Application parameter portrait determination method and device, equipment, medium and program product

Abstract

The invention provides an application parameter portrait determination method and device, electronic equipment, a medium and a computer program product, which can be used in the technical field of financial science and technology. The method comprises the steps that m application parameters used in an application program are extracted, and each application parameter comprises a parameter name and a parameter value; defining parameter types of the m application parameters according to a parameter type determination rule; when a preset parameter type set corresponding to the parameter type of the application parameter exists, dividing the application parameter into the parameter type set; when the preset parameter type set corresponding to the parameter type of the application parameter does not exist, adding the corresponding parameter type set according to the parameter type of the application parameter, and dividing the application parameter into the added parameter type set; determining a parameter distribution curve of the application program according to a preset parameter type set and the added parameter type set; and taking the parameter distribution curve as a portrait of the application parameter.

Description

Technical Field

[0001] The present disclosure relates to the field of financial technology, and more specifically, to a method, device, electronic device, medium and computer program product for determining a portrait of application parameters. Background Art

[0002] In the prior art, the development process of an application involves the collaboration of a development team, a testing team, and a configuration management team. In the actual development process, each team only cares about the values ​​of some parameters related to the work tasks, but lacks an overall management of all parameters used by the application. For the overall parameters of the application, development and maintenance personnel are required to manually check the numbers, and then manually organize and count the data, which is time-consuming and inefficient. If the amount of parameter data reaches tens of thousands, it is almost an impossible task. Therefore, how to automatically organize and count the application parameters of the application to provide R&D personnel with a data basis for parameter optimization has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0003] In view of this, the present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for automatically determining parameter distribution curves to provide R&D personnel with a data-based profile of application parameters for parameter optimization.

[0004] One aspect of the present disclosure provides a method for determining a portrait of an application parameter, comprising: extracting m application parameters used in an application, wherein each of the application parameters includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; defining parameter types of the m application parameters according to a parameter type determination rule, wherein the determination rule includes a mapping relationship between parameter names, parameter values, and parameter types; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, dividing the application parameter into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, adding a corresponding parameter type set according to the parameter type of the application parameter, and dividing the application parameter into the added parameter type set; determining a parameter distribution curve of the application according to the pre-set parameter type set and the added parameter type set; and using the parameter distribution curve as a portrait of the application parameter.

[0005] According to the method for determining the portrait of application parameters of the embodiment of the present disclosure, the method includes extracting m application parameters used in the application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; defining the parameter types of the m application parameters according to the parameter type determination rule, wherein the determination rule includes the mapping relationship between the parameter name, the parameter value and the parameter type; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set; according to the pre-set parameter type set and the added parameter type set, the parameter distribution curve of the application is determined; and the parameter distribution curve is used as the portrait of the application parameter. The present disclosure can realize the automatic determination of the parameter distribution curve, and the parameter distribution curve can characterize the distribution of the number of parameters under the pre-set parameter type and the newly added parameter type dimensions, and the parameter distribution curve can provide the data basis for parameter optimization to the R&D personnel.

[0006] In some embodiments, the step of determining the parameter distribution curve of the application based on a pre-set parameter type set and an added parameter type set includes: using the parameter type corresponding to the parameter type set as the horizontal coordinate of the parameter distribution curve; using the number of application parameters in the parameter type set as the vertical coordinate of the parameter distribution curve; and drawing the parameter distribution curve based on the horizontal coordinate and the vertical coordinate.

[0007] In some embodiments, the method for determining the application parameter optimization strategy further includes: using bars on the parameter distribution curve to represent the number of application parameters in the parameter type set, and rendering the bars corresponding to different parameter type sets with different colors.

[0008] In some embodiments, the method for determining the application parameter optimization strategy further includes: calling g of each historical time point determined in advance at n historical time points i A set of parameter types, where g i is an integer greater than or equal to 1, i is an integer greater than or equal to 1 and less than or equal to n; according to g of each historical time point determined at the n historical time points i A parameter type set, a pre-set parameter type set and an added parameter type set are used to determine the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type; and the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type is used as a portrait of the application parameter.

[0009] In some embodiments, g of each historical time point is determined in advance at n historical time points. iThe steps of the parameter type set include: at each of the n historical time points, extracting k parameters used in the application program at the historical time point j application parameters, where k j is an integer greater than or equal to 1, j is an integer greater than or equal to 1 and less than or equal to n; according to the parameter type determination rule, the k is defined j The parameter type of an application parameter; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set; the pre-set parameter type set and the added parameter type set are used as g at the historical time point i A collection of parameter types.

