A pick-up pen voice content semantic analysis method fusing industry knowledge base

Abstract

The application discloses a kind of industry knowledge base fusion pick-up pen voice content semantic analysis method, belong to computer data processing technical field, it includes obtaining the original voice stream that is collected by pick-up pen is transcribed into transcription text and extracts acoustic context information and interactive role information to obtain multidimensional context information;Transcription text and multidimensional context information are input dynamic knowledge network and are handled with semantic fusion, and combined with industry document framework to generate structured document;Acquire structured document formed in the aggregate document of previous session generation, and based on dynamic knowledge network carries out pattern mining, generates user behavior mode;User behavior mode is compared with benchmark practice model, and generates personalized optimization suggestion.The multidimensional context information in voice stream is handled with semantic fusion with dynamic knowledge network, which can realize the deep understanding of voice content, accurate modeling of user behavior and automatic generation of personalized optimization suggestion.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer data processing, and in particular to a voice content semantic analysis method of a voice pickup pen fusing an industry knowledge base. BACKGROUND

[0002] As a portable audio acquisition device, the voice pickup pen has been widely used in many scenarios such as conference recording, business negotiation, customer interview, training and teaching. With the development of automatic speech recognition technology, the original voice stream collected by the voice pickup pen can be efficiently converted into electronic text, laying a foundation for subsequent data processing and information extraction. Automatic semantic analysis of these transcribed texts to extract key information and understand communication patterns has become an important technical direction to improve work efficiency and achieve knowledge management.

[0003] The existing voice content analysis technology of the voice pickup pen usually relies on general natural language processing algorithms to process the transcribed text after completing speech transcription. These methods include extracting keywords, generating text summaries, performing topic modeling or sentiment analysis, etc. The processing flow is relatively simple, simplifying the speech analysis problem into a pure text analysis problem, i.e. after converting speech into text, the rich information in the original voice stream is discarded, and the focus of analysis is completely on the text content itself.

[0004] However, analyzing only the transcribed text will lose a lot of key contextual information, such as the emphasis intention expressed by the speaker through changes in speech speed, volume and stress, and the thinking or hesitation reflected by abnormal pauses, which are crucial for accurately understanding the deep meaning of the conversation. Secondly, general natural language processing models lack the background knowledge of specific industries, making it difficult to accurately understand industry terminology and identify the logical relationships between professional concepts, resulting in analysis results that are often superficial and lack professional depth. Finally, the existing analysis is usually one-time and isolated event recording, lacking the ability to continuously track and mine long-term behavior patterns of users, and unable to form a comprehensive assessment of the communication ability of users, let alone provide targeted improvement suggestions. SUMMARY

[0005] To solve the above problems, the present application provides a voice content semantic analysis method of a voice pickup pen fusing an industry knowledge base, which can realize deep understanding of voice content, accurate modeling of user behavior and automatic generation of personalized optimization suggestions by performing semantic fusion processing of multi-dimensional contextual information in the voice stream and dynamic knowledge network.

[0006] The above-mentioned object can be achieved by the following scheme:

[0007] The method comprises the following steps: acquiring an original voice stream collected by a voice pickup pen; performing voice transcription on the original voice stream to generate a transcribed text; extracting acoustic context information and interactive role information based on the original voice stream and the transcribed text; combining the acoustic context information and the interactive role information into multi-dimensional context information; constructing a dynamic knowledge network based on a preset industry knowledge base; inputting the transcribed text and the multi-dimensional context information into the dynamic knowledge network for semantic fusion processing to generate a semantic fusion result containing key knowledge points; constructing an industry document framework based on the industry knowledge base and generating a structured document using the semantic fusion result and the industry document framework; forming an aggregated document by acquiring the structured documents generated in previous sessions; performing pattern mining based on the aggregated document and the dynamic knowledge network to generate a user behavior pattern; acquiring a benchmark practice model used to define an idealized standard from the dynamic knowledge network; comparing the user behavior pattern with the benchmark practice model to generate personalized optimization suggestions.

