Government affair service method and system for processing intelligent dialogue based on multi-modal semantic information

Abstract

The invention relates to the field of intelligent dialogue technology and government affair service, in particular to a government affair service method and system for processing intelligent dialogue based on multi-modal semantic information, comprising the following steps: a) multi-modal data acquisition, b) data preprocessing, c) deep learning tree construction, d) multi-modal fusion and semantic understanding, e) intelligent dialogue generation, and f) feedback circulation and optimization. The system can comprehensively capture the input of the user by integrating information of various modes such as texts, voices and images, richer information sources are provided, efficient feature extraction is realized, feature extraction is performed on data of each mode by using a deep learning algorithm (such as a convolutional neural network and a recurrent neural network), and the feature extraction efficiency is improved; the deep learning model can perform deep semantic understanding on the multi-modal feature representation, the accurate semantic vector is generated, and the accuracy and the naturalness of the dialogue are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent dialogue technology and government service, specifically to a government service method and system based on multi-modal semantic information processing intelligent dialogue. BACKGROUND

[0002] With the rapid development of information technology, government services are gradually developing towards intelligence and personalization. Traditional government service systems mainly rely on text input and output, which cannot fully utilize information from multiple modalities such as images and voice, resulting in poor user experience and low service efficiency. In addition, traditional semantic processing methods often rely on rules and shallow models, making it difficult to cope with complex and variable user demands. Therefore, it is of great significance to develop a government service method and system based on multi-modal semantic information processing intelligent dialogue to improve the quality and efficiency of government services.

[0003] Disadvantages of Prior Art Single modal information processing: Traditional government service systems mainly rely on text input and output, which cannot fully utilize information from multiple modalities such as images and voice, resulting in insufficient comprehensiveness and accuracy of information processing.

[0004] Single modal information processing is easily affected by environmental noise, user input errors, and other factors, reducing service quality and user experience.

[0005] Shallow semantic understanding: Traditional semantic processing methods mainly rely on rules and shallow models, making it difficult to cope with complex and variable user demands.

[0006] Shallow models have limited performance in processing high-dimensional data and complex tasks, and cannot provide deep semantic understanding and dialogue generation.

[0007] Lack of personalized service: Traditional government service systems lack consideration of user historical dialogue records and personal preferences, and cannot provide personalized services.

[0008] User feedback and knowledge base updating mechanism are not perfect, and the system cannot be continuously optimized according to user's actual needs.

[0009] Single user interaction mode: Traditional government service systems provide single user interaction mode, which cannot meet the individual needs of different users.

[0010] Lack of diversified input and output modalities limits the way users interact with the system. SUMMARY

[0011] In view of the deficiencies of the prior art, the present application provides a government service method and system based on intelligent dialogue of multi-modal semantic information processing.

[0012] To achieve the above object, the present application provides the following technical solution: the government service method based on intelligent dialogue of multi-modal semantic information processing of the present application comprises the following steps: a) Multi-modal data acquisition: used for acquiring multi-modal data input by the user, including but not limited to text, voice and image data, so as to realize all-round capture of the user's demand; b) Data preprocessing: the acquired multi-modal data is cleaned, formatted and normalized, so as to ensure data quality and provide a basis for subsequent processing; c) Deep learning tree construction: a model based on deep learning tree is constructed, and the model comprises multiple branches, each branch is used for processing data of one mode, and a deep learning algorithm is used for feature extraction and preliminary semantic understanding; d) Multi-modal fusion and semantic understanding: advanced attention mechanism or fusion algorithm is used to effectively fuse the feature information output by each branch, so as to comprehensively and deeply understand the user's intention and demand; e) Intelligent dialogue generation: based on the fused semantic information, natural language generation technology is used to generate intelligent dialogue content matched with the user's demand, and the intelligent dialogue content is presented to the user through a suitable output mode, and the output mode includes text output, image output and voice output; f) Feedback cycle and optimization: according to the instant feedback and historical data of the user, the model is continuously optimized to improve the accuracy and naturalness of the dialogue, and according to the characteristics of the government service process, the intelligent dialogue is seamlessly connected with the business process.

[0013] Preferably, the multi-modal information acquisition further comprises: an information input interface, the input modes of the input interface include text information, image information and voice information; an information input device, the information input device includes a keyboard, a camera and a microphone.

