Testing method, device, storage medium and electronic equipment for testing system

Abstract

The present application discloses a test method, device, storage medium and electronic device for a test system. Relating to the field of artificial intelligence technology, the method includes: collecting multiple business data contained in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business; inputting the multiple business data and target parameter information into the artificial intelligence model, and outputting a target feature set; determining a test data set based on the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each area; and testing the test system corresponding to the target business through the test data set. Through this application, the problem of low testing efficiency of the business test system in the related art is solved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to a testing method, device, storage medium, and electronic device for a testing system. Background Art

[0002] In related technologies, when testing business systems, there are gradually higher standards for test data. For example, for the business systems of financial enterprises, with the continued in-depth development of financial business and the rise of personalized financial service needs in different industries and regions, the shortcomings of traditional test data are: 1. Generating simulation data according to functional business logic depends on the tester's understanding of the business and system framework. Cognitive biases lead to deviations in the accuracy and completeness of the prepared data, affecting the test coverage of system functions; 2. The introduction of some typical production data is often concentrated in a certain region or industry. Due to the large deviations in the development of different financial businesses in different regions, this data cannot actually represent the business level of various regions across the country, that is, there is a certain degree of authenticity deviation. 3. Both simulation data and typical production data are smaller than the data volume of actual production data, and there is a possibility of missing some business branches. At the same time, it cannot meet the performance pressure of the verification system. Therefore, due to the above factors, the test data is not comprehensive, resulting in low testing efficiency of the business testing system.

[0003] Currently, no effective solution has been proposed to address the problem of low testing efficiency of business testing systems in related technologies. Summary of the Invention

[0004] The main purpose of this application is to provide a testing method, device, storage medium and electronic equipment for a testing system to solve the problem of low testing efficiency of business testing systems in related technologies.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a testing method for a test system is provided. The method includes: collecting multiple business data contained in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business; inputting the multiple business data and target parameter information into the artificial intelligence model, and outputting a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, the proportion of each industry in multiple industries under each region, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region in multiple regions, the preference proportion of each industry in multiple industries in each region for the target business, wherein the multiple regions at least include the target region; determining a test data set according to the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each region; testing the test system corresponding to the target business through the test data set.

[0006] Furthermore, collecting the multiple business data included in the target business includes: determining initial business data of the target business; and collecting data with a preset proportion from the initial business data as the multiple business data.

[0007] Furthermore, multiple business data and target parameter information are input into the artificial intelligence model, and the target feature set is output, including: in the artificial intelligence model, multiple business data are divided according to preset dimensions through the target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions include at least: the population dimension of each region and the industry proportion of each industry; based on the artificial intelligence model, each dimensional data in the multiple dimensional data sets is iteratively processed to output the target feature set.

[0008] Furthermore, determining the test data set based on the target feature set and the target parameter information includes: determining the test data set based on the proportion of each industry in multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in multiple industries in each region for the target business.

[0009] Furthermore, after determining the test data set based on the target feature set and the target parameter information, the method also includes: predicting the business scale of each region within a preset time period based on the test data set and the target prediction function to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each region within the preset time period, wherein the expected business data includes at least: the expected business generation amount.

[0010] Furthermore, after predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, the method also includes: generating a target fitting curve based on the target prediction function; and displaying the distribution of the business scale of each region within the preset time period based on the target fitting curve.

[0011] Furthermore, before predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, the method also includes: dividing multiple business data according to the time dimension through target parameter information to obtain multiple time data sets; iteratively processing each time data in the multiple time data sets based on the artificial intelligence model, and outputting the target prediction function.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a test device for a test system is provided. The device includes: an acquisition unit for collecting multiple business data contained in a target business, wherein the multiple business data at least include: business data of multiple industries in a target area under the target business; an output unit for inputting the multiple business data and target parameter information into an artificial intelligence model, and outputting a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, the proportion of each industry in each region in multiple industries, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region in multiple regions, the preference proportion of each industry in each region for the target business, wherein the multiple regions at least include the target region; a determination unit for determining a test data set based on the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each region; a testing unit for testing the test system corresponding to the target business through the test data set.