[0010] In some embodiments, according to the g of each historical time point determined at the n historical time points i The step of determining an application parameter quantity change trend curve of a parameter type corresponding to a parameter type set of the same type for a parameter type set, a pre-set parameter type set and an added parameter type set, comprises: taking a time point as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type, taking the number of application parameters in the parameter type set of the same type as the vertical coordinate of the application parameter quantity change trend curve, and drawing the application parameter quantity change trend curve; repeatedly performing the step of taking the time point as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type, taking the number of application parameters in the parameter type set of the same type as the vertical coordinate of the application parameter quantity change trend curve, and drawing the application parameter quantity change trend curve, until application parameter quantity change trend curves are drawn for all parameter type sets.

[0011] In some embodiments, after the step of extracting m application parameters used in the application, the method further includes: cleaning the m application parameters, the cleaning including removing duplicate data, removing logically deleted data, filling missing values, converting data format and standardizing data; according to the parameter type determination rule, the step of defining the parameter types of the m application parameters is: according to the parameter type determination rule, defining the parameter types of the m application parameters after cleaning.

[0012] Another aspect of the present disclosure provides a device for determining a portrait of an application parameter, comprising: an extraction module, the extraction module being used to extract m application parameters used in an application, wherein each of the application parameters includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; a definition module, the definition module being used to define parameter types of the m application parameters according to a determination rule of parameter types, wherein the determination rule includes a mapping relationship between parameter names, parameter values, and parameter types; a first division module, the first division module being used to divide the application parameter into a parameter type set when there is a preset parameter type set corresponding to the parameter type of the application parameter; a second division module, the second division module being used to add a corresponding parameter type set according to the parameter type of the application parameter when there is no preset parameter type set corresponding to the parameter type of the application parameter, and divide the application parameter into the added parameter type set; a first determination module, the first determination module being used to determine a parameter distribution curve of the application according to a preset parameter type set and an added parameter type set; and a second determination module, the second determination module being used to use the parameter distribution curve as a portrait of the application parameter.

[0013] Another aspect of the present disclosure provides an electronic device, comprising one or more processors and one or more memories, wherein the memories are used to store executable instructions, and when the executable instructions are executed by the processors, the above-mentioned method is implemented.

[0014] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0015] Another aspect of the present disclosure provides a computer program product, comprising a computer program, wherein the computer program comprises computer executable instructions, and the instructions are used to implement the method as described above when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 An exemplary system architecture to which the method and apparatus according to an embodiment of the present disclosure can be applied is schematically shown;

[0018] Figure 2 A flowchart of a method for determining a portrait of application parameters according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 3A flowchart schematically illustrates the steps of determining a parameter distribution curve of an application program according to a preset parameter type set and an added parameter type set according to an embodiment of the present disclosure;

[0020] Figure 4 Schematically shows an example diagram of a parameter distribution curve according to an embodiment of the present disclosure;

[0021] Figure 5 A flowchart of a method for determining a portrait of application parameters according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 6 The schematic diagram shows that the g of each historical time point is determined in advance at n historical time points according to an embodiment of the present disclosure. i A flowchart of the steps for a set of parameter types;

[0023] Figure 7 Schematically illustrates the g of each historical time point determined at n historical time points according to an embodiment of the present disclosure. i A flowchart of the steps of determining the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type, the preset parameter type set and the added parameter type set;

[0024] Figure 8 An example diagram schematically shows an application parameter quantity variation trend curve according to an embodiment of the present disclosure;

[0025] Fig. 9 A block diagram schematically shows a device for determining a portrait of application parameters according to an embodiment of the present disclosure;

[0026] Fig.10 A block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0028] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0029] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0030] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0031] When using expressions such as "at least one of A, B or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the said features.

[0032] In the prior art, the development process of an application involves the collaboration of a development team, a testing team, and a configuration management team. In the actual development process, each team only cares about the values ​​of some parameters related to the work tasks, but lacks an overall management of all parameters used by the application. For the overall parameters of the application, development and maintenance personnel are required to manually check the numbers, and then manually organize and count the data, which is time-consuming and inefficient. If the amount of parameter data reaches tens of thousands, it is almost an impossible task. Therefore, how to automatically organize and count the application parameters of the application to provide R&D personnel with a data basis for parameter optimization has become a technical problem that technical personnel in this field urgently need to solve.

