Multi-scene self-adaptive semantic analysis writing assisting method and system

Abstract

The invention discloses a multi-scene self-adaptive semantic analysis writing assistance method and system, and relates to the technical field of writing assistance, and the method comprises the steps: monitoring a physiological signal of a user in real time to determine a concentration state, fusing text, voice, images and other semantic information based on a multi-modal semantic fusion algorithm, and dynamically adjusting the weight according to the concentration. Personalized writing suggestions are generated through dynamically updated semantic association graph matching, and related algorithms and strategies are optimized by utilizing a historical writing scene database. According to the method, the writing state of the user can be accurately grasped, multi-mode semantic information is fully utilized, the real-time performance and accuracy of writing suggestions are ensured, meanwhile, the performance is continuously improved through self-optimization, and more intelligent and efficient writing auxiliary experience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of writing assistance technology, and in particular to a multi-scenario adaptive semantic analysis writing assistance method and system. Background Art

[0002] With the development of artificial intelligence and natural language processing technologies, semantic analysis writing assistance systems are gradually emerging, providing users with a platform for writing suggestions and text optimization. Current writing assistance methods are mostly based on text semantic analysis, using machine learning or deep learning models to understand the semantics of input text. These systems then provide writing suggestions based on preset rules or model predictions, such as grammar checking, vocabulary richness analysis, and text style optimization.

[0003] However, these existing semantic analysis writing assistance methods have significant shortcomings. On the one hand, they ignore the impact of the user's physiological state on writing quality and are unable to dynamically adjust semantic analysis and writing suggestion strategies based on physiological indicators such as the user's concentration and stress. On the other hand, they lack multimodal semantic information fusion, relying solely on text semantics while making limited use of semantic information from other modalities such as speech and images, resulting in insufficiently comprehensive and in-depth semantic analysis. Furthermore, semantic association graphs are not updated promptly and accurately enough, and cannot quickly adapt to dynamic changes in semantic knowledge. This results in less than ideal writing assistance and makes it difficult to meet the high requirements of multi-scenario adaptive writing assistance. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a multi-scenario adaptive semantic analysis writing assistance method to solve the problems of existing semantic analysis writing assistance methods, such as the inability to dynamically adjust the semantic analysis strategy according to the user's physiological state, insufficient fusion of multimodal semantic information, and how to achieve real-time updating and precise matching of semantic association maps.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides a multi-scenario adaptive semantic analysis writing assistance method, characterized in that the method includes:

[0008] S1: Monitor the user's heart rate, brain waves and other physiological signals in real time, and analyze the physiological signals through a preset physiological signal processing model to determine the user's current concentration state.

[0009] S2: Obtain text information input by the user, and fuse the text information with related multimodal semantic information such as voice and image based on a multimodal semantic fusion algorithm to obtain comprehensive semantic features, wherein the multimodal semantic fusion algorithm is dynamically adjusted according to the concentration state.

[0010] S3: Match the comprehensive semantic features with a dynamically updated semantic association graph, where the semantic association graph is updated in real time according to user input and the latest knowledge, and generate personalized writing suggestions for the user based on the matching results, wherein the generation method of the personalized writing suggestions is dynamically determined according to the matching results of the concentration state and the semantic association graph.

[0011] S4: Receive user feedback on the personalized writing suggestion, and automatically adjust the update strategy of the multimodal semantic fusion algorithm and the semantic association graph based on the feedback information and the concentration state to optimize the subsequent writing assistance process.

[0012] As a preferred solution of the multi-scenario adaptive semantic analysis writing assistance method described in the present invention, wherein: in the step 1, the physiological signal processing model is trained through a pre-established user physiological signal database, and the database contains typical physiological signal features under different concentration states. The model uses the features to perform pattern matching to determine the user's current concentration state.

[0013] As a preferred solution of the multi-scenario adaptive semantic analysis writing assistance method described in the present invention, wherein: in the step 2, the multimodal semantic fusion algorithm dynamically adjusts the weight of each modal semantic information according to the concentration state. When the concentration is low, the weight of the text semantic information is increased, and the weight of the speech and image semantic information is reduced to improve the accuracy of the semantic analysis.

[0014] As a preferred solution of the multi-scenario adaptive semantic analysis writing assistance method described in the present invention, the semantic association graph is updated in real time through distributed storage and incremental update mechanism. The update mechanism includes regularly acquiring the latest knowledge from multiple knowledge sources and integrating the new knowledge with the existing semantic association graph to ensure the timeliness and accuracy of the semantic association graph.

