Fault positioning method and device, equipment and storage medium

Abstract

The invention relates to the technical field of fault data positioning, and discloses a fault positioning method and device, equipment and a storage medium, and the method comprises the steps: obtaining original business data, and carrying out the preprocessing of the original business data, and obtaining target business data; determining candidate fault features based on the target business data; and fault point positioning is carried out based on the candidate fault features to obtain fault data, and the fault data at least comprises a fault position and a fault reason. According to the invention, the transaction fault can be rapidly positioned, and the fault reason can be accurately positioned.

Description

Technical Field

[0001] The present application relates to the technical field of fault data location, and in particular to a fault location method, device, equipment and storage medium. Background Art

[0002] Locating the fault point of the business system is a key step in ensuring the high availability of the system during system operation and maintenance. Common monitoring technologies in the industry focus on fault identification (such as monitoring the increase in the overall failure rate), but lack automated analysis of the root cause of the fault. Identifying the root cause, quickly locating the precise part of the fault and providing targeted intervention will help the system return to normal as soon as possible. However, there are many reasons for failures. The traditional approach is to conduct targeted monitoring of high-frequency problems that have occurred in the past. The disadvantage is that it is difficult to foresee the failure before it occurs for the first time, and after the failure occurs, it needs to be personalized for the specific problem, which is difficult to expand.

[0003] Therefore, how to quickly locate transaction faults and accurately locate the cause of the fault is a problem that urgently needs to be solved.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a fault location method, device, equipment and storage medium, aiming to solve the technical problem of how to quickly locate transaction faults and accurately locate the cause of the fault.

[0006] To achieve the above object, the present application proposes a fault location method, the method comprising:

[0007] Acquire original business data, and pre-process the original business data to obtain target business data;

[0008] Determining candidate fault features based on the target service data;

[0009] The fault point is located based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0010] In one embodiment, the obtaining of original business data and preprocessing the original business data to obtain target business data includes:

[0011] Obtaining original business data, and performing low-latency data extraction based on the original business data to obtain low-latency business data;

[0012] Identifying non-final data in the low-latency service data;

[0013] The low-latency service data is cleaned according to the non-final data to obtain target service data.

[0014] In one embodiment, determining candidate fault features based on the target service data includes:

[0015] Obtain historical fault data;

[0016] The target service data is analyzed based on the historical fault data to obtain candidate fault features.

[0017] In one embodiment, the analyzing the target service data based on the historical fault data to obtain candidate fault features includes:

[0018] Extracting features from the target business data and the historical fault data respectively to obtain transaction data features and historical fault data features;

[0019] Calculate similarity based on the transaction data features and the historical fault data features to obtain feature similarity;

[0020] A candidate fault feature is determined based on the feature similarity.

[0021] In one embodiment, the locating the fault point based on the candidate fault features to obtain fault data includes:

[0022] determining a fault index based on the candidate fault features;

[0023] The fault point is located based on the fault index to obtain fault data.

[0024] In one embodiment, determining a fault index based on the candidate fault feature includes:

[0025] Classifying the candidate fault features based on transaction types to obtain multiple candidate fault feature sets;

[0026] Obtaining a transaction data set corresponding to each of the fault feature sets;

[0027] Determining a transaction volume and a transaction failure rate based on the transaction data set;

[0028] The failure index of each type of transaction data is determined according to the transaction volume and the transaction failure rate.

[0029] In one embodiment, locating the fault point based on the fault index to obtain fault data includes:

[0030] Determining a fault level of each candidate fault feature based on the fault index;

[0031] determining a target fault feature based on the fault level;

[0032] Based on the target fault characteristics, the fault point is located to obtain the fault data location and the cause of the fault data;

[0033] The fault data is determined according to the fault data location and the cause of the fault data.

[0034] In addition, to achieve the above purpose, the present application also proposes a fault location device, the fault location device comprising:

[0035] A preprocessing module, used to obtain original business data and preprocess the original business data to obtain target business data;

[0036] A determination module, configured to determine candidate fault features based on the target service data;

[0037] A positioning module is used to locate the fault point based on the candidate fault characteristics to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0038] In addition, to achieve the above objectives, the present application also proposes a fault location device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the fault location method described above.

