Performance test data model generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a performance test data model generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an operation log file of a business platform in a preset historical period, carrying out the preprocessing of the operation log file, and obtaining a plurality of operation logs and a plurality of corresponding operation feature values; performing matching query on the operation characteristic value of each operation log through a characteristic dictionary database, and determining an operation type corresponding to each operation log; determining customer information corresponding to each operation log, and generating an operation record table according to the operation logs, the operation types and the customer information; and determining operation type proportion distribution information and customer proportion distribution information corresponding to each operation type according to the operation record table, and generating a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information. The accuracy of the performance test data model is improved, so that the accuracy of the performance test result and the stability of the service system are improved, and the method can be applied to the technical field of information.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method, device, electronic equipment and storage medium for generating a performance test data model. Background Art

[0002] Performance testing is an important means to satisfy customer experience and ensure system stability, and the data model of performance testing is an important condition to ensure the accuracy of performance testing results. Therefore, in business scenario performance testing, whether an accurate performance testing data model can be obtained directly determines whether the performance testing conclusion is valid. Inaccurate performance testing data models will lead to inaccurate results of stress testing through the performance testing data model, thus leading to problems such as poor customer experience and poor system stability. Summary of the invention

[0003] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0004] To this end, an object of an embodiment of the present invention is to provide a method for generating a performance test data model, which improves the accuracy of the performance test data model, thereby improving the accuracy of the performance test results and the stability of the business system.

[0005] Another object of an embodiment of the present invention is to provide a device for generating a performance test data model.

[0006] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0007] In one aspect, an embodiment of the present invention provides a method for generating a performance test data model, comprising the following steps:

[0008] Obtaining an operation log file of the business platform in a preset historical period, preprocessing the operation log file, and obtaining a plurality of operation logs and a corresponding plurality of operation characteristic values;

[0009] Performing a matching query on the operation feature value of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log;

[0010] Determine the customer information corresponding to each of the operation logs, and generate an operation record table according to the operation log, the operation type and the customer information;

[0011] The operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type are determined according to the operation record table, and then a performance test data model is generated according to the operation type proportion distribution information and the customer operation proportion distribution information.

[0012] Further, in one embodiment of the present invention, the preprocessing of the operation log file to obtain multiple operation logs and corresponding multiple operation feature values ​​specifically includes:

[0013] Performing data cleaning, data denoising and data segmentation on the operation log file to obtain a plurality of the operation logs;

[0014] Performing feature extraction on the operation log to obtain the operation feature value;

[0015] The operation characteristic values ​​include log type, calling method, request mode, client ID and operation time.

[0016] Furthermore, in one embodiment of the present invention, the feature dictionary library is constructed by the following steps:

[0017] Obtain multiple historical operation logs and corresponding multiple historical feature values;

[0018] Determining the operation type label of each of the historical operation logs by manual labeling;

[0019] Generate a corresponding feature value file according to the plurality of historical feature values ​​and the operation type label of the historical operation log;

[0020] The feature dictionary library is constructed according to the feature value files of each of the historical operation logs.

[0021] Further, in one embodiment of the present invention, matching query is performed on the operation feature value of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log, which specifically includes:

[0022] Determine, according to the feature dictionary library, a TF-IDF value of each of the operation feature values ​​in the operation log for a target operation type label;

[0023] Determining a matching degree between the operation log and the target operation type label according to the sum of TF-IDF values ​​of each of the operation feature values;

[0024] The operation type corresponding to the operation log is determined according to the target operation type tag with the highest matching degree.