[0008] Optionally, the combining of the acoustic context information and the interactive role information into multi-dimensional context information comprises: acquiring an original voice stream collected by a voice pickup pen; performing voice transcription on the original voice stream to generate a transcribed text; performing acoustic context analysis on the original voice stream to extract sudden speed increase points, volume accent points, and abnormal pause intervals to generate acoustic context information; performing interactive role analysis based on the transcribed text and the original voice stream to distinguish different speakers and mark interactive moments to generate interactive role information; and combining the acoustic context information and the interactive role information to obtain multi-dimensional context information.

[0009] Optionally, the generating of the semantic fusion result containing key knowledge points comprises: acquiring industry data through a preset industry knowledge base and analyzing the industry data to construct a dynamic knowledge network; locating potential emphasized content in the transcribed text using acoustic context information in the multi-dimensional context information; matching and anchoring the potential emphasized content in the dynamic knowledge network to activate corresponding associated knowledge nodes to confirm that the potential emphasized content is a key knowledge point; and integrating all the potential emphasized content confirmed as key knowledge points and the corresponding associated knowledge nodes activated in the dynamic knowledge network to form a semantic fusion result.

[0010] Optionally, the constructing an industry document framework based on the industry knowledge base and generating a structured document using the semantic fusion result and the industry document framework comprises: defining a document structure based on the industry knowledge base to construct an industry document framework; analyzing the interactive heat in different structural units in the industry document framework in combination with the interactive role information in the multi-dimensional context information; performing highlight display processing on the key knowledge points according to the interactive heat and the key knowledge points in the semantic fusion result; querying the dynamic knowledge network to obtain supplementary knowledge related to the key knowledge points, and taking the supplementary knowledge as associated recommended content; and constructing a structured document based on the key knowledge points after highlight display processing and the associated recommended content.

[0011] Optionally, the obtaining the structured documents generated in previous sessions to form an aggregated document, and performing pattern mining based on the aggregated document and the dynamic knowledge network to generate a user behavior pattern comprises: obtaining the structured documents generated in previous sessions to form an aggregated document; extracting data of the knowledge coverage dimension, the explanation logic dimension and the interactive guidance dimension from the aggregated document to form multi-dimensional behavior data; performing behavior analysis and evaluation in combination with the multi-dimensional behavior data and the dynamic knowledge network to obtain a quantitative result; and generating a user behavior pattern based on the quantitative result.

[0012] Optionally, the performing behavior analysis and evaluation in combination with the multi-dimensional behavior data and the dynamic knowledge network to obtain a quantitative result comprises: comparing the data of the knowledge coverage dimension in the multi-dimensional behavior data with the knowledge nodes in the dynamic knowledge network to quantify the knowledge coverage rate; fitting the explanation logic dimension data in the multi-dimensional behavior data with the logic paths between the knowledge nodes in the dynamic knowledge network to evaluate the logic coherence; mapping the interactive guidance dimension data in the multi-dimensional behavior data to the interactive frequency of specific knowledge nodes in the dynamic knowledge network to analyze the interactive effectiveness; and integrating the knowledge coverage rate, the logic coherence and the interactive effectiveness to form a quantitative behavior result.

[0013] Optionally, the obtaining a benchmark practice model for defining an idealized standard from the dynamic knowledge network, comparing the user behavior pattern with the benchmark practice model, and generating a personalized optimization suggestion comprises: obtaining a benchmark practice model for defining an idealized standard from the dynamic knowledge network; calculating the gap between the user behavior pattern and the benchmark practice model in the knowledge coverage dimension, the explanation logic dimension and the interactive guidance dimension to generate a gap analysis result; constructing a rule template library for converting the gap analysis result into a natural language strategy; and generating a personalized optimization suggestion based on the gap analysis result and the rule template library.

[0014] Optionally, the method further comprises: performing part-of-speech tagging and phrase extraction on the transcribed text to obtain a candidate concept set; calculating the similarity between each candidate concept in the candidate concept set and a knowledge node in the dynamic knowledge network, respectively, selecting a candidate concept with a similarity less than a preset similarity threshold, and constructing a new knowledge point set; based on the new knowledge point set, adding a new knowledge node to the dynamic knowledge network, and updating the dynamic knowledge network.

