Test data tag generation method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of system testing, discloses a test data tag generation method and device, electronic equipment and a storage medium, and is suitable for financial and medical scenes. The method comprises the following steps: receiving at least one piece of test user identification information data sent by a tester and corresponding input label data; according to the test user identification information data, identifying at least one type of service system corresponding to a corresponding target test user in a target service system; matching a corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; and if the matched preset system tag type is not null, generating a test user data tag corresponding to the target test user under the at least one type of service system according to the input tag data and the matched preset system tag type. Through the mode, the test labor cost is reduced, and the test data marking intelligence is improved.

Description

Technical Field

[0001] The present application relates to the field of system testing technology, is applicable to financial and medical scenarios, and in particular, to a test data label generation method, device, electronic device and storage medium. Background Art

[0002] In a system with a large number of users, in order to better and more accurately serve users, it is necessary to label different types of users with different attributes and apply labels with different attributes in various business links. Therefore, in the testing process of each business link, it is necessary to use various different labels for testing. For example, in an insurance system with a large number of users, the sources of insurance user labels are different. For example, registration, real name, insurance, renewal, channel, reinsurance, high-risk operations, etc. can all generate different labels. For example, the diagnosis results, past medical history, medication records, allergy history, etc. in the medical system can also generate corresponding labels. For example, newly registered users can generate new customer labels, old customer labels after insurance, channel labels for different channels, and risky customer labels for high-risk operations. There are also overlaps and time controls between various insurance business scenarios. For example, patients who are registering for the first visit can generate first-visit patient labels, and patients who have completed the first treatment can generate labels for first-visit patients. Generate follow-up patient labels after treatment, generate appointment channel labels through different appointment methods (such as online APP, telephone, on-site window), and generate high-risk patient labels when serious adverse drug reactions occur. There are overlaps and time controls between various medical business scenarios. Some chronic disease patients not only meet the follow-up patient labels, but also have the risk of adverse drug reactions due to long-term medication. They may also be included in the high-risk patient label category. In terms of time control, statistics on the time interval from initial visit to follow-up visit can help optimize the allocation of medical resources. The frequency analysis of different appointment channels in different time periods can help hospitals reasonably arrange human and material resources, and the continuous monitoring time regulations for high-risk patients are more related to the life, health and safety of patients. Before testing the corresponding business system, due to the complex source of labels, difficult operation and long cycle of generating labels, testers need to spend a lot of time and go through complex business processes to obtain customer data of target labels for testing. Summary of the invention

[0003] The main technical problem solved by the implementation methods of the present application is that the generation process of labeled test data used in the testing process is complicated and time-consuming.

[0004] To solve the above technical problems, the first technical solution adopted in the implementation mode of the present application is: providing a test data label generation method, comprising: receiving at least one test user identification information data and corresponding input label data sent by a tester; identifying at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data; matching the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; if the matched preset system label type is not empty, then generating a test user data label corresponding to the target test user under the at least one type of business system according to the input label data and the matched preset system label type.

[0005] Optionally, after the step of receiving at least one test user identification information data and corresponding input label data sent by the tester, it also includes: verifying whether the test user identification information data exists in the target business system; if it exists in the target business system, verifying whether the corresponding input label data is empty; if the input label data is not empty, calculating the first similarity between the input label data and the preset system label type already stored in the target business system; if the first similarity is not within the preset first similarity threshold range, generating a first label match warning prompt information according to the preset system label type; and returning the first label match warning prompt information to the tester.

[0006] Optionally, after the step of matching the corresponding preset system tag type according to the input tag data, wherein the preset system tag type corresponds to the type of the business system, it also includes: generating system tag type matching failure information if the matched preset system tag type is empty; returning the system tag type matching failure information to the tester; receiving a new system tag type generation instruction sent by the tester, and generating a new system tag type according to the new system tag type generation instruction; generating the test user data tag according to the new system tag type and the input tag data.

[0007] Optionally, the step of matching the corresponding preset system label type according to the input label data includes: obtaining all stored test user data labels corresponding to each of the preset system label types in the target business system; clustering the stored test user data labels according to the description data of the preset system label types to obtain different data clusters corresponding to the preset system label types, and corresponding cluster centers; calculating different first distance values ​​from the input label data to different cluster centers; and setting the preset system label type corresponding to the cluster center associated with the smallest first distance value as the matching result corresponding to the input label data.

