Fault diagnosis method and device for communication equipment

Abstract

This invention relates to the field of communication technology, and particularly to a method and apparatus for fault diagnosis of communication equipment. The method includes the following steps: acquiring communication equipment operating status data for bandwidth detection and transmission delay detection analysis to obtain communication equipment operating bandwidth utilization data and communication equipment operating transmission delay data; performing performance adjustment evaluation analysis and performance packet loss correction analysis on the communication equipment operating bandwidth utilization data and communication equipment operating transmission delay data to obtain communication equipment performance packet loss correction data; detecting abnormal operating modes on the communication equipment performance packet loss correction data according to a preset equipment operating performance anomaly detection model to obtain communication equipment operating anomaly mode data; and performing fault location diagnosis and fault response recording on the communication equipment operating status data based on the communication equipment operating anomaly mode data to generate a communication equipment fault diagnosis report. This invention can improve the stability and reliability of communication networks.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a fault diagnosis method and device for communication equipment. Background Art

[0002] In the field of communications, with the increasing complexity of communication networks and the continuous updating of communication equipment, the stable operation of communication equipment is crucial to ensuring the reliability of communication networks, and fault diagnosis has become increasingly critical. However, due to the complex structure of communication equipment and the huge network topology, traditional fault diagnosis methods usually rely on manual intervention or a single performance indicator, lacking efficient and intelligent fault diagnosis methods. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a fault diagnosis method for a communication device to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for diagnosing a fault of a communication device comprises the following steps:

[0005] Step S1: Acquire the operation status data of the communication device; perform bandwidth detection and transmission delay detection analysis on the operation status data of the communication device to obtain the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device; perform performance adjustment evaluation analysis on the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device to obtain the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor;

[0006] Step S2: performing performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor to obtain communication device performance packet loss correction data;

[0007] Step S3: performing operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation abnormality detection model to obtain communication device operation abnormality mode data;

[0008] Step S4: performing fault location diagnosis on the operation status data of the communication equipment according to the abnormal operation mode data of the communication equipment to obtain the communication equipment operation fault diagnosis result; and performing fault response recording on the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

[0009] The present invention firstly obtains the operation status data of the communication equipment, which can provide a detailed data basis for subsequent operation and maintenance and performance optimization. The key of this step is to realize the comprehensive perception of the communication equipment, and provide high-quality data support for subsequent analysis and optimization. At the same time, by performing bandwidth detection on the operation status data of the communication equipment, the operation bandwidth utilization status data of the communication equipment can be obtained. This step provides important data for real-time monitoring and optimization of the network traffic of the communication equipment, which helps to timely discover potential problems and take corresponding adjustment measures. By performing transmission delay detection and analysis on the operation status data of the communication equipment, the transmission delay status data of the communication equipment can be obtained. The key of this step is to deeply understand the data transmission efficiency of the communication equipment, so as to better optimize the speed and reliability of data transmission in the subsequent performance adjustment. Through this analysis, the transmission delay of the communication equipment can be fully understood, which provides in-depth data support for performance adjustment. In addition, by performing in-depth evaluation and analysis on the operation bandwidth utilization status data and the transmission delay status data of the communication equipment, the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor are obtained. These factors reflect the performance of the communication equipment in actual operation, and provide a powerful reference indicator for the system administrator to optimize the equipment performance, so as to provide basic data guarantee for subsequent performance correction and abnormality detection. Secondly, by using the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor, the communication equipment operation status data is accurately analyzed for performance packet loss correction. This process can not only accurately identify the cause of performance degradation by considering the two aspects of performance indicators, but also effectively correct the packet loss in the data, thereby obtaining more reliable communication equipment performance packet loss correction data, which provides clear and reliable input data for subsequent anomaly detection. Then, by using the preset equipment operation performance anomaly detection model, the corrected communication equipment performance packet loss data is subjected to operation anomaly mode detection. Through this step, the abnormal mode in the operation of the communication equipment can be efficiently identified, providing key information for subsequent fault location and diagnosis. Such anomaly detection mechanism helps to monitor the equipment status in real time and reduce the adverse effects of potential faults on the communication network. Finally, by performing fault location diagnosis on the communication equipment operation status data based on the obtained communication equipment operation abnormal mode data, it is possible to quickly and accurately locate the possible problems of the communication equipment and generate the communication equipment operation fault diagnosis results. This process is followed by the formation of a communication equipment fault diagnosis report through the fault response record, which includes detailed fault information, solutions and future prevention suggestions. This complete fault diagnosis system helps to respond to faults in a timely manner, improve network stability, and provide valuable reference for the continuous improvement of communication equipment performance.

[0010] Preferably, the present invention further provides a fault diagnosis device of a communication device, which is used to execute the fault diagnosis method of the communication device as described above, and the fault diagnosis device of the communication device comprises:

[0011] The equipment operation evaluation and analysis module is used to obtain the communication equipment operation status data; perform bandwidth detection and delay analysis on the communication equipment operation status data to obtain the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data; perform operation evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data to obtain the bandwidth utilization operation adjustment factor and the operation delay adjustment factor;

[0012] The device performance packet loss correction module is used to perform performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization operation adjustment factor and the operation delay adjustment factor to obtain communication device performance packet loss correction data;

[0013] The device operation abnormality detection module is used to perform operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation performance abnormality detection model to obtain communication device operation abnormality mode data;

[0014] The equipment operation fault diagnosis module is used to perform fault location diagnosis on the communication equipment operation status data according to the communication equipment operation abnormal mode data to obtain the communication equipment operation fault diagnosis result; and to record the fault response of the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

[0015] In summary, the present invention provides a fault diagnosis device for communication equipment, which consists of an equipment operation evaluation and analysis module, an equipment performance packet loss correction module, an equipment operation abnormality detection module and an equipment operation fault diagnosis module. It can implement any one of the communication equipment fault diagnosis methods described in the present invention, and is used to combine the operations between computer programs running on each module to implement a communication equipment fault diagnosis method. The internal structures of the device cooperate with each other, and by combining real-time performance monitoring with machine learning, it can more comprehensively understand the operating status of the communication equipment, thereby achieving more accurate fault diagnosis, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient communication equipment fault diagnosis process, thereby simplifying the operating process of the communication equipment fault diagnosis system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1A schematic diagram of the steps of the method for diagnosing a fault of a communication device according to the present invention;

[0018] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0019] Figure 3 for Figure 2 Detailed step flow chart of step S12 in FIG. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for diagnosing a fault of a communication device, the method comprising the following steps:

[0024] Step S1: Acquire the operation status data of the communication device; perform bandwidth detection and transmission delay detection analysis on the operation status data of the communication device to obtain the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device; perform performance adjustment evaluation analysis on the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device to obtain the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor;

[0025] Step S2: performing performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor to obtain communication device performance packet loss correction data;

[0026] Step S3: performing operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation abnormality detection model to obtain communication device operation abnormality mode data;

[0027] Step S4: performing fault location diagnosis on the operation status data of the communication equipment according to the abnormal operation mode data of the communication equipment to obtain the communication equipment operation fault diagnosis result; and performing fault response recording on the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

[0028] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic flow chart of the steps of the method for diagnosing a fault of a communication device according to the present invention. In this example, the method for diagnosing a fault of a communication device includes:

[0029] Step S1: Acquire the operation status data of the communication device; perform bandwidth detection and transmission delay detection analysis on the operation status data of the communication device to obtain the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device; perform performance adjustment evaluation analysis on the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device to obtain the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor;

[0030] The embodiment of the present invention performs real-time perception monitoring of the operation status of the communication equipment by deploying an omni-directional perception sensor network to collect the operation status of the communication equipment, including but not limited to various operation parameters, performance indicators and possible abnormal conditions of the communication equipment, so as to obtain the operation status data of the communication equipment. Then, the operation status data of the communication equipment is subjected to bandwidth detection and transmission delay detection analysis by using corresponding detection methods to detect and analyze the utilization of the operation bandwidth of the communication equipment and the transmission delay of the communication equipment data from the source to the destination, so as to obtain the operation bandwidth utilization status data of the communication equipment and the operation transmission delay status data of the communication equipment. Finally, the operation bandwidth utilization status data of the communication equipment and the operation transmission delay status data of the communication equipment are analyzed by using a performance evaluation analysis method to analyze and compare the actual performance with the expected performance, and identify the performance bottleneck and improvement space therefrom, and finally obtain the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor.

[0031] Step S2: performing performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor to obtain communication device performance packet loss correction data;

[0032] The embodiment of the present invention first detects the operation status data of the communication equipment by using a data performance packet loss detection algorithm, so as to timely capture and analyze the performance packet loss phenomenon occurring in the communication equipment, and then analyzes the corrected bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor by using a difference analysis method, so as to analyze the difference between the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor, and formulates a corresponding performance packet loss correction strategy according to the analysis result, so as to be able to adapt to different network environments and workloads more flexibly, and makes a more refined and targeted correction to the corresponding performance packet loss phenomenon according to the formulated performance packet loss correction strategy, so as to further improve the performance of the communication equipment, and finally obtain the communication equipment performance packet loss correction data.

