be used for subsequent frequency interaction interference identification; ensure that the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data are prepared. Based on the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data, identify the frequency interaction interference of the node signal transmission frequency intensity data. The specific steps include: Data integration: Integrate the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data.Interference identification: Appropriate interference identification algorithms or techniques, such as pattern recognition and machine learning, are applied to identify the mutual interference between node signal transmission frequencies. Result analysis: Analyze the identification results, assess the impact of frequency mutual interference on system performance, and propose appropriate interference management or optimization recommendations. The analysis process generates identified data reflecting the mutual interference between node signal transmission frequencies. This data can be used to guide network optimization or the development of interference management strategies.

[0093] The present invention analyzes the frequency-space power law distribution of the frequency-space power law distribution data to understand the distribution law of the signal in the space-time domain, which helps to identify the changing trend of the signal strength at different space-time scales and provides a basis for understanding the signal propagation mechanism. The stability of the signal strength can be evaluated by performing the critical stability analysis of the intensity based on the frequency-space power law distribution data, which helps to determine the stability boundary of the signal strength and helps predict the performance of the signal under different environmental conditions. The frequency intermittent identification of the frequency intensity critical stability data can discover the intermittent change of the signal strength in time, which helps to identify the periodic change pattern of the signal emission and provide clues for subsequent frequency interference identification. The frequency intensity critical stability data and the frequency intermittent data are used to perform interactive signal frequency correlation mining on the node signal emission frequency intensity data to discover the correlation between different frequency signals, which helps to understand the interaction relationship between different frequency signals and provide deeper information for interference identification. The frequency interactive interference identification of the node signal emission frequency intensity data based on the interactive signal frequency correlation data, the frequency intensity critical stability data and the frequency intermittent data can identify the interference between different frequency signals, which helps to timely discover and resolve the interactive interference between frequencies and improve the stability and reliability of the communication network.

[0094] Preferably, step S24 includes the following steps:

[0095] Step S241: constructing a frequency interaction interference matrix for the frequency interaction interference identification data to obtain a frequency interaction interference matrix;

[0096] Step S242: performing diagonalization analysis based on the frequency interaction interference matrix to obtain frequency interaction interference diagonalization data;

[0097] Step S243: performing orthogonal basis vector identification on the frequency interaction interference matrix according to the frequency interaction interference diagonalization data to obtain frequency interference orthogonal basis vector data;

[0098] Step S244: performing basis vector distortion analysis on the frequency interference orthogonal basis vector data to obtain frequency interference basis vector distortion data;

[0099] Step S245: normalizing the frequency interaction interference matrix according to the frequency interference basis vector distortion data to obtain distortion rule normalized data;

[0100] Step S246: Identify the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the distortion law normalization data and the frequency interference basis vector distortion data to obtain frequency intensity attenuation law data.

