A method, device, equipment and medium for constructing an official document instruction data set

Abstract

The present invention relates to the technical field of combining artificial intelligence with quantum computing, and in particular to a method, device, equipment and medium for constructing a document instruction data set. First, by preprocessing text, image, voice and structured data, corresponding feature data is generated; then, multimodal data is subjected to deep semantic association analysis through quantum state distributed node initialization and quantum entangled graph convolution network to generate optimized semantic links; finally, multiple document versions are generated through quantum modal coupling calculation and adversarial generation network, and a complete document data set is generated through global quantum semantic consistency optimization. The present invention significantly improves the semantic consistency, information integration capability and generation efficiency of document generation.

Description

Technical Field

[0001] The present invention relates to the technical field of combining artificial intelligence with quantum computing, and in particular to a method, device, equipment and medium for constructing an official document instruction data set. Background Art

[0002] In the modern official document writing process, the construction of official document instruction data sets is crucial. This is because in complex administrative and corporate management, efficient and accurate generation and processing of official documents is of great significance to maintaining the normal operation of the organization. The existing technology (Chinese invention patent, publication number: CN117992601B, name: Official document generation method and device based on artificial intelligence) usually relies on official document generation methods based on artificial intelligence, mainly through natural language processing (NLP) technology to encode and analyze historical official document data, so as to automatically generate official documents. However, these technologies have many shortcomings:

[0003] Existing methods mainly rely on preset natural semantic recognition models to analyze historical document data, and use named entity recognition and other technologies to extract key information and determine the logical structure of the text. This method is prone to poor matching between the format and content of official documents in practical applications, especially when faced with complex multimodal data (such as text, images, voice, and structured data). Existing technologies perform poorly in semantic matching and logical reasoning.

[0004] Since existing methods mostly rely on pre-trained models for document generation, and the training of these models is often based on a single data model, this leads to poor semantic consistency and information integration capabilities when processing multimodal data. The final generated documents may have conflicts or incoherence at the semantic level. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a method, device, equipment and medium for constructing a document instruction data set. The present invention generates a comprehensive semantic network by performing preliminary semantic matching, deep semantic association analysis and global semantic consistency correction on multimodal data, and further optimizes semantic links through quantum state distributed node initialization and quantum entangled graph convolution network, and finally generates a high-quality document data set. The present invention improves the semantic consistency and content integration capabilities in the document generation process through the introduction of quantum computing technology.

[0006] A method for constructing an official document instruction data set comprises the following steps:

[0007] Collecting multimodal data including text, image, voice and structured data, and preprocessing the multimodal data to generate text semantic data, image feature data, voice feature data and structured feature data;

[0008] Perform preliminary semantic matching, deep semantic association analysis, and global semantic consistency correction on text semantic data, image feature data, speech feature data, and structured feature data to generate a comprehensive semantic network;

[0009] Perform quantum state distributed node initialization on semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization;

[0010] According to user feedback, a multi-path adaptive feedback path is established to generate multi-path feedback semantic data; the multi-path feedback semantic data is adversarially generated and selected through the adversarial generative feedback network, and quantum cross-domain semantic transfer is used to generate quantum cross-domain optimized semantic data;

[0011] Perform quantum modal coupling calculations on quantum cross-domain optimized semantic data, generate at least two versions of official documents through a quantum adversarial generative network, and finally generate a complete official document dataset through global quantum semantic consistency optimization;

[0012] Key instructions are extracted from the complete official document data set, structured and sorted, and the sorted instruction data is quantum-link associated with the optimized quantum semantic link data. Finally, after format normalization and instruction set optimization, an official document instruction data set is generated.

[0013] Preferably, the preliminary semantic matching of the text semantic data, the image feature data, the voice feature data and the structured feature data is achieved by calculating the semantic similarity between the text semantic data, the image feature data, the voice feature data and the structured feature data; wherein the similarity calculation formula is as follows:

[0014] S m =f(T,I,A,V)

[0015] Among them, S m represents the similarity value; T represents the semantic representation of text semantic data; I represents the feature vector of image feature data; A represents the timbre feature vector of speech feature data; V represents the numerical feature of structured feature data;

[0016] The formula is used to analyze the semantic relationship between text semantic data, image feature data, voice feature data and structured feature data, and generate a unified similarity value to achieve semantic matching of multimodal data.

[0017] Preferably, deep semantic association analysis is achieved by constructing a multi-layer semantic association graph, which includes multiple semantic nodes and semantic edges, wherein each semantic node represents a semantic unit, and the semantic edge represents the semantic relationship between semantic units; by constructing and analyzing the multi-layer semantic association graph at multiple levels, deep semantic association graph data is generated to describe and capture the complex semantic relationship between multimodal data.

[0018] Preferably, the quantum state distributed node initialization includes the following steps:

[0019] Applying quantum state initialization techniques to each semantic node in the comprehensive semantic network;

[0020] Each semantic node in the comprehensive semantic network is initialized through quantum superposition state, so that each semantic node can simultaneously represent multiple potential semantic states and generate quantum state semantic node data;

[0021] The quantum entangled graph convolutional network processes quantum state semantic node data through convolution operations. The convolution calculation formula is as follows:

[0022] C=∑(W×Q)

[0023] Among them, C represents the output of the convolution operation, W represents the weight matrix in the quantum entangled graph convolution network, and Q represents the quantum state semantic node data;

[0024] Through convolution operations, the quantum entangled graph convolutional network transmits and shares information between multiple semantic nodes, thereby generating convolutional quantum semantic link data, which is used to reflect the deep semantic associations between semantic nodes.

[0025] Preferably, the self-organizing quantum topology optimization comprises the following steps:

[0026] Dynamically adjust the semantic node connections and weights in the convolutional quantum semantic link data through an adaptive algorithm;

[0027] According to a predetermined optimization criterion, the structure of the convolution quantum semantic link data is iteratively adjusted so that the convolution quantum semantic link data reaches an optimal state in the global scope;

[0028] Generate optimized quantum semantic link data, which is used to realize information transmission and semantic reasoning in the comprehensive semantic network.

[0029] Preferably, establishing a multi-path adaptive feedback path comprises the following steps:

[0030] Generate multiple semantic feedback paths based on user feedback data, each semantic feedback path corresponds to a different semantic adjustment strategy, and generate multi-path feedback semantic data;

[0031] Each semantic feedback path contains different semantic adjustment weights and adjustment directions. By analyzing user feedback data, these semantic feedback paths are dynamically generated and adjusted to adapt to different semantic contexts and user needs.

[0032] Preferably, the adversarial generative feedback network comprises the following steps:

[0033] Generate multiple semantic optimization schemes through adversarial training of the generator and the discriminator, where the generator is used to generate quantum semantic links after semantic adjustment, and the discriminator is used to evaluate the quantum semantic links generated by the generator and judge their quality;

[0034] Generate an optimized quantum semantic link scheme through multiple rounds of adversarial training;

[0035] Optimizing the quantum semantic link scheme through a quantum cross-domain semantic transmission mechanism, and finally generating quantum cross-domain optimized semantic data;

[0036] Quantum modal coupling calculation includes multimodal quantum state coupling of quantum cross-domain optimized semantic data to generate at least two different versions of official documents;

[0037] The document version is adversarially selected through a quantum adversarial generation network, and finally a complete document dataset is generated through global quantum semantic consistency optimization;

[0038] Quantum modal coupling computing establishes a quantum state coupling relationship between multimodal data, making the generated document version consistent and coherent in content, semantics and logic.

