Communication fault rapid diagnosis method and system based on data analysis

Abstract

The invention discloses a communication fault rapid diagnosis method and system based on data analysis, and belongs to the technical field of network fault diagnosis, and the method comprises a multi-source data obtaining module which obtains data parameters related to a communication fault from a plurality of data terminals; the data processing module is used for processing the acquired data parameters to obtain standardized data parameters; the fault analysis module is used for inputting the acquired data parameters into a trained fault risk model and judging whether the communication has a fault or not under the current condition; and the fault response module correspondingly responds to the communication network with the fault. According to the invention, when the communication network does not have a fault, comprehensive analysis can be carried out according to the acquired change condition of the fault risk value and the temperature change condition of the corresponding network equipment in combination with the fault influence factors, so as to pre-judge the potential impending fault of the communication network and carry out early warning in time, thereby ensuring the normal network communication.

Description

Technical Field

[0001] The present invention belongs to the technical field of network fault diagnosis, and particularly relates to a method and system for quickly diagnosing communication faults based on data analysis. Background Art

[0002] In today's digital age, communication systems have become key infrastructure for social operation, widely used in various fields, including but not limited to mobile communication, Internet, Internet of Things, and industrial automation. With the rapid development of communication technology, the scale of communication networks is increasing day by day, the structure is becoming more and more complex, and the requirements for their reliability and stability have reached an unprecedented height. The occurrence of any communication fault, even a short interruption, may cause serious consequences, such as business transaction interruption, traffic command failure, information leakage, etc., bringing huge losses to society and economy.

[0003] Traditional communication fault diagnosis mainly relies on the experience of operation and maintenance personnel and a pre-established rule base. Operation and maintenance personnel judge the type and location of faults by observing the alarm information, log records of network devices and their own work experience. However, this method has obvious limitations. First, with the increase in network scale and complexity, fault modes become more and more complex, and it is difficult to comprehensively cover them with limited rules and experience. Second, manual diagnosis is inefficient and easily affected by subjective factors, and it is difficult to guarantee the accuracy and consistency of diagnosis results. Moreover, when diagnosing existing communication faults, it can only detect the faults that have occurred and cannot warn of potential or upcoming faults. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for quickly diagnosing communication faults based on data analysis to solve the problems faced in the above background art.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A communication fault quick diagnosis system based on data analysis, the system includes: a multi-source data acquisition module, a data processing module, a fault analysis module, and a fault response module;

[0007] The multi-source data acquisition module is used to acquire data parameters related to communication faults from multiple data terminals;

[0008] The data processing module is used to process the acquired data parameters to obtain standardized data parameters;

[0009] The fault analysis module inputs the acquired data parameters into a trained fault risk model to obtain the fault risk value under the current situation, so as to judge whether a communication fault occurs under the current situation;

[0010] The fault response module is used to make corresponding responses to the communication network with faults.

[0011] Further, the working method of the fault analysis module is as follows:

[0012] The communication network is divided into n sub-monitoring units, the values of various performance index parameters in each monitoring unit are obtained, and they are respectively compared with their respective fault thresholds. When the obtained performance index parameter value exceeds its respective fault threshold, it is determined that the communication network of the current sub-monitoring unit has a fault, and a first response signal is generated;

[0013] At the same time, the values of various performance index parameters of each sub-monitoring unit at time t i , ,

[0019] , i , ,

[0018] , i , i , , , 0i , n , i , , n , n ,

[0015] , n ,

[0014] , i , , , , i , v ,

[0017] ,

[0016] are obtained, so as to obtain the performance status value WR of each performance index parameter value at time t n through the formula, where maxW i is the maximum parameter value of the i-th performance index at time t i , minW n is the minimum parameter value of the i-th performance index at time t i , ΔW n is the set comparison value of the i-th performance index parameter, a is the proportionality coefficient, i and is the average parameter value of the i-th performance index at time t ; n The performance status values WR

[0014] of the obtained various performance indexes are input into the trained fault risk model to obtain the fault risk value Pr of the sub-monitoring unit; i

[0015] If the fault risk value Pr exceeds the preset fault risk judgment threshold Pr v , it is determined that the communication network of the current sub-monitoring unit has a fault, and a second response signal is generated.

[0016] Further, the method for obtaining the fault risk model is as follows:

[0017] The fault risk model is constructed through the formula ;

[0018] where WR 0i is the historical standard performance status value of the i-th performance index, and i ∈ [1, n], σ i is the standard deviation of the performance status value of the i-th performance index, and β i is the assignment weight of the i-th performance index.

[0019] Further, the working method of the fault response module is as follows:

[0020] When it is determined that a communication network fails, obtain the location of the fault area. At the same time, the system automatically generates an alarm message and sends it to relevant personnel through multiple channels;

[0021] The alarm message includes the location of the fault, the time when the fault occurred, the type of response signal generated by the fault, and preliminary countermeasures recommended according to the type of response signal.

[0022] Further, the working method of the data processing module is as follows:

[0023] Perform data cleaning on the obtained multi-source data parameters, remove noise data, fill in missing data, filter invalid data, and then use the normalization method to standardize the data to unify data with different dimensions, thereby obtaining standardized data parameters.

[0024] A fast communication fault diagnosis method based on data analysis. The diagnosis method is controlled and implemented by the fast communication fault diagnosis system based on data analysis. The diagnosis method includes:

[0025] Step 1: Obtain data parameters related to the communication fault from multiple data terminals and process them to obtain standardized data parameters;

[0026] Step 2: Divide the communication network into multiple sub-monitoring units, and analyze according to the obtained data parameters to determine whether the communication network of each sub-monitoring unit fails;

[0027] Step 3: When the communication network does not fail, further analyze the obtained parameters to determine whether there are potential faults in the communication network of each sub-monitoring unit;

[0028] Step 4: Perform corresponding responses to the communication network with faults.

[0029] Further, the method for determining whether there are potential faults in the communication network of each sub-monitoring unit in Step 3 is as follows:

[0030] When the communication network does not fail, at this time, obtain the fault risk values within consecutive m t n time periods and the self-temperature values of the corresponding network devices, and formulate a curve function Pr(x) of the fault risk value changing with the time period and a curve function S T (x) of the temperature changing with the time period, input them into the fault probability model, and obtain the fault probability value RU. The fault probability model is:

[0031] When RU > RU0, it is determined that there are potential faults in the communication network of the sub-monitoring unit;

[0032] Among them, x1 is the first t n time period, and x m is the last t n time period. S Tv (x) is the standard temperature change curve function formulated according to historical data, and Pr v (x) is the fault risk judgment threshold change curve function, and maxPr t is the duration of the maximum fault risk value, and max S Tt is the duration of the highest temperature. τ is the fault impact coefficient, and RU0 is the set fault probability judgment threshold.

[0033] Furthermore, the method for obtaining the fault impact coefficient τ is as follows:

[0034] Obtain the average service life of relevant devices in the sub-monitoring unit The historical fault times k and fault frequency μ of the sub-monitoring unit are obtained. At the same time, the environmental factors of the area where the sub-monitoring unit is located are obtained, including the humidity value emp S , temperature value emp T , electromagnetic intensity emp E , the number of large obstacles on the communication line emp n and the total area emp W ;

[0035] Through the formula The fault impact coefficient τ is obtained;

[0036] Among them, maxy is the longest service life of the device, and emp S0 is the set optimal humidity value, Δemp S is the humidity ratio value, emp T0 is the set optimal temperature value, Δemp T is the temperature ratio value, Δemp E is the electromagnetic intensity ratio value, and Δemp W is the area ratio value.

[0037] Advantages of the present invention:

[0038] The present invention can divide network communication into multiple sub-monitoring units, can quickly identify the section where the problem is located, thereby narrowing the scope of fault troubleshooting, and does not require manual monitoring. Multi-source data can be used for comprehensive analysis, which can comprehensively cover the entire network communication. According to the individual changes and comprehensive changes of multiple performance indicators, obvious and difficult-to-identify fault phenomena can be identified, so as to more accurately judge the network fault situation and greatly improve the accuracy of the diagnosis result.

[0039] When the communication network is not faulty, the present invention can comprehensively analyze the obtained changes in the fault risk value, the corresponding changes in the temperature of network devices, and combine the fault influencing factors to predict potential upcoming faults in the communication network and give timely warnings to ensure normal network communication.

[0040] Of course, any product implementing the present invention does not necessarily need to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for describing the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is the system module block diagram of the present invention;

[0043] Figure 2 It is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0045] In one embodiment, a communication fault rapid diagnosis system based on data analysis is disclosed. As Figure 1 shown, the system includes: a multi-source data acquisition module, a data processing module, a fault analysis module, and a fault response module;

[0046] The multi-source data acquisition module is used to acquire data parameters related to communication faults from multiple data terminals;

[0047] The data processing module is used to process the acquired data parameters to obtain standardized data parameters. The method is: perform data cleaning on the acquired multi-source data parameters, remove noise data, fill in missing data, filter invalid data, and then use the normalization method to standardize the data to unify data with different dimensions, thereby obtaining standardized data parameters;

[0048] The fault analysis module inputs the acquired data parameters into a trained fault risk model to obtain the fault risk value in the current situation, thereby determining whether a communication fault has occurred in the current situation;

[0049] The fault response module is used to make corresponding responses to the communication network with faults.

[0050] Through the above technical solutions, this application obtains multiple communication-related data parameters from multi-source data ends, and after preprocessing by the data processing module, standardized data parameters are obtained. The processing includes data cleaning, removing noise data, filling missing data, and filtering invalid data, and then the data is standardized using the normalization method to unify data with different dimensions, thereby obtaining standardized data parameters to facilitate subsequent calculations. Then, the communication network is divided into multiple sub-monitoring units. By monitoring and analyzing the performance indicators (such as packet loss rate, latency, response duration, etc.) of each sub-monitoring unit, the fault risk value of each sub-monitoring unit is obtained, and thus it is determined whether a communication fault occurs under the current situation according to the fault risk value. Once a fault occurs, a timely response is made to achieve the rapid diagnosis of network communication faults. Dividing network communication into multiple sub-monitoring units can quickly identify the section where the problem lies, thereby narrowing the scope of fault troubleshooting. And it does not require manual monitoring. Comprehensive analysis can be carried out using multi-source data, which can comprehensively cover the entire network communication and can more accurately judge the network fault situation, greatly improving the accuracy of the diagnosis results.

[0051] The working method of the fault analysis module is as follows: The communication network is divided into n sub-monitoring units, the parameter values of each performance indicator in each monitoring unit are obtained, and they are respectively compared with their respective fault thresholds. When the obtained performance indicator parameter values exceed their respective fault thresholds, it is determined that the communication network of the current sub-monitoring unit has a fault, and a first response signal is generated.

[0052] At the same time, the parameter values of each performance indicator of each sub-monitoring unit within time t n are obtained, and thus through the formula the performance status value WR n of each performance indicator parameter value within time t i is obtained, where maxW i is the maximum parameter value of the i-th performance indicator within time t n , minW i is the minimum parameter value of the i-th performance indicator within time t n , ΔW i is the set comparison value of the i-th performance indicator parameter, a is the proportionality coefficient, is the average parameter value of the i-th performance indicator within time t n ;

[0053] The obtained performance status values WR i of each performance indicator are input into the trained fault risk model to obtain the fault risk value Pr of the sub-monitoring unit. The fault risk model is:

[0054] If the failure risk value Pr exceeds the preset failure risk judgment threshold Pr v , it is determined that the communication network of the current sub-monitoring unit has failed, and a second response signal is generated;

[0055] where WR 0i is the historical standard performance status value of the i-th performance index, and i ∈ [1, n], σ i is the standard deviation of the performance status value of the i-th performance index, and β i is the assignment weight of the i-th performance index.

[0056] Through the above technical solution, this embodiment provides a method for determining whether there is a network failure in each sub-monitoring unit of the communication network. First, the communication network is divided into n sub-monitoring units, and the parameter values of each performance index in each monitoring unit are obtained (such as packet loss rate, delay, response duration, etc.). Each performance index parameter corresponds to a failure threshold. The obtained parameter values of each performance index are compared with their respective failure thresholds. When the obtained parameter value of the performance index exceeds its respective failure threshold, it indicates that the performance index of the sub-monitoring unit is abnormal, indicating that there is a network communication failure. Then, it is determined that the communication network of the current sub-monitoring unit has failed, and a first response signal is generated to alert the remote management personnel; similarly, this method can only identify the situation where the network performance index parameters are significantly abnormal, and it is difficult to identify the non-obvious abnormal phenomena. Therefore, the parameter values of each performance index in each sub-monitoring unit within t n time are obtained. The t n time can be determined manually, and then through the formula the performance status value WR n of each performance index parameter within t i time is obtained. In the formula, maxW i is the maximum parameter value of the i-th performance index within t n time, minW i is the minimum parameter value of the i-th performance index within t n time, is the average parameter value of the i-th performance index within t n time, ΔW i is the set comparison value of the i-th performance index parameter, which is obtained based on experimental comparison data. a is a proportionality coefficient, which is obtained based on empirical data. Generally speaking, if the average parameter value of each performance index is larger or the fluctuation is larger within a certain period of time, it indicates that the possibility of a failure is greater. Therefore, when the formula The larger the value, the greater the likelihood that the network has a fault. Finally, the performance status value WR of each performance index obtained will be i input into the trained fault risk model to obtain the fault risk value Pr of the sub-monitoring unit. Obviously, the larger the fault risk value, the greater the possibility that the communication network has a fault. Therefore, compare it with the preset fault risk judgment threshold Pr v If the fault risk value Pr exceeds the preset fault risk judgment threshold Pr v , it is determined that the communication network of the current sub-monitoring unit has a fault, and a second response signal is generated to remind the remote management personnel to take corresponding measures. In this way, the network communication can be divided into multiple sub-monitoring units, which can quickly identify the section where the problem is located, thereby narrowing the scope of fault troubleshooting. Moreover, it does not require manual monitoring and can use multi-source data for comprehensive analysis, which can comprehensively cover the entire network communication. According to the individual and comprehensive change situations of multiple performance indicators, it can identify obvious and difficult-to-identify fault phenomena, thereby more accurately judging the network fault situation and greatly improving the accuracy of the diagnosis result.

[0057] The working method of the fault response module is as follows: when it is determined that the communication network has a fault, obtain the location of the fault area. At the same time, the system automatically generates alarm information and sends it to relevant personnel through multiple channels. The alarm information includes the location of the fault, the time when the fault occurred, the type of response signal generated by the fault, and the preliminary countermeasures recommended according to the type of response signal. This facilitates the relevant management and maintenance personnel to understand the fault location and the general cause of the fault in the first time, so as to quickly identify the fault and timely restore the normal network communication to ensure the communication quality.

[0058] In one embodiment, a rapid communication fault diagnosis method based on data analysis is also disclosed. The diagnosis method is controlled and implemented by a rapid communication fault diagnosis system based on data analysis, as Figure 2 shown. The diagnosis method includes:

[0059] Step 1: Obtain data parameters related to communication faults from multiple data terminals and process them to obtain standardized data parameters;

[0060] Step 2: Divide the communication network into multiple sub-monitoring units and analyze according to the obtained data parameters to determine whether the communication network of each sub-monitoring unit has a fault;

[0061] Step 3: When the communication network has no fault, further analyze the obtained parameters to determine whether there are potential faults in the communication network of each sub-monitoring unit;

[0062] Step 4: Make corresponding responses to the faulty communication network.

[0063] Through the above technical solution, the present application can not only quickly detect existing network communication faults, but also, when the network has no faults, comprehensively analyze according to the change of the obtained fault risk value, the change of the temperature of the corresponding network device, and the fault influencing factors, to predict potential upcoming faults in the communication network and give early warnings in time to ensure the normal network communication.

[0064] The method for judging whether there are potential faults in the communication network of each sub-monitoring unit in Step 3 is as follows: when the communication network has no faults, obtain the fault risk values and the self-temperature values of the corresponding network devices within consecutive m t n time periods, and formulate a curve function Pr(x) of the fault risk value changing with the time period and a curve function S T (x) of the temperature changing with the time period, and input them into the fault probability model to obtain the fault probability value RU. The fault probability model is:

[0065] When RU > RU0, it is determined that there are potential faults in the communication network of the sub-monitoring unit;

[0066] Among them, x1 is the first t n time period, x m is the last t n time period, S Tv (x) is the standard temperature change curve function formulated according to historical data, Pr v (x) is the curve function of the fault risk judgment threshold change, maxPr t is the duration of the maximum fault risk value, max S Tt is the duration of the highest temperature, τ is the fault influence coefficient, RU0 is the set fault probability judgment threshold, and the method for obtaining the fault influence coefficient τ is: obtain the average service life of the relevant equipment in the sub-monitoring unit the historical fault times k and the fault frequency μ of the sub-monitoring unit, and at the same time obtain the environmental factors in the area where the sub-monitoring unit is located, including the humidity value emp S 、temperature value emp T 、electromagnetic intensity emp E 、the number of large obstacles on the communication line emp n and the total area emp W ;

[0067] Through the formula obtain the fault influence coefficient τ;

[0068] Among them, maxy is the longest service life of the device, emp S0 is the set optimal humidity value, Δemp S is the humidity ratio value, emp T0 is the set optimal temperature value, Δemp T is the temperature ratio value, Δemp E is the electromagnetic intensity ratio value, Δemp W is the area ratio value.

[0069] Through the above technical solution, this embodiment provides a method for judging whether there is a potential fault in the communication network of the sub-monitoring unit. First, when the communication network does not fail, at this time, obtain the fault risk values within m consecutive t n time periods and the self-temperature values of the corresponding network devices, and formulate a curve function Pr(x) of the fault risk value changing with the time period and a curve function S T (x) of the temperature changing with the time period, and input them into the fault probability model to obtain the fault probability value RU. The fault probability model is: In the formula, represents the difference change between the temperature change and the standard temperature change. The larger the difference, the greater the possibility of potential faults. The formula dx represents the difference situation between the change of the fault risk judgment threshold and the change of the fault risk value. Although the fault risk value does not exceed the judgment threshold, if the fault risk value has been near the judgment threshold and is slowly increasing, it indicates that the network has a high possibility of potential fault risk. Therefore, the smaller the difference, the greater the possibility of potential faults. And the formula represents the proportion of the duration of the maximum fault risk value and the duration of the highest temperature. The higher the proportion, the greater the possibility of potential faults; and the method for obtaining the fault influence coefficient τ is: obtain the average service life of the relevant devices in the sub-monitoring unit the historical fault times k and the fault frequency μ of the sub-monitoring unit, and at the same time obtain the environmental factors in the area where the sub-monitoring unit is located, including the humidity value emp S , the temperature value emp T , the electromagnetic intensity emp E , the number of large obstacles on the communication line emp n and the total area emp W , and obtain the fault influence coefficient τ through the formula . In the formula, emp S0 is the set optimal humidity value, Δemp S is the humidity ratio value, emp T0 is the set optimal temperature value, Δemp T is the temperature ratio value, Δemp Eis the electromagnetic intensity ratio, Δemp W is the area ratio, both of which can be obtained based on historical data combined with experimental data; it can be derived from the formula that the greater the fault influence coefficient, the greater its impact on network communication, and the greater the likelihood of network communication failures; therefore, finally, a comprehensive analysis is carried out by combining the temperature change of the network device itself, the change of the network fault risk value in the corresponding section, the duration, and the combined fault influence factors to obtain the fault probability value RU. Then, it is compared with the fault probability judgment threshold RU0 set according to experience. When RU > RU0, it is determined that there is a potential fault in the communication network of the sub-monitoring unit. Through this method, when the communication network has not failed, a comprehensive analysis can be carried out based on the change of the obtained fault risk value, the temperature change of the corresponding network device, and the combined fault influence factors to predict the potential upcoming faults in the communication network and give early warnings in a timely manner to ensure the normal network communication.

[0070] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claim book, they should all belong to the protection scope of the present invention.

Claims

1. A rapid communication fault diagnosis system based on data analysis, characterized in that, The system includes: a multi-source data acquisition module, a data processing module, a fault analysis module, and a fault response module; The multi-source data acquisition module is used to acquire data parameters related to communication faults from multiple data terminals; The data processing module is used to process the acquired data parameters to obtain standardized data parameters; The fault analysis module inputs the acquired data parameters into a trained fault risk model to obtain the fault risk value under the current condition, so as to judge whether a communication fault occurs under the current condition; The fault response module is used to perform corresponding responses to the communication network with faults.