[0015] As a preferred solution of the multi-scenario adaptive semantic analysis writing assistance method described in the present invention, wherein: in the step 4, the automatic adjustment includes constructing a historical writing scene database to store the user's physiological signals, text input, multimodal semantic information, personalized writing suggestions and feedback information, and optimizing the multimodal semantic fusion algorithm and the update strategy of the semantic association map by analyzing the historical data in the database.

[0016] The beneficial effects of the present invention are as follows: by monitoring the user's physiological signals to determine the state of concentration, and dynamically adjusting the multimodal semantic fusion algorithm accordingly, the semantic analysis process is made more targeted and adaptable, the accuracy of semantic analysis is significantly improved, and writing suggestions that are more in line with the user's current state are provided to the user. Secondly, a dynamically updated semantic association graph is constructed to timely incorporate the latest knowledge, ensuring the timeliness and comprehensiveness of semantic information, so that the writing assistance system can better meet the user's needs for semantic accuracy and richness in different scenarios. Thirdly, the update strategy of the multimodal semantic fusion algorithm and the semantic association graph is automatically optimized in combination with user feedback information, achieving self-improvement and continuous improvement of the system, and improving the quality and efficiency of writing assistance. Finally, the fusion processing of multimodal semantic information fully integrates a variety of semantic resources such as text, voice, and images, greatly enriching the dimension and depth of semantic analysis, and bringing users a more comprehensive and intelligent writing assistance experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flowchart of the multi-scenario adaptive semantic analysis writing assistance method in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0022] Reference Figure 1 , which is the first embodiment of the present invention, provides a multi-scenario adaptive semantic analysis writing assistance method, characterized in that the method includes:

[0023] S1: Monitor the user's heart rate, brain waves and other physiological signals in real time, and analyze the physiological signals through a preset physiological signal processing model to determine the user's current concentration state.

[0024] S2: Obtain text information input by the user, and fuse the text information with related multimodal semantic information such as voice and image based on a multimodal semantic fusion algorithm to obtain comprehensive semantic features, wherein the multimodal semantic fusion algorithm is dynamically adjusted according to the concentration state.

[0025] S3: Match the comprehensive semantic features with a dynamically updated semantic association graph, where the semantic association graph is updated in real time according to user input and the latest knowledge, and generate personalized writing suggestions for the user based on the matching results, wherein the generation method of the personalized writing suggestions is dynamically determined according to the matching results of the concentration state and the semantic association graph.

[0026] S4: Receive user feedback on the personalized writing suggestion, and automatically adjust the update strategy of the multimodal semantic fusion algorithm and the semantic association graph based on the feedback information and the concentration state to optimize the subsequent writing assistance process.

[0027] It should be noted that this step focuses on real-time monitoring of the user's physiological signals and accurate determination of their concentration state. During the writing process, the user's body emits physiological signals such as heart rate and brain waves, which contain a wealth of state information. We use professional monitoring equipment, such as high-precision heart rate sensors and brainwave detectors, to continuously and frequently collect these signals. The collected raw physiological signals are then input into a pre-constructed physiological signal processing model. This model uses advanced filtering algorithms to reduce noise on the signals, remove impurities, and retain the core data. Feature extraction technology is then used to accurately identify key physiological features that reflect concentration. Finally, through pattern matching, the extracted features are compared with typical physiological features of different concentration states pre-stored in the model to accurately determine the user's current concentration state.

[0028] By monitoring physiological signals in real time and determining the state of concentration, the user's writing state can be precisely controlled. This method can promptly capture the user's physiological changes during the writing process, providing a basis for subsequent semantic analysis and writing suggestions that are tailored to the user's actual state, ensuring the personalization and pertinence of the entire writing assistance process, thereby effectively improving the accuracy and effectiveness of writing assistance.