[0039] A device for constructing an official document instruction data set, comprising:

[0040] An acquisition module, used to acquire multimodal data including text, image, voice and structured data, and preprocess the multimodal data to generate text semantic data, image feature data, voice feature data and structured feature data;

[0041] The semantic processing module is used to perform preliminary semantic matching, deep semantic association analysis and global semantic consistency correction on text semantic data, image feature data, speech feature data and structured feature data to generate a comprehensive semantic network;

[0042] The quantum processing module is used to initialize the quantum state distributed nodes of the semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization;

[0043] An adaptive feedback module, used to establish a multi-path adaptive feedback path according to user feedback and generate multi-path feedback semantic data;

[0044] The adversarial generation module is used to perform adversarial generation and selection on multi-path feedback semantic data through an adversarial generation feedback network, and to generate quantum cross-domain optimized semantic data using quantum cross-domain semantic transfer;

[0045] The modal coupling module is used to perform quantum modal coupling calculations on quantum cross-domain optimized semantic data, generate at least two versions of official documents through a quantum adversarial generative network, and finally generate a complete official document data set through global quantum semantic consistency optimization;

[0046] The data set generation module is used to extract key instructions from the complete official document data set, perform structured processing and sorting, and associate the sorted instruction data with the optimized quantum semantic link data through quantum links. Finally, after format normalization and instruction set optimization, an official document instruction data set is generated.

[0047] A device for constructing an official document instruction data set, the device comprising a memory and a processor, wherein the memory is used to store executable instructions, and the processor is used to execute the instructions to implement the steps of the method for constructing the official document instruction data set.

[0048] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for constructing an official document instruction data set.

[0049] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0050] Through quantum state distributed node initialization and quantum entangled graph convolutional network, the present invention effectively realizes deep semantic association analysis between multimodal data. This method overcomes the problems of semantic inconsistency and insufficient information integration in the prior art.

[0051] The present invention generates multiple optimized versions of official documents through quantum modal coupling calculation and quantum adversarial generation network, and finally generates a complete and coherent official document data set through global quantum semantic consistency optimization, which significantly improves the quality and efficiency of official document generation.

[0052] The present invention dynamically generates and adjusts a multi-path adaptive feedback path through user feedback data, thereby achieving real-time optimization of the content of official documents, so that the generated official documents are more in line with user needs and context. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the process of the present invention;

[0054] Figure 2 This is a flowchart of quantum state distributed node initialization and quantum entanglement graph convolution network processing in the present invention;

[0055] Figure 3 A diagram for the calculation of quantum mode coupling and the document version generation in the present invention;

[0056] Figure 4 It is a structural block diagram of the device of the present invention. DETAILED DESCRIPTION

[0057] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0058] like Figure 1 As shown, a method for constructing an official document instruction data set includes the following steps:

[0059] Collecting multimodal data including text, image, voice and structured data, and preprocessing the multimodal data to generate text semantic data, image feature data, voice feature data and structured feature data;

[0060] Multimodal data collection refers to obtaining different types of data from multiple sources, including text, images, voice, and structured data. These data come from a wide range of sources, such as official document text, attached pictures, voice command recordings, and structured data such as system logs or database records. The introduction of multimodal data is to enrich the semantic information of the data set, ensuring that when generating the official document instruction data set, not only can the text information be accurately captured, but also other forms of data can be combined to provide a more comprehensive understanding and analysis.

[0061] Text data is usually the most direct form of expression in official documents. Through word segmentation, grammatical analysis and semantic extraction, key semantic information such as instructions, commands or important decision points can be extracted from it. The technologies used here may include word segmentation algorithms, dependency syntactic analysis and topic models in natural language processing (NLP). These technologies can effectively convert text content into structured text semantic data, laying the foundation for subsequent semantic analysis and matching.

[0062] Image data is usually charts, photos or other visual information attached to official documents. These images may contain key information related to the content of the official document, such as flowcharts, charts or schematics. Through computer vision technology, such as convolutional neural networks (CNN), features in images can be extracted, such as the shape, color, texture of objects, and text information in the image (through optical character recognition, OCR). These extracted features form image feature data, which provides image-level information support for subsequent multimodal data fusion.

[0063] Voice data may come from voice commands, meeting records, or voice memos. When processing voice data, speech recognition technology is usually used to convert speech into text, and voice features such as the speaker's emotional state, speaking speed, and pitch are extracted through timbre analysis. These voice feature data include not only textual content, but also the voice features themselves, which is especially important when processing commands with emotions or contexts.

[0064] Structured data refers to data in official documents in the form of tables, database records, etc. These data usually contain clear fields and values, such as statistical data, budget tables, or database query results. When processing structured data, it is usually necessary to clean and standardize the data to ensure the consistency and integrity of the data, and finally generate structured feature data. These data can be directly used in the quantitative analysis part of the official document instruction generation to ensure the accuracy and consistency of the instruction generation.

[0065] By processing text, images, speech and structured data into text semantic data, image feature data, speech feature data and structured feature data respectively, the multimodal information in the official document can be standardized and structured. The main effects of this preprocessing step are:

[0066] Through the introduction of multimodal data, the official document instruction dataset is no longer limited to text information, but can comprehensively utilize information in images, voice and structured data, which greatly improves the expressiveness and semantic richness of the dataset.

[0067] The processing of multimodal data enables official document instructions to more accurately reflect the intent of the original document. For example, by introducing image feature data, specific charts or schematic diagrams mentioned in the document can be referenced when generating instructions, making the instructions more operational and valuable for reference.

[0068] By preprocessing multimodal data, it is ensured that all types of data are standardized and comparable when entering the subsequent processing stage. This consistency provides a solid foundation for subsequent multimodal data fusion, semantic matching, and instruction generation, avoiding potential problems caused by inconsistencies between data sources.

[0069] Example: Assume that there is an official document containing the following content: a text describing the company's quarterly financial situation, a line graph showing sales trends, a voice recording of a high-level meeting, and a spreadsheet recording the budget. When constructing the official document instruction dataset, firstly, the text content is segmented and semantic extraction is performed to generate the text semantic data of the "financial analysis report"; the chart in the image is identified by OCR technology, the trend line data in the chart is extracted, and the image feature data is generated; the conference speech is converted into text and the speaker's tone and emotion are analyzed to generate speech feature data; the data in the budget form is cleaned and standardized to generate structured feature data. Ultimately, these pre-processed multimodal data will be used to generate comprehensive instructions covering text, images, voice, and structured information, providing a complete and semantically rich instruction set for company decision-making.

[0070] Perform preliminary semantic matching, deep semantic association analysis, and global semantic consistency correction on text semantic data, image feature data, speech feature data, and structured feature data to generate a comprehensive semantic network;

[0071] In the present invention, it is a crucial step to perform preliminary semantic matching, deep semantic association analysis and global semantic consistency correction on text semantic data, image feature data, speech feature data and structured feature data. Its purpose is to generate a unified and semantically consistent comprehensive semantic network by integrating multimodal data, thereby providing a solid foundation for subsequent instruction generation.

[0072] Preliminary semantic matching is the first step of the whole process, and its purpose is to make preliminary association matching for data from different modalities. In this step, the semantic similarity between text, image, voice and structured data is determined by a specific calculation model. In the present invention, the matching between these data is not just a simple content comparison, but is achieved by analyzing the similarity at the semantic level. This process adopts a multimodal semantic similarity calculation method, which can quantify and compare the semantic similarity between data of different modalities by mapping them into a common semantic space. For example, the "meeting decision" mentioned in the text data may be associated with a decision flow chart in the image data, a meeting recording clip in the voice data, and a meeting minutes in the structured data. Through this semantic matching, the potential associations in the multimodal data can be preliminarily identified, providing a basis for further semantic analysis.

[0073] The next step is deep semantic association analysis, which is to further explore the deep semantic relationship between data based on the preliminary matching. Deep semantic association analysis is achieved by constructing a semantic association graph, in which each node represents a specific semantic unit, such as a keyword in the text, an object in the image, an emotional state in the voice, or a numerical record in the structured data. The edges of the semantic association graph represent the complex relationships between these semantic units, such as causal relationships, time order, causal association, etc. Through this multi-level semantic association analysis, deeper connections between data can be discovered and a semantic network that can reflect the full picture of multimodal data can be formed. For example, the "market growth" mentioned in the text may have direct or indirect connections with the growth curve in the image data, the high-level discussion in the voice data, and the sales data in the structured data. By analyzing these connections, a comprehensive semantic network reflecting market dynamics can be constructed to help understand the core content of the official document.