[0015] Optionally, the method further comprises: calculating the attention degree of a knowledge node corresponding to the interaction heat in the dynamic knowledge network based on the interaction heat in different structural units in the industry document framework and the multi-dimensional context information, and generating a knowledge node change parameter; and adjusting the weight of the corresponding knowledge node in the dynamic knowledge network based on the knowledge node change parameter.

[0016] Based on the same inventive concept, the application also provides a voice content semantic analysis system of a note-taking pen fused with an industry knowledge base, characterized in that the system comprises: a context analysis module configured to acquire an original voice stream collected by a note-taking pen, perform voice transcription on the original voice stream to generate a transcribed text, and extract acoustic context information and interactive role information based on the original voice stream and the transcribed text, and combine the acoustic context information and the interactive role information into multi-dimensional context information; a semantic fusion module configured to construct a dynamic knowledge network through a preset industry knowledge base, input the transcribed text and the multi-dimensional context information into the dynamic knowledge network for semantic fusion processing, and generate a semantic fusion result containing key knowledge points; a document generation module configured to construct an industry document framework based on the industry knowledge base, and generate a structured document using the semantic fusion result and the industry document framework; a pattern mining module configured to acquire the structured documents generated in previous sessions to form an aggregated document, perform pattern mining based on the aggregated document and the dynamic knowledge network, and generate a user behavior pattern; and an optimization suggestion generation module configured to acquire a benchmark practice model used to define an idealized standard from the dynamic knowledge network, compare the user behavior pattern with the benchmark practice model, and generate an individualized optimization suggestion.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1. The application improves the depth and accuracy of semantic understanding of voice content by deeply fusing multi-dimensional context information with a dynamic industry knowledge network; it not only analyzes the literal meaning of the transcribed text, but also matches and anchors the acoustic characteristics such as the speaking speed, volume, and pause of the speaker and the role interaction information as key context with industry knowledge, can identify the core knowledge points and implicit intentions in the dialogue, and overcomes the defects of information loss and understanding deviation caused by the traditional method of relying only on text;

[0019] 2. By constructing an industry document framework and combining it with interaction popularity analysis, this invention can automatically organize the conversation logic and highlight the issues that the two parties are most concerned about. At the same time, it links relevant supplementary knowledge, which improves the efficiency of reading conversation content and the value of knowledge accumulation, and transforms a single voice recording into a knowledge asset that can be used for a long time.

[0020] 3. This invention, through the aggregation and analysis of data from past conversations, creates a comprehensive behavioral profile of users from multiple dimensions such as knowledge coverage, explanation logic, and interactive guidance, and compares it with industry benchmark practice models. This allows for the diagnosis of users' strengths and weaknesses in professional communication, providing data support for personal ability assessment and development, and breaking away from the limitations of traditional reliance on subjective feelings.

[0021] 4. This invention can not only identify problems, but also automatically generate optimization suggestions based on the gap analysis results, providing users with a growth path; at the same time, the system has the ability to learn and evolve on its own, and can automatically discover new knowledge points and adjust knowledge weights from new conversations, ensuring the continuous updating and iteration of its knowledge system and analysis model, keeping it in sync with business development.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for semantic analysis of voice content using a microphone pen that integrates an industry knowledge base, according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram showing the comparison results of user behavior patterns and benchmark practice models in multiple dimensions according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of the structure of a speech content semantic analysis system for a microphone pen that integrates an industry knowledge base, according to an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram illustrating the extraction of acoustic context information from the original speech stream according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 One embodiment of the present invention proposes a semantic analysis method for voice content of a microphone pen that integrates an industry knowledge base. By semantically fusing multidimensional contextual information in the voice stream with a dynamic knowledge network, it is possible to achieve a deep understanding of voice content, accurate modeling of user behavior, and automatic generation of personalized optimization suggestions.

[0030] The method described in this embodiment specifically includes:

[0031] The system acquires the original speech stream collected by the microphone, performs speech-to-text transcription on the original speech stream to generate transcribed text, and extracts acoustic context information and interactive role information based on the original speech stream and the transcribed text, and combines the acoustic context information and the interactive role information into multi-dimensional context information.

