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

Abstract

This disclosure provides a document difference detection method, apparatus, device, and storage medium, particularly relating to the computer technology field, and more specifically to the technology fields of data processing, difference detection, and software development. [Solution] A specific solution involves obtaining at least two documents to be detected, wherein the at least two documents to be detected include documents at different stages of the collaborative design process; determining multiple contents in each of the documents to be detected; determining the relationships between the contents in the different documents to be detected; and using pre-defined detection rules and the relationships to detect whether there are differences between the at least two documents to be detected.

Description

Technical Field

[0001] This application claims the priority of a Chinese patent application filed with the China National Intellectual Property Administration on March 20, 2024, with an application number of 202410324187.4 and an invention title of "Document Difference Detection Method, Apparatus, Device, and Storage Medium", and all the contents of the said application are incorporated herein by reference.

[0002] The present disclosure relates to the field of computer technology, particularly to technical fields such as data processing, difference detection, software development, etc.

Background Art

[0003] Collaborative design is a way in which multiple people cooperate in the design process for the purpose of improving design efficiency and quality. Based on the idea of cooperation, collaborative design realizes the efficient completion of design tasks through communication, coordination, and resource sharing among designers. In the collaborative design process, it is related to multiple stages, different users participate in each stage, corresponding documents are generated at each stage, and consistency needs to be maintained among the documents of different stages. However, in related technologies, it is difficult to timely detect the differences between documents of different stages, which easily leads to problems such as misunderstanding and information leakage in the collaborative design process, and is likely to affect the effect of collaborative design.

Summary of the Invention

Problems to be Solved by the Invention

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

Means for Solving the Problems

[0005] In one aspect of the present disclosure, a document difference detection method is provided, and the method includes: obtaining at least two detection target documents, where the at least two detection target documents include documents at different stages of a collaborative design process, Determining multiple contents in each document to be detected, Determining the linking relationships between the content in different documents to be detected, This includes detecting whether there are differences between at least two documents to be detected, using pre-configured detection rules and associated relationships.

[0006] Another aspect of this disclosure provides a document difference detection device, which includes: An acquisition module for acquiring at least two documents to be detected, wherein the at least two documents to be detected include documents at different stages of a collaborative design process, A content determination module for determining multiple contents in each of the documents to be detected, A relationship determination module for determining the linking relationships between content in different documents to be detected, The system includes a detection module for detecting whether there are differences between at least two target documents, using pre-configured detection rules and associated relationships.

[0007] In another aspect of this disclosure, an electronic device is provided, which is At least one processor, The system comprises at least one processor and memory that is communicated with, The memory stores instructions that are executable by the at least one processor, and when executed by the at least one processor, the instructions cause one of the methods in the embodiments of the present disclosure to be performed.

[0008] Another aspect of the present disclosure provides a non-temporary computer-readable storage medium that stores computer instructions for causing a computer to perform any one of the methods of the embodiments of the present disclosure.

[0009] In another aspect of the present disclosure, a program product is provided which, when executed by a processor, includes a program for performing any of the methods in the embodiments of the present disclosure.

[0010] This disclosure determines the relationships between contents in documents at different stages of collaborative design, and automatically detects documents at different stages of collaborative design by determining whether there are differences between documents at different stages of collaborative design through pre-defined detection rules and said relationships. This reduces or avoids problems such as misunderstandings and information leaks in the collaborative design process, thereby improving the effectiveness of collaborative design.

[0011] It should be understood that the content contained herein is not intended to describe any key points or important features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Further details of other features of this disclosure are provided in the specification below.

[0012] The attached drawings are intended to provide a better understanding of the solutions described herein and do not constitute a limitation of this disclosure. [Brief explanation of the drawing]

[0013] [Figure 1] This is a schematic diagram illustrating application scenarios according to the embodiments of this disclosure. [Figure 2] This is a document difference detection method according to an embodiment of the present disclosure. [Figure 3] This is a schematic diagram illustrating the relationships according to the embodiments of this disclosure. [Figure 4] This is a schematic diagram showing the difference content according to the embodiments of this disclosure. [Figure 5] This is a flowchart of user data flow interaction according to an embodiment of the present disclosure. [Figure 6] This is a schematic diagram showing an interaction interface according to an embodiment of the present disclosure. [Figure 7]It is a schematic diagram showing the configuration of a document difference detection device 700 according to an embodiment of the present disclosure. [Figure 8] It is a schematic diagram showing the configuration of a document difference detection device 800 according to an embodiment of the present disclosure. [Figure 9] It is a block diagram of an electronic device 900 according to an embodiment of the present disclosure.