[0008] Optionally, after the step of calculating different first distance values ​​from the input label data to different cluster centers, it also includes: determining whether the minimum first distance value is within a preset distance threshold range; if it is not within the preset distance threshold range, generating corresponding system label type matching failure information; and returning the system label type matching failure information to the tester.

[0009] Optionally, after the step of generating a test user data label corresponding to the target test user in at least one type of business system according to the input label data and the matched preset system label type, the step further includes: monitoring whether a new type of new business system is generated in the target business system; if a new type of new business system is generated, calculating a second similarity between the test user data label in the target business system and the new business system according to the text description data of the new business system; if the second similarity is within a preset second similarity threshold range, establishing a first association relationship between the test user data label and the new business system.

[0010] Optionally, after the step of generating a test user data label corresponding to the target test user in at least one type of business system according to the input label data and the matched preset system label type, the step further includes: monitoring whether a new preset system label type is generated in the target business system; if a new preset system label type is generated, calculating a third similarity between the test user data label in the target business system and the new preset system label type according to the text description data of the new preset system label type; if the third similarity is within a preset third similarity threshold range, establishing a second association relationship between the test user data label and the new preset system label type.

[0011] To solve the above technical problems, the second technical solution adopted in the implementation mode of the present application is: to provide a test data label generation device, including: an input data receiving module, used to receive at least one test user identification information data and corresponding input label data sent by a tester; a business system identification module, used to identify at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data; a label type matching module, used to match the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; a data label generation module, used to generate a test user data label corresponding to the target test user under the at least one type of business system according to the input label data and the matched preset system label type if the matched preset system label type is not empty.

[0012] To solve the above technical problems, the third technical solution adopted in the implementation mode of the present application is: to provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the test data label generation method as described above.

[0013] In order to solve the above technical problems, the fourth technical solution adopted in the implementation mode of the present application is: providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device executes the test data label generation method as described above.

[0014] Different from the related art, the present application receives at least one test user identification information data and corresponding input label data sent by the tester; identifies at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data; matches the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; if the matched preset system label type is not empty, then generates the test user data label corresponding to the target test user in at least one type of business system according to the input label data and the matched preset system label type. The above method reduces the manpower input in the testing process, and realizes the automatic and intelligent generation of test data labeling, which significantly improves the test quality and iteration cycle efficiency of the projects to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings, which do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and the figures in the drawings do not constitute proportional limitations unless otherwise stated.

[0016] Figure 1 It is a schematic diagram of the operating environment of the test data label generation method provided in an embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the execution flow of the test data label generation method provided in an embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of the execution flow of generating matching warning prompt information in the test data label generation method provided in an embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the execution flow of matching preset system label types in the test data label generation method provided in an embodiment of the present application.

[0020] Figure 5 It is a system structure diagram of the test data label generating device provided in an embodiment of the present application.

[0021] Figure 6 It is a schematic diagram of the hardware structure of an electronic device for executing the test data label generation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device schematic diagram or the order in the flow chart.

[0024] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0025] To facilitate understanding of this embodiment, a test data label generation method disclosed in the embodiment of the present application is first described in detail. Figure 1 , Figure 1 Schematic diagram of the operating environment of the test data label generation method provided in the embodiment of the present application. Figure 1 As shown, the execution subject of the test data label generation method provided in the embodiment of the present application is generally an electronic device with certain computing capabilities, such as a computer device. In some possible implementations, the test data label generation method can be implemented by a processor calling a computer-readable instruction stored in a memory. Figure 1 The computer device in the above description may be a server. The server may be an independent server or 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, content delivery networks (CDN), and big data and artificial intelligence platforms. For example, a cloud server that deploys a financial system or a medical system, and it can be understood that Figure 1 The number of computer devices in the figure is only for reference and can be expanded to any number according to actual needs.

[0026] Please continue reading Figure 2 , Figure 2 is a schematic diagram of the execution flow of the test data label generation method provided in the embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S1 to S4.

[0027] S1. Receive at least one test user identification information data and corresponding input label data sent by a tester.