[0101] In an embodiment of the present invention, ensure that the frequency interaction interference identification data is prepared, which may be obtained through the previous steps, and construct a frequency interaction interference matrix based on the frequency interaction interference identification data. The specific steps include: data sorting: the frequency interaction interference identification data is sorted into a matrix form according to certain rules or formats, wherein each row and each column respectively represents a different node signal transmission frequency. Matrix construction: based on the identified frequency interaction interference, the corresponding elements in the matrix are filled with interference intensity values or interference degree indicators. During the construction process, a matrix reflecting the frequency interaction interference relationship is obtained, which can be used for subsequent diagonalization analysis and orthogonal basis vector identification. Ensure that the frequency interaction interference matrix is prepared, which will be used for diagonalization analysis. Based on the frequency interaction interference matrix, a diagonalization analysis is performed. The specific steps include: calculating eigenvalues and eigenvectors: using mathematical tools or algorithms to calculate the eigenvalues and corresponding eigenvectors of the frequency interaction interference matrix. Diagonalize the matrix: Diagonalize the frequency interaction interference matrix to obtain a diagonalized matrix, where the elements on the diagonal are the eigenvalues. During the diagonalization analysis, the diagonalized results of the frequency interaction interference matrix are obtained, including the eigenvalues and corresponding eigenvectors. Ensure that the frequency interaction interference diagonalized data, including the eigenvalues and corresponding eigenvectors, are prepared. Based on the frequency interaction interference diagonalized data, the frequency interference orthogonal basis vectors are identified. Specific steps include: Orthogonalization: Use the eigenvectors to construct orthogonal basis vectors, ensuring that the basis vectors are mutually orthogonal. Identify the main frequency interference pattern: By analyzing the eigenvalues and corresponding eigenvectors, the main frequency interference pattern or main interference direction is determined. Extract orthogonal basis vector data: Extract the obtained orthogonal basis vector data as the basic features describing the frequency interaction interference. During the identification process, orthogonal basis vector data reflecting the frequency interaction interference is obtained. This data can be used for subsequent analysis and interference treatment. Ensure that the frequency interference orthogonal basis vector data is prepared. This data was obtained in the previous steps. The frequency interference orthogonal basis vector data is used for distortion analysis. The specific steps include: Data processing: pre-processing the basis vector data, including data cleaning, denoising, etc. Distortion measurement: use appropriate measurement methods (such as Euclidean distance, correlation coefficient, etc.) to quantitatively analyze the degree of distortion between basis vector data. Distortion feature extraction: identify and extract distortion features in the basis vector data, such as frequency offset, amplitude distortion, etc. During the analysis process, data reflecting the degree of distortion of the frequency interference basis vector is obtained. These data will be used for subsequent distortion law normalization. According to the frequency interference basis vector distortion data, the distortion law normalization calculation is performed. The specific steps include: Normalization processing: normalize the basis vector distortion data so that it takes values within a certain range.Distortion Pattern Analysis: Analyze the regularity of the normalized distortion data to explore the distribution characteristics and influencing factors of the distortion. During the calculation process, normalized data reflecting the distortion patterns of the frequency interference basis vectors is obtained. This data will be used to normalize the distortion patterns of the frequency interaction interference matrix. Ensure that the normalized distortion pattern data and the frequency interference basis vector distortion data are prepared. Based on the normalized distortion pattern data and the frequency interference basis vector distortion data, the frequency intensity attenuation pattern of the node signal transmission frequency strength is identified. Specific steps include: Establishing an intensity attenuation model: Based on the distortion pattern and basis vector distortion data, a model of the node signal transmission frequency intensity attenuation is established. Regularity Analysis: Analyze the regularity of the frequency intensity attenuation to determine the attenuation trend, amplitude, and frequency correlation. Data Fitting and Validation: Through fitting algorithms or model validation, the specific parameters of the frequency intensity attenuation pattern are obtained. During the identification process, data reflecting the frequency intensity attenuation pattern of the node signal transmission is obtained. This data can be used for subsequent interference analysis and the formulation of compensation measures.

[0102] The present invention constructs a frequency interaction interference matrix based on the frequency interaction interference identification data, and can clearly express the interference relationship between different frequencies in the form of a matrix, which helps system managers to quickly understand the degree of interference between different frequencies and provide a basis for subsequent interference processing. According to the diagonalization analysis of the frequency interaction interference matrix, the interference matrix can be converted into a diagonal matrix, so as to better understand the characteristics of inter-frequency interference, which helps to discover the main interference mode and optimize the interference processing strategy. According to the frequency interaction interference diagonalization data, the frequency interaction interference matrix is identified by orthogonal basis vectors, and the orthogonal basis vectors describing the frequency interference can be found, which helps to decompose the interference information into basic orthogonal modes, provide a basis for interference analysis and elimination, and orthogonal frequency interference. Basis vector distortion analysis of basis vector data can evaluate the accuracy and completeness of orthogonal basis vectors in describing interference, which helps to identify errors and deviations in the interference model and guide the improvement and optimization of the model. Normalizing the distortion law of the frequency interaction interference matrix based on the frequency interference basis vector distortion data can correct the distortion effect in the interference matrix and improve the accuracy of the interference model, which helps to more accurately describe the impact of interference and provide a more reliable basis for interference suppression and management. Identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data based on the distortion law normalized data and the frequency interference basis vector distortion data can analyze the change of signal strength with frequency, which helps to predict the attenuation trend of the signal at different frequencies and provide a reference for optimizing signal transmission parameters.