Embodiments for Carrying out the Invention

[0014] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings. These drawings include various details of the embodiments of the present disclosure for the purpose of assisting understanding, and these should be considered to be merely exemplary. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described in this specification without departing from the scope of the present disclosure. Similarly, descriptions of well-known features and structures are omitted in the following description for the sake of clarity and brevity.

[0015] The term "and / or" in the present disclosure merely describes the relationship of related objects and indicates that three types of relationships may exist. For example, A and / or B refers to three situations where A exists alone, A and B exist simultaneously, and B exists alone. The term "at least one" in the present disclosure indicates any one or any combination of at least two among a plurality. For example, at least one of A, B, and C indicates that any one element or a plurality of elements can be selected from the set consisting of A, B, and C. The terms "first" and "second" in the present disclosure are used to refer to and distinguish a plurality of similar technical terms, and do not mean to limit the order or limit to only two. For example, the first feature and the second feature mean the existence of two types / two features, the first feature may be one or more, and the second feature may be one or more.

[0016] Collaborative design is a method in which multiple people cooperate jointly in the design process for the purpose of improving design efficiency and quality. Based on the idea of cooperation, it realizes the efficient completion of design tasks through communication, coordination, and resource sharing among designers. Collaborative design has the following characteristics.

[0017] (1) Collaboration by multiple people: It involves the participation of multiple designers who can be designers from different departments, different companies, or different regions.

[0018] (2) Real-time communication: Designers can communicate through methods such as instant communication tools and online discussion areas, and share their design ideas, opinions, and advice.

[0019] (3) Resource sharing: Designers can share design materials, document templates, design standards, etc., save time and energy, and maintain the consistency and unity of the design.

[0020] (4) Equal cooperation: It emphasizes equal cooperation. Each designer can participate in the design process based on their expertise and interests, and give full play to their maximum potential.

[0021] Collaborative design can be applied to various industries, such as multiple positions and scenarios in multiple processes such as software project development, medical system consultation, examination and treatment, industrial design, production manufacturing, circuit design, etc.

[0022] Taking software project development as an example, in the complete process of software project development, the product manager first conducts product research and creates requirements documentation. The designer creates a design draft based on the requirements documentation. The research and development team develops the software project based on the requirements documentation and the design draft. The tester organizes test cases according to the requirements documentation, generates test cases, tests the software based on the test cases, and the software can be put into use after passing the tests.

[0023] In the overall process of software project development, functional documentation from three main perspectives—requirements documents, design drafts, and test cases—comes into play. If any of these documents are modified, it is necessary to synchronize them with stakeholders at other stages in a timely manner. For example, after a requirements document is changed, it needs to be synchronized with the designers and testers, and the corresponding content changes must be made. Similarly, after a design draft is changed, it needs to be synchronized with the developers, R&D developers, and testers. Failure to timely detect differences between documents at different stages and synchronize document information can lead to discrepancies between understanding and implementation, gradually widening these gaps, significantly increasing the complexity of the developers' work, increasing communication costs, or resulting in iterative development, wasted development resources, and errors or invalid, redundant work in subsequent testing phases.

[0024] The embodiments of this disclosure propose a document difference detection method that can automatically detect differences between documents at different stages of a collaborative design process and solve information synchronization problems for different roles in collaborative design. The document difference detection method according to the embodiments of this disclosure can be applied to various different scenarios, such as software project development scenarios, and can be used to detect differences between documents at different stages of a software project development scenario, such as differences between requirements documents, design drafts, and test cases. This solution can detect differences between documents at different stages of collaborative design through an artificial intelligence automatic identification method, and in the field of software project development, it can automatically understand requirements documents created by product managers, design drafts created by designers, and test cases created by testers, and uncover differences and problems between documents created by these three parties, ultimately providing suggestions, warnings, or design proposals.

[0025] Figure 1 is a schematic diagram illustrating an application scenario according to an embodiment of the present disclosure. As shown in Figure 1, the schematic diagram illustrating an application scenario according to an embodiment of the present disclosure may include a plurality of document generation devices 110 and a document difference detection device 120, where the document generation devices 110 generate documents for different stages of a collaborative design according to the instructions of stakeholders at different stages of the collaborative design and transmit the generated documents to the document difference detection device 120. The document difference detection device 120 receives documents corresponding to different stages of the same collaborative design from each document generation device 110 and detects whether there are differences between the different documents. The document generation devices 110 and the document difference detection device 120 can be connected by wired or wireless communication. Here, the document generation devices 110 proposed in the embodiment of the present disclosure include, but are not limited to, electronic devices such as mobile phones, computers, and smart voice interaction devices, and the document difference detection device 120 may include an electronic device or server for providing document difference detection to each document generation device 110. The document difference detection method proposed in the embodiment of the present disclosure can be performed by the document difference detection device 120.

