Document difference detection method and apparatus, device, and storage medium

Abstract

The present disclosure relates to the technical field of computers, in particular to the technical fields such as data processing, difference detection, and software development, and provides a document difference detection method and apparatus, a device, and a storage medium. The specific implementation comprises: acquiring at least two documents to be detected, said at least two documents including documents at different stages in a collaborative design process; determining a plurality of pieces of content in each of said documents; determining binding relationships between content in different documents among said documents; and using a preset detection rule and the binding relationships to detect whether there is a difference between said at least two documents. According to the present disclosure, a difference between documents at different stages of collaborative design can be detected.

Description

Document difference detection method, device, equipment and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 20, 2024, with application number 202410324187.4 and invention name “Document Difference Detection Method, Device, Equipment and Storage Medium”. The entire contents of the application are incorporated by reference into this application. Technical Field

[0002] The present disclosure relates to the field of computer technology, and in particular to technical fields such as data processing, difference detection, and software development. Background Art

[0003] Collaborative design is a collaborative approach in which multiple people collaborate during the design process, aiming to improve design efficiency and quality. Based on the concept of collaboration, collaborative design achieves efficient completion of design tasks through communication, collaboration, and resource sharing among designers. The collaborative design process involves multiple steps, each involving different personnel. Each step generates corresponding documentation, and the documentation across different steps must remain consistent. However, in related technologies, discrepancies between documents in different steps are difficult to detect in a timely manner, which can easily lead to misunderstandings and information omissions during the collaborative design process, compromising the effectiveness of collaborative design. Summary of the Invention

[0004] The present disclosure provides a document difference detection method, apparatus, device, and storage medium.

[0005] According to one aspect of the present disclosure, a document difference detection method is provided, comprising:

[0006] Obtaining at least two documents to be tested, wherein the at least two documents to be tested include documents of different stages in the collaborative design process;

[0007] Determining multiple contents in each of the documents to be detected;

[0008] Determine the binding relationship between the contents in different documents to be detected;

[0009] Using the preset detection rule and the binding relationship, it is detected whether there is a difference between the at least two documents to be detected.

[0010] According to another aspect of the present disclosure, there is provided a document difference detection device, comprising:

[0011] An acquisition module, configured to acquire at least two documents to be detected, wherein the at least two documents to be detected include documents from different stages of the collaborative design process;

[0012] A content determination module, configured to determine a plurality of contents in each of the documents to be detected;

[0013] A relationship determination module, used to determine the binding relationship between the contents in different documents to be detected;

[0014] The detection module is configured to detect whether there is a difference between the at least two documents to be detected using a preset detection rule and the binding relationship.

[0015] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any method in the embodiments of the present disclosure.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any method according to the embodiments of the present disclosure.

[0020] According to another aspect of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements any one of the methods according to the embodiments of the present disclosure.

[0021] The present invention determines the binding relationship between the contents in documents of different links of collaborative design, and determines whether there are differences between documents of different links of collaborative design through pre-set detection rules and the binding relationship, thereby automatically detecting documents of different links of collaborative design, reducing or avoiding problems such as misunderstanding deviation and information omission in the collaborative design process, and improving the effect of collaborative design.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0024] FIG1 is a schematic diagram of an application scenario according to an embodiment of the present disclosure.

[0025] FIG2 is a diagram of a document difference detection method according to an embodiment of the present disclosure.

[0026] FIG3 is a schematic diagram of a binding relationship according to an embodiment of the present disclosure.

[0027] FIG4 is a schematic diagram of difference content according to an embodiment of the present disclosure.

[0028] FIG5 is a flowchart of personnel data flow interaction according to an embodiment of the present disclosure.

[0029] FIG6 is a schematic diagram of an interactive interface according to an embodiment of the present disclosure.

[0030] FIG7 is a schematic structural diagram of a document difference detection device 700 according to an embodiment of the present disclosure.

[0031] FIG8 is a schematic structural diagram of a document difference detection device 800 according to an embodiment of the present disclosure.

