Method, device and equipment for processing configuration error log and storage medium

Abstract

The application provides a processing method, device and equipment for configuration error log and a storage medium. The method comprises the following steps: obtaining configuration error logs of a target object within a preset time range; grouping and processing the configuration error logs according to the types of the target objects to obtain a plurality of sets of error logs; for each set of error logs, determining a corresponding configuration error influencing factor according to the corresponding configuration error information, and determining the correlation degree between the influence result data corresponding to each configuration error influencing factor in each set of error logs and the benchmark result data; determining the configuration key influencing factor of the corresponding target object type according to the correlation degrees, so as to locate the error configuration parameter according to the configuration key influencing factor. The method of the application improves the problem that the efficiency of locating the error configuration parameter is low when the user manually finds the configuration error reason.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for processing configuration error logs. Background Technology

[0002] With the development of mobile internet, a large number of online shopping applications have emerged. Users can place orders for desired items through these applications. To ensure smooth order placement and prevent errors, the attributes of the target item, such as its location, sales region, and price, are typically configured before it is listed for sale.

[0003] In existing technologies, the configuration of attribute information for target objects to be sold is generally done manually by the management user. Therefore, configuration errors are inevitable, resulting in target objects with incorrect attribute information being unable to be ordered after they are launched.

[0004] Therefore, when faced with the problem of incorrect configuration of attribute information of the target object to be sold, the current practice is generally for management users to manually search the configuration error log to determine the cause of the configuration error, which results in low efficiency in locating the incorrect configuration parameters. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for processing configuration error logs, in order to solve the problem of low efficiency in locating configuration error parameters when users manually search configuration error logs to determine the cause of configuration errors.

[0006] Firstly, this application provides a method for handling configuration error logs, including:

[0007] Obtain the configuration error log of the target object within a preset time range; the configuration error log includes the type of the target object and configuration error information.

[0008] The configuration error logs are grouped according to the type of the target object to obtain multiple sets of error logs;

[0009] For each set of error logs, determine the corresponding configuration error influencing factors based on the corresponding configuration error information, and determine the correlation between the impact result data and the baseline result data for each configuration error influencing factor in each set of error logs.

[0010] Based on the aforementioned correlation, key configuration influencing factors for the corresponding target object type are determined, so as to locate erroneous configuration parameters based on the aforementioned key configuration influencing factors.

[0011] Optionally, before grouping the configuration error logs according to the type of the target object to obtain multiple sets of error logs, the method further includes:

[0012] The configuration error logs are formatted in a unified manner to obtain structured error logs;

[0013] The configuration error logs are grouped according to the type of the target object to obtain multiple sets of error logs, including:

[0014] The structured error logs are grouped according to the type of the target object to obtain multiple sets of error logs.

[0015] Optionally, determining the corresponding configuration error influencing factors based on the corresponding configuration error information includes:

[0016] Extract the configuration error influencing factor field from the configuration error information to determine the corresponding configuration error influencing factors.

[0017] Optionally, the benchmark result data is a benchmark result sequence, and the influence result data is an influence result sequence;

[0018] Determining the correlation between the impact result data and the baseline result data for each configuration error influencing factor in each set of error logs includes:

[0019] The number of structured error logs in each error log set is determined according to a preset time period;

[0020] The baseline result sequence for each set of error logs is determined based on the number of structured error logs in each preset time period within each set of error logs.

[0021] Determine the number of structured error logs for each preset time period under each configuration error influencing factor in each error log set;

[0022] The sequence of impact results for each configuration error factor is determined based on the number of structured error logs in each preset time period under each configuration error factor.

[0023] Determine the correlation between the sequence of impact results for each configuration error influencing factor in each set of error logs and the corresponding baseline sequence of results.

[0024] Optionally, determining the correlation between the sequence of impact results for each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence includes:

[0025] Input the sequence of impact results for each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence into the gray-scale correlation analysis model;

[0026] The gray-scale correlation analysis model is used to determine and output the correlation degree between the influence sequence and the corresponding baseline sequence under each configuration error influencing factor.

[0027] Optionally, determining the key influencing factors for the configuration of the corresponding target object type based on each of the aforementioned correlation degrees includes:

[0028] Sort the correlation scores of objects of the same type in descending order;

[0029] Obtain the relevance of the top preset number of items, and determine the configuration error influencing factors corresponding to the relevance of the top preset number of items;

[0030] The configuration error influencing factors corresponding to the first preset number of correlation degrees are determined as the key configuration influencing factors for the corresponding target object type.

[0031] Optionally, the method further includes:

[0032] In response to a configuration request for a target object triggered by a management user, the type of the target object is determined;

[0033] Obtain the corresponding key configuration influencing factors based on the type of the target object;

[0034] The corresponding key configuration factors will be displayed to remind the management users.

[0035] Secondly, this application provides a processing apparatus for configuring error logs, comprising:

[0036] The first acquisition module is used to acquire configuration error logs of target objects within a preset time range; the configuration error logs include the type of the target object and configuration error information.

[0037] The grouping module is used to group the configuration error logs according to the type of the target object to obtain multiple sets of error logs.

[0038] The first determination module is used to determine the corresponding configuration error influencing factors for each set of error logs based on the corresponding configuration error information, and to determine the correlation between the influence result data and the baseline result data for each configuration error influencing factor in each set of error logs.

[0039] The second determining module is used to determine the key influencing factors of the corresponding target object type based on the correlation degree, so as to locate the erroneous configuration parameters based on the key influencing factors of the configuration.