[0026] Figure 2 shows a document difference detection method according to an embodiment of the present disclosure. S210, obtain at least two documents to be detected, and these at least two documents to be detected include documents at different stages of the collaborative design process. S220, determining multiple contents in each of the aforementioned documents to be detected, S230 determines the linking relationships between content in different documents to be detected, S240 includes detecting whether there are differences between at least two documents to be detected, using pre-configured detection rules and association relationships.

[0027] The embodiments of this disclosure first determine the content of each document for documents at different stages of collaborative design, then determine the relationships between the contents of different documents, and based on that, determine whether there are differences between the contents that have those relationships. This allows for timely detection of differences between documents at different stages, reduces misunderstandings and implementation discrepancies, and lowers the operational complexity in the collaborative design process.

[0028] The collaborative design process involves multiple stages, each with a corresponding document, and each stage's document contains content for multiple functions. For the same function, content corresponding to that function exists in documents at different stages, and in the embodiments of this disclosure, content corresponding to the same function in documents at different stages is considered to have a linking relationship. For example, Document 1 and Document 2 are two documents at different stages of the collaborative design process. Document 1 contains three content items corresponding to functions A, B, and C, respectively, and Document 2 also contains three content items corresponding to functions A, B, and C, respectively. In the embodiments of this disclosure, it can be determined that there is a linking relationship between the content corresponding to function A in Document 1 and the content corresponding to function A in Document 2, between the content corresponding to function B in Document 1 and the content corresponding to function B in Document 2, and between the content corresponding to function C in Document 1 and the content corresponding to function C in Document 2.

[0029] Taking software project development as an example, in collaborative design for software project development, there are three main stages in the overall software development process, each with documents corresponding to different perspectives: requirements documents, design drafts, and test cases. Each document contains content corresponding to multiple functions. Figure 3 is a schematic diagram showing the relationships between content in documents at different stages of collaborative design according to an embodiment of this disclosure. As shown in Figure 3, the requirements document, design draft, and test cases all contain content 1, content 2, and content 3. Of these, 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 are linking relationships between Content 1 in the requirements document, Content 1 in the design draft, and Content 1 in the test case; there are linking relationships between Content 2 in the requirements document, Content 2 in the design draft, and Content 2 in the test case; and there are linking relationships 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 this disclosure identifies the linking relationships between content in different target documents, and detects whether there are differences between content with linking relationships according to pre-set detection rules and linking relationships. For example, if there are differences between content with linking relationships in any one of the sets shown in Figure 3, it can be determined that there are differences between the target documents.

[0030] In some embodiments of this disclosure, determining the linking relationships between content in different documents to be detected is: The process involves obtaining N pieces of content, each of which is obtained from N different documents to be detected, where N is a positive number greater than or equal to 2. To determine the function corresponding to each of the N pieces of content, This includes determining that if N pieces of content have the same functionality, then a linking relationship exists between those N pieces of content.

[0031] Using the example shown in Figure 3, if Content 1 in the requirements document contains "Homepage Synchronized Display Software Title [XXX]", then Content 1 in the design draft includes a picture corresponding to the software title of the display interface, and the text content in that picture is [XXX]. Content 1 in the test case includes the function of displaying the homepage and a description of that function, specifically, "The title is [XXX]". In this case, the function corresponding to Content 1 in all three documents mentioned above is the title of the homepage display software. Therefore, it can be determined that there is a linking relationship between Content 1 in the three documents mentioned above. Accordingly, corresponding to the same function described in the embodiments of this disclosure refers to the function corresponding at different stages of collaborative design.

[0032] Each document at each stage of the collaborative design is designed for the same or corresponding function. Based on this feature, embodiments of the present disclosure can improve the accuracy of determining the linking relationships between the content of different target documents by determining the function corresponding to the content in each of the different target documents, thereby providing a basis for subsequent difference detection.

[0033] After determining the content with a linking relationship, it is possible to determine whether there are differences between the documents to be detected by determining whether there are differences between the linked content. For example, using pre-configured detection rules and the linking relationships, it is possible to detect whether there are differences between the at least two documents to be detected. This involves detecting whether there are differences between related content using pre-defined detection rules, where these detection rules are used to define logical AND / OR conditions for identifying the differences. This includes determining that there are differences between at least two documents being detected if there are differences between related content.

[0034] A detection rule can be a clear and concise decision rule for determining whether there are differences between documents being detected, by detecting related content using the logic and conditions defined in the detection rule, thereby improving the accuracy of determining differences between related content. For example, in a software project development scenario, if two documents being detected each contain content related to the software title, there is a related relationship between the two contents, and the relevant detection rule includes indicating that the software titles defined in the related content are exactly the same, or that if they are different, there is a difference between the related contents. Difference detection between contents can be performed using this detection rule.