[0028] Among them, the test user identification information data can be the user's ID (such as the user's digital number in the system), mobile phone number, user name, user's email address or other information that can distinguish different users. The main purpose of the identification information is to associate subsequent operations and results with a specific user, so as to facilitate tracking and analysis by testers and developers. For example, in the test of an insurance sales management system, the test user identification may be the user's mobile phone number. During the test, when it is necessary to operate the user (such as purchasing insurance, viewing insurance orders, etc.) or check the user's information, the corresponding user information and operation records can be accurately found based on this identification. For another example, taking the test of a hospital information management system as an example, the test user identification information data often uses the patient's medical insurance card number. The medical insurance card number is the patient's unique identity in the medical insurance system and the hospital's medical treatment process. The tester can quickly locate the patient's entire medical treatment information in the hospital based on the medical insurance card number, including registration records, diagnosis reports, examination and test results, medication lists, etc. Input label data usually refers to the specific input information provided by the tester for the test user during the test, and the corresponding labels are added to these input information. The labels can help the tester classify and manage the test data, and facilitate the execution of test cases and result analysis.

[0029] As an optional implementation, please continue to refer to Figure 3 , Figure 3 It is a schematic diagram of the execution flow of generating matching warning prompt information in the test data label generation method provided in an embodiment of the present application, which specifically includes the following steps S11 to S15.

[0030] S11. Verify whether the test user identification information data exists in the target business system.

[0031] S12. If it exists in the target business system, check whether the corresponding input tag data is empty.

[0032] S13: If the input tag data is not empty, calculate a first similarity between the input tag data and a preset system tag type already stored in the target business system.

[0033] Among them, the system tag type is used to classify and describe the tag categories of various data or operations in the system, reflecting the normal business functions and data types of the system. For example, the system may have "user registration information tags", "insurance product information tags", "insurance purchase record tags", etc. For another example, in the medical system, there may be "patient basic information tags", "disease diagnosis tags", "examination and inspection report tags", "drug prescription tags", "hospitalization record tags", etc. The calculation method of the above-mentioned first similarity can be cosine similarity, Euclidean distance, Jaccard similarity coefficient, etc. By calculating the similarity, it can be determined whether the tags entered by the tester match the existing function and data tags in the system. If the similarity is very high, it means that the test input may be consistent with the expected function of the system, which helps to verify whether the system correctly handles the corresponding input. If the similarity is very low or there is no suitable match, it may mean that the tags used by the tester do not match the system's expectations, there may be input errors or the system is not covered, which helps to find potential problems in the testing stage. For example, the tester may use the wrong tag, or the system may miss the tag setting of certain functions during development.

[0034] S14: If the first similarity is not within a preset first similarity threshold range, generating first tag matching warning prompt information according to a preset system tag type.

[0035] S15. Return the first tag matching warning prompt information to the tester.

[0036] Among them, when the first similarity is not within the preset first similarity threshold range, it means that there is a large difference between the input label data and the preset system label type, which may indicate that there is a problem with the tester's input or the system setting. By generating the first label matching warning prompt information, this abnormal situation can be discovered at the first time, avoiding potential errors or mismatches from continuing to affect the subsequent test process or the normal operation of the system. It helps to ensure that the test process strictly follows the system's preset rules and functional scope. By reminding the tester of the mismatch of his input label, the tester is prompted to re-examine the input content and the system's preset label, thereby ensuring that the test is based on the correct system function and data category, thereby improving the effectiveness and accuracy of the test. For example, the tester may mistakenly believe that "insurance product type identification" and "insurance product identification" are the same function, but in fact they may correspond to different processing logic or data storage structures in the system. The warning prompt can guide the tester to use the correct label to ensure that the test can correctly verify the system function. For example, similar situations may occur in medical system testing. For example, testers may mistakenly believe that "blood routine test result label" is equivalent to "blood biochemical test result label". In fact, blood routine mainly checks indicators such as the number and morphology of red blood cells, white blood cells, platelets, etc., while blood biochemical tests focus on detecting the content of biochemical substances such as blood sugar, blood lipids, liver and kidney function. If the tester confuses the two when entering label data, when calculating its similarity with the preset system label type, it will exceed the preset similarity threshold range, and the system will issue a first label match warning prompt message.

[0037] S2. Identify at least one type of business system corresponding to the target test user in the target business system according to the test user identification information data.