[0074] After completing the deep semantic association analysis, global semantic consistency correction is performed. This step aims to ensure that the comprehensive semantic network generated in the multimodal data fusion process is consistent at the semantic level. The key to this process is to detect and adjust potential semantic conflicts between multimodal data. Since different data modalities may come from different contexts or perspectives, there may be semantic inconsistencies between them. For example, the text may describe a positive market outlook, while the image data may show a negative market trend chart. Global semantic consistency correction ensures that the overall semantic expression of the comprehensive semantic network is coordinated and consistent by adjusting the weights of nodes and edges in the semantic association graph, or by introducing additional correction steps such as redistributing semantic weights and correcting semantic relationships. The final comprehensive semantic network not only integrates all the important information of multimodal data, but also is coherent and conflict-free in semantic expression.

[0075] Through this series of steps, the final generated comprehensive semantic network is highly consistent at the semantic level and can fully express the content of official documents covered by multimodal data. This semantic network not only captures the explicit information of each modal data, but also reveals the potential relationship between the data through in-depth analysis, and ensures the semantic coherence through global correction. Its direct effect is to provide a strong and reliable semantic foundation for the generation of subsequent official document instructions, so that the generated instructions not only accurately reflect the content of the original official document, but also maintain consistency and logic between different modal data. The formation of this comprehensive semantic network has greatly improved the semantic expression ability and overall quality of the official document instruction dataset, ensuring that the instructions generated in the end can accurately guide actual operations or decisions.

[0076] Example: For example, suppose you are processing a company's annual summary document. The text part describes in detail the company's achievements and challenges in marketing, product development, financial management, etc.; the image part includes several charts reflecting market share and product sales trends; the voice part is a recording of the senior leadership's evaluation of the annual performance; and the structured data part is the financial statements and market analysis data. In this process, the initial semantic matching first calculates the semantic similarity between the information of "market share" in the text description and the market share chart in the image, the leader's evaluation of market performance in the voice, and the market analysis data in the structured data, and establishes a preliminary semantic connection. Subsequently, the deep semantic association analysis deepens these preliminary connections and connects the key semantic units in each part of the data through the semantic association graph, such as associating "market share" with "sales growth" and "financial health" to form a more complex semantic network. Finally, the global semantic consistency correction coordinates the possibly inconsistent semantic expressions from different data modalities so that the generated semantic network can coherently reflect the core content of the company's annual summary as a whole. Through this example, we can see how the construction of a comprehensive semantic network ensures the efficient integration of multimodal data and provides a solid foundation for the ultimate generation of accurate and consistent official document instructions.

[0077] Through these steps, the official document instruction dataset is able to make full use of multimodal data to generate a content-rich and semantically coherent instruction set, ensuring that it can effectively guide relevant decisions and operations in practical applications.

[0078] Preferably, the preliminary semantic matching of the text semantic data, the image feature data, the voice feature data and the structured feature data is achieved by calculating the semantic similarity between the text semantic data, the image feature data, the voice feature data and the structured feature data; wherein the similarity calculation formula is as follows:

[0079] S m =f(T,I,A,V)

[0080] Among them, S m represents the similarity value; T represents the semantic representation of text semantic data; I represents the feature vector of image feature data; A represents the timbre feature vector of speech feature data; V represents the numerical feature of structured feature data;

[0081] The formula is used to analyze the semantic relationship between text semantic data, image feature data, voice feature data and structured feature data, and generate a unified similarity value to achieve semantic matching of multimodal data.

[0082] In this formula, each variable represents the data features of different modalities, and the similarity function f(T, I, A, V) is used to measure the semantic similarity between these features. The goal of this process is to map data of different modalities into a common semantic space through the calculation of semantic similarity, so that these data can be uniformly semantically analyzed. For text data, T represents the semantic representation after word segmentation, grammatical parsing and semantic extraction, which may include key phrases, subject-predicate structures, etc.; for image data, I is a feature vector generated by a feature extraction algorithm (such as a convolutional neural network), reflecting the visual elements in the image; for speech data, A captures the timbre, intonation and emotional characteristics of the speech; and V, as a feature representation of structured data, contains numerical features of both numerical and categorical types.

[0083] By calculating the similarity between these different modal data, preliminary semantic matching can identify the potential semantic associations between text and images, speech, and structured data. For example, the "quarterly report" mentioned in the text may have a direct semantic relationship with the bar chart in the image, the meeting summary mentioned in the speech, and the financial data in the structured data. By mapping these modal data into a unified semantic space, S m This provides a quantitative result of semantic similarity for subsequent steps, allowing these associations to be further analyzed and integrated.

[0084] The effect of preliminary semantic matching is that it provides a semantic association basis for multimodal data, so that different types of data can be effectively integrated and analyzed. By calculating semantic similarity, these different modal data are no longer isolated, but can be uniformly processed and analyzed at the semantic level. Specifically, this calculation of semantic similarity enables the automatic identification of the association between text and other modal data when processing complex official document data, thus laying the foundation for building a more comprehensive and accurate official document instruction data set.

[0085] Example: For example, suppose you are processing a complex official document that includes a text description, accompanying charts, a voice recording of a meeting, and a spreadsheet containing financial data. In this scenario, the text may mention "quarterly sales growth of the company", which is closely related to the sales trend chart in the image, the executive comments in the voice recording, and the sales data in the structured data. Through the preliminary semantic matching process, the semantic similarity S between the text semantic representation T and the image feature vector I, the voice feature vector A, and the structured feature data V can be calculated. m This similarity value reflects the proximity of these data in the semantic space, thus revealing the potential association between them.

[0086] Through this semantic similarity calculation, the system can identify the semantic connection between the "quarterly sales growth" mentioned in the text and the growth curve in the chart, the discussion content in the voice recording, and the actual growth figures in the financial data. In this way, all relevant multimodal data can be unified into a semantic network to support the generation of more accurate and comprehensive official documents and instructions.

[0087] Preferably, deep semantic association analysis is achieved by constructing a multi-layer semantic association graph, which includes multiple semantic nodes and semantic edges, wherein each semantic node represents a semantic unit, and the semantic edge represents the semantic relationship between semantic units; by constructing and analyzing the multi-layer semantic association graph at multiple levels, deep semantic association graph data is generated to describe and capture the complex semantic relationship between multimodal data.

[0088] The principle of deep semantic association analysis is to characterize the complex semantic relationships between multimodal data through a multi-level semantic association graph. In this process, the system first extracts important semantic units from data of different modalities (such as text, images, speech, and structured data) and represents them as semantic nodes. Each semantic node represents a specific semantic unit in the data, which may be a keyword in the text, a visual object in the image, a feature fragment in the speech, or a numerical field in the structured data.

[0089] The relationship between these semantic nodes is represented by semantic edges. The definition of semantic edges depends on the semantic relevance between these nodes. For example, there may be causal, temporal, or logical relationships between concepts described in text and objects in images, topics in speech, and items in structured data. Through these semantic edges, the system can establish connections between semantic nodes, thereby forming a preliminary semantic association graph.

[0090] In order to more comprehensively capture the complex semantic relationships in the data, deep semantic association analysis not only builds a semantic association graph at a single level, but also associates at multiple levels. For example, "corporate strategy" in the text may be directly associated with the company structure diagram in the image and the strategic discussion fragment in the voice in the first-level semantic association graph; in the second-level semantic association graph, "corporate strategy" is further associated with the market analysis report in the structured data; at a deeper level, these associations may also have a broader connection with financial data or external market data.