[0103] Preferably, step S3 includes the following steps:

[0104] Step S31: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency interaction interference identification data and the frequency strength attenuation law data to obtain frequency attenuation coverage data;

[0105] Step S32: performing distance influence inference on the frequency attenuation coverage data to obtain frequency attenuation distance influence data;

[0106] Step S33: performing frequency difference impact inference on the frequency attenuation coverage data to obtain frequency difference attenuation impact data;

[0107] Step S34: performing multi-factor environmental state perception on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

[0108] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0109] Step S31: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency interaction interference identification data and the frequency strength attenuation law data to obtain frequency attenuation coverage data;

[0110] In an embodiment of the present invention, it is ensured that the frequency interaction interference identification data and the frequency strength attenuation law data are prepared, and the frequency attenuation coverage range of the node signal transmission frequency strength data is calculated. The specific steps include: attenuation model application: according to the frequency strength attenuation law data, an appropriate attenuation model (such as a free space transmission model, a multipath transmission model, etc.) is applied. Coverage calculation: using the attenuation model, combined with the frequency interaction interference identification data, the frequency attenuation coverage range of the node signal transmission is calculated. Data processing: the calculated coverage data is cleaned and screened to ensure the accuracy and reliability of the data. During the calculation process, the coverage data of the node signal transmission frequency attenuation is obtained.

[0111] Step S32: performing distance influence inference on the frequency attenuation coverage data to obtain frequency attenuation distance influence data;

[0112] In this embodiment of the present invention, frequency attenuation coverage data is prepared and distance influence is inferred from the frequency attenuation coverage data. Specific steps include: Data Analysis: Analyzing the frequency attenuation coverage data to understand how attenuation varies with distance. Attenuation Distance Impact Calculation: Based on the data analysis results, inferring the impact of different distances on frequency attenuation. This calculation may be performed using statistical methods or mathematical models. During this inference process, data on the distance influence of frequency attenuation is obtained.

[0113] Step S33: performing frequency difference impact inference on the frequency attenuation coverage data to obtain frequency difference attenuation impact data;

[0114] In this embodiment of the present invention, the frequency attenuation coverage data is used to infer the impact of frequency differences. The specific steps include: Data analysis: Analyzing the frequency attenuation coverage data to understand the attenuation differences between different frequencies. Frequency difference impact calculation: Based on the data analysis results, inferring the degree of attenuation impact between different frequencies, which may be calculated using mathematical models or statistical methods. During the inference process, data on the impact of frequency difference attenuation is obtained.

[0115] Step S34: performing multi-factor environmental state perception on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

[0116] In an embodiment of the present invention, based on the frequency attenuation distance influence data, the frequency difference attenuation influence data is perceived in terms of multi-factor environmental status. The specific steps include: Data association: Correlation analysis is performed on the frequency attenuation distance influence data and the frequency difference attenuation influence data. Multi-factor impact assessment: Comprehensively considering the distance factor and the frequency difference factor, the impact of frequency attenuation in different environments is assessed. Environmental status perception: Based on the assessment results, the frequency attenuation influence under multiple factors is perceived to understand the comprehensive impact of frequency attenuation in the actual environment. In the perception process, data reflecting the frequency attenuation influence under the multi-factor environmental state is obtained.

[0117] The present invention calculates the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency interaction interference identification data and the frequency strength attenuation law data, which helps to determine the transmission range of the signal at different frequencies and helps to evaluate the coverage capability of the communication system at different frequencies. The frequency attenuation coverage data is inferred to have a distance impact, and the relationship between frequency attenuation and distance can be analyzed, which helps to understand the attenuation of the signal at different distances and provide guidance for optimizing the communication network layout and signal transmission. The frequency attenuation coverage data is inferred to have a frequency difference impact, and the attenuation difference between different frequencies can be studied, which helps to identify the characteristics and limitations of signal transmission at different frequencies and provide a reference for frequency selection and channel allocation. The frequency difference attenuation impact data is perceived by multiple factors of the environmental status according to the frequency attenuation distance impact data, and the impact of distance and frequency differences on signal transmission can be comprehensively considered, which helps to more accurately evaluate the reliability and stability of signal transmission in actual environments and provide support for system optimization and troubleshooting.

[0118] Preferably, step S34 includes the following steps:

[0119] Step S341: performing an influencing factor correlation analysis on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency influencing factor correlation data;

[0120] Step S342: performing an influencing factor significance test on the frequency influencing factor correlation data to obtain influencing factor significance data;

[0121] Step S343: Estimating the environmental demand factor based on the significance data of the influencing factors to obtain the environmental impact demand factor;

[0122] Step S344: Perform multi-factor environmental state perception based on the environmental impact demand factor and the impact factor significance data to obtain frequency attenuation multi-factor environmental perception data.