[0038] Among them, the user system usually involves the information, operations and permissions of ordinary users in the system. For the user system in the insurance business system, it may include the user's registration information (such as name, age, contact information), user operation records (such as login records, policy viewing records), user permissions (such as ordinary users can view their own policies, apply for claims, etc.). In the medical system, the user system also covers many aspects. Taking the hospital information management system as an example, the patient is a system user, and its user system contains the patient's basic identity information, such as name, gender, age, ID number, home address, etc.; operation records include the patient's registration record, appointment examination record, and visit check-in record in the hospital; in terms of permissions, ordinary patients can view their own test report, outpatient medical records and other basic medical information. The customer system focuses on the information related to the business when the user is a customer, such as the customer's purchase record (purchased insurance products, premium payment status), the customer's risk assessment information, customer service information (such as customer complaints, consultations), etc. In the medical field, the customer system corresponding to the patient includes the patient's medical consumption record, such as registration fees, drug costs, and payment details of examination and treatment costs; the patient's health risk assessment information, such as the risk of suffering from a certain disease based on a comprehensive assessment of the patient's past medical history, family medical history, physical examination results, etc.; customer service information includes patient complaints and feedback on medical services, medical consultation records, etc. The operation of step S2 helps to ensure that the test covers users of different business systems during the testing phase. Testers can use different types of user identifiers to verify user functions and operations under different business systems to ensure that the system functions normally for users of different business systems. For example, testers can use the identifiers of different types of users to verify whether the user's login, information modification, and product purchase operations under the user system and the customer system are normal, and ensure the stability and reliability of the system under various user scenarios. In the medical system test, testers can use the identifiers of different patients to verify whether the patient can normally register for an appointment and query medical records under the user system, and whether the patient can accurately view the medical consumption details and receive health risk assessment reports under the customer system, so as to ensure that the medical system can operate stably under various patient usage scenarios and provide technical support for the smooth development of medical services.

[0039] S3. Match the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system.

[0040] As an optional implementation, after the above step S3, if the matched preset system tag type is empty, a system tag type matching failure message is generated, and the system tag type matching failure message is returned to the tester. Then, a new system tag type generation instruction sent by the tester is received, and a new system tag type is generated according to the new system tag type generation instruction. Finally, a test user data tag is generated according to the new system tag type and the input tag data. When the matched preset system tag type is empty, it means that the existing system tag cannot meet the requirements of the current test input, and a system tag type matching failure message is generated and returned to the tester to ensure that the tester knows that the system cannot process the current input tag data, which helps the tester adjust the test strategy or take further action to avoid the test being unable to proceed due to the lack of system tags. For example, in a constantly updated financial business system, newly developed functions may require new tag types. If the existing preset system tag type cannot be matched, this step can ensure that the test will not be interrupted due to this problem.

[0041] As an optional implementation, please continue to refer to Figure 4 , Figure 4 It is a schematic diagram of the execution flow of matching the preset system label type in the test data label generation method provided in an embodiment of the present application, which specifically includes the following steps S31 to S34.

[0042] S31. Obtain all stored test user data tags corresponding to each preset system tag type in the target business system.

[0043] S32: Clustering the stored test user data labels according to the description data of the preset system label type to obtain different data clusters corresponding to the preset system label type and corresponding cluster centers.

[0044] S33, calculating different first distance values ​​from the input label data to different cluster centers.

[0045] For example, if the first distance value of the input label data from the center of the data cluster representing "high-risk customers" is small, it means that the insured's situation is closer to the characteristics of high-risk customers, and his insurance needs, risk level, etc. may be similar to those of customers in this cluster. On the contrary, if it is closer to the center of the data cluster of "low-risk customers", it is more likely to belong to the low-risk category. In the medical scenario, assuming that the cluster center is divided according to the severity of the patient's disease and the difficulty of treatment, when the various label data of a patient are input, including symptoms, examination indicators, past medical history, etc., if the calculated first distance value of the input label data of the patient from the center of the data cluster representing "severe patients" is small, it indicates that the severity of the patient's condition and the complexity of treatment are similar to the characteristics of severe patients, and the doctor may need to formulate a more targeted and high-intensity treatment plan for him and allocate more medical resources. On the contrary, if it is closer to the center of the data cluster of "mild patients", it means that the patient's condition is relatively mild and more suitable for conventional treatment methods and nursing methods.