[0039] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, the steps of the fault location method described above are implemented.

[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the fault location method described above are implemented.

[0041] The present application provides a fault location method, which first obtains original business data and preprocesses the original business data to obtain target business data; determines candidate fault features based on the target business data; locates the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause, and can quickly locate the transaction fault and accurately locate the fault cause.

[0042] In summary, the present application quickly and accurately determines the target business data by preprocessing the original business data, and then determines the candidate fault characteristics and locates the fault point based on the target business data, thereby quickly and accurately determining fault data such as the fault location and fault cause, overcoming the technical defects of low fault location efficiency and poor accuracy, and can quickly locate transaction faults and accurately locate the cause of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 A flowchart of the first embodiment of the fault location method of the present application is provided;

[0046] Figure 2 A schematic diagram of data extraction for multi-database hybrid deployment provided in Example 1 of the fault location method of this application;

[0047] Figure 3 The overall flow chart of fault point location provided in the first embodiment of the fault location method of the present application;

[0048] Figure 4 A flowchart of the second embodiment of the fault location method of the present application is provided;

[0049] Figure 5 A schematic diagram of a fault index provided in Example 2 of the fault location method of the present application;

[0050] Figure 6 This is a schematic diagram of the module structure of the fault location device according to an embodiment of the present application;

[0051] Figure 7 A schematic diagram of the device structure of the hardware operating environment involved in the fault locating method of the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of the embodiment of the present application is: obtain original business data, and pre-process the original business data to obtain target business data; determine candidate fault features based on the target business data; locate the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0056] Locating the fault point of the business system is a key step in ensuring the high availability of the system during system operation and maintenance. Common monitoring technologies in the industry focus on fault identification (such as monitoring the increase in the overall failure rate), but lack automated analysis of the root cause of the fault. Identifying the root cause, quickly locating the precise part of the fault and providing targeted intervention will help the system return to normal as soon as possible. However, there are many reasons for failures. The traditional approach is to conduct targeted monitoring of high-frequency problems that have occurred in the past. The disadvantage is that it is difficult to foresee the failure before it occurs for the first time, and after the failure occurs, it needs to be personalized for specific problems, which is difficult to expand. Therefore, how to quickly locate transaction failures and accurately locate the cause of the failure is a problem that needs to be solved urgently.

[0057] The present application quickly and accurately determines the target business data by preprocessing the original business data, and then determines the candidate fault characteristics and locates the fault point based on the target business data, thereby quickly and accurately determining fault data such as the fault location and fault cause, overcoming the technical defects of low fault location efficiency and poor accuracy, and can quickly locate transaction faults and accurately locate the cause of the fault.

[0058] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a fault location device, etc. The following takes the fault location device as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the present application embodiment provides a fault location method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the fault location method of the present application.

[0060] In this embodiment, the fault location method includes steps S10 to S30:

[0061] Step S10, obtaining original business data, and preprocessing the original business data to obtain target business data.

[0062] It should be noted that business data refers to data generated by various business activities in a business system, such as transaction records, user behavior logs, system operation logs, etc., which are not specifically limited in this embodiment. These data usually exist in various forms such as text, numerical values, timestamps, etc., and the data volume is huge and contains rich information. Raw business data is often unprocessed, and direct use for fault location may result in low efficiency and poor accuracy.

[0063] It is understandable that the preprocessing steps include but are not limited to data cleaning, data normalization, data dimension reduction, etc., the purpose of which is to improve the accuracy and efficiency of subsequent fault location. The preprocessed target business data will be more suitable for fault feature extraction and analysis.

[0064] In a feasible implementation, step S10 may include: obtaining original business data, and performing low-latency data extraction based on the original business data to obtain low-latency business data; identifying non-final data in the low-latency business data; and performing data cleaning on the low-latency business data according to the non-final data to obtain target business data.