[0032] FIG9 is a schematic block diagram of an example electronic device 900 according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0034] The “and / or” in the embodiments of the present disclosure indicates that there may be three relationships. For example, A and / or B may indicate three situations: A exists alone, A and B exist at the same time, and B exists alone. The term “at least one” herein indicates any combination of at least two of any one or more of a plurality of. For example, at least one of A, B, and C may indicate any one or more elements selected from the set consisting of A, B, and C. The terms “first” and “second” herein refer to and distinguish between multiple similar technical terms, and do not mean to limit the order or to limit the meaning to only two. For example, the first feature and the second feature refer to two categories / two features. The first feature may be one or more, and the second feature may also be one or more.

[0035] Collaborative design is a way for multiple people to work together during the design process, aiming to improve design efficiency and quality. Based on the concept of collaboration, it achieves efficient completion of design tasks through communication, collaboration, and resource sharing among designers. Collaborative design has the following characteristics:

[0036] (1) Multi-person collaboration: involves the participation of multiple designers, who can be from different departments, different companies or different regions.

[0037] (2) Real-time communication: Designers can communicate through instant messaging tools, online discussion forums, etc. to share their design ideas, opinions and suggestions.

[0038] (3) Shared resources: Designers can share design materials, document templates, design standards, etc., saving time and energy and maintaining design consistency and unity.

[0039] (4) Equal cooperation: Emphasis on equal cooperation. Each designer can participate in the design process according to his or her own expertise and interests and give full play to his or her maximum potential.

[0040] Collaborative design can be applied to various industries, such as software project development, medical system consultation, testing and treatment, industrial design, production and manufacturing, circuit design and other multi-process and multi-role scenarios.

[0041] Taking software project development as an example, in the complete process of software project development, the product manager first conducts product research and produces a requirements document; the designer produces a design draft based on the requirements document; the R&D personnel develop the software project based on the requirements document and the design draft; the tester sorts out the test cases based on the requirements document, generates test cases, and tests the software based on the test cases. After passing the test, the software can be put into use.

[0042] During the entire software project development process, functional documents from three main perspectives appear, namely requirement documents, design drafts, and test cases. When any document is modified, it needs to be synchronized with relevant personnel in other links in a timely manner. For example, after the requirement document is changed, it needs to be synchronized with designers and testers to make corresponding content changes; after the design draft is changed, it also needs to be synchronized with developers, R&D personnel, and testers; and so on. If the differences between documents in different links are not detected in a timely manner and the document information is not synchronized, it will cause differences in understanding and implementation, and the differences will be magnified layer by layer, greatly increasing the complexity of the developer's work, increasing communication costs, or leading to repeated development, increasing the waste of development resources, and may also lead to errors or ineffective and repetitive work in subsequent testing links.

[0043] The disclosed embodiment proposes a document difference detection method, which can automatically detect the differences between documents in different links of the collaborative design process, and solve the information synchronization problem of different roles in collaborative design. The document difference detection method proposed in the disclosed embodiment can be applied to a variety of different scenarios, such as software project development scenarios, and is used to detect the differences between documents in different links of software project development scenarios, such as requirement documents, design drafts, and test cases. This solution can detect the differences between documents in different links of collaborative design through artificial intelligence automatic recognition. In the field of software project development, it can automatically understand the requirement documents produced by product managers, the design drafts produced by designers, and the test cases produced by testers, and explore the differences and problems between the documents produced by these three roles, and finally give prompts, reminders, or design suggestions.

[0044] FIG1 is a schematic diagram of an application scenario according to an embodiment of the present disclosure. As shown in FIG1 , the schematic diagram of the application scenario of the embodiment of the present disclosure may include multiple document generation devices 110 and a document difference detection device 120. The document generation device 110 is configured to generate documents for different stages of the collaborative design process based on instructions from relevant personnel at different stages of the collaborative design process, and to send the generated documents to the document difference detection device 120. The document difference detection device 120 is configured to receive documents corresponding to different stages of the same collaborative design process from each document generation device 110 and detect whether there are differences between the different documents. The document generation device 110 and the document difference detection device 120 may be connected via wired or wireless communication. The document generation device 110 proposed in the embodiment of the present disclosure includes, but is not limited to, electronic devices such as mobile phones, computers, and intelligent voice interaction devices. The document difference detection device 120 may include an electronic device or server for providing document difference detection for each document generation device 110. The document difference detection method proposed in the embodiment of the present disclosure may be executed by the document difference detection device 120.