[0033] The embodiments of the present disclosure provide a method, device, electronic device, computer-readable storage medium and computer program product for determining the portrait of an application parameter. The method for determining the portrait of an application parameter includes: extracting m application parameters used in an application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; defining parameter types of the m application parameters according to a parameter type determination rule, wherein the determination rule includes a mapping relationship between a parameter name, a parameter value and a parameter type; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set; according to the pre-set parameter type set and the added parameter type set, a parameter distribution curve of the application is determined; and the parameter distribution curve is used as a portrait of the application parameter.

[0034] It should be noted that the application parameter portrait determination method, device, electronic device, computer-readable storage medium and computer program product disclosed in the present invention can be used in the field of financial technology technology, and can also be used in any field outside the field of financial technology technology. The application parameter portrait determination method can be used for parameter management of banking applications in the financial technology field, specifically for portrait determination of application parameters of banking applications.

[0035] Figure 1 The exemplary system architecture 100 of the method, apparatus, electronic device, computer-readable storage medium and computer program product for determining a portrait of application parameters according to an embodiment of the present disclosure is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0036] like Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0037] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0038] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0039] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0040] It should be noted that the portrait determination method of the application parameters provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the portrait determination device of the application parameters provided in the embodiment of the present disclosure can generally be set in the server 105. The portrait determination method of the application parameters provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the portrait determination device of the application parameters provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0041] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0042] The following will be based on Figure 1 The scene described by Figure 2~Figure 8 The method for determining the portrait of application parameters in the embodiment of the present disclosure is described in detail.

[0043] Figure 2 The flowchart of the method for determining the portrait of application parameters according to an embodiment of the present disclosure is schematically shown.

[0044] like Figure 2 As shown, the method for determining the portrait of application parameters of this embodiment includes operations S210 to S260.

[0045] In operation S210, m application parameters used in the application are extracted, where each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1.

[0046] Here, the m application parameters used in the application may include static parameter data, dynamic parameter data, parameter data used in a platform as a service (Paas) environment and complying with specific standards or specifications, parameter data not specific to the Paas environment and complying with specific standards or specifications, and parameter data used in the Paas environment and not complying with specific standards or specifications.

[0047] In operation S220, the parameter types of the m application parameters are defined according to the parameter type determination rule, wherein the determination rule includes a mapping relationship between parameter name, parameter value and parameter type. Here, the parameter type may include, for example, interface type parameters, domain name type parameters, key type parameters, certificate type parameters and performance type parameters.

[0048] In operation S230, when there is a preset parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set.

[0049] In operation S240, when there is no preset parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set.

[0050] In operation S250, a parameter distribution curve of the application is determined according to the preset parameter type set and the added parameter type set.

[0051] In operation S260 , the parameter distribution curve is used as a portrait of the application parameters.

[0052] According to the method for determining the portrait of application parameters of the embodiment of the present disclosure, the method includes extracting m application parameters used in the application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; defining the parameter types of the m application parameters according to the parameter type determination rule, wherein the determination rule includes the mapping relationship between the parameter name, the parameter value and the parameter type; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set; according to the pre-set parameter type set and the added parameter type set, the parameter distribution curve of the application is determined; and the parameter distribution curve is used as the portrait of the application parameter. The present disclosure can realize the automatic determination of the parameter distribution curve, and the parameter distribution curve can characterize the distribution of the number of parameters under the pre-set parameter type and the newly added parameter type dimensions, and the parameter distribution curve can provide the data basis for parameter optimization to the R&D personnel.

[0053] Figure 3 The flowchart schematically shows the steps of determining the parameter distribution curve of an application program according to a preset parameter type set and an added parameter type set according to an embodiment of the present disclosure.

[0054] like Figure 3 As shown, operation S250 of this embodiment is a step of determining a parameter distribution curve of an application program according to a preset parameter type set and an added parameter type set, and includes operations S251 to S253.

[0055] In operation S251, the parameter type corresponding to the parameter type set is used as the abscissa of the parameter distribution curve.

[0056] In operation S252, the number of application parameters in the parameter type set is used as the ordinate of the parameter distribution curve.

[0057] In operation S253, a parameter distribution curve is drawn according to the abscissa and the ordinate.

[0058] In order to better understand operations S251 to S253, for example, the parameter types corresponding to the preset parameter type set and the added parameter type set include domain name parameters, key parameters and performance parameters. Assuming that there are 38 application parameters in the parameter type set corresponding to the domain name parameters, 50 application parameters in the parameter type set corresponding to the key parameters, and 82 application parameters in the parameter type set corresponding to the performance parameters, the parameter distribution curve of the application determined by the above specific data can be as follows: Figure 4 shown. Figure 4The parameter distribution curve shown is only used as an example to better understand operations S251 to S253, and should not be construed as a limitation of the present disclosure. In the specific implementation of the present disclosure, the parameter types corresponding to the preset parameter type set and the added parameter type set can be any actually required type, and the number of application parameters in the parameter type set corresponding to the parameter type can be any actually required number.