[0029] After obtaining the user's input text, we immediately initiate a multimodal semantic fusion algorithm to deeply fuse the text and related multimodal semantic information, including speech and image information. Text semantic information is extracted using natural language processing techniques, which perform in-depth analysis of the text at the lexical, syntactic, and semantic levels to uncover the core semantic content expressed in the text. Speech semantic information is extracted using speech recognition and semantic conversion techniques, converting the speech signal into semantic text and extracting its key semantic points. Image semantic information is extracted using computer vision and image semantic analysis techniques to identify key elements in the image and their relationships, converting them into image semantic descriptions. Based on the extracted semantic information from each modality, the multimodal semantic fusion algorithm is dynamically adjusted according to the focus level determined in step S1. For example, the weighting of each modal semantic information is adjusted. When focus is low, the weighting of text semantic information is appropriately increased, while the weighting of speech and image semantic information is reduced. Then, through a specific fusion strategy, the semantic information from each modality is mapped into a unified semantic space and fused. Ultimately, a comprehensive semantic feature is generated that comprehensively and accurately reflects the semantic connotations of the user's input information.

[0030] Through processing based on a multimodal semantic fusion algorithm, the comprehensive utilization of semantic information from multiple modalities, such as text, speech, and images, is achieved. This fusion approach fully taps into the semantic value inherent in information from different modalities, avoiding the limitations of single-modal semantic analysis and enriching the dimensionality and depth of semantic analysis. Furthermore, the fusion algorithm is dynamically adjusted based on the state of focus, making the semantic fusion process more adaptable and targeted. This ensures that the comprehensive semantic features accurately reflect the user's true semantic intent, laying a solid foundation for the subsequent generation of precise writing suggestions and effectively improving the comprehensiveness and accuracy of semantic analysis.

[0031] After obtaining the comprehensive semantic features in step S2, we match them with the dynamically updated semantic association graph. The semantic association graph is a huge semantic knowledge network composed of multiple semantic nodes and association edges. Semantic nodes represent different semantic concepts, and association edges represent the association relationship between concepts. This graph is not static, but is updated in real time based on user input and the latest knowledge. On the one hand, the text information entered by the user will be parsed and converted into new semantic elements and integrated into the graph; on the other hand, the system regularly obtains the latest knowledge from multiple authoritative knowledge sources. After semantic analysis and knowledge fusion, the new knowledge is added to the graph, and the old knowledge is adjusted and optimized to ensure the timeliness and accuracy of the graph. In the matching process, the comprehensive semantic features are used as query vectors to search for the most similar and relevant semantic paths and concept sets in the graph. The matching results are determined by calculating the semantic similarity and association. Then, based on the matching results and concentration status, the method of generating personalized writing suggestions is dynamically determined. For example, elements such as writing ideas, vocabulary expressions, sentence structures, etc. related to the matching results are extracted from the graph, and combined according to certain logical rules and language styles to generate a personalized writing suggestion that fits the user's writing scenario and semantic intention, providing specific guidance and help for the user's writing.

[0032] By matching the dynamically updated semantic association graph and generating personalized writing suggestions, writing assistance achieves intelligent and dynamic adaptability. Real-time updates to the semantic association graph ensure that writing suggestions are based on the latest knowledge and semantic information, keeping pace with the times and meeting users' writing needs for cutting-edge knowledge and hot topics. At the same time, writing suggestions are dynamically generated based on matching results and focus status, allowing them to accurately match the user's writing intentions and current state, providing users with writing guidance that is more in line with their personalized needs, effectively improving the pertinence and practicality of writing assistance.

[0033] After users receive and use personalized writing suggestions, we proceed to step S4 to receive user feedback on these suggestions. Feedback can be obtained in various ways, such as direct user evaluations of the suggestions (positive or negative), modifications and adjustments to the suggestions, and subsequent user writing behaviors and achievements. Simultaneously, we conduct a comprehensive analysis of the feedback information, taking into account the focus state determined in step S1. On the one hand, we analyze the user's satisfaction, acceptance, and perceived challenges with the writing suggestions as reflected in the feedback information. On the other hand, we analyze the differences in user demand for writing suggestions and the effectiveness of their use at different levels of focus, taking into account the focus state. Based on these analysis results, we automatically adjust the multimodal semantic fusion algorithm and the semantic association graph update strategy. For the multimodal semantic fusion algorithm, we may adjust the algorithm's parameter settings, fusion strategy, and weight distribution to optimize the fusion of semantic information from each modality. For the semantic association graph, we may adjust the graph update frequency, scope, and rules for adding or deleting semantic nodes to optimize the graph's knowledge updates and semantic organization. Through this automatic adjustment mechanism based on feedback information and concentration status, the entire writing assistance system can be continuously optimized, and the system performance and quality of writing assistance can be continuously improved.

