Communication aggregation display method

Abstract

The invention relates to the technical field of aggregation display, in particular to a communication aggregation display method. The method comprises the following steps: performing communication node wireless bandwidth extraction and signal emission frequency intensity evaluation on cell communication node data to obtain node signal emission frequency intensity data; performing frequency interaction interference identification and frequency intensity attenuation rule identification on the node signal emission frequency intensity data to obtain frequency intensity attenuation rule data; performing multi-factor environment state sensing according to the frequency intensity attenuation rule data to obtain frequency attenuation multi-factor environment sensing data; performing signal quality optimization processing according to the frequency attenuation multi-factor environmental perception data to obtain signal quality optimization data; and constructing a wireless communication quality knowledge graph according to the signal quality optimization data to obtain the wireless communication quality knowledge graph so as to execute wireless communication cell aggregation display. According to the invention, aggregation display can be 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 the data transmission speed and network capacity, but the resulting spectrum resource shortage and network congestion problems have become increasingly prominent. As one of the core innovations of LTE-Advanced and 5G technologies, Carrier Aggregation (CA) aims to increase the available bandwidth of users by using multiple carrier frequency bands at the same time, thereby improving the data transmission rate and overall network performance. Cell aggregation technology can not only significantly improve the throughput of data transmission, but also optimize spectrum utilization and alleviate network congestion. With the application of cell aggregation technology, user terminals need to understand the status and information of multiple cells currently connected in real time, which is crucial to user experience. Existing wireless communication equipment cannot intuitively display the cell aggregation status and related information, which not only limits the user's understanding of the network status, but also is not conducive to the operator's monitoring and optimization of network performance. Therefore, a communication aggregation display method is proposed. By intuitively and in real time displaying the detailed information of the cell aggregation status and its constituent cells on the terminal device, it can significantly improve the user's perception and experience of the network, and provide operators with more accurate network status feedback, thereby facilitating network optimization and resource management. The core of this method is to display the multi-carrier information of cell aggregation in a friendly user interface, so that users and operators can easily obtain and understand the complex network status, providing support for efficient wireless communication. However, the traditional communication aggregation display method has the problem of being unable to accurately analyze and display the blind spots of wireless signals, as well as inaccurate analysis of environmental impact. 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 according to 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 strength attenuation law identification based on the frequency interaction interference identification data to obtain frequency strength attenuation law data;

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

[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 allocation 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, so as to timely discover and locate frequency interference problems existing in the network, 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, so as to better plan network layout and optimize 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 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 influence of various environmental factors on signal transmission, such as terrain, buildings, and weather, can be comprehensively considered, which helps to improve the network's adaptability to environmental changes, thereby maintaining the stability and reliability of the network. By identifying the signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, the 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 the signal coverage blind spots. The network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve the signal coverage and transmission quality in the blind spot area. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. By sorting the data according to the optimization difficulty level, we 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 to integrate the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network managers with a clear guidance map to help them understand the problems and optimization directions in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, we can aggregate and display information such as the signal quality and coverage of each communication cell to form an intuitive network status display, which helps network managers 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 processing of the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that is unable to accurately analyze and display the blind spots of wireless signals and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze the blind spots of wireless signals and improves the accuracy of environmental impact analysis.

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

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

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

[0014] Step S13: Performing operation power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operation 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 the data of cell communication nodes, which is the basis for understanding the topological structure and node distribution of the communication network. These data include information such as node location, connection mode, and device type. By obtaining these data, network management personnel can fully understand the composition of the network, provide basic data support for subsequent optimization and management work, extract wireless bandwidth information from the communication node data, and accurately understand the bandwidth resource allocation of each node, which helps to optimize bandwidth utilization, reasonably allocate resources, avoid network congestion and bandwidth bottleneck problems, and analyze the operating power differences between nodes. The signal transmission capability and coverage of different nodes can be evaluated, which helps to discover potential signal coverage problems, optimize node layout and parameter settings, and improve signal coverage quality. According to the node operating power difference data, the signal transmission frequency intensity of the node is evaluated, and the transmission range and quality of the signal can be understood, which helps to determine the signal coverage area and signal quality distribution, and provides a reference for network optimization.

