A digital identity generation method, device and equipment and computer storage medium

Abstract

This application provides a digital identity generation method, apparatus, device, and computer storage medium, applied in the computer field, to reduce the difficulty of generating digital identities and simultaneously improve their generation efficiency. The method includes at least: obtaining target test cases corresponding to a target application; wherein the target test cases include: description information of a set of test operations executed when testing an application function in the target application based on a test digital identity; performing semantic recognition processing on the description information of the test operation set included in the target test cases to obtain corresponding semantic recognition results; obtaining identity attribute conditions required when executing the test operation set based on the semantic recognition results; the identity attribute conditions represent at least one identity feature required during testing; and generating a test digital identity that satisfies the description information in the target test cases based on the identity attribute conditions.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a digital identity generation method, apparatus, device, and computer storage medium. Background Technology

[0002] With the continuous development of science and technology, the social practices contained on the Internet are becoming increasingly rich, and various application software can provide more and more functional modules. In order to ensure that the various functions provided by application software can operate normally for the public, an important step in the development phase is testing.

[0003] Testing refers to a series of activities involving the inspection, verification, and evaluation of a target application from code writing to official release. This process aims to capture errors, verify functionality, and evaluate performance. Specifically, the testing process begins with developing a test plan, clearly defining the test objectives, scope, strategies, and schedule. Then, test cases are designed based on the test plan; each test case is a collection of specific operational steps and expected results. During test execution, testers record and manage any issues discovered (defects) until they are fixed and the application passes retesting.

[0004] In some target applications, testing requires various digital identities and data with different identity attributes to serve as entities and aid in the testing process. Therefore, the testing process not only involves designing test cases but also providing corresponding test digital identities based on the requirements of those test cases.

[0005] In related technologies, generating corresponding test digital identities for the testing process requires experienced testers to manually construct the corresponding test digital identities and related data based on actual needs and test case descriptions; or, based on pre-configured rules, to generate the corresponding test digital identities and related data.

[0006] However, manually constructing test digital identities and data by testers is very time-consuming and requires a lot of manpower. Furthermore, if multiple test digital identities are needed during the testing process, the corresponding generation cost increases exponentially. On the other hand, generating test digital identities and data based on rules requires pre-defining the corresponding rules, which can only handle some test digital identities with simple attributes, but is difficult to handle complex data structures and condition requirements.

[0007] Therefore, there is an urgent need for a new method for generating digital identities to reduce the difficulty of generating digital identities while improving their generation efficiency. Summary of the Invention

[0008] This application provides a digital identity generation method, apparatus, device, and computer storage medium to reduce the difficulty of generating digital identities and improve their generation efficiency.

[0009] In a first aspect, embodiments of this application provide a digital identity generation method, including:

[0010] Obtain the target test cases corresponding to the target application; wherein, the target test cases include: description information of the set of test operations executed when testing an application function in the target application based on a test digital identity;

[0011] The description information of the test operation set included in the target test case is subjected to semantic recognition processing to obtain the corresponding semantic recognition results;

[0012] Based on the semantic recognition results, the identity attribute conditions required to execute the test operation set are obtained; the identity attribute conditions represent at least one identity feature required when conducting the test.

[0013] Based on the identity attribute conditions, a test digital identity that satisfies the description information in the target test case is generated.

[0014] Secondly, embodiments of this application provide a digital identity generation apparatus, comprising:

[0015] The acquisition module is used to acquire target test cases corresponding to the target application; wherein, the target test cases include: description information of the set of test operations executed when testing an application function in the target application based on a test digital identity;

[0016] The identification module is used to perform semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition result;

[0017] The processing module is used to obtain the identity attribute conditions required when executing the test operation set based on the semantic recognition result; the identity attribute conditions represent at least one identity feature required when conducting the test.

[0018] The generation module is used to generate a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions.

[0019] Optionally, when the generation module generates a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions, it is specifically used for:

[0020] Based on a preset data transmission format, a corresponding feature generation request is generated for each identity feature in the identity attribute conditions.

[0021] The obtained at least one feature generation request is sent to the backend server corresponding to the target application, so that the backend server generates the test digital identity with the at least one identity feature based on the at least one feature generation request.

[0022] Optionally, when the generation module generates a corresponding feature generation request for each identity feature in the identity attribute conditions, it is specifically used for:

[0023] Based on a preset request generation order, corresponding feature generation requests are generated sequentially; wherein, each time a feature generation request is generated, the current identity feature is combined with the previous identity feature preceding the current identity feature in the request generation order to generate the corresponding feature generation request.

[0024] When the generation module sends the obtained at least one feature generation request to the backend server corresponding to the target application, it is specifically used for:

[0025] Based on the order in which the requests are generated, the corresponding feature generation requests are sent to the backend server in sequence.

[0026] After the last feature generation request in the request generation sequence is sent, the corresponding test digital identity is obtained from the backend server.

[0027] Optionally, when the generation module generates a feature generation request based on the current identity feature and combining the current identity feature with the preceding identity feature in the request generation sequence, it is specifically used for:

[0028] When the current identity feature is the first identity feature in the request generation sequence, a corresponding feature generation request is generated based on the current identity feature;

[0029] When the current identity feature is not the first identity feature in the request generation order, a corresponding feature generation request is generated based on the current identity feature and the association between the current identity feature and the preceding identity feature.

[0030] Optionally, when the identification module performs semantic recognition processing on the descriptive information of the test operation set included in the target test case to obtain the corresponding semantic recognition result, it is specifically used for:

[0031] The description information of the test operation set included in the target test case is processed by word segmentation to obtain at least one word segmentation result corresponding to the description information;

[0032] Based on a preset attention mechanism, multi-dimensional semantic features are extracted from the at least one word segmentation result to obtain the corresponding semantic features for each dimension; the semantic features for each dimension include at least one of the following: grammatical structure features, semantic sentiment features, and lexical type features;

[0033] The semantic features corresponding to each dimension are weighted and fused to obtain the semantic recognition result.

[0034] Optionally, when the processing module obtains the identity attribute conditions required for executing the test operation set based on the semantic recognition result, it is specifically used for:

[0035] Based on the semantic information represented by the semantic recognition results, corresponding to each identity feature, a generation guide word is generated for each identity feature.

[0036] Based on the obtained generation guide words, the identity attribute conditions required when executing the test operation set are obtained.

[0037] Optionally, when the generation module generates a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions, it is specifically used for:

[0038] When there is a target created digital identity that meets the identity attribute conditions in the created digital identities stored in the database, the target created digital identity is used as the test digital identity.

[0039] If no target created digital identity that meets the identity attribute conditions exists in the database of created digital identities, then a test digital identity that meets the description information in the target test case is generated based on the identity attribute conditions.

[0040] Optionally, the at least one identity feature includes one or more of the following:

[0041] Organizational identity features, wherein the organizational identity features represent: organizational information of the organization to which the test digital identity belongs;

[0042] Type identity feature, wherein the type identity feature represents: the identity type information of the test digital identity in the organization;

[0043] Relational identity features, wherein the relational identity features represent: the identity information of other digital identities associated with the test digital identity, and the association relationship between the other digital identities and the test digital identity;

[0044] Group identity features, which represent the group information to which the test digital identity has joined.