[0035] The embodiments of this disclosure can also visualize and display the detection results to the user, such as by highlighting the differences or providing interactive navigation, which can help the user better understand the problem and advice and improve the user experience.

[0036] For example, the document detection method according to the embodiments of this disclosure is: If there are differences between at least two documents being detected, the system further includes displaying difference information, which is used to indicate the content that differs in the documents being detected.

[0037] Let us continue by using the example of performing difference detection on documentation in software project development. Figure 4 is a schematic diagram showing the display of difference information according to an embodiment of this disclosure. In the example shown in Figure 4, the detection results are displayed, and the display screen includes a requirements document, a design draft, and a test case. In this example, there are differences between "Top Page Synchronized Display Software Title [AI Assist Product]" in the requirements document, a picture with text content that reads [AI Assist Design] in the design draft, and the description of the top page display function "Title [AI Assist Product]" in the test case. The differences can be clearly shown to the user by highlighting, emphasizing, or displaying the specific content with differences using a different color, font, or other method than other content. For example, in the example in Figure 4, there are differences between software titles in different files, and in Figure 4, the content with differences is highlighted.

[0038] Furthermore, the difference information provided in the embodiments of this disclosure can also be used to indicate other documents that have differences from the document being detected. As shown in Figure 4, near the difference content of the requirements document (i.e., "Top Page Synchronization Display Software Title [AI Assist Product]"), other documents that have differences from that content can be further displayed using a message box, as shown in Figure 4, which indicates "This is not synchronized with the design draft." Similarly, near the difference content between the design draft and the test cases, it can be indicated which or which documents have differences.

[0039] Embodiments of this disclosure can provide users with interactive navigation, for example, the above method can: Receiving synchronization commands for content with differences, The further includes updating the content with the difference in accordance with the synchronization command and removing the difference between at least two documents to be detected.

[0040] Using the example shown in Figure 4, a message box is displayed near the differential content in the requirements document (i.e., "Homepage Synchronized Display Software Title [AI Assisted Product]") stating "This is not synchronized with the design draft." The user can send a synchronization command; for example, the user clicks the message box and then selects "Synchronize with Design Draft." After receiving the user's synchronization command, the differential content can be updated. In the example in Figure 4, "Homepage Synchronized Display Software Title [AI Assisted Product]" in the requirements document can be changed to "Homepage Synchronized Display Software Title [AI Assisted Design]," ensuring consistency in the content related to the software title display function between the requirements document and the design draft, and eliminating the difference between the requirements document and the design draft. Interface interaction makes it convenient for users to update differential content in documents, proactively provides users with selectable updates, and allows for timely and easy synchronization of documents at different stages.

[0041] To detect differences between texts at different stages of a collaborative design process, embodiments of this disclosure relate primarily to two aspects: firstly, determining multiple contents in a document to be detected, and, given that the contents in each document to be detected have been determined, determining the relationships between the contents of different documents to be detected; and secondly, using pre-defined detection rules, detecting whether there are differences between the linked contents.

[0042] In the first aspect, determining multiple contents in the document to be detected according to the embodiments of this disclosure is: To determine at least one of the semantic information and logical relationships contained in the document to be detected, This includes determining multiple contents in the document to be detected according to at least one of the semantic information and logical relationships.

[0043] Since the content of a document contains multiple meanings and different semantic information or logical relationships between surrounding sentences, it is possible to improve the accuracy of distinguishing between multiple content items in a document based on the semantic information and logical relationships contained within the document, and to provide a basis for detecting differences between documents.

[0044] The documents to be detected include text and / or pictures. For example, requirements documents and test cases in software project development primarily contain text, while design drafts primarily contain both text and pictures.

[0045] In some embodiments, determining at least one of the semantic information and logical relationships contained in the document to be detected is: Identifying text in the target document and obtaining text information, This includes using the text information to determine at least one of the semantic information and logical relationships contained in the document to be detected.

[0046] For example, embodiments of this disclosure can leverage the multilingual processing capabilities of a Large Language Model (LLM) to automatically identify the language of text and perform semantic analysis and understanding of text content in requirements documents, design drafts, and test cases. In some examples, accurate identification of technical terms and specific expressions can be achieved by constructing at least one of domain-specific vocabulary bases, grammatical rules, and knowledge graphs.

[0047] In some other embodiments, the document to be detected includes a picture, and determining at least one of the semantic information and logical relationships contained in the document to be detected is: The process involves extracting key information from the picture using image recognition technology, and converting the key information into text information. This includes using the text information to determine at least one of the semantic information and logical relationships contained in the document to be detected.