[0025] Further, in an embodiment of the present invention, determining the TF-IDF value of each of the operation feature values ​​in the operation log for the target operation type label according to the feature dictionary library specifically includes:

[0026] Determine the frequency of occurrence of the characteristic value of each of the operation characteristic values ​​in the operation log;

[0027] Determine a plurality of target feature value files corresponding to the target operation type label in the feature dictionary library, and determine the number of the target feature value files containing each of the operation feature values;

[0028] Determining, according to the total number of the target feature value files and the number of the target feature value files containing each of the operation feature values, a reverse file frequency of each of the operation feature values ​​for the target operation type label;

[0029] The TF-IDF value of each of the operation feature values ​​for the target operation type label is determined according to the feature value occurrence frequency and the reverse file frequency.

[0030] Further, in one embodiment of the present invention, the determining the customer information corresponding to each operation log and generating an operation record table according to the operation log, the operation type and the customer information specifically includes:

[0031] Determine the customer information corresponding to each of the operation logs according to the customer ID;

[0032] Annotate the operation log according to the operation type and the customer information to obtain a corresponding operation record;

[0033] The operation record table is obtained by summarizing the operation records corresponding to each operation log.

[0034] Further, in an embodiment of the present invention, determining the operation type proportion distribution information and the customer operation proportion distribution information corresponding to each operation type according to the operation record table specifically includes:

[0035] Determine a number of target operation records corresponding to each operation type according to the operation record table;

[0036] Determine the operation type ratio of the corresponding operation type according to the quantity ratio of the target operation record in the operation record table, and then determine the operation type ratio distribution information according to the operation type ratio of each operation type;

[0037] Determine a number of customer operation records corresponding to the target customer according to the target operation record;

[0038] The customer operation ratio of the corresponding target customer is determined according to the quantity ratio of the customer operation records in the target operation records, and then the customer operation ratio distribution information of the corresponding operation type is determined according to the customer operation ratio of each target customer.

[0039] On the other hand, an embodiment of the present invention provides a performance test data model generation device, including:

[0040] A log processing module is used to obtain the operation log files of the business platform in a preset historical period, pre-process the operation log files, and obtain multiple operation logs and corresponding multiple operation feature values;

[0041] A matching query module, used to perform a matching query on the operation feature value of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log;

[0042] An operation record table generating module, used for determining the customer information corresponding to each operation log, and generating an operation record table according to the operation log, the operation type and the customer information;

[0043] The data model generation module is used to determine the operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type according to the operation record table, and then generate a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information.

[0044] On the other hand, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein the program, when executed by the processor, realizes the performance test data model generation method as described above.

[0045] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the performance test data model generation method as described above.

[0046] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0047] The embodiment of the present invention obtains the operation log file of the business platform in a preset historical period, pre-processes the operation log file, obtains multiple operation logs and corresponding multiple operation feature values, matches and queries the operation feature values ​​of each operation log through a pre-built feature dictionary library, determines the operation type corresponding to each operation log, determines the customer information corresponding to each operation log, generates an operation record table according to the operation log, the operation type and the customer information, determines the operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type according to the operation record table, and then generates a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information. The embodiment of the present invention improves the accuracy of the performance test data model, thereby improving the accuracy of the performance test results and the stability of the business system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of the steps of the method for generating a performance test data model provided by an embodiment of the present invention;

[0050] Figure 2 A step flow chart of step S101 provided in an embodiment of the present invention;

[0051] Figure 3 A flowchart of the steps of constructing a feature dictionary library provided by an embodiment of the present invention;

[0052] Figure 4 A step flow chart of step S102 provided in an embodiment of the present invention;

[0053] Figure 5 A step flow chart of step S1021 provided in an embodiment of the present invention;

[0054] Figure 6 An instruction schematic diagram of step S103 provided in an embodiment of the present invention;

[0055] Figure 7 Another step flow chart of step S104 provided in an embodiment of the present invention;

[0056] Figure 8 A schematic diagram of the structure of a performance test data model generation device provided by an embodiment of the present invention;

[0057] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention;

[0058] Fig.10 A schematic diagram of the structure of a storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, although the functional module division is performed in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order from the module division in the system schematic diagram or the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0060] In the description of the present invention, the meaning of "a plurality" is two or more. If there is a description of the first or the second, it is only for the purpose of distinguishing the technical features, and it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0061] The performance test data model generation method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the performance test data model generation method, etc., but is not limited to the above forms.