[0014] Further preferably, the data preprocessing further comprises: a text preprocessing unit, which performs word segmentation on the text information collected in the multi-modal information acquisition step to obtain a word sequence; an image preprocessing unit, which performs feature extraction on the picture information collected in the multi-modal information acquisition step to identify objects and scenes in the image; a voice preprocessing unit, which performs voice recognition on the voice information collected in the multi-modal information acquisition step to obtain text information, and performs voiceprint recognition and emotion analysis.

[0015] Further preferably, the multi-modal information fusion further comprises: a feature extraction unit for converting the pre-processed text, image and voice information into feature vectors; a feature fusion unit for fusing the feature vectors using an AI deep learning tree model to generate a unified multi-modal feature representation.

[0016] Preferably, the semantic understanding and dialogue generation further comprises: a semantic parsing unit for performing semantic understanding on the multi-modal feature representation using a deep learning model to generate a semantic vector; a dialogue strategy unit for generating a dialogue strategy based on the semantic vector and the user's historical dialogue record; a natural language generation unit for converting the dialogue strategy into natural language text to generate reply information; a multi-modal output unit for selecting an appropriate output modality for reply according to the user's preferences, the output modality including text information, voice information and image information.

[0017] Further preferably, the deep learning tree model comprises a knowledge base, the knowledge base comprising: a government affair knowledge base for storing information related to government affairs, such as laws and regulations, guidelines for handling affairs, and common questions; a user information base for storing information related to the user's personal information, historical record, and preference settings.

[0018] Further preferably, the feedback loop and optimization further comprises: an information output interface through which the user can view or listen to the system's reply information and provide feedback; a deep learning tree construction step for updating the government affair knowledge base and adding new government affair information based on the user's feedback and new knowledge; a deep learning tree construction step for updating the user information base and recording the user's personal information, historical record and preference settings based on the user's feedback and new knowledge.

[0019] Compared with the prior art, the present application provides a government affair service method and system based on multi-modal semantic information processing intelligent dialogue, which has the following advantages: Multi-modal information processing: Comprehensive capture of user needs: By integrating information from multiple modalities such as text, voice and image, the system can comprehensively capture the user's input and provide more comprehensive information sources.

[0020] Improved information accuracy: The combination of multi-modal information can complement each other, reducing the uncertainty of single modality information and improving the accuracy and reliability of information.

[0021] Deep learning techniques: Efficient feature extraction: Use deep learning algorithms (such as convolutional neural networks, recurrent neural networks, etc.) to extract features from each modality's data, generating high-quality feature vectors.

[0022] Strong semantic understanding ability: Deep learning models can perform deep semantic understanding on multi-modal feature representations, generating accurate semantic vectors and improving the accuracy and naturalness of conversations.

[0023] Feature fusion and semantic understanding: Effective feature fusion: Use attention mechanisms or fusion algorithms (such as weighted averaging, concatenation, etc.) to effectively fuse feature vectors from different modalities, generating unified multi-modal feature representations.

[0024] In-depth semantic analysis: Use deep learning models to understand the semantics of multi-modal feature representations, generating semantic vectors and further understanding user intentions and needs.

[0025] Intelligent dialogue generation: Personalized dialogue strategy: Based on semantic vectors and user's historical dialogue records, generate personalized dialogue strategies to improve the relevance and effectiveness of conversations.

[0026] Natural language generation: Convert dialogue strategies into natural language text to generate reply information, ensuring the natural flow of reply content.

[0027] Multi-modal output: According to user preferences, choose appropriate output modalities (such as text, speech, images, etc.) for replies, providing diverse interaction methods.

[0028] Feedback loop and optimization: Real-time feedback: Users view or listen to system replies through the information output interface, providing real-time feedback to help the system continuously improve.

[0029] Knowledge base update: Update the government affairs knowledge base and user information base based on user feedback and new knowledge, recording user personal information, historical records, and preference settings.

[0030] Model optimization: Continuously optimize the AI deep learning tree model based on user feedback and new knowledge to improve the accuracy and naturalness of conversations.

[0031] User-friendly: Diverse input methods: Provide multiple input methods (text, image, voice) to meet the individual needs of different users.