[0040] Thirdly, this application provides an electronic device, comprising:

[0041] A processor, and a memory communicatively connected to the processor;

[0042] The memory stores computer-executed instructions;

[0043] The processor executes computer execution instructions stored in the memory to implement the method described in any of the first aspects.

[0044] Fourthly, this application provides a computer-readable storage medium, comprising:

[0045] The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method described in any of the first aspects.

[0046] This application provides a method, apparatus, device, and storage medium for processing configuration error logs. The method involves acquiring configuration error logs of a target object within a preset time range. The configuration error logs include the type of the target object and configuration error information. The configuration error logs are grouped according to the type of the target object to obtain multiple sets of error logs. For each set of error logs, corresponding configuration error influencing factors are determined based on the corresponding configuration error information, and the correlation between the impact result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs is determined. Based on each correlation, key configuration influencing factors for the corresponding target object type are determined, so as to locate the incorrect configuration parameters based on the key configuration influencing factors. Since incorrect configuration parameters are often related to the key configuration influencing factors of the same type of target object when configuring parameters, and the key influencing factors will differ depending on the target object type, the configuration error logs are first grouped according to the type of the target object to determine the key configuration influencing factors for each set of error logs corresponding to the target object type. When determining key influencing factors for configuration errors, the correlation between the impact result data corresponding to each configuration error influencing factor and the baseline result data is determined. Then, based on the correlation, the key influencing factors for configuration of the corresponding target object type are determined. This method can accurately and quickly identify key influencing factors for configuration errors. Furthermore, when users configure incorrect configuration parameters, the method can quickly locate the incorrect configuration parameters based on the key influencing factors for configuration errors, effectively improving the efficiency of locating incorrect configuration parameters. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] Figure 1 This is an application scenario diagram of the configuration error log processing method provided in the embodiments of this application;

[0049] Figure 2 A diagram showing the parameter configuration interface changes in another application scenario of the configuration error log processing method provided in the embodiments of this application;

[0050] Figure 3 A flowchart illustrating a method for processing configuration error logs according to an embodiment of this application;

[0051] Figure 4 A flowchart illustrating a method for processing configuration error logs provided in another embodiment of this application;

[0052] Figure 5 A flowchart illustrating the method for processing configuration error logs provided in a further embodiment of this application;

[0053] Figure 6 A flowchart of a configuration error log processing method provided in an embodiment of this application is also included.

[0054] Figure 7 A flowchart illustrating a method for processing configuration error logs provided in another embodiment of this application;

[0055] Figure 8 A schematic diagram of the structure of a configuration error log processing device provided in an embodiment of this application;

[0056] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0057] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0058] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0059] To clearly understand the technical solution of this application, the solutions of the prior art will be described in detail first.

[0060] The existing technical solution involves administrators manually searching for error configuration log files in the log management backend server when the attribute information of the target product is misconfigured. This involves identifying the incorrect configuration information based on date, time, and product type. However, manually searching the configuration error logs and determining the cause of the error is inefficient for locating the incorrect parameters.

[0061] Addressing the low efficiency of locating configuration errors in existing technologies, the inventors, through creative research, discovered that when users configure parameters for each target object to be purchased, incorrect configuration parameters are often closely related to the key influencing factors of configuration for similar target objects. However, the key influencing factors differ for different types of target objects. Therefore, the inventors can determine the corresponding key influencing factors for each type of target object. To determine these key influencing factors, the correlation between the impact data of each configuration error influencing factor and the baseline data can be determined based on each set of error logs after classifying the target objects. This correlation then determines the key influencing factors for the corresponding target object type. Therefore, once it is determined that a user has configured incorrect parameters, obtaining the corresponding key influencing factors allows for rapid location of the incorrect configuration parameters, effectively improving the efficiency of error configuration parameter location.

[0062] The following section describes the network structure and application scenarios corresponding to the configuration error log handling method provided in this application.

[0063] Figure 1 This is an application scenario diagram of the configuration error log processing method provided in the embodiments of this application, such as... Figure 1As shown, the network structure corresponding to the configuration error log processing method provided in this application embodiment may include: a database 1, an electronic device 2, and a user terminal 3. The electronic device 2 can be a backend server. Specifically, configuration error logs for various target objects are pre-stored in the database 1. The electronic device 2 periodically accesses the database 1 to obtain the configuration error logs of target objects within a preset time range. It then executes the configuration error log processing method according to this application embodiment, ultimately obtaining the key influencing factors for each type of target object and storing the corresponding key influencing factors. The configuration user can configure the parameters of the target object on the electronic device 2. Furthermore, the purchasing user can access the backend server through the user terminal 3 to place an order for the target object to be sold. When the purchasing user cannot place an order through the user terminal 3, the user terminal 3 requests the incorrect configuration parameters and obtains the key influencing factors of the configuration error log returned by the electronic device 2, displaying them to the user to locate the incorrect configuration parameters based on the key influencing factors. For example, in... Figure 1 In the process, when a user is unable to place an order through user terminal 3, the order placement interface displays the following order failure message: The key influencing factors for the configuration of target object A are attribute D, attribute C and attribute E.