[0045] FIG2 is a document difference detection method according to an embodiment of the present disclosure, comprising:

[0046] S210: Obtain at least two documents to be tested, where the at least two documents to be tested include documents from different stages of the collaborative design process;

[0047] S220, determining multiple contents in each of the documents to be detected;

[0048] S230, determining the binding relationship between the contents in different documents to be detected;

[0049] S240: Detect whether there is a difference between the at least two documents to be detected using pre-set detection rules and binding relationships.

[0050] The disclosed embodiment aims at documents in different links of collaborative design. First, the content in each document is determined, and the binding relationship between the contents of different documents is determined. On this basis, whether there are differences between the contents with binding relationships is determined. The differences between documents in different links can be detected in time, the understanding deviation and implementation difference can be reduced, and the operational complexity in the collaborative design process can be reduced.

[0051] The collaborative design process includes multiple links, and each link has a corresponding document; the documents of each link include content for multiple functions. For the same function, the content corresponding to the function exists in the documents of different links; in the embodiment of the present disclosure, the content corresponding to the same function in the documents of different links is regarded as content with a binding relationship. For example, Document 1 and Document 2 are two documents in different links of the collaborative design process. Document 1 includes 3 contents, corresponding to Function A, Function B and Function C respectively; Document 2 also includes 3 contents, corresponding to Function A, Function B and Function C respectively; in the example of the present disclosure, it can be determined that the content corresponding to Function A in Document 1 has a binding relationship with the content corresponding to Function A in Document 2, the content corresponding to Function B in Document 1 has a binding relationship with the content corresponding to Function B in Document 2, and the content corresponding to Function C in Document 1 has a binding relationship with the content corresponding to Function C in Document 2.

[0052] Taking software project development as an example, in the collaborative design of software project development, there are three main links in the entire software development process, and there are documents corresponding to different perspectives, namely, requirements documents, design drafts, and test cases; each document contains content corresponding to multiple functions. Figure 3 is a schematic diagram of the binding relationship between the content in documents of different links of collaborative design according to an embodiment of the present disclosure. As shown in Figure 3, the requirements document, design draft, and test case all include content 1, content 2, and content 3. Among them, content 1 in the requirements document, content 1 in the design draft, and content 1 in the test case correspond to the same function, content 2 in the requirements document, content 2 in the design draft, and content 2 in the test case correspond to the same function, and content 3 in the requirements document, content 3 in the design draft, and content 3 in the test case correspond to the same function. Therefore, there is a binding relationship between content 1 in the requirements document, content 1 in the design draft, and content 1 in the test case, a binding relationship between content 2 in the requirements document, content 2 in the design draft, and content 2 in the test case, and a binding relationship between content 3 in the requirements document, content 3 in the design draft, and content 3 in the test case. When performing difference detection on documents, the embodiment of the present disclosure can identify the binding relationship between the contents in different documents to be detected, and detect whether there are differences between the contents with binding relationships based on pre-set detection rules and binding relationships; taking the example shown in Figure 3 as an example, if there are differences between any group of contents with binding relationships shown in Figure 3, it can be determined that there are differences between the documents to be detected.

[0053] In some implementations, determining the binding relationship between contents in different documents to be detected in the embodiment of the present disclosure includes:

[0054] Obtain N contents, where the N contents are obtained from N different documents to be detected; N is a positive number greater than or equal to 2;

[0055] Determine the function corresponding to each of the N contents;

[0056] In the case that the functions corresponding to the N contents are the same, it is determined that there is a binding relationship between the N contents.

[0057] Still taking the example shown in Figure 3 as an example, if the content one in the requirement document includes "the software title [XXX] is displayed synchronously on the homepage"; the content one in the design draft includes a picture of the corresponding software title in the display interface, and the text content in the picture is [XXX]; the content one of the test case includes the function displayed on the homepage, and a description of the function, specifically "the title is [XXX]". In this case, the functions corresponding to the content one in the aforementioned three documents are all the titles of the software displayed on the homepage. Therefore, it can be determined that there is a binding relationship between the contents one in the aforementioned three documents. It can be seen that the corresponding same function described in the embodiment of the present disclosure refers to the corresponding functions in different links of the corresponding collaborative design.