[0059] Operations S251 to S253 can facilitate the step of determining the parameter distribution curve of the application program according to the preset parameter type set and the added parameter type set.

[0060] According to some embodiments of the present disclosure, the method for determining an application parameter optimization strategy may further include operation S254.

[0061] In operation S254, the number of application parameters in the parameter type set is represented by bars on the parameter distribution curve, and the bars corresponding to different parameter type sets are rendered in different colors, so that it is easier for developers to obtain the information reflected by the parameter distribution curve and improve their satisfaction.

[0062] Figure 5 The flowchart of the method for determining the portrait of application parameters according to an embodiment of the present disclosure is schematically shown.

[0063] like Figure 5 As shown, the method for determining the portrait of application parameters of this embodiment may also include operations S270 to S290.

[0064] In operation S270, the g of each historical time point determined in advance at the n historical time points is retrieved. i A set of parameter types, where g i is an integer greater than or equal to 1, and i is an integer greater than or equal to 1 and less than or equal to n.

[0065] As a possible way to achieve this, Figure 6 As shown, g of each historical time point is determined in advance at n historical time points i The step of performing a parameter type set includes operations S310 to S350.

[0066] In operation S310, at each of the n historical time points, k used in the application program at the historical time point is extracted. j application parameters, where k j is an integer greater than or equal to 1, and j is an integer greater than or equal to 1 and less than or equal to n.

[0067] In operation S320, according to the parameter type determination rule, k is defined. jThe parameter type of the application parameter.

[0068] In operation S330, when there is a preset parameter type set corresponding to the parameter type of the application parameter, the application parameter is classified into the parameter type set.

[0069] In operation S340, when there is no preset parameter type set corresponding to the parameter type of the application parameter, a corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set.

[0070] In operation S350, the preset parameter type set and the added parameter type set are used as the g at the historical time point. i A collection of parameter types.

[0071] It is understandable that, at each of the n historical time points, the g of the historical time point can be obtained by executing operations S310 to S350. i Therefore, through operations S310 to S350, it is possible to facilitate the pre-determination of g at each historical time point at n historical time points. i The steps of performing parameter type sets at n historical time points are finally performed to obtain parameter type sets at n historical time points.

[0072] In operation S280, according to g of each historical time point determined at n historical time points i A parameter type set, a preset parameter type set and an added parameter type set are selected to determine an application parameter quantity change trend curve of a parameter type corresponding to a parameter type set of the same type.

[0073] As some feasible ways, such as Figure 7 As shown, operation S280 determines the g of each historical time point at the n historical time points. i The step of determining the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type includes operation S281 and operation S282.

[0074] In operation S281, the time point is used as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, and the number of application parameters in the same type of parameter type set is used as the vertical coordinate of the application parameter quantity change trend curve to draw the application parameter quantity change trend curve.

[0075] In operation S282, the time point is repeatedly used as the abscissa of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, and the number of application parameters in the same type of parameter type set is used as the ordinate of the application parameter quantity change trend curve, and the application parameter quantity change trend curve is drawn until all parameter type sets have drawn the application parameter quantity change trend curve. All parameter type sets here include parameter type sets determined at n historical time points obtained through operations S310 to S350, and parameter type sets determined at the current time point obtained through operations S210 to S240.

[0076] To better understand operations S281 and S282, take domain name parameters as an example, assuming that n historical time points are May 1, 24, June 1, 24, July 1, 24, August 1, 24, September 1, 24, October 1, 24, and November 1, 24, and the current time point is December 1, 24. The parameter type set of the domain name parameters determined on May 1, 24 includes 5 application parameters; the parameter type set of the domain name parameters determined on June 1, 24 includes 5 application parameters; the parameter type set of the domain name parameters determined on July 1, 24 includes 5 application parameters. The parameter type set of domain name parameters includes 6 application parameters; the parameter type set of domain name parameters determined on August 1, 24 includes 7 application parameters; the parameter type set of domain name parameters determined on September 1, 24 includes 5 application parameters; the parameter type set of domain name parameters determined on October 1, 24 includes 6 application parameters; the parameter type set of domain name parameters determined on November 1, 24 includes 7 application parameters; the parameter type set of domain name parameters determined on December 1, 24 includes 15 application parameters.