2. The rapid communication fault diagnosis system based on data analysis according to claim 1, characterized in that The working method of the fault analysis module is as follows: The communication network is divided into n sub-monitoring units, the values of various performance index parameters in each monitoring unit are acquired, and are respectively compared with their respective fault thresholds. When the acquired performance index parameter values exceed their respective fault thresholds, it is judged that the communication network of the current sub-monitoring unit has a fault, and a first response signal is generated; Meanwhile, obtain the parameter values of various performance indicators of each sub-monitoring unit within time t n so as to obtain, through the formula the performance status value WR of each performance indicator parameter value within time t n , where maxW i is the maximum parameter value of the i-th performance indicator within time t i , minW n is the minimum parameter value of the i-th performance indicator within time t i , ΔW n is the set comparison value of the i-th performance indicator parameter, a is the proportionality coefficient, i and is the average parameter value of the i-th performance indicator within time t n ; The performance status value WR of each obtained performance index i is input into the trained fault risk model to obtain the fault risk value Pr of this sub-monitoring unit; If the fault risk value Pr exceeds the preset fault risk judgment threshold Pr v , it is determined that the communication network of the current sub-monitoring unit fails, and a second response signal is generated.

3. A rapid communication fault diagnosis system based on data analysis according to claim 2, characterized in that, The method for obtaining the fault risk model is as follows: Through the formula Construct a fault risk model; Among them, WR 0i is the historical standard performance status value of the i-th performance indicator, and i ∈ [1, n], σ i is the standard deviation of the performance status value of the i-th performance indicator, β i is the assignment weight of the i-th performance indicator.

4. A rapid communication fault diagnosis system based on data analysis according to claim 2, characterized in that, The working method of the fault response module is as follows: When it is judged that the communication network has a fault, the location of the fault area is acquired, and at the same time, the system automatically generates an alarm message and issues it to relevant personnel through multiple channels; The alarm message includes the location of the fault, the time when the fault occurs, the type of the response signal generated by the fault, and the preliminary countermeasures recommended according to the type of the response signal.

5. The quick communication fault diagnosis system based on data analysis according to claim 2, characterized in that The working method of the data processing module is as follows: The multi-source data parameters acquired are subjected to data cleaning, and noise data are removed, missing data are filled, and invalid data are filtered. Then, the normalization method is used to perform standardized processing on the data to unify data with different dimensions, so as to obtain standardized data parameters.

6. A rapid communication fault diagnosis method based on data analysis, wherein the diagnosis method is controlled and implemented by the rapid communication fault diagnosis system based on data analysis according to any one of claims 1-5, characterized in that, The diagnostic method includes: Step 1: Acquire data parameters related to communication faults from multiple data terminals and process them to obtain standardized data parameters; Step 2: Divide the communication network into multiple sub-monitoring units, and analyze according to the acquired data parameters to judge whether the communication network of each sub-monitoring unit has a fault; Step 3: When the communication network has no fault, further analyze the acquired parameters to judge whether there are potential faults in the communication network of each sub-monitoring unit; Step 4: Perform corresponding responses to the communication network with faults.

7. A rapid communication fault diagnosis method based on data analysis according to claim 5, characterized in that, The method for judging whether there are potential faults in the communication network of each sub-monitoring unit in Step 3 is as follows: When the communication network is not faulty, obtain the fault risk values within consecutive m t n time periods and the self-temperature values of the corresponding network devices, and formulate the curve function Pr(x) of the fault risk value varying with the time period, and the curve function S T (x) of the temperature varying with the time period, and input them into the fault probability model to obtain the fault probability value RU. The fault probability model is: When RU > RU0, it is judged that there are potential faults in the communication network of the sub-monitoring unit; Among them, x1 is the first t n time period, x m is the last t n time period, S Tv (x) is the standard temperature change curve function formulated according to historical data, Pr v (x) is the fault risk judgment threshold change curve function, maxPr t is the duration of the maximum fault risk value, max S Tt is the duration of the highest temperature, τ is the fault impact coefficient, and RU0 is the set fault probability judgment threshold.

8. A rapid communication fault diagnosis method based on data analysis according to claim 7, characterized in that, The method for obtaining the fault influence coefficient τ is as follows: Obtain the average service life of relevant devices within the sub-monitoring unit The historical failure times k and failure frequency μ of the sub-monitoring unit, and at the same time obtain the environmental factors in the area where the sub-monitoring unit is located, including the humidity value emp S , temperature value emp T , electromagnetic intensity emp E , the number of large obstacles on the communication line emp n and the total area emp W ; Through the formula the fault influence coefficient τ is obtained; Among them, maxy is the longest service life of the device, emp S0 is the set optimal humidity value, Δemp S is the humidity ratio value, emp T0 is the set optimal temperature value, Δemp T is the temperature ratio value, Δemp E is the electromagnetic intensity ratio value, Δemp W is the area ratio value.

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