[0065] It should be noted that in this embodiment, preprocessing mainly includes low-latency business data extraction, data cleaning and fault data result marking, among which low-latency business data extraction refers to the rapid extraction of data closely related to current business activities from the original business data to reduce the delay in data processing. Data cleaning is to eliminate non-final data to ensure the accuracy of subsequent analysis. Fault data result marking refers to marking possible fault data during data preprocessing to facilitate subsequent fault feature extraction and fault point location.

[0066] It is understandable that low-latency business data extraction is to minimize the coupling effect of the monitoring system on the business system, and use the Kafka queue method to exchange low-latency business data, that is, the business system stores the original data in the queue as a producer, and the monitoring system receives the original data from the queue as a consumer.

[0067] like Figure 2 As shown, Figure 2 This is a schematic diagram of data extraction for mixed deployment of multiple databases. The A01, A02, A03, A04, A99 and other data in the Oracle multi-database and the X01, X02, X03, X04, X99 and other data in the TD-SQL multi-database are mixed and extracted through the Kafka queue and analyzed using the data analysis engine, and then used as the data source of the monitoring system.

[0068] It is worth noting that non-final data refers to data that has not yet reached the final state during the business processing process, that is, unfinished transactions, temporary records, etc. Eliminating non-final data can ensure that only complete and valid data enters the next step of processing.

[0069] It is worth noting that the fault data result marking means that the monitoring system marks the result of the original business data details according to specific monitoring purposes. Mark 1 represents normal and 0 represents a fault.

[0070] Step S20: determining candidate fault features based on the target service data.

[0071] It should be noted that candidate fault features refer to those features that may indicate system abnormalities or faults. These features can be abnormal values ​​in business data, sudden changes in data patterns, abnormal fluctuations in business performance indicators, etc. By analyzing the target business data, candidate fault features can be identified, thus providing a basis for subsequent fault location.

[0072] It is understandable that the determination of candidate fault features usually involves statistical analysis, pattern recognition, machine learning and other technologies to ensure that features with potential fault indication significance can be accurately screened out from a large amount of data, and this embodiment does not impose specific limitations on this. After determining the candidate fault features, these features can be further analyzed to determine the specific location and cause of the fault.

[0073] In the specific implementation, candidate fault features are determined based on statistical analysis, that is, candidate fault features are defined through experience summary. Common fault causes are sorted out, for example, faults originating from common faults such as internal system changes and external sporadic faults such as the bank to which the payment card belongs and the branch to which the transaction belongs, and then candidate fault features are defined, for example, @BN_BANK: UnionPay bank number, @CARD_TYPE: card type, @TRX_TYPE: transaction type, @DIV_NAME: branch name, @DB_TYPE: database type, @CARD_END1: the last digit of the card number, @BRANCH: the branch to which the device belongs, the country of the customer, etc.

[0074] In a feasible implementation, step S20 may include: acquiring historical fault data; and analyzing the target business data based on the historical fault data to obtain candidate fault features.

[0075] It should be noted that historical fault data refers to various types of information related to faults recorded during the operation of the system, including but not limited to the time, duration, type, cause, handling process, and result of the fault, and the system state when the fault occurs. Historical fault data usually consists of system logs, fault reports, user feedback, maintenance records, etc., and this embodiment does not specifically limit this.

[0076] It is understandable that by automatically analyzing historical fault data, patterns and trends related to faults can be discovered, thereby helping to determine candidate fault features. After obtaining historical fault data, a variety of analysis methods can be used to extract candidate fault features. For example, time series analysis can be used to identify periodic patterns in the data, cluster analysis can be used to discover abnormal clusters in the data, or association rule mining can be used to find correlations between fault events. Through these analysis methods, features with potential fault indication significance can be effectively extracted from historical fault data, providing a more accurate basis for fault location. This embodiment does not impose specific restrictions on this.

[0077] It is worth noting that in order to improve the accuracy of fault location, it is also possible to combine real-time monitoring data and real-time business data for comprehensive analysis. Real-time monitoring data can provide instant information on the system's operating status, while real-time business data reflects the current business's operating status. By combining historical fault data, real-time monitoring data, and real-time business data, the system status can be analyzed more comprehensively, thereby more accurately determining the characteristics of candidate faults and providing a more reliable basis for fault location.