[0077] The application parameter quantity change trend curve of the domain name type parameters determined by the above specific data can be shown as follows: Figure 8 shown. Figure 8 The application parameter quantity change trend curve of the domain name class parameter shown is only an example for better understanding of operation S281 and operation S282, and cannot be understood as a limitation of the present disclosure. In the specific implementation of the present disclosure, n historical time points within any time period can be determined through operation S310 to operation S350 to determine the parameter type set of each historical time point, and the number of application parameters in the parameter type set corresponding to the parameter type can be any actual required number. Figure 8 This is only a trend curve of application parameter quantity changes for domain name type parameters. Similarly, a trend curve of application parameter quantity changes for any parameter type can be drawn, which will not be described here one by one.

[0078] When developers see Figure 8When the application parameter quantity change trend curve is shown, it can be determined whether the application parameters are abnormal. For example, from May to November, the number of application parameters of domain name parameters is stable at around 5 to 7, but in December of the same month, the number of application parameters of domain name parameters suddenly increased to 15. At this time, the R&D personnel may determine the sudden increase in the number of application parameters of domain name parameters as an abnormality, and then conduct a cause analysis to further determine whether there is indeed an abnormality. If there is indeed an abnormality, timely countermeasures can be taken.

[0079] Operations S281 and S282 can facilitate the implementation of g at each historical time point determined at n historical time points. i The steps of determining the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type are as follows: a parameter type set, a pre-set parameter type set and an added parameter type set. On this basis, the application parameter quantity change trend curve of the parameter type determined by the operation S281 and the operation S282 of the present disclosure can help R&D personnel to quickly discover abnormalities and take countermeasures in time, thereby improving the user's satisfaction with the application of the present disclosure.

[0080] In operation S290, the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type is used as the portrait of the application parameter.

[0081] According to the method for determining the portrait of application parameters in the embodiment of the present disclosure, by calling the g of each historical time point determined in advance at n historical time points, i A set of parameter types, where g i is an integer greater than or equal to 1, i is an integer greater than or equal to 1 and less than or equal to n; according to g at each historical time point determined at n historical time points i A parameter type set, a pre-set parameter type set and an added parameter type set are used to determine the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type; the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type is used as a portrait of the application parameter; so that the present invention can realize automatic determination of the application parameter quantity change trend curve of the same parameter type within a certain time period, the application parameter quantity change trend curve of the same parameter type within a certain time period can characterize the application parameter quantity change trend of the same parameter type within the time period, and the application parameter quantity change trend curve of the same parameter type within a certain time period can also provide R&D personnel with a data basis for parameter optimization.

[0082] According to some embodiments of the present disclosure, after the step of extracting m application parameters used in the application in operation S210, the method for determining the portrait of the application parameters may further include operation S001.

[0083] In operation S001 , the m application parameters are cleaned, where the cleaning includes at least one of removing duplicate data, removing logically deleted data, filling missing values, converting data formats, and standardizing data.

[0084] The step of defining the parameter types of the m application parameters according to the parameter type determination rule in operation S220 is as follows: defining the parameter types of the cleaned m application parameters according to the parameter type determination rule. Thus, high-quality basic data (m application parameters) can be obtained through operation S001, and operation S220 processes the high-quality basic data so that the processing result (defining the parameter type of each application parameter) is more accurate.

[0085] Based on the above-mentioned application parameter portrait determination method, the present disclosure also provides an application parameter portrait determination device. Fig. 9 A device for determining a portrait of application parameters is described in detail.

[0086] Fig. 9 The structural block diagram of the device for determining the portrait of application parameters according to an embodiment of the present disclosure is schematically shown.

[0087] The device 10 for determining the portrait of application parameters includes an extraction module 1, a definition module 2, a first division module 3, a second division module 4, a first determination module 5 and a second determination module 6.

[0088] The extraction module 1 is used to extract m application parameters used in the application program, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1.

[0089] The definition module 2 is used to define the parameter types of the m application parameters according to the parameter type determination rule, wherein the determination rule includes a mapping relationship between a parameter name, a parameter value and a parameter type.

[0090] The first classification module 3 is used to classify the application parameter into the parameter type set when there is a preset parameter type set corresponding to the parameter type of the application parameter.

[0091] The second division module 4 is used to add a corresponding parameter type set according to the parameter type of the application parameter when there is no preset parameter type set corresponding to the parameter type of the application parameter, and divide the application parameter into the added parameter type set.