[0091] Through this multi-level semantic association graph construction, deep semantic association analysis can not only capture explicit associations in the data (such as the one-to-one correspondence between text descriptions and image content), but also reveal implicit or complex semantic connections (such as the potential impact between emotions in speech and strategic decisions in text). These multi-level association graphs ultimately form a complete deep semantic association graph, which describes in detail the complex semantic relationships between multimodal data.

[0092] The deep semantic association graph generated by deep semantic association analysis has many significant effects in constructing the official document instruction dataset. First, it can effectively integrate multimodal data to form a unified semantic framework, making the association between data clearer and more organic. Second, the deep semantic association graph can reveal the hidden semantic relationships in the data. Especially when the interaction of multimodal data is more complex, the structure of this graph can help identify the key relationships and potential decision points in the data. In addition, the deep semantic association graph also provides a solid foundation for subsequent global semantic consistency correction and instruction generation, ensuring that the official document instruction dataset has a high level of semantic coherence and consistency during the generation process.

[0093] Example: Assume that a company's annual summary report is being processed. The text part describes in detail the market changes, strategic adjustments, and financial performance during the year; the image part includes a line chart of market share changes, a pie chart of product sales, etc.; the voice part is the annual summary speech of senior management; and the structured data part contains specific financial data and market analysis results. When performing deep semantic association analysis, the system first extracts keywords in the text (such as "market share", "strategic adjustment"), key elements in the image (such as "growth curve", "sales ratio"), core themes in the voice (such as "annual performance", "market expectations"), and important fields in the structured data (such as "quarterly revenue", "market share") as semantic nodes. Next, by analyzing the semantic relationship between these nodes, the system associates the "market share" node with the growth curve in the image, the market expectation discussion in the voice, and the market share field in the structured data in the first-level semantic association graph. Subsequently, at a deeper level, the system further associates the relationship between the "strategic adjustment" node and these data to form a more complex semantic association graph. This process not only reveals information that is directly related on the surface, but also reveals potential connections such as between "market expectations" and "strategic adjustments", further strengthening the semantic consistency between the data.

[0094] Ultimately, this deep semantic association graph enables the official document instruction dataset to include complex semantic relationships between multiple modal data, ensuring that the generated instructions can accurately reflect the content of the official document and maintain consistent semantic expressions between data. This method not only improves the accuracy and practicality of the instructions, but also enhances the overall logic and information integrity of the official document dataset.

[0095] Perform quantum state distributed node initialization on semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization;

[0096] like Figure 2 As shown in the figure, quantum state distributed node initialization is the first step of this step, and its purpose is to enhance the representation ability of semantic nodes through the unique properties of quantum computing, such as quantum superposition and quantum entanglement. During the quantum state initialization process, each semantic node is initialized to a quantum state, which means that these nodes can simultaneously represent multiple potential semantic states, rather than a single state in traditional computing. In this way, semantic nodes can consider multiple possible semantic interpretations simultaneously when processing complex multimodal data, thereby maintaining efficient and flexible semantic expression in the semantic network of the data. This polymorphism is particularly important when dealing with complex, multidimensional data, because it can effectively reduce the semantic ambiguity and information loss problems that may occur in traditional computing.

[0097] The Quantum Entanglement Graph Convolutional Network (QEGCN) processes these quantum state semantic nodes based on quantum state initialization. The concept of quantum entanglement is particularly critical here, as it enables strong associations between semantic nodes, even if these nodes may not have direct connections under traditional computing frameworks. Through convolution operations, QEGCN can transfer and share information between multiple quantum state semantic nodes, thereby generating a more compact and consistent semantic network. The introduction of convolutional networks means that information can be efficiently propagated between semantic nodes, while quantum entanglement ensures that these information transfers are non-local and can capture deep associations in the semantic network. In other words, QEGCN is not just a simple connection and weight adjustment of semantic nodes, but through the mechanism of quantum entanglement, it ensures that the entire semantic network remains coherent and consistent at the global level.

[0098] After the quantum entangled graph convolutional network generates preliminary quantum semantic link data, self-organizing quantum topology optimization is performed. The main goal of this step is to further optimize the quantum semantic link to achieve the best state globally. Self-organizing quantum topology optimization is a dynamic adjustment mechanism that continuously adjusts the connections and weights between semantic nodes through an adaptive algorithm, iterates according to specific optimization criteria (such as minimizing semantic conflicts, maximizing information transmission efficiency, etc.), and finally generates optimized quantum semantic link data. This process can be understood as the application of an evolutionary algorithm in a quantum computing environment. By continuously adjusting the network topology, the semantic link can achieve the best in terms of global semantic consistency, information fluidity, and semantic expression accuracy.

[0099] The final effect of this series of processes is the generation of optimized quantum semantic link data, which plays a core role in the generation of official document instruction data sets. First, through the initialization of quantum state distributed nodes, semantic nodes can simultaneously represent multiple semantic states, greatly enhancing the ability of data sets to express complex semantics. Secondly, through the quantum entangled graph convolutional network, it ensures that the information transmission between different semantic nodes has global consistency and deep associations, and improves the coherence and accuracy of the semantic link. Finally, self-organizing quantum topology optimization enables the entire semantic network to reach the optimal state at the global level, ensuring the overall performance and reliability of the data set.

[0100] Example: For example, when processing a company's strategic planning document, the text part describes the company's future development direction, the image part shows the market analysis chart, the voice part records the discussion content of the high-level meeting, and the structured data contains specific market data and financial forecasts. When constructing the document instruction data set, first initialize the core semantic nodes such as "market analysis", "strategic adjustment", and "financial forecast" as quantum state nodes through quantum state distributed node initialization, so that these nodes can consider multiple possible semantic interpretations at the same time. Subsequently, through the quantum entangled graph convolution network, the system establishes a deep association between the "market analysis" node and the "strategic adjustment" and "financial forecast" nodes to form a globally consistent quantum semantic link to ensure that all relevant information is efficiently propagated and integrated in the network. Finally, through self-organizing quantum topology optimization, the system continuously adjusts the connection and weight between semantic nodes so that the semantic link data reaches the optimal state, and finally generates an optimized quantum semantic link that can accurately reflect the semantic associations of various aspects in the company's strategic planning and generate a high-quality document instruction data set.

[0101] Through the above process, the official document instruction dataset can show higher accuracy and consistency in semantic expression, information integration and instruction generation, providing more intelligent and reliable support for enterprise decision-making. This method not only improves the quality of the dataset, but also ensures the semantic coherence and logical consistency in the instruction generation process, greatly enhancing the practical application value of the official document dataset.

[0102] Preferably, the quantum state distributed node initialization includes the following steps:

[0103] Applying quantum state initialization techniques to each semantic node in the comprehensive semantic network;

[0104] Each semantic node in the comprehensive semantic network is initialized through quantum superposition state, so that each semantic node can simultaneously represent multiple potential semantic states and generate quantum state semantic node data;

[0105] The quantum entangled graph convolutional network processes quantum state semantic node data through convolution operations. The convolution calculation formula is as follows:

[0106] C=∑(W×Q)

[0107] Among them, C represents the output of the convolution operation, W represents the weight matrix in the quantum entangled graph convolution network, and Q represents the quantum state semantic node data;

[0108] Through convolution operations, the quantum entangled graph convolutional network transmits and shares information between multiple semantic nodes, thereby generating convolutional quantum semantic link data, which is used to reflect the deep semantic associations between semantic nodes.

[0109] In the present invention, quantum state distributed node initialization and subsequent quantum entangled graph convolution network processing steps are key links in the entire process. These steps provide multimodal semantic networks with higher expressive power and depth of association analysis through the unique properties of quantum computing, thereby optimizing semantic link data, thereby achieving more efficient and accurate document instruction generation in complex data processing.