[0078] In a feasible implementation, the target business data is analyzed based on the historical fault data to obtain candidate fault features, including: extracting features from the target business data and the historical fault data respectively to obtain transaction data features and historical fault data features; calculating similarity between the transaction data features and the historical fault data features to obtain feature similarity; and determining candidate fault features based on the feature similarity.

[0079] It should be noted that in the present embodiment, the candidate fault features are determined by feature similarity calculation, and the feature similarity can be calculated by a variety of methods, such as cosine similarity, Euclidean distance or Manhattan distance. Cosine similarity evaluates the similarity between two vectors by measuring the angle between them, and is suitable for evaluating the similarity between feature vectors. Euclidean distance measures the difference between two points by calculating the straight-line distance between them in multidimensional space, and is suitable for similarity calculation of continuous numerical features. Manhattan distance takes into account the sum of the absolute wheelbase in each dimension, and is suitable for evaluating the absolute position difference of data.

[0080] It is understandable that transaction data features refer to various data indicators related to the target business, such as transaction volume, transaction amount, transaction failure rate, user access volume, etc. These features can reflect the health status and potential problems of business operations. By real-time monitoring and analysis of these features, abnormal situations in business operations can be discovered in a timely manner, thereby providing strong support for fault location. Based on the similarity calculation between transaction data features and historical fault data features, transaction data features whose feature similarity reaches the set threshold are used as candidate fault features.

[0081] It is worth noting that after determining the candidate fault features, machine learning algorithms can be further used to screen and optimize the features. For example, algorithms such as random forests, support vector machines, or neural networks can be used to sort the features according to their contribution to fault prediction, thereby screening out the most valuable features for fault location. In this way, the feature dimension can be effectively reduced and the efficiency and accuracy of fault location can be improved.

[0082] In order to further improve the accuracy of fault location, an expert system or knowledge base can be introduced. The expert system simulates the decision-making process of human experts and combines domain knowledge and empirical rules to diagnose and locate faults. The knowledge base stores a large number of fault cases and solutions. By matching the current fault characteristics with the cases in the knowledge base, the cause of the fault can be quickly located and the corresponding solution can be provided.

[0083] Step S30, locating the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0084] It should be noted that in the process of locating the fault point, a variety of technical means can be used, such as rule-based reasoning, pattern matching, signal processing, etc. The rule-based reasoning technology analyzes the relationship between fault characteristics and faults through a predefined set of rules to determine the fault point; the pattern matching technology identifies possible fault points by comparing the similarity between the current fault characteristics and historical fault cases; the signal processing technology is suitable for analyzing various signals in the operation of the system, such as current, voltage, temperature, etc., and locating the fault point through abnormal changes in the signal. This embodiment does not impose specific restrictions on this.

[0085] It is understandable that after obtaining the fault data, further fault cause analysis and fault impact assessment can be conducted. Fault cause analysis aims to determine the specific factors that cause the fault, which may involve in-depth inspection of system components, analysis of operation logs, and consideration of relevant environmental factors. Fault impact assessment focuses on the impact of the fault on the operation of the entire system, including the impact of the fault on business processes, the impact on user services, and possible economic losses.

[0086] It is worth noting that in order to ensure the timeliness and accuracy of fault location, a fault response mechanism can be established, which includes fault detection, fault notification, fault response and fault recovery. The fault detection link is responsible for real-time monitoring of the system status and timely detection of abnormal conditions. The fault notification link quickly notifies the relevant maintenance personnel or team of relevant information after the fault is detected. The fault response link involves preliminary analysis and processing of the fault to minimize the impact of the fault on the system operation. The fault recovery link is responsible for restoring the system to normal operation after analyzing and processing the cause of the fault.

[0087] In the specific implementation, transaction data can use candidate fault features to mark the same data from different dimensions, and use features and classifications under the features to perform detailed statistics on the data. In addition to single-dimensional marking, transaction data can also use candidate fault features to mark combined dimensions, that is, multiple dimensions are arranged and combined to generate new tag values ​​to mark the data. If the fault point has been found in a single dimension, the candidate features of the combined dimension will not be marked in order to save computing resources; if the fault point cannot be found in a single dimension, the data can continue to be marked with candidate features of multiple combined dimensions, and finally the system fault point with a high error rate can be found.