[0045] Optionally, after generating a test digital identity that satisfies the description information in the target test case, the generation module is further configured to:

[0046] Generate the correspondence between the test digital identity and the target test case;

[0047] The test digital identity and the corresponding relationship are saved together in the database.

[0048] Thirdly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0049] Fourthly, embodiments of this application provide a computer device, including:

[0050] Memory, used to store program instructions;

[0051] A processor is configured to invoke program instructions stored in the memory and execute the method described in the first aspect according to the obtained program instructions.

[0052] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method described in the first aspect.

[0053] The beneficial effects of this application are as follows:

[0054] In the digital identity generation method proposed in this application, semantic recognition processing can be performed on descriptive information containing test operation sets. Using a preset semantic processing method, the corresponding semantic recognition results are automatically obtained, and then the corresponding identity attribute conditions are obtained based on the semantic recognition results. Thus, manual generation of test digital identities for test cases is unnecessary, and the required identity attribute conditions in the descriptive information of different test cases can be quickly and flexibly obtained for different test cases, thereby ensuring rapid and low-cost generation of test digital identities.

[0055] On the other hand, based on the obtained identity attribute conditions, corresponding test digital identities that meet the description information in the target test cases are generated. After the identity features represented in the identity attribute conditions have been determined, test digital identities with these identity features can be generated in a targeted manner based on the requirements of the target test cases for the identity features of the test digital identities, thereby meeting the requirements of the target test cases. Moreover, generating corresponding test digital identities for different identity features can ensure the accuracy of the generated test digital identities in meeting the requirements, thereby improving the reliability of the implementation of this solution. Attached Figure Description

[0056] Figure 1 This is one application scenario of the digital identity generation method provided in the embodiments of this application;

[0057] Figure 2 A flowchart illustrating a digital identity generation method provided in an embodiment of this application;

[0058] Figure 3 A schematic diagram illustrating a type of target application provided in an embodiment of this application;

[0059] Figure 4 This application provides a schematic diagram illustrating the correspondence between test cases and application functions in an embodiment of the present application.

[0060] Figure 5 A logical diagram illustrating the acquisition of semantic recognition results provided in an embodiment of this application;

[0061] Figure 6 A flowchart illustrating a method for obtaining semantic recognition results provided in an embodiment of this application;

[0062] Figure 7 A flowchart illustrating a method for obtaining identity attribute conditions provided in an embodiment of this application;

[0063] Figure 8 A schematic diagram illustrating the content of an identity type provided in an embodiment of this application;

[0064] Figure 9 A schematic diagram illustrating the relational identity features of a digital identity as provided in an embodiment of this application;

[0065] Figure 10 This is a schematic diagram illustrating information corresponding to an identity feature provided in an embodiment of this application;

[0066] Figure 11 A flowchart illustrating a method for generating a test digital identity, provided as an embodiment of this application;

[0067] Figure 12 A logical diagram illustrating the storage of a test digital identity is provided in an embodiment of this application.

[0068] Figure 13 A logical schematic diagram of a method for obtaining a test digital identity provided in an embodiment of this application;

[0069] Figure 14 A logical schematic diagram of another method for obtaining a test digital identity provided in an embodiment of this application;

[0070] Figure 15 A flowchart illustrating a method for generating a feature generation request provided in an embodiment of this application;

[0071] Figure 16 A logical diagram illustrating the interaction between a server and a backend server, provided as an embodiment of this application;

[0072] Figure 17 A logical schematic diagram of a method for generating a test digital identity provided in an embodiment of this application;

[0073] Figure 18 A logical schematic diagram of a method for generating identity features provided in an embodiment of this application;

[0074] Figure 19 A logical schematic diagram of a method for generating identity features based on sequence, provided in an embodiment of this application;

[0075] Figure 20 A logical schematic diagram of a test digital identity generation method provided in an embodiment of this application;

[0076] Figure 21 This is a schematic diagram of the structure of a digital identity generation device provided in an embodiment of this application;

[0077] Figure 22 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application;

[0078] Figure 23 This is a schematic diagram of the hardware structure of another electronic device in an embodiment of this application. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0080] The following explanations of some terms used in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0081] (1) Test Case: A test case is a set of test inputs, execution conditions, and expected results designed for a specific purpose, in order to test whether the functionality of a program or system meets the requirements. Simply put, it is like a detailed "test manual" that tells the tester how to test the software.

[0082] (2) Test Digital Identity: This is a digital identity specifically designed for testing purposes. During software development, developers create test digital identities to check whether various functions of the software are working properly. For example, for a social networking application, a test digital identity can be used to simulate user registration, login, posting updates, adding friends, and other operations. The purpose of this is to discover as many vulnerabilities, errors, or areas that do not conform to the expected design as possible before the software is officially released to users.

[0083] (3) Large Language Models (LLM): A language model is a deep neural network with hundreds of billions of parameters. It is usually trained using self-supervised learning methods with a large amount of unlabeled text and can predict and generate text and other content by training on large-scale datasets.

[0084] It should be noted that the embodiments of this application involve operations such as obtaining digital identity generation and test case acquisition. When the following embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, when it is necessary to obtain relevant data, relevant volunteers can be recruited and relevant agreements authorizing the volunteers to authorize data can be signed, and then the data of these volunteers can be used for implementation; or, it can be implemented within the authorized scope of the organization, and the following implementation methods can be used to identify internal members; or, the relevant data used in the specific implementation are all simulated data, such as simulated data generated in a virtual scene.

[0085] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0086] The technical concept of the technical solution of the embodiments of this application will be briefly described below.

[0087] Testing refers to a series of activities involving the inspection, verification, and evaluation of a target application from code writing to official release. This process aims to capture errors, verify functionality, and evaluate performance. Specifically, the testing process begins with developing a test plan, clearly defining the test objectives, scope, strategies, and schedule. Then, test cases are designed based on the test plan; each test case is a collection of specific operational steps and expected results. During test execution, testers record and manage any issues discovered (defects) until they are fixed and the application passes retesting.

[0088] In the testing process for various target applications, it is necessary to use various test digital identities and data with different identity attributes to help complete the testing of related functions. For example, in social software, test digital identities are needed to test functions such as communication, adding friends, and posting updates.

[0089] Therefore, for some test cases, the specified testing methods require the use of corresponding test digital identities in order to complete the corresponding tests.

[0090] In related technologies, generating corresponding test digital identities for the testing process requires experienced testers to manually construct the corresponding test digital identities and related data based on actual needs and test case descriptions; or, based on pre-configured rules, to generate the corresponding test digital identities and related data.

[0091] However, manually constructing test digital identities and data by testers is very time-consuming and requires significant manpower. Furthermore, if multiple test digital identities are needed during testing, the generation cost increases exponentially. Additionally, generating test digital identities and data based on rules requires pre-defining the rules, which can only handle test digital identities with simple attributes and struggles to handle complex data structures and conditions.

[0092] In view of this, embodiments of this application provide a digital identity generation method to reduce the difficulty of generating digital identities while improving their generation efficiency.

[0093] Specifically, this method first requires obtaining the target test cases corresponding to the target application. These target test cases include a description of the set of test operations executed when testing an application function within the target application based on a test digital identity. Then, semantic recognition processing can be performed on the description information of the test operation set included in the target test cases to obtain the corresponding semantic recognition results.