[0034] By receiving feedback and automatically adjusting relevant strategies based on concentration status, the writing assistance system achieves self-optimization and continuous improvement. This adjustment mechanism based on user feedback and physiological status ensures that the system is always user-oriented, constantly adapting to the changing writing needs of users at different concentration levels, optimizing semantic analysis and writing suggestion generation processes, and improving the accuracy and effectiveness of writing assistance. At the same time, it also helps to enhance the system's intelligence level and user experience, enabling the writing assistance system to better serve users and promote the improvement of their writing skills and efficiency.

[0035] Specifically, in step 1, the physiological signal processing model is trained using a pre-established user physiological signal database, which contains typical physiological signal features under different concentration states. The model uses the features to perform pattern matching to determine the user's current concentration state.

[0036] It should be noted that in step S1, to accurately determine the user's concentration state, we employ an analysis method based on a physiological signal processing model. First, high-precision sensors collect physiological signals such as the user's heart rate and brain waves in real time. These sensors utilize optical principles and electrode technology to continuously and stably acquire physiological data without disrupting the user's writing. The collected physiological signals are often mixed with environmental noise and other interfering signals. Therefore, we preprocess them using advanced filtering algorithms to remove noise components and retain the core physiological features. The processed signals are then input into a pre-trained physiological signal processing model. This model is built on a vast database of user physiological signals, which contains a large number of typical physiological signal features associated with different concentration states, such as the stable frequency range of heart rate during concentration and the specific waveform patterns of brain waves. Using a machine learning algorithm, the model extracts features and performs pattern matching on the input physiological signals, comparing and analyzing each feature in the database. During the matching process, the similarity between the input signal and the features of each concentration state is calculated. Ultimately, the user's current concentration state is determined based on the level of similarity, and the results are output for use in subsequent steps.

[0037] Through the detailed physiological signal monitoring and analysis steps described above, an accurate assessment of the user's concentration state is achieved. This physiological signal-based monitoring method can objectively reflect the user's internal physiological state without relying on subjective user input, improving the accuracy and reliability of concentration assessment. Accurate concentration state information provides a key basis for the dynamic adjustment of semantic analysis strategies in subsequent steps, enabling the entire writing assistance system to be personalized according to the user's actual state, thereby significantly improving the pertinence and effectiveness of writing assistance and providing users with more writing support.

[0038] The main purpose of step S1 is to monitor the user's physiological signals and determine their concentration state, providing a basis for the personalized service of the entire writing assistance system. In this process, high-precision sensors and advanced filtering algorithms are used to ensure the accuracy and stability of physiological signal acquisition; with the help of machine learning models and a rich physiological signal database, accurate identification of concentration states is achieved. This process not only improves the objectivity and reliability of concentration assessment, but also provides key user status information for subsequent steps, enabling the entire writing assistance system to be adjusted and optimized according to the user's actual concentration level. In this way, step S1 strongly supports the entire writing assistance system in achieving the goal of personalized services and providing users with more accurate and effective writing assistance.

[0039] Specifically, in step 2, the multimodal semantic fusion algorithm dynamically adjusts the weight of each modal semantic information according to the concentration state. When the concentration is low, the weight of the text semantic information is increased and the weight of the speech and image semantic information is reduced to improve the accuracy of semantic analysis.

[0040] It should be noted that in step S2, we obtain the text information input by the user, and fuse the text information with related multimodal semantic information such as speech and image based on the multimodal semantic fusion algorithm. First, through text analysis technology, the text input by the user is deeply analyzed at the lexical, syntactic and semantic levels to extract key semantic information such as keywords, phrases, sentence structures and semantic themes in the text. At the same time, speech recognition technology is used to convert the speech input that the user may provide into text form, and semantic features such as emotion, intonation and key content in the speech are extracted through speech semantic analysis. For image information, computer vision technology is used to identify elements such as objects, scenes, text in the image, and convert them into corresponding semantic descriptions. Under the guidance of the concentration state, the multimodal semantic fusion algorithm dynamically adjusts the weight of each modal semantic information. For example, when the concentration is low, the algorithm automatically increases the weight of text semantic information and reduces the weight of speech and image semantic information. This adjustment is achieved through a specific weight calculation formula, such as the weight adjustment formula can be expressed as:

[0041] W fusion = α × W text + β × W speech + γ × W image

[0042] Here, α, β, and γ are weight coefficients for text, speech, and image semantic information, respectively, and they change dynamically based on the state of focus. Ultimately, a fusion strategy maps the semantic information of each modality into a unified semantic space, performs comprehensive calculations, and obtains comprehensive semantic features, providing comprehensive semantic input for subsequent semantic analysis.