[0013] Through this application, the following steps are adopted: collecting multiple business data contained in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business; inputting multiple business data and target parameter information into the artificial intelligence model, and outputting the target feature set; determining the test data set based on the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each area; testing the test system corresponding to the target business through the test data set. Through this application, the problem of low testing efficiency of the business test system in the related art is solved. The test system corresponding to the target business is tested through the test data set, thereby achieving the effect of improving the testing efficiency of the business test system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0015] Figure 1 is a flow chart of a testing method of a testing system provided in an embodiment of the present application;

[0016] Figure 2 1 is a schematic diagram of auxiliary parameters of artificial intelligence machine learning according to the test method of the test system provided in an embodiment of the present application;

[0017] Figure 3 Schematic diagram of the artificial intelligence multi-round learning iterative process of the test method of the test system provided in an embodiment of the present application;

[0018] Figure 4 This is a schematic diagram of automatic test data generation according to a test method of a test system provided in an embodiment of the present application;

[0019] Figure 5 2 is a schematic diagram of an iterative trend function of a test method of a test system provided in an embodiment of the present application;

[0020] Figure 6 1 is a schematic diagram of a trend function example of a test method of a test system provided in an embodiment of the present application;

[0021] Figure 7 is a schematic diagram of a test device of a test system provided according to an embodiment of the present application;

[0022] Figure 8 2 is a schematic diagram of a network architecture of a test electronic device of a test system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0027] The present invention will be described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a testing method of a testing system provided in an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0028] Step S101 : collecting a plurality of business data included in a target business, wherein the plurality of business data at least includes: business data of a plurality of industries in a target area under the target business.

[0029] For example, by extracting a certain amount (more than 10%) of existing production data, typical production data sets with broad urban and industry data characteristics are obtained as multiple business data in this application. This application can identify typical characteristics of urban dimensions and industry dimensions through artificial intelligence machines based on a small amount of production business data.

[0030] Optionally, in the testing method of the testing system provided in an embodiment of the present application, collecting multiple business data contained in the target business includes: determining initial business data of the target business; and collecting data with a preset proportion from the initial business data as multiple business data.

[0031] Specifically, the initial business data in this application corresponds to the existing production data in the above example, and the existing production data is extracted according to a preset proportion to obtain multiple business data, where the preset proportion can be more than 10%. By acquiring multiple business data, the processing efficiency of subsequent artificial intelligence models is further improved.

[0032] Step S102: Input multiple business data and target parameter information into the artificial intelligence model, and output a target feature set, wherein the target parameter information includes at least: the population size of multiple regions, the proportion of each industry in each region in multiple industries, wherein the target feature set includes at least: the production amount of the target business corresponding to the total population of each region in multiple regions, the preference proportion of each industry in multiple industries in each region for the target business, wherein the multiple regions include at least the target region.

[0033] For example, before inputting each business data and target parameter information into the artificial intelligence model, the target parameter information is first determined and used as an auxiliary parameter for artificial intelligence machine learning, such as Figure 2 As shown, the target parameter information may include city parameter information and industry parameter information, wherein the city parameter information at least includes the city number, city name, city region, population, etc., wherein the industry parameter information at least includes the industry number, industry name, industry debt-to-asset ratio, number of employees, etc.

[0034] Optionally, in the testing method of the testing system provided in the embodiment of the present application, multiple business data and target parameter information are input into the artificial intelligence model, and the target feature set is output, including: in the artificial intelligence model, multiple business data are divided according to preset dimensions through the target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions include at least: the population dimension of each region and the industry proportion of each industry; each dimensional data in the multiple dimensional data sets is iteratively processed based on the artificial intelligence model to output the target feature set.

[0035] For example, the typical production data set obtained (corresponding to multiple business data in this application) can be referred to Figure 2 The cities (corresponding to the regions in this application) and industry parameter information sets shown are divided into multiple small sets: Di (i=1, 2, ...). For example, Figure 3As shown, artificial intelligence machine learning is used to perform multiple rounds of iterations on the small set Di, during which data division, data processing and data classification are performed, and data extraction and data calculation are performed on similar data according to the city dimension and industry dimension, and a small set C(i, j) with city dimension information (that is, the population dimension of each region) and a small set H(i, j) with industry dimension information (that is, the industry proportion of each industry) are output. The above small sets C(i, j) and the small set H(i, j) with industry dimension information are summarized as the set C of typical characteristics of the city dimension (that is, the population dimension of multiple regions) and the set H of typical characteristics of the industry dimension (corresponding to the present application (that is, the industry proportion of each industry in multiple regions). The present application can identify the typical characteristics of the city dimension and industry dimension through an artificial intelligence machine on the basis of a small amount of production business data, and combine the city industry parameter information to help achieve the sufficiency and completeness of the test data for the business.