[0058] Because the documents in each link of collaborative design are designed for the same or corresponding functions, based on this feature, the disclosed embodiment can improve the accuracy of determining the binding relationship between the contents of different documents to be detected by separately determining the functions corresponding to the contents in different documents to be detected, thereby providing a basis for subsequent difference detection.

[0059] After determining the contents with a binding relationship, it is possible to determine whether there are differences between the contents with the binding relationship, thereby determining whether there are differences between the documents to be detected. For example, using a pre-set detection rule and the binding relationship, detecting whether there are differences between the at least two documents to be detected includes:

[0060] Using a pre-set detection rule, detecting whether there are differences between the contents having a binding relationship; the detection rule is used to define logic and / or conditions for identifying the differences;

[0061] When there is a difference between the contents having the binding relationship, it is determined that there is a difference between the at least two documents to be detected.

[0062] Detection rules can be a set of clear and concise judgment rules. By using the logic and conditions defined by the detection rules to detect content with binding relationships, it is possible to improve the accuracy of judging differences between binding content, thereby determining whether there are differences between the documents to be tested. For example, in a software project development scenario, if two documents to be tested each contain content about the software title, there is a binding relationship between the two contents; the relevant detection rules include: the software titles defined in the binding content must be exactly the same; if they are not, it indicates that there are differences between the binding content. Using this detection rule, it is possible to detect differences between content.

[0063] The disclosed embodiment can also present the detection results to the user in a visual manner, such as highlighting the differences, providing interactive navigation, etc., to help the user better understand the problems and suggestions and improve the user experience.

[0064] For example, the document detection method proposed in the embodiment of the present disclosure may further include:

[0065] In the case where there are differences between at least two documents to be detected, difference information is displayed; the difference information is used to identify the content in which the differences exist in the documents to be detected.

[0066] Still taking the difference detection of documents in the software project development process as an example. Figure 4 is a schematic diagram of the difference information display according to an embodiment of the present disclosure. In the example shown in Figure 4, the detection results are displayed, and the display page includes the requirements document, the design draft and the test case. Among them, there are differences between the "Homepage synchronously displays the software title [AI assisted product]" in the requirements document, the picture with the text content of [AI assisted design] in the design draft, and the description of the homepage display function "Title [AI assisted product]" in the test case. The specific content with differences can be highlighted, highlighted, or displayed in a color, font, etc. that is different from other content, so that the differences are clearly displayed to the user. For example, in the example of Figure 4, there are differences between the software titles in different files, and the content with differences is highlighted in Figure 4.

[0067] In addition, the difference information proposed in the embodiment of the present disclosure can also be used to identify other documents to be detected that are different from the document to be detected. As shown in Figure 4, near the difference content of the requirement document (i.e., "The software title [AI-assisted product] is displayed synchronously on the homepage"), other documents to be detected that are different from the content can be further displayed, such as the prompt box in Figure 4 that displays "This is not synchronized with the design draft"; similarly, near the difference content between the design draft and the test case, it is also displayed which document or documents have differences with it.

[0068] The embodiments of the present disclosure may provide interactive navigation for users. For example, the above method may further include:

[0069] receiving synchronization instructions for the content that is different;

[0070] According to the synchronization instruction, the content with differences is updated to eliminate the differences between at least two documents to be detected.

[0071] Still taking the example shown in FIG4 as an example, near the difference content of the requirements document (i.e., “Synchronize the display of software title [AI-assisted product] on the homepage”), a prompt box is used to display “This is not synchronized with the design draft”; the user can send a synchronization instruction, for example, the user clicks on the prompt box and further selects “Synchronize with design draft”. After receiving the user's synchronization instruction, the difference content can be updated. In the example of FIG4, the “Synchronize the display of software title [AI-assisted product] on the homepage” in the requirements document can be modified to “Synchronize the display of software title [AI-assisted design] on the homepage” to make the content of the display software title function in the requirements document consistent with that in the design draft, eliminating the difference between the requirements document and the design draft. By providing interface interaction, it is convenient for users to update the difference content in the document, and it can actively provide users with optional update plans to synchronize documents in different links in a timely and convenient manner.