[0092] The first determination module 5 is used to determine the parameter distribution curve of the application program according to a preset parameter type set and an added parameter type set.

[0093] The second determination module 6 is used to perform profiling using the parameter distribution curve as the application parameter.

[0094] According to some embodiments of the present disclosure, the first determining module may include a first determining unit, a second determining unit, and a first drawing unit.

[0095] The first determining unit is used to use the parameter type corresponding to the parameter type set as the abscissa of the parameter distribution curve.

[0096] The second determining unit is configured to use the number of application parameters in the parameter type set as the ordinate of the parameter distribution curve.

[0097] The first drawing unit is used to draw a parameter distribution curve according to the abscissa and the ordinate.

[0098] According to some embodiments of the present disclosure, the apparatus for determining a portrait of application parameters further includes a rendering module.

[0099] The rendering module is used to represent the number of application parameters in the parameter type set with bars on the parameter distribution curve, and to render the bars corresponding to different parameter type sets with different colors.

[0100] According to some embodiments of the present disclosure, the device for determining the portrait of application parameters also includes a calling module, a third determining module and a fourth determining module.

[0101] The calling module is used to call the g of each historical time point determined in advance at n historical time points i A set of parameter types, where g i is an integer greater than or equal to 1, and i is an integer greater than or equal to 1 and less than or equal to n.

[0102] The third determining module is used to determine the g of each historical time point according to the g of each historical time point determined at the n historical time points. i A parameter type set, a preset parameter type set and an added parameter type set are selected to determine an application parameter quantity change trend curve of a parameter type corresponding to a parameter type set of the same type.

[0103] The fourth determination module is used to use the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type as the portrait of the application parameter.

[0104] According to some embodiments of the present disclosure, the apparatus for determining the image of application parameters further includes a pre-determination module. The pre-determination module is used to pre-determine the g of each historical time point at n historical time points. i The pre-determination module includes an extraction unit, a definition unit, a first division unit, a second division unit and a third determination unit.

[0105] The extraction unit is used to extract k used in the application program at each historical time point among the n historical time points. j application parameters, where k j is an integer greater than or equal to 1, and j is an integer greater than or equal to 1 and less than or equal to n.

[0106] The definition unit is used to define k according to the determination rules of the parameter type. j The parameter type of the application parameter.

[0107] The first dividing unit is used to divide the application parameter into the parameter type set when there is a preset parameter type set corresponding to the parameter type of the application parameter.

[0108] The second division unit is used to add a corresponding parameter type set according to the parameter type of the application parameter when there is no preset parameter type set corresponding to the parameter type of the application parameter, and divide the application parameter into the added parameter type set.

[0109] The third determining unit is used to use the preset parameter type set and the added parameter type set as g at the historical time point. i A collection of parameter types.

[0110] According to some embodiments of the present disclosure, the third determining module includes a second drawing unit and a repeated execution unit.

[0111] The second drawing unit is used to use the time point as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, and the number of application parameters in the same type of parameter type set as the vertical coordinate of the application parameter quantity change trend curve to draw the application parameter quantity change trend curve.

[0112] The repeated execution unit is used to repeatedly execute the process of taking the time point as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, and taking the number of application parameters in the same type of parameter type set as the vertical coordinate of the application parameter quantity change trend curve, and drawing the application parameter quantity change trend curve until the application parameter quantity change trend curve is drawn for all parameter type sets.

[0113] According to some embodiments of the present disclosure, the device for determining the portrait of application parameters also includes a cleaning module, and after the step of extracting m application parameters used in the application, the cleaning module is used to clean the m application parameters, and the cleaning includes removing duplicate data, removing logically deleted data, filling missing values, converting data format and standardizing data; according to the parameter type determination rule, the step of defining the parameter types of the m application parameters is: according to the parameter type determination rule, define the parameter types of the cleaned m application parameters.

[0114] According to the application parameter portrait determination device 10 of the embodiment of the present disclosure, it includes extracting m application parameters used in the application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; defining the parameter types of the m application parameters according to the parameter type determination rule, wherein the determination rule includes the mapping relationship between the parameter name, the parameter value and the parameter type; when there is a pre-set parameter type set corresponding to the parameter type of the application parameter, the application parameter is divided into the parameter type set; when there is no pre-set parameter type set corresponding to the parameter type of the application parameter, the corresponding parameter type set is added according to the parameter type of the application parameter, and the application parameter is divided into the added parameter type set; according to the pre-set parameter type set and the added parameter type set, the parameter distribution curve of the application is determined; the parameter distribution curve is used as the portrait of the application parameter. The present disclosure can realize the automatic determination of the parameter distribution curve, the parameter distribution curve can characterize the distribution of the number of parameters under the pre-set parameter type and the newly added parameter type dimensions, and the parameter distribution curve can provide the data basis for parameter optimization to the R&D personnel.