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

[0018] Step S21: Performing a time-space intensity distribution characteristic analysis on the node signal transmission frequency intensity data to obtain signal frequency time-space 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: Identify the frequency strength attenuation law of the node signal transmission frequency strength data according to the frequency interaction interference identification data to obtain frequency strength attenuation law data.

[0022] The present invention can understand the distribution of signals in the space-time domain by performing spatiotemporal intensity distribution feature analysis on the node signal transmission frequency strength data, which helps to identify strong and weak areas within the signal coverage range, find dead spots or weak coverage areas of signal coverage, and thus optimize node layout and parameter settings. The non-uniformity density calculation of the signal frequency spatiotemporal intensity distribution data can evaluate whether the spatial distribution of 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, which can 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 intensity attenuation law of the node signal transmission frequency strength data is identified based on the frequency interaction interference identification data, so as to understand the attenuation law of signal strength with distance, which helps to predict the signal coverage range, guide signal power adjustment and node layout, and improve network coverage effect.

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

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

[0025] Step S232: performing intensity critical stability analysis on the frequency-time-space intensity density data according to the frequency-time-space 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 association data, the frequency strength critical stability data and the frequency intermittency data to obtain frequency interaction interference identification data.

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

[0030] Preferably, step S24 comprises 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 according to 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 acquiring the cell communication node data and extracting the wireless bandwidth information therein, it can help network administrators understand the bandwidth utilization of the entire communication network; by analyzing the wireless bandwidth, it can determine the bandwidth allocation between nodes, which helps to optimize network resource utilization and avoid congestion and bandwidth bottleneck problems; by evaluating the node signal transmission frequency strength, it can understand the signal coverage and signal strength distribution between nodes, which helps to optimize the 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, it can timely discover and locate frequency interference problems in the network, 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 the signal during the propagation process, so as to better plan the network layout and optimize the signal transmission plan, 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 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 influence of various environmental factors on signal transmission, such as terrain, buildings, and weather, can be comprehensively considered, which helps to improve the network's adaptability to environmental changes, thereby maintaining the stability and reliability of the network. By identifying the signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, the 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 the signal coverage blind spots. The network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve the signal coverage and transmission quality in the blind spot area. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. By sorting the data according to the optimization difficulty level, we 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 to integrate the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network managers with a clear guidance map to help them understand the problems and optimization directions in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, we can aggregate and display information such as the signal quality and coverage of each communication cell to form an intuitive network status display, which helps network managers 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 processing of the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that is unable to accurately analyze and display the blind spots of wireless signals and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze the blind spots of wireless signals and improves the accuracy of environmental impact analysis. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0040] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

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

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

[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 described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

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

[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 according to 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 strength attenuation law identification based on the frequency interaction interference identification data to obtain frequency strength attenuation law data;

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

[0052] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram 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 according to 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, and 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, and the calculation result includes the signal transmission frequency of each node and the corresponding signal strength.

[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 strength attenuation law identification based on the frequency interaction interference identification data to obtain frequency strength attenuation law data;

[0056] In an embodiment of the present invention, the frequency strength 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 interference source nodes, interfered nodes, interference frequency range, and interference intensity. Based on the frequency interaction interference identification data, the frequency strength 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 strength 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 according to the frequency strength attenuation law data to obtain frequency attenuation coverage data; perform distance influence inference on the frequency attenuation coverage data to obtain frequency attenuation distance influence data; perform multi-factor environmental state perception according to 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 strength 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 considered 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 the intensity change of the signal at different distances is inferred. 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 signal strength change with distance. The frequency attenuation distance influence data is used to consider various environmental factors (such as buildings, trees, weather conditions, etc.), and the comprehensive impact of perception on signal attenuation is perceived. 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, identify signal coverage blind spot areas, and 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, obtain signal coverage blind spot data, including the specific location and area information of all blind spots, and use signal coverage blind spot data and frequency attenuation multi-factor environmental perception data to improve signal coverage and quality by adjusting the transmission power, optimizing the antenna direction, and adding relay equipment. A network optimization algorithm (such as a power control algorithm and a frequency allocation algorithm) is 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: sorting 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; 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.