[0123] In this embodiment of the present invention, frequency attenuation distance impact data and frequency difference attenuation impact data are prepared. Correlation analysis is performed on these data using appropriate statistical methods (such as the Pearson correlation coefficient and the Spearman rank correlation coefficient). The analysis results can reflect the correlations between different influencing factors, such as the relationship between frequency, distance, and attenuation. During the analysis, data reflecting the correlations of frequency influencing factors is obtained. The frequency influencing factor correlation data is prepared. A significance test is performed on the frequency influencing factor correlation data using appropriate statistical methods (such as hypothesis testing and analysis of variance). The factors that statistically significantly affect frequency attenuation are determined. During the test, data reflecting the significance of the influencing factors is obtained. The significance data of the influencing factors is prepared. Based on the significance data of the influencing factors, the environmental demand factor for frequency attenuation is estimated. This can be achieved using statistical models or regression analysis. During the estimation process, data on the environmental demand factor is obtained. The environmental demand factor and the significance data of the influencing factors are combined to perform multi-factor environmental state perception. This involves weighing the impact of different factors on frequency attenuation, and making adjustments and optimizations based on actual conditions. During the perception process, data reflecting the impact of frequency attenuation under multi-factor environmental conditions is obtained.

[0124] The present invention can determine the correlation between different factors by performing an influencing factor correlation analysis on the frequency attenuation distance influence data, which helps to understand the relationship between the influencing factors of frequency difference attenuation and provides a basis for subsequent analysis. A significance test is performed on the frequency influencing factor correlation data to determine which factors have a significant impact on the frequency difference attenuation, which helps to identify which factors are the most important in a multi-factor environment and provide a basis for the weight allocation of environmental factors. The environmental demand factor is estimated based on the influencing factor significance data to quantify the degree of influence of different environmental factors on frequency attenuation, which helps to determine the frequency attenuation under different environmental conditions and provide data support for the comprehensive evaluation of environmental factors. In combination with the environmental impact demand factor and the influencing factor significance data, multi-factor environmental state perception is performed, which helps to comprehensively consider the influence of various factors on frequency attenuation and provide decision support for system performance optimization and environmental adaptability design.

[0125] Preferably, step S4 includes the following steps:

[0126] Step S41: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data;

[0127] Step S42: extracting blind spot characteristic parameters from the signal coverage blind spot data to obtain blind spot characteristic parameter data;

[0128] Step S43: classifying the signal coverage blind spot data into blind spot types according to the blind spot characteristic parameter data to obtain blind spot type classification data;

[0129] Step S44: performing signal quality optimization processing on the signal coverage blind spot data according to the blind spot type classification data and the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data.

[0130] In this embodiment of the present invention, frequency attenuation multi-factor environmental perception data and frequency attenuation coverage data are prepared. The frequency attenuation coverage data is analyzed using the multi-factor environmental perception data to identify signal coverage blind spots. A blind spot is an area within the coverage area that is not covered by normal signals. During the identification process, the location and range of the signal coverage blind spot are determined, and corresponding data is generated. The signal coverage blind spot data is analyzed to extract characteristic parameters of the blind spot, such as coverage, signal strength, and terrain. These characteristic parameters can help further understand the nature and influencing factors of the blind spot. During the extraction process, data reflecting the characteristics of the blind spot is obtained. Based on the blind spot characteristic parameters, the signal coverage blind spot data is classified, for example, by coverage, signal strength, and other indicators. This helps understand the characteristics and causes of different types of blind spots. During the classification process, the distribution and characteristics of different blind spot types are determined. Signal quality optimization is performed on the signal coverage blind spot by combining the blind spot type classification data and the frequency attenuation multi-factor environmental perception data. This includes measures such as adjusting signal transmission power, optimizing antenna direction, and improving network layout to improve signal coverage quality. During the optimization process, data reflecting the optimization effect is generated, including the improved signal coverage and related parameters.