[0046] S34. Set the preset system label type corresponding to the cluster center associated with the smallest first distance value as the matching result corresponding to the input label data.

[0047] As another optional implementation, after the above step S33, the following steps S35 to S37 are further included.

[0048] S35: Determine whether the minimum first distance value is within a preset distance threshold range.

[0049] S36: If the distance is not within the preset distance threshold, a corresponding system tag type matching failure message is generated.

[0050] S37. Return the system tag type matching failure information to the tester.

[0051] Among them, the above steps S35 to S37 can enable the tester to quickly detect abnormal situations in the system tag type matching process. By judging whether the minimum first distance value is within the preset distance threshold range, the tester can promptly find out whether there is a problem in the matching of the input tag data with the existing data clusters in the system. When a match failure message appears, the tester will realize that the system processing result does not meet expectations and needs to conduct a more in-depth inspection of the current test situation. Taking property insurance testing as an example, if the minimum first distance value calculated by the tag data of the input property information and customer information is not within the preset distance threshold range, the tester can determine whether there is a deviation in the system when processing this type of property insurance business. This helps prevent erroneous test results from being misjudged as normal, ensures the accuracy of the test, and avoids incorrect evaluation of system functions and performance.

[0052] S4. If the matched preset system tag type is not empty, a test user data tag corresponding to the target test user in at least one type of business system is generated according to the input tag data and the matched preset system tag type.

[0053] As an optional implementation, after the above step S4, it is also possible to monitor whether a new type of new business system is generated in the target business system. If a new type of new business system is generated, the second similarity between the test user data label in the target business system and the new business system is calculated based on the text description data of the new business system. If the second similarity is within the preset second similarity threshold range, a first association relationship between the test user data label and the new business system is established.

[0054] Among them, it is crucial for testers to be able to monitor whether new types of new business systems appear in the target business system. In complex business systems, such as financial or medical systems, businesses will continue to develop and innovate, and new business systems may appear at any time. For example, in the insurance field, new business systems such as "network insurance business" and "shared insurance business" may be added. The same is true in the medical field. New business systems such as "remote home care business system" and "Internet mental health diagnosis and treatment business system" may be added. Through this step, testers can detect the emergence of these new business systems in a timely manner, ensure that the test coverage does not miss new businesses, and avoid the risks caused by not testing new businesses.

[0055] As another optional implementation, after the above step S4, it is also possible to monitor whether a new preset system label type is generated in the target business system. If a new preset system label type is generated, the third similarity between the test user data label in the target business system and the new preset system label type is calculated based on the text description data of the new preset system label type. If the third similarity is within the preset third similarity threshold range, a second association relationship between the test user data label and the new preset system label type is established.

[0056] Among them, it is crucial for testers to monitor whether new preset system label types are generated in the insurance business system. As the market environment and customer needs continue to evolve, the insurance business will continue to launch new products and services, and new preset system label types may be introduced accordingly. For example, new label types such as "cyber insurance product label", "green insurance label", "pet insurance label" may appear to meet the emerging insurance customer needs. For example, in the medical system, new label types such as "telemedicine service label", "genetic test result label", "smart wearable device health data label" may appear. Through continuous monitoring, testers can timely discover these new label types, ensure that the testing work keeps up with the pace of business system updates, enable testers to quickly test new functions or new products, avoid potential problems caused by untimely testing after system updates, and ensure that new system functions have a certain quality assurance when they are launched.

[0057] The test data label generation method provided in the embodiment of the present application receives at least one test user identification information data sent by the tester, and the corresponding input label data; identifies at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data; matches the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; if the matched preset system label type is not empty, then generates the test user data label corresponding to the target test user in at least one type of business system according to the input label data and the matched preset system label type. The above method reduces the manpower input in the testing process, and realizes the automatic and intelligent generation of test data labeling, which significantly improves the test quality and iteration cycle efficiency of the projects to be tested.

[0058] Please continue reading Figure 5 , Figure 5 is a schematic diagram of the system structure of the test data label generating device provided in an embodiment of the present application, such as Figure 5 As shown, the test data label generating device 50 includes: an input data receiving module 51 , a business system identifying module 52 , a label type matching module 53 and a data label generating module 54 .

[0059] The input data receiving module 51 is used to receive at least one test user identification information data and corresponding input tag data sent by a tester.