[0072] In order to detect the differences between texts in different links of the collaborative design process, the embodiments of the present disclosure mainly involve two aspects. First, multiple contents in the document to be detected are determined, and on the premise of determining the contents in each document to be detected, the binding relationship between the contents of different documents to be detected is determined; second, pre-set detection rules are used to detect whether there are differences between the contents with binding relationships.

[0073] With respect to the first aspect, the method for determining multiple contents in the document to be detected in the embodiment of the present disclosure may include:

[0074] Determining at least one of semantic information and logical relationship contained in the document to be detected;

[0075] According to at least one of the semantic information and the logical relationship, multiple contents in the document to be detected are determined.

[0076] Since the content in a document includes multiple semantics and logical relationships between different semantic information or contexts, using the semantic information and logical relationships contained in the document as a basis can improve the accuracy of dividing multiple contents in the document and provide a basis for detecting differences between documents.

[0077] The document to be tested contains text and / or images. Taking the requirements documents, design drafts, and test cases in the software project development process as an example, the requirements documents and test cases mainly contain text, while the design drafts mainly contain text and images.

[0078] In some embodiments, determining at least one of semantic information and logical relationships contained in the document to be detected includes:

[0079] Identify the text in the document to be detected and obtain text information;

[0080] At least one of semantic information and logical relationship contained in the document to be detected is determined using the text information.

[0081] For example, embodiments of the present disclosure can leverage the multilingual processing capabilities of a Large Language Model (LLM) to automatically identify the language of a text and perform semantic analysis and understanding of the textual content in requirements documents, design drafts, and test cases. In some examples, accurate recognition of specialized terminology and specific expressions can be achieved by constructing at least one of a domain-specific vocabulary, grammatical rules, and a knowledge graph.

[0082] In some other embodiments, the document to be detected includes an image; and determining at least one of semantic information and logical relationships contained in the document to be detected may include:

[0083] Extracting key information from the image using image recognition technology, and converting the key information into text information;

[0084] At least one of semantic information and logical relationship contained in the document to be detected is determined using the text information.

[0085] For example, the embodiments of the present disclosure can use image recognition technologies such as optical character recognition (OCR) to extract key information from the design draft image (mainly including text information in the image), and after converting the extracted key information into text information, the text information can be analyzed through a deep learning model to understand its design intent and logical relationship.

[0086] Based on the text information carried in the document to be detected, artificial intelligence technology can be used to adopt a pre-trained neural network model to identify the semantic information and logical relationships contained in the document to be detected, thereby improving the accuracy of recognition and detection.

[0087] In some examples, using text information to determine at least one of semantic information and logical relationships contained in the document to be detected includes:

[0088] Based on at least one of a pre-built vocabulary library, grammatical rules, and knowledge graph, a pre-trained deep learning model is used to perform semantic analysis on the text information to obtain at least one of semantic information and logical relationships contained in the document to be detected;

[0089] Among them, at least one of the vocabulary library, grammatical rules and knowledge graph is constructed based on the design field of collaborative design.

[0090] For example, embodiments of the present disclosure can pre-build a vocabulary library and / or grammatical rules specific to the software project development domain to improve the accuracy of identifying specialized terms and specific expressions in software project development. Furthermore, based on this vocabulary library and / or grammatical rules, a knowledge graph for the software project development domain can be constructed to integrate concepts, attributes, and relationships in software development and design, providing contextual information for identifying differences.

[0091] The disclosed embodiment can also design a set of rule engines, which predefine detection rules for detecting whether there are differences between contents with binding relationships; the detection rules can define the logic and / or conditions for identifying differences. Using the detection rules, the design contents of different links and different roles can be compared to automatically identify design differences. In response to these differences, the system can give specific problem descriptions and suggestions. For example, when the contents belonging to different documents to be detected and having a binding relationship meet the logic and / or conditions corresponding to the detection rules, the rule engine outputs a detection result, which indicates that there are differences between the contents with binding relationships.