[0033] Step S3: performing operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation abnormality detection model to obtain communication device operation abnormality mode data;

[0034] The embodiment of the present invention selects an appropriate device operation performance anomaly detection model (such as a deep learning model, a machine learning model, etc.), and then uses the selected device operation performance anomaly detection model to perform anomaly detection on the communication device performance packet loss correction data to identify and record abnormal patterns in the operation of the communication device, including abnormal events that occur, abnormal output methods, etc., and finally obtains the communication device operation abnormal pattern data.

[0035] Step S4: performing fault location diagnosis on the operation status data of the communication equipment according to the abnormal operation mode data of the communication equipment to obtain the communication equipment operation fault diagnosis result; and performing fault response recording on the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

[0036] The embodiment of the present invention uses data statistical analysis, pattern matching and machine learning technologies to comprehensively analyze the abnormal mode data of the operation of the communication equipment to identify the abnormal type corresponding to the abnormal mode data of the operation of the communication equipment. At the same time, by establishing a fault location model, secondly, by using the established fault location model to analyze the operation status data of the communication equipment, the specific location of the communication equipment fault is quickly and accurately determined, and the fault diagnosis is performed on the specific location of the fault located to determine the cause of the fault of the communication equipment at the specific location of the fault, thereby obtaining the diagnosis result of the operation fault of the communication equipment. Then, by defining the rules of fault alarm, including which situations need to issue an alarm and the urgency of the alarm, and judging the diagnosis result of the operation fault of the communication equipment through the defined fault alarm rules, when the diagnosis result of the operation fault of the communication equipment triggers the alarm condition, an alarm notification is issued by sending an email, a text message or an alarm signal to notify the relevant personnel. In addition, each operation fault diagnosis result of the communication equipment is recorded in detail, including the time of the fault, the specific fault abnormality type, the emergency treatment measures taken for the fault, and the future fault prevention measures, etc., to form a complete fault response record, and finally generate a communication equipment fault diagnosis report.

[0037] The present invention firstly obtains the operation status data of the communication equipment, which can provide a detailed data basis for subsequent operation and maintenance and performance optimization. The key of this step is to realize the comprehensive perception of the communication equipment, and provide high-quality data support for subsequent analysis and optimization. At the same time, by performing bandwidth detection on the operation status data of the communication equipment, the operation bandwidth utilization status data of the communication equipment can be obtained. This step provides important data for real-time monitoring and optimization of the network traffic of the communication equipment, which helps to timely discover potential problems and take corresponding adjustment measures. By performing transmission delay detection and analysis on the operation status data of the communication equipment, the transmission delay status data of the communication equipment can be obtained. The key of this step is to deeply understand the data transmission efficiency of the communication equipment, so as to better optimize the speed and reliability of data transmission in the subsequent performance adjustment. Through this analysis, the transmission delay of the communication equipment can be fully understood, which provides in-depth data support for performance adjustment. In addition, by performing in-depth evaluation and analysis on the operation bandwidth utilization status data and the transmission delay status data of the communication equipment, the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor are obtained. These factors reflect the performance of the communication equipment in actual operation, and provide a powerful reference indicator for the system administrator to optimize the equipment performance, so as to provide basic data guarantee for subsequent performance correction and abnormality detection. Secondly, by using the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor, the communication equipment operation status data is accurately analyzed for performance packet loss correction. This process can not only accurately identify the cause of performance degradation by considering the two aspects of performance indicators, but also effectively correct the packet loss in the data, thereby obtaining more reliable communication equipment performance packet loss correction data, which provides clear and reliable input data for subsequent anomaly detection. Then, by using the preset equipment operation performance anomaly detection model, the corrected communication equipment performance packet loss data is subjected to operation anomaly mode detection. Through this step, the abnormal mode in the operation of the communication equipment can be efficiently identified, providing key information for subsequent fault location and diagnosis. Such anomaly detection mechanism helps to monitor the equipment status in real time and reduce the adverse effects of potential faults on the communication network. Finally, by performing fault location diagnosis on the communication equipment operation status data based on the obtained communication equipment operation abnormal mode data, it is possible to quickly and accurately locate the possible problems of the communication equipment and generate the communication equipment operation fault diagnosis results. This process is followed by the formation of a communication equipment fault diagnosis report through the fault response record, which includes detailed fault information, solutions and future prevention suggestions. This complete fault diagnosis system helps to respond to faults in a timely manner, improve network stability, and provide valuable reference for the continuous improvement of communication equipment performance.

[0038] Preferably, step S1 comprises the following steps:

[0039] Step S11: monitoring the operation status of the communication device through an omni-directional sensing sensor network to obtain operation status data of the communication device;

[0040] Step S12: performing noise classification filtering on the communication device operation status data to obtain communication device operation noise filtering data;

[0041] Step S13: performing real-time traffic monitoring on the noise filtering data of the communication equipment operation to obtain the real-time network traffic data of the communication equipment operation; performing bandwidth detection on the noise filtering data of the communication equipment operation according to the real-time network traffic data of the communication equipment operation to obtain the bandwidth utilization status data of the communication equipment operation;

[0042] Step S14: performing transmission delay detection and analysis on the noise filtering data of the communication device operation to obtain the transmission delay status data of the communication device operation;

[0043] Step S15: Perform performance adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain a bandwidth utilization performance adjustment factor and a transmission delay performance adjustment factor.

[0044] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0045] Step S11: monitoring the operation status of the communication device through an omni-directional sensing sensor network to obtain operation status data of the communication device;

[0046] The embodiment of the present invention deploys an omni-directional perception sensor network to cover various key areas of the communication equipment to ensure that the operation status of the communication equipment can be fully perceived. Then, the operation status of the communication equipment is monitored in real time by using the omni-directional perception sensor network to collect the operation status of the communication equipment, including but not limited to various operation parameters, performance indicators and possible abnormal conditions of the communication equipment, and finally obtain the operation status data of the communication equipment.

[0047] Step S12: performing noise classification filtering processing on the communication equipment operation status data to obtain communication equipment operation noise filtering data;

[0048] The embodiment of the present invention performs graded processing on the noise fluctuation in the operation status data of the communication device by using an appropriate noise grading method, and adopts different noise filtering processing methods for different levels of noise to filter out different levels of noise, and finally obtains the communication device operation noise filtering data.

[0049] Step S13: performing real-time traffic monitoring on the noise filtering data of the communication equipment operation to obtain the real-time network traffic data of the communication equipment operation; performing bandwidth detection on the noise filtering data of the communication equipment operation according to the real-time network traffic data of the communication equipment operation to obtain the bandwidth utilization status data of the communication equipment operation;

[0050] The embodiment of the present invention uses a flow monitoring method to analyze and monitor the noise filtering data of the communication equipment operation, so as to analyze and monitor the flow of the real-time network traffic transmitted and transmitted by the communication equipment operation, thereby obtaining the real-time network traffic data of the communication equipment operation. Then, the bandwidth of the noise filtering data of the communication equipment operation is detected according to the real-time network traffic flow of the real-time network traffic data of the communication equipment operation, so as to detect and analyze the utilization of the communication equipment operation bandwidth, and finally obtain the communication equipment operation bandwidth utilization data.

[0051] Step S14: performing transmission delay detection and analysis on the noise filtering data of the communication device operation to obtain the transmission delay status data of the communication device operation;

[0052] The embodiment of the present invention uses a transmission delay detection method to analyze the noise filtering data of the communication device operation to measure the transmission delay of the data from the source to the destination, including various delay conditions in the transmission process, and finally obtains the transmission delay status data of the communication device operation.

[0053] Step S15: Perform performance adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain a bandwidth utilization performance adjustment factor and a transmission delay performance adjustment factor.

[0054] The embodiment of the present invention uses a performance evaluation and analysis method to analyze the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data, so as to analyze and compare the actual performance with the expected performance, identify performance bottlenecks and room for improvement, and finally obtain the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor.