[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, evaluate the optimization difficulty, and the evaluation indicators of the 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 to determine the priority, and a sorting algorithm (such as a weight sorting method, a priority queue) is used to generate blind spot optimization difficulty level sorting data to obtain blind spot optimization difficulty level sorting data, 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, and the graph data is used to generate the blind spot optimization difficulty level sorting data. 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 data. The constructed knowledge graph is optimized and adjusted to improve the query efficiency and display effect. The indexing, caching and other technologies are used to optimize the performance of the graph to obtain the wireless communication quality knowledge graph, which includes 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 allocation 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, so as to timely discover and locate frequency interference problems existing in the network, 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, so as to better plan network layout and optimize 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 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 influence of various environmental factors on signal transmission, such as terrain, buildings, and weather, can be comprehensively considered, which helps to improve the network's adaptability to environmental changes, thereby maintaining the stability and reliability of the network. By identifying the signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, the 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 the signal coverage blind spots. The network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve the signal coverage and transmission quality in the blind spot area. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. By sorting the data according to the optimization difficulty level, we 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 to integrate the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network managers with a clear guidance map to help them understand the problems and optimization directions in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, we can aggregate and display information such as the signal quality and coverage of each communication cell to form an intuitive network status display, which helps network managers 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 processing of the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that is unable to accurately analyze and display the blind spots of wireless signals and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze the blind spots of wireless signals and improves the accuracy of environmental impact analysis.

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

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

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

[0067] Step S13: Performing operation power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operation 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 of each communication node (such as a base station, a router, and a hotspot) in a cell, wherein the data includes basic information such as node location, node type, node status, number of connected users, and working frequency band. The collected communication node data is integrated, deduplicated, cleaned, and the data format is standardized to ensure data integrity and consistency, and detailed node information required for subsequent analysis is prepared; wireless bandwidth information of each node is extracted from the integrated communication node data, wherein the wireless bandwidth information includes the total bandwidth, uplink and downlink bandwidth, and bandwidth utilization of each node; operating power data of each communication node is collected, and the power output of the node under different time periods and different loads is recorded; according to the wireless bandwidth data and the operating power data, the power output difference of each node under different load conditions is analyzed, the power difference value is 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, wherein the data includes statistical information such as the power output average value, standard deviation, maximum value, and minimum value of each node; signal transmission frequency data of each communication node is collected, and the working frequency band and transmission frequency information of the node are recorded; according to the node operating power difference data and the signal transmission frequency data, the signal transmission frequency strength of each node is evaluated. 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 strength data.

[0070] The present invention obtains the data of cell communication nodes, which is the basis for understanding the topological structure and node distribution of the communication network. These data include information such as node location, connection mode, and device type. By obtaining these data, network management personnel can fully understand the composition of the network, provide basic data support for subsequent optimization and management work, extract wireless bandwidth information from the communication node data, and accurately understand the bandwidth resource allocation of each node, which helps to optimize bandwidth utilization, reasonably allocate resources, avoid network congestion and bandwidth bottleneck problems, and analyze the operating power differences between nodes. The signal transmission capability and coverage of different nodes can be evaluated, which helps to discover potential signal coverage problems, optimize node layout and parameter settings, and improve signal coverage quality. According to the node operating power difference data, the signal transmission frequency intensity of the node is evaluated, and the transmission range and quality of the signal can be understood, which helps to determine the signal coverage area and signal quality distribution, and provides a reference for network optimization.