[0062] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0063] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0064] like Figure 1 FIG. 1 is a flow chart showing a method for generating a performance test data model according to an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a method for generating a performance test data model, which specifically includes the following steps:

[0065] S101, obtaining an operation log file of a business platform in a preset historical period, preprocessing the operation log file, and obtaining a plurality of operation logs and a plurality of corresponding operation characteristic values;

[0066] S102, performing a matching query on the operation feature values ​​of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log;

[0067] S103, determining the customer information corresponding to each operation log, and generating an operation record table according to the operation log, operation type and customer information;

[0068] S104: Determine the operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type according to the operation record table, and then generate a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information.

[0069] The embodiment of the present invention proposes a method for generating a performance test data model. According to the conditions on which the performance test data model depends, the operation log files are first collected, and the data is collected on a designated server. Then, data preprocessing is performed, and the data is cleaned, denoised, and segmented into multiple operation logs. Then, operation feature values ​​are extracted according to the operation logs. Based on the pre-built feature dictionary library, the operation type corresponding to each operation log is quickly and accurately identified through the TF-IDF algorithm to form an operation record table. According to the operation record table, the distribution information of the operation type ratio and the distribution information of the customer ratio corresponding to each operation type are statistically calculated, thereby outputting the performance test data model, which can effectively improve the accuracy of the performance test and reduce the performance test model analysis time. The embodiment of the present invention improves the accuracy of the performance test data model, thereby improving the accuracy of the performance test results and the stability of the business system.

[0070] like Figure 2 FIG. 1 is a flowchart of step S101 provided in an embodiment of the present invention, referring to FIG. Figure 2 As an optional implementation, the operation log file is preprocessed to obtain multiple operation logs and corresponding multiple operation feature values, which specifically include:

[0071] S1011, performing data cleaning, data denoising, and data segmentation on the operation log file to obtain multiple operation logs;

[0072] S1012, extracting features from the operation log to obtain operation feature values;

[0073] The operation characteristic values ​​include log type, calling method, request mode, customer ID and operation time.

[0074] Specifically, the user's operations on the platform will be recorded by the system and written into the log. The platform's interactive operations with other platforms are also recorded and written into the log, such as the scheduled synchronization of data with external systems, the call of external system functions, and the operations called by external systems. The service collects log files for a whole natural month in a specified directory. Such files are generally .log or tar.gz files.

[0075] After the operation log files are collected, data preprocessing is required, that is, the data is first cleaned and denoised, and then a single operation log is segmented and the operation feature values ​​of the operation log are extracted, including log type, calling method, request method, customer ID and operation time.

[0076] like Figure 3 FIG. 1 is a flowchart of a step of constructing a feature dictionary library provided by an embodiment of the present invention, referring to FIG. Figure 3 As an optional implementation, the feature dictionary library is constructed by the following steps:

[0077] S201, obtaining multiple historical operation logs and corresponding multiple historical feature values;

[0078] S202, determining the operation type label of each historical operation log through manual labeling;

[0079] S203, generating a corresponding feature value file according to multiple historical feature values ​​and operation type labels of the historical operation log;

[0080] S204: Construct a feature dictionary library according to the feature value files of each historical operation log.

[0081] Specifically, the embodiment of the present invention uses the TF-IDF algorithm to quickly match the operation type corresponding to each operation log. The specific principle is to identify and classify the operation type through a pre-built feature dictionary library. The feature library dictionary library contains multiple feature value files corresponding to various operation types, and each feature value file includes several operation feature values. The frequency of occurrence of each operation feature value in the operation log is weighted by the feature library dictionary library, so as to accurately match the operation type corresponding to the operation log, and the relevant data will be recorded in the operation record table for subsequent analysis and use.