[0131] The present invention can identify blind spots in the signal coverage range, that is, areas that are not covered, by utilizing frequency attenuation multi-factor environmental perception data. This helps to determine weak areas of network coverage and provide goals and directions for subsequent optimization. Feature parameter extraction is performed on the identified signal coverage blind spot data, and the characteristics of the blind spots can be quantified from multiple aspects, which helps to gain an in-depth understanding of the causes and characteristics of the blind spots and provide a basis for subsequent analysis. Based on the extracted blind spot feature parameter data, the signal coverage blind spots are classified and different types of blind spots are distinguished, which helps to take corresponding optimization measures for different types of blind spots and improve the quality and efficiency of network coverage. According to the blind spot type classification data and the frequency attenuation multi-factor environmental perception data, the signal coverage blind spots are optimized, which includes adjusting antenna direction, power control, spectrum allocation and other strategies to improve signal quality and coverage.

[0132] Preferably, step S44 includes the following steps:

[0133] Step S441: Analyze the impact of obstacles on signal path transmission using the geometric ray method and frequency attenuation multi-factor environmental perception data to obtain obstacle path transmission impact data;

[0134] Step S442: performing type weight association analysis on the obstacle path transfer impact data according to the blind spot type classification data to obtain conduction impact type weight association data;

[0135] Step S443: adjusting the direction of the signal transmitting antenna according to the obstacle path transmission impact data and the conduction impact type weight association data to obtain signal transmitting antenna direction adjustment data;

[0136] Step S444: performing signal transmission adaptive power adjustment according to the obstacle path transmission impact data and the conduction impact type weight association data to obtain signal transmission adaptive power data;

[0137] Step S445: performing signal quality optimization processing on the signal coverage blind spot data according to the signal transmission antenna direction adjustment data and the signal transmission adaptive power data to obtain signal quality optimization data.

[0138] In this embodiment of the present invention, the geometric ray method is used to simulate the signal propagation path in an environment, taking into account the impact of obstacles such as terrain and buildings. Based on frequency attenuation multi-factor environmental perception data, the signal propagation path and the impact of obstacles are determined. Analysis yields data on the impact of obstacles on signal propagation, including parameters such as path delay and signal attenuation. Weights for different blind spot types are determined based on blind spot classification data. The obstacle path transmission impact data is correlated with blind spot type weights to determine the significance of different blind spot types. This analysis yields weights for each type of blind spot and the impact of the obstacle path transmission impact. This data, combined with the correlation data for the obstacle path transmission impact and the conduction impact type weights, is used to formulate a signal transmission antenna direction adjustment strategy. This involves adjusting the antenna's direction angle to minimize or minimize the impact of obstacles on signal propagation. Based on the adjustment strategy, specific data for the signal transmission antenna direction adjustment is generated, including information such as the adjustment angle and position. Based on the correlation data for the obstacle path transmission impact data and the conduction impact type weights, a signal transmission adaptive power adjustment strategy is formulated. This involves increasing or decreasing signal transmission power to address the impact of different types of blind spots. Based on the adjustment strategy, specific data for adaptive power adjustment of signal transmission is generated, including parameters such as power gain and power attenuation. This data is combined with the signal transmission antenna direction adjustment data and the adaptive power adjustment data to formulate a signal quality optimization strategy. This includes comprehensively considering antenna direction and power adjustment to maximize signal coverage quality. Based on the optimization strategy, the signal coverage blind spot data is processed to generate optimized signal quality data.

[0139] The present invention uses the geometric ray method and frequency attenuation multi-factor environmental perception data to analyze the impact of obstacles on signals during propagation. This helps identify obstacles in the signal propagation path and quantify their impact on signal coverage. Based on the blind spot type classification data, the obstacle path transmission impact data is subjected to type weight association analysis, which can determine the weights of the impact of obstacles on different types of blind spots, so as to perform subsequent optimization more accurately. The direction of the signal transmitting antenna is adjusted based on the obstacle path transmission impact data and the conduction impact type weight association data. By adjusting the antenna direction, the obstruction of obstacles to signal propagation can be minimized, and the coverage range and quality can be improved. Based on the obstacle path transmission impact data and the conduction impact type weight association data, the signal transmission power is adaptively adjusted. The adaptive power adjustment can ensure that during the signal propagation process, the signal attenuation caused by obstacles is overcome and the stability and consistency of coverage are maintained. Combined with the signal transmitting antenna direction adjustment data and the signal transmission adaptive power data, the signal coverage blind spots are further optimized, including optimizing the direction and power of the signal coverage to eliminate blind spots to the greatest extent and ensure that the signal quality within the coverage range is optimal.