[0092] The embodiments of the present disclosure can utilize a deep learning model to identify documents to be detected. Based on at least one of a pre-built vocabulary library, grammatical rules, and knowledge graph, the content in the document to be detected can be determined, and the content with a binding relationship can be determined. Furthermore, the embodiments of the present disclosure can utilize the above-mentioned rule engine to determine whether there are differences between the content with a binding relationship. In some embodiments, the rule engine can also be implemented using a deep learning model.

[0093] In addition, as the design content and requirements continue to change, the models involved in the embodiments of the present disclosure (for example, including a neural network model for determining the content of the document to be detected, the binding relationship between the content, and the difference between the content with the binding relationship) can also be continuously updated and optimized. For example, by collecting user feedback and system data, the model can be continuously learned and tuned to improve the accuracy and efficiency of model recognition. In addition, new model update technologies and methods can be introduced, such as using transfer learning, incremental learning and other technologies to further improve the performance of the model.

[0094] The triggering of document detection proposed in the embodiments of the present disclosure includes, but is not limited to, real-time monitoring based on modification triggering, batch timed monitoring, and alarm reminders with a limit on the number of modifications.

[0095] Figure 5 is a flowchart of the personnel data flow interaction according to an embodiment of the present disclosure. As shown in Figure 5, in the collaborative design of software project development, documents from various stages (such as requirements documents, design drafts, and test cases) are uploaded to the document difference detection model proposed in the embodiment of the present disclosure. The model analyzes and compares the documents, determines the differences between the documents, and identifies the content in the documents where the differences exist, for example, displaying the requirements document, design draft, and test case with content difference identification.

[0096] Figure 6 is a schematic diagram of the interactive interface according to an embodiment of the present disclosure. In the example shown in Figure 6, the person involved modified the design draft and submitted the modified design draft, which is the design draft 2.0 in Figure 6. The embodiment of the present disclosure adopts a model for detecting document differences, and pre-classifies the data of the same function description in the three documents; after receiving the modified design draft, the differences between the modified design draft and the previously uploaded requirement documents and test cases are analyzed based on the modified design draft, and a document with a difference mark is obtained. When displaying document differences, the requirement document, design draft and test case can be displayed separately. In the requirement document and test case, the content that differs from the modified design draft is displayed; in the design draft, the modified content is displayed. Furthermore, the document differences can be synchronized to the personnel in the relevant links. For example, the situation where there are differences between the requirement document and the design draft is synchronized to the upstream product manager, and the situation where there are differences between the test case and the design draft is synchronized to the downstream tester.

[0097] The present disclosure also provides a document difference detection device. FIG7 is a schematic structural diagram of a document difference detection device 700 according to an embodiment of the present disclosure, including:

[0098] An acquisition module 710 is configured to acquire at least two documents to be detected, wherein the at least two documents to be detected include documents from different stages of the collaborative design process;

[0099] A content determination module 720 is configured to determine a plurality of contents in each of the documents to be detected;

[0100] The relationship determination module 730 is used to determine the binding relationship between the contents in different documents to be detected;

[0101] The detection module 740 is configured to detect whether there is a difference between the at least two documents to be detected using a preset detection rule and the binding relationship.

[0102] In some implementations, the relationship determination module 730 is configured to:

[0103] Obtain N contents, where the N contents are obtained from N different documents to be detected; N is a positive number greater than or equal to 2;

[0104] Determine the function corresponding to each of the N contents;

[0105] In the case that the functions corresponding to the N contents are the same, it is determined that there is a binding relationship between the N contents.

[0106] In some embodiments, the detection module 740 is configured to:

[0107] Using a pre-set detection rule, detecting whether there are differences between the contents having a binding relationship; the detection rule is used to define logic and / or conditions for identifying the differences;

[0108] When there is a difference between the contents having the binding relationship, it is determined that there is a difference between the at least two documents to be detected.

[0109] FIG8 is a schematic diagram of the structure of a document difference detection device 800 according to an embodiment of the present disclosure. As shown in FIG8 , in some embodiments, the device further includes:

[0110] The display module 850 is configured to display difference information when there is a difference between the at least two documents to be detected; the difference information is used to identify the content of the difference in the documents to be detected.