[0064] In another application scenario, such as Figure 2 As shown, after the electronic device 2 periodically executes the configuration error log processing method provided in this application embodiment, the key configuration influencing factors corresponding to each target object type are stored. When the management user configures the parameters of the target object to be sold, the type of the target object is determined, the corresponding key configuration influencing factors are obtained according to the type of the target object, and the corresponding key configuration influencing factors are displayed to remind the management user. For example, in... Figure 2 In the top left image, when a user configures parameters for a target object to be sold on the target object parameter configuration page, if the target object is configured as type A and the parameters are attribute A and attribute B, then... Figure 2 In the upper right image, the target object parameter configuration section indicates that the key influencing factors for the configuration of target object A are attribute C and attribute D. Figure 2 In the lower right diagram, after the administrator configures the corresponding key influencing factors for the parameters of target object A according to the prompts, a configuration request for target object A is initiated. Electronic device 2 receives the configuration information of target object A, stores it, and returns a message indicating that the target object has been successfully configured. Figure 2 The lower left image shows the target object parameter configuration page, which is displayed to prompt the management user.

[0065] The configuration error log processing method provided in this application can be specifically applied in scenarios such as e-commerce and telecom operators configuring target objects like items and services to be sold, for locating and troubleshooting incorrect configuration parameters. For example, when a telecom operator manages users to configure parameters for services to be sold, such as broadband or data packages, the configuration parameters are prone to errors because the configured services contain multiple attributes. When errors occur in the user's configuration parameters, this method can indicate the key influencing factors of the corresponding service type's incorrect configuration, improving the efficiency of administrators in locating configuration error information.

[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0067] Example 1

[0068] Figure 3 This is a flowchart illustrating a configuration error log processing method provided in Embodiment 1 of this application. This embodiment addresses the problem of low efficiency in locating configuration error parameters when users manually search configuration error logs to determine the cause of configuration errors, and provides a configuration error log processing method. The method in this embodiment is applied to a configuration error log processing device, which can be located in an electronic device. The electronic device can be a digital computer of various forms, such as cellular phones, smartphones, laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers.

[0069] like Figure 3 As shown, the specific steps of this method are as follows:

[0070] Step S101: Obtain the configuration error log of the target object within a preset time range; the configuration error log includes the type of the target object and configuration error information.

[0071] The target object is the object that requires parameter configuration. This target can be a service or item that needs to be sold. For example, it could be a converged service, broadband service, etc.

[0072] The configuration error log is generated when parameters of the target object are misconfigured. In addition to the type of the target object and the configuration error information, the configuration error log may also include the time the log was generated.

[0073] In the configuration error log, the type of the target object can be represented by a type identifier code, with different type identifier codes representing different types. Configuration error information can include fields for configuration error influencing factors and detailed configuration error information. For example, the configuration error influencing factor field could be "Missing attribute A" or "Missing product B." For instance, if a package should include a ringback tone or call forwarding service, but this product is not configured in the configuration parameters, the configuration error influencing factor field would be "Missing ringback tone" or "Missing call forwarding." Similarly, broadband services generally require configuring traffic attributes; if traffic is not configured in the configuration parameters, the configuration error influencing factor field would be "Missing traffic attribute."

[0074] Specifically, in this embodiment, after a user configures the parameters of a target object on an electronic device, if the configured parameters are incorrect during the sale of that target object, a configuration error log for the target object will be generated. After the electronic device generates the configuration error log, it can be stored in the database in real time. Therefore, the database will store configuration error logs for multiple target objects. The electronic device can access the database to retrieve configuration error logs for all target objects within a preset time range.

[0075] Alternatively, in this embodiment, after the electronic device generates the configuration error log of the target object, it stores the generated configuration error log of the target object in the local storage area. Then, the configuration error log of the target object within a preset time range can be obtained by accessing the local storage area.

[0076] The preset time range can be the most recent month, the most recent six months, etc., but this embodiment does not limit it.

[0077] Step S102: Group the configuration error logs according to the type of the target object to obtain multiple sets of error logs.

[0078] Specifically, the electronic device reads the type of the target object and groups the configuration error logs of the same type of target object into a group, thereby obtaining multiple sets of configuration error logs.

[0079] Step S103: For each set of error logs, determine the corresponding configuration error influencing factors based on the corresponding configuration error information, and determine the correlation between the impact result data and the baseline result data for each configuration error influencing factor in each set of error logs.

[0080] The configuration error influencing factors are the fields extracted from the configuration error information. The impact result data are the statistical results of configuration error logs generated due to this configuration error influencing factor within the error log group set. The baseline result data are the statistical results of configuration error logs within the error log group set.

[0081] Specifically, for each set of error logs, the electronic device extracts the configuration error influencing factor field from the corresponding configuration error information, performs deduplication, and identifies the corresponding configuration error influencing factors. For each configuration error influencing factor, it determines whether it falls within a preset time period based on the configuration error log generation time. For configuration error logs whose generation time falls within the preset time period, the frequency of occurrence of the corresponding configuration error influencing factor field is counted for each preset time period, and the statistical results are used as the influence result data for the corresponding configuration error influencing factor. Correspondingly, for each set of error logs, the electronic device counts the number of configuration error logs in each preset time period within the error log set based on the configuration error log generation time, using this as the baseline result data. Then, the correlation between the influence result data corresponding to each configuration error influencing factor and the baseline data is calculated. The specific algorithm is not limited. For example, a cosine similarity model can be used to calculate the correlation between the influence result data and the baseline data.

[0082] Specifically, assume there are m factors that may cause configuration errors and n preset time periods.

[0083] First, normalize the baseline results and the impact data of each configuration error factor. No specific limitations are imposed on the normalization method.