[0032] Intuitive operation interface: Users can choose the appropriate input modality through the input interface, making the operation simple and intuitive.

[0033] Flexible output mode: According to the user's preference, choose the appropriate output mode (such as text, voice, image, etc.), provide diversified interactive experience.

[0034] Knowledge base management: Rich government knowledge: The government knowledge base stores information related to laws and regulations, work guidelines, common problems, etc., providing comprehensive government service support.

[0035] Detailed user information: The user information base stores user personal information, historical records, preference settings, etc., supporting personalized services of the system.

[0036] Efficient service: Quick response: Through deep learning technology, the system can quickly understand and generate reply information, improving service efficiency.

[0037] Seamless integration: The intelligent dialogue system is seamlessly integrated with the government service process, providing an integrated government service experience. BRIEF DESCRIPTION OF DRAWINGS

[0038] Fig. 1 The mind map of the present invention; Fig. 2 The data processing flowchart of the present invention; Fig. 3 The intelligent dialogue generation flowchart of the present invention. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present invention.

[0040] Please refer to Figs. 1-3 The government service method based on multi-modal semantic information processing intelligent dialogue of the present invention includes the following steps: a) Multi-modal data acquisition: used to collect multi-modal data input by users, including but not limited to text, voice and image data, to achieve comprehensive capture of user needs; b) Data preprocessing: clean, format and normalize the collected multi-modal data to ensure data quality and provide a basis for subsequent processing; c) Deep learning tree construction: a model based on deep learning tree is constructed, and the model contains multiple branches, each branch is specially designed to process data of one modality, and deep learning algorithm is used for feature extraction and preliminary semantic understanding; d) Multimodal fusion and semantic understanding: Employing advanced attention mechanisms or fusion algorithms, the feature information output from each branch is effectively fused to form a comprehensive and in-depth understanding of user intent and needs; e) Intelligent dialogue generation: Based on the fused semantic information, natural language generation technology is used to generate intelligent dialogue content that matches the user's needs and present it to the user through appropriate output methods, including text output, image output and voice output. f) Feedback loop and optimization: Based on real-time user feedback and historical data, the model is continuously optimized to improve the accuracy and naturalness of the dialogue. At the same time, based on the characteristics of government service processes, the intelligent dialogue is seamlessly integrated with business processes.

[0041] This approach integrates information from multiple modalities, including text, voice, and images, and utilizes deep learning technology for semantic understanding and dialogue generation, thereby improving the accuracy of government services and user satisfaction. The specific steps are as follows: Multimodal data acquisition: Information input interface: Provides a user input interface that supports the input of text, image and voice information.

[0042] Information input devices: including keyboards, cameras, and microphones, used to collect user input information.

[0043] Data preprocessing: Text preprocessing unit: performs word segmentation on the collected text information to obtain word sequences.

[0044] Image preprocessing unit: Extracts features from the acquired image information and identifies objects and scenes in the image.

[0045] Speech preprocessing unit: performs speech recognition on the collected speech information to obtain text information, and performs voiceprint recognition and sentiment analysis.

[0046] Deep learning tree construction: Construct a model based on deep learning trees, which contains multiple branches, each branch specializing in processing data of a specific modality.

[0047] Deep learning algorithms are used for feature extraction and preliminary semantic understanding.

[0048] Multimodal fusion and semantic understanding: Feature extraction unit: Converts preprocessed text, image and speech information into feature vectors.

[0049] Feature fusion unit: Uses an AI deep learning tree model to fuse feature vectors and generate a unified multimodal feature representation.

[0050] ‌AI deep learning tree model is a model that combines decision tree and deep learning technology, aiming to improve prediction performance and interpretability.

[0051] ‌Model background: Decision trees are widely used in classification and regression tasks due to their intuitive and easy-to-understand characteristics. However, traditional decision trees have limited performance in handling high-dimensional data and complex models. With the development of deep learning technology, people began to explore the combination of deep learning algorithms and decision trees.

[0052] ‌Combination method: By embedding deep learning components such as convolutional neural networks (CNN) and recurrent neural networks (RNN) in the decision tree structure, the complexity and expression ability of the model are improved. At the same time, deep learning algorithms are used to automatically optimize the decision tree structure and parameters, improving the adaptability and generalization ability of the model.