[0048] For example, the embodiments of this disclosure utilize image recognition technologies such as Optical Character Recognition (OCR) to extract key information (mainly including text information within the picture) from a design draft picture, convert the extracted key information into text information, and then analyze the text information using a deep learning model to understand the design intent and logical relationships.

[0049] Based on the text information contained in the target document, artificial intelligence technology can be used to employ a pre-trained neural network model to identify semantic information and logical relationships contained in the target document, thereby improving the accuracy of identification and detection.

[0050] In some cases, using text information to determine at least one of the semantic information and logical relationships contained in the document being detected is possible. This includes employing a pre-trained deep learning model based on at least one of a pre-constructed vocabulary base, grammatical rules, and knowledge graph to perform semantic analysis on the text information and obtain at least one of the semantic information and logical relationships contained in the document to be detected.

[0051] Here, at least one of the vocabulary base, grammatical rules, and knowledge graph is constructed based on the design domain of the collaborative design.

[0052] For example, embodiments of this disclosure can pre-construct a vocabulary base and / or grammatical rules specific to the software project development domain in order to improve the accuracy of identifying specialized terminology and specific expressions in software project development. Furthermore, based on the vocabulary base and / or grammatical rules, a knowledge graph of the software project development domain can be constructed to integrate concepts, attributes, relationships, etc. in software development design and to provide contextual information to identify differences.

[0053] Embodiments of this disclosure also allow for the design of a rule engine that predefines detection rules for detecting differences between related content, and these detection rules can define logical AND / OR conditions for identifying differences. Using these inspection rules, design content from different stages and roles can be compared and design differences can be automatically identified. For these differences, the system can provide a description of the specific problem and advice. For example, if related content belonging to different target documents satisfies the logical AND / OR conditions corresponding to the detection rule, the rule engine outputs a detection result indicating that there are differences between related content.

[0054] Embodiments of this disclosure can identify target documents using a deep learning model, determine content within the target documents and identify related content based on at least one of a pre-built vocabulary base, grammatical rules, and knowledge graph. Furthermore, embodiments of this disclosure can use the aforementioned rule engine to determine whether there are differences between related content. In some embodiments, the rule engine can be implemented using a deep learning model.

[0055] Furthermore, as design content and requirements constantly change, the models relating to the embodiments of this disclosure (including, for example, neural network models for determining the content of the documents to be detected, the relationships between the content, and the differences between the content with relationships) can be continuously updated and optimized. For example, by collecting user feedback and system data, the model can be continuously trained and optimized to improve the accuracy and efficiency of model identification. New model updating techniques and methods can also be introduced, such as using techniques like transfer learning and incremental learning to further improve the model's performance.

[0056] The document detection triggers proposed in the embodiments of this disclosure include, but are not limited to, real-time monitoring based on modification triggers, batch timing monitoring, and modification limit alert reminders.

[0057] Figure 5 is a flowchart of user data flow interaction according to an embodiment of the present disclosure. As shown in Figure 5, in collaborative design of software project development, documents from each stage (e.g., requirements documents, design drafts, test cases) are uploaded to a model for detecting document differences proposed in an embodiment of the present disclosure. The model analyzes and compares the documents, determines the differences between them, and marks the content that differs in the documents. For example, it displays requirements documents, design drafts, and test cases with content difference markings.

[0058] Figure 6 is a schematic diagram showing an interaction interface according to an embodiment of the present disclosure. The example shown in Figure 6 relates to a user modifying a design draft and submitting the modified design draft, i.e., design draft 2.0 in Figure 6. The embodiment of the present disclosure uses a model for detecting document differences to pre-classify data for identical functional descriptions in three documents. After receiving the modified design draft, it analyzes the differences between the modified design draft and previously uploaded requirements documents and test cases to obtain a document with difference markings. When displaying document differences, the requirements document, design draft, and test cases can be displayed respectively. Content that differs from the modified design draft can be displayed in the requirements document and test cases, and the modified content can be displayed in the design draft. Furthermore, document differences can be synchronized to users at the relevant stage; for example, differences between the requirements document and the design draft can be synchronized to the upstream product manager, and differences between the test cases and the design draft can be synchronized to the downstream tester.

[0059] In the embodiments of this disclosure, a document difference detection device 700 is further provided, and Figure 7 is a schematic diagram showing the configuration of the document difference detection device 700 according to the embodiments of this disclosure, and the document difference detection device 700 is Acquisition module 710 for acquiring at least two documents to be detected, wherein the at least two documents to be detected include documents at different stages of a collaborative design process, A content determination module 720 for determining multiple contents in each of the documents to be detected, A relationship determination module 730 for determining the linking relationships between content in different documents to be detected, The system includes a detection module 740 for detecting whether there are differences between at least two documents to be detected, using pre-configured detection rules and associated relationships.