[0094] Next, based on the semantic recognition result, the identity attribute conditions required to execute the aforementioned test operation set are obtained. These identity attribute conditions represent at least one identity feature required to perform the test. Thus, by obtaining the identity attribute conditions required to execute the test operations from the description information of the target test case based on the semantic recognition result, the corresponding test digital identity can be obtained specifically based on these identity attribute conditions in subsequent processes. That is, based on the identity attribute conditions, a test digital identity that satisfies the description information in the target test case is generated.

[0095] The following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application are applicable. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0096] See Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application, such as... Figure 1 As shown, this scenario may include multiple terminal devices 101 and a server 102.

[0097] Terminal device 101 can be any device capable of network communication, such as a mobile phone, laptop, tablet computer (PAD), notebook computer, desktop computer, smart TV, smart in-vehicle device, smart wearable device, e-book reader, etc. Terminal device 101 can be used to obtain download operations triggered by a user targeting target data in a cloud server, and send these download operations to the cloud server. It can also be used to provide the target address to the cloud server. Server 102 is used to respond to download operations targeting target data and to provide data management for the target data. It can be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms, but is not limited to these.

[0098] Both server 102 and terminal device 101 may include one or more processors, memory, and I / O interfaces for interaction. The memory of server 102 and terminal device 101 may also store program instructions required for execution in the digital identity generation method provided in this application embodiment. These program instructions, when executed by the processor, can be used to implement the digital identity generation method provided in this application embodiment.

[0099] In this embodiment, each terminal device 101 and the server 102 can communicate directly or indirectly through one or more networks 103. The network 103 can be a wired network or a wireless network. For example, the wireless network can be a mobile cellular network or a Wireless-Fidelity (WIFI) network. Of course, it can also be other possible networks, and this embodiment does not limit them.

[0100] It should be noted that, Figure 1 The examples shown are merely illustrative; in reality, the number of terminal devices and servers is unlimited and not specifically limited in this embodiment. The digital identity generation method proposed in this embodiment can be performed solely by the server 102, or jointly by the server and terminal device 101. For example, when performed solely by the server, the server can obtain various test cases corresponding to the target application and treat each test case as a target test case, executing the corresponding digital identity generation method. Specifically, it obtains the semantic recognition result corresponding to the description information of the target test case, and then, based on the semantic recognition result, obtains the identity attribute conditions for executing the test operation set. These identity attribute conditions represent at least one identity feature required during testing. Thus, the server can generate a test digital identity that satisfies the description information in the target test case based on these identity attribute conditions.

[0101] The following describes the digital identity generation method provided by exemplary embodiments of this application in conjunction with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way in this respect.

[0102] See Figure 2 This is a flowchart illustrating a digital identity generation method provided in an embodiment of this application. The executing entity can be, for example, […]. Figure 1 The server or terminal device shown. For ease of explanation, the following description will use a server as the execution subject. Figure 2 As shown, the specific implementation steps of this method are as follows:

[0103] Step S21: Obtain the target test cases corresponding to the target application. The target test cases include: description information of the set of test operations executed when testing an application function within the target application based on a test digital identity.

[0104] First, it should be noted that, as Figure 3As shown, the target application can be any application software or system that needs to be tested. For example, the target application type can be social software, drawing software, engineering modeling software, video software, and so on. These different application software programs require a large number of different test cases to complete the testing of their various functions.

[0105] For example, such as Figure 4 As shown, for social software, it may be necessary to test the communication function, the posting function, the friend adding function, etc., separately. Therefore, for this social software, at least three test cases are needed to describe the process, the required conditions, and the expected results during the testing. Furthermore, each type of function may include several different sub-functions that require specific testing, so each type of function may also correspond to multiple different test cases.

[0106] Therefore, test cases should include at least the following: a description of the set of test operations executed when testing a function of the target application.

[0107] Furthermore, since the embodiments of this application are aimed at generating digital identities corresponding to test cases, the test cases involved in the application scenarios of the solutions proposed in the embodiments of this application specifically include: description information of the set of test operations executed when testing an application function in a target application based on a test digital identity.

[0108] For example, assuming the target application is a social software application, then the content included in target test case 1 for this target application could be:

[0109] 1. Test case number: TC-001;

[0110] 2. Test item: Text message sending function;

[0111] 3. Test case title: Verify whether text messages can be sent to friends normally in different scenarios;

[0112] 4. Test environment: Device model: Device 1 with operating system version 3.1.5 running;

[0113] 5. Prerequisites: You have successfully logged into your digital identity, and your digital identity is in normal use. You have also added at least one friend, and that friend is online.

[0114] 6. Test steps: Select a friend to chat with, enter a piece of ordinary text about 10 Chinese characters in the input box (e.g., "The weather is so nice today"), click the send button, and observe whether the message can be sent successfully and whether the interface displays normally after sending.

[0115] 7. Expected Results: Messages containing plain text, special symbols, and pure English text should all be sent successfully. Messages should be instantly visible in the chat interface, and the message format should be correct. Very long messages should also be sent successfully. If the interface has character limit rules (such as folding messages exceeding a certain character limit), the messages should be displayed correctly according to those rules.

[0116] As shown above, a test case includes a set of test operations (such as selecting a friend to send a message, etc.) performed when testing a specific application function (text message sending function) within the target application (i.e., a social software application). Since it involves the mutual sending of messages, it also involves a digital identity: a digital identity that is in normal use, has added at least one friend, and that friend is online.

[0117] The above describes the possible content of a single test case. For a target application, there may be multiple test cases. Therefore, when the server generates the corresponding test digital identity for each test case, it can either execute the digital identity generation method proposed in this application sequentially for each of the multiple test cases, or it can directly handle multiple test cases and generate digital identities simultaneously. To more clearly explain the digital identity generation method proposed in this application, the following will first use the generation of a digital identity for a single test case as an example, where this single test case is the target test case.

[0118] Step S22: Perform semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition results.

[0119] After obtaining the target test cases corresponding to the target application, the server can perform semantic recognition processing on the descriptive information therein to obtain the corresponding semantic recognition results.

[0120] Among them, the semantic recognition processing of descriptive information can adopt the following different types of methods, namely:

[0121] For example, semantics can be identified in descriptive information using a pre-built dictionary, or by analyzing sentence structure based on grammatical rules of the language. Alternatively, supervised learning algorithms such as Naive Bayes classifiers, support vector machines, and decision trees can be used to train models for specialized semantic recognition of test cases. Furthermore, deep learning methods, such as recurrent neural network models, convolutional neural networks, or Transformer architectures, can be employed to perform semantic recognition on descriptive information in test cases.

[0122] For example, such as Figure 5 As shown, a pre-trained large language model can also be used to perform semantic recognition processing on the descriptive information of the test operation set in the test cases, thereby obtaining the corresponding semantic recognition results.

[0123] In one possible implementation, when the server performs semantic recognition processing on the description information to obtain the semantic recognition result, it may specifically perform the following operations:

[0124] See Figure 6 The flowchart below shows a method for obtaining semantic recognition results provided in an embodiment of this application. Figure 6 As shown, the specific implementation steps of this method are as follows:

[0125] Step S301: Perform word segmentation on the description information of the test operation set included in the target test case, and obtain at least one word segmentation result corresponding to the description information.