[0110] Quantum state distributed node initialization is the first step, which aims to utilize the unique characteristics of quantum states to enhance the representation capabilities of semantic nodes. In traditional computing frameworks, each semantic node can usually only represent a single semantic state. However, through quantum state initialization technology, the system is able to initialize each semantic node to a quantum state, enabling it to represent multiple potential semantic states at the same time. This is achieved through quantum superposition. The quantum superposition state allows a semantic node to be in a combination of multiple semantic states at the same time, similar to the "multiple-choice" mechanism in classical probability, but in quantum computing, this polymorphism is more powerful because all potential states are processed in parallel during the calculation process. Therefore, quantum state semantic node data is able to comprehensively consider multiple possible semantic interpretations during processing, thereby capturing more complex semantic associations in subsequent analysis.

[0111] After completing the quantum state initialization, the system processes the data of these quantum state semantic nodes through the Quantum Entanglement Graph Convolutional Network (QEGCN). Quantum entanglement is another core feature in quantum computing, which enables different semantic nodes to establish close associations, even if these nodes may seem unrelated in the traditional framework. Through quantum entanglement, QEGCN can transmit information between multiple semantic nodes to ensure that this information is shared and enhanced in the global semantic network. Specifically, the convolution operation formula used by QEGCN is: C = ∑ (W × Q). Through this convolution operation, the association between semantic nodes is not only strengthened, but also deeper semantic relationships can be captured at the global level.

[0112] The result of this process is the generation of convolutional quantum semantic link data, which reflects the deep semantic associations between semantic nodes. These quantum semantic link data not only reflect the direct relationship between semantic nodes, but also reveal the complex semantic interactions generated by quantum entanglement and convolution operations. This kind of data provides more accurate semantic links when constructing official document instruction data sets, making the generated official document instructions more consistent with the true semantics of multimodal data.

[0113] Through the initialization of quantum state distributed nodes and the processing of quantum entangled graph convolutional networks, the semantic expression in the official document instruction data set has been greatly enhanced. First, quantum state semantic nodes can represent multiple potential semantic states at the same time, which improves the data set's ability to accommodate ambiguity and complex semantics. Secondly, the quantum entangled graph convolutional network closely links these semantic nodes through convolution operations, so that the entire semantic network maintains coherence and consistency on a global scale. The final generated convolutional quantum semantic link data ensures the accuracy and depth of the semantic link, greatly improves the generation quality of official document instructions, and ensures that the instructions can accurately reflect and integrate information from different data modalities.

[0114] Embodiment: For example, suppose a document involving market analysis is processed. The text part describes the current market trend, the image part includes a sales curve chart, the voice part records the opinions of market experts, and the structured data part contains historical sales data. In this process, the system first initializes the quantum state distributed nodes of semantic nodes such as "market trends" and "sales analysis" so that these nodes can simultaneously reflect the different development directions of the market. Then, through the quantum entangled graph convolution network, the system establishes a strong association between these quantum state semantic nodes, and through the convolution operation formula C = ∑ (W × Q), generates a convolution quantum semantic link data that reflects market changes and sales trends. This data can comprehensively consider the complex interactions between text descriptions, image trends, voice analysis, and structured data, and ultimately provide an accurate semantic basis for the generation of official document instructions.

[0115] In this way, the generated official documents and instructions can not only accurately capture the various aspects of the current market information, but also express this information in the form of consistent semantic links, providing a reliable basis for corporate decision-making. The core advantage of this method is that it can integrate complex semantic associations in different data modalities through quantum computing, thereby achieving efficient, accurate and intelligent semantic expression in the official document and instruction data set.

[0116] Preferably, the self-organizing quantum topology optimization comprises the following steps:

[0117] Dynamically adjust the semantic node connections and weights in the convolutional quantum semantic link data through an adaptive algorithm;

[0118] According to a predetermined optimization criterion, the structure of the convolution quantum semantic link data is iteratively adjusted so that the convolution quantum semantic link data reaches an optimal state in the global scope;

[0119] Generate optimized quantum semantic link data, which is used to realize information transmission and semantic reasoning in the comprehensive semantic network.

[0120] The core of self-organizing quantum topology optimization is to dynamically adjust the convolution quantum semantic link data through an adaptive algorithm. The convolution quantum semantic link data is generated by the previous steps, which reflects the complex relationships between semantic nodes. Since these relationships play a vital role in constructing the official document instruction data set, optimizing them is a necessary step to ensure the efficient operation of the entire semantic network.

[0121] The system dynamically adjusts the connections and weights between semantic nodes through an adaptive algorithm. This adaptive algorithm can automatically adjust the connection mode and weight value of each node based on factors such as the strength of association between semantic nodes and the global consistency of semantic links. Specifically, this adjustment is made in a continuous iterative process, and each round of iteration will be fine-tuned based on the results of the previous round to gradually optimize the topological structure of the semantic network. In this way, the system can discover and correct potential weaknesses or redundant connections in the semantic network, making the structure of the entire network more compact and efficient.

[0122] During the adjustment process, the system evaluates and adjusts the semantic network according to predetermined optimization criteria. These optimization criteria usually include minimizing semantic conflicts in the network, maximizing information transfer efficiency, maintaining semantic consistency, etc. In each iteration, the system evaluates the current network structure and makes corresponding adjustments based on these criteria. For example, if the connection strength between some semantic nodes is low, the system may enhance the overall coherence of the network by increasing the connection between other nodes or adjusting the weights of existing connections.

[0123] This iterative optimization process ultimately generates optimized quantum semantic link data. These data are not only optimized in local structure, but more importantly, they have reached the optimal state globally. This means that the optimized semantic network can transmit information more efficiently and provide more accurate and consistent results in the semantic reasoning process.

[0124] Through self-organizing quantum topology optimization, the quantum semantic link data finally generated shows significant improvements in many aspects. First, the connection between semantic nodes is more reasonable and tight, eliminating possible semantic conflicts and redundant connections. Secondly, the structure of the entire semantic network becomes more efficient, and information transmission and semantic reasoning can be completed in a shorter time. In addition, the optimized semantic link data shows better performance in global semantic consistency, ensuring the consistency and coherence of semantic expression when transmitting information between different modal data. These improvements directly improve the quality of the official document instruction dataset, making the final generated official document instructions more accurate and reliable, and better able to reflect and integrate information from different data sources.

[0125] Example: For example, suppose you are processing a company annual report that contains a large amount of text descriptions, image analysis, voice recordings, and structured data. After generating preliminary quantum semantic link data, the system finds that the connections between some nodes are not tight enough, or that the weights of some connections are not sufficient to support effective information transfer in the semantic network. In this case, the system dynamically adjusts these problems through self-organizing quantum topology optimization. For example, the system may increase the connection strength between the "annual sales" node and the "market share growth" node, or adjust the weight between the "financial forecast" node and the "investment strategy" node, so that the information transfer between these nodes is smoother and more effective.

[0126] After multiple rounds of iterative optimization, the final generated quantum semantic link data can more accurately reflect the key information in the annual report and pass this information to the official document instruction data set in an efficient and consistent manner. This optimization process ensures that the final generated official document instruction not only fully covers the core content of the report, but also maintains semantic consistency and logical coherence between various data, providing more reliable support for corporate decision-making.

[0127] According to user feedback, a multi-path adaptive feedback path is established to generate multi-path feedback semantic data; the multi-path feedback semantic data is adversarially generated and selected through the adversarial generative feedback network, and quantum cross-domain semantic transfer is used to generate quantum cross-domain optimized semantic data;

[0128] Preferably, establishing a multi-path adaptive feedback path comprises the following steps:

[0129] Generate multiple semantic feedback paths based on user feedback data, each semantic feedback path corresponds to a different semantic adjustment strategy, and generate multi-path feedback semantic data;

[0130] Each semantic feedback path contains different semantic adjustment weights and adjustment directions. By analyzing user feedback data, these semantic feedback paths are dynamically generated and adjusted to adapt to different semantic contexts and user needs.