[0088] like Figure 3 As shown, Figure 3 The overall flow chart of fault point location includes: 1. Screening data within the time range; 2. Data collection and cleaning; 3. Basic data analysis, in which the basic data analysis uses a data analysis engine; 4. Classification of candidate features; 5. Calculation of classification correlation coefficient; 6. Classification sorting; 7. Visual display; Determine whether the correlation coefficient is greater than the threshold, if so, perform 8. Complete the Chinese description of the classification; Determine whether it is a high-level fault, if so, execute 9.1 (high-level) outbound call, if not, execute 9.2 (low-level) instant messaging.

[0089] The present embodiment provides a fault location method, which first obtains original business data and pre-processes the original business data to obtain target business data; determines candidate fault features based on the target business data; locates the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause, and can quickly locate the transaction fault and accurately locate the fault cause.

[0090] In summary, this embodiment quickly and accurately determines the target business data by preprocessing the original business data, and then determines the candidate fault characteristics and locates the fault point based on the target business data, thereby quickly and accurately determining fault data such as the fault location and fault cause, overcoming the technical defects of low fault location efficiency and poor accuracy, and can quickly locate transaction faults and accurately locate the cause of the fault.

[0091] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 4 , the step S30 further includes steps S301-S302:

[0092] Step S301: determining a fault index based on the candidate fault features.

[0093] It should be noted that the fault index refers to a numerical indicator used to measure the severity of a fault. It is used to characterize the correlation between the fault and the current candidate fault characteristics, and to facilitate horizontal comparison of the fault degree between different set slices. The larger the fault index, the more significant the fault.

[0094] In a feasible implementation, step S301 may include: classifying the candidate fault features based on transaction type to obtain multiple candidate fault feature sets; obtaining transaction data sets corresponding to each of the fault feature sets; determining the transaction volume and transaction failure rate based on the transaction data sets; and determining the fault index of each type of transaction data based on the transaction volume and the transaction failure rate.

[0095] It should be noted that dividing the candidate fault features into multiple sets helps to analyze different types of transaction faults in more detail, thereby improving the accuracy of fault location.

[0096] It is understandable that the fault index is greatly affected by the proportion of transaction volume and failure rate of the set slice. Therefore, by counting the transaction data sets corresponding to each fault feature set and then determining the transaction volume and transaction failure rate, the fault index of each candidate fault feature can be calculated more accurately, thereby providing a more accurate basis for subsequent fault location.

[0097] It is understandable that the failure index is greatly affected by the proportion of transaction volume of the collection section (i.e., weight, defined as x, ∈ [0, 1]) and the failure rate (defined as y ∈ [0, 1]). The failure index z is as follows:

[0098]

[0099] Among them, z is the failure index, x is the transaction volume ratio, y is the transaction failure rate, and the failure rate y and the failure index z are positively correlated. However, simply processing the weight x in a linear manner will result in a classification with a very low weight. Even if the transaction fails 100% (y), the final failure index z is still not high. Therefore, the weight x is calculated by taking the square root twice.

[0100] like Figure 5 As shown, Figure 5 This is a schematic diagram of the fault index. When the weight x=0 or the failure rate y=0, the fault index z=0, and it is in a blue healthy state. When x and y increase from 0, the closer they are to 1, the larger the fault index is, and the fault index z also approaches 100, and it is in a dark red fault state.

[0101] Step S302: locate the fault point based on the fault index to obtain fault data.

[0102] It should be noted that the larger the fault index, the more serious the fault. The candidate fault features are sorted from large to small according to the fault index. According to the Pareto principle, the problems with higher ranking are displayed in a rolling manner, that is, more important fault problems are displayed first, which can ensure that within limited resources and time, the faults that have the greatest impact on system operation can be solved first, thereby improving the efficiency and effectiveness of fault handling. Fault priority sorting can also be combined with factors such as the time and frequency of fault occurrence to achieve a more reasonable fault handling strategy.