[0126] When the server performs semantic recognition processing on the description information, different operations need to be performed in sequence. The first operation is to perform word segmentation on the description information to obtain at least one word segmentation result.

[0127] For example, the server can use the pre-trained large language model proposed above to perform word segmentation on the descriptive information, breaking the text in the descriptive information into basic units such as words, subwords, or characters, so that the model can more easily and quickly understand the components in the descriptive information later. During word segmentation, the server can use methods such as Byte Pair Encoding (BPE) or WordPiece, and this application does not impose any restrictions on this.

[0128] Step S302: Based on the preset attention mechanism, perform multi-dimensional semantic feature extraction on at least one word segmentation result to obtain the corresponding semantic features for each dimension.

[0129] The semantic features corresponding to each dimension include at least one of the following: grammatical structure features, semantic sentiment features, and lexical type features.

[0130] After obtaining the corresponding word segmentation results, the server can perform word vector conversion to ensure that the model can easily perform calculations and processing when extracting features later.

[0131] In this way, the server can continue to perform feature extraction processing on at least one word segmentation result through an attention mechanism. Different attention mechanisms can be used by the server during feature extraction.

[0132] For example, the server can employ a self-attention mechanism to obtain the degree of correlation between each segment and other segments in at least one segmentation result. Specifically, the self-attention mechanism can assign different weights to each segment to reflect its importance in the current context, thereby capturing the semantic and syntactic structure of the text describing the information, obtaining corresponding semantic features from various dimensions, and understanding its contextual relationships.

[0133] For example, the server can also adopt a multi-head attention mechanism, using multiple self-attention heads to perform parallel computation on the word segmentation results. Each self-attention head focuses on different aspects of the word segmentation results, thereby obtaining different semantic features under multiple dimensions, and thus capturing richer and more comprehensive semantic information, further improving the model's ability to understand text.

[0134] Regardless of the attention mechanism used, the server can obtain semantic features in different dimensions. These different dimensions of semantic features include: grammatical structure features, semantic sentiment features, lexical type features, and may also include entity features corresponding to words, semantic role features of words, etc.

[0135] Step S303: Perform weighted fusion processing on the semantic features corresponding to each dimension to obtain the semantic recognition result.

[0136] After acquiring the semantic features of each dimension, the server can perform weighted fusion processing on these different semantic features to finally obtain the semantic recognition result corresponding to the description information.

[0137] In this approach, the server can employ a pre-defined attention mechanism to acquire semantic features of the descriptive information from multiple dimensions, thereby comprehensively and richly capturing the semantic information of the descriptive information. This provides a better data foundation for subsequent acquisition of semantic recognition results and improves the reliability of the implementation of this solution.

[0138] Thus, after obtaining the semantic recognition results, the server can continue to perform the following operations:

[0139] Step S23: Based on the semantic recognition results, obtain the identity attribute conditions required when executing the test operation set; wherein, the identity attribute conditions represent at least one identity feature required when conducting the test.

[0140] After the server obtains the semantic recognition results, it can extract the identity attribute conditions represented in the description information: the identity attribute conditions required when executing the test operation set. These identity attribute conditions represent at least one identity feature required during testing.

[0141] Taking the test cases proposed above for social software applications as an example, since the test involves the interaction between digital identities, the description information of the test operation set includes the potential requirements for the test digital identity, namely: the test digital identity needs to be usable, needs to have added at least one friend, and that friend needs to be online and able to receive messages.

[0142] After extracting and refining this information, we can obtain the requirements of the test case for the identity attributes of the tested digital identity.

[0143] For the server, the process of obtaining identity attribute conditions based on semantic recognition results can be accomplished through one of the following possible implementation methods:

[0144] See Figure 7 The flowchart below shows a method for obtaining identity attribute conditions provided in an embodiment of this application. Figure 7 As shown, the specific implementation steps of this method are as follows:

[0145] Step S401: Based on the semantic information corresponding to each identity feature represented in the semantic recognition result, generate the generation guide word corresponding to each identity feature.

[0146] After obtaining the semantic recognition results, the server can generate generation prompts for each identity feature based on the semantic information corresponding to the identity features required for testing the target test cases, as represented in the semantic recognition results. These generation prompts are used to clarify the topic and content of the text to be generated when the large language model generates text. In the scheme proposed in the embodiments of this application, these generation prompts can guide the model in the server to generate relevant content of the identity features required in the description information, such as information about the organization to which the test digital identity belongs; information about the position related to the test digital identity in that organization, etc.

[0147] Step S402: Based on the obtained generation guide words, obtain the identity attribute conditions required when executing the test operation set.

[0148] After obtaining the generation guide word, the server can generate corresponding requirements for various identity features based on the content indicated by the generation guide word, and then combine the requirements of various identity features to obtain the corresponding identity attribute conditions.

[0149] In this way, by constructing the generation guide word, the server can use the generation guide word to guide the generation of identity attribute conditions that meet the requirements, thereby improving the accuracy of test digital identity generation.

[0150] The above describes the extraction of identity attribute conditions for a single target test case. However, when the server needs to process multiple test cases simultaneously, it can first utilize methods such as... Figure 5 The large language model shown identifies the corresponding semantic recognition result from each test case. Then, according to the one-to-one correspondence between the test cases and the semantic recognition results, the semantic recognition results are classified based on the test cases to ensure that each test case corresponds to its own semantic recognition result, thereby enabling the acquisition of the corresponding identity attribute conditions.

[0151] Furthermore, in one possible embodiment, for identity attribute conditions that characterize at least one identity feature, such as Figure 8 As shown, at least one identity feature can be categorized into different types, such that each test case contains one or more of the following:

[0152] Organizational identity features, whereby the organizational identity features represent: the organizational information of the organization to which the test digital identity belongs; for example, assuming the target application is an internal management software of company A, and the function to be tested is the internal access control function of company A, then for this test case, the identity feature required for the corresponding test digital identity is the organizational identity feature, which indicates that the test digital identity belongs to company A.

[0153] Type identity features, type identity feature representation: test the identity type information of digital identity in the organization; again, taking the above target application as the internal management software of company A as an example, the test digital identity belongs to company A and also corresponds to a corresponding position within company A. For example, the test digital identity can be a department head in the company. Therefore, in the identity attribute conditions corresponding to the test case, one of the identity features is used to represent the department head in the company structure.

[0154] Relational identity features, representing: the identity information of other digital identities associated with the test digital identity, and the association relationships between other digital identities and the test digital identity; for example, suppose that during testing, the test digital identity needs to interact with other digital identities, then for the test digital identity, such as Figure 9As shown, it requires the existence of corresponding digital identities such as internal company contacts, external company contacts, upstream and downstream contacts, etc. Therefore, the identity features of this relationship need to represent the identity information of other digital identities, at least the organizational structure and basic information of other digital identities.

[0155] Group identity features represent the group information that the test digital identity joins; the group identity features correspond to the group information such as internal groups, external groups, customer service groups, etc. that the test digital identity joins.