[0084] Optionally, normalization can be performed using the mean method. Specifically, the mean of the results in each column is subtracted from the mean of the results in that column to obtain the normalized result.

[0085] The processed baseline data and the impact data of each configuration error factor are respectively formed into corresponding vectors. For each impact data vector, the baseline data vector and the impact data vector are input into the cosine similarity model. After cosine similarity calculation, the cosine similarity between the baseline data vector and each impact data vector is output. The higher the similarity, the greater the correlation.

[0086] Specifically, the formula corresponding to the cosine similarity model can be expressed as shown in equation (1):

[0087]

[0088] in, The mean of the baseline results data, x is the mean of the impact data corresponding to the i-th configuration error influencing factor. 0k For the k-th row of the baseline results data, x ik This is the k-th row of data corresponding to the impact result of the i-th configuration error influencing factor. i = 0, 1, 2, 3, ..., m.

[0089] The preset time period can be seven days, ten days, or one month, etc. This embodiment does not limit this.

[0090] Step S104: Determine the key configuration influencing factors for the corresponding target object type based on the correlation degree, so as to locate the erroneous configuration parameters based on the key configuration influencing factors.

[0091] Among them, the key configuration influencing factors are configuration error influencing factors that have a key impact on incorrect configuration parameters.

[0092] Specifically, the value range of key influencing factors can be set, and the corresponding configuration error influencing factors with correlation values ​​within the value range can be used as the configuration key influencing factors for the corresponding target object type. When a user makes a parameter configuration error, the electronic device returns the configuration key influencing factors according to the target object type, thereby locating the incorrect configuration parameters.

[0093] It is understood that other methods can also be used to determine the key influencing factors of the corresponding target object type based on the aforementioned correlation degree, and this embodiment does not limit this.

[0094] In this embodiment, configuration error logs of target objects within a preset time range are obtained; the configuration error logs are grouped according to the type of the target object to obtain multiple sets of error logs; for each set of error logs, the corresponding configuration error influencing factors are determined based on the corresponding configuration error information, and the correlation between the impact result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs is determined; the configuration key influencing factors for the corresponding target object type are determined based on the correlation, so as to locate the incorrect configuration parameters based on the configuration key influencing factors. Since incorrect configuration parameters are often related to the configuration key influencing factors of the same type of target object when configuring parameters, and different types of target objects have different configuration key influencing factors, the target objects can be grouped by type to determine the configuration key influencing factors for each type of target object. When determining the configuration key influencing factors, by determining the correlation between the impact result data and the baseline result data of each configuration error influencing factor, the configuration key influencing factors for the target object type can be determined quickly and accurately. When a user configures parameters incorrectly, the configuration key influencing factors for that type of target object can be quickly prompted, thereby improving the efficiency of locating incorrect configuration parameters.

[0095] Example 2

[0096] Based on the above embodiments, this embodiment involves the following technical solutions before step S102, which configures error logs to be grouped according to the type of the target object to obtain multiple sets of error logs:

[0097] The configuration error logs are formatted in a unified manner to obtain structured error logs.

[0098] Accordingly, S102 specifically includes:

[0099] The structured error logs are grouped according to the type of the target object to obtain multiple sets of error logs.

[0100] Specifically, in this embodiment, the configuration error log can be in free text format, with different fields separated by preset delimiters, such as spaces, colons, etc.

[0101] Therefore, electronic devices can extract valid field content according to preset delimiters and perform structured processing on the valid field content according to the requirements of unified formatting to form a structured error log.

[0102] Accordingly, in step S102, the electronic device groups the structured error logs according to the type of the target object, thereby obtaining multiple sets of error logs.

[0103] In this embodiment, the configuration error log is uniformly formatted to obtain a structured error log. This uniform formatting improves the reading speed of the log text content, thereby increasing processing efficiency.

[0104] Example 3

[0105] Based on Embodiment 1 or Embodiment 2, this embodiment relates to an implementable method for step S103 to determine the corresponding configuration error influencing factors according to the corresponding configuration error information.

[0106] Specifically, step 103 includes:

[0107] Extract the configuration error influencing factor field from the configuration error information to determine the corresponding configuration error influencing factors.

[0108] In this embodiment, the configuration error information may contain at least one field. If it is a single field, then that field represents the corresponding configuration error influencing factor. For each error log set, the electronic device reads the corresponding configuration error information and identifies that field as the configuration error influencing factor. If the configuration error information includes multiple fields, for each error log set, the electronic device reads the corresponding configuration error information, determines the field location of the configuration error influencing factor, and extracts the configuration error influencing factor based on its field location.

[0109] Understandably, after extracting the configuration error influencing factors from a configuration error message, the newly extracted configuration error influencing factors are compared with the previously extracted configuration error influencing factors. If they are not completely the same, the newly extracted configuration error influencing factor is saved as a new configuration error influencing factor.

[0110] In this embodiment, the configuration error influencing factor field is extracted from the configuration error information to determine the corresponding configuration error influencing factors. Since the configuration error information always includes the configuration error influencing factor field, the configuration error influencing factors can be obtained quickly and accurately.

[0111] Example 4

[0112] Figure 4 This is a flowchart of the configuration error log processing method provided in Embodiment 4 of this application, as follows: Figure 4As shown, based on Embodiment 2 or Embodiment 3, this embodiment relates to a specific feasible method for determining the correlation between the impact result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs. Here, the baseline result data is a baseline result sequence, and the impact result data is an impact result sequence. Therefore, the configuration error log processing method provided in this embodiment, determining the correlation between the impact result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs, includes the following steps:

[0113] Step S401: Determine the number of structured error logs in each error log set according to the preset time period.