[0060] In some embodiments, the relationship determination module 730 is The objective is to retrieve N pieces of content, each of which is obtained from N different documents to be detected, and where N is a positive number greater than or equal to 2. To determine the function corresponding to each of the N pieces of content, This is used to determine that if N pieces of content have the same function, then those N pieces of content are considered to have a linking relationship.

[0061] In some embodiments, the detection module 740 is This involves detecting whether there are differences between related content using pre-defined detection rules, where these detection rules are used to define logical AND / OR conditions for identifying the differences. This is used to determine that there are differences between at least two documents being detected if there are differences between related content.

[0062] Figure 8 is a schematic diagram showing the configuration of a document difference detection device 800 according to an embodiment of the present disclosure. As shown in Figure 8, in some embodiments, the document difference detection device 800 is configured as follows: If there are differences between the at least two documents to be detected, the system further includes a display module 850 for displaying the difference information. This difference information is used to indicate the content that has differences in the document being detected.

[0063] In some embodiments, the document difference detection device 800 is A receiving module 860 for receiving synchronization commands for content that has the difference, The system further includes an update module 870 for updating the content with the difference in accordance with the synchronization command and for removing the difference between the at least two documents to be detected.

[0064] In some embodiments, the content determination module 720 is: To determine at least one of the semantic information and logical relationships contained in the document to be detected, It is used to determine multiple contents in the document to be detected according to at least one of the semantic information and logical relationships.

[0065] In some embodiments, the document to be detected contains text, The content determination module 720 is, Identifying the text in the document to be detected and obtaining text information, This text information is used to determine at least one of the semantic information and logical relationships contained in the document to be detected.

[0066] In some embodiments, the document to be detected includes a picture. The content determination module 720 is, Extracting key information from the picture using image recognition technology, and converting that key information into text information, This text information is used to determine at least one of the semantic information and logical relationships contained in the document to be detected.

[0067] In some embodiments, the content determination module 720 is: It is used to perform semantic analysis on text information by employing a pre-trained deep learning model based on at least one of a pre-constructed vocabulary base, grammatical rules, and knowledge graph, and to obtain at least one of the semantic information and logical relationships contained in the target document. Here, at least one of the vocabulary base, grammatical rules, and knowledge graph is constructed based on the design domain of the collaborative design.

[0068] In some embodiments, the collaborative design process is used in software project development. The at least two documents to be detected include two or more of the following generated during the development of the software project: requirements documents, design drafts, and test cases.

[0069] A description of the specific functions and examples of each unit of the apparatus of the embodiments of this disclosure can be found in the relevant descriptions of the corresponding steps in the embodiments of the method described above and will not be repeated here.

[0070] The technical solutions disclosed herein ensure that the acquisition, storage, and application of users' personal information comply with applicable laws and regulations and do not violate public order or morality.

[0071] According to embodiments of the present disclosure, the present disclosure further provides electronic devices, non-temporary computer-readable storage media, and program products.

[0072] Figure 9 is a block diagram of an electronic device 900 according to an embodiment of the present disclosure. An electronic device refers to any form of digital computer, such as a laptop computer, desktop computer, workstation, personal digital assistant, server, blade server, mainframe computer, and other compatible computers. An electronic device further refers to any form of mobile device, such as a personal digital assistant, cellular phone, intelligent phone, wearable device, and other similar computer equipment. The components, their connections, and functions described in this disclosure are illustrative and do not limit the realization of anything described or specified in this disclosure.

[0073] As shown in Figure 9, device 900 includes a computing unit 901 that can perform various appropriate operations and processes based on computer program instructions stored in read-only memory (ROM) 902 or computer program instructions loaded from storage unit 908 into random access memory (RAM) 903. RAM 903 can further store various programs and data necessary for the operation of device 900. The computing unit 901, ROM 902, and RAM 903 are connected to each other via bus 904. An input / output (I / O) interface 905 is also connected to bus 904.

[0074] Multiple components in device 900 are connected to an I / O interface 905, which includes an input unit 906 such as a keyboard and mouse, an output unit 907 such as various displays and speakers, a storage unit 908 such as a magnetic disk or optical disk, and a communication unit 909 such as a network card, modem, or wireless communication transceiver. The communication unit 909 allows device 900 to exchange information / data with other devices via computer networks such as the Internet and / or various carrier networks.

[0075] The computing unit 901 may be a variety of general-purpose and / or dedicated processing components having 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, a computing unit that executes various machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs each of the 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 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 into 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. Additionally, in other embodiments, the computing unit 901 may be configured to perform the detection method by any other suitable method (e.g., firmware).