[0156] For example, let's take a test case as an example to illustrate the above identity characteristics:

[0157] Suppose that the content of a target test case 2 is as follows:

[0158] 1. Test module: Application entry point;

[0159] 2. Function entry point: Homepage;

[0160] 3. Test function points: Checkpoints: Workbench arrangement;

[0161] 4. Prerequisites: Role: Administrator; Organization Type: K-12 Preschool Education;

[0162] 5. Test case steps: 1) Click on the workbench; 2) Add the home-school communication circle to the home-school communication group;

[0163] 6. Verification content: Displayed normally.

[0164] Based on the preconditions of the above test case, it can be determined that the organization type required to run this test case is the education industry, and the digital identity test requires a super administrator.

[0165] Therefore, when obtaining the corresponding identity attribute conditions from the descriptive information, the corresponding information can be obtained according to the types of identity features mentioned above. For example, such as Figure 10 As shown, for organizational identity features, the corresponding information can be obtained from the test cases, namely: organizations in the education industry; for type identity features, the corresponding information is: administrator; since the test cases do not require the information corresponding to relationship identity features and group identity features, the test digital identity corresponding to these test cases can be left unrestricted.

[0166] Thus, for the identity features corresponding to the test digital identity, they can be restricted to different types of identity features based on different representation information. In this way, when generating identity attribute conditions, the server can obtain relevant content from the semantic recognition results of the description information according to the type of identity feature, thereby improving the accuracy and efficiency of generating identity attribute conditions corresponding to the test digital identity.

[0167] After obtaining the identity attribute conditions, the server can continue to perform the following operations:

[0168] Step S24: Based on the identity attribute conditions, generate a test digital identity that satisfies the description information in the target test case.

[0169] After the server obtains the identity attribute conditions, it can generate a test digital identity based on at least one identity feature represented by the identity attribute conditions, so that the test digital identity contains the corresponding identity feature.

[0170] Therefore, in the digital identity generation method proposed in this application embodiment, for descriptive information containing test operation sets, semantic recognition processing can be performed on it. Using a preset semantic processing method, the corresponding semantic recognition results can be automatically obtained, and then the corresponding identity attribute conditions can be obtained based on the semantic recognition results. In this way, manual generation of test digital identities for test cases is unnecessary, and the required identity attribute conditions in the descriptive information of different test cases can be quickly and flexibly obtained for different test cases, thereby ensuring the rapid and low-cost generation of test digital identities.

[0171] On the other hand, based on the obtained identity attribute conditions, corresponding test digital identities that meet the description information in the target test cases are generated. After the identity features represented in the identity attribute conditions have been determined, test digital identities with these identity features can be generated in a targeted manner based on the requirements of the target test cases for the identity features of the test digital identities, thereby meeting the requirements of the target test cases. Moreover, generating corresponding test digital identities for different identity features can ensure the accuracy of the generated test digital identities in meeting the requirements, thereby improving the reliability of the implementation of this solution.

[0172] In one possible implementation, after the server generates a test digital identity that satisfies the description information in the target test case, the server may also perform the following operations:

[0173] See Figure 11 The flowchart below illustrates a method for generating a test digital identity, as provided in an embodiment of this application. Figure 11 As shown, the specific implementation steps of this method are as follows:

[0174] Step S501: Generate the correspondence between test digital identities and target test cases.

[0175] Step S502: Save the test digital identity and the above-mentioned correspondence to the database.

[0176] After the server completes the generation of the test digital identity, it obtains the test digital identity corresponding to the target test case. In order to improve the reusability of the test digital identity, the server can store the identity features corresponding to the test digital identity and related digital identity data in the database associated with the server, so that the test digital identity can be reused when the same identity features are needed in the future.

[0177] And, as Figure 12 As shown, the correspondence between test digital identities and target test cases is recorded and saved to the database. In this way, if the number of existing generated test digital identities does not meet the needs of the target test case, the server can call the interface for generating test digital identities again based on the correspondence and generate test digital identities with the same identity feature type. In this way, the server only needs to perform the corresponding parallel expansion without investing additional manual costs or performing semantic recognition and identity attribute condition extraction operations again, which improves the flexibility and reliability of the implementation of this solution.

[0178] The above details the specific implementation methods for obtaining identity attribute conditions when generating a test digital identity. The following will describe several possible implementation methods for generating a corresponding test digital identity based on attribute identity conditions.

[0179] In one possible implementation, when the server executes step S24, it can specifically complete the acquisition of the test digital identity in the following manner.

[0180] Specifically, the server can first check the created digital identities stored in the above database to determine whether there is a target created digital identity that meets the identity attribute conditions corresponding to the target test case.

[0181] If it is determined that a target has already created a digital identity that satisfies the identity attribute conditions corresponding to the target test case, i.e., such as Figure 13 As shown, if the target's created digital identity contains all the identity features represented by the identity attribute conditions, then the server can directly use this target's created digital identity as a test digital identity to meet the requirements of the target test cases.

[0182] When the server determines that there is no target digital identity that meets the identity attribute conditions among the created digital identities in the database, that is, if... Figure 14As shown, if none of the created digital identities have the corresponding identity features, including all the identity features in the identity attribute conditions, the server needs to generate a new test digital identity that meets the description information of the target test case based on the identity attribute conditions.

[0183] Thus, in this approach, the server can first select a suitable digital identity from the existing digital identities in the database as a test digital identity, thereby improving the reuse rate of each existing digital identity and increasing the efficiency of digital identity generation. On the other hand, if there is no digital identity in the database that meets the conditions, the server can also continue to generate new digital identities as test digital identities, thereby improving the reliability of the implementation of this solution.

[0184] In one possible implementation, when the server generates a test digital identity that promises to meet the description information in the target test case based on identity attribute conditions, the server can specifically implement it in the following way:

[0185] See Figure 15 The flowchart below shows a method for generating a feature generation request provided in an embodiment of this application. Figure 15 As shown, the specific implementation steps of this method are as follows:

[0186] Step S601: Based on the preset data transmission format, generate a corresponding feature generation request for each identity feature in the identity attribute conditions.

[0187] When generating a test digital identity, the server usually communicates with the backend server corresponding to the target application and sends a corresponding generation request to the backend server.

[0188] Therefore, the server executing the digital identity generation method needs to determine the data transmission format with the backend server in advance. This allows the determined data transmission format to be pre-configured in the server so that the server can generate the corresponding feature generation request according to the requirements of the data transmission format.

[0189] The data transmission format may conform to the Hypertext Transfer Protocol (HTTP) or the Extensible Markup Language (XML) protocol, etc., and this application does not impose any restrictions on this.

[0190] When generating feature generation requests, the server needs to generate corresponding feature generation requests for each identity feature. In generating these requests, the server can also choose to use a large language model to generate corresponding guide words from the acquired identity features. This allows the large language model to generate feature generation requests that meet the data transmission format requirements based on the guide words.

[0191] After obtaining the feature generation request corresponding to each identity feature, the following operations can then be performed:

[0192] Step S602: Send the obtained at least one feature generation request to the backend server corresponding to the target application, so that the backend server generates a test digital identity with at least one identity feature based on the at least one feature generation request.