[0114] Specifically, for each set of error logs, the electronic device counts the number of structured error logs in each set of error logs within each preset time period according to the configuration error log generation time.

[0115] For example, the structured error logs in each set of error logs are generated within a preset time range, which can be 30 days, specifically from March 1st to March 30th. Alternatively, a preset time period of 7 days can be used, allowing the number of structured error logs in each set to be counted for every 7-day period.

[0116] Step S402: Determine the baseline result sequence corresponding to each set of error logs based on the number of structured error logs for each preset time period in each set of error logs.

[0117] In this embodiment, the baseline result sequence is the statistical result data of the structured error logs within the error log group set, calculated according to each preset time period. In this embodiment, the number of structured error logs in each preset time period within each error log group set is determined as the baseline result sequence corresponding to each error log group set.

[0118] For example, continuing with the example of step S401, if the number of structured error logs in a certain set of error logs is counted according to each 7-day period as follows: March 1-7: 20; March 8-14: 10; March 1-March 21: 8; March 22-March 28: 5. Then the baseline result sequence corresponding to the structured error logs in this set of error logs is: 20, 10, 8, 5.

[0119] Step S403: Determine the number of structured error logs for each preset time period under each configuration error influencing factor in each error log set.

[0120] Specifically, for each set of error logs, for each configuration error influencing factor, it is determined whether it falls within a preset time period based on the generation time of each configuration error log. For structured error logs whose generation time falls within the preset time period, the number of structured error logs corresponding to the configuration error influencing factor is counted for each preset time period, and the statistical results are used as the impact result data of the corresponding configuration error influencing factor.

[0121] For example, the structured error logs in each set of error logs are generated within a preset time range, which can be 30 days, specifically from March 1st to March 30th. The preset time period can be 7 days, and the factors affecting the configuration error can be attribute A and attribute B of the target object. Therefore, the number of structured error logs generated due to configuration errors in attribute A and attribute B of the target object can be counted for each 7-day time period.

[0122] Step S404: Determine the sequence of impact results for each configuration error factor based on the number of structured error logs in each preset time period under each configuration error influencing factor.

[0123] Among them, the impact result sequence is the statistical result data of the structured error logs generated by the factors affecting the configuration error according to each preset time period.

[0124] In this embodiment, the number of structured error logs in each preset time period under each configuration error influencing factor is determined as the sequence of impact results under each configuration error influencing factor.

[0125] For example, continuing with the example of step S403, if statistics are compiled in 7-day time periods, for the target object attribute A of the first configuration error influencing factor, the sequence is as follows: March 1-March 7: 12; March 8-14: 5; March 1-March 21: 2; March 22-March 28: 3. Therefore, the influence result sequence for the target object attribute A of this configuration error influencing factor is 12, 5, 2, 3. Similarly, for the target object attribute B of the second configuration error influencing factor, the sequence is as follows: March 1-March 7: 8; March 8-14: 5; March 1-March 21: 6; March 22-March 28: 2. Therefore, the influence result sequence for the target object attribute B of this configuration error influencing factor is 8, 5, 6, 2.

[0126] Step S405: Determine the correlation between the impact result sequence of each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence.

[0127] Specifically, a preset correlation calculation model is obtained where the input data is a sequence. For each set of error logs, the baseline result sequence of the error log set and the impact result sequence under each configuration error influencing factor are input into the preset correlation calculation model. The correlation between the impact result sequence under each configuration error influencing factor and the corresponding baseline result sequence is output.

[0128] The preset correlation calculation model can be a gray-scale correlation analysis model or other correlation calculation models, which are not limited in this embodiment.

[0129] In this embodiment, the number of structured error logs in each error log set is determined according to a preset time period. A baseline result sequence corresponding to each error log set is determined based on the number of structured error logs in each preset time period within each error log set. The number of structured error logs in each preset time period under each configuration error influencing factor in each error log set is determined. An impact result sequence under each configuration error influencing factor is determined based on the number of structured error logs in each preset time period under each configuration error influencing factor. The correlation between the impact result sequence under each configuration error influencing factor in each error log set and the corresponding baseline result sequence is determined. By statistically determining the number of structured error logs to establish the baseline sequence and impact result sequence, the log text processing process is transformed into a statistical information processing process, reducing information redundancy and enabling rapid and accurate determination of the correlation between each configuration error influencing factor and the error configuration parameters of the target object type.

[0130] Example 5

[0131] Figure 5 A flowchart of the configuration error log processing method provided in another embodiment of this application is shown below. Figure 5 As shown, this embodiment, based on embodiment four, relates to a specific feasible method for determining the correlation between the impact result sequence under each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence. Therefore, the method for processing configuration error logs provided in this embodiment, determining the correlation between the impact result sequence under each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence, includes the following steps:

[0132] Step S501: Input the impact result sequence of each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence into the gray-scale correlation analysis model.

[0133] Specifically, this embodiment uses a gray-scale correlation model.

[0134] Among them, the gray-scale correlation model is a multi-factor statistical analysis model that uses sample data of various factors as a basis and gray-scale correlation to describe the strength, magnitude and order of the relationship between factors.