[0043] By dynamically adjusting the weight of each modal semantic information according to the state of concentration, the rational use and optimized integration of semantic information of different modalities is achieved. When the concentration is low, increasing the weight of text semantic information can more accurately capture the user's core semantic intention and avoid semantic deviation caused by insufficient concentration. At the same time, reducing the weight of voice and image semantic information can reduce the interference that these modal information may introduce when the concentration is low, thereby improving the accuracy and reliability of semantic analysis. This dynamic adjustment mechanism can ensure that the comprehensive semantic features more truly reflect the user's writing intention, provide a more accurate semantic basis for the subsequent generation of personalized writing suggestions, and effectively improve the performance and user experience of the entire writing assistance system.

[0044] Specifically, the semantic association graph is updated in real time through distributed storage and incremental update mechanism. The update mechanism includes regularly acquiring the latest knowledge from multiple knowledge sources and integrating the new knowledge with the existing semantic association graph to ensure the timeliness and accuracy of the semantic association graph.

[0045] It should be noted that in step S3, we generate personalized writing suggestions for users through a dynamically updated semantic association graph. The semantic association graph is a vast semantic knowledge network consisting of multiple semantic nodes and associated edges. Nodes represent semantic concepts, and edges represent the relationships between concepts. The graph is updated through a distributed storage and incremental update mechanism. Specifically, the system regularly obtains the latest knowledge from multiple authoritative knowledge sources (such as online encyclopedias, academic databases, news platforms, etc.). The information provided by these knowledge sources undergoes preprocessing, including steps such as text cleaning and structured extraction, to ensure a uniform and accurate format. The system then uses semantic analysis technology to convert the new knowledge into semantic elements and fuses them with the existing graph. The fusion process involves calculating the semantic similarity and association between new and old knowledge, and seamlessly integrating the new knowledge into the existing graph through specific fusion algorithms (such as graph-based fusion algorithms). At the same time, the distributed storage mechanism ensures that the graph data is stored in a distributed manner across multiple servers, avoiding single points of failure and improving data access efficiency. During the matching process, the comprehensive semantic features obtained in step S2 are used as the query vector. The most similar semantic paths and concept sets are searched in the graph, and the semantic similarity and relevance are calculated to determine the matching results. Finally, based on the matching results and the focus state, personalized writing suggestions are dynamically generated to provide users with more accurate writing guidance.

[0046] By dynamically updating the semantic association graph and combining it with matching results to generate personalized writing suggestions, the intelligent and dynamic adaptability of writing assistance is achieved. Distributed storage and incremental update mechanisms ensure that the graph can obtain and integrate the latest knowledge in real time, avoiding the inefficiency and lag of traditional centralized updates, and improving the timeliness and robustness of the system. Dynamically generating writing suggestions based on matching results and concentration status can accurately match the user's writing intentions and current status, provide more targeted writing guidance, and effectively improve the practicality of writing assistance. This mechanism can also help users keep up with hot topics and cutting-edge knowledge, meet writing needs in multiple scenarios, and significantly enhance the competitiveness and user experience of the writing assistance system.

[0047] Specifically, in step 4, the automatic adjustment includes constructing a historical writing scene database to store the user's physiological signals, text input, multimodal semantic information, personalized writing suggestions and feedback information, and optimizing the multimodal semantic fusion algorithm and the update strategy of the semantic association map by analyzing the historical data in the database.

[0048] It should be noted that in step S4, we continuously improve the entire writing assistance system by constructing a database of historical writing scenarios and using it to optimize the multimodal semantic fusion algorithm and semantic association graph update strategy. Specifically, the system first collects and stores various data from the user during the writing process, including physiological signals, text input content, extracted multimodal semantic information, generated personalized writing suggestions, and subsequent user feedback. This data is structured and stored in the historical writing scenario database, with each data item associated with a specific writing scenario and timestamp to facilitate subsequent retrieval and analysis. The system regularly conducts in-depth mining and analysis of the historical data in the database, applying data mining algorithms and machine learning models to identify which multimodal semantic fusion strategies are most effective in specific scenarios, as well as the update requirements and optimization directions for the semantic association graph in different scenarios. For example, by analyzing user feedback on writing suggestions, it is found that certain types of suggestions are frequently marked as useful during specific concentration states. The system then adjusts the weight distribution and fusion strategy in the multimodal semantic fusion algorithm accordingly. At the same time, based on user feedback and changes in the quality of writing output, we optimize the update frequency and knowledge fusion rules of the semantic association graph to ensure that the graph can better support semantic analysis and writing suggestion generation. This process is a continuous, iterative, closed-loop optimization mechanism that continuously improves system performance.