[0082] like Figure 4 FIG. 1 is a flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 4 As an optional implementation, a pre-built feature dictionary is used to match and query the operation feature values ​​of each operation log to determine the operation type corresponding to each operation log, which specifically includes:

[0083] S1021, determining the TF-IDF value of each operation feature value in the operation log for the target operation type label according to the feature dictionary library;

[0084] S1022, determining the matching degree between the operation log and the target operation type label according to the sum of the TF-IDF values ​​of each operation feature value;

[0085] S1023: Determine the operation type corresponding to the operation log according to the target operation type tag with the highest matching degree.

[0086] Specifically, the TF-IDF value of a certain operation feature value in the operation log for a certain operation type label is calculated according to the feature dictionary library, and the TF-IDF value reflects the importance of the operation feature value to the multiple feature value files corresponding to the operation type label; for the operation log, the sum of the TF-IDF values ​​of all its operation feature values ​​for a certain operation type label is counted to obtain the matching degree of the operation log and the operation type label; for each operation log, its matching degree with different operation type labels is counted through the aforementioned steps, and the operation type label with the highest matching degree is screened out to determine the operation type corresponding to the operation log.

[0087] like Figure 5 FIG. 1 is a flow chart of step S1021 provided in an embodiment of the present invention, referring to FIG. Figure 5 As an optional implementation, determining the TF-IDF value of each operation feature value in the operation log for the target operation type label according to the feature dictionary library specifically includes:

[0088] S10211. Determine the frequency of occurrence of characteristic values ​​of each operation characteristic value in the operation log;

[0089] S10212, determining a plurality of target feature value files corresponding to the target operation type label in the feature dictionary library, and determining the number of target feature value files containing each operation feature value;

[0090] S10213, determining the reverse file frequency of each operation characteristic value for the target operation type label according to the total number of target characteristic value files and the number of target characteristic value files containing each operation characteristic value;

[0091] S10214. Determine the TF-IDF value of each operation feature value for the target operation type label according to the feature value occurrence frequency and the reverse file frequency.

[0092] Specifically, for a certain operation log, the operation characteristic value t i The frequency of occurrence of the eigenvalue is represented by TF, and the formula calculated by normalization is as follows:

[0093]

[0094] Where n i,j is the operation characteristic value t in the operation log i The denominator is the total number of occurrences of all eigenvalues ​​in the operation log.

[0095] Then calculate the reverse file frequency. Here we need to use the feature dictionary library of the operation log prepared previously to simulate the actual matching degree of the log feature value:

[0096]

[0097] Where |D| is the total number of target feature value files corresponding to a certain operation type label in the feature dictionary library, |{j:t i ∈d j}| means including eigenvalue t i The number of target feature value files. Since the feature value is not in the dictionary corpus |{j:t i ∈d j}| may be 0, so +1 is added to ensure that the denominator is not 0.

[0098] The final TF-IDF value is the product of the two, that is, the product of the eigenvalue occurrence frequency and the inverse file frequency.

[0099] The sum of the TF-IDF values ​​of all operation feature values ​​and the target operation type label in each operation log is thus counted. The larger the value, the higher the matching degree, that is, the higher the fit between the target operation type label and the operation log feature, thereby determining the log operation type.

[0100] like Figure 6 FIG. 1 is a flow chart of step S103 provided in an embodiment of the present invention, referring to FIG. Figure 6 As an optional implementation, the customer information corresponding to each operation log is determined, and an operation record table is generated according to the operation log, the operation type and the customer information, which specifically includes:

[0101] S1031, determining the customer information corresponding to each operation log according to the customer ID;

[0102] S1032, marking the operation log according to the operation type and customer information to obtain the corresponding operation record;

[0103] S1033. Obtain an operation record table according to the operation records corresponding to each operation log.