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

[0072] Step S21: Performing a time-space intensity distribution characteristic analysis on the node signal transmission frequency intensity data to obtain signal frequency time-space 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: Identify the frequency strength attenuation law of the node signal transmission frequency strength data according to the frequency interaction interference identification data to obtain frequency strength 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 a time-space intensity distribution characteristic analysis on the node signal transmission frequency intensity data to obtain signal frequency time-space 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. The data is sorted and sorted by time and space (geographic location), and a time series and a spatial grid are created to facilitate subsequent analysis, analyze the changes in signal strength in different time periods, identify peak periods and trough periods, generate a signal strength time distribution diagram, display the trend of signal strength over time, analyze the distribution of signal strength in different geographic locations, and identify areas with high and low signal strength. Generate a signal strength spatial distribution diagram to display the distribution characteristics of signal strength in space, and analyze the spatiotemporal distribution characteristics of signal strength in combination with time and space dimensions. Generate a signal strength spatiotemporal distribution diagram to display 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, signal strength, etc., the study area is divided into several small spatiotemporal grid units (such as time periods, geographical cells), and density calculation indicators are defined, such as the mean, variance, maximum, and minimum values ​​of the signal strength; 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, the mean and variance of the signal strength in each spatiotemporal grid unit are calculated, and a non-uniform density distribution map is generated 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 distribution, 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 an 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.), identify the signal strength distribution of different nodes at the same frequency, analyze the signal strength superposition between the co-frequency nodes, determine the co-frequency interference area, identify the signal strength distribution of different nodes at adjacent frequencies, analyze the signal strength influence between adjacent frequency nodes, determine the adjacent frequency interference area, analyze the signal interaction between multiple nodes, identify possible intermodulation interference, calculate the intermodulation interference signal strength, determine the intermodulation interference area, process the identified interference data, and generate frequency interaction interference identification data.

[0083] Step S24: Identify the frequency strength attenuation law of the node signal transmission frequency strength data according to the frequency interaction interference identification data to obtain frequency strength 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 free space model, logarithmic distance model, segmented model, etc.) under different environments, and model parameters are set according to different environmental factors (such as buildings, trees, weather, etc.). In combination with signal strength and distance data, the attenuation model is used to calculate the basic attenuation law of signal strength. A basic attenuation curve of signal strength varying with distance is generated, and the basic attenuation model is corrected according to the frequency interaction interference identification data, considering the influence of interference on signal strength, and the attenuation law of signal strength is recalculated to generate a corrected attenuation curve. In combination with multi-factor environmental data, the attenuation model is further corrected, considering the influence of environmental factors on signal attenuation, and finally frequency strength attenuation law data is generated to reflect the signal attenuation in the actual environment, and the identified attenuation law data is processed to generate frequency strength attenuation law data, the data includes information such as attenuation model, attenuation parameter, attenuation curve, and frequency strength attenuation law data is obtained, including the attenuation law of signal strength under different environments and interference conditions.

[0085] The present invention can understand the distribution of signals in the space-time domain by performing spatiotemporal intensity distribution feature analysis on the node signal transmission frequency strength data, which helps to identify strong and weak areas within the signal coverage range, find dead spots or weak coverage areas of signal coverage, and thus optimize node layout and parameter settings. The non-uniformity density calculation of the signal frequency spatiotemporal intensity distribution data can evaluate whether the spatial distribution of 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, which can 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 intensity attenuation law of the node signal transmission frequency strength data is identified based on the frequency interaction interference identification data, so as to understand the attenuation law of signal strength with distance, which helps to predict the signal coverage range, guide signal power adjustment and node layout, and improve network coverage effect.

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

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

[0088] Step S232: performing intensity critical stability analysis on the frequency-time-space intensity density data according to the frequency-time-space 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 association 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 and other processing. 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, the relevant data of the frequency-space-time power-law distribution, including the power-law exponent and the fitting curve, are obtained; ensuring that the 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 the rate of signal change, the degree of frequency interference, 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: Analyze the stability indicators, identify possible weaknesses or areas with low stability, and propose improvement suggestions. During the analysis process, relevant data on frequency intensity critical stability are obtained, which can be used to guide network optimization or the formulation of interference management strategies; identify frequency intermittency in frequency intensity critical stability data. This includes the following steps: Determine the definition and indicators of frequency intermittency: Determine the characteristics or indicators used to identify frequency intermittency, such as the amplitude of frequency change, the frequency of frequency change, etc. Data processing: Preprocess the frequency intensity critical stability data, such as removing noise, smoothing data, etc. Identify frequency intermittency: Apply appropriate algorithms or techniques, such as time series analysis, spectrum analysis, etc., to identify the pattern or characteristics of frequency intermittency; Ensure that frequency intensity critical stability data and frequency intermittency data are prepared, which will be used to mine the interactive signal frequency association of node signal transmission frequency Based on the frequency intensity critical stability data and frequency intermittency data, mine the interactive signal frequency association of node signal transmission frequency intensity data. The specific steps include: Data association: Associate 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, which can be used for subsequent identification of frequency interaction interference; ensure that the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data are prepared, and identify the frequency interaction interference of the node signal transmission frequency intensity data based on the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data. Specific steps include: Data integration: Integrate the interactive signal frequency association data, frequency intensity critical stability data, and frequency intermittent data.Interference identification: Apply appropriate interference identification algorithms or technologies, such as pattern recognition, machine learning, etc., to identify the mutual interference between node signal transmission frequencies. Result analysis: Analyze the identification results, evaluate the impact of frequency mutual interference on system performance, and put forward corresponding interference management or optimization suggestions. During the analysis process, identification data reflecting the mutual interference between node signal transmission frequencies is obtained, which can be used to guide network optimization or the formulation of interference management strategies.

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

[0094] Preferably, step S24 comprises 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 according to 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. 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: According to 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. According to the frequency interaction interference matrix, diagonalization analysis is performed. Specific steps include: Calculate eigenvalues ​​and eigenvectors: Use mathematical tools or algorithms to calculate the eigenvalues ​​and corresponding eigenvectors of the frequency interaction interference matrix. Diagonalization matrix: diagonalize the frequency interaction interference matrix to obtain a diagonalization matrix, where the elements on the diagonal are the eigenvalues. In the diagonalization analysis process, the diagonalization result of the frequency interaction interference matrix is ​​obtained, including the eigenvalues ​​and the corresponding eigenvectors. Ensure that the frequency interaction interference diagonalization data, including the eigenvalues ​​and the corresponding eigenvectors, are prepared. According to the frequency interaction interference diagonalization data, the frequency interference orthogonal basis vectors are identified. The specific steps include: orthogonal processing: construct orthogonal basis vectors using eigenvectors to ensure that the basis vectors are mutually orthogonal. Determine the main frequency interference mode: determine the main frequency interference mode or the main interference direction by analyzing the eigenvalues ​​and the corresponding eigenvectors. Extract orthogonal basis vector data: extract the obtained orthogonal basis vector data as the basic features to describe the frequency interaction interference. In the identification process, the orthogonal basis vector data reflecting the frequency interaction interference are obtained. These data can be used for subsequent analysis and interference processing; ensure that the frequency interference orthogonal basis vector data are prepared. These data are obtained through the previous steps. The frequency interference orthogonal basis vector data is used for distortion analysis. The specific steps include: Data processing: pre-process 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 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 are 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 law analysis: Regularity analysis is performed on the normalized distortion data to explore the distribution characteristics and influencing factors of the distortion. In the calculation process, normalized data reflecting the distortion law of the frequency interference basis vector is obtained. These data will be used to normalize the distortion law of the frequency interaction interference matrix; ensure that the distortion law normalized data and the frequency interference basis vector distortion data are prepared, and identify the frequency intensity attenuation law of the node signal transmission frequency intensity based on the distortion law normalized data and the frequency interference basis vector distortion data. The specific steps include: Establishment of intensity attenuation model: According to the distortion law and basis vector distortion data, a node signal transmission frequency intensity attenuation model is established. Regularity analysis: Analyze the regularity of frequency intensity attenuation, determine the attenuation trend, amplitude and frequency correlation, etc. Data fitting and verification: Through fitting algorithm or model verification, the specific parameters of the frequency intensity attenuation law are obtained. In the identification process, data reflecting the node signal transmission frequency intensity attenuation law are obtained. These data can be used for subsequent interference analysis and 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 comprises the following steps:

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

[0105] Step S32: inferring the distance influence of the frequency attenuation coverage data to obtain the frequency attenuation distance influence data;

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

[0107] Step S34: Perform 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 range of the node signal transmission frequency strength data according to the frequency interaction interference identification data and the frequency strength attenuation law data to obtain the frequency attenuation coverage range data;

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

[0111] Step S32: inferring the distance influence of the frequency attenuation coverage data to obtain the frequency attenuation distance influence data;

[0112] In the embodiment of the present invention, it is ensured that the frequency attenuation coverage data is prepared, and the distance influence of the frequency attenuation coverage data is inferred. The specific steps include: Data analysis: Analyze the frequency attenuation coverage data to understand the law of how the attenuation degree changes with the distance. Calculate the influence of attenuation distance: Based on the data analysis results, infer the influence of different distances on frequency attenuation, which may be calculated by using statistical methods or establishing a mathematical model. In the inference process, the data of the frequency attenuation distance influence is obtained.

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

[0114] In an embodiment of the present invention, the frequency attenuation coverage data is used to infer the frequency difference impact. The specific steps include: Data analysis: Analyze the frequency attenuation coverage data to understand the attenuation differences between different frequencies. Calculate the frequency difference impact: Based on the data analysis results, infer the degree of attenuation between different frequencies, which may be calculated using a mathematical model or statistical method. In the inference process, the frequency difference attenuation impact data is obtained.

[0115] Step S34: Perform 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, multi-factor environmental state perception is performed on the frequency difference attenuation influence data based on the frequency attenuation distance influence data. 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 consider the distance factor and the frequency difference factor to assess the impact of frequency attenuation in different environments. Environmental state perception: Based on the evaluation results, the frequency attenuation influence under various 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 effect, 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 effect, 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 influence data is perceived by multiple factors of the environmental status based on the frequency attenuation distance influence data, and the influence of distance and frequency difference 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 influence factor correlation analysis on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency influence 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 according to the significance data of the influencing factors to obtain the environmental impact demand factor;