[0115] In addition, according to an embodiment of the present disclosure, any multiple modules among the extraction module 1, the definition module 2, the first division module 3, the second division module 4, the first determination module 5 and the second determination module 6 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0116] According to an embodiment of the present disclosure, at least one of the extraction module 1, the definition module 2, the first division module 3, the second division module 4, the first determination module 5 and the second determination module 6 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware and firmware, or in an appropriate combination of any of them.

[0117] Alternatively, at least one of the extraction module 1, the definition module 2, the first division module 3, the second division module 4, the first determination module 5 and the second determination module 6 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is executed.

[0118] The following describes in detail an apparatus for determining a portrait of application parameters according to an embodiment of the present disclosure. It is worth noting that the following description is merely an exemplary description and is not a specific limitation of the present disclosure.

[0119] In order to provide an overall perspective of all parameters for the application, so that the application has a comprehensive view of the overall parameters, and to provide a technical analysis method for the rationality of parameter configuration, parameter exit, parameter configuration optimization, and parameter sharing, the present disclosure proposes a device for determining the portrait of application parameters. The modules of the device for determining the portrait of application parameters disclosed in the present disclosure are as follows.

[0120] 101 Parameter data source module: Parameter data source refers to the application that centrally extracts the parameters needed in the program, and compiles and packages them together with the program to produce a version package.

[0121] 102 Parameter cleaning module: Parameter data cleaning refers to improving data quality through a series of specific operations to ensure that data can be effectively used for analysis and profiling. It mainly involves some specific steps such as: removing duplicate data, removing logically deleted data, filling missing values, converting data formats, standardizing data, checking data consistency, and data verification.

[0122] 103 Parameter analysis module: refers to a process of analyzing all the attributes of a parameter in various dimensions. For example, semantic analysis is performed on the two attributes of parameter name and remark to understand the exact meaning of the word. Regular expression judgment is performed on the parameter value to form a consistency judgment on the parameter value, parameter name, remark, etc. Trend analysis of parameter data volume and rationality of parameter configuration are performed. Parameters such as ports and directories that do not require dynamic updates should exist in the form of non-dynamic parameters.

[0123] 104 Parameter definition module: refers to the process of completing the parameter conceptualization after the previous round of parameter analysis module. It is a process from concrete to abstract after parameter analysis, and finally forms parameter types. For example, ports in the network, switches, or thresholds. Each parameter type should be unique.

[0124] 105 Parameter classification module: When traversing application parameters, first determine whether the parameter belongs to an existing parameter type. If yes, directly classify it into the parameter type. For example, port and IP information in the network. If not, re-analyze the parameter and form a new parameter type.

[0125] 106 Parameter labeling module: After the parameter classification is completed, the parameter labeling step is entered. A parameter can have one or more labels. The purpose of parameter labeling is to allow the application to see the overall status of its own parameters from a multi-dimensional perspective.

[0126] 107 Parameter portrait module: Finally, after completing all the above steps, the parameter portrait module gives the definition and usage of the application's own parameters as a whole, and gives specific optimization strategies for the optimization of application parameters.

[0127] The device for determining the portrait of application parameters disclosed in the present invention utilizes all parameter data stored in the configuration center by the application, and completes parameter analysis, parameter definition, parameter classification and other processing through technical means, thereby realizing parameter portrait and improving the efficiency of parameter analysis in the configuration center.

[0128] Fig.10 A block diagram of an electronic device suitable for implementing the above method according to an embodiment of the present disclosure is schematically shown.

[0129] like Fig.10 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0130] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0131] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0132] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0133] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0134] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

[0135] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0136] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0137] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0138] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0139] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a 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 box can also occur in a different order from the order marked in the accompanying drawings. For example, two 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 box in the block diagram or flow chart, and the combination of the boxes in the block diagram 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.