[0103] In a feasible implementation, step S301 may include: determining the fault level of each candidate fault feature based on the fault index; determining the target fault feature based on the fault level; locating the fault point based on the target fault feature to obtain the fault data location and the cause of the fault data; determining the fault data according to the fault data location and the cause of the fault data.

[0104] It should be noted that the fault level is divided according to the numerical range of the fault index. For example, the fault level can be divided into general faults and severe faults according to the fault index. For example, if the fault index is in [30, 50], it is a general fault, and if the fault index is greater than 50, it is a severe fault. Different processing measures are taken according to the fault level.

[0105] It can be understood that according to the fault level, the candidate fault features of the serious fault can be used as the target fault features, and then the fault point is located to determine the location of the fault data and the cause of the fault data.

[0106] It is worth noting that, according to the fault level, for general level faults (i.e. 30 < fault index < 50), the developer is alerted by enterprise instant messaging; for serious problems (i.e. fault index > 50), additional strong contact methods such as outbound calls are used. For example, if an abnormality is detected in the ATM's acceptance of bank cards, an alarm message will automatically pop up in the developer communication group and further intervention and disposal will be arranged.

[0107] In this embodiment, the fault index is determined according to the candidate fault features, and then the fault data is quickly and accurately located according to the fault index, thereby effectively improving the efficiency and effect of fault processing.

[0108] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the fault location method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0109] This application also provides a fault location device, please refer to Figure 6 , the fault locating device comprises:

[0110] The preprocessing module 10 is used to obtain original business data and preprocess the original business data to obtain target business data.

[0111] The determination module 20 is used to determine candidate fault features based on the target service data.

[0112] The positioning module 30 is used to locate the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0113] The present embodiment provides a fault locating device, which obtains original business data and pre-processes the original business data to obtain target business data; determines candidate fault features based on the target business data; locates the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause, and can quickly locate the transaction fault and accurately locate the fault cause.

[0114] In summary, this embodiment quickly and accurately determines the target business data by preprocessing the original business data, and then determines the candidate fault characteristics and locates the fault point based on the target business data, thereby quickly and accurately determining fault data such as the fault location and fault cause, overcoming the technical defects of low fault location efficiency and poor accuracy, and can quickly locate transaction faults and accurately locate the cause of the fault.

[0115] Optionally, the preprocessing module 10 is also used to obtain original business data, and perform low-latency data extraction based on the original business data to obtain low-latency business data; identify non-final data in the low-latency business data; and clean the low-latency business data according to the non-final data to obtain target business data.

[0116] Optionally, the determination module 20 is further configured to obtain historical fault data; and analyze the target business data based on the historical fault data to obtain candidate fault features.

[0117] Optionally, the determination module 20 is also used to perform feature extraction on the target business data and the historical fault data respectively to obtain transaction data features and historical fault data features; perform similarity calculation based on the transaction data features and the historical fault data features to obtain feature similarity; and determine candidate fault features based on the feature similarity.

[0118] Optionally, the positioning module 30 is further configured to determine a fault index based on the candidate fault feature; and locate the fault point based on the fault index to obtain fault data.

[0119] Optionally, the positioning module 30 is also used to classify the candidate fault features based on transaction type to obtain multiple candidate fault feature sets; obtain transaction data sets corresponding to each of the fault feature sets; determine the transaction volume and transaction failure rate based on the transaction data sets; and determine the fault index of each type of transaction data based on the transaction volume and the transaction failure rate.

[0120] Optionally, the positioning module 30 is also used to determine the fault level of each candidate fault feature based on the fault index; determine the target fault feature based on the fault level; locate the fault point based on the target fault feature to obtain the fault data location and the cause of the fault data; determine the fault data according to the fault data location and the cause of the fault data.

[0121] The fault location device provided by the present application adopts the fault location method in the above embodiment, which can solve the technical problem of how to quickly locate transaction faults and accurately locate the cause of the fault. Compared with the prior art, the beneficial effects of the fault location device provided by the present application are the same as the beneficial effects of the fault location method provided by the above embodiment, and other technical features in the fault location device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0122] The present application provides a fault location device, which includes: 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the fault location method in the above-mentioned embodiment 1.