[0140] Preferably, step S5 includes the following steps:

[0141] Step S51: constructing an optimization directed graph for the signal quality optimization data to obtain a signal quality optimization directed graph;

[0142] Step S52: sorting the signal coverage blind spot optimization difficulty levels for the signal coverage blind spot data ports according to the signal quality optimization directed graph to obtain blind spot optimization difficulty level sorting data;

[0143] Step S53: constructing a wireless communication quality knowledge graph based on the signal quality optimization directed graph and the blind spot optimization difficulty ranking data to obtain a wireless communication quality knowledge graph for performing wireless communication cell aggregation display.

[0144] In an embodiment of the present invention, signal quality optimization data is prepared, including signal quality data processed by step S445, and the nodes in the signal quality optimization data are regarded as vertices in the graph. According to the relationship between the nodes, a directed edge is constructed to represent the influence relationship between the nodes. For example, if the optimization of one node affects the optimization of another node, a directed edge is established between the two nodes. According to the above steps, a signal quality optimization directed graph is obtained, which contains the relationship and influence between the nodes in the signal quality optimization process. Based on the signal quality optimization directed graph, the optimization difficulty of each signal coverage blind spot is evaluated. Considering factors such as the in-degree and out-degree of the node and the position of the node in the graph, the signal coverage blind spot is ranked by the optimization difficulty level. According to the evaluation results, the optimization difficulty level ranking data of the signal coverage blind spot is generated. This data can be used for the formulation and execution of subsequent optimization strategies. Combining the signal quality optimization directed graph and the blind spot optimization difficulty level ranking data, a wireless communication quality knowledge graph is constructed. The node and edge information in the signal quality optimization directed graph and the blind spot optimization difficulty ranking data are integrated into a comprehensive knowledge graph. Based on the data integration results, a wireless communication quality knowledge graph is constructed, which contains the relationship between each node, optimization difficulty ranking information, etc. The constructed wireless communication quality knowledge graph is used to perform cell aggregation display and visualize the information in the graph to enable users to understand and analyze the wireless communication quality optimization situation.

[0145] The present invention constructs an optimization directed graph for signal quality optimization data, and can represent different signal quality optimization schemes and optimization results in the form of a graph. Such a graphical representation helps to intuitively understand the signal quality optimization situation, including the relationship and influence between various optimization schemes. According to the signal quality optimization directed graph, the signal coverage blind spots are sorted by optimization difficulty level. This step can sort different blind spots according to the difficulty of optimization, and provide priority guidance for subsequent optimization work. The signal quality optimization directed graph and blind spot optimization difficulty level sorting data are used to construct a wireless communication quality knowledge graph. This knowledge graph comprehensively represents various signal optimization schemes, blind spot optimization difficulty levels and the correlation between them, and provides a comprehensive reference framework for wireless communication quality optimization.

[0146] By acquiring cell communication node data and extracting wireless bandwidth information therein, the present invention can help network administrators understand the bandwidth utilization of the entire communication network. By analyzing the wireless bandwidth, the bandwidth distribution between nodes can be determined, which helps to optimize network resource utilization and avoid congestion and bandwidth bottleneck problems. By evaluating the node signal transmission frequency intensity, the signal coverage and signal strength distribution between nodes can be understood, which helps to optimize network coverage and signal transmission quality, improve communication efficiency and stability, and perform frequency interaction interference identification on the node signal transmission frequency intensity data. Frequency interference problems in the network can be discovered and located in a timely manner, which helps to reduce the impact of interference on communication quality and improve network stability and reliability. Identifying the frequency intensity attenuation law helps to understand the attenuation of signals during propagation, thereby better planning network layout and optimizing signal transmission schemes, which helps to improve the coverage and signal transmission distance of the communication network and reduce the impact of signal attenuation on communication quality. By calculating the frequency attenuation coverage range based on the frequency intensity attenuation law data, the signal coverage range in space can be accurately predicted, which is helpful for rationally planning network layout and optimizing signal coverage. By inferring the distance impact of the frequency attenuation coverage range data, the degree of influence of different distances on signal strength can be evaluated, providing an important reference for network optimization. By analyzing the frequency attenuation multi-factor environmental perception data, the impact of various environmental factors on signal transmission can be comprehensively considered, such as terrain, buildings, and weather. This helps to improve the network's adaptability to environmental changes, thereby maintaining network stability and reliability. By identifying signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, coverage blind spot problems in the network can be discovered in a timely manner, so that corresponding optimization measures can be taken to optimize the signal quality of signal coverage blind spots. Network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve signal coverage and transmission quality in blind spot areas. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. Sorting the data according to the optimization difficulty level can provide network administrators with optimization strategy priorities so that they can solve network problems in a targeted manner. Based on the blind spot optimization difficulty level sorting data, we can construct a wireless communication quality knowledge graph, which integrates the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network management personnel with a clear guidance map to help them understand the problems and optimization direction in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, the signal quality, coverage and other information of each communication cell can be aggregated and displayed to form an intuitive network status display, which helps network management personnel quickly understand the operating status of the entire communication network, discover problems in a timely manner and take corresponding measures to ensure the normal operation of the network and the user's communication experience.Therefore, the present invention is an optimization process for the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that the blind spots of wireless signals cannot be accurately analyzed and displayed, and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze wireless signal blind spots, and improves the accuracy of environmental impact analysis.