[0111] In some embodiments, further comprising:

[0112] The receiving module 860 is configured to receive a synchronization instruction for the content having the difference;

[0113] The updating module 870 is configured to update the content with differences according to the synchronization instruction to eliminate the differences between the at least two documents to be detected.

[0114] In some implementations, the content determination module 720 is configured to:

[0115] Determining at least one of semantic information and logical relationship contained in the document to be detected;

[0116] According to at least one of the semantic information and the logical relationship, multiple contents in the document to be detected are determined.

[0117] In some embodiments, the document to be detected contains text;

[0118] The content determination module 720 is used to:

[0119] Recognize the text in the document to be detected and obtain text information;

[0120] At least one of semantic information and logical relationship contained in the document to be detected is determined using the text information.

[0121] In some embodiments, the document to be detected includes a picture;

[0122] The content determination module 720 is used to:

[0123] Using image recognition technology to extract key information from the image and convert the key information into text information;

[0124] At least one of semantic information and logical relationship contained in the document to be detected is determined using the text information.

[0125] In some implementations, the content determination module 720 is configured to:

[0126] Based on at least one of a pre-built vocabulary library, grammatical rules, and knowledge graph, a pre-trained deep learning model is used to perform semantic analysis on the text information to obtain at least one of semantic information and logical relationships contained in the document to be detected;

[0127] At least one of the vocabulary library, grammatical rules and knowledge graph is constructed based on the design domain of the collaborative design.

[0128] In some embodiments, the collaborative design process is used for software project development;

[0129] The at least two documents to be tested include two or more of a requirement document, a design draft, and a test case generated during the software project development process.

[0130] For the description of specific functions and examples of each module and submodule of the device in the embodiment of the present disclosure, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0131] In the technical solution disclosed herein, the acquisition, storage and application of personal information of users involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0132] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0133] FIG9 is a schematic block diagram of an example electronic device 900 according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0134] As shown in Figure 9, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the device 900 can also be stored in the RAM 903. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0135] Multiple components in the device 900 are connected to the I / O interface 905, including an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0136] The computing unit 901 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as the detection method. For example, in some embodiments, the detection method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the detection method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the detection method in any other appropriate manner (e.g., by means of firmware).

[0137] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0142] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0143] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0144] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A document difference detection method, comprising: Acquire at least two documents to be tested, wherein the at least two documents to be tested include documents from different stages of the collaborative design process; Determining multiple contents in each of the documents to be detected; Determining the binding relationship between the contents in different documents to be detected; Using the preset detection rules and the binding relationship, it is detected whether there is a difference between the at least two documents to be detected.

2. The method according to claim 1, wherein Determining the binding relationship between the contents in different documents to be detected includes: Obtaining N contents, wherein the N contents are respectively obtained from N different documents to be detected; wherein N is a positive number greater than or equal to 2; Determining the function corresponding to each of the N contents; In a case where the functions corresponding to the N contents are the same, it is determined that there is a binding relationship between the N contents.

3. The method according to claim 1 or 2, wherein: The detecting whether there is a difference between the at least two documents to be detected by using the preset detection rule and the binding relationship includes: Using pre-set detection rules, detecting whether there are differences between the contents having a binding relationship; the detection rules are used to define logic and / or conditions for identifying differences; In the case that there is a difference between the contents having the binding relationship, it is determined that there is a difference between the at least two documents to be detected.

4. The method according to any one of claims 1 to 3, further comprising: If there is a difference between the at least two documents to be detected, display the difference information; The difference information is used to identify the content with differences in the document to be detected.

5. The method according to claim 4, further comprising: receiving a synchronization instruction for the content having the difference; According to the synchronization instruction, the content with differences is updated to eliminate the differences between the at least two documents to be detected.

6. The method according to any one of claims 1 to 5, wherein: Determining multiple contents in the document to be detected includes: Determining at least one of semantic information and logical relationship contained in the document to be detected; Determine multiple contents in the document to be detected based on at least one of the semantic information and the logical relationship.