[0055] The present invention firstly senses and monitors the operation status of the communication equipment by using an omni-directional sensing sensor network, thereby realizing comprehensive monitoring of the operation status of the communication equipment, which includes various operation parameters, performance indicators and possible abnormal conditions of the communication equipment. Through comprehensive perception, the working status of the communication equipment can be obtained in real time, providing a detailed data basis for subsequent operation and maintenance and performance optimization. The key to this step is to realize comprehensive perception of the communication equipment, providing high-quality data support for subsequent analysis and optimization. Secondly, by performing noise classification filtering on the operation status data of the communication equipment, the interference and noise in the data are eliminated, and the quality and reliability of the data are improved. Through a clever filtering algorithm, unnecessary noise can be accurately identified and filtered out to ensure that subsequent analysis and decision-making are based on clear and accurate data. The implementation of this step makes the operation data of the communication equipment more credible, and provides a more reliable basis for subsequent performance evaluation and adjustment. Then, by performing real-time traffic monitoring on the noise-filtered data of the communication equipment operation, accurate monitoring of the real-time network traffic of the communication equipment operation can be realized. By analyzing the real-time traffic data and combining the noise-filtered data for bandwidth detection, the bandwidth utilization status data of the communication equipment operation can be obtained. This step provides important data for real-time monitoring and optimization of the network traffic of communication equipment, which helps to timely discover potential problems and take corresponding adjustment measures. Next, by performing transmission delay detection and analysis on the noise filtering data of the communication equipment, the transmission delay status data of the communication equipment can be obtained. The key to this step is to have a deep understanding of the data transmission efficiency of the communication equipment, so as to better optimize the speed and reliability of data transmission in the subsequent performance adjustment. Through this analysis, the transmission delay of the communication equipment can be fully understood, providing in-depth data support for performance adjustment. Finally, by conducting in-depth evaluation and analysis on the bandwidth utilization status data and transmission delay status data of the communication equipment, the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor are obtained. These factors reflect the performance of the communication equipment in actual operation and provide a powerful reference indicator for system administrators to optimize equipment performance. Through this step, a reasonable performance adjustment strategy can be formulated based on the evaluation results to ensure that the communication equipment operates in the best state and improve the stability and efficiency of the network. This comprehensive evaluation provides a scientific basis for operation and maintenance decisions, so that the performance of the communication equipment can be effectively adjusted and improved.

[0056] Preferably, step S12 comprises the following steps:

[0057] Step S121: performing noise recognition analysis on the communication equipment operation status data to obtain the communication equipment operation noise data;

[0058] Step S122: using a multi-scale wavelet transform algorithm to perform frequency domain conversion on the communication equipment operation noise data to obtain the communication equipment noise frequency domain status data;

[0059] Step S123: inputting the communication device noise frequency domain status data into a preset FIFO storage queue for storage processing to obtain noise FIFO storage sequence data; performing noise fluctuation calculation on the noise FIFO storage sequence data to obtain the communication device noise frequency domain fluctuation value;

[0060] Step S124: performing fluctuation threshold calculation on the noise frequency domain status data of the communication device using the noise fluctuation threshold range calculation formula to obtain the noise fluctuation threshold range of the communication device;

[0061] Step S125: performing a comparison and judgment of the fluctuation level of the noise frequency domain fluctuation value of the communication device according to the noise fluctuation threshold range of the communication device, and when the noise frequency domain fluctuation value of the communication device is less than the noise fluctuation threshold range of the communication device, it is judged that the noise fluctuation level is at the first level; performing local filtering processing on the noise FIFO storage sequence data corresponding to the first level using a local noise filtering algorithm to obtain first-level noise filtering data;

[0062] Step S126: when the noise frequency domain fluctuation value of the communication device is within the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the secondary level; a median filtering algorithm is used to perform median filtering on the noise FIFO storage sequence data corresponding to the secondary level to obtain secondary noise filtering data;

[0063] Step S127: when the noise frequency domain fluctuation value of the communication device is greater than the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the third level; a deep joint filtering algorithm is used to perform joint filtering processing on the noise FIFO storage sequence data corresponding to the third level to obtain third-level noise filtering data;

[0064] Step S128: Time-sequentially merge the first-level noise filtering data, the second-level noise filtering data, and the third-level noise filtering data to obtain communication device operation noise filtering data.

[0065] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S12 in the embodiment, step S12 includes the following steps:

[0066] Step S121: performing noise recognition analysis on the communication equipment operation status data to obtain the communication equipment operation noise data;

[0067] The embodiment of the present invention analyzes the operating status data of the communication device by using a noise recognition algorithm to identify and extract noise data that is unrelated to the operating status of the device, and ensures that various types of noise that may exist during the operation of the communication device can be accurately captured, and finally the operating noise data of the communication device is obtained.

[0068] Step S122: using a multi-scale wavelet transform algorithm to perform frequency domain conversion on the communication equipment operation noise data to obtain the communication equipment noise frequency domain status data;

[0069] The embodiment of the present invention processes the communication equipment operation noise data by using a multi-scale wavelet transform algorithm to perform detailed frequency domain conversion on the communication equipment operation noise data at different scales, better reflect the spectral characteristics of the noise, and finally obtain the communication equipment noise frequency domain status data.

[0070] Step S123: inputting the communication device noise frequency domain status data into a preset FIFO storage queue for storage processing to obtain noise FIFO storage sequence data; performing noise fluctuation calculation on the noise FIFO storage sequence data to obtain the communication device noise frequency domain fluctuation value;

[0071] The embodiment of the present invention first inputs the converted communication equipment noise frequency domain status data into a pre-set FIFO storage queue for storage to maintain the timing of the data, thereby obtaining the noise FIFO storage sequence data. Then, the noise FIFO storage sequence data is calculated by using a corresponding noise fluctuation detection algorithm to quantify the change trend of the noise, and finally obtain the communication equipment noise frequency domain fluctuation value.

[0072] Step S124: performing fluctuation threshold calculation on the noise frequency domain status data of the communication device using the noise fluctuation threshold range calculation formula to obtain the noise fluctuation threshold range of the communication device;

[0073] The embodiment of the present invention combines the noise fluctuation frequency parameter, the minimum value of the noise fluctuation frequency range, the maximum value of the noise fluctuation frequency range, the overall fluctuation amplitude control parameter, the noise fluctuation frequency distribution mean, the noise fluctuation frequency distribution standard deviation, the noise fluctuation frequency influence adjustment parameter, the noise fluctuation cosine influence change parameter, the noise fluctuation sine influence change parameter and related parameters to form a suitable noise fluctuation threshold range calculation formula to calculate the fluctuation threshold of the communication equipment noise frequency domain status data, so as to quantify the fluctuation range of the communication equipment noise, and finally obtain the communication equipment noise fluctuation threshold range. In addition, the noise fluctuation threshold range calculation formula can also use any noise fluctuation range detection algorithm in the field to replace the fluctuation threshold calculation process, and is not limited to the noise fluctuation threshold range calculation formula.

[0074] Step S125: performing a comparison and judgment of the fluctuation level of the noise frequency domain fluctuation value of the communication device according to the noise fluctuation threshold range of the communication device, and when the noise frequency domain fluctuation value of the communication device is less than the noise fluctuation threshold range of the communication device, it is judged that the noise fluctuation level is at the first level; performing local filtering processing on the noise FIFO storage sequence data corresponding to the first level using a local noise filtering algorithm to obtain first-level noise filtering data;

[0075] The embodiment of the present invention compares and judges the noise frequency domain fluctuation value of the communication device by using the calculated communication device noise fluctuation threshold range. If the noise frequency domain fluctuation value of the communication device is less than the communication device noise fluctuation threshold range, it means that the noise fluctuation level of the noise FIFO storage sequence data corresponding to the noise frequency domain fluctuation value of the communication device is low, and then the noise fluctuation level is determined to be level 1. Then, the noise FIFO storage sequence data corresponding to the first level is subjected to noise filtering processing by using a local noise filtering algorithm to locally filter out the noise in the noise FIFO storage sequence data, and finally obtain the first level noise filtering data.

[0076] Step S126: when the noise frequency domain fluctuation value of the communication device is within the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the secondary level; a median filtering algorithm is used to perform median filtering on the noise FIFO storage sequence data corresponding to the secondary level to obtain secondary noise filtering data;

[0077] In the embodiment of the present invention, if it is determined that the noise frequency domain fluctuation value of the communication device is within the noise fluctuation threshold range of the communication device, it means that the noise fluctuation level of the noise FIFO storage sequence data corresponding to the noise frequency domain fluctuation value of the communication device is relatively medium, and the noise fluctuation level is determined to be the secondary level. Then, the noise FIFO storage sequence data corresponding to the secondary level is subjected to noise filtering processing by using the median filtering algorithm, so as to perform more refined processing on the medium-level noise, and finally obtain the secondary noise filtering data.

[0078] Step S127: when the noise frequency domain fluctuation value of the communication device is greater than the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the third level; a deep joint filtering algorithm is used to perform joint filtering processing on the noise FIFO storage sequence data corresponding to the third level to obtain third-level noise filtering data;

[0079] In the embodiment of the present invention, if it is determined that the noise frequency domain fluctuation value of the communication device is greater than the noise fluctuation threshold range of the communication device, it means that the noise fluctuation level of the noise FIFO storage sequence data corresponding to the noise frequency domain fluctuation value of the communication device is high, and the noise fluctuation level is determined to be level 3. Then, by using a deep joint filtering algorithm combined with a plurality of filtering techniques, the noise FIFO storage sequence data corresponding to the level 3 is subjected to noise filtering processing, so that the high-level noise therein can be accurately filtered, and finally the level 3 noise filtering data is obtained.