[0053] Semantic analysis unit: Use deep learning models to understand the semantics of multi-modal feature representations and generate semantic vectors.

[0054] Intelligent dialogue generation: Dialogue strategy unit: Generate dialogue strategies based on semantic vectors and user's historical dialogue records.

[0055] Natural language generation unit: Convert dialogue strategies into natural language text to generate reply information.

[0056] Multi-modal output unit: According to the user's preference, select the appropriate output mode (such as text, voice, image, etc.) for reply.

[0057] Feedback loop and optimization: Information output interface: Users view or listen to the system's reply information through this interface and provide feedback.

[0058] Knowledge base update: Update the government knowledge base and user information base according to user feedback and new knowledge.

[0059] Model optimization: Continuously optimize the deep learning tree model based on user feedback and new knowledge to improve the accuracy and naturalness of dialogue.

[0060] Working principle of preferred technical solution Multi-modal information collection: Information input interface: Provide user input interface, support text information, image information and voice information input.

[0061] Information input device: Including keyboard, camera and microphone, used for collecting user input information.

[0062] Working principle: The user selects the appropriate input modality (text, image, voice) through the input interface, and the system collects the user's input information through the corresponding input device (keyboard, camera, microphone).

[0063] Data preprocessing: Text preprocessing unit: The collected text information is segmented into words to obtain a sequence of words.

[0064] Image preprocessing unit: The collected image information is feature extracted to identify objects and scenes in the image.

[0065] Speech preprocessing unit: The collected speech information is speech recognized to obtain text information, and voiceprint recognition and sentiment analysis are performed.

[0066] Working principle: Through techniques such as word segmentation, feature extraction, and speech recognition, the collected multi-modal data is preprocessed to ensure data quality and consistency, providing a foundation for subsequent processing.

[0067] Deep learning tree construction: Model structure: A model based on deep learning tree is constructed, which contains multiple branches, each branch specifically processes data of one modality.

[0068] Feature extraction: Deep learning algorithms (such as convolutional neural networks, recurrent neural networks, etc.) are used to extract features and preliminarily understand semantics for each modality of data.

[0069] Working principle: Through deep learning algorithms, feature extraction is performed on data of each modality to generate feature vectors, providing a foundation for multi-modal fusion.

[0070] Multi-modal fusion and semantic understanding: Feature extraction unit: The preprocessed text, image, and speech information are converted into feature vectors.

[0071] Feature fusion unit: AI deep learning tree model is used to fuse feature vectors to generate unified multi-modal feature representation.

[0072] Semantic analysis unit: Deep learning model is used to understand the semantics of multi-modal feature representation to generate semantic vector.

[0073] Working principle: Through the feature extraction unit, data of different modalities is converted into feature vectors, and the feature fusion unit is used to fuse these feature vectors to generate unified multi-modal feature representation. The semantic analysis unit uses a deep learning model to understand the semantics of multi-modal feature representation to generate a semantic vector.

[0074] Intelligent dialogue generation: Dialogue strategy unit: generates dialogue strategies based on semantic vectors and user's historical dialogue records.

[0075] Natural language generation unit: converts dialogue strategies into natural language text to generate reply information.

[0076] Multi-modal output unit: selects appropriate output modalities (such as text, speech, images, etc.) for replies based on user preferences.

[0077] Working principle: Dialogue strategy unit generates dialogue strategies based on semantic vectors and user's historical dialogue records. Natural language generation unit converts dialogue strategies into natural language text to generate reply information. Multi-modal output unit selects appropriate output modalities (such as text, speech, images, etc.) for replies based on user preferences.

[0078] Feedback loop and optimization: Information output interface: users view or listen to system's reply information through this interface and provide feedback.

[0079] Knowledge base update: updates government knowledge base and user information base based on user feedback and new knowledge.

[0080] Model optimization: continuously optimizes deep learning tree model based on user feedback and new knowledge to improve dialogue accuracy and naturalness.

[0081] Working principle: users view or listen to system's reply information through information output interface and provide feedback. System updates government knowledge base and user information base based on user feedback and new knowledge, records user's personal information, historical records, and preference settings. Meanwhile, system continuously optimizes deep learning tree model based on user feedback and new knowledge to improve dialogue accuracy and naturalness.