[0036] Among them, the set C of typical characteristics of the city dimension can be expressed as:

[0037]

[0038] The set H of typical characteristics of industry dimensions can be expressed as:

[0039]

[0040] Step S103 : determining a test data set according to the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each region.

[0041] For example, based on the generated typical feature set C of the city dimension and the typical feature set H of the industry dimension, and then referring to Figure 2 The city industry parameter information in the is used to generate the final full test data set Q (corresponding to the test data set in this application).

[0042] Optionally, in the testing method of the testing system provided in the embodiment of the present application, determining the test data set according to the target feature set and the target parameter information includes: determining the test data set according to the target feature set and the target parameter information includes: determining the test data set according to the proportion information of each industry in multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in multiple industries in each region for the target business.

[0043] For example, as shown in Table 1, suppose city S is entered Figure 2Medium-sized cities and cities in the industry parameter information, according to their information in the city industry parameter information set: the population is more than 10 million, there are 20,000 enterprises, high-tech industries account for 20%, large-scale industrial enterprises account for 30%, and general industrial enterprises account for 50%.

[0044] Table 1

[0045] S City Population of 5 million-10 million 200 million companies High-tech industries account for 20% Industrial enterprises above designated size account for 30% General industrial enterprises 50%

[0046] Taking the target business Y as an example, the set C of typical characteristics of the city dimension and the set H of typical characteristics of the industry dimension are shown in Table 2 and Table 3 respectively:

[0047] Table 2

[0048]

[0049] Table 3

[0050]

[0051] Assume that the scale of Y business involved in cities with a population of more than 10 million is 90 million (corresponding to the business sales in this application), that is, C4(Y,...) is 90 million, among which: the preference of high-tech industries for Y business, that is, H1(Y,...) is 70%; the preference of large-scale industrial enterprises for Y business, that is, H1(Y,...) is 50%; the preference of general industrial enterprises for Y business, that is, H1(Y,...) is 20%.

[0052] Then the Q corresponding to the Y business data of city S (s城市) The data can actually be calculated as:

[0053] 90 million*(20%*70%+30%*50%+50%*20%).

[0054] Similarly, each city in the target parameter information entered in this application is calculated in the same way as above. The principle is similar and will not be described in detail here. The result of each calculation is set as a small set Qi, that is, the full test data set Q can be expressed as:

[0055]

[0056] Step S104: testing the test system corresponding to the target business using the test data set.

[0057] For example, the above steps can be used to obtain the full test data set for business Y, thereby expanding the test data and making the test data set more comprehensive and complete. By testing the test system corresponding to business Y through the test data set, the test efficiency of the system can be further improved.

[0058] Specifically, Figure 4 This is a schematic diagram of automatic test data generation according to the test method of the test system provided in an embodiment of the present application. As shown in 4, after obtaining the test data set through artificial intelligence data analysis and automatic data expansion, the present application also studies the scale trend of business data.

[0059] Optionally, in the testing method of the testing system provided in the embodiment of the present application, after determining the test data set based on the target feature set and the target parameter information, the method further includes: predicting the business scale of each region within a preset time period based on the test data set and the target prediction function to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each region within the preset time period, wherein the expected business data includes at least: the expected business generation amount.

[0060] Specifically, before obtaining the prediction results through the above steps, determining the target prediction function step is also one of the invention points of this application. The specific steps are as follows: multiple business data are divided according to the time dimension through target parameter information to obtain multiple time data sets; each time data in the multiple time data sets is iteratively processed based on the artificial intelligence model to output the target prediction function.

[0061] For example, Figure 5 As shown, in the process of machine learning, machine learning is carried out through artificial intelligence, refer to Figure 2 The city and industry parameter information set in the process of artificial intelligence machine learning is used to perform multiple rounds of iterations on the small set Di. The iteration is divided according to time to form small sets Ti at different times. The above small sets Ti are summarized to generate trend functions T(t) at different times (corresponding to the target prediction function in this application). By using the target prediction function, the test efficiency of the system is further improved.

[0062] Optionally, in the testing method of the testing system provided in an embodiment of the present application, after predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, the method also includes: generating a target fitting curve based on the target prediction function; and displaying the distribution of the business scale of each region within the preset time period based on the target fitting curve.