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

[0123] In the embodiment of the present invention, it is ensured that the frequency attenuation distance influence data and the frequency difference attenuation influence data are prepared, and the frequency attenuation distance influence data and the frequency difference attenuation influence data are subjected to correlation analysis using appropriate statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.). The analysis results can reflect the correlation between different influencing factors, such as the relationship between frequency, distance and attenuation. During the analysis process, data reflecting the correlation of frequency influencing factors are obtained, and it is ensured that the frequency influencing factor correlation data are prepared, and appropriate statistical methods (such as hypothesis testing, variance analysis, etc.) are used to perform significance tests on the frequency influencing factor correlation data. Determine which influencing factors statistically significantly affect frequency attenuation. During the test process, data reflecting the significance of the influencing factors are obtained; ensure that the significance data of the influencing factors are prepared, and estimate the environmental demand factor for frequency attenuation based on the significance data of the influencing factors. This can be achieved through statistical models or regression analysis. During the estimation process, data of environmental impact demand factors are obtained, and the environmental impact demand factors 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 correlation analysis of influencing factors on frequency attenuation distance influencing data, which helps to understand the relationship between 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 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. Environmental demand factors are estimated based on the significance data of influencing factors 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. Multi-factor environmental state perception is performed in combination with environmental impact demand factors and significance data of influencing factors, 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 comprises 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 the embodiment of the present invention, it is ensured that the frequency attenuation multi-factor environmental perception data and the frequency attenuation coverage data are prepared, and the frequency attenuation coverage data is analyzed using the frequency attenuation multi-factor environmental perception data to identify the signal coverage blind spot. A blind spot refers to an area that is not covered by a normal signal within the coverage range. During the identification process, the location and range of the signal coverage blind spot are determined, and corresponding data are generated. The signal coverage blind spot data is analyzed to extract characteristic parameters of the blind spot, such as coverage range, signal strength, terrain and other related information. 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 characteristic parameters of the blind spot, the signal coverage blind spot data is classified, such as classification according to indicators such as coverage range and signal strength. This helps to understand the characteristics and causes of different types of blind spots. During the classification process, the distribution and characteristics of different types of blind spots are determined. Combined with the blind spot type classification data and the frequency attenuation multi-factor environmental perception data, the signal coverage blind spot is subjected to signal quality optimization processing. 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 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, which 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 influence of obstacles on signal path transmission by using the geometric ray method and frequency attenuation multi-factor environment perception data to obtain obstacle path transmission influence 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 influence data and the conduction influence type weight association data to obtain signal transmitting antenna direction adjustment data;

[0136] Step S444: adjusting the signal transmission adaptive power according to the obstacle path transmission influence data and the conduction influence type weight association data to obtain the 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 the embodiment of the present invention, the geometric ray method is used to simulate the propagation path of the signal in the environment, taking into account the influence of obstacles such as terrain and buildings. According to the frequency attenuation multi-factor environmental perception data, the propagation path of the signal and the influence of obstacles are determined, and the influence data of obstacles on signal propagation are obtained after analysis, including parameters such as path delay and signal attenuation. According to the blind spot type classification data, the weights of different types of blind spots are determined. The obstacle path transmission influence data and the blind spot type weight are associated and analyzed to determine the importance of the influence of different types of blind spots on obstacles. In the analysis process, the relevant weight data of the influence of various types of blind spots and obstacle path transmission are obtained. Combined with the obstacle path transmission influence data and the conduction influence type weight association data, a signal transmission antenna direction adjustment strategy is formulated. This includes adjusting the direction angle of the antenna to avoid or reduce the influence of obstacles on signal propagation to the greatest extent. According to the adjustment strategy, specific data for adjusting the direction of the signal transmission antenna is generated, including information such as adjustment angle and position. According to the obstacle path transmission influence data and the conduction influence type weight association data, a signal transmission adaptive power adjustment strategy is formulated. This involves increasing or decreasing the signal transmission power to cope with the impact of different types of blind spots, generating specific data for signal transmission adaptive power adjustment according to the adjustment strategy, including parameters such as power gain and power attenuation, and formulating a signal quality optimization strategy in combination with the signal transmission antenna direction adjustment data and the signal transmission adaptive power adjustment data. This includes comprehensive consideration of antenna direction and power adjustment to maximize signal coverage quality, processing signal coverage blind spot data according to the optimization strategy, and generating optimized signal quality data.

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

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

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

[0142] Step S52: sorting the signal coverage blind spot optimization difficulty levels of 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: construct 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.