[0082] Summary The present application provides a government service method and system based on multi-modal semantic information processing intelligent dialogue, which integrates various modalities of information such as text, speech, and images, uses deep learning technology for semantic understanding and dialogue generation, and improves the accuracy and user satisfaction of government services. This method has the advantages of multi-modal information processing, deep learning technology, intelligent dialogue generation, user-friendly, knowledge base management, and efficient service, and is suitable for the intelligent needs of modern government services.

[0083] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for government service based on intelligent dialogue processing of multi-modal semantic information, characterized in that, Comprising the following steps: a) Multi-modal data collection: Collecting various modalities of user input data, including but not limited to text, speech, and image data, to comprehensively capture user needs; b) Data preprocessing: Cleaning, formatting, and normalizing the collected multi-modal data to ensure data quality and provide a foundation for subsequent processing; c) Deep learning tree construction: Building a model based on deep learning trees, which contains multiple branches, each dedicated to processing data from a specific modality, using deep learning algorithms for feature extraction and preliminary semantic understanding; d) Multi-modal fusion and semantic understanding: Using advanced attention mechanisms or fusion algorithms to effectively fuse the feature information output by each branch, forming a comprehensive and in-depth understanding of user intent and needs; e) Intelligent dialogue generation: Based on the fused semantic information, using natural language generation technology to generate intelligent dialogue content that matches user needs, and presenting it to the user through appropriate output methods, including text output, image output, and speech output; f) Feedback loop and optimization: Continuously optimizing the model based on user feedback and historical data to improve the accuracy and naturalness of the dialogue, and seamlessly integrating intelligent dialogue with business processes according to the characteristics of government service processes. 2.The government service system based on intelligent dialogue of multi-modal semantic information processing intelligent dialogue according to claim 1, characterized in that, The multi-modal information collection also includes: An information input interface, the input modalities of which include text information, image information, and voice information; Information input devices, including keyboards, cameras, and microphones.

3. The government service system based on intelligent conversation processing of multi-modal semantic information according to claim 2, characterized in that, The data preprocessing also includes: A text preprocessing unit that tokenizes the text information collected in the multi-modal information collection step to obtain a sequence of words; An image preprocessing unit that extracts features from the image information collected in the multi-modal information collection step to identify objects and scenes in the image; A speech preprocessing unit that performs speech recognition on the voice information collected in the multi-modal information collection step to obtain text information, and performs voiceprint recognition and sentiment analysis.

4. The government service system based on intelligent conversation processing of multi-modal semantic information according to claim 3, characterized in that, The multi-modal information fusion also includes: A feature extraction unit that converts preprocessed text, image, and voice information into feature vectors; A feature fusion unit that uses an AI deep learning tree model to fuse the feature vectors to generate a unified multi-modal feature representation.

5. The government service system based on intelligent conversation processing of multi-modal semantic information according to claim 4, characterized in that, The semantic understanding and dialogue generation also include: A semantic analysis unit that uses a deep learning model to understand the semantic meaning of the multi-modal feature representation and generates a semantic vector; A dialogue strategy unit that generates a dialogue strategy based on the semantic vector and the user's historical dialogue records; A natural language generation unit that converts the dialogue strategy into natural language text to generate a reply; A multi-modal output unit that selects the appropriate output modality based on the user's preferences to reply, including text information, voice information, and image information.

6. The government service system based on intelligent conversation processing of multi-modal semantic information according to claim 5, characterized in that, The deep learning tree model includes a knowledge base, which includes: A government knowledge base that stores information related to government laws, regulations, guidelines, and common questions; A user information base that stores user personal information, historical records, and preference settings.

7. The government service system based on intelligent conversation processing of multi-modal semantic information according to claim 6, characterized in that, The feedback cycle and optimization further comprise: An information output interface, through which the user views or listens to the system's reply information and provides feedback; The deep learning tree construction step updates the government affair knowledge base and adds new government affair information according to the user's feedback and new knowledge; The deep learning tree construction step updates the user information base and records the user's personal information, history record and preference setting according to the user's feedback and new knowledge.

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