[0131] In the present invention, a multi-path adaptive feedback path is established based on user feedback, and the adversarial generative feedback network and quantum cross-domain semantic transfer are used to generate optimized quantum semantic data, in order to ensure that the generated official document instructions can dynamically adapt to different semantic contexts and user needs. This process achieves deep optimization and intelligent adjustment of multimodal data through the unique advantages of multi-path feedback mechanism and quantum computing, thereby improving the accuracy and practicality of official document instructions.

[0132] The establishment of a multi-path adaptive feedback path is the basis of the whole process, which generates multiple semantic feedback paths based on user feedback data. Each feedback path corresponds to a different semantic adjustment strategy, which may include strengthening, weakening, reorganizing or deleting certain semantic nodes according to user needs. The core of this process is that by analyzing user feedback data, the system can identify which parts of the semantics need to be adjusted, and how to adjust the connections and weights of these semantic nodes to better meet the actual needs of users.

[0133] Specifically, the system first dynamically generates preliminary semantic feedback paths by analyzing user feedback data. These paths cover multiple possible semantic adjustment directions, and each path represents a potential semantic optimization solution. For example, if user feedback indicates that certain official document instructions have deviations in semantic understanding, the system may generate a path to correct this deviation by adjusting the weights and connections of relevant semantic nodes. After generating these preliminary paths, the system will further analyze the effectiveness of each path and dynamically adjust the weights and directions of these paths according to predetermined criteria (such as the priority of user feedback, the complexity of semantic adjustment, contextual adaptability, etc.).

[0134] These multi-path feedback semantic data are adversarially generated and selected through the Adversarial Generative Feedback Network (AGFN). The Adversarial Generative Feedback Network consists of two parts: the generator and the discriminator. The generator is responsible for generating possible optimization solutions, while the discriminator evaluates the pros and cons of these solutions. Through multiple rounds of adversarial training, the system can screen out the optimal semantic adjustment solution and apply it to the quantum cross-domain semantic transmission process.

[0135] Quantum cross-domain semantic transfer is the last step of this process. It uses the cross-domain transfer capability of quantum computing to transfer and optimize the selected optimal semantic adjustment scheme between multiple semantic domains. This mechanism ensures that the final generated quantum cross-domain optimized semantic data can maintain consistency and coherence in different semantic contexts. Quantum cross-domain semantic transfer not only considers the optimization effect within a single semantic domain, but also ensures the semantic fidelity and optimization effect when transferring between different semantic domains. This is particularly important for processing complex, multimodal official document data, because different data modalities and semantic contexts may have different effects on the generation of the same official document instruction.

[0136] Through this series of steps, the quantum cross-domain optimized semantic data finally generated is highly adaptable and optimized. First, the establishment and adjustment of the multi-path adaptive feedback path enables the official document instructions to be dynamically adjusted according to user feedback, ensuring that the semantic expression of the instructions is closer to actual needs. Secondly, through the screening of the adversarial generation feedback network, the system can automatically select the optimal semantic adjustment scheme, avoiding the subjective bias caused by human intervention. Finally, quantum cross-domain semantic transmission ensures that these adjustment schemes remain efficient and consistent during the transmission process between different semantic domains, greatly improving the global semantic consistency and information transmission efficiency of official document instructions.

[0137] Example: Suppose you are processing a complex corporate decision-making document that involves collaborative information from multiple departments. User feedback indicates that the currently generated instructions have some semantic deviations when transmitting information across departments, causing some departments to misunderstand the content of the instructions. To address this problem, the system first generates multiple semantic feedback paths through user feedback. For example, in response to feedback from the "Finance Department", the system may generate a path that strengthens the semantic weight of the "Budget Allocation" node, while adjusting the connection method between the "Expenditure Plan" and "Revenue Forecast" nodes. Subsequently, through the adversarial generation feedback network, the system adversarially generates and evaluates these paths to screen out the optimal semantic adjustment plan. Finally, quantum cross-domain semantic transmission optimizes and transmits this adjustment plan between the semantic domains of each department to ensure that the "budget allocation" instruction can be consistently understood and executed between the finance department, marketing department, and R&D department.

[0138] Through this example, we can see how the establishment of multi-path adaptive feedback paths, adversarial generation feedback networks, and quantum cross-domain semantic transmission work together to ensure that official documents and instructions can maintain efficient, accurate, and consistent transmission effects in complex semantic environments. This method not only improves the execution efficiency of instructions, but also reduces the execution risks caused by semantic deviations, providing more reliable and intelligent support for corporate decision-making.

[0139] like Figure 3 As shown, quantum modal coupling calculation is performed on quantum cross-domain optimized semantic data, and at least two official document versions are generated through a quantum adversarial generation network, and finally a complete official document data set is generated through global quantum semantic consistency optimization;

[0140] Preferably, the adversarial generative feedback network comprises the following steps:

[0141] Generate multiple semantic optimization schemes through adversarial training of the generator and the discriminator, where the generator is used to generate quantum semantic links after semantic adjustment, and the discriminator is used to evaluate the quantum semantic links generated by the generator and judge their quality;

[0142] Generate an optimized quantum semantic link scheme through multiple rounds of adversarial training;

[0143] Optimizing the quantum semantic link scheme through a quantum cross-domain semantic transmission mechanism, and finally generating quantum cross-domain optimized semantic data;

[0144] Quantum modal coupling calculation includes multimodal quantum state coupling of quantum cross-domain optimized semantic data to generate at least two different versions of official documents;

[0145] The document version is adversarially selected through a quantum adversarial generation network, and finally a complete document dataset is generated through global quantum semantic consistency optimization;

[0146] Quantum modal coupling computing establishes a quantum state coupling relationship between multimodal data, making the generated document version consistent and coherent in content, semantics and logic.

[0147] The core of quantum modal coupling computing lies in the multimodal quantum state coupling of quantum cross-domain optimized semantic data. The advantage of quantum state coupling is that it can process data of multiple modes at the same time and deeply integrate these modes in the semantic space. In this process, the system will identify the key semantic nodes in the multimodal data and generate different quantum semantic links through the superposition and coupling of quantum states. These links can retain their respective characteristics when processing different modal data while maintaining consistency in the global semantic space. The final result of quantum modal coupling computing is to generate at least two different versions of official documents, which may have slight differences in content and semantics, but are coherent in logical structure and information consistency. The purpose of this multi-version generation is to ensure that the system can provide the most suitable version of official documents when facing different application scenarios or user needs.

[0148] The system uses a quantum generative adversarial network (QGAN) to perform adversarial selection on the generated versions of official documents. QGAN consists of two parts: the generator and the discriminator. The generator is responsible for generating official document versions after quantum modal coupling, while the discriminator evaluates the quality and consistency of these versions. The generator attempts to generate optimized quantum semantic links between different versions, while the discriminator selects the best version to retain and further optimize by evaluating the performance of these links in terms of global semantic consistency. This process is similar to traditional adversarial generative networks, but the introduction of quantum computing makes the adversarial process more complex and sophisticated, and can handle more complex multimodal data structures.

[0149] After generating multiple optimized versions of the document, the system integrates these versions into a complete document data set through global quantum semantic consistency optimization. The main goal of this step is to ensure semantic consistency and logical coherence between multiple versions. Through global quantum semantic consistency optimization, the system can evaluate and adjust the differences between versions on a global scale to ensure that the final generated data set is consistent in semantic expression, information transmission, and logical structure.

[0150] Through the above process, the generated complete official document data set is not only rich and diverse in content, but also highly consistent in semantics and logic. First, quantum modal coupling calculation ensures the deep fusion between different modal data, so that the generated official document version can provide appropriate responses when facing different contexts and needs. Secondly, the introduction of quantum adversarial generative networks makes the selection between versions more intelligent and optimized, ensuring that the final retained version of the official document is the best and can play the greatest role in different application scenarios. Finally, through global quantum semantic consistency optimization, it is ensured that the final data set is coherent as a whole, whether in terms of information content or semantic logic, efficient information transmission and correct instruction execution can be achieved.