[0060] The business system identification module 52 is used to identify at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data.

[0061] The tag type matching module 53 is used to match the corresponding preset system tag type according to the input tag data, wherein the preset system tag type corresponds to the type of the business system.

[0062] The data tag generation module 54 is used to generate a test user data tag corresponding to the target test user in the at least one type of business system according to the input tag data and the matched preset system tag type if the matched preset system tag type is not empty.

[0063] As an optional implementation, the business system identification module 52 is also specifically used to verify whether the test user identification information data exists in the target business system; if it exists in the target business system, verify whether the corresponding input label data is empty; if the input label data is not empty, calculate the first similarity between the input label data and the preset system label type already stored in the target business system; if the first similarity is not within the preset first similarity threshold range, generate a first label match warning prompt information according to the preset system label type; and return the first label match warning prompt information to the tester.

[0064] As an optional implementation, the tag type matching module 53 is further specifically configured to generate system tag type matching failure information if the matched preset system tag type is empty;

[0065] Return the system tag type matching failure information to the tester; receive a new system tag type generation instruction sent by the tester, and generate a new system tag type according to the new system tag type generation instruction; generate the test user data tag according to the new system tag type and the input tag data.

[0066] As an optional implementation, the label type matching module 53 is also specifically used to obtain all stored test user data labels corresponding to each of the preset system label types in the target business system; cluster the stored test user data labels according to the description data of the preset system label types to obtain different data clusters corresponding to the preset system label types, and corresponding cluster centers; calculate different first distance values ​​from the input label data to different cluster centers; and set the preset system label type corresponding to the cluster center associated with the smallest first distance value as the matching result corresponding to the input label data.

[0067] As an optional implementation, the tag type matching module 53 is also specifically used to determine whether the minimum first distance value is within a preset distance threshold range; if it is not within the preset distance threshold range, a corresponding system tag type matching failure information is generated; and the system tag type matching failure information is returned to the tester.

[0068] As an optional implementation, the data label generation module 54 is also specifically used to monitor whether a new type of new business system is generated in the target business system; if a new type of new business system is generated, the second similarity between the test user data label in the target business system and the new business system is calculated based on the text description data of the new business system; if the second similarity is within a preset second similarity threshold range, a first association relationship between the test user data label and the new business system is established.

[0069] As an optional implementation, the data label generation module 54 is also specifically used to monitor whether a new preset system label type is generated in the target business system; if a new preset system label type is generated, the third similarity between the test user data label in the target business system and the new preset system label type is calculated based on the text description data of the new preset system label type; if the third similarity is within a preset third similarity threshold range, a second association relationship between the test user data label and the new preset system label type is established.

[0070] It should be noted that the above-mentioned test data label generation device can execute the test data label generation method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in the embodiment of the test data label generation device, please refer to the test data label generation method provided in the embodiment of the present application.

[0071] Please continue reading Figure 6 , Figure 6 is a hardware structure diagram of an electronic device 600 for executing a test data label generation method provided in an embodiment of the present application, such as Figure 6 As shown, the electronic device 600 includes:

[0072] One or more processors 610 and memory 620, Figure 6 A processor 610 is taken as an example.

[0073] The processor 610 and the memory 620 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0074] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the test data label generation method in the embodiment of the present application. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 620, that is, the test data label generation method of the above method embodiment is implemented.

[0075] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the test data label generating device, etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely arranged relative to the processor 610, and these remote memories may be connected to the test data label generating device 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.

[0076] The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, execute the test data label generation method in any of the above method embodiments, for example, execute the above described Figure 2 Steps S1 to S4 of the method, Figure 3 Steps S11 to S15 of the method, Figure 4 Steps S31 to S34 of the method are implemented Figure 5 The functions of modules 51-54 in.

[0077] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0078] The embodiment of the present application provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, such as Figure 6 A processor 610 in the embodiment may enable the one or more processors to execute the test data label generation method in any of the above method embodiments, for example, to execute the above described Figure 2 Steps S1 to S4 of the method, Figure 3 Steps S11 to S15 of the method, Figure 4Steps S31 to S34 of the method are implemented Figure 5 The functions of modules 51-54 in.