7. The method according to claim 6, wherein: The document to be detected contains text; The determining of at least one of semantic information and logical relationship contained in the document to be detected includes: Identifying text in the document to be detected to obtain text information; At least one of semantic information and logical relationship contained in the document to be detected is determined by using the text information.

8. The method according to claim 6 or 7, wherein: The document to be detected contains a picture; The determining of at least one of semantic information and logical relationship contained in the document to be detected includes: Extracting key information from the image using image recognition technology and converting the key information into text information; At least one of semantic information and logical relationship contained in the document to be detected is determined by using the text information.

9. The method according to claim 7 or 8, wherein The step of determining at least one of semantic information and logical relationship contained in the document to be detected by using the text information includes: Based on at least one of a pre-built vocabulary library, grammatical rules, and knowledge graph, a pre-trained deep learning model is used to perform semantic analysis on the text information to obtain at least one of semantic information and logical relationships contained in the document to be detected; Wherein, at least one of the vocabulary library, grammatical rules and knowledge graph is constructed based on the design domain of the collaborative design.

10. The method according to any one of claims 1 to 9, wherein: The collaborative design process is used for software project development; The at least two documents to be tested include two or more of the requirement documents, design drafts and test cases generated during the software project development process.

11. A document difference detection device, comprising: An acquisition module, configured to acquire at least two documents to be detected, wherein the at least two documents to be detected include documents from different stages of the collaborative design process; A content determination module, configured to determine a plurality of contents in each of the documents to be detected; A relationship determination module, configured to determine the binding relationship between the contents in different documents to be detected; The detection module is used to detect whether there is a difference between the at least two documents to be detected using a preset detection rule and the binding relationship.

12. The document difference detection device according to claim 11, wherein: The relationship determination module is used to: Obtaining N contents, wherein the N contents are respectively obtained from N different documents to be detected; wherein N is a positive number greater than or equal to 2; Determining the function corresponding to each of the N contents; In a case where the functions corresponding to the N contents are the same, it is determined that there is a binding relationship between the N contents.

13. The document difference detection device according to claim 11 or 12, wherein: The detection module is used for: Using pre-set detection rules, detecting whether there are differences between the contents having a binding relationship; the detection rules are used to define logic and / or conditions for identifying differences; In the case that there is a difference between the contents having the binding relationship, it is determined that there is a difference between the at least two documents to be detected.

14. The document difference detection device according to any one of claims 11 to 13, further comprising: The display module is used to display difference information when there is a difference between the at least two documents to be detected; the difference information is used to identify the content with differences in the documents to be detected.

15. The document difference detection device according to claim 14, further comprising: A receiving module, configured to receive a synchronization instruction for the content having the difference; An updating module is used to update the content with differences according to the synchronization instruction to eliminate the differences between the at least two documents to be detected.

16. The document difference detection device according to any one of claims 11 to 15, wherein: The content determination module is used to: Determining at least one of semantic information and logical relationship contained in the document to be detected; Determine multiple contents in the document to be detected based on at least one of the semantic information and the logical relationship.

17. The document difference detection device according to claim 16, wherein: The document to be detected contains text; The content determination module is used to: Identifying text in the document to be detected to obtain text information; At least one of semantic information and logical relationship contained in the document to be detected is determined by using the text information.

18. The document difference detection device according to claim 16 or 17, wherein: The document to be detected contains a picture; The content determination module is used to: Extracting key information from the image using image recognition technology, and converting the key information into text information; At least one of semantic information and logical relationship contained in the document to be detected is determined by using the text information.

19. The document difference detection device according to claim 17 or 18, wherein: The content determination module is used to: Based on at least one of a pre-built vocabulary library, grammatical rules, and knowledge graph, a pre-trained deep learning model is used to perform semantic analysis on the text information to obtain at least one of semantic information and logical relationships contained in the document to be detected; Wherein, at least one of the vocabulary library, grammatical rules and knowledge graph is constructed based on the design domain of the collaborative design.

20. The document difference detection device according to any one of claims 11 to 19, wherein: The collaborative design process is used for software project development; The at least two documents to be tested include two or more of the requirement documents, design drafts and test cases generated during the software project development process.

21. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-10.

23. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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