[0147] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0148] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A communication aggregation display method, characterized in that: The following steps are involved: Step S1: Acquire cell communication node data; extract the communication node wireless bandwidth from the cell communication node data to obtain the communication node wireless bandwidth data; evaluate the signal transmission frequency strength based on the communication node wireless bandwidth data to obtain the node signal transmission frequency strength data; Step S2: performing frequency interaction interference identification on the node signal transmission frequency strength data to obtain frequency interaction interference identification data; Identify the frequency intensity attenuation law based on the frequency interaction interference identification data to obtain the frequency intensity attenuation law data; Step S3: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency strength attenuation law data to obtain frequency attenuation coverage data; infer the distance influence of the frequency attenuation coverage data to obtain frequency attenuation distance influence data; and perform multi-factor environmental state perception based on the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data. Step S4: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data; performing signal quality optimization processing on the signal coverage blind spot data according to the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data; Step S5: sorting the signal coverage blind spot optimization difficulty level according to the signal quality optimization data and the signal coverage blind spot data to obtain blind spot optimization difficulty level sorting data; constructing a wireless communication quality knowledge graph according to the blind spot optimization difficulty level sorting data to obtain a wireless communication quality knowledge graph to perform wireless communication cell aggregation display. Step S5 includes the following steps: Step S51: constructing an optimized directed graph for the signal quality optimization data to obtain a signal quality optimized directed graph; including: The nodes in the signal quality optimization data are regarded as vertices in the graph, and directed edges are constructed according to the relationships between the nodes to obtain a signal quality optimization directed graph; Step S52: Sorting the signal coverage blind spot data by signal coverage blind spot optimization difficulty level according to the signal quality optimization directed graph to obtain blind spot optimization difficulty level ranking data; comprising: evaluating the optimization difficulty of each signal coverage blind spot based on the signal quality optimization directed graph, and sorting the optimization difficulty level to generate signal coverage blind spot optimization difficulty level ranking data; Step S53: constructing a wireless communication quality knowledge graph based on the signal quality optimization directed graph and the blind spot optimization difficulty level ranking data to obtain a wireless communication quality knowledge graph to perform wireless communication cell aggregation display; including: integrating the node and edge information in the signal quality optimization directed graph and the blind spot optimization difficulty level ranking data into a comprehensive knowledge graph, constructing a wireless communication quality knowledge graph based on the data integration results, which includes the relationship between each node, optimization difficulty level ranking information, etc., using the constructed wireless communication quality knowledge graph to perform cell aggregation display and visualize the information in the graph.

2. The communication aggregation display method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire cell communication node data; Step S12: extracting wireless bandwidth of communication nodes from cell communication node data to obtain wireless bandwidth data of communication nodes; Step S13: performing an operating power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operating power difference data; Step S14: performing signal transmission frequency strength evaluation on the node operation power difference data to obtain node signal transmission frequency strength data.

3. The communication aggregation display method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Analyze the spatiotemporal intensity distribution characteristics of the node signal transmission frequency intensity data to obtain signal frequency spatiotemporal intensity distribution data; Step S22: performing non-uniform density calculation on the signal frequency-time-space intensity distribution data to obtain frequency-time-space intensity density data; Step S23: performing frequency interaction interference identification on the node signal transmission frequency intensity data according to the frequency-time-space intensity density data to obtain frequency interaction interference identification data; Step S24: identifying the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the frequency interaction interference identification data to obtain frequency intensity attenuation law data.