[0079] The present application provides a computer program product, wherein the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by the electronic device, the electronic device is enabled to execute the test data label generation method in any of the above method embodiments, for example, to execute the above described Figure 2 Steps S1 to S4 of the method, Figure 3 Steps S11 to S15 of the method, Figure 4 Steps S31 to S34 of the method are implemented Figure 5 The functions of modules 51-54 in.

[0080] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] Through the description of the above implementation methods, ordinary technicians in this field can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Ordinary technicians in this field can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present application as described above, which are not provided in detail for the sake of simplicity. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A test data label generation method, characterized in that: include: Receive at least one test user identification information data and corresponding input label data sent by the tester; Identify at least one type of business system corresponding to the target test user in the target business system according to the test user identification information data; Matching a corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; If the matched preset system tag type is not empty, a test user data tag corresponding to the target test user in the at least one type of business system is generated according to the input tag data and the matched preset system tag type.

2. The test data label generation method according to claim 1, characterized in that: After the step of receiving at least one test user identification information data sent by the tester and the corresponding input tag data, the method further includes: Verifying whether the test user identification information data exists in the target business system; If it exists in the target business system, verify whether the corresponding input tag data is empty; If the input tag data is not empty, calculating a first similarity between the input tag data and the preset system tag type already stored in the target business system; If the first similarity is not within a preset first similarity threshold range, generating first tag matching warning prompt information according to the preset system tag type; Return the first tag matching warning prompt information to the tester.

3. The test data label generation method according to claim 1, characterized in that: After the step of matching the input tag data with the corresponding preset system tag type, wherein the preset system tag type corresponds to the type of the business system, the step further includes: If the matched preset system tag type is empty, a system tag type matching failure message is generated; Return the system tag type matching failure information to the tester; Receiving a new system label type generation instruction sent by the tester, and generating a new system label type according to the new system label type generation instruction; The test user data tag is generated according to the new system tag type and the input tag data.

4. The test data label generation method according to claim 1, characterized in that: The step of matching the corresponding preset system label type according to the input label data includes: Acquire all stored test user data tags corresponding to each of the preset system tag types in the target business system; Clustering the stored test user data labels according to the description data of the preset system label type to obtain different data clusters corresponding to the preset system label type and corresponding cluster centers; Calculating different first distance values ​​from the input label data to different cluster centers; The preset system label type corresponding to the cluster center associated with the smallest first distance value is set as the matching result corresponding to the input label data.

5. The test data label generation method according to claim 4, characterized in that: After the step of calculating different first distance values ​​from the input label data to different cluster centers, the method further includes: Determining whether the minimum first distance value is within a preset distance threshold range; If it is not within the preset distance threshold range, a corresponding system tag type matching failure information is generated; Return the system tag type matching failure information to the tester.

6. The test data label generation method according to claim 1, characterized in that: After the step of generating a test user data label corresponding to the target test user in the at least one type of business system according to the input label data and the matched preset system label type, the method further includes: Monitoring whether a new type of new business system is generated in the target business system; If a new type of new business system is generated, calculating a second similarity between the test user data label in the target business system and the new business system according to the text description data of the new business system; If the second similarity is within a preset second similarity threshold range, a first association relationship between the test user data tag and the new business system is established.

7. The test data label generation method according to claim 1, characterized in that: After the step of generating a test user data label corresponding to the target test user in the at least one type of business system according to the input label data and the matched preset system label type, the method further includes: Monitor whether a new preset system tag type is generated in the target business system; If a new preset system label type is generated, calculating a third similarity between the test user data label in the target business system and the new preset system label type according to the text description data of the new preset system label type; If the third similarity is within a preset third similarity threshold range, a second association relationship between the test user data tag and the new preset system tag type is established.

8. A test data label generating device, characterized in that: include: An input data receiving module, used to receive at least one test user identification information data sent by a tester, and corresponding input tag data; A business system identification module, used to identify at least one type of business system corresponding to the corresponding target test user in the target business system according to the test user identification information data; A label type matching module, used to match the corresponding preset system label type according to the input label data, wherein the preset system label type corresponds to the type of the business system; The data label generation module is used to generate a test user data label corresponding to the target test user in the at least one type of business system according to the input label data and the matched preset system label type if the matched preset system label type is not empty.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the test data label generating method according to any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device executes the test data label generating method according to any one of claims 1 to 7.

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