[0063] Specifically, the example data in the target fitting curve can be shown in Table 4 below:

[0064] Table 4

[0065]

[0066] For example, the distribution of business scale in each region within a preset time period can be displayed as follows through the target fitting curve: Figure 6 Specifically, based on the base period data of the past three or four years obtained from typical production data, the trend function curve JX(t) is fitted using machine learning, and the data F of the simulated city S in 2025 in Tables 1, 5, and 6 can be expanded to:

[0067] F(S city, 2025) = Q(S city) * JX(2025)

[0068] according to Figure 2 The above small set Fi is aggregated in different regions, and the test data of multiple regions in 2025 is predicted:

[0069]

[0070] Among them, Table 5 and Table 6 are respectively as follows:

[0071] Table 5

[0072]

[0073]

[0074] Table 6

[0075]

[0076] This application forms a trend function by analyzing the data trend over a period of time, and then forms a full test data set (expected time T) after targeted expansion of the data according to the trend function, so as to perform expected stress testing on the business system and further improve the testing efficiency of the business system.

[0077] In summary, the testing method of the testing system provided in the embodiment of the present application collects multiple business data contained in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business; inputs the multiple business data and target parameter information into the artificial intelligence model, and outputs a target feature set; determines a test data set based on the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each area; and tests the test system corresponding to the target business through the test data set. Through this application, the problem of low testing efficiency of the business testing system in the related art is solved. The test system corresponding to the target business is tested through the test data set, thereby achieving the effect of improving the testing efficiency of the business testing system.

[0078] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0079] The present application also provides a test device for a test system. It should be noted that the test device for a test system in the present application can be used to execute the test method for a test system provided in the present application. The following describes the test device for a test system provided in the present application.

[0080] Figure 7 Schematic diagram of a test device of a test system according to an embodiment of the present application. Figure 7 As shown, the device includes: a collection unit 701, an output unit 702, a determination unit 703, and a testing unit 704.

[0081] Specifically, the collection unit 701 is configured to collect multiple business data included in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business;

[0082] Output unit 702 is configured to input multiple business data and target parameter information into the artificial intelligence model and output a target feature set, wherein the target parameter information includes at least: the population size of multiple regions, and the proportion of each industry in each region among multiple industries, wherein the target feature set includes at least: the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in each region for the target business, wherein the multiple regions include at least the target region;

[0083] A determining unit 703 is configured to determine a test data set according to the target feature set and the target parameter information, wherein the test data set at least includes a target production amount of a target business in each region;

[0084] The testing unit 704 is configured to test the test system corresponding to the target business using a test data set.

[0085] In summary, the test device of the test system provided by the embodiment of the present application collects multiple business data contained in the target business through the collection unit 701, wherein the multiple business data at least include: business data of multiple industries under the target business in the target area; the output unit 702 inputs the multiple business data and target parameter information into the artificial intelligence model, and outputs a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, the proportion of each industry in multiple industries under each region, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region in multiple regions, and the preference proportion of each industry in multiple industries in each region for the target business, wherein the multiple regions at least include the target region; the determination unit 703 determines the test data set according to the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each region; the test unit 704 tests the test system corresponding to the target business through the test data set, thereby solving the problem of low test efficiency of the business test system in the related art. By testing the test system corresponding to the target business through the test data set, the test efficiency of the business test system is improved.

[0086] Optionally, in the test device of the test system provided in the embodiment of the present application, the collection unit includes: a first determination module for determining the initial business data of the target business; and a second determination module for collecting a preset proportion of data from the initial business data as multiple business data.

[0087] Optionally, in the test device of the test system provided in the embodiment of the present application, the output unit includes: a division module, which is used to divide multiple business data according to preset dimensions through target parameter information in the artificial intelligence model to obtain multiple dimensional data sets, wherein the preset dimensions include at least: the population dimension of each region and the industry proportion of each industry; a processing module, which is used to iteratively process each dimensional data in the multiple dimensional data sets based on the artificial intelligence model, and output a target feature set.

[0088] Optionally, in the test device of the test system provided in the embodiment of the present application, the determination unit includes: a third determination module, used to determine the test data set based on the proportion information of each industry in multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in multiple industries in each region for the target business.

[0089] Optionally, in the test device of the test system provided in the embodiment of the present application, the device also includes: a prediction unit, which is used to predict the business scale of each area within a preset time period based on the test data set and the target prediction function after determining the test data set based on the target feature set and the target parameter information, to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each area within the preset time period, wherein the expected business data includes at least: the expected business generation amount.