[0076] Various embodiments of the systems or technologies described in this disclosure can be implemented by digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standards (ASSPs), systems-on-a-chip (SOCs), complex-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. Each of these embodiments may be implemented by one or more computer programs that run and / or interpret on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] Program code for performing the methods of this disclosure can be written in any combination of one or more programming languages. These program codes are provided to a processor or controller of a general-purpose computer, a dedicated computer, or other programming data processing device, so that when the program code is executed by the processor or controller, it can perform the functions / operations defined in the flowcharts and / or block diagrams. The program code may run entirely in a mainscan, partially in a mainscan, partially as an independent soft encapsulation and partially in a remote mainscan, or entirely in a remote mainscan or server.

[0078] In this disclosure, machine-readable media may be tangible media containing or storing programs used by or in conjunction with instruction execution systems, devices, or equipment. Machine-readable media may be machine-readable signal media or machine-readable storage media. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any suitable combination of the contents described above. Further specific examples of machine-readable storage media include one or more wired electrical connections, portable computer disk cartridges, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any combination of the contents described above.

[0079] To provide user interaction, a computer may implement the systems and technologies described herein, which may include a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor), a keyboard and pointing device for the user to provide input to the computer (e.g., a mouse or trackball). Other types of devices may also be used to provide user interaction; for example, the feedback provided to the user may be any form of sensor feedback (e.g., visual feedback, auditory feedback, or haptic feedback), and input from the user may be accepted in any form (e.g., acoustic input, voice input, haptic input).

[0080] The systems and technologies described herein can be implemented in computing systems that include background components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include front-end components (e.g., user computers having a graphics user interface or network browser, through which users can interact with embodiments of the systems and technologies described herein), or in any combination of such background components, middleware components, or front-end components. Components of the system can be connected to one another via digital data communication in any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0081] A computer system can include a client and a server. Typically, the client and server are geographically separated and interact via a communication network. The client-server relationship is created by a computer program that operates on the corresponding computer. The server may be a cloud server, a server in a distributed system, or a server incorporating blockchain technology, etc.

[0082] It should be understood that steps can be newly ranked, added, or deleted using the various forms of flows shown above. For example, each step described in this disclosure may be executed in parallel, sequentially, or in a different order. This disclosure is not limited to this, as long as the technical solutions disclosed herein can achieve the desired results.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, subcombinations, and substitutions are possible due to design considerations and other factors. Any changes, equivalent substitutions, and improvements within the gist and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A document difference detection method, To obtain at least two documents to be detected, wherein the at least two documents to be detected include documents at different stages of the collaborative design process, Determining multiple contents in each of the aforementioned documents to be detected, Determining the linking relationships between the contents in different documents to be detected, This includes detecting whether there are differences between the at least two documents to be detected, using pre-configured detection rules and the aforementioned linking relationships. Document difference detection method.

2. Determining the linking relationships between the contents in the different documents to be detected is: The method involves obtaining N pieces of content, where each of the N pieces of content is obtained from N different documents to be detected, and where N is a positive number greater than or equal to 2. To determine the function corresponding to each of the N contents, This includes determining that if N content items have the same function, then the N content items are linked to each other. The document difference detection method according to claim 1.

3. Using the aforementioned pre-configured detection rules and the aforementioned linking relationships, detecting whether there are differences between the at least two documents to be detected is: The process involves detecting whether there are differences between related content using pre-configured detection rules, wherein the detection rules are used to define logical AND / OR conditions for identifying the differences. If there are differences between related content, the process includes determining that there are differences between at least two of the documents to be detected. The document difference detection method according to claim 1 or 2.

4. The aforementioned document difference detection method is: If there are differences between the two documents to be detected, the method further includes displaying the difference information. The aforementioned difference information is used to indicate the content that has differences in the document to be detected. A document difference detection method according to any one of claims 1 to 3.

5. The aforementioned document difference detection method is: Receiving a synchronization command for the content that has the aforementioned difference, The process further includes updating the content with the difference in accordance with the synchronization command and removing the difference between the at least two documents to be detected, The document difference detection method according to claim 4.

6. Determining multiple contents in the aforementioned document to be detected is: To determine at least one of the semantic information and logical relationships contained in the document to be detected, This includes determining multiple contents in the document to be detected according to at least one of the aforementioned semantic information and logical relationships, A document difference detection method according to any one of claims 1 to 5.

7. The aforementioned document to be detected contains text, Determining at least one of the semantic information and logical relationships contained in the aforementioned document to be detected is: Identifying the text in the aforementioned document to be detected and obtaining text information, This includes determining at least one of the semantic information and logical relationships contained in the document to be detected using the aforementioned text information, The document difference detection method according to claim 6.