[0135] The baseline sequence corresponding to the error log set of each type of target object is used as the reference sequence, and the impact result sequence of each configuration error influencing factor in the error log set of that type of target object is used as the comparison sequence. Assume there are m configuration error influencing factors and n preset time periods.

[0136] Following step S501, the following steps are included:

[0137] Step S5011: Normalize the baseline sequence and the affected result sequence. The specific method is not limited.

[0138] Optionally, the initial value method can be used for normalization. The difference between each element in the sequence and the first element of the sequence is used to obtain the normalization result of the initial value method.

[0139] Optionally, the mean method can be used for normalization. The mean normalization result is obtained by subtracting the mean of the sequence from each element in the sequence.

[0140] Step S502: Determine and output the correlation degree between the influence sequence and the corresponding benchmark sequence under each configuration error influencing factor through the gray-scale correlation analysis model.

[0141] Specifically, step S502 includes the following steps:

[0142] Step S5021: Subtract the corresponding row element of each row in the comparison sequence from the corresponding row element of the reference sequence to obtain the difference between the comparison sequence and the benchmark sequence. The formula is as follows:

[0143] Δx i (k)=|x i (k)-x0(k)|

[0144] Where, x i (k) represents the data in the k-th row of the impact result sequence corresponding to the configuration error in column i, x0(k) represents the data in the k-th row of the baseline result sequence, and Δx i (k) represents the absolute value of the difference between the data in the k-th row of the result sequence corresponding to the error in column i and the data in the k-th row of the baseline result sequence. k = 1, 2, 3, ..., n, i = 0, 1, 2, 3, ..., m.

[0145] Step S5022: Calculate the grey relational coefficient (GRC), expressed by the formula:

[0146]

[0147] Where, maxi mxk k Δx i (k) represents the global maximum difference between the resulting sequence and the benchmark result sequence, min i min k Δx i (k) represents the minimum global difference between the affected sequence and the baseline result sequence. ρ is the resolution coefficient, ρ∈(0,1), usually ρ=0.5, k=1,2,3,…n,i=0,1,2,3,…m.

[0148] Step S5023: Calculate the average of all correlation coefficients within each influencing sequence to obtain the grey relational degree of each misconfiguration influencing factor:

[0149]

[0150] Grey relational degree represents the similarity between the reference sequence and the comparison sequence. The closer the grey relational degree is to 1, the higher the correlation between the samples.

[0151] In this embodiment, the impact result sequence and the corresponding baseline result sequence for each configuration error influencing factor in each set of error logs are input into a gray-scale correlation analysis model. The gray-scale correlation analysis model determines and outputs the correlation degree between the impact sequence for each configuration error influencing factor and the corresponding baseline sequence. By calculating the gray correlation degree between the impact result sequence for each configuration error influencing factor and the corresponding baseline result sequence, the correlation degree between each configuration error influencing factor and the error configuration parameters of the target object type can be obtained quickly and accurately.

[0152] Example 6

[0153] Figure 6 A flowchart of the configuration error log processing method provided in this application is shown in the embodiment. Figure 6 As shown, based on any one of Embodiments 1 to 4, this embodiment relates to a specific implementable method for determining the key configuration influencing factors of the corresponding target object type according to the aforementioned correlation. Therefore, the configuration error log processing method provided in this embodiment, which determines the key configuration influencing factors of the corresponding target object type according to the aforementioned correlation, includes the following steps:

[0154] Step S601: Sort the correlation degrees of objects of the same type in descending order.

[0155] This embodiment does not specifically limit the sorting algorithm. For example, bubble sort or quicksort can be used.

[0156] Optionally, a bubble sort algorithm can be used. Specifically, adjacent pairs of relatedness are compared sequentially, and those with higher relatedness are swapped to the front, until no adjacent pairs of relatedness need to be swapped, at which point the sorting is complete.

[0157] Optionally, a quicksort algorithm can be used. Specifically, determine the range of all correlation values, select a median value, and rank correlations greater than the median value on the left and correlations less than the median value on the right. When the first grouping is completed, the left side contains correlations greater than the median value, and the right side contains correlations less than the median value. Apply an independent quicksort algorithm to the correlations on both sides until all correlations are sorted.

[0158] Step S602: Obtain the correlation degree of the first preset number of items, and determine the configuration error influencing factors corresponding to the correlation degree of the first preset number of items.

[0159] Among them, the preset number is greater than or equal to 1 and less than or equal to the number of factors affecting configuration errors.

[0160] Specifically, the correlation degree of the preset number of items ranked first is obtained in sequence, and the configuration error influencing factors corresponding to each obtained correlation degree are determined.

[0161] Step S603: Determine the configuration error influencing factors corresponding to the first preset number of correlation degrees as the key configuration influencing factors for the corresponding target object type.

[0162] In this embodiment, since the correlation degree of the first preset number is a correlation degree with a large value, the configuration error influencing factor corresponding to the correlation degree of the first preset number is a key influencing factor. Therefore, the configuration error influencing factor corresponding to the correlation degree of the first preset number is determined as the key configuration influencing factor for the corresponding target object type.

[0163] In this embodiment, the correlation degrees of objects of the same target type are sorted in descending order; the top preset number of correlation degrees are obtained, and the configuration error influencing factors corresponding to the top preset number of correlation degrees are determined; the configuration error influencing factors corresponding to the top preset number of correlation degrees are determined as the key configuration influencing factors of the corresponding target object type. Since the top preset number of correlation degrees are correlation degrees with larger values, they are also more correlated with configuration error logs. Therefore, determining the configuration error influencing factors corresponding to the top preset number of correlation degrees as the key configuration influencing factors of the corresponding target object type can accurately determine the key configuration influencing factors.