[0080] Step S128: Time-sequentially merge the first-level noise filtering data, the second-level noise filtering data, and the third-level noise filtering data to obtain communication device operation noise filtering data.

[0081] The embodiment of the present invention combines and integrates the first-level noise filtering data, the second-level noise filtering data and the third-level noise filtering data in chronological order to ensure that the noise can be processed more comprehensively and smoothly at different times and in different scenarios, maintain the stability of the communication signal quality, and finally obtain the communication equipment operation noise filtering data.

[0082] The present invention firstly realizes accurate identification of noise components in the operation of communication equipment by performing noise identification and analysis on the operation status data of communication equipment. By deeply analyzing the operation status data of communication equipment, noise data irrelevant to the operation status of the equipment can be identified and extracted, providing an accurate noise basis for subsequent filtering processing. The key to this step is to accurately capture various types of noise that may exist in the communication equipment through advanced identification technology to ensure the accuracy and effectiveness of subsequent processing. By using a multi-scale wavelet transform algorithm to perform frequency domain conversion on the operation noise data of the communication equipment, the frequency domain information of the communication equipment noise can be obtained. The multi-scale wavelet transform technology has efficient frequency domain analysis capabilities and can perform detailed frequency domain conversion on noise data at different scales to better reflect the spectral characteristics of noise. This step improves the resolution of noise frequency domain information by introducing multi-scale wavelet transform, providing a richer data basis for subsequent processing. At the same time, by inputting the noise frequency domain status data of the communication equipment into a preset FIFO storage queue for storage processing, the noise FIFO storage sequence data is obtained, and the noise frequency domain fluctuation value of the communication equipment is obtained by noise fluctuation calculation. Through the ingenious design of the FIFO storage queue, the orderly storage of the noise frequency domain status data of the communication equipment can be realized, providing a stable data source for subsequent fluctuation calculations. In addition, by calculating the noise fluctuation value, the degree of noise fluctuation in the frequency domain can be obtained, laying the foundation for the subsequent fluctuation level judgment. By using a suitable noise fluctuation threshold range calculation formula to calculate the fluctuation threshold of the noise frequency domain status data of the communication equipment, the fluctuation threshold can be flexibly adjusted according to the actual situation, so that the noise fluctuation range is more in line with the actual working environment. The key to this step is to ensure the accuracy and adaptability of the fluctuation threshold range through a scientific calculation method, providing a scientific basis for subsequent fluctuation level judgment. Secondly, by using the calculated communication equipment noise fluctuation threshold range to compare and judge the fluctuation level of the communication equipment noise frequency domain fluctuation value, a quantitative evaluation of the noise fluctuation level can be achieved. By comparing the fluctuation value with the threshold range, the noise fluctuation level can be accurately judged, and different filtering algorithms can be used for processing based on different levels. The judgment accuracy of this step and the flexibility of level division enable better response to filtering requirements under different noise environments. When the noise frequency domain fluctuation value of the communication device is less than the noise fluctuation threshold range of the communication device, it is determined to be the first level. Subsequently, the corresponding noise FIFO storage sequence data is processed by using the local noise filtering algorithm to obtain the first level noise filtering data. The key to this step is to dynamically adapt to the noise level and ensure that different processing strategies are used for different levels of noise, thereby improving the filtering effect. Then, when the noise frequency domain fluctuation value of the communication device is within the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the second level.By using the median filtering algorithm to process the noise FIFO storage sequence data at the secondary level, the secondary noise filtering data after the median filtering is obtained. The median filtering algorithm has a good effect in suppressing noise, can effectively smooth the signal and improve the readability of the signal. This step introduces the median filtering algorithm to process the medium-level noise more finely, ensuring the superiority of the filtering effect. Next, when the noise frequency domain fluctuation value of the communication equipment is greater than the noise fluctuation threshold range of the communication equipment, it is judged that the noise fluctuation level is at the third level. The noise FIFO storage sequence data at the third level is processed by the deep joint filtering algorithm. The deep joint filtering algorithm combines a variety of filtering technologies and has a strong noise suppression ability, especially suitable for the processing of high-level noise. The key to this step is to introduce deep joint filtering to process high-level noise more comprehensively and effectively, thereby improving the accuracy and effect of filtering. Finally, by combining the time series of the first, second and third level noise filtering data, the time series characteristics of the filtering data at different levels can be comprehensively considered, so that the data processed by different filtering processes can be organically integrated to form the noise filtering data of the communication equipment operation. The core of this step is the method of time series merging, which combines the advantages of noise data at different levels, making it possible to process noise more comprehensively and smoothly, and maintain the stability of communication signal quality. The entire process builds a multi-level, multi-strategy noise filtering system that is highly adaptable and can provide efficient noise suppression capabilities in complex noise environments.

[0083] Preferably, the noise fluctuation threshold range calculation formula in step S124 is specifically:

[0084]

[0085] In the formula, β max is the maximum threshold of the communication equipment noise fluctuation threshold range, β min is the minimum threshold of the communication equipment noise fluctuation threshold range, ω is the noise fluctuation frequency parameter of the communication equipment noise frequency domain status data, f min is the minimum value of the noise fluctuation frequency range, f max is the maximum value of the noise fluctuation frequency range, A is the overall fluctuation amplitude control parameter of the noise frequency domain status data of the communication equipment, μ is the mean value of the noise fluctuation frequency distribution of the noise frequency domain status data of the communication equipment, σ is the standard deviation of the noise fluctuation frequency distribution of the noise frequency domain status data of the communication equipment, B is the noise fluctuation frequency influence adjustment parameter of the noise frequency domain status data of the communication equipment, C is the noise fluctuation cosine influence variation parameter of the noise frequency domain status data of the communication equipment, D is the noise fluctuation sine influence variation parameter of the noise frequency domain status data of the communication equipment, ε1 is the correction value of the maximum threshold, and ε2 is the correction value of the minimum threshold.

[0086] The present invention constructs a noise fluctuation threshold range calculation formula for calculating the fluctuation threshold of the noise frequency domain status data of the communication equipment. The noise fluctuation threshold range calculation formula uses the noise fluctuation frequency parameter to represent the frequency of analyzing the noise in the frequency domain. The minimum and maximum values ​​of the noise fluctuation frequency range are used. These two parameters specify the frequency range of the noise fluctuation. The amplitude of the overall fluctuation is adjusted by the overall fluctuation amplitude control parameter, thereby affecting the intensity of the overall fluctuation. The center position of the noise fluctuation in the frequency domain is described by the mean value of the noise fluctuation frequency distribution. The breadth of the noise fluctuation frequency distribution is reflected by the standard deviation of the noise fluctuation frequency distribution. The influence of the noise fluctuation frequency on the overall fluctuation is adjusted by using the noise fluctuation frequency influence adjustment parameter. At the same time, the morphology of the noise fluctuation in the frequency domain is affected by using the noise fluctuation cosine and sine influence variation parameters. In addition, the maximum threshold and the minimum threshold correction value are introduced to fine-tune the calculated threshold to more accurately match the actual noise situation. The entire calculation formula describes a complex noise fluctuation threshold range calculation process, which includes considerations of frequency, amplitude, distribution shape and other aspects. This calculation method can dynamically adjust the threshold range according to the actual situation to more effectively classify and filter noise. This formula fully considers the maximum threshold β of the noise fluctuation threshold range of the communication equipment. max , the minimum threshold β of the communication equipment noise fluctuation threshold range min , the noise fluctuation frequency parameter ω of the noise frequency domain status data of the communication equipment, the minimum value of the noise fluctuation frequency range f min , the maximum value of the noise fluctuation frequency range f max , the overall fluctuation amplitude control parameter A of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution mean μ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution standard deviation σ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency influence adjustment parameter B of the noise frequency domain status data of the communication equipment, the noise fluctuation cosine influence variation parameter C of the noise frequency domain status data of the communication equipment, the noise fluctuation sine influence variation parameter D of the noise frequency domain status data of the communication equipment, the maximum threshold correction value ε1, the minimum threshold correction value ε2, wherein, through the noise fluctuation frequency parameter ω of the noise frequency domain status data of the communication equipment, the minimum value f of the noise fluctuation frequency range min , the maximum value of the noise fluctuation frequency range f max , the overall fluctuation amplitude control parameter A of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution mean μ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution standard deviation σ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency influence adjustment parameter B of the noise frequency domain status data of the communication equipment, and the noise fluctuation cosine influence variation parameter C of the noise frequency domain status data of the communication equipment constitute a maximum threshold β of the noise fluctuation threshold range of the communication equipmentmax Functional relationship:

[0087]

[0088] The noise fluctuation frequency parameter ω and the minimum value f of the noise fluctuation frequency range are also obtained by the noise frequency domain status data of the communication equipment. min , the maximum value of the noise fluctuation frequency range f max , the overall fluctuation amplitude control parameter A of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution mean μ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency distribution standard deviation σ of the noise frequency domain status data of the communication equipment, the noise fluctuation frequency influence adjustment parameter B of the noise frequency domain status data of the communication equipment, and the noise fluctuation sinusoidal influence variation parameter D of the noise frequency domain status data of the communication equipment constitute a minimum threshold β of the noise fluctuation threshold range of the communication equipment min Functional relationship:

[0089]

[0090] This formula can realize the fluctuation threshold calculation process of the noise frequency domain status data of the communication equipment. At the same time, by introducing the correction value ε1 of the maximum threshold and the correction value ε2 of the minimum threshold, it can be adjusted according to the error situation occurring in the calculation process, thereby improving the accuracy and applicability of the noise fluctuation threshold range calculation formula.