[0104] Specifically, for the operation logs obtained by processing the operation type through the TF-IDF algorithm, the corresponding customer information is determined according to the customer ID, and then the operation logs are labeled according to the operation type and customer information to obtain the corresponding operation records, which are finally summarized into an operation record table.

[0105] like Figure 7 FIG. 1 is a flowchart of step S104 provided in an embodiment of the present invention, referring to FIG. Figure 7 As an optional implementation, the operation type proportion distribution information and the customer operation proportion distribution information corresponding to each operation type are determined according to the operation record table, which specifically includes:

[0106] S1041, determining a number of target operation records corresponding to each operation type according to the operation record table;

[0107] S1042, determining the operation type ratio of the corresponding operation type according to the number ratio of the target operation record in the operation record table, and then determining the operation type ratio distribution information according to the operation type ratio of each operation type;

[0108] S1043, determining a number of customer operation records corresponding to the target customer according to the target operation record;

[0109] S1044. Determine the customer operation ratio of the corresponding target customer according to the quantity ratio of the customer operation records in the target operation records, and then determine the customer operation ratio distribution information of the corresponding operation type according to the customer operation ratio of each target customer.

[0110] Specifically, the operation record table is statistically processed to obtain the operation type proportion distribution information, and then the customer operation proportion of each operation type is statistically processed to obtain the customer operation proportion distribution information corresponding to each operation type.

[0111] The calculation formula for the operation type ratio is:

[0112]

[0113] The operation records of each operation type are grouped by customer, and the customer operation ratio corresponding to each operation type is calculated. The calculation formula is:

[0114]

[0115] The proportion of operations in each scenario is calculated, and finally the system performance test data model is obtained.

[0116] The method steps of the embodiment of the present invention are described above. It can be appreciated that the embodiment of the present invention improves the accuracy of the performance test data model, thereby improving the accuracy of the performance test results and the stability of the business system.

[0117] like Figure 8 FIG. 1 is a schematic diagram of a structure of a performance test data model generation device provided by an embodiment of the present invention, referring to FIG. Figure 8 The embodiment of the present invention provides a performance test data model generation device, comprising:

[0118] The log processing module is used to obtain the operation log files of the business platform in a preset historical period, pre-process the operation log files, and obtain multiple operation logs and corresponding multiple operation feature values;

[0119] A matching query module is used to perform a matching query on the operation feature values ​​of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log;

[0120] An operation record sheet generation module is used to determine the customer information corresponding to each operation log, and generate an operation record sheet according to the operation log, operation type and customer information;

[0121] The data model generation module is used to determine the operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type according to the operation record table, and then generate a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information.

[0122] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] The embodiment of the present invention also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned performance test data model generation method. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0124] like Fig. 9 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention, referring to FIG. Fig. 9 , an embodiment of the present invention provides an electronic device, including:

[0125] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0126] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the performance test data model generation method of the embodiment of the present invention;

[0127] Input / output interface 903, used to implement information input and output;

[0128] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0129] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0130] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0131] like Fig.10 FIG. 1 is a schematic diagram of a storage medium according to an embodiment of the present invention. Fig.10 The embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs 1001, and the one or more programs 1001 can be executed by one or more processors to implement the above-mentioned performance test data model generation method.

[0132] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0134] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0135] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0136] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0138] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0142] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for generating a performance test data model, characterized in that: The following steps are involved: Obtaining an operation log file of the business platform in a preset historical period, preprocessing the operation log file, and obtaining a plurality of operation logs and a corresponding plurality of operation characteristic values; Performing a matching query on the operation feature value of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log; Determine the customer information corresponding to each of the operation logs, and generate an operation record table according to the operation log, the operation type and the customer information; The operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type are determined according to the operation record table, and then a performance test data model is generated according to the operation type proportion distribution information and the customer operation proportion distribution information.