[0140] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0141] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining a portrait of an application parameter, characterized in that: include: Extract m application parameters used in the application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; Defining parameter types of the m application parameters according to a parameter type determination rule, wherein the determination rule includes a mapping relationship between a parameter name, a parameter value, and a parameter type; When there is a preset parameter type set corresponding to the parameter type of the application parameter, classifying the application parameter into the parameter type set; When there is no preset parameter type set corresponding to the parameter type of the application parameter, adding a corresponding parameter type set according to the parameter type of the application parameter, and dividing the application parameter into the added parameter type set; Determining a parameter distribution curve of the application program according to a preset parameter type set and an added parameter type set; The parameter distribution curve is used as a profile of the application parameters.

2. The method for determining the application parameter optimization strategy according to claim 1, characterized in that: The step of determining the parameter distribution curve of the application according to the preset parameter type set and the added parameter type set comprises: The parameter type corresponding to the parameter type set is used as the abscissa of the parameter distribution curve; The number of application parameters in the parameter type set is used as the ordinate of the parameter distribution curve; A parameter distribution curve is drawn according to the abscissa and the ordinate.

3. The method for determining the application parameter optimization strategy according to claim 2, characterized in that: Also includes: On the parameter distribution curve, bars are used to represent the number of application parameters in the parameter type set, and bars corresponding to different parameter type sets are rendered in different colors.

4. The method for determining the application parameter optimization strategy according to claim 1, characterized in that: Also includes: Retrieve g at each historical time point determined in advance at n historical time points i A set of parameter types, where g i is an integer greater than or equal to 1, i is an integer greater than or equal to 1 and less than or equal to n; According to the g of each historical time point determined at the n historical time points i parameter type sets, pre-set parameter type sets and added parameter type sets, and determine the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type; The application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type is used as the portrait of the application parameter.

5. The method for determining the application parameter optimization strategy according to claim 4, characterized in that: Determine g at each historical time point in advance at n historical time points i The steps for a collection of parameter types include: At each of the n historical time points, extract the k used in the application at that historical time point. j application parameters, where k j is an integer greater than or equal to 1, j is an integer greater than or equal to 1 and less than or equal to n; According to the parameter type determination rule, define the k j The parameter type of the application parameter; When there is a preset parameter type set corresponding to the parameter type of the application parameter, classifying the application parameter into the parameter type set; When there is no preset parameter type set corresponding to the parameter type of the application parameter, adding a corresponding parameter type set according to the parameter type of the application parameter, and dividing the application parameter into the added parameter type set; The pre-set parameter type set and the added parameter type set are used as g at this historical time point i A collection of parameter types.

6. The method for determining the application parameter optimization strategy according to claim 4, characterized in that: According to the g of each historical time point determined at the n historical time points i The step of determining the application parameter quantity change trend curve of the parameter type corresponding to the parameter type set of the same type includes: Using the time point as the abscissa of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, and using the number of application parameters in the same type of parameter type set as the ordinate of the application parameter quantity change trend curve, the application parameter quantity change trend curve is drawn; Repeat the process of using the time point as the horizontal coordinate of the application parameter quantity change trend curve of the parameter type corresponding to the same type of parameter type set, using the number of application parameters in the same type of parameter type set as the vertical coordinate of the application parameter quantity change trend curve, and drawing the application parameter quantity change trend curve until application parameter quantity change trend curves are drawn for all parameter type sets.

7. The method for determining an application parameter optimization strategy according to any one of claims 1 to 6, characterized in that: After the step of extracting m application parameters used in the application program, the method further comprises: Cleaning the m application parameters, wherein the cleaning includes at least one of removing duplicate data, removing logically deleted data, filling missing values, converting data formats, and standardizing data; According to the parameter type determination rule, the step of defining the parameter types of the m application parameters is as follows: according to the parameter type determination rule, defining the parameter types of the m application parameters after cleaning.

8. A device for determining an image of an application parameter, characterized in that: include: An extraction module, the extraction module is used to extract m application parameters used in the application, wherein each application parameter includes a parameter name and a parameter value, and m is an integer greater than or equal to 1; A definition module, the definition module is used to define the parameter types of the m application parameters according to a determination rule of the parameter type, wherein the determination rule includes a mapping relationship between a parameter name, a parameter value and a parameter type; A first division module, the first division module is used to divide the application parameter into the parameter type set when there is a preset parameter type set corresponding to the parameter type of the application parameter; A second division module, the second division module is used to add a corresponding parameter type set according to the parameter type of the application parameter when there is no preset parameter type set corresponding to the parameter type of the application parameter, and divide the application parameter into the added parameter type set; A first determination module, the first determination module is used to determine a parameter distribution curve of the application according to a preset parameter type set and an added parameter type set; A second determination module, wherein the second determination module is used to execute profiling using the parameter distribution curve as an application parameter.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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