[0091] Preferably, step S15 comprises the following steps:

[0092] Step S151: Performing performance impact analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain bandwidth utilization performance impact data and transmission delay performance impact data;

[0093] The embodiment of the present invention uses a performance impact analysis method to conduct an in-depth analysis of the bandwidth utilization status data of the communication equipment and the transmission delay status data of the communication equipment, so as to analyze and identify the performance impact status of the communication equipment during operation (including bandwidth utilization impact status and transmission delay impact status), and finally obtain bandwidth utilization performance impact data and transmission delay performance impact data.

[0094] Step S152: performing influence pattern exploration and analysis on the bandwidth utilization performance influence data and the transmission delay performance influence data to obtain bandwidth utilization influence behavior pattern data and transmission delay influence behavior pattern data;

[0095] The embodiment of the present invention analyzes the bandwidth utilization performance impact data and the transmission delay performance impact data by using data mining or statistical analysis methods to analyze and explore the patterns and associations in the bandwidth utilization performance impact data and the transmission delay performance impact data, and identify and understand the impact behavior patterns of bandwidth utilization and transmission delay, and finally obtain bandwidth utilization impact behavior pattern data and transmission delay impact behavior pattern data.

[0096] Step S153: performing bandwidth adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data according to the bandwidth utilization influencing behavior pattern data to obtain a bandwidth utilization performance adjustment factor;

[0097] The embodiment of the present invention evaluates and analyzes the communication equipment operation bandwidth utilization status data by using the influencing behavior pattern in the bandwidth utilization influencing behavior pattern data, so as to analyze the specific suggestions and directions for the communication equipment bandwidth adjustment, so that the communication equipment can achieve the best performance under different load and network conditions, and finally obtain the bandwidth utilization performance adjustment factor.

[0098] Step S154: performing delay adjustment evaluation and analysis on the communication equipment operation transmission delay status data according to the transmission delay impact behavior pattern data to obtain a transmission delay performance adjustment factor.

[0099] The embodiment of the present invention evaluates and analyzes the transmission delay status data of the communication equipment by using the influencing behavior pattern in the transmission delay influencing behavior pattern data, so as to analyze the relative advantages and disadvantages of the transmission delay performance of the communication equipment or the specific suggestions and directions that need to be adjusted, and finally obtain the transmission delay performance adjustment factor.

[0100] The present invention firstly performs an in-depth performance impact analysis on the bandwidth utilization status and transmission delay status data of the communication equipment, and can obtain accurate bandwidth utilization performance impact data and transmission delay performance impact data. This process includes a comprehensive evaluation of actual data, reveals the performance status of the communication equipment in operation, and provides a practical basis for subsequent optimization. By analyzing the data, the performance of the equipment under different conditions can be fully understood, laying a foundation for performance adjustment and optimization. Secondly, by further exploring and analyzing the impact pattern of the bandwidth utilization performance impact data and the transmission delay performance impact data, the impact behavior pattern of bandwidth utilization and transmission delay can be identified and understood. By deeply mining the correlation and trend between the data, the potential performance impact mechanism can be discovered, providing a deep understanding for formulating effective performance optimization strategies. This helps to predict performance problems more accurately and take targeted measures to adjust them. Then, by using the bandwidth utilization impact behavior pattern data to perform bandwidth adjustment evaluation and analysis on the bandwidth utilization status data of the communication equipment, this process is not only a measurement of performance impact, but also provides specific suggestions and directions for the bandwidth adjustment of the communication equipment through in-depth analysis. Through this step, bandwidth management can be performed more intelligently, so that the communication equipment can achieve optimal performance under different loads and network conditions. Finally, by using the transmission delay impact behavior pattern data to conduct delay adjustment evaluation and analysis on the transmission delay status data of the communication equipment, this process provides strong support for the delay optimization of the communication equipment. By evaluating the actual impact of different factors on the delay, it is possible to formulate an effective delay management strategy and improve the overall performance of the communication equipment in real-time communication and data transmission.

[0101] Preferably, step S2 comprises the following steps:

[0102] Step S21: performing performance packet loss detection on the communication device operation status data to obtain communication device operation performance packet loss data;

[0103] The embodiment of the present invention detects the operation status data of the communication device by using a data performance packet loss detection algorithm, so as to timely capture and analyze the performance packet loss phenomenon occurring in the communication device, and finally obtain the communication device operation performance packet loss data.

[0104] Step S22: using a performance packet loss correction index calculation formula to perform correction calculation on the communication equipment operation performance packet loss data to obtain a communication equipment performance packet loss correction index;

[0105] The embodiment of the present invention forms a suitable performance packet loss correction index calculation formula by combining the time variable parameter of the correction calculation, the time decay rate parameter, the benchmark performance parameter, the performance packet loss loss parameter, the performance packet loss efficiency index parameter, the performance packet loss impact change adjustment parameter, the performance packet loss impact change decay parameter and related parameters to perform correction calculation on the communication equipment operation performance packet loss data, so as to quantify the correction of the performance packet loss data, and finally obtain the communication equipment performance packet loss correction index. In addition, the performance packet loss correction index calculation formula can also use any packet loss correction algorithm in the art to replace the correction calculation process, and is not limited to the performance packet loss correction index calculation formula.

[0106] Step S23: assigning a first performance adjustment weight to the bandwidth utilization performance adjustment factor and assigning a second performance adjustment weight to the transmission delay performance adjustment factor; dynamically modifying and optimizing the first performance adjustment weight and the second performance adjustment weight according to the communication device performance packet loss correction index to obtain a first modified optimization weight for performance adjustment and a second modified optimization weight for performance adjustment;

[0107] The embodiment of the present invention first assigns a first performance adjustment weight and a second performance adjustment weight to a bandwidth utilization performance adjustment factor and a transmission delay performance adjustment factor respectively, and then dynamically adjusts the assigned first performance adjustment weight and the second performance adjustment weight by using a calculated communication equipment performance packet loss correction index to optimize the weight parameters of the performance adjustment, so as to better adapt to the dynamic changes in performance requirements under different workloads and network conditions, and finally obtain the first performance adjustment correction optimization weight and the second performance adjustment correction optimization weight.

[0108] Step S24: Correct and adjust the bandwidth utilization performance adjustment factor according to the first correction optimization weight of performance adjustment to obtain the bandwidth utilization performance adjustment factor; correct and adjust the transmission delay performance adjustment factor according to the second correction optimization weight of performance adjustment to obtain the transmission delay performance adjustment factor;

[0109] The embodiment of the present invention corrects and adjusts the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor by using the corrected and optimized performance adjustment first correction optimization weight and the performance adjustment second correction optimization weight, so as to more finely affect different performance factors, thereby optimizing the overall performance of the communication equipment, and finally obtaining the bandwidth utilization performance correction factor and the transmission delay performance correction factor.

[0110] Step S25: performing a difference correction strategy formulation analysis on the bandwidth utilization performance correction factor and the transmission delay performance correction factor to generate a differentiated performance packet loss correction strategy;

[0111] The embodiment of the present invention analyzes the bandwidth utilization performance correction factor and the transmission delay performance correction factor by using a difference analysis method to analyze the difference between the bandwidth utilization performance correction factor and the transmission delay performance correction factor, and formulates a corresponding performance packet loss correction strategy based on the analysis results, so as to be able to more flexibly adapt to different network environments and workloads, and finally generate a differentiated performance packet loss correction strategy.

[0112] Step S26: Correct, optimize and adjust the communication equipment operation performance packet loss data through a differentiated performance packet loss correction strategy to obtain communication equipment performance packet loss correction data.

[0113] The embodiment of the present invention processes the communication equipment operation performance packet loss data by using the generated differentiated performance packet loss correction strategy, and performs more precise and targeted corrections on the performance packet loss data to further improve the performance of the communication equipment, and finally obtains the communication equipment performance packet loss correction data.