8. The aforementioned documents to be detected include pictures. Determining at least one of the semantic information and logical relationships contained in the aforementioned document to be detected is: The process involves extracting key information from the picture using image recognition technology, and converting the key information into text information. This includes determining at least one of the semantic information and logical relationships contained in the document to be detected using the aforementioned text information, The document difference detection method according to claim 6 or 7.

9. Using the aforementioned text information, determining at least one of the semantic information and logical relationships contained in the document to be detected is: This includes performing semantic analysis on the text information by employing a pre-trained deep learning model based on at least one of a pre-constructed vocabulary base, grammatical rules, and knowledge graph, and obtaining at least one of the semantic information and logical relationships contained in the target document. Here, at least one of the vocabulary base, grammatical rules, and knowledge graph is constructed based on the design domain of the collaborative design. The document difference detection method according to claim 7 or 8.

10. The aforementioned collaborative design process is used in software project development. The aforementioned at least two documents to be detected include two or more of the following generated during the development of the software project: requirements documents, design drafts, and test cases. A document difference detection method according to any one of claims 1 to 9.

11. A document difference detection device, An acquisition module for acquiring at least two documents to be detected, wherein the at least two documents to be detected include documents at different stages of a collaborative design process, A content determination module for determining multiple contents in each of the aforementioned documents to be detected, A relationship determination module for determining the linking relationships between the contents in different documents to be detected, The system includes a detection module for detecting whether there are differences between at least two documents to be detected, using pre-configured detection rules and the aforementioned linking relationships. Document difference detection device.

12. The aforementioned relationship determination module is The method involves obtaining N pieces of content, where each of the N pieces of content is obtained from N different documents to be detected, and where N is a positive number greater than or equal to 2. To determine the function corresponding to each of the N contents, If the functions corresponding to N pieces of content are the same, it is determined that there is a linking relationship between the N pieces of content, and this is used in The document difference detection device according to claim 11.

13. The detection module is The process involves detecting whether there are differences between related content using pre-configured detection rules, wherein the detection rules are used to define logical AND / OR conditions for identifying the differences. If there are differences between related content, it is determined that there are differences between at least two of the documents to be detected, and this is used to determine that there are differences between the documents that have differences between them. The document difference detection device according to claim 11 or 12.

14. The aforementioned document difference detection device is If there are differences between the two documents to be detected, the system further includes a display module for displaying the difference information. The aforementioned difference information is used to indicate the content that has differences in the document to be detected. A document difference detection device according to any one of claims 11 to 13.

15. The aforementioned document difference detection device is A receiving module for receiving synchronization commands for the content with the aforementioned difference, The system further comprises an update module for updating the content with differences in accordance with the synchronization command and for removing differences between the at least two documents to be detected. The document difference detection device according to claim 14.

16. The aforementioned content determination module is To determine at least one of the semantic information and logical relationships contained in the document to be detected, To determine multiple contents in the document to be detected according to at least one of the aforementioned semantic information and logical relationships, and used for this purpose, A document difference detection device according to any one of claims 11 to 15.

17. The aforementioned document to be detected contains text, The aforementioned content determination module is Identifying the text in the aforementioned document to be detected and obtaining text information, The method used to determine at least one of the semantic information and logical relationships contained in the document to be detected, using the aforementioned text information, The document difference detection device according to claim 16.

18. The aforementioned documents to be detected include pictures. The aforementioned content determination module is The process involves extracting key information from the picture using image recognition technology, and converting the key information into text information. The method used to determine at least one of the semantic information and logical relationships contained in the document to be detected, using the aforementioned text information, The document difference detection device according to claim 16 or 17.

19. The aforementioned content determination module is This system employs a pre-trained deep learning model based on at least one of a pre-constructed vocabulary base, grammatical rules, and knowledge graph to perform semantic analysis on the text information and obtain at least one of the semantic information and logical relationships contained in the target document. Here, at least one of the vocabulary base, grammatical rules, and knowledge graph is constructed based on the design domain of the collaborative design. The document difference detection device according to claim 17 or 18.

20. The aforementioned collaborative design process is used in software project development. The aforementioned at least two documents to be detected include two or more of the following generated during the development of the software project: requirements documents, design drafts, and test cases. A document difference detection device according to any one of claims 11 to 19.

21. At least one processor, The system comprises at least one processor and a memory that is communicated with by it, The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor causes the at least one processor to perform the method according to any one of claims 1 to 10. Electronic devices.

22. A non-temporary computer-readable storage medium storing computer instructions that cause a computer to perform the method described in any one of claims 1 to 10.

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

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