[0164] Example 7

[0165] Figure 7 A flowchart of a configuration error log processing method provided in another embodiment of this application is shown below. Figure 7As shown, based on any one of Embodiments 1 to 6, the configuration error log processing method provided in this embodiment further includes the following steps:

[0166] Step 701: In response to the configuration request for the target object triggered by the management user, determine the type of the target object.

[0167] Among them, the management user is the user who manages the target object. The management user can configure the parameters of the target object.

[0168] Specifically, when a management user initiates a configuration request, the configuration request may include the identification information of the target object. The electronic device can obtain the pre-stored mapping relationship between the identification information of the target object and the type of the target object based on the identification information of the target object, and determine the type of the target object based on the mapping relationship.

[0169] Step 702: Obtain the corresponding key influencing factors of the configuration based on the type of the target object.

[0170] Specifically, in this embodiment, after determining the key configuration influencing factors corresponding to each target object error log set, each target object type is associated with and stored in relation to its corresponding key configuration influencing factors. If stored in a database or local storage area, the association between each target object type and its corresponding key configuration influencing factors is retrieved from the database or local storage area. Then, based on this association, the key configuration influencing factors corresponding to the target object type in the configuration request triggered by the management user are determined.

[0171] Step 703: Display the corresponding key configuration factors to remind the management user.

[0172] Specifically, the front end receives the key configuration factors corresponding to the target object type sent by the electronic device and displays them on the front end user interface to provide prompts to the management user.

[0173] In this embodiment, in response to a configuration request for a target object triggered by a management user, the type of the target object is determined. Based on the type of the target object, corresponding key configuration influencing factors are obtained. These key configuration influencing factors are then displayed to remind the management user. By obtaining the key configuration influencing factors corresponding to the target object type and responding to the front-end management user's request in real time, the electronic device returns the key configuration influencing factors to prompt the user, thus improving the accuracy of the management user's configuration parameters for the target object type.

[0174] Example 8

[0175] Figure 8 This is a schematic diagram of the structure of a configuration error log processing device provided in an embodiment of this application, as shown below. Figure 8 As shown, the configuration error log processing device 80 provided in this embodiment includes: a first acquisition module 81, a group processing module 82, a first determination module 83, and a second determination module 84.

[0176] The system comprises the following modules: a first acquisition module 81, used to acquire configuration error logs of target objects within a preset time range; the configuration error logs include the type of the target object and configuration error information. A grouping processing module 82, used to group the configuration error logs according to the type of the target object to obtain multiple sets of error logs. A first determination module 83, used to determine the corresponding configuration error influencing factors for each set of error logs based on the corresponding configuration error information, and to determine the correlation between the influence result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs. A second determination module 84, used to determine the key configuration influencing factors for the corresponding target object type based on the correlation, so as to locate the error configuration parameters based on the key configuration influencing factors.

[0177] The configuration error log processing device provided in this embodiment can execute... Figure 3 The technical solution of the method embodiment shown is implemented in the same way as the technical effect of the method. Figure 3 The methods and embodiments shown are similar and will not be described in detail here.

[0178] Optionally, the configuration error log processing device provided in this embodiment further includes a formatting processing module.

[0179] The formatting module is used to perform unified formatting on the configuration error logs to obtain structured error logs. Correspondingly, the grouping module 82 is specifically used to group the structured error logs according to the type of the target object to obtain multiple sets of error logs.

[0180] Optionally, the first determining module 83, when determining the corresponding configuration error influencing factors based on the corresponding configuration error information, is specifically used to extract the configuration error influencing factor field from the configuration error information to determine the corresponding configuration error influencing factors.

[0181] Optionally, the benchmark result data is a benchmark result sequence, and the influence result data is an influence result sequence.

[0182] Accordingly, the first determining module 83, when determining the correlation between the impact result data and the baseline result data corresponding to each configuration error influencing factor in each set of error logs, is specifically used for:

[0183] The number of structured error logs in each error log set is determined according to a preset time period. The baseline result sequence for each error log set is determined based on the number of structured error logs in each preset time period within each error log set. The number of structured error logs in each preset time period under each configuration error influencing factor is determined. The impact result sequence under each configuration error influencing factor is determined based on the number of structured error logs in each preset time period under each configuration error influencing factor. The correlation between the impact result sequence under each configuration error influencing factor in each error log set and the corresponding baseline result sequence is determined.

[0184] Optionally, the first determining module 83, when determining the correlation between the sequence of impact results for each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence, is specifically used for:

[0185] The impact result sequence for each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence are input into a gray-scale correlation analysis model. The gray-scale correlation analysis model is used to determine and output the correlation degree between the impact sequence for each configuration error influencing factor and the corresponding baseline sequence.

[0186] Optionally, the second determining module 84 is specifically used for:

[0187] Sort the relevance scores of objects of the same target object type in descending order. Obtain the top preset number of relevance scores and determine the configuration error influencing factors corresponding to these top preset number of relevance scores. Identify the configuration error influencing factors corresponding to these top preset number of relevance scores as the key configuration influencing factors for the corresponding target object type.

[0188] Optionally, the configuration error log processing device provided in this embodiment further includes: a third determination module, a second acquisition module, and a reminder module.