4. The communication aggregation display method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing a spatiotemporal power law distribution analysis on the frequency-spatiotemporal intensity density data to obtain frequency-spatiotemporal power law distribution data; Step S232: performing intensity critical stability analysis on the frequency-space-time intensity density data according to the frequency-space-time power law distribution data to obtain frequency-intensity critical stability data; Step S233: performing frequency intermittency identification on the frequency intensity critical stability data to obtain frequency intermittency data; Step S234: performing interactive signal frequency correlation mining on the node signal transmission frequency strength data according to the frequency strength critical stability data and the frequency intermittency data to obtain interactive signal frequency correlation data; Step S235: performing frequency interaction interference identification on the node signal transmission frequency strength data according to the interaction signal frequency correlation data, the frequency strength critical stability data, and the frequency intermittency data to obtain frequency interaction interference identification data.

5. The communication aggregation display method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: constructing a frequency interaction interference matrix for the frequency interaction interference identification data to obtain a frequency interaction interference matrix; Step S242: performing diagonalization analysis based on the frequency interaction interference matrix to obtain frequency interaction interference diagonalization data; Step S243: performing orthogonal basis vector identification on the frequency interaction interference matrix according to the frequency interaction interference diagonalization data to obtain frequency interference orthogonal basis vector data; Step S244: performing basis vector distortion analysis on the frequency interference orthogonal basis vector data to obtain frequency interference basis vector distortion data; Step S245: normalizing the frequency interaction interference matrix according to the frequency interference basis vector distortion data to obtain distortion rule normalized data; Step S246: Identify the frequency intensity attenuation law of the node signal transmission frequency intensity data according to the distortion law normalization data and the frequency interference basis vector distortion data to obtain frequency intensity attenuation law data.

6. The communication aggregation display method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data based on the frequency interaction interference identification data and the frequency strength attenuation law data to obtain frequency attenuation coverage data; Step S32: performing distance influence inference on the frequency attenuation coverage data to obtain frequency attenuation distance influence data; Step S33: performing frequency difference impact inference on the frequency attenuation coverage data to obtain frequency difference attenuation impact data; Step S34: performing multi-factor environmental state perception on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency attenuation multi-factor environmental perception data.

7. The communication aggregation display method according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: performing an influencing factor correlation analysis on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency influencing factor correlation data; Step S342: performing an influencing factor significance test on the frequency influencing factor correlation data to obtain influencing factor significance data; Step S343: Estimating the environmental demand factor based on the significance data of the influencing factors to obtain the environmental impact demand factor; Step S344: Perform multi-factor environmental state perception based on the environmental impact demand factor and the impact factor significance data to obtain frequency attenuation multi-factor environmental perception data.

8. The communication aggregation display method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing signal coverage blind spot identification on the frequency attenuation coverage range data according to the frequency attenuation multi-factor environment perception data to obtain signal coverage blind spot data; Step S42: extracting blind spot characteristic parameters from the signal coverage blind spot data to obtain blind spot characteristic parameter data; Step S43: classifying the signal coverage blind spot data into blind spot types according to the blind spot characteristic parameter data to obtain blind spot type classification data; Step S44: performing signal quality optimization processing on the signal coverage blind spot data according to the blind spot type classification data and the frequency attenuation multi-factor environment perception data to obtain signal quality optimization data.

9. The communication aggregation display method according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: Analyze the impact of obstacles on signal path transmission using the geometric ray method and frequency attenuation multi-factor environmental perception data to obtain obstacle path transmission impact data; Step S442: performing type weight association analysis on the obstacle path transfer impact data according to the blind spot type classification data to obtain conduction impact type weight association data; Step S443: adjusting the direction of the signal transmitting antenna according to the obstacle path transmission impact data and the conduction impact type weight association data to obtain signal transmitting antenna direction adjustment data; Step S444: performing signal transmission adaptive power adjustment according to the obstacle path transmission impact data and the conduction impact type weight association data to obtain signal transmission adaptive power data; Step S445: performing signal quality optimization processing on the signal coverage blind spot data according to the signal transmission antenna direction adjustment data and the signal transmission adaptive power data to obtain signal quality optimization data.

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