[0151] Example: Assume that a complex document is processed, which involves multiple aspects of the company's strategic decision-making, including market analysis, financial planning, technology research and development, etc. After generating quantum cross-domain optimization semantic data, the system generates two different versions of the document through quantum modal coupling calculation. The first version focuses on the relationship between the market and finance, emphasizing the impact of strategic decisions on market share; the second version focuses on the progress of technology research and development, and focuses on describing the application potential of new technologies in the market.

[0152] Through the quantum adversarial generative network, the system conducted an adversarial selection of the two versions. The generator tried to optimize the semantic association between market and finance, while evaluating the connection between technology research and development and market demand, while the discriminator evaluated the global semantic consistency of the two versions and finally determined that the first version showed higher global consistency in strategic decision-making, so it was selected as the main version.

[0153] The system integrates the advantages of the two versions through global quantum semantic consistency optimization and generates a complete document data set. In this data set, the relationship between market analysis, financial planning and technological research and development is organically combined to ensure the semantic and logical consistency of the document, while providing a comprehensive strategic decision-making perspective that adapts to different user needs and application scenarios.

[0154] Through this example, we show how quantum modal coupling computing, quantum adversarial generative networks, and global quantum semantic consistency optimization work together to ensure that the final generated document data set maintains a high degree of consistency and coherence in content, semantics, and logic. This method not only improves the quality of document generation, but also enhances its practicality and flexibility in different application scenarios, providing more intelligent and accurate support for corporate decision-making.

[0155] Key instructions are extracted from the complete official document data set, structured and sorted, and the sorted instruction data is quantum-link associated with the optimized quantum semantic link data. Finally, after format normalization and instruction set optimization, an official document instruction data set is generated.

[0156] Extracting key instructions is one of the core steps of the whole process. In this link, the system identifies and extracts the most important directive content from the complete document data set that has been optimized and integrated. These key instructions are usually the parts of the document that have a direct impact on execution, decision-making, or operation. For example, in a strategic planning document, key instructions may include "specific steps to increase market share", "allocation plan for R&D budget", etc. The extraction process of these instructions not only relies on semantic analysis and natural language processing technology, but also needs to be combined with the global optimization results of the quantum semantic link to ensure that the extracted instructions are highly consistent and coherent in semantics.

[0157] The system performs structured processing and sorting on the extracted key instructions. The purpose of structured processing is to extract these instructions from unstructured text content and convert them into an operational and logically clear format. This process may involve converting natural language instructions into structured data formats, such as tables, lists, or tree structures, to facilitate subsequent processing and execution. Sorting is to arrange these instructions in the optimal execution order based on the importance, time sequence, logical relationship and other criteria of the instructions. This sorting process helps to optimize the execution process of the instructions and ensure that the tasks can be completed efficiently in actual operations.

[0158] After completing the structured processing and sorting, the system associates the sorted instruction data with the optimized quantum semantic link data through quantum links. The core of quantum link association is to use the unique advantages of quantum computing to deeply integrate structured instructions with the global quantum semantic network. Through this association, the system can ensure that each instruction finds the best position and association point in the semantic link, so that these instructions are not only isolated operation steps, but also closely connected with other related information through semantic links. This process ensures that when the instructions are executed, the global semantic consistency of the entire official document semantic network can be fully considered, thereby improving the execution effect of the instructions.

[0159] After format normalization and instruction set optimization, the system generates the final document instruction data set. The purpose of format normalization is to convert these optimized instruction data into instruction sets that conform to specific standards or formats for use by different systems or user groups. For example, the system may convert these instructions into standardized XML format, JSON format, or other structured data formats to ensure that the instructions have consistent presentation on different platforms. Instruction set optimization is to further refine and adjust the generated instruction set to ensure that it has the highest execution efficiency and the least ambiguity in practical applications. This process may include deleting redundant instructions, merging similar instructions, or making customized adjustments based on user needs.

[0160] Through the above series of steps, the official document instruction data set finally generated has achieved optimization effects in content, format and execution order. First, the extraction and structured processing of key instructions ensure the accuracy and operability of the instruction data set; second, through the association with quantum semantic link data, the coherence and consistency of the instructions in the global semantic network are ensured; finally, format standardization and instruction set optimization ensure the adaptability and practicality of the instruction set in different application scenarios. Overall, this process greatly improves the quality of the official document instruction data set, making it not only accurate in content, but also able to be efficiently applied in different execution environments.

[0161] Example: Assume that you are processing a document for the company's annual strategic plan. The document contains the company's main strategic direction, market expansion plan, product development route, and financial budget allocation for the next year. In this process, the system first extracts key instructions such as "priority steps for market expansion", "milestone goals for new product development", and "specific amount of budget allocation" from the complete document data set. The system then structures these instructions, converts them into a structured instruction list, and sorts them according to strategic priority and time sequence. For example, "priority steps for market expansion" may be sorted as the highest priority instruction, while "milestone goals for product development" are sorted according to time nodes.

[0162] The system associates these sorted instruction data with the optimized quantum semantic link data to ensure that each instruction finds the best position in the global semantic network. This association enables the instructions between "market expansion" and "product development" to support and reinforce each other semantically. Finally, the system normalizes the format of these associated instruction data, converts them into a standard format suitable for use by different departments, and further optimizes the instruction set to ensure its efficiency and consistency in actual execution.

[0163] Through this example, the whole process of extracting key instructions from a complete official document data set, structural processing and sorting, quantum link association, format normalization processing and instruction set optimization is demonstrated, and how to ensure that the final generated official document instruction data set is consistent, coherent and highly adaptable to actual operation needs in terms of content, semantics and execution logic. This method not only improves the quality of official document instruction generation, but also enhances its practicality and operability in different application scenarios, providing more accurate and efficient support for enterprise decision-making and operations.

[0164] like Figure 4 As shown, a device for constructing an official document instruction data set includes:

[0165] The acquisition module is used to acquire multimodal data including text, image, speech and structured data, and preprocess the multimodal data to generate text semantic data, image feature data, speech feature data and structured feature data; the purpose of preprocessing is to convert these different types of data into a unified semantic representation form to generate text semantic data, image feature data, speech feature data and structured feature data. This step ensures that subsequent semantic processing can be carried out under the same semantic framework, reducing the complexity in cross-modal data processing.

[0166] The semantic processing module is used to perform preliminary semantic matching, deep semantic association analysis and global semantic consistency correction on text semantic data, image feature data, speech feature data and structured feature data to generate a comprehensive semantic network. The core of this module is to establish semantic relationships between different data types to ensure that the generated comprehensive semantic network can accurately reflect the internal logic and connections of the data.

[0167] The quantum processing module is used to initialize the quantum state distributed nodes of the semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization. First, the diversity and expression ability of the semantic nodes are enhanced through quantum state distributed node initialization; then, the quantum entangled graph convolutional network and self-organizing quantum topology optimization are used to generate optimized quantum semantic link data. This module uses the characteristics of quantum computing to improve the global consistency and information transmission efficiency of semantic links.

[0168] The adaptive feedback module is used to establish a multi-path adaptive feedback path based on user feedback and generate multi-path feedback semantic data; this module ensures that the generated official document instructions can adapt to different contexts and user needs through real-time analysis and adjustment.

[0169] The adversarial generation module is used to perform adversarial generation and selection on multi-path feedback semantic data through the adversarial generation feedback network, and generate quantum cross-domain optimization semantic data using quantum cross-domain semantic transfer; the multi-path feedback semantic data is adversarially generated and selected through the adversarial generation feedback network. Quantum cross-domain semantic transfer technology is used to generate quantum cross-domain optimization semantic data. This process ensures that the generated semantic data has higher adaptability and optimization effect based on multi-path feedback.