[0049] By building a database of historical writing scenarios and optimizing related algorithms and strategies based on this, the self-evolution and performance improvement of the writing assistance system are achieved. This data-driven optimization method can accurately identify the strengths and weaknesses of the system in different writing scenarios, and specifically adjust the update strategy of the multimodal semantic fusion algorithm and the semantic association graph to ensure that the system always operates in an optimal state. In the long run, this self-optimization mechanism can significantly improve the quality and efficiency of writing assistance, enabling the system to more intelligently adapt to the writing needs and preferences of different users, thereby providing users with a more personalized, efficient and accurate writing assistance experience, and enhancing users' trust and reliance on the system.

[0050] In summary, the present invention determines the state of concentration by monitoring the user's physiological signals, and dynamically adjusts the multimodal semantic fusion algorithm accordingly, making the semantic analysis process more targeted and adaptable, significantly improving the accuracy of semantic analysis, and providing users with writing suggestions that are more in line with their current state. Secondly, a dynamically updated semantic association graph is constructed to promptly incorporate the latest knowledge, ensuring the timeliness and comprehensiveness of semantic information, so that the writing assistance system can better meet the user's needs for semantic accuracy and richness in different scenarios. Thirdly, the update strategy of the multimodal semantic fusion algorithm and the semantic association graph is automatically optimized in combination with user feedback information, achieving self-improvement and continuous improvement of the system, and improving the quality and efficiency of writing assistance. Finally, the fusion processing of multimodal semantic information fully integrates multiple semantic resources such as text, voice, and images, greatly enriching the dimension and depth of semantic analysis, and bringing users a more comprehensive and intelligent writing assistance experience.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-scenario adaptive semantic analysis writing assistance method, characterized in that: The method comprises: S1: Monitor the user's heart rate, brain waves and other physiological signals in real time, and analyze the physiological signals through a preset physiological signal processing model to determine the user's current concentration state. S2: Obtain text information input by the user, and fuse the text information with related multimodal semantic information such as voice and image based on a multimodal semantic fusion algorithm to obtain comprehensive semantic features, wherein the multimodal semantic fusion algorithm is dynamically adjusted according to the concentration state. S3: Match the comprehensive semantic features with a dynamically updated semantic association graph, where the semantic association graph is updated in real time according to user input and the latest knowledge, and generate personalized writing suggestions for the user based on the matching results, wherein the generation method of the personalized writing suggestions is dynamically determined according to the matching results of the concentration state and the semantic association graph. S4: Receive user feedback on the personalized writing suggestion, and automatically adjust the update strategy of the multimodal semantic fusion algorithm and the semantic association graph based on the feedback information and the concentration state to optimize the subsequent writing assistance process.

2. The method and system according to claim 1, characterized in that: In step 1, the physiological signal processing model is trained using a pre-established user physiological signal database, which contains typical physiological signal features under different concentration states. The model uses the features to perform pattern matching to determine the user's current concentration state.

3. The method and system according to claim 1, characterized in that: In step 2, the multimodal semantic fusion algorithm dynamically adjusts the weight of each modal semantic information according to the concentration state. When the concentration is low, the weight of text semantic information is increased and the weight of speech and image semantic information is reduced to improve the accuracy of semantic analysis.

4. The method and system according to claim 1, characterized in that: The semantic association graph is updated in real time through distributed storage and incremental update mechanism. The update mechanism includes regularly acquiring the latest knowledge from multiple knowledge sources and fusing the new knowledge with the existing semantic association graph to ensure the timeliness and accuracy of the semantic association graph.

5. The method and system according to claim 1, characterized in that: In step 4, the automatic adjustment includes constructing a historical writing scene database to store the user's physiological signals, text input, multimodal semantic information, personalized writing suggestions and feedback information, and optimizing the multimodal semantic fusion algorithm and the update strategy of the semantic association map by analyzing the historical data in the database.

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