[0193] like Figure 16 As shown, when the server communicates with the backend server corresponding to the target application, it can send feature generation requests to the backend server. The backend server can then add the corresponding identity features to a blank digital identity based on the various description requirements for identity features included in these feature generation requests. In this way, as the identity features are gradually generated, the original blank digital identity gradually transforms into a test digital identity that meets the description information of the target test cases.

[0194] In this way, after obtaining the identity attribute conditions, the server directly generates a corresponding feature generation request for each identity feature in the identity attribute conditions, and then sends it to the backend server, so that the backend server calls the corresponding interface to generate the test digital identity. This makes the generation of digital identity completely automated, ensuring that the test digital identity can be generated accordingly after obtaining the corresponding test cases, improving the generation efficiency of test digital identity, and further reducing the generation cost of test digital identity.

[0195] For example, the generation of the test digital identity will be introduced using target test case 2 as an example.

[0196] For target test case 2, the corresponding identity attribute conditions have already been obtained, and the relevant information of the identity features contained therein has been clarified. Therefore, when testing the generation of digital identity, it can be achieved through the following steps:

[0197] like Figure 17As shown, based on the organizational identity features in the identity attribute conditions, an organization containing the test digital identity is generated. Then, based on the relevant information in the organizational identity features, the characteristics possessed by the organization are generated, such as an educational organization including multiple hierarchical structures. Next, based on the type identity features, a new member (i.e., the test digital identity) is added to the generated organization, and based on the relevant information in the type identity features, the identity features of this member are constructed accordingly, i.e., the administrator identity type. Finally, the corresponding data is added to the test digital identity, thus completing the generation of the entire test digital identity.

[0198] Thus, for different target applications and different backend servers, the server executing this method can generate a feature generation request that can be accepted by the backend server through a preset data transmission format, thereby enabling the backend server to call the corresponding functional interface based on the feature generation request to complete the generation of the test digital identity.

[0199] In one possible implementation, the accuracy of test digital identity generation can be further improved by means of the above-described embodiments.

[0200] Specifically, when generating feature generation requests, the server can first generate the corresponding feature generation requests based on a preset request generation order.

[0201] Among them, the request generation order is preset in the server, such as Figure 18 As shown, the settings can be configured according to the specific type of identity feature. For example, for organizational identity features and type identity features, the organizational identity feature needs to be generated first, then the type identity feature needs to be generated, and then other identity features need to be generated.

[0202] Furthermore, during the process of generating feature generation requests sequentially, each time a feature generation request is generated, the corresponding feature generation request can be generated based on the current identity feature and the preceding identity feature in the request generation order.

[0203] For example, such as Figure 19 As shown, each time a feature generation request is generated, in addition to the identity feature corresponding to this time, it is also necessary to combine the information in the previous identity feature to generate the corresponding feature generation request.

[0204] Optionally, the server can also perform different operations based on the identity features in different positions in the request generation order. Specifically, when the current identity feature is the first identity feature in the request generation order, the server can directly generate the corresponding feature generation request based on the current identity feature.

[0205] When the current identity feature is not the first identity feature in the request generation sequence, the server needs to generate the corresponding feature generation request based on the current identity feature and the relationship between the current identity feature and the previous identity features.

[0206] For example, for a test digital identity to be generated, the identity features required for testing include: the organization to which it belongs, the position of the test digital identity within the organization, the connections between the test digital identity and other digital identities within the organization, and the other digital identities and groups associated with the test digital identity, etc. Therefore, in the request generation order, the identity feature corresponding to the organization is usually placed first. Thus, when generating the feature generation request corresponding to this identity feature, it is only necessary to generate the corresponding feature generation request based on the information corresponding to this identity feature.

[0207] When generating requests for other identity features in the request generation sequence, it is necessary to combine the information from the preceding identity features to generate the corresponding requests.

[0208] For example, when generating type identity features, it is necessary to combine information from organizational identity features and the relationship between those organizational identity features and type identity features. For instance, the test digital identity might be the deputy head of department A within company A. Therefore, when generating feature generation requests corresponding to type identity features, it is necessary to combine the relationship between organizational identity features and type identity features to generate appropriate feature generation requests.

[0209] While completing the feature generation request in the above manner, the server can also continue to perform the following operations:

[0210] Based on the order in which requests are generated, corresponding feature generation requests are sent to the backend server in sequence; once the last feature generation request in the request generation order is sent, the corresponding test digital identity is obtained from the backend server.

[0211] For example, when a server sends a feature generation request, it needs to send the requests in the order they are generated. However, as for the timing of sending, there are two ways to send them: 1. Send as soon as a feature generation request is generated, that is, the server sends the corresponding request to the backend server as soon as it generates a feature generation request; 2. Send the requests to the backend server in sequence after all feature generation requests have been sent.

[0212] In this way, the backend server can generate the corresponding identity features in sequence based on the requirements of each feature generation request by receiving the feature generation requests in sequence, so as to ensure that the final test digital identity has all the identity features in the identity attribute conditions.

[0213] Thus, in this approach, by using a preset request generation order, corresponding identity features are generated for the test digital identity in sequence. At the same time, by combining the correlation between the previous identity features and the current identity features, it is ensured that the generated identity features can accurately meet the requirements of the identity attribute conditions, thereby improving the reliability of the implementation of this solution.

[0214] The above describes various possible embodiments of the digital identity generation method provided in this application. It should be noted that the above possible embodiments can be freely combined in practical applications. For ease of understanding, the digital identity generation method will be described below through a combination method.

[0215] like Figure 20 As shown, for a test case set of a target application, the server needs to generate a corresponding test digital identity for each test case. Therefore, the server can obtain the corresponding identity attribute conditions from the description information of each test case through the semantic recognition result after semantic recognition processing.

[0216] Then, after completing the mapping between identity attribute conditions and test cases, based on each identity feature in each identity attribute condition, a feature generation request corresponding to each test case is generated and sent to the backend server. This allows the backend server to generate corresponding test digital identities for each test case based on each feature generation request.

[0217] Before sending to the backend server, the server can first retrieve the created digital identities from the database, determine whether there are any target created digital identities that can meet any identity attribute conditions, use the ones that meet the conditions as the corresponding test digital identities, and continue to generate feature generation requests and send them to the backend server.

[0218] Based on the same inventive concept, embodiments of this application provide a digital identity generation apparatus capable of realizing the functions corresponding to the aforementioned digital identity generation method. Please refer to... Figure 21 The device includes an acquisition module 2101, an identification module 2102, a processing module 2103, and a generation module 2104, wherein:

[0219] The acquisition module 2101 is used to acquire the target test cases corresponding to the target application; wherein, the target test cases include: description information of the set of test operations executed when testing an application function in the target application based on a test digital identity;

[0220] The identification module 2102 is used to perform semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition result;

[0221] Processing module 2103 is used to obtain the identity attribute conditions required when executing the test operation set based on the semantic recognition result; the identity attribute conditions represent at least one identity feature required when conducting the test;

[0222] The generation module 2104 is used to generate a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions.

[0223] Optionally, when the generation module 2104 generates a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions, it is specifically used for:

[0224] Based on a preset data transmission format, a corresponding feature generation request is generated for each identity feature in the identity attribute conditions.

[0225] The obtained at least one feature generation request is sent to the backend server corresponding to the target application, so that the backend server generates the test digital identity with the at least one identity feature based on the at least one feature generation request.

[0226] Optionally, when the generation module 2104 generates a corresponding feature generation request for each identity feature in the identity attribute conditions, it is specifically used for:

[0227] Based on a preset request generation order, corresponding feature generation requests are generated sequentially; wherein, each time a feature generation request is generated, the current identity feature is combined with the previous identity feature preceding the current identity feature in the request generation order to generate the corresponding feature generation request.

[0228] When the generation module sends the obtained at least one feature generation request to the backend server corresponding to the target application, it is specifically used for:

[0229] Based on the order in which the requests are generated, the corresponding feature generation requests are sent to the backend server in sequence.

[0230] After the last feature generation request in the request generation sequence is sent, the corresponding test digital identity is obtained from the backend server.

[0231] Optionally, the generation module 2104 is used to generate the corresponding feature generation request based on the current identity feature and in combination with the previous identity feature preceding the current identity feature in the request generation order. Specifically, it is used to:

[0232] When the current identity feature is the first identity feature in the request generation sequence, a corresponding feature generation request is generated based on the current identity feature;

[0233] When the current identity feature is not the first identity feature in the request generation order, a corresponding feature generation request is generated based on the current identity feature and the association between the current identity feature and the preceding identity feature.

[0234] Optionally, when the identification module 2102 performs semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition result, it is specifically used for:

[0235] The description information of the test operation set included in the target test case is processed by word segmentation to obtain at least one word segmentation result corresponding to the description information;

[0236] Based on a preset attention mechanism, multi-dimensional semantic features are extracted from the at least one word segmentation result to obtain the corresponding semantic features for each dimension; the semantic features for each dimension include at least one of the following: grammatical structure features, semantic sentiment features, and lexical type features;

[0237] The semantic features corresponding to each dimension are weighted and fused to obtain the semantic recognition result.

[0238] Optionally, when the processing module 2103 obtains the identity attribute conditions required for executing the test operation set based on the semantic recognition result, it is specifically used for:

[0239] Based on the semantic information represented by the semantic recognition results, corresponding to each identity feature, a generation guide word is generated for each identity feature.

[0240] Based on the obtained generation guide words, the identity attribute conditions required when executing the test operation set are obtained.

[0241] Optionally, when the generation module generates a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions, it is specifically used for:

[0242] When there is a target created digital identity that meets the identity attribute conditions in the created digital identities stored in the database, the target created digital identity is used as the test digital identity.

[0243] If no target created digital identity that meets the identity attribute conditions exists in the database of created digital identities, then a test digital identity that meets the description information in the target test case is generated based on the identity attribute conditions.

[0244] Optionally, the at least one identity feature includes one or more of the following:

[0245] Organizational identity features, wherein the organizational identity features represent: organizational information of the organization to which the test digital identity belongs;

[0246] Type identity feature, wherein the type identity feature represents: the identity type information of the test digital identity in the organization;

[0247] Relational identity features, wherein the relational identity features represent: the identity information of other digital identities associated with the test digital identity, and the association relationship between the other digital identities and the test digital identity;

[0248] Group identity features, which represent the group information to which the test digital identity has joined.

[0249] Optionally, after generating a test digital identity that satisfies the description information in the target test case, the generation module 2104 is further configured to:

[0250] Generate the correspondence between the test digital identity and the target test case;

[0251] The test digital identity and the corresponding relationship are saved together in the database.

[0252] Based on the same inventive concept, embodiments of this application also provide an electronic device. In one possible implementation, the electronic device may be a server, such as... Figure 1 The server 102 is shown. In this embodiment, the electronic device 2200 has the following structure. Figure 22 As shown, it may include at least a memory 2201, a communication module 2203, and at least one processor 2202.

[0253] The memory 2201 is used to store computer programs executed by the processor 2202. The memory 2201 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0254] Memory 2201 may be volatile memory, such as random-access memory (RAM); memory 2201 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 2201 may be any other medium capable of carrying or storing a desired computer program having the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 2201 may be a combination of the above-described memories.

[0255] Processor 2202 may include one or more central processing units (CPUs) or digital processing units, etc. Processor 2202 is used to implement the above-described digital identity generation method when calling computer programs stored in memory 2201.

[0256] The communication module 2203 is used to communicate with terminal devices and other servers.

[0257] This application embodiment does not limit the specific connection medium between the memory 2201, communication module 2203, and processor 2202. This application embodiment... Figure 22 The memory 2201 and the processor 2202 are connected via a bus 2204, and the bus 2204 is in Figure 22 The diagram uses thick lines to describe the connections between other components; these are for illustrative purposes only and should not be considered limiting. The 2204 bus can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 22 It is described using only a thick line, but does not indicate that there is only one bus or one type of bus.

[0258] The memory 2201 stores a computer storage medium containing computer-executable instructions for implementing the digital identity generation method of this application embodiment. The processor 2202 is used to execute the digital identity generation method described above.

[0259] In another embodiment, the electronic device may also be other electronic devices, such as... Figure 1 The terminal device 101 shown. In this embodiment, the electronic device can be structured as follows: Figure 23As shown, it includes components such as: communication component 2310, memory 2320, display unit 2330, camera 2340, sensor 2350, audio circuit 2360, Bluetooth module 2370, processor 2380, etc.

[0260] The communication component 2310 is used to communicate with the server. In some embodiments, it may include a Wireless Fidelity (WiFi) module, which is a short-range wireless transmission technology, and the electronic device can send and receive information through the WiFi module.

[0261] The memory 2320 can be used to store software programs and data. The processor 2380 executes various functions of the terminal device 101 and performs data processing by running the software programs or data stored in the memory 2320. The memory 2320 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The memory 2320 stores an operating system that enables the terminal device 101 to run. In this application, the memory 2320 may store the operating system and various application programs, and may also store a computer program that executes the digital identity generation method of the embodiments of this application.

[0262] The display unit 2330 can also be used to display information input by the object or information provided to the object, as well as a graphical user interface (GUI) for various menus of the terminal device 101. Specifically, the display unit 2330 may include a display screen 2332 disposed on the front of the terminal device 101. The display screen 2332 may be configured as a liquid crystal display, a light-emitting diode, or the like. The display unit 2330 can be used to display relevant interfaces of the digital identity generation method in the embodiments of this application.

[0263] The display unit 2330 can also be used to receive input digital or character information and generate signal inputs related to object settings and function control of the terminal device 101. Specifically, the display unit 2330 may include a touch screen 2331 disposed on the front of the terminal device 101, which can collect touch operations on or near the object, such as clicking a button, dragging a scroll bar, etc.

[0264] The touchscreen 2331 can be placed on top of the display screen 2332, or the touchscreen 2331 and the display screen 2332 can be integrated to realize the input and output functions of the physical terminal device 101. After integration, it can be referred to as a touch display screen. In this application, the display unit 2330 can display the application and the corresponding operation steps.

[0265] Camera 2340 can be used to capture still images, and objects can publish images captured by camera 2340 through an application. There can be one or multiple cameras 2340. An object generates an optical image through a lens, which is projected onto a photosensitive element. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then transmitted to processor 2380 to be converted into a digital image signal.

[0266] The physical terminal device may also include at least one sensor 2350, such as an accelerometer 2351, a proximity sensor 2352, a fingerprint sensor 2353, and a temperature sensor 2354. The terminal device may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, thermometer, infrared sensor, light sensor, and motion sensor.

[0267] Audio circuitry 2360, speaker 2361, and microphone 2362 provide an audio interface between the physical terminal device 101 and the physical terminal device 101. Audio circuitry 2360 converts received audio data into electrical signals, transmits them to speaker 2361, and then converts them into sound signals for output. Physical terminal device 101 may also be equipped with volume buttons for adjusting the volume of the sound signal. On the other hand, microphone 2362 converts collected sound signals into electrical signals, which are then received by audio circuitry 2360, converted into audio data, and output to communication component 2310 for transmission to, for example, another physical terminal device 101, or to memory 2320 for further processing.

[0268] The Bluetooth module 2370 is used to interact with other Bluetooth devices that also have a Bluetooth module via the Bluetooth protocol. For example, a physical terminal device can establish a Bluetooth connection with a wearable electronic device (such as a smartwatch) that also has a Bluetooth module through the Bluetooth module 2370, thereby exchanging data.

[0269] The processor 2380 is the control center of the physical terminal device, connecting various parts of the terminal through various interfaces and lines. It executes various functions and processes data by running or executing software programs stored in the memory 2320 and calling data stored in the memory 2320. In some embodiments, the processor 2380 may include one or more processing units; the processor 2380 may also integrate an application processor and a baseband processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the baseband processor mainly handles wireless communication. It is understood that the baseband processor may not be integrated into the processor 2380. In this application, the processor 2380 can run the operating system, applications, user interface display and touch response, and the digital identity generation method of this embodiment. Furthermore, the processor 2380 is coupled to the display unit 2330.

[0270] In some possible implementations, various aspects of the digital identity generation method provided in this application may also be implemented in the form of a computer program product, which includes a computer program that, when the program product is run on an electronic device, causes the electronic device to perform the steps in the digital identity generation method according to the various exemplary embodiments of this application described above.

[0271] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0272] The program product of the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on an electronic device. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with a command execution system, apparatus, or device.

[0273] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with a command execution system, apparatus, or device.

[0274] Computer programs contained on readable media may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0275] Computer programs for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The computer program can execute entirely on the user's electronic device, partially on the user's electronic device, as a standalone software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on a remote electronic device. In cases involving remote electronic devices, the remote electronic device can be connected to the user's electronic device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external electronic device (e.g., via the Internet using an Internet service provider).

[0276] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0277] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0278] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0279] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0280] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0281] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0282] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for generating a digital identity, characterized in that, include: Obtain the target test cases corresponding to the target application; wherein, the target test cases include: description information of the set of test operations executed when testing an application function in the target application based on a test digital identity; The description information of the test operation set included in the target test case is subjected to semantic recognition processing to obtain the corresponding semantic recognition results; Based on the semantic recognition results, the identity attribute conditions required to execute the test operation set are obtained; the identity attribute conditions represent at least one identity feature required when conducting the test. Based on the identity attribute conditions, a test digital identity that satisfies the description information in the target test case is generated.

2. The method as described in claim 1, characterized in that, The step of generating a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions includes: Based on a preset data transmission format, a corresponding feature generation request is generated for each identity feature in the identity attribute conditions. The obtained at least one feature generation request is sent to the backend server corresponding to the target application, so that the backend server generates the test digital identity with the at least one identity feature based on the at least one feature generation request.

3. The method as described in claim 2, characterized in that, The step of generating a corresponding feature generation request for each identity feature in the identity attribute conditions includes: Based on a preset request generation order, corresponding feature generation requests are generated sequentially; wherein, each time a feature generation request is generated, the current identity feature is combined with the previous identity feature preceding the current identity feature in the request generation order to generate the corresponding feature generation request. The step of generating a request for at least one obtained feature and sending it to the backend server corresponding to the target application includes: Based on the order in which the requests are generated, the corresponding feature generation requests are sent to the backend server in sequence. After the last feature generation request in the request generation sequence is sent, the corresponding test digital identity is obtained from the backend server.

4. The method as described in claim 3, characterized in that, The step of generating a feature generation request based on the current identity feature, combined with the previous identity feature preceding the current identity feature in the request generation sequence, includes: When the current identity feature is the first identity feature in the request generation sequence, a corresponding feature generation request is generated based on the current identity feature; When the current identity feature is not the first identity feature in the request generation order, a corresponding feature generation request is generated based on the current identity feature and the association between the current identity feature and the preceding identity feature.

5. The method as described in claim 1, characterized in that, The step of performing semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition result includes: The description information of the test operation set included in the target test case is processed by word segmentation to obtain at least one word segmentation result corresponding to the description information; Based on a preset attention mechanism, multi-dimensional semantic features are extracted from the at least one word segmentation result to obtain the corresponding semantic features for each dimension; the semantic features for each dimension include at least one of the following: grammatical structure features, semantic sentiment features, and lexical type features; The semantic features corresponding to each dimension are weighted and fused to obtain the semantic recognition result.

6. The method as described in claim 1, characterized in that, The identity attribute conditions required for executing the test operation set based on the semantic recognition result include: Based on the semantic information represented by the semantic recognition results, corresponding to each identity feature, a generation guide word is generated for each identity feature. Based on the obtained generation guide words, the identity attribute conditions required when executing the test operation set are obtained.

7. The method as described in claim 1, characterized in that, The step of generating a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions includes: When there is a target created digital identity that meets the identity attribute conditions in the created digital identities stored in the database, the target created digital identity is used as the test digital identity. If no target created digital identity that meets the identity attribute conditions exists in the database of created digital identities, then a test digital identity that meets the description information in the target test case is generated based on the identity attribute conditions.

8. The method according to any one of claims 1-7, characterized in that, The at least one identity feature includes one or more of the following: Organizational identity features, wherein the organizational identity features represent: organizational information of the organization to which the test digital identity belongs; Type identity feature, wherein the type identity feature represents: the identity type information of the test digital identity in the organization; Relational identity features, wherein the relational identity features represent: the identity information of other digital identities associated with the test digital identity, and the association relationship between the other digital identities and the test digital identity; Group identity features, which represent the group information to which the test digital identity has joined.

9. The method according to any one of claims 1-7, characterized in that, After generating a test digital identity that satisfies the description information in the target test case, the method further includes: Generate the correspondence between the test digital identity and the target test case; The test digital identity and the corresponding relationship are saved together in the database.

10. A digital identity generation device, characterized in that, The device includes: The acquisition module is used to acquire target test cases corresponding to the target application; wherein, the target test cases include: description information of the set of test operations executed when testing an application function in the target application based on a test digital identity; The identification module is used to perform semantic recognition processing on the description information of the test operation set included in the target test case to obtain the corresponding semantic recognition result; The processing module is used to obtain the identity attribute conditions required when executing the test operation set based on the semantic recognition result; the identity attribute conditions represent at least one identity feature required when conducting the test. The generation module is used to generate a test digital identity that satisfies the description information in the target test case based on the identity attribute conditions.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-9.

12. A computer device, characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method as described in any one of claims 1-9 according to the obtained program instructions.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the method as described in any one of claims 1-9.

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