2. A method for generating a performance test data model according to claim 1, characterized in that: The preprocessing of the operation log file to obtain a plurality of operation logs and a plurality of corresponding operation characteristic values ​​specifically includes: Performing data cleaning, data denoising and data segmentation on the operation log file to obtain a plurality of the operation logs; Performing feature extraction on the operation log to obtain the operation feature value; The operation characteristic values ​​include log type, calling method, request mode, client ID and operation time.

3. A method for generating a performance test data model according to claim 1, characterized in that: The feature dictionary library is constructed by the following steps: Obtain multiple historical operation logs and corresponding multiple historical feature values; Determining the operation type label of each of the historical operation logs by manual labeling; Generate a corresponding feature value file according to the plurality of historical feature values ​​and the operation type label of the historical operation log; The feature dictionary library is constructed according to the feature value files of each of the historical operation logs.

4. A method for generating a performance test data model according to claim 3, characterized in that: The matching query of the operation feature value of each operation log by using a pre-built feature dictionary library to determine the operation type corresponding to each operation log specifically includes: Determine, according to the feature dictionary library, a TF-IDF value of each of the operation feature values ​​in the operation log for a target operation type label; Determining a matching degree between the operation log and the target operation type label according to the sum of TF-IDF values ​​of each of the operation feature values; The operation type corresponding to the operation log is determined according to the target operation type tag with the highest matching degree.

5. A method for generating a performance test data model according to claim 4, characterized in that: The determining, according to the feature dictionary library, a TF-IDF value of each of the operation feature values ​​in the operation log for a target operation type label specifically includes: Determine the frequency of occurrence of the characteristic value of each of the operation characteristic values ​​in the operation log; Determine a plurality of target feature value files corresponding to the target operation type label in the feature dictionary library, and determine the number of the target feature value files containing each of the operation feature values; Determining, according to the total number of the target feature value files and the number of the target feature value files containing each of the operation feature values, a reverse file frequency of each of the operation feature values ​​for the target operation type label; The TF-IDF value of each of the operation feature values ​​for the target operation type label is determined according to the feature value occurrence frequency and the reverse file frequency.

6. A method for generating a performance test data model according to claim 2, characterized in that: The determining of the customer information corresponding to each operation log and generating an operation record table according to the operation log, the operation type and the customer information specifically includes: Determine the customer information corresponding to each of the operation logs according to the customer ID; Annotate the operation log according to the operation type and the customer information to obtain a corresponding operation record; The operation record table is obtained by summarizing the operation records corresponding to each operation log.

7. A method for generating a performance test data model according to any one of claims 1 to 6, characterized in that: The determining, according to the operation record table, the operation type proportion distribution information and the customer operation proportion distribution information corresponding to each operation type specifically includes: Determine a number of target operation records corresponding to each operation type according to the operation record table; Determine the operation type ratio of the corresponding operation type according to the quantity ratio of the target operation record in the operation record table, and then determine the operation type ratio distribution information according to the operation type ratio of each operation type; Determine a number of customer operation records corresponding to the target customer according to the target operation record; The customer operation ratio of the corresponding target customer is determined according to the quantity ratio of the customer operation records in the target operation records, and then the customer operation ratio distribution information of the corresponding operation type is determined according to the customer operation ratio of each target customer.

8. A performance test data model generation device, characterized in that: include: A log processing module is used to obtain the operation log files of the business platform in a preset historical period, pre-process the operation log files, and obtain multiple operation logs and corresponding multiple operation feature values; A matching query module, used to perform a matching query on the operation feature value of each operation log through a pre-built feature dictionary library to determine the operation type corresponding to each operation log; An operation record table generating module, used to determine the customer information corresponding to each operation log, and generate an operation record table according to the operation log, the operation type and the customer information; The data model generation module is used to determine the operation type proportion distribution information and the customer proportion distribution information corresponding to each operation type according to the operation record table, and then generate a performance test data model according to the operation type proportion distribution information and the customer operation proportion distribution information.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the performance test data model generation method as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the performance test data model generation method as described in any one of claims 1 to 7.

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