[0123] Reference below Figure 7 , which shows a schematic diagram of the structure of a fault location device suitable for implementing the embodiment of the present application. The fault location device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The fault location device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Figure 7 As shown, the fault location device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the fault location device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the fault location device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a fault location device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0125] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0126] The fault location device provided by the present application adopts the fault location method in the above embodiment, which can solve the technical problem of how to quickly locate transaction faults and accurately locate the cause of the fault. Compared with the prior art, the beneficial effects of the fault location device provided by the present application are the same as the beneficial effects of the fault location method provided by the above embodiment, and the other technical features in the fault location device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0127] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0129] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the fault location method in the above-mentioned embodiment.

[0130] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0131] The computer-readable storage medium may be included in the fault location device; or may exist independently without being assembled into the fault location device.

[0132] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the fault location device, the fault location device: obtains original business data, and preprocesses the original business data to obtain target business data; determines candidate fault features based on the target business data; locates the fault point based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

[0133] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0134] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0135] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0136] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned fault location method, and can solve the technical problem of how to quickly locate transaction faults and accurately locate the causes of faults. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the fault location method provided by the above-mentioned embodiment, and will not be elaborated here.

[0137] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned fault location method when executed by a processor.

[0138] The computer program product provided by the present application can solve the technical problem of how to quickly locate transaction faults and accurately locate the causes of the faults. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the fault location method provided by the above embodiment, which will not be described in detail here.

[0139] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A fault location method, characterized in that: The method comprises: Acquire original business data, and pre-process the original business data to obtain target business data; Determining candidate fault features based on the target service data; The fault point is located based on the candidate fault features to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

2. The method according to claim 1, characterized in that The obtaining of original business data and preprocessing of the original business data to obtain target business data includes: Obtaining original business data, and performing low-latency data extraction based on the original business data to obtain low-latency business data; Identifying non-final data in the low-latency service data; The low-latency service data is cleaned according to the non-final data to obtain target service data.

3. The method according to claim 1, characterized in that The determining of candidate fault features based on the target service data includes: Obtain historical fault data; The target service data is analyzed based on the historical fault data to obtain candidate fault features.

4. The method according to claim 3, characterized in that The analyzing the target service data based on the historical fault data to obtain candidate fault features includes: Extracting features from the target business data and the historical fault data respectively to obtain transaction data features and historical fault data features; Calculate similarity based on the transaction data features and the historical fault data features to obtain feature similarity; A candidate fault feature is determined based on the feature similarity.

5. The method according to claim 1, characterized in that The locating the fault point based on the candidate fault features to obtain fault data includes: determining a fault index based on the candidate fault features; The fault point is located based on the fault index to obtain fault data.

6. The method according to claim 5, characterized in that The determining of the fault index based on the candidate fault feature comprises: Classifying the candidate fault features based on transaction types to obtain multiple candidate fault feature sets; Obtaining a transaction data set corresponding to each of the fault feature sets; Determining a transaction volume and a transaction failure rate based on the transaction data set; The failure index of each type of transaction data is determined according to the transaction volume and the transaction failure rate.

7. The method according to claim 5, characterized in that The locating the fault point based on the fault index to obtain fault data includes: Determining a fault level of each candidate fault feature based on the fault index; determining a target fault feature based on the fault level; Based on the target fault characteristics, the fault point is located to obtain the fault data location and the cause of the fault data; The fault data is determined according to the fault data location and the cause of the fault data.

8. A fault location device, characterized in that: The fault locating device comprises: A preprocessing module, used to obtain original business data and preprocess the original business data to obtain target business data; A determination module, configured to determine candidate fault features based on the target service data; A positioning module is used to locate the fault point based on the candidate fault characteristics to obtain fault data, wherein the fault data at least includes the fault location and the fault cause.

9. A fault location device, characterized in that: The fault location device comprises: a memory, a processor, and a fault location program stored in the memory and executable on the processor, wherein the fault location program is configured to implement the fault location method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a fault location program, and when the fault location program is executed by the processor, the fault location method according to any one of claims 1 to 7 is implemented.

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