[0032] A dynamic knowledge network is constructed by building a pre-set industry knowledge base. The transcribed text and the multi-dimensional contextual information are input into the dynamic knowledge network for semantic fusion processing to generate a semantic fusion result containing key knowledge points.

[0033] An industry document framework is constructed based on the industry knowledge base, and structured documents are generated using the semantic fusion results and the industry document framework.

[0034] The structured documents generated in each session are obtained to form an aggregated document. Based on the aggregated document and the dynamic knowledge network, pattern mining is performed to generate user behavior patterns.

[0035] A benchmark practice model for defining idealized standards is obtained from the dynamic knowledge network. The user behavior pattern is compared with the benchmark practice model to generate personalized optimization suggestions.

[0036] This invention achieves a progressive, layered analysis of voice content from a microphone pen by deeply integrating acoustic context, interactive roles, and industry knowledge. First, by extracting nonverbal information such as speech rate and volume, and the speaker's role, a multi-dimensional contextual information rich in context is constructed. Then, this information is injected into a dynamic knowledge network built from an industry knowledge base to identify key knowledge points. Based on this, using an industry-standard document framework, the identified key knowledge points are systematically reorganized to generate structured documents. By aggregating and analyzing the structured documents generated from each conversation, the mined user behavior patterns are quantitatively compared with the benchmark practice models defined in the knowledge network, thereby generating targeted optimization suggestions and achieving a complete closed loop from data analysis to capability empowerment.

[0037] This invention enhances the depth of analysis and application value of voice content from microphone pens, transforming raw, unstructured speech streams into structured, information-rich knowledge assets. It not only solves the problem of difficulty in reviewing and utilizing conversation content but also improves information retrieval efficiency and comprehension depth by highlighting key knowledge points and interactive focal points. It can outline users' long-term communication behavior patterns, revealing their stable characteristics and potential weaknesses in knowledge coverage, logical organization ability, and interactive guidance skills, providing a data-driven basis for individual competency assessment. Finally, through comparison with industry benchmark practices, it can generate personalized optimization suggestions, turning each voice recording into a learning and improvement opportunity, thereby constructing a continuously self-optimizing intelligent feedback system that effectively promotes the growth of individual professional capabilities.

[0038] Optionally, combining the acoustic context information and the interactive role information into multidimensional context information includes:

[0039] Acquire the original speech stream captured by the microphone pen, and perform speech-to-text transcription on the original speech stream to generate transcribed text;

[0040] Acoustic context analysis is performed on the original speech stream to extract speech rate spikes, volume stress points, and abnormal pause intervals, generating acoustic context information.

[0041] Based on the transcribed text and the original speech stream, interactive role analysis is performed to distinguish different speakers and mark interaction moments, thereby generating interactive role information.

[0042] The acoustic context information and the interactive role information are combined to obtain multidimensional context information.

[0043] Specifically, the system first acquires raw speech streams captured or stored in real-time using a microphone, and then processes these streams into time-aligned transcribed text using an automatic speech recognition engine. Next, to extract nonverbal cues from the communication, the system performs independent acoustic context analysis on the raw speech stream. During this analysis, it identifies and marks points of speech rate spikes significantly higher than average by calculating the rate of change of syllables or words within a sliding time window; it identifies and marks points where the energy value exceeds a preset dynamic threshold as volume stress points by analyzing the short-term energy of the speech signal; and it identifies silent segments in the speech stream and defines silent segments whose duration exceeds normal conversation intervals as abnormal pauses. These identified speech rate spikes, volume stress points, and abnormal pauses collectively constitute the acoustic context information. Simultaneously, the system performs interactive role analysis based on the transcribed text and the original speech stream. This step utilizes speaker log technology to extract acoustic features from the speech signal, such as fundamental frequency and formants, to cluster speech segments, thereby distinguishing different speakers and assigning a unique speaker identifier to each sentence or word in the transcribed text. Further, changes in speaker identifiers are monitored to mark the transition points between different speakers; these transition points are the interaction moments. Finally, the interactive role information, containing speaker identity and interaction moments, is timestamped and fused with the aforementioned acoustic contextual information, including speech rate, volume, and pause features, to generate a unified multi-dimensional contextual information. This information, based on a timeline, organically binds text content with unstructured contextual elements such as speakers, intonation, and communication rhythm.