[0114] The present invention firstly detects the performance packet loss of the communication equipment operation status data, and can timely capture and analyze the performance packet loss phenomenon occurring in the communication equipment, thereby providing an accurate data basis for optimizing the performance of the communication equipment, which helps to improve the stability and reliability of the communication equipment and ensure the timeliness of data transmission. Secondly, the communication equipment operation performance packet loss data is corrected and calculated by using a suitable performance packet loss correction index calculation formula, thereby obtaining the communication equipment performance packet loss correction index. The key to this process is that through index correction, the severity of the packet loss data is more comprehensively considered, the accuracy of the performance packet loss situation is improved, and a more accurate correction factor is provided for subsequent performance adjustment. At the same time, by assigning different weights to the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor, and dynamically correcting and optimizing the weight parameters according to the communication equipment performance packet loss correction index, the key to this step is to flexibly adjust the weight of the performance adjustment according to the actual situation, so as to better adapt to the dynamic changes in performance requirements under different workloads and network conditions. Then, the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor are corrected and adjusted by using the first correction optimization weight of the performance adjustment after correction optimization and the second correction optimization weight of the performance adjustment, respectively, so as to more finely affect different performance factors, thereby optimizing the overall performance of the communication equipment. Next, a differential correction strategy is formulated and analyzed for the bandwidth utilization performance correction factor and the transmission delay performance correction factor to generate a differentiated performance packet loss correction strategy. The key to this step is to formulate differentiated strategies that can more flexibly adapt to different network environments and workloads, and improve the adaptability and solution capabilities of communication equipment to diverse performance problems. Finally, the communication equipment operation performance packet loss data is corrected, optimized and adjusted by using differentiated performance packet loss correction strategies. The key to this step is to actually apply differentiated strategies to make more detailed and targeted corrections to the performance packet loss data, thereby further improving the performance of the communication equipment and ensuring the quality and reliability of data transmission. Overall, the key to the above steps is to build a systematic communication equipment performance packet loss correction mechanism, which effectively improves the overall performance and stability of the communication equipment.

[0115] Preferably, the performance packet loss correction index calculation formula in step S22 is specifically:

[0116]

[0117] Where PI is the packet loss correction index of communication equipment performance, T is the time range parameter of correction calculation, t is the integral time variable parameter of correction calculation, α is the time attenuation rate parameter of communication equipment operation performance packet loss data, P base is the benchmark performance parameter of the communication equipment operating performance packet loss data, P loss(t) is the performance packet loss loss parameter of the communication equipment operation performance packet loss data at time t, η is the performance packet loss efficiency index parameter, γ is the performance packet loss impact change adjustment parameter, δ is the performance packet loss impact change attenuation parameter, and ξ is the correction value of the communication equipment performance packet loss correction index.

[0118] The present invention constructs a performance packet loss correction index calculation formula for correcting and calculating the performance packet loss data of the communication equipment. The performance packet loss correction index calculation formula defines the time range for calculating the performance packet loss correction index by using a time range parameter, which affects the time period of the correction calculation, represents the current time point of the calculation by using an integral time variable parameter, represents the time decay rate of the performance packet loss data of the communication equipment by using a time decay rate parameter, which affects the decay speed of the packet loss data, uses a benchmark performance parameter to compare and correct with the actual performance packet loss, uses a performance packet loss efficiency index parameter to adjust the degree of influence of the performance packet loss, uses a performance packet loss influence change adjustment parameter to adjust the influence change of the performance packet loss, and uses a performance packet loss influence change attenuation parameter to determine the decay speed of the performance packet loss influence. The integral part in the formula comprehensively considers factors such as the time decay of the performance packet loss, the benchmark performance, the performance packet loss loss, the performance packet loss efficiency, and the performance packet loss influence change to obtain a comprehensive correction index. This correction index can be used to optimize and adjust the performance adjustment weight in subsequent steps, thereby realizing differentiated corrections to the bandwidth utilization performance and the transmission delay performance. In summary, this formula fully considers the communication equipment performance packet loss correction index PI, the time range parameter T of the correction calculation, the integral time variable parameter t of the correction calculation, the time decay rate parameter α of the communication equipment operation performance packet loss data, and the benchmark performance parameter P of the communication equipment operation performance packet loss data. base , the performance packet loss parameter P of the communication equipment operation performance packet loss data at time t loss (t), performance packet loss efficiency index parameter η, performance packet loss impact change adjustment parameter γ, performance packet loss impact change attenuation parameter δ, correction value ξ of communication equipment performance packet loss correction index, according to the mutual correlation between the communication equipment performance packet loss correction index PI and the above parameters, a functional relationship is formed:

[0119]

[0120] The formula can realize the correction calculation process of the communication equipment operation performance packet loss data. At the same time, by introducing the correction value ξ of the communication equipment performance packet loss correction index, it can be adjusted according to the error situation occurring in the calculation process, thereby improving the accuracy and applicability of the performance packet loss correction index calculation formula.

[0121] Preferably, step S3 comprises the following steps:

[0122] Step S31: dividing the communication device performance packet loss correction data into a communication device performance packet loss training data set and a communication device performance packet loss verification data set according to a preset division rule;

[0123] The embodiment of the present invention divides the communication equipment performance packet loss correction data into two parts according to a preset division rule (division ratio is 8:2), one is a communication equipment performance packet loss training data set for model training, and the other is a communication equipment performance packet loss verification data set for verifying and learning the optimization model performance.

[0124] Step S32: using the communication device performance packet loss training data set to perform model training on the preset device operation performance anomaly detection model, and using the back propagation algorithm to adjust the parameters of the device operation performance anomaly detection model to obtain a performance anomaly detection verification model;

[0125] The embodiment of the present invention selects an appropriate device operation performance anomaly detection model (such as a deep learning model, a machine learning model, etc.), and then trains the selected model by using the divided communication device performance packet loss training data set, and continuously adjusts the parameters of the device operation performance anomaly detection model by using a back propagation algorithm to adapt to the training data set, and finally obtains a performance anomaly detection verification model.

[0126] Step S33: Adaptively adjust the performance anomaly detection verification model by introducing an attention mechanism to obtain a performance anomaly detection attention model;

[0127] The embodiment of the present invention introduces an attention mechanism into a trained performance anomaly detection verification model to improve the criticality of the performance anomaly detection verification model to key information, and adaptively adjusts the performance anomaly detection verification model's attention to performance anomalies, thereby improving the sensitivity and accuracy of the model, and finally obtaining a performance anomaly detection attention model.

[0128] Step S34: using the communication device performance packet loss verification data set to perform migration verification learning on the performance anomaly detection focus model to optimize the network hyperparameters of the performance anomaly detection focus model and obtain a performance anomaly detection optimization model;

[0129] The embodiment of the present invention uses the divided communication equipment performance packet loss verification data set to perform transfer verification learning on the performance anomaly detection focus model obtained after adjustment, migrates the performance anomaly detection focus model to another model space for learning to improve the model performance, and fine-tunes the model's learning rate, regularization term and other network hyperparameters to adapt to the characteristics of the communication equipment performance packet loss verification data set, optimizes and adjusts the network hyperparameters of the performance anomaly detection focus model to further improve the accuracy and generalization ability of performance anomaly detection, and finally obtains a performance anomaly detection optimization model.

[0130] Step S35: Utilize the performance anomaly detection optimization model to perform operation anomaly mode detection on the communication device performance packet loss correction data to obtain communication device operation anomaly mode data.

[0131] The embodiment of the present invention performs anomaly detection on communication equipment performance packet loss correction data by using an optimized performance anomaly detection optimization model to identify and record abnormal modes in the operation of communication equipment, including abnormal events that occur, abnormal output methods, etc., and finally obtains communication equipment operation abnormal mode data.

[0132] The present invention first divides the communication equipment performance packet loss correction data into two key parts: a training data set and a verification data set according to a preset division rule. Such a division process is to effectively train and verify the equipment operation performance anomaly detection model in the subsequent process to ensure that the model has robustness and generalization ability in real scenarios. Secondly, the preset equipment operation performance anomaly detection model is trained by using the communication equipment performance packet loss training data set, and the parameters of the equipment operation performance anomaly detection model are continuously adjusted with the help of the back propagation algorithm to gradually optimize it to obtain a performance anomaly detection verification model, which ensures that the model can accurately capture the abnormal mode of communication equipment performance and improve the accuracy and reliability of detection. Then, by introducing the attention mechanism, adaptive attention adjustment of the performance anomaly detection verification model can be achieved. This mechanism enables the model to pay more attention to different parts of the input data more flexibly, thereby enhancing the perception of abnormal patterns and improving the robustness and adaptability of the model. Next, the performance anomaly detection attention model is transferred and verified by using the communication equipment performance packet loss verification data set. The key to this step is to optimize the network hyperparameters of the performance anomaly detection attention model through the feedback of the communication equipment performance packet loss verification data set, thereby further improving the performance and generalization ability of the model and ensuring its effectiveness in different scenarios. Finally, the communication equipment performance packet loss correction data is used to detect the operation anomaly mode by using the optimized performance anomaly detection model. The main goal of this step is to identify and record the abnormal patterns in the operation of the equipment, and provide strong support for subsequent fault diagnosis and performance optimization. Through this comprehensive process, performance anomalies can be efficiently detected and responded to in a complex communication equipment environment.