[0189] The third determining module is used to determine the type of the target object in response to a configuration request triggered by the management user. The second obtaining module is used to obtain the corresponding key configuration influencing factors based on the type of the target object. The reminder module is used to display the corresponding key configuration influencing factors to remind the management user.

[0190] The configuration error log processing device provided in this embodiment can execute... Figures 4-7 The technical solution of the method embodiment shown has the same implementation principle and technical effect as... Figures 4-7 The methods and embodiments shown are similar and will not be described in detail here.

[0191] Example 9

[0192] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device 90 includes a processor 901 and a memory 902 communicatively connected to the processor 901.

[0193] The memory 902 stores computer-executed instructions.

[0194] The processor 901 executes the computer execution instructions stored in the memory 902 to implement the configuration error log processing method provided in any of the above method embodiments.

[0195] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method provided in any of the above-described method embodiments.

[0196] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in any embodiment of this application.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0198] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0199] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in a combination of hardware and software functional modules.

[0200] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to the processor or controller of a general-purpose computer, special-purpose computer, or other programmable compliance testing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0201] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0202] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0203] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0204] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for processing configuration error logs, characterized in that, include: Retrieve configuration error logs for the target object within a preset time range; The configuration error log includes the type of the target object and configuration error information; The configuration error logs are uniformly formatted to obtain structured error logs; the structured error logs are then grouped according to the type of the target object to obtain multiple sets of error logs. For each set of error logs, determine the corresponding configuration error influencing factors based on the corresponding configuration error information, and determine the correlation between the impact result data and the baseline result data for each configuration error influencing factor in each set of error logs. Based on the aforementioned correlation, key configuration influencing factors for the corresponding target object type are determined, so as to locate erroneous configuration parameters based on the aforementioned key configuration influencing factors; The benchmark result data is a benchmark result sequence, and the influence result data is an influence result sequence; Determining the correlation between the impact result data and the baseline result data for each configuration error influencing factor in each set of error logs includes: determining the number of structured error logs in each set of error logs according to a preset time period; determining the baseline result sequence corresponding to each set of error logs based on the number of structured error logs in each preset time period in each set of error logs; determining the number of structured error logs in each preset time period under each configuration error influencing factor in each set of error logs; determining the impact result sequence under each configuration error influencing factor based on the number of structured error logs in each preset time period under each configuration error influencing factor; and determining the correlation between the impact result sequence under each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence.

2. The method according to claim 1, characterized in that, The step of determining the corresponding configuration error influencing factors based on the corresponding configuration error information includes: Extract the configuration error influencing factor field from the configuration error information to determine the corresponding configuration error influencing factors.

3. The method according to claim 1, characterized in that, Determining the correlation between the impact result sequence and the corresponding baseline result sequence for each configuration error influencing factor in each set of error logs includes: Input the sequence of impact results for each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence into the gray-scale correlation analysis model; The gray-scale correlation analysis model is used to determine and output the correlation degree between the impact result sequence and the corresponding baseline result sequence under each configuration error influencing factor.

4. The method according to any one of claims 1-3, characterized in that, The key influencing factors for determining the configuration of the corresponding target object type based on each of the aforementioned correlation degrees include: Sort the correlation scores of objects of the same type in descending order; Obtain the relevance of the top preset number of items, and determine the configuration error influencing factors corresponding to the relevance of the top preset number of items; The configuration error influencing factors corresponding to the first preset number of correlation degrees are determined as the key configuration influencing factors for the corresponding target object type.

5. The method according to any one of claims 1-3, characterized in that, Also includes: In response to a configuration request for a target object triggered by a management user, the type of the target object is determined; Obtain the corresponding key configuration influencing factors based on the type of the target object; The corresponding key configuration factors will be displayed to remind the management users.

6. A processing apparatus for configuring error logs, comprising: The first acquisition module is used to acquire configuration error logs of target objects within a preset time range; The configuration error log includes the type of the target object and configuration error information; The grouping module is used to group the configuration error logs according to the type of the target object to obtain multiple sets of error logs. The first determination module is used to determine the corresponding configuration error influencing factors for each set of error logs based on the corresponding configuration error information, and to determine the correlation between the influence result data and the baseline result data for each configuration error influencing factor in each set of error logs. The second determining module is used to determine the key influencing factors of the corresponding target object type based on the correlation degree, so as to locate the erroneous configuration parameters based on the key influencing factors of the configuration. The device further includes: a formatting processing module, used to perform unified formatting processing on the configuration error log to obtain a structured error log; After uniformly formatting the configuration error log to obtain a structured error log, the grouping module is specifically used to group the structured error log according to the type of the target object to obtain multiple sets of error logs. The baseline result data is a baseline result sequence, and the influence result data is an influence result sequence. The first determining module, when determining the correlation between the influence result data corresponding to each configuration error influencing factor in each set of error logs and the baseline result data, specifically performs the following: determining the number of structured error logs in each set of error logs according to a preset time period; determining the baseline result sequence corresponding to each set of error logs based on the number of structured error logs in each preset time period in each set of error logs; determining the number of structured error logs in each preset time period under each configuration error influencing factor in each set of error logs; determining the influence result sequence under each configuration error influencing factor based on the number of structured error logs in each preset time period under each configuration error influencing factor; and determining the correlation between the influence result sequence under each configuration error influencing factor in each set of error logs and the corresponding baseline result sequence.

7. An electronic device, comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-5.

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