[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, each signal coverage blind spot is optimized. The difficulty level 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 according to the optimization difficulty level. According to the evaluation results, the optimization difficulty level ranking data of the signal coverage blind spot is generated. The data can be used for the formulation and execution of subsequent optimization strategies. Combined with 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 level ranking data are integrated into a comprehensive knowledge graph. Based on the data integration results, a wireless communication quality knowledge graph is constructed, which includes the relationship between each node, optimization difficulty level ranking information, etc. The constructed wireless communication quality knowledge graph is used to perform cell aggregation display and visualize the information in the graph so that users can 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 ranked by optimization difficulty level. This step can rank 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 ranking 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 correlations therebetween, 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 allocation 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, so as to timely discover and locate frequency interference problems existing in the network, 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, so as to better plan network layout and optimize 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 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 influence of various environmental factors on signal transmission, such as terrain, buildings, and weather, can be comprehensively considered, which helps to improve the network's adaptability to environmental changes, thereby maintaining the stability and reliability of the network. By identifying the signal coverage blind spots based on the frequency attenuation multi-factor environmental perception data, the 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 the signal coverage blind spots. The network parameters can be adjusted in a targeted manner, signal transmission equipment can be added, etc., to improve the signal coverage and transmission quality in the blind spot area. By analyzing and evaluating the signal quality optimization data, we can determine the optimization difficulty level of different signal coverage blind spots. By sorting the data according to the optimization difficulty level, we 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 to integrate the optimization difficulty, optimization plan, optimization effect and other information of each signal coverage blind spot. This knowledge graph can provide network managers with a clear guidance map to help them understand the problems and optimization directions in the network and make corresponding decisions. Using the constructed wireless communication quality knowledge graph, we can aggregate and display information such as the signal quality and coverage of each communication cell to form an intuitive network status display, which helps network managers 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 processing of the traditional communication aggregation display method, which solves the problems of the traditional communication aggregation display method that is unable to accurately analyze and display the blind spots of wireless signals and the inaccurate analysis of environmental impacts, improves the ability to accurately analyze the blind spots of wireless signals and improves the accuracy of environmental impact analysis.

[0147] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 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 according to 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; According to the frequency interaction interference identification data, the frequency intensity attenuation law is identified to obtain the frequency intensity attenuation law data; Step S3: Calculate the frequency attenuation coverage of the node signal transmission frequency strength data according to the frequency strength attenuation law data to obtain frequency attenuation coverage data; perform distance influence inference on the frequency attenuation coverage data to obtain frequency attenuation distance influence data; perform multi-factor environmental state perception according to 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 of the signal coverage blind spot data port according to the signal quality optimization 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.

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 the wireless bandwidth of the communication nodes from the cell communication node data to obtain the wireless bandwidth data of the communication nodes; Step S13: Performing operation power difference analysis based on the wireless bandwidth data of the communication nodes to obtain node operation 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 the 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: Identify the frequency strength attenuation law of the node signal transmission frequency strength data according to the frequency interaction interference identification data to obtain frequency strength 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 space-time power law distribution analysis on the frequency-space-time intensity density data to obtain frequency-space-time power law distribution data; Step S232: performing intensity critical stability analysis on the frequency-time-space intensity density data according to the frequency-time-space 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 association 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 according to 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 range of the node signal transmission frequency strength data according to the frequency interaction interference identification data and the frequency strength attenuation law data to obtain the frequency attenuation coverage range data; Step S32: inferring the distance influence of the frequency attenuation coverage data to obtain the frequency attenuation distance influence data; Step S33: performing frequency difference influence inference on the frequency attenuation coverage data to obtain frequency difference attenuation influence data; Step S34: Perform 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 influence factor correlation analysis on the frequency difference attenuation influence data according to the frequency attenuation distance influence data to obtain frequency influence 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 according to the significance data of the influencing factors to obtain the environmental impact demand factor; Step S344: Perform multi-factor environmental state perception according to 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 influence of obstacles on signal path transmission by using the geometric ray method and frequency attenuation multi-factor environment perception data to obtain obstacle path transmission influence 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 influence data and the conduction influence type weight association data to obtain signal transmitting antenna direction adjustment data; Step S444: adjusting the signal transmission adaptive power according to the obstacle path transmission influence data and the conduction influence type weight association data to obtain the 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.

10. The communication aggregation display method according to claim 1, characterized in that: 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; Step S52: sorting the signal coverage blind spot optimization difficulty levels of the signal coverage blind spot data ports according to the signal quality optimization directed graph to obtain blind spot optimization difficulty level sorting data; Step S53: construct 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.

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