[0044] Optionally, generating a semantic fusion result containing key knowledge points includes:

[0045] Industry data is acquired through a pre-set industry knowledge base, and a dynamic knowledge network is constructed by analyzing the industry data.

[0046] The acoustic context information in the multidimensional context information is used to locate potential emphasized content in the transcribed text;

[0047] The potential emphasized content is matched and anchored in the dynamic knowledge network to activate the corresponding associated knowledge nodes and confirm that the potential emphasized content is a key knowledge point;

[0048] Integrate all the potential emphasized content identified as key knowledge points and the corresponding associated knowledge nodes activated in the dynamic knowledge network to form a semantic fusion result.

[0049] Specifically, the system first analyzes various industry documents in the industry knowledge base, such as technical manuals, sales scripts, and regulatory documents. Using natural language processing (NLP) technology, it extracts core entities, concepts, and their relationships, constructing a graph-like dynamic knowledge network. Nodes represent knowledge points, and edges represent logical or relational relationships between these knowledge points. Next, the system uses acoustic contextual information from the multi-dimensional contextual information generated in the previous step—namely, sudden increases in speech rate, volume stress points, and abnormal pause intervals—to align their timestamps with the transcribed text. This alignment allows the system to accurately locate phrases or sentences that are particularly emphasized in pronunciation within the transcribed text and preliminarily label these as potential emphasized content. Then, for each labeled potential emphasized content, the system uses a deep learning language model to convert it into a high-dimensional semantic vector. Deep learning language models such as BERT and GPT can be used. Simultaneously, each knowledge node in the dynamic knowledge network is pre-represented as a corresponding semantic vector. The system evaluates the semantic matching degree by calculating the cosine similarity between the semantic vector of the potential emphasized content and the vectors of all knowledge nodes in the dynamic knowledge network. This calculation process can be represented as:

[0050] ,

[0051] in, Represents the similarity score. It is a vector obtained by vectorizing the potential emphasis content. It is the vector corresponding to a knowledge node in a dynamic knowledge network. When the similarity score... When a match exceeds a preset threshold, it indicates that the potential emphasized content is semantically highly relevant to the knowledge node. The system then anchors the potential emphasized content in the dynamic knowledge network and activates the corresponding knowledge node. This formally confirms the potential emphasized content as a key knowledge point. Finally, the system integrates all content fragments identified as key knowledge points in this session, along with their activated knowledge nodes and neighboring related knowledge nodes in the dynamic knowledge network, forming a structured semantic fusion result containing contextual information.

[0052] Optionally, the step of constructing an industry document framework based on the industry knowledge base and generating structured documents using the semantic fusion result and the industry document framework includes:

[0053] Based on the industry knowledge base, define the document structure and construct an industry document framework;

[0054] By combining the interaction role information in the multidimensional contextual information, the interaction popularity within different structural units of the industry document framework is analyzed.

[0055] Based on the interaction popularity and the key knowledge points in the semantic fusion results, the key knowledge points are highlighted.

[0056] The dynamic knowledge network is queried to obtain supplementary knowledge related to the key knowledge points, and the supplementary knowledge is used as associated recommended content.

[0057] Based on the highlighted key knowledge points and the associated recommended content, a structured document is constructed.

[0058] Specifically, the system pre-analyzes typical communication patterns and information organization paradigms in the industry knowledge base, defining a standardized document structure to form an industry document framework. This framework typically includes logical structural units such as "opening remarks," "requirements exploration," "solution presentation," and "objection handling." After a session ends, the system uses the interaction role information from the multi-dimensional contextual information generated in the preceding steps to segment the session and maps the content of each segment to the corresponding structural unit of the industry document framework. Next, the system calculates the interaction heat within each structural unit, which quantifies the frequency of communication between the two parties on a specific topic. The calculation of interaction heat can be based on the number of times the speaