WO2023179042A1

WO2023179042A1 - Data updating method, fault diagnosis method, electronic device, and storage medium

Info

Publication number: WO2023179042A1
Application number: PCT/CN2022/130723
Authority: WO
Inventors: 刘煌; 梁帅
Original assignee: 中兴通讯股份有限公司
Priority date: 2022-03-21
Filing date: 2022-11-08
Publication date: 2023-09-28
Also published as: CN116821141A

Abstract

Provided are a data updating method, a fault diagnosis method, an electronic device, and a storage medium, which relate to the field of communications. The data updating method comprises: acquiring historical index data of a service, according to a change rule of the historical index data over time, classifying the historical index data, and acquiring a classification result (101); according to an anomaly detection algorithm corresponding to the classification result, performing anomaly detection on the historical index data, and determining abnormal data in the historical index data (102); and according to the distribution of the abnormal data, updating a preset index threshold of the service (103).

Description

Data update method, fault diagnosis method, electronic equipment and storage media

Related applications

This application claims priority to the Chinese patent application with application number 202210283753.2, which was filed on March 21, 2022.

Technical field

Embodiments of the present application relate to the field of communications, and in particular to a data update method, fault diagnosis method, electronic device and storage medium.

Background technique

Big data systems are widely used not only in the Internet industry, but also in key fields such as government, finance, communications, aerospace, and military. This type of system usually has a massive user base of hundreds of millions. Service interruption or service quality degradation caused by any minor fault will bring huge losses. Moreover, such systems are large in scale, complex in composition and operation logic, resulting in frequent system failures. occurs, and it is difficult to find, locate and diagnose the fault after it occurs, and it is difficult to analyze and debug the system after it goes wrong.

The currently commonly used fault diagnosis method is fault diagnosis based on the graph neural network algorithm. This method requires training the graph neural network based on existing abnormal data to obtain a fault diagnosis model, and completing the root cause of the fault through the trained fault diagnosis model and real-time data. diagnosis. However, this method has low accuracy of fault diagnosis model due to the small sample size of abnormal data.

Contents of the invention

The main purpose of the embodiments of this application is to propose a data update method, fault diagnosis method, electronic device and storage medium, and update the fault diagnosis model through dynamic thresholds so that the fault diagnosis model can accurately locate faults and be applicable to various scenarios.

In order to achieve the above purpose, embodiments of the present application provide a data update method, which includes: obtaining historical indicator data of the business, classifying the historical indicator data according to the change pattern of the historical indicator data over time, and obtaining the classification results. ; Perform anomaly detection on the historical indicator data according to the anomaly detection algorithm corresponding to the classification result, and determine the abnormal data in the historical indicator data; update the preset indicators of the business according to the distribution of the abnormal data threshold.

Embodiments of the present application provide a fault diagnosis method, which includes: obtaining fault data of a service; when the indicator threshold of the service is not updated, analyzing the fault data according to a preset fault diagnosis model, generating and outputting Fault diagnosis results, wherein the preset fault diagnosis model includes a preset indicator threshold; when the indicator threshold of the service has been updated, the latest indicator threshold is obtained, and the preset indicator threshold is calculated based on the latest indicator threshold. The fault diagnosis model is updated, the updated fault diagnosis model is used to analyze the fault data, and fault diagnosis results are generated and output, wherein the latest index threshold is obtained through the data update method described in the above embodiment.

In order to achieve the above object, an embodiment of the present application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores information that can be processed by the at least one processor. Instructions executed by a processor, the instructions are executed by the at least one processor, so that the at least one processor can perform the data update method described in the above embodiment, or can perform the fault diagnosis described in the above embodiment. method.

In order to achieve the above purpose, embodiments of the present application also propose a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, the data update method described in the above embodiments is implemented, or the above implementation can be performed. The fault diagnosis method described in the example.

This application proposes a data update method that obtains classification results by classifying historical indicator data of the business. The classification results indicate the change pattern of historical indicator data over time. The corresponding anomaly detection algorithm is selected according to the type of historical indicator data to analyze the historical indicators. Detect the data, identify abnormal data, and then determine or update the business indicator thresholds based on the distribution of abnormal data. In other words, this application can periodically or continuously obtain historical indicator data of the business, determine the latest indicator threshold based on the latest historical indicator data, and obtain the current operating status of the business in a timely manner through this dynamic threshold.

A fault diagnosis method proposed in this application obtains the fault data of the service in real time, and then determines whether the indicator threshold of the service has been updated. If the indicator threshold of the service has not been updated, the preset fault diagnosis model and the preset indicator threshold are used. Analyze the fault data and obtain the fault diagnosis results (the cause of the fault). If the indicator threshold of the business has been updated, the preset fault diagnosis model is updated according to the latest indicator threshold, and the updated fault diagnosis model is used to complete fault diagnosis and determine the cause of the fault. In other words, the fault diagnosis model of this application is not static, but is constantly updated based on the latest indicator thresholds, and the latest indicator thresholds are determined based on the latest historical indicator data. In this way, the fault diagnosis model can be timely based on the current operation of the business. The adaptive adjustment of state changes makes the model more suitable for actual business scenarios, thereby improving the accuracy of fault diagnosis results.

Description of the drawings

One or more embodiments are exemplified by the pictures in the corresponding drawings, and these exemplary illustrations do not constitute limitations to the embodiments.

Figure 1 is a flow chart of a data update method provided by an embodiment of the present application;

Figure 2 is a flow chart of a fault diagnosis method provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, each embodiment of the present application will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in each embodiment of the present application, many technical details are provided to enable readers to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solution claimed in this application can also be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present application. The various embodiments can be combined with each other and referenced with each other on the premise that there is no contradiction.

The currently commonly used fault diagnosis method is fault diagnosis based on the graph neural network algorithm. This method often abstracts the big data system into a network graph and obtains exceptions from all nodes in the network graph (such as servers, clients, etc.) Data is used to train the pre-created graph neural network to obtain a fault diagnosis model. When a fault occurs, abnormal data is input into the fault diagnosis model in real time to complete the root cause diagnosis of the fault. However, this method needs to prepare a large amount of abnormal data in the early training stage, and it is abnormal data that occurs when a fault occurs, and this kind of abnormal data often has a small sample size. If abnormal data with a small sample size is used for training, the fault diagnosis obtained The accuracy of the model is low; if the model is iteratively trained by accumulating abnormal data, the cost is extremely high and it takes a long time.

The embodiment of the present application relates to a data update method, as shown in Figure 1, including:

Step 101: Obtain the historical indicator data of the business, classify the historical indicator data according to the change pattern of the historical indicator data over time, and obtain the classification results.

In this embodiment, the historical indicator data of the business includes: various indicator data related to the operation of the business itself and indicator data of the underlying platform on which the business operation depends. For example: [Business Service A] is located in the business center. The various data required by business service A come from [Other Services 1] and [Other Services 2] in the data center, and the data center relies on the underlying PaaS platform to complete it. In order to obtain the support of industry applications, when obtaining historical indicator data, you need to obtain the indicator data of the PaaS platform itself, the indicator data of the data center, the indicator data of the business center, etc. It is understandable that for different businesses, different scenarios, and different devices, the content of historical indicator data may be different.

In addition, when obtaining historical indicator data, it is necessary to select indicator data for an appropriate time period based on various factors such as business type, fault diagnosis requirements (data volume, time, accuracy, etc.), application scenarios, etc. It should be noted that the updated indicator thresholds must conform to the current operating status of the business as much as possible. Therefore, when obtaining historical indicator data, try to select the data closest to the current moment, that is, the latest historical indicator data. For example: For a certain business, it is necessary to obtain a week's historical indicator data to determine the indicator threshold. Then it is best to select the data of the week before the current time as the historical indicator data, rather than selecting the data of a certain week a year ago to calculate the indicator threshold. .

It should be noted that if the indicator threshold of the business has not been determined, the indicator threshold of the business at the current moment can be obtained through the data update method of this application. If the indicator threshold of the business has been determined, the index threshold of the business can be continuously updated through the data update method of this application. Some indicator thresholds, or in other words, historical indicator thresholds are constantly updated. In other words, when this application obtains historical indicator data, it can be obtained continuously, in real time, or periodically.

In one embodiment, classifying the historical indicator data according to the change pattern of the historical indicator data over time and obtaining the classification results includes: obtaining the autocorrelation coefficient amplitude spectrum of the historical indicator data, and based on the The autocorrelation coefficient amplitude spectrum determines whether the historical indicator data is periodic; obtains the mean and variance of the historical indicator data, and determines whether the historical indicator data is volatile based on the mean and variance; The indicator data is linearly fitted, a fitting function is obtained, and whether the historical indicator data has a trend is determined based on the fitting function.

Specifically, to determine the periodicity of historical indicator data, first obtain the autocorrelation coefficient map of the historical indicator data, perform Fourier transform on the autocorrelation coefficient map to obtain the autocorrelation coefficient amplitude spectrum, and calculate the maximum value of the autocorrelation coefficient amplitude spectrum. , mean and variance. When the maximum value is greater than the sum of the mean and the variance, the historical indicator data is determined to be periodic data. When the maximum value is less than or equal to the sum of the mean and the variance, the historical indicator data is determined to be aperiodic data.

In addition, for the determination of non-periodic data, the autocorrelation coefficient of historical indicator data can also be directly calculated. If the autocorrelation coefficient is less than the preset threshold, the historical indicator data can be directly determined to be non-periodic data. However, when the autocorrelation coefficient is greater than the preset threshold, it cannot be determined that the historical indicator data is periodic data, because the autocorrelation coefficient of non-periodic data may also be greater than the preset threshold. In one embodiment, for historical indicator data whose autocorrelation coefficient is greater than a preset threshold, its autocorrelation coefficient amplitude spectrum can be obtained. If its amplitude spectrum has no peaks, it can be directly determined that the historical indicator data is aperiodic data.

In this embodiment, for periodic data, the strength of the periodicity can be further determined, and different anomaly detection algorithms can be selected according to the strength of the periodicity. For example: for strongly periodic data, you can choose a simpler and less complex anomaly detection algorithm; for weakly periodic data, you can choose an anomaly detection algorithm with better detection results and stronger compatibility. To judge the strength of periodic data, the maximum peak value of the main peak of the autocorrelation coefficient amplitude spectrum can be obtained, which is determined by the following formula:

Among them, value _max is the maximum value of the amplitude spectrum, μ is the mean value, σ is the variance, peak _max is the maximum peak value of the main wave peak, and λ is the preset discrimination rate.

In one embodiment, for detecting data volatility, the mean and variance of historical indicator data can be obtained. When the historical indicator data meets any of the following conditions, it is determined that it has no volatility. Otherwise, the historical indicator data has volatility:

Among them, μ is the mean value of historical indicator data, σ is the variance of historical indicator data, and λ is the preset adjustment parameter.

In one embodiment, to determine the trend data, the historical indicator data can be linearly fitted, a fitting function is obtained, and whether the overall data has an upward or downward trend is determined based on the type of the fitting function or the slope of the fitting function. trend.

Step 102: Detect the historical indicator data according to the anomaly detection algorithm corresponding to the classification result, and determine the abnormal data in the historical indicator data.

In one embodiment, when the classification result is that the historical indicator data is periodic data, a regression algorithm is used to perform anomaly detection on the historical indicator data; when the classification result is that the historical indicator data is aperiodic data, a clustering algorithm is used to detect the historical indicator data. Anomaly detection is performed on the indicator data; when the classification result is that the historical indicator data is non-volatile data, the box plot algorithm is used to detect anomalies in the historical indicator data; when the classification result is that the historical indicator data is trend data, time series decomposition is used The algorithm performs anomaly detection on historical indicator data.

Specifically, the corresponding anomaly detection algorithm is selected according to the type of historical indicator data. For cyclical historical indicator data, the data trend shows a certain periodic pattern over time and there is a correlation between the data, so the regression algorithm is used to detect anomalies. Detection and regression algorithms can be Histogram-based decision tree algorithms, Leaf-wise algorithms with depth restrictions, unilateral gradient sampling algorithms, etc. For non-periodic historical indicator data, the data is volatile, but the fluctuation patterns are messy and non-periodic, so a clustering algorithm is used to detect anomalies. The clustering algorithm can be Gaussian mixture model algorithm, K-means clustering algorithm, mean shift clustering algorithm, density-based clustering algorithm, agglomerative hierarchical clustering algorithm, etc. For historical indicator data without volatility, the data is basically in a constant value state, or basically oscillates in a very small narrow range, so the box plot algorithm is used to detect anomalies. For trending historical indicator data, the data will fluctuate locally, but the overall trend will be upward or downward, so the time series decomposition algorithm is used to detect anomalies. Time series decomposition algorithms can be X11 decomposition algorithm (X11 decomposition), classic decomposition algorithm, SEATS decomposition algorithm (SEATS decomposition), STL decomposition algorithm (STL decomposition), etc.

Step 103: Update the business indicator threshold according to the distribution of abnormal data.

In this embodiment, updating the preset indicator threshold of the business according to the distribution of the abnormal data includes: determining the upper quartile and lower quartile of the abnormal data according to the distribution of the abnormal data. number and interquartile range; determine the current indicator threshold of the business based on the upper quartile, the lower quartile and the interquartile range, and use the current indicator threshold to replace the preset indicator threshold.

Specifically, after arranging the abnormal data in order from small to large, and dividing the set of data into four equal parts, the lower quartile Q1, the median Q2, and the upper quartile Q3 are obtained. After all the values are arranged from small to large, the 25th, 50th, and 75th percentile numbers are the lower quartile Q1, the median Q2, and the upper quartile Q3 respectively. The upper quartile and the lower quartile The difference between the quartiles is the interquartile range IQR, the upper limit of the indicator threshold is Q3+1.5×IQR, and the lower limit of the indicator threshold is Q3+1.5×IQR. For example: There is a set of abnormal data arranged from small to large: (2710, 2755, 2850, 2880, 2880, 2890, 2920, 2940, 2950, 3050, 3130, 3325). After being divided into four equal parts, it is (2710, 2755 , 2850|2880, 2880, 2890|2920, 2940, 2950|3050, 3130, 3325), Q1＝2850+(2880-2850)/2, Q2＝2890+(2920-2890)/2, Q3＝2950+ (3050-2950)/2. It should be noted that for different businesses and different indicator data, the indicator threshold can be a range or a value.

Taking the historical indicator data as CPU usage as an example, the data update method is explained. (1) First, select the CPU usage every minute of the day, classify the CPU usage data, and calculate the mean and variance of the data to determine whether it has Volatility. Calculate the autocorrelation coefficient amplitude spectrum of the CPU usage data to perform period discrimination. Calculate the fitting function of the CPU usage data to identify trends. It can be determined that the CPU usage data is non-periodic data. (2) Since the CPU usage data is non-periodic data, a clustering algorithm is used for anomaly detection, that is, the entire CPU usage data is clustered according to the set number of categories, and then the abnormal ones are found according to the discrimination rules. That category or categories and label them. In order to achieve the purpose of anomaly detection. (3) Finally, based on the distribution of abnormal data, the normal CPU usage indicator threshold interval is obtained.

The embodiment of the present application relates to a fault diagnosis method, as shown in Figure 2, including:

Step 201: Obtain service fault data.

In this embodiment, the fault data includes: log data, alarm data, indicator data, third-party operation and maintenance data, etc. The specific fault data acquisition time can be to obtain the business fault data in real time when the fault occurs, and then store the fault data so that it can be called during subsequent fault diagnosis. It can also be obtained from relevant equipment or platforms during fault diagnosis. For example: fault diagnosis methods are deployed in the data center in the form of modules. The data center is based on the data warehouse and data platform, and produces data into data API services to provide it to the business in a more efficient way. [Business Service A] is located in the business center. The various data required by business service A come from [Other Services 1] and [Other Services 2] in the data center. The data center relies on the underlying PaaS platform to complete industry applications. Support, then when obtaining fault data, you need to obtain fault data of the PaaS platform, fault data of the data center, fault data of the business center, etc.

Step 202: When the service indicator threshold is not updated, the fault data is analyzed according to a preset fault diagnosis model, and a fault diagnosis result is generated and output, where the preset fault diagnosis model includes the preset indicator threshold.

Step 203: When the indicator threshold of the service has been updated, the latest indicator threshold is obtained, the preset fault diagnosis model is updated according to the latest indicator threshold, and the fault data is analyzed using the updated fault diagnosis model. Analyze, generate and output fault diagnosis results, where the latest indicator threshold is obtained through the data update method described in the above embodiment.

In this embodiment, the update of the indicator threshold is obtained through the data update method described in the above embodiment, and the data update method can be deployed on a certain device or platform in the form of a module, and the fault diagnosis method can also be deployed on a certain device or platform in the form of a module. On the same or different device or platform as the data update method. When performing fault diagnosis on a business, you can first obtain the fault data of the business, and then determine whether the indicator threshold of the business has been updated. If the indicator threshold has been updated, obtain the updated indicator threshold and replace the preset one with the updated indicator threshold. The preset indicator threshold in the fault diagnosis model (the preset indicator threshold can be initially manually set, or it can be the indicator threshold at a historical moment obtained through the data update method), and the updated fault diagnosis model is obtained.

In one embodiment, the preset fault diagnosis model includes multiple fault diagnosis templates, and the fault diagnosis templates are created according to a rule engine and preset fault diagnosis conditions. The rule engine can be a Drools engine, an Open Tablets engine, an Easy Rules engine, etc. The rule engine converts preset fault diagnosis conditions into fault diagnosis templates. A fault diagnosis model usually includes multiple fault diagnosis templates.

It should be noted that through the rule engine, users can create fault diagnosis models based on preset fault diagnosis conditions without writing any code or training, which avoids the problems of requiring professionals to write code, large amounts of training data, and long training time. However, the thresholds in the rule engine are generally fixed values, which cannot satisfy fault diagnosis in various scenarios of the same business. Therefore, this application also combines the data update method to obtain dynamic thresholds. In this way, the fault diagnosis model of this application does not need to collect a large amount of abnormal data. Conduct training and adaptively adjust the threshold according to the current operating status of the business. The entire fault diagnosis method has low cost, short model construction time and high accuracy.

The fault diagnosis method of this application is explained by taking the sudden drop in the number of Yarn's running tasks as an example. For this fault, the preset fault diagnosis conditions include: (1) If the task submission service is down, then the number of Yarn's running tasks will be caused. The reason for the decrease is that the submitting node is down; (2) If the submitting task service is not down, but the Pg service is abnormal, then the reason for the decrease in the number of Yarn running tasks is that the Pg service is abnormal; (3) If the submitting task service is different from the Pg service None of them are abnormal, but the task initialized at the current moment is abnormal, so the reason for the decrease in the number of running tasks in Yarn is the task initialization exception. Create multiple fault diagnosis templates through the above fault diagnosis conditions and rule engine, and create a fault diagnosis model based on multiple fault diagnosis templates.

The indicator data involved in this failure are (1) the number of submitted task services; (2) the number of Pg service processes; (3) the number of initial tasks. For (1) and (2), generally the corresponding indicator thresholds are fixed values, so there is no need for data update methods to update. And whether there is any abnormality in the initial number of tasks in the current hour (how many tasks are initialized before how many tasks need to be submitted), This cannot be judged using a fixed threshold, because different nodes have different task volumes, so the task volume is different at different hours every day, and the number of tasks at the same hour on different days is not necessarily the same, so the data update method is used to continuously update The indicator threshold of the number of initial tasks is continuously updated, and the fault diagnosis model is continuously updated to complete the root cause diagnosis of the sudden drop in the number of running tasks in Yarn.

In addition, it should be understood that the division of steps in the various methods above is only for the purpose of clear description. During implementation, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, all It is within the protection scope of this application; adding insignificant modifications to the process or introducing insignificant designs without changing the core design of the process are all within the protection scope of this application.

The embodiment of the present application relates to an electronic device, as shown in Figure 3, including: at least one processor 301; and a memory 302 communicatively connected with the at least one processor 301; wherein the memory 302 stores data that can Instructions executed by the at least one processor 301, the instructions are executed by the at least one processor 301, so that the at least one processor 301 can perform the fault diagnosis method of the above embodiment, or can perform the above embodiment data update method.

Among them, the memory and the processor are connected using a bus. The bus can include any number of interconnected buses and bridges. The bus connects one or more processors and various circuits of the memory together. The bus may also connect various other circuits together such as peripherals, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. The bus interface provides the interface between the bus and the transceiver. A transceiver may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. The data processed by the processor is transmitted over the wireless medium through the antenna. Further, the antenna also receives the data and transmits the data to the processor.

The processor is responsible for managing the bus and general processing, and can also provide a variety of functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

Embodiments of the present application relate to a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the above fault diagnosis method or data update method is implemented.

That is, those skilled in the art can understand that all or part of the steps in the method of implementing the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium and includes several instructions to cause a device ( It may be a microcontroller, a chip, etc.) or a processor (processor) that executes all or part of the steps of the method described in each embodiment of the application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

Those of ordinary skill in the art can understand that the above-mentioned embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made in form and details without departing from the spirit and spirit of the present application. scope.

Claims

A data update method including:

Obtain the historical indicator data of the business, classify the historical indicator data according to the change pattern of the historical indicator data over time, and obtain the classification results;

Perform anomaly detection on the historical indicator data according to the anomaly detection algorithm corresponding to the classification result, and determine the abnormal data in the historical indicator data;

According to the distribution of the abnormal data, the preset indicator threshold of the business is updated.
The data update method according to claim 1, wherein said classifying the historical indicator data according to the change pattern of the historical indicator data over time and obtaining the classification result includes:

Obtain the autocorrelation coefficient amplitude spectrum of the historical indicator data, and determine whether the historical indicator data is periodic based on the autocorrelation coefficient amplitude spectrum;

Obtain the mean and variance of the historical indicator data, and determine whether the historical indicator data is volatile based on the mean and variance;

Linear fitting is performed on the historical indicator data, a fitting function is obtained, and whether the historical indicator data has a trend is determined based on the fitting function.
The data update method according to claim 2, wherein the determining whether the historical indicator data is periodic based on the autocorrelation coefficient amplitude spectrum includes:

Calculate the maximum value, mean value and variance of the autocorrelation coefficient amplitude spectrum;

When the maximum value is greater than the sum of the mean value and the variance, the historical indicator data is periodic data;

When the maximum value is less than or equal to the sum of the mean value and the variance, the historical indicator data is aperiodic data.
The data updating method according to claim 2, wherein whether the historical indicator data has volatility is determined through the following formula, including:

Among them, μ is the mean value of historical indicator data, σ is the variance of historical indicator data, and λ is the preset adjustment parameter.
The data update method according to any one of claims 1-4, wherein the method further includes:

When the classification result shows that the historical indicator data is periodic data, a regression algorithm is used to perform anomaly detection on the historical indicator data;

When the classification result shows that the historical indicator data is non-periodic data, a clustering algorithm is used to perform anomaly detection on the historical indicator data;

When the classification result is that the historical indicator data is non-volatile data, a box plot algorithm is used to perform anomaly detection on the historical indicator data;

When the classification result shows that the historical indicator data is trend data, a time series decomposition algorithm is used to perform anomaly detection on the historical indicator data.
The data update method according to claim 1, wherein updating the preset indicator threshold of the business according to the distribution of the abnormal data includes:

According to the distribution of the abnormal data, determine the upper quartile, lower quartile and interquartile range of the abnormal data;

The current indicator threshold of the business is determined based on the upper quartile, the lower quartile and the interquartile range, and the current indicator threshold is used to replace the preset indicator threshold.
A fault diagnosis method including:

Obtain business fault data;

When the indicator threshold of the service is not updated, the fault data is analyzed according to a preset fault diagnosis model, and a fault diagnosis result is generated and output, wherein the preset fault diagnosis model includes the preset indicator threshold;

When the indicator threshold of the service has been updated, the latest indicator threshold is obtained, the preset fault diagnosis model is updated according to the latest indicator threshold, and the fault data is analyzed using the updated fault diagnosis model. , generate and output fault diagnosis results, wherein the latest indicator threshold is obtained through the data update method as described in any one of claims 1-6.
The fault diagnosis method according to claim 7, wherein the preset fault diagnosis model includes a plurality of fault diagnosis templates, and the fault diagnosis templates are created according to a rule engine and preset fault diagnosis conditions.
An electronic device including:

at least one processor; and,

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor, so that the at least one processor can perform as claimed in any one of claims 1 to 6 The data update method described above, or the fault diagnosis method described in any one of claims 7 to 8 can be performed.
A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the data update method according to any one of claims 1 to 6 is implemented, or the computer program is capable of executing the data update method according to any one of claims 7 to 8. The fault diagnosis method described in any one of them.