[0090] Optionally, in the test device of the test system provided in the embodiment of the present application, the device also includes: a generation unit for generating a target fitting curve according to the target prediction function after predicting the business scale of each region within a preset time period based on the test data set and the target prediction function; and a display unit for displaying the distribution of the business scale of each region within the preset time period according to the target fitting curve.

[0091] Optionally, in the test device of the test system provided in the embodiment of the present application, the device also includes: a division unit, which is used to divide multiple business data according to the time dimension according to target parameter information before predicting the business scale of each area within a preset time period based on the test data set and the target prediction function, so as to obtain multiple time data sets; a processing unit, which is used to iteratively process each time data in the multiple time data sets based on an artificial intelligence model, and output a target prediction function.

[0092] The test device of the test system includes a processor and a memory. The above-mentioned acquisition unit 701, output unit 702, determination unit 703, test unit 704, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0093] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the test system can be tested by adjusting the kernel parameters.

[0094] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0095] An embodiment of the present invention provides a computer-readable storage medium storing a program, which implements a test method of a test system when executed by a processor.

[0096] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a test method of a test system when the program is running.

[0097] like Figure 8 As shown, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: collecting multiple business data contained in the target business, wherein the multiple business data include at least: business data of multiple industries in the target area under the target business; inputting the multiple business data and target parameter information into an artificial intelligence model, and outputting a target feature set, wherein the target parameter information includes at least: the population size of multiple regions, the proportion of each industry in each region in multiple industries, wherein the target feature set includes at least: the production amount of the target business corresponding to the total population of each region in multiple regions, the preference proportion of each industry in multiple industries in each region for the target business, wherein the multiple regions include at least the target region; determining a test data set based on the target feature set and the target parameter information, wherein the test data set includes at least the target production amount of the target business in each region; and testing the test system corresponding to the target business through the test data set.

[0098] When the processor executes the program, the following steps are also implemented: determining initial business data of the target business; and collecting data with a preset proportion from the initial business data as multiple business data.

[0099] When the processor executes the program, the following steps are also implemented: in the artificial intelligence model, multiple business data are divided according to preset dimensions through target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions include at least: the population dimension of each region and the industry proportion of each industry; based on the artificial intelligence model, each dimensional data in the multiple dimensional data sets is iteratively processed to output the target feature set.

[0100] When the processor executes the program, it also implements the following steps: determining a test data set based on the proportion of each industry in multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in multiple industries in each region for the target business.

[0101] When the processor executes the program, the following steps are also implemented: after determining the test data set based on the target feature set and the target parameter information, the business scale of each region within a preset time period is predicted based on the test data set and the target prediction function to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each region within the preset time period, wherein the expected business data includes at least: the expected business generation amount.

[0102] When the processor executes the program, it also implements the following steps: after predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, generating a target fitting curve based on the target prediction function; and displaying the distribution of the business scale of each region within the preset time period based on the target fitting curve.

[0103] When the processor executes the program, it also implements the following steps: before predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, multiple business data are divided according to the time dimension through the target parameter information to obtain multiple time data sets; each time data in the multiple time data sets is iteratively processed based on the artificial intelligence model, and the target prediction function is output.

[0104] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0105] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: collecting multiple business data contained in the target business, wherein the multiple business data at least include: business data of multiple industries in the target area under the target business; inputting the multiple business data and target parameter information into an artificial intelligence model, and outputting a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, the proportion of each industry in each region in multiple industries, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region in multiple regions, the preference proportion of each industry in multiple industries in each region for the target business, wherein the multiple regions at least include the target region; determining a test data set based on the target feature set and the target parameter information, wherein the test data set at least includes the target production amount of the target business in each region; and testing the test system corresponding to the target business through the test data set.

[0106] When executed on a data processing device, it is also suitable for executing an initialization program having the following method steps: determining initial business data of a target business; and collecting data with a preset proportion from the initial business data as a plurality of business data.

[0107] When executed on a data processing device, it is also suitable for executing an initialized program having the following method steps: in an artificial intelligence model, multiple business data are divided according to preset dimensions through target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions include at least: the population dimension of each region and the industry proportion of each industry; based on the artificial intelligence model, each dimensional data in the multiple dimensional data sets is iteratively processed to output a target feature set.

[0108] When executed on a data processing device, it is also suitable for executing an initialization program with the following method steps: determining a test data set based on the proportion information of each industry in multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference proportion of each industry in multiple industries in each region for the target business.

