Communication fault rapid diagnosis system and method

Abstract

The invention belongs to the technical field of communication management, and discloses a communication fault rapid diagnosis system, which is characterized in that a data acquisition module sequentially acquires equipment operation state data, network flow data and signal intensity data in a communication process, and sends the data to a data processing module; the data processing module cleans, converts and normalizes the received data to obtain preprocessed data, and sends the preprocessed data to the data analysis module; the data analysis module analyzes the processed data, extracts feature information related to the fault and sends an analysis result to the fault diagnosis module; and the fault diagnosis module diagnoses the communication fault and determines the type and reason of the fault according to the analysis result of the data analysis module in combination with the fault knowledge base, thereby solving the problems of low diagnosis speed and low accuracy in the existing communication fault diagnosis mode, realizing rapid positioning and diagnosis of the communication fault, and improving the fault diagnosis efficiency. And the reliability and stability of the communication network are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication management, and particularly relates to a communication fault rapid diagnosis system and method. Background Art

[0002] With the rapid development of communication technology, the scale of communication networks is increasing day by day, and the network structure is becoming more and more complex. During the operation of communication networks, various faults occur from time to time, such as equipment faults, line faults, software faults, etc.

[0003] However, for the diagnosis of communication link faults, it mostly relies on manual inspections and empirical judgments. This method is inefficient and prone to missed judgments and misjudgments. Although there are also some automated diagnosis systems, these systems cannot quickly locate the cause of faults and cannot meet the requirements of rapid fault diagnosis in communication networks. Therefore, there is an urgent need for a system and method that can quickly and accurately diagnose communication link faults and other communication faults. Summary of the Invention

[0004] The purpose of the present invention is to provide a communication fault rapid diagnosis system and method to solve the above technical problems.

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

[0006] A communication fault rapid diagnosis system, the system includes: a data acquisition module, a data processing module, a data analysis module, and a fault diagnosis module;

[0007] The data acquisition module is used to collect various parameters in the communication network in real time, and the parameters include but are not limited to equipment operation status data, network traffic data, and signal strength data;

[0008] The data processing module receives the data collected by the data acquisition module and is used to clean, transform, and normalize the data;

[0009] The data analysis module is used to analyze the processed data and extract feature information related to faults;

[0010] The fault diagnosis module diagnoses communication faults according to the feature information extracted by the data analysis module.

[0011] As a further description of the technical solution of the present invention, the working process of the data acquisition module includes:

[0012] According to the type of device and communication protocol, the data acquisition module will automatically adapt to the corresponding connection method and connect to each device in the communication network through the network interface; in a polling manner, at a preset time interval, it sends data acquisition requests to the devices in the communication network. After sending the requests, the data acquisition module waits for the devices to respond and receives the data returned by the devices;

[0013] According to the set communication protocol and transmission method, it transmits the acquired data to the data processing module.

[0014] As a further description of the technical solution of the present invention, the device operating state data includes the CPU usage rate, memory usage rate, and port traffic of each device;

[0015] The network traffic data includes the incoming and outgoing data volumes of each link;

[0016] The signal strength data includes signal-to-noise ratio, bit error rate, and carrier-to-noise ratio.

[0017] As a further description of the technical solution of the present invention, the specific working process of the data analysis module includes:

[0018] Number all the communication devices in the communication process, and the numbers are successively: 1, 2,..., n;

[0019] Obtain the CPU usage rate C i of the i-th communication device, the memory usage rate M i and the port traffic F i , and construct the mathematical model of the failure coefficient of the i-th communication device, and the expression is:

[0020]

[0021] In the formula, i belongs to n, R i is the failure coefficient of the i-th communication device, and α i and β i are weight coefficients respectively.

[0022] As a further description of the technical solution of the present invention, the specific working process of the data analysis module further includes:

[0023] Number all the links in the communication process, and the numbers are successively: 1, 2,..., m;

[0024] Obtain the incoming data volume I j and the outgoing data volume O j of the j-th link;

[0025] First, calculate the traffic difference rate of the j-th link according to the acquired data:

[0026]

[0027] Then, calculate the average value of all link traffic difference rates

[0028] Next, according to the formula calculate the average value of the incoming data volume, and according to the formula calculate the average value of the outgoing data volume;

[0029] Next, calculate all based on the average value of the incoming data volume and the average value of the outgoing data volume

[0030]

[0031] Finally, calculate the network traffic fault coefficient according to the average value of all link traffic difference rates and the dispersion coefficient of all link traffic: S = γ * D AVE + δ * CV;

[0032] In the formula, j belongs to m, and γ and δ are weight coefficients respectively.

[0033] As a further description of the technical solution of the present invention, the specific working process of the data analysis module further includes:

[0034] Obtain the signal-to-noise ratio SNR, bit error rate BER, and carrier-to-noise ratio CNR within a set time period of the communication process, and construct a mathematical model of the signal strength fault coefficient, and the expression is:

[0035]

[0036] In the formula, ε, ∈, and θ are weight coefficients respectively.

[0037] As a further description of the technical solution of the present invention, the working process of the fault diagnosis module includes:

[0038] Compare the fault coefficient R of the i-th communication device i with the threshold of the fault coefficient of the i-th communication device set by the system. If the fault coefficient R of the i-th communication device i is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the i-th communication device;

[0039] Calculate the fault coefficients of n communication devices in sequence. If the fault coefficients of n communication devices are all lower than the corresponding thresholds, the communication fault is not caused by the communication device;

[0040] Compare the network traffic fault coefficient S with the threshold of the network traffic fault coefficient set by the system. If the network traffic fault coefficient is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the network traffic fault, otherwise, it is not caused by the network traffic;

[0041] Compare the signal strength fault coefficient FSI with the signal strength fault coefficient threshold set by the system. If the signal strength fault coefficient is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the signal strength fault; otherwise, it is not caused by the signal strength.

[0042] A diagnostic method for a communication fault rapid diagnosis system, the method comprising the following steps:

[0043] Step S1, data acquisition: sequentially obtain the device operation status data, network traffic data, and signal strength data during the communication process, and send them to the data processing module;

[0044] Step S2, data processing: the data processing module performs cleaning, conversion, and normalization processing on the received data to obtain preprocessed data, and sends it to the data analysis module;

[0045] Step S3, fault analysis: the data analysis module analyzes the processed data, extracts the feature information related to the fault, and sends the analysis result to the fault diagnosis module;

[0046] Step S4, fault diagnosis: the fault diagnosis module diagnoses the communication fault according to the analysis result of the data analysis module, in combination with the fault knowledge base, to determine the type and cause of the fault.

[0047] Advantages of the present invention:

[0048] 1. Rapid diagnosis: By collecting and processing communication network data in real time and combining advanced fault analysis and diagnosis algorithms, it is possible to locate and diagnose communication faults, including link faults, in a short time, greatly improving the speed of fault diagnosis.

[0049] 2. Automatic processing: The system can automatically complete a series of operations such as data acquisition, processing, fault analysis, and diagnosis, reducing the workload of maintenance personnel and improving work efficiency.

[0050] 3. Improve network reliability: Timely and accurately diagnosing and handling communication faults, especially link faults, can effectively improve the reliability and stability of the communication network, ensure the quality of communication services, and avoid data transmission interruption and service loss caused by link problems.

[0051] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of 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.

[0053] Figure 1 It is a schematic structural diagram of the communication fault rapid diagnosis system of the present invention. Specific embodiments

[0054] 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 a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0055] Please refer to Figure 1 As shown, a communication fault rapid diagnosis system is disclosed. The system includes: a data acquisition module, a data processing module, a data analysis module, and a fault diagnosis module;

[0056] The data acquisition module is used to collect various parameters in the communication network in real time. The parameters include but are not limited to device operation status data, network traffic data, and signal strength data. The data acquisition module is connected to each device in the communication network through a network interface and uses a combination of polling and event triggering to collect data.

[0057] The data processing module receives the data collected by the data acquisition module and is used to clean, transform, and normalize the data. The cleaning operation removes noise and outliers in the data. The transformation operation converts data in different formats into a unified format. The normalization process makes the data within the same numerical range for subsequent analysis.

[0058] The data analysis module is used to analyze the processed data and extract feature information related to faults;

[0059] The fault diagnosis module diagnoses communication faults based on the feature information extracted by the data analysis module.

[0060] Through the above technical solution, the present invention provides a communication fault rapid diagnosis system and method. The data acquisition module sequentially acquires the device operation status data, network traffic data, and signal strength data during the communication process, and sends them to the data processing module; the data processing module performs cleaning, conversion, and normalization processing on the received data to obtain preprocessed data, and sends it to the data analysis module; the data analysis module analyzes the processed data, extracts the feature information related to the fault, and sends the analysis result to the fault diagnosis module; the fault diagnosis module diagnoses the communication fault according to the analysis result of the data analysis module, in combination with the fault knowledge base, determines the type and cause of the fault, solves the problems of slow diagnosis speed and low accuracy existing in the existing communication fault diagnosis methods, realizes the rapid location and diagnosis of communication faults, and improves the reliability and stability of the communication network.

[0061] The working process of the data acquisition module includes:

[0062] According to the type of device and the communication protocol, the data acquisition module will automatically adapt the corresponding connection method and connect to each device in the communication network through the network interface; these network interfaces can be Ethernet interfaces, optical fiber interfaces, wireless communication interfaces, etc., to ensure stable and reliable data transmission with the devices.

[0063] Adopt a polling method, and send data acquisition requests to the devices in the communication network at a preset time interval. After sending the requests, the data acquisition module waits for the devices to respond and receives the data returned by the devices;

[0064] Transmit the acquired data to the data processing module according to the set communication protocol and transmission method. During the transmission process, in order to ensure the security and stability of the data, the data acquisition module may adopt technologies such as encrypted transmission and retransmission mechanism to ensure that the data can reach the data preprocessing module accurately and provide a reliable data basis for the subsequent fault diagnosis work.

[0065] The device operation status data includes the CPU usage rate, memory usage rate, and port traffic of each device;

[0066] The network traffic data includes the incoming and outgoing data volumes of each link;

[0067] The signal strength data includes signal-to-noise ratio, bit error rate, and carrier-to-noise ratio.

[0068] The specific working process of the data analysis module includes:

[0069] Number all the communication devices during the communication process, and the numbers are successively: 1, 2,..., n;

[0070] Obtain the CPU usage rate C of the i-th communication device i , the memory usage rate M i and the port traffic F i , and construct a mathematical model for the failure coefficient of the i-th communication device. The expression is as follows:

[0071]

[0072] In the formula, i belongs to n, and R i is the failure coefficient of the i-th communication device, and α i and β i are weight coefficients respectively.

[0073] Through the above technical solution, this embodiment provides a process for analyzing the failures of communication devices during the communication process. By numbering all the communication devices in the communication process, sequentially obtaining the CPU usage rate, memory usage rate, and port traffic of each device, and then according to the formula

[0074] calculate the failure coefficients of each communication device. The above formula combines the cube sum and square root operations of the indicators and the relationship between their product and the average value, more comprehensively reflecting the comprehensive impact of the three indicators on device failures, and being able to reflect the interaction and non-linear relationship between the indicators. For example, when one of the three indicators is particularly high while the other two are relatively low, the cubic operation can highlight the impact of this high-value indicator; while through the part of the relationship between the product and the average value, the synergistic effect between the indicators can be reflected. When the three indicators are all relatively high in a balanced manner, the value of this part will also increase accordingly.

[0075] The specific working process of the data analysis module further includes:

[0076] Number all the links in the communication process, and the numbers are sequentially: 1, 2,..., m;

[0077] Obtain the incoming data volume I j and the outgoing data volume O j of the j-th link;

[0078] First, calculate the traffic difference rate of the j-th link according to the obtained data:

[0079]

[0080] Then, calculate the average value of the traffic difference rates of all links

[0081] Next, calculate the average value of the incoming data volume according to the formula , and calculate the average value of the outgoing data volume according to the formula ;

[0082] Next, calculate the coefficient of variation of all link traffic based on the average value of the incoming data volume and the average value of the outgoing data volume.

[0083]

[0084] Finally, calculate the network traffic fault coefficient based on the average value of the difference rate of all link traffic and the coefficient of variation of all link traffic: S = γ * D AVE + δ * CV;

[0085] In the formula, j belongs to m, and γ and δ are weight coefficients respectively.

[0086] Through the above technical solution, this embodiment provides a process for analyzing network traffic faults during communication. By numbering all links in the communication process and according to the formula calculate the difference rate of the traffic of the j-th link, then calculate the average value of the difference rates of all link traffic. According to the formula calculate the average value of the incoming data volume, according to the formula calculate the average value of the outgoing data volume, and then according to the formula

[0087] calculate the coefficient of variation of all link traffic. Finally, perform a weighted sum of the average value of the difference rates of all link traffic and the coefficient of variation of all link traffic to obtain the network traffic fault coefficient. The above formula comprehensively evaluates the network fault coefficient by considering the traffic difference and dispersion degree of the links. The average value of the difference rate reflects the average imbalance degree of the incoming and outgoing data volumes of each link, while the coefficient of variation reflects the stability of the link traffic. When the traffic difference of the link is large and the dispersion degree is high, the network fault coefficient will increase accordingly, and vice versa.

[0088] The specific working process of the data analysis module further includes:

[0089] Obtain the signal-to-noise ratio SNR, bit error rate BER, and carrier-to-noise ratio CNR during a set time period of the communication process, and construct a mathematical model for the signal strength fault coefficient. The expression is:

[0090]

[0091] In the formula, ε, ∈, and θ are weight coefficients respectively.

[0092] Through the above technical solution, this embodiment provides a process for analyzing signal strength faults during communication. Obtain the signal-to-noise ratio SNR, bit error rate BER, and carrier-to-noise ratio CNR during a set time period of the communication process, and then through the formula

[0093] Calculate the signal strength fault coefficient. In the above formula, the higher the signal-to-noise ratio and the carrier-to-noise ratio, the better the signal quality. Their reciprocals are positively correlated with the fault coefficient, that is, the larger the value, the higher the probability of failure. The bit error rate directly reflects the probability of errors occurring in signal transmission. The higher the bit error rate, the greater the probability of signal strength failure. Therefore, the bit error rate is directly incorporated into the formula. By adjusting the weight coefficient, the influence of these three factors on the fault coefficient can be balanced according to the actual situation.

[0094] The working process of the fault diagnosis module includes:

[0095] Compare the fault coefficient R of the i-th communication device i with the threshold of the fault coefficient of the i-th communication device set by the system. If the fault coefficient R of the i-th communication device i is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the i-th communication device;

[0096] Calculate the fault coefficients of n communication devices in sequence. If the fault coefficients of all n communication devices are lower than the corresponding thresholds, the communication fault is not caused by the communication devices;

[0097] Compare the network traffic fault coefficient S with the threshold of the network traffic fault coefficient set by the system. If the network traffic fault coefficient is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the network traffic fault. Otherwise, the communication fault is not caused by the network traffic;

[0098] Compare the signal strength fault coefficient FSI with the threshold of the signal strength fault coefficient set by the system. If the signal strength fault coefficient is greater than or equal to the corresponding threshold, it indicates that the communication fault is caused by the signal strength fault. Otherwise, the communication fault is not caused by the signal strength.

[0099] Through the above technical solution, this embodiment is used to diagnose the cause of the communication fault according to the characteristic information related to the communication fault extracted by the data analysis module, and quickly determine the type of communication fault.

[0100] A diagnostic method for a communication fault rapid diagnosis system, the method includes the following steps:

[0101] Step S1, data acquisition: sequentially obtain the device operation status data, network traffic data, and signal strength data during the communication process, and send them to the data processing module;

[0102] Step S2, data processing: The data processing module performs cleaning, conversion, and normalization processing on the received data to obtain the preprocessed data, and sends it to the data analysis module;

[0103] Step S3, Fault Analysis: The data analysis module analyzes the processed data, extracts the feature information related to the fault, and sends the analysis result to the fault diagnosis module;

[0104] Step S4, Fault Diagnosis: The fault diagnosis module diagnoses the communication fault according to the analysis result of the data analysis module and in combination with the fault knowledge base, and determines the type and cause of the fault.

[0105] It should be noted that the formulas in this application are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The thresholds and weight coefficients involved in this application are all empirical values, and the selection is set by those skilled in the art according to the actual situation.

[0106] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements or use similar methods to replace the specific embodiments described, 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, characterized in that, The system includes: a data acquisition module, a data processing module, a data analysis module, and a fault diagnosis module; The data acquisition module is used to collect various parameters in the communication network in real time. The parameters include device operation status data, network traffic data, and signal strength data; The data processing module receives the data collected by the data acquisition module and is used to clean, transform, and normalize the data; The data analysis module is used to analyze the processed data and extract feature information related to faults; The fault diagnosis module diagnoses communication faults based on the feature information extracted by the data analysis module.

2. The quick communication fault diagnosis system according to claim 1, characterized in that The working process of the data acquisition module includes: According to the type of device and the communication protocol, the data acquisition module will automatically adapt the corresponding connection method and connect to each device in the communication network through the network interface; in a polling manner, according to a preset time interval, send a data acquisition request to the devices in the communication network. After sending the request, the data acquisition module waits for the device to respond and receives the data returned by the device; Transmit the collected data to the data processing module according to the set communication protocol and transmission method.

3. The quick communication fault diagnosis system according to claim 2, characterized in that, The device operation status data includes the CPU usage rate, memory usage rate, and port traffic of each device; The network traffic data includes the incoming and outgoing data volumes of each link; The signal strength data includes signal-to-noise ratio, bit error rate, and carrier-to-noise ratio.

4. A communication fault rapid diagnosis system according to claim 3, characterized in that The specific working process of the data analysis module includes: Number all communication devices in the communication process, and the numbers are in sequence: 1, 2,..., n; Get the CPU usage C of the i-th communication device i , memory usage M i and port flow F i , construct the mathematical model of the failure coefficient of the i-th communication equipment, the expression is: where \(i\in n\), \(R\) i is the failure coefficient of the \(i\)-th communication device, \(\alpha\) i and \(\beta\) i are weight coefficients respectively.

5. A communication fault rapid diagnosis system according to claim 3, characterized in that, The specific working process of the data analysis module also includes: Number all links in the communication process, and the numbers are in sequence: 1, 2,..., m; Obtain the inflow data volume I of the j-th link j and the outflow data volume O j ; First, calculate the traffic difference rate of the jth link according to the acquired data: Then, calculate the average value of the traffic difference rates of all links Next, according to the formula calculate the average value of the inflow data volume, and according to the formula calculate the average value of the outflow data volume; Next, calculate the traffic dispersion coefficient of all links according to the average value of the incoming data volume and the average value of the outgoing data volume Finally, calculate the network traffic fault coefficient based on the average value of all link traffic difference rates and the coefficient of variation of all link traffic: S = γ * DAVE + δ * CV; In the formula, j belongs to m, and γ and δ are weight coefficients respectively.

6. A rapid communication fault diagnosis system according to claim 3 of the network traffic fault coefficient, characterized in that, The specific working process of the data analysis module also includes: Obtain the signal-to-noise ratio SNR, bit error rate BER, and carrier-to-noise ratio CNR within a set time period of the communication process, and construct a mathematical model of the signal strength fault coefficient, and the expression is: In the formula, ε, ∈, and θ are weight coefficients respectively.

7. A communication fault rapid diagnosis system according to claim 1, characterized in that, The working process of the fault diagnosis module includes: Compare the failure coefficient R of the i-th communication device i with the threshold of the failure coefficient of the i-th communication device set by the system. If the failure coefficient R of the i-th communication device i is greater than or equal to the corresponding threshold, it indicates that the communication failure is caused by the failure of the i-th communication device; Calculate the fault coefficients of n communication devices in sequence. If the fault coefficients of the n communication devices are all lower than the corresponding thresholds, it is not a communication fault caused by communication devices; Compare the network traffic fault coefficient S with the system-set network traffic fault coefficient threshold. If the network traffic fault coefficient is greater than or equal to the corresponding threshold, it means that the communication fault is caused by network traffic faults. Otherwise, it is not a communication fault caused by network traffic; Compare the signal strength fault coefficient FSI with the system-set signal strength fault coefficient threshold. If the signal strength fault coefficient is greater than or equal to the corresponding threshold, it means that the communication fault is caused by signal strength faults. Otherwise, it is not a communication fault caused by signal strength.

8. A diagnostic method for a communication fault rapid diagnosis system according to any one of claims 1-7, characterized in that, The method includes the following steps: Step S1, Data Acquisition: Sequentially obtain the device operation status data, network traffic data, and signal strength data during the communication process, and send them to the data processing module; Step S2, Data Processing: The data processing module performs cleaning, transformation, and normalization on the received data to obtain preprocessed data, and sends it to the data analysis module; Step S3, Fault Analysis: The data analysis module analyzes the processed data, extracts the feature information related to the fault, and sends the analysis result to the fault diagnosis module; Step S4, Fault Diagnosis: The fault diagnosis module diagnoses the communication fault based on the analysis result of the data analysis module and in combination with the fault knowledge base to determine the type and cause of the fault.

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