[0170] The modal coupling module is used to perform quantum modal coupling calculations on quantum cross-domain optimized semantic data, and generate at least two versions of official documents through a quantum adversarial generation network, and finally generate a complete official document data set through global quantum semantic consistency optimization; perform quantum modal coupling calculations to generate multiple official document versions, and select the optimal version through a quantum adversarial generation network. Finally, a complete and optimized official document data set is generated through global quantum semantic consistency optimization.

[0171] The data set generation module is used to extract key instructions from the complete official document data set, perform structured processing and sorting, associate the sorted instruction data with the optimized quantum semantic link data, and finally generate the official document instruction data set after format normalization and instruction set optimization. After format normalization and instruction set optimization, the official document instruction data set is finally generated. This module ensures that the generated official document instruction set meets high-quality standards in both content and form, and has operability for practical applications.

[0172] A device for constructing an official document instruction data set, the device comprising a memory and a processor, wherein the memory is used to store executable instructions, and the processor is used to execute the instructions to implement the steps of the method for constructing the official document instruction data set.

[0173] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for constructing an official document instruction data set.

[0174] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for constructing an official document instruction data set, characterized in that: The following steps are involved: Collecting multimodal data including text, image, voice and structured data, and preprocessing the multimodal data to generate text semantic data, image feature data, voice feature data and structured feature data; Perform preliminary semantic matching, deep semantic association analysis, and global semantic consistency correction on text semantic data, image feature data, speech feature data, and structured feature data to generate a comprehensive semantic network; Perform quantum state distributed node initialization on semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization; According to user feedback, a multi-path adaptive feedback path is established to generate multi-path feedback semantic data; the multi-path feedback semantic data is adversarially generated and selected through the adversarial generative feedback network, and quantum cross-domain semantic transfer is used to generate quantum cross-domain optimized semantic data; Perform quantum modal coupling calculations on quantum cross-domain optimized semantic data, generate at least two versions of official documents through a quantum adversarial generative network, and finally generate a complete official document dataset through global quantum semantic consistency optimization; Key instructions are extracted from the complete official document data set, structured and sorted, and the sorted instruction data is quantum-link associated with the optimized quantum semantic link data. Finally, after format normalization and instruction set optimization, an official document instruction data set is generated.

2. The method according to claim 1, characterized in that The preliminary semantic matching of text semantic data, image feature data, voice feature data and structured feature data is achieved by calculating the semantic similarity between the text semantic data, image feature data, voice feature data and structured feature data; wherein the similarity calculation formula is as follows: S m =f(T,I,A,V) Among them, S m represents the similarity value; T represents the semantic representation of text semantic data; I represents the feature vector of image feature data; A represents the timbre feature vector of speech feature data; V represents the numerical feature of structured feature data; The formula is used to analyze the semantic relationship between text semantic data, image feature data, voice feature data and structured feature data, and generate a unified similarity value to achieve semantic matching of multimodal data.

3. The method according to claim 1, characterized in that Deep semantic association analysis is achieved by constructing a multi-layer semantic association graph, which includes multiple semantic nodes and semantic edges, where each semantic node represents a semantic unit and the semantic edge represents the semantic relationship between semantic units; by constructing and analyzing the multi-layer semantic association graph at multiple levels, deep semantic association graph data is generated to describe and capture the complex semantic relationship between multimodal data.

4. The method according to claim 1, characterized in that: The quantum state distributed node initialization includes the following steps: Applying quantum state initialization techniques to each semantic node in the comprehensive semantic network; Each semantic node in the comprehensive semantic network is initialized through quantum superposition state, so that each semantic node can simultaneously represent multiple potential semantic states and generate quantum state semantic node data; The quantum entangled graph convolutional network processes quantum state semantic node data through convolution operations. The convolution calculation formula is as follows: C=Σ(W×Q) Among them, C represents the output of the convolution operation, W represents the weight matrix in the quantum entangled graph convolution network, and Q represents the quantum state semantic node data; Through convolution operations, the quantum entangled graph convolutional network transmits and shares information between multiple semantic nodes, thereby generating convolutional quantum semantic link data, which is used to reflect the deep semantic associations between semantic nodes.

5. The method according to claim 4, characterized in that Self-organizing quantum topology optimization includes the following steps: Dynamically adjust the semantic node connections and weights in the convolutional quantum semantic link data through an adaptive algorithm; According to a predetermined optimization criterion, the structure of the convolution quantum semantic link data is iteratively adjusted so that the convolution quantum semantic link data reaches an optimal state in the global scope; Generate optimized quantum semantic link data, which is used to realize information transmission and semantic reasoning in the comprehensive semantic network.

6. The method according to claim 1, characterized in that Establishing a multi-path adaptive feedback path includes the following steps: Generate multiple semantic feedback paths based on user feedback data, each semantic feedback path corresponds to a different semantic adjustment strategy, and generate multi-path feedback semantic data; Each semantic feedback path contains different semantic adjustment weights and adjustment directions. By analyzing user feedback data, these semantic feedback paths are dynamically generated and adjusted to adapt to different semantic contexts and user needs.

7. The method according to claim 1, characterized in that The adversarial generative feedback network consists of the following steps: Generate multiple semantic optimization schemes through adversarial training of the generator and the discriminator, where the generator is used to generate quantum semantic links after semantic adjustment, and the discriminator is used to evaluate the quantum semantic links generated by the generator and judge their quality; Generate an optimized quantum semantic link scheme through multiple rounds of adversarial training; Optimizing the quantum semantic link scheme through a quantum cross-domain semantic transmission mechanism, and finally generating quantum cross-domain optimized semantic data; Quantum modal coupling calculation includes multimodal quantum state coupling of quantum cross-domain optimized semantic data to generate at least two different versions of official documents; The document version is adversarially selected through a quantum adversarial generation network, and finally a complete document dataset is generated through global quantum semantic consistency optimization; Quantum modal coupling computing establishes a quantum state coupling relationship between multimodal data, making the generated document version consistent and coherent in content, semantics and logic.

8. A device for constructing a document instruction data set, characterized in that: include: An acquisition module, used to acquire multimodal data including text, image, voice and structured data, and preprocess the multimodal data to generate text semantic data, image feature data, voice feature data and structured feature data; The semantic processing module is used to perform preliminary semantic matching, deep semantic association analysis, and global semantic consistency correction on text semantic data, image feature data, speech feature data, and structured feature data to generate a comprehensive semantic network; The quantum processing module is used to initialize the quantum state distributed nodes of the semantic nodes in the comprehensive semantic network, and generate optimized quantum semantic link data through quantum entangled graph convolutional network and self-organizing quantum topology optimization; An adaptive feedback module, used to establish a multi-path adaptive feedback path according to user feedback and generate multi-path feedback semantic data; The adversarial generation module is used to perform adversarial generation and selection on multi-path feedback semantic data through an adversarial generation feedback network, and to generate quantum cross-domain optimized semantic data using quantum cross-domain semantic transfer; The modal coupling module is used to perform quantum modal coupling calculations on quantum cross-domain optimized semantic data, generate at least two versions of official documents through a quantum adversarial generative network, and finally generate a complete official document data set through global quantum semantic consistency optimization; The data set generation module is used to extract key instructions from the complete official document data set, perform structured processing and sorting, and associate the sorted instruction data with the optimized quantum semantic link data through quantum links. Finally, after format normalization and instruction set optimization, an official document instruction data set is generated.

9. A device for constructing a document instruction data set, characterized in that: The device includes a memory and a processor, wherein the memory is used to store executable instructions, and the processor is used to execute the instructions to implement the steps of the method for constructing an official document instruction data set as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for constructing an official document instruction data set as claimed in any one of claims 1 to 7 are implemented.

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