[0133] Preferably, step S4 comprises the following steps:

[0134] Step S41: performing abnormal type detection on the abnormal mode data of the communication device to obtain abnormal type data of the communication device;

[0135] The embodiment of the present invention uses data statistical analysis, pattern matching, machine learning and other technologies to comprehensively analyze the abnormal operation mode data of communication equipment to identify the abnormal type corresponding to the abnormal operation mode data of communication equipment, ensure that the abnormal operation of the communication equipment can be detected efficiently and accurately, and finally obtain the abnormal type data of the communication equipment.

[0136] Step S42: performing fault location diagnosis on the operation status data of the communication device according to the abnormal type data of the communication device, and obtaining a diagnosis result of the operation fault of the communication device;

[0137] The embodiment of the present invention first establishes a fault location model, and then associates the communication equipment abnormality type data with the communication equipment operation status data to ensure that the fault location model can effectively use this information. At the same time, by using the established fault location model for analysis, the specific location of the communication equipment fault is quickly and accurately determined, and the fault diagnosis is performed on the located specific fault location to determine the cause of the fault of the communication equipment at the specific fault location, and finally obtain the communication equipment operation fault diagnosis result.

[0138] Step S43: performing fault alarm processing on the communication equipment operation fault diagnosis result to generate communication equipment operation fault alarm information;

[0139] The embodiment of the present invention first defines the rules for fault alarm, including the situations in which an alarm needs to be issued and the urgency of the alarm, etc., and then judges the communication equipment operation fault diagnosis results according to the defined fault alarm rules. When the communication equipment operation fault diagnosis results trigger the alarm condition, an alarm notification is issued by notifying relevant personnel by sending an email, text message or alarm signal, and finally generating communication equipment operation fault alarm information.

[0140] Step S44: Record the fault response of the communication equipment operation fault diagnosis result based on the communication equipment operation fault alarm information to generate a communication equipment fault diagnosis report.

[0141] The embodiment of the present invention records the results of each communication equipment operation fault diagnosis in detail according to the communication equipment operation fault alarm information, including the time when the fault occurred, the specific fault abnormality type, the fault emergency handling measures taken, and future fault prevention measures, so as to form a complete fault response record and finally generate a communication equipment fault diagnosis report.

[0142] The present invention firstly detects the abnormal type of the communication equipment operation abnormal mode data, and can accurately identify various abnormal types in the communication equipment operation abnormal mode data. Through this step, a comprehensive and accurate data set about the abnormal type of the communication equipment can be established, which provides strong support for subsequent fault location and diagnosis. This helps to monitor the operation status of the equipment in real time, discover and deal with potential problems as early as possible, and thus improve the stability and reliability of the communication network. Secondly, based on the acquired communication equipment abnormal type data, the communication equipment operation status data is deeply analyzed and the fault location diagnosis is performed. By mapping the abnormal type to the specific fault cause, the possible problems of the equipment can be quickly and accurately determined, and a detailed operation fault diagnosis result can be generated. This process helps to shorten the time of fault location, improve the timely responsiveness to the operation status of the equipment, and thus enhance the stability and maintainability of the communication network. Then, by performing fault alarm processing on the communication equipment operation fault diagnosis result, the communication equipment operation fault alarm information can be generated in time, and the relevant personnel can also be effectively notified so that they can quickly take necessary measures to solve the problem. This automated fault alarm processing helps to reduce the fault response time, minimize the impact of the abnormal operation of the communication equipment on the entire network, and improve the availability and stability of the communication network. Finally, the previous operation fault diagnosis results are recorded in detail based on the communication equipment operation fault alarm information, thereby generating a communication equipment fault diagnosis report, which contains detailed information about the fault, solutions, and future preventive measures. Through this step, a complete fault diagnosis report system can be formed, providing valuable experience for solving similar problems in the future. This not only helps to restore the normal operation of the equipment in a timely manner, but also provides a reference for the continuous optimization of the performance of the communication equipment, ensuring the long-term and stable operation of the communication network.

[0143] Preferably, the present invention further provides a fault diagnosis device of a communication device, which is used to execute the fault diagnosis method of the communication device as described above, and the fault diagnosis device of the communication device comprises:

[0144] The equipment operation evaluation and analysis module is used to obtain the communication equipment operation status data; perform bandwidth detection and delay analysis on the communication equipment operation status data to obtain the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data; perform operation evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data to obtain the bandwidth utilization operation adjustment factor and the operation delay adjustment factor;

[0145] The device performance packet loss correction module is used to perform performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization operation adjustment factor and the operation delay adjustment factor to obtain communication device performance packet loss correction data;

[0146] The device operation abnormality detection module is used to perform operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation performance abnormality detection model to obtain communication device operation abnormality mode data;

[0147] The equipment operation fault diagnosis module is used to perform fault location diagnosis on the communication equipment operation status data according to the communication equipment operation abnormal mode data to obtain the communication equipment operation fault diagnosis result; and to record the fault response of the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

[0148] In summary, the present invention provides a fault diagnosis device for communication equipment, which consists of an equipment operation evaluation and analysis module, an equipment performance packet loss correction module, an equipment operation abnormality detection module and an equipment operation fault diagnosis module. It can implement any one of the communication equipment fault diagnosis methods described in the present invention, and is used to combine the operations between computer programs running on each module to implement a communication equipment fault diagnosis method. The internal structures of the device cooperate with each other, and by combining real-time performance monitoring with machine learning, it can more comprehensively understand the operating status of the communication equipment, thereby achieving more accurate fault diagnosis, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient communication equipment fault diagnosis process, thereby simplifying the operating process of the communication equipment fault diagnosis system.

[0149] 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.

[0150] 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 method for diagnosing a fault of a communication device, characterized in that: The following steps are involved: Step S1: Acquire the operation status data of the communication device; perform bandwidth detection and transmission delay detection analysis on the operation status data of the communication device to obtain the operation bandwidth utilization status data of the communication device and the operation transmission delay status data of the communication device; Performing performance adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain a bandwidth utilization performance adjustment factor and a transmission delay performance adjustment factor; Step S2: performing performance packet loss correction analysis on the communication device operation status data according to the bandwidth utilization performance adjustment factor and the transmission delay performance adjustment factor to obtain communication device performance packet loss correction data; wherein step S2 includes the following steps: Step S21: performing performance packet loss detection on the communication device operation status data to obtain communication device operation performance packet loss data; Step S22: using a performance packet loss correction index calculation formula to perform correction calculation on the communication equipment operation performance packet loss data to obtain a communication equipment performance packet loss correction index; wherein the performance packet loss correction index calculation formula is specifically: ； In the formula, is the packet loss correction index of communication equipment performance, To modify the time range parameters of the calculation, To correct the calculation of the integral time variable parameter, is the time decay rate parameter of packet loss data of communication equipment operation performance, It is the benchmark performance parameter of the packet loss data of the communication equipment operation performance. Performance packet loss data for communication equipment running at time Performance packet loss parameter at, is the performance packet loss efficiency index parameter, Adjust parameters for performance changes due to packet loss, To change the attenuation parameter for performance packet loss impact, It is the correction value of the packet loss correction index of the communication equipment performance; Step S23: assigning a first performance adjustment weight to the bandwidth utilization performance adjustment factor and assigning a second performance adjustment weight to the transmission delay performance adjustment factor; dynamically modifying and optimizing the first performance adjustment weight and the second performance adjustment weight according to the communication device performance packet loss correction index to obtain a first modified optimization weight for performance adjustment and a second modified optimization weight for performance adjustment; Step S24: Correct and adjust the bandwidth utilization performance adjustment factor according to the first correction optimization weight of performance adjustment to obtain the bandwidth utilization performance adjustment factor; correct and adjust the transmission delay performance adjustment factor according to the second correction optimization weight of performance adjustment to obtain the transmission delay performance adjustment factor; Step S25: performing a difference correction strategy formulation analysis on the bandwidth utilization performance correction factor and the transmission delay performance correction factor to generate a differentiated performance packet loss correction strategy; Step S26: Correcting, optimizing and adjusting the communication equipment operation performance packet loss data through a differentiated performance packet loss correction strategy to obtain communication equipment performance packet loss correction data; Step S3: performing operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation abnormality detection model to obtain communication device operation abnormality mode data; Step S4: performing fault location diagnosis on the operation status data of the communication equipment according to the abnormal operation mode data of the communication equipment to obtain the communication equipment operation fault diagnosis result; and performing fault response recording on the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

2. The method for diagnosing a fault of a communication device according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: monitoring the operation status of the communication device through an omni-directional sensing sensor network to obtain operation status data of the communication device; Step S12: performing noise classification filtering on the communication device operation status data to obtain communication device operation noise filtering data; Step S13: performing real-time traffic monitoring on the noise filtering data of the communication equipment operation to obtain the real-time network traffic data of the communication equipment operation; performing bandwidth detection on the noise filtering data of the communication equipment operation according to the real-time network traffic data of the communication equipment operation to obtain the bandwidth utilization status data of the communication equipment operation; Step S14: performing transmission delay detection and analysis on the noise filtering data of the communication device operation to obtain the transmission delay status data of the communication device operation; Step S15: Perform performance adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain a bandwidth utilization performance adjustment factor and a transmission delay performance adjustment factor.

3. The fault diagnosis method of communication equipment according to claim 2, characterized in that: Step S12 includes the following steps: Step S121: performing noise recognition analysis on the communication equipment operation status data to obtain the communication equipment operation noise data; Step S122: using a multi-scale wavelet transform algorithm to perform frequency domain conversion on the communication equipment operation noise data to obtain the communication equipment noise frequency domain status data; Step S123: inputting the communication device noise frequency domain status data into a preset FIFO storage queue for storage processing to obtain noise FIFO storage sequence data; performing noise fluctuation calculation on the noise FIFO storage sequence data to obtain the communication device noise frequency domain fluctuation value; Step S124: performing fluctuation threshold calculation on the noise frequency domain status data of the communication device using the noise fluctuation threshold range calculation formula to obtain the noise fluctuation threshold range of the communication device; Step S125: performing a comparison and judgment of the fluctuation level of the noise frequency domain fluctuation value of the communication device according to the noise fluctuation threshold range of the communication device, and when the noise frequency domain fluctuation value of the communication device is less than the noise fluctuation threshold range of the communication device, it is judged that the noise fluctuation level is at the first level; performing local filtering processing on the noise FIFO storage sequence data corresponding to the first level using a local noise filtering algorithm to obtain first-level noise filtering data; Step S126: when the noise frequency domain fluctuation value of the communication device is within the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the secondary level; a median filtering algorithm is used to perform median filtering on the noise FIFO storage sequence data corresponding to the secondary level to obtain secondary noise filtering data; Step S127: when the noise frequency domain fluctuation value of the communication device is greater than the noise fluctuation threshold range of the communication device, it is determined that the noise fluctuation level is at the third level; a deep joint filtering algorithm is used to perform joint filtering processing on the noise FIFO storage sequence data corresponding to the third level to obtain third-level noise filtering data; Step S128: Time-sequentially merge the first-level noise filtering data, the second-level noise filtering data, and the third-level noise filtering data to obtain communication device operation noise filtering data.

4. The method for diagnosing a fault of a communication device according to claim 3, characterized in that: The noise fluctuation threshold range calculation formula in step S124 is specifically: ； ； In the formula, is the maximum threshold of the noise fluctuation threshold range of the communication device, is the minimum threshold of the noise fluctuation threshold range of the communication equipment, is the noise fluctuation frequency parameter of the noise frequency domain status data of the communication equipment, is the minimum value of the noise fluctuation frequency range, is the maximum value of the noise fluctuation frequency range, is the overall fluctuation amplitude control parameter of the noise frequency domain status data of the communication equipment, is the mean value of the noise fluctuation frequency distribution of the noise frequency domain status data of the communication equipment, is the standard deviation of the noise fluctuation frequency distribution of the noise frequency domain status data of the communication equipment, The noise fluctuation frequency of the noise frequency domain status data of the communication equipment affects the adjustment parameters. It is the noise fluctuation cosine influence variation parameter of the noise frequency domain status data of the communication equipment. It is the noise fluctuation sinusoidal influence variation parameter of the noise frequency domain status data of the communication equipment. is the correction value of the maximum threshold, is the correction value of the minimum threshold.

5. The fault diagnosis method of communication equipment according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Performing performance impact analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation transmission delay status data to obtain bandwidth utilization performance impact data and transmission delay performance impact data; Step S152: performing influence pattern exploration and analysis on the bandwidth utilization performance influence data and the transmission delay performance influence data to obtain bandwidth utilization influence behavior pattern data and transmission delay influence behavior pattern data; Step S153: performing bandwidth adjustment evaluation and analysis on the communication equipment operation bandwidth utilization status data according to the bandwidth utilization influencing behavior pattern data to obtain a bandwidth utilization performance adjustment factor; Step S154: performing delay adjustment evaluation and analysis on the communication equipment operation transmission delay status data according to the transmission delay impact behavior pattern data to obtain a transmission delay performance adjustment factor.

6. The method for diagnosing a fault of a communication device according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: dividing the communication device performance packet loss correction data into a communication device performance packet loss training data set and a communication device performance packet loss verification data set according to a preset division rule; Step S32: using the communication device performance packet loss training data set to perform model training on the preset device operation performance anomaly detection model, and using the back propagation algorithm to adjust the parameters of the device operation performance anomaly detection model to obtain a performance anomaly detection verification model; Step S33: Adaptively adjust the performance anomaly detection verification model by introducing an attention mechanism to obtain a performance anomaly detection attention model; Step S34: using the communication device performance packet loss verification data set to perform migration verification learning on the performance anomaly detection focus model to optimize the network hyperparameters of the performance anomaly detection focus model and obtain a performance anomaly detection optimization model; Step S35: Utilize the performance anomaly detection optimization model to perform operation anomaly mode detection on the communication device performance packet loss correction data to obtain communication device operation anomaly mode data.

7. The fault diagnosis method of communication equipment according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing abnormal type detection on the abnormal mode data of the communication device to obtain abnormal type data of the communication device; Step S42: performing fault location diagnosis on the operation status data of the communication device according to the abnormal type data of the communication device, and obtaining a diagnosis result of the operation fault of the communication device; Step S43: performing fault alarm processing on the communication equipment operation fault diagnosis result to generate communication equipment operation fault alarm information; Step S44: Record the fault response of the communication equipment operation fault diagnosis result based on the communication equipment operation fault alarm information to generate a communication equipment fault diagnosis report.

8. A fault diagnosis device for communication equipment, characterized in that: For executing the fault diagnosis method of the communication device according to claim 1, the fault diagnosis device of the communication device comprises: The equipment operation evaluation and analysis module is used to obtain the communication equipment operation status data; perform bandwidth detection and delay analysis on the communication equipment operation status data to obtain the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data; perform operation evaluation and analysis on the communication equipment operation bandwidth utilization status data and the communication equipment operation delay status data to obtain the bandwidth utilization operation adjustment factor and the operation delay adjustment factor; The equipment performance packet loss correction module is used to perform performance packet loss detection on the communication equipment operation status data to obtain the communication equipment operation performance packet loss data; use the performance packet loss correction index calculation formula to correct the communication equipment operation performance packet loss data to obtain the communication equipment performance packet loss correction index; wherein the performance packet loss correction index calculation formula is specifically: ； In the formula, is the packet loss correction index of communication equipment performance, To modify the time range parameters of the calculation, To correct the calculation of the integral time variable parameter, is the time decay rate parameter of packet loss data of communication equipment operation performance, It is the benchmark performance parameter of the packet loss data of the communication equipment operation performance. Performance packet loss data for communication equipment running at time Performance packet loss parameter at, is the performance packet loss efficiency index parameter, Adjust parameters for performance changes due to packet loss, To change the attenuation parameter for performance packet loss impact, The correction value of the communication equipment performance packet loss correction index is obtained by assigning a first performance adjustment weight to the bandwidth utilization performance adjustment factor and a second performance adjustment weight to the transmission delay performance adjustment factor; the first performance adjustment weight and the second performance adjustment weight are dynamically corrected and optimized according to the communication equipment performance packet loss correction index to obtain a first performance adjustment correction optimization weight and a second performance adjustment correction optimization weight; the bandwidth utilization performance adjustment factor is corrected and adjusted according to the first performance adjustment correction optimization weight to obtain a bandwidth utilization performance correction factor; the transmission delay performance adjustment factor is corrected and adjusted according to the second performance adjustment correction optimization weight to obtain a transmission delay performance correction factor; a difference correction strategy is formulated and analyzed for the bandwidth utilization performance correction factor and the transmission delay performance correction factor to generate a differentiated performance packet loss correction strategy; the communication equipment operation performance packet loss data is corrected, optimized and adjusted through the differentiated performance packet loss correction strategy to obtain the communication equipment performance packet loss correction data; The device operation abnormality detection module is used to perform operation abnormality mode detection on the communication device performance packet loss correction data according to a preset device operation performance abnormality detection model to obtain communication device operation abnormality mode data; The equipment operation fault diagnosis module is used to perform fault location diagnosis on the communication equipment operation status data according to the communication equipment operation abnormal mode data to obtain the communication equipment operation fault diagnosis result; and to record the fault response of the communication equipment operation fault diagnosis result to generate a communication equipment fault diagnosis report.

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