[0109] When executed on a data processing device, it is also suitable for executing an initialized program having the following method steps: after determining a test data set based on a target feature set and target parameter information, predicting the business scale of each region within a preset time period based on the test data set and the target prediction function to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each region within the preset time period, wherein the expected business data includes at least: the expected business generation amount.

[0110] When executed on a data processing device, it is also suitable for executing an initialization program with the following method steps: after predicting the business scale of each region within a preset time period based on a test data set and a target prediction function, generating a target fitting curve based on the target prediction function; and displaying the distribution of the business scale of each region within the preset time period based on the target fitting curve.

[0111] When executed on a data processing device, it is also suitable for executing an initialized program having the following method steps: before predicting the business scale of each region within a preset time period based on a test data set and a target prediction function, multiple business data are divided according to the time dimension through target parameter information to obtain multiple time data sets; each time data in the multiple time data sets is iteratively processed based on an artificial intelligence model to output a target prediction function.

[0112] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0117] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0118] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A testing method for a testing system, characterized in that: include: Collecting multiple business data included in the target business, wherein the multiple business data at least includes: business data of multiple industries in the target area under the target business; Input the multiple business data and target parameter information into an artificial intelligence model, and output a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, the proportion of each industry in each region in the multiple industries, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region, the preference proportion of each of the multiple industries in each region for the target business, wherein the multiple regions at least include the target region, in the artificial intelligence model, divide the multiple business data according to preset dimensions through the target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions at least include: the population dimension of each region, the industry proportion of each industry; perform iterative processing on each dimensional data in the multiple dimensional data sets based on the artificial intelligence model, and output the target feature set; Determining a test data set based on the target feature set and the target parameter information, wherein the test data set includes at least the target production amount of the target business in each region, and determining the test data set based on the proportion of each industry in the multiple industries, the production amount of the target business corresponding to the total population of each region, and the preference ratio of each industry in the multiple industries in each region for the target business; The test system corresponding to the target business is tested using the test data set.

2. The method according to claim 1, characterized in that The target business data includes: Determining initial business data of the target business; Data with a preset proportion is collected from the initial business data as the multiple business data.

3. The method according to claim 1, characterized in that After determining a test data set according to the target feature set and the target parameter information, the method further includes: The business scale of each region within a preset time period is predicted based on the test data set and the target prediction function to obtain a prediction result, wherein the business scale is the expected business data corresponding to the target business in each region within the preset time period, wherein the expected business data at least includes: the expected business generation amount.

4. The method according to claim 3, characterized in that After predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, the method further includes: generating a target fitting curve according to the target prediction function; The distribution of the business scale of each region within the preset time period is displayed according to the target fitting curve.

5. The method according to claim 3, characterized in that Before predicting the business scale of each region within a preset time period based on the test data set and the target prediction function, the method further includes: Dividing the plurality of business data according to the time dimension using the target parameter information to obtain a plurality of time data sets; Based on the artificial intelligence model, each time data in the multiple time data sets is iteratively processed to output the target prediction function.

6. A testing device for a testing system, used to implement the testing method for a testing system according to claim 1, characterized in that: include: A collection unit is configured to collect a plurality of business data included in a target business, wherein the plurality of business data at least includes: business data of a plurality of industries in a target area under the target business; An output unit is used to input the multiple business data and target parameter information into an artificial intelligence model, and output a target feature set, wherein the target parameter information at least includes: the population size of multiple regions, and the proportion of each industry in each region in the multiple industries, wherein the target feature set at least includes: the production amount of the target business corresponding to the total population of each region, and the preference proportion of each of the multiple industries in each region for the target business, wherein the multiple regions at least include the target region, and in the artificial intelligence model, the multiple business data are divided according to preset dimensions through the target parameter information to obtain multiple dimensional data sets, wherein the preset dimensions at least include: the population dimension of each region and the industry proportion of each industry; each dimensional data in the multiple dimensional data sets is iteratively processed based on the artificial intelligence model, and the target feature set is output; a determining unit, configured to determine a test data set based on the target feature set and the target parameter information, wherein the test data set includes at least a target production amount of the target business in each region, and the test data set is determined based on information on a proportion of each industry in the multiple industries, the production amount of the target business corresponding to the total population of each region, and a preference ratio of each of the multiple industries in each region for the target business; The testing unit is configured to test the test system corresponding to the target business using the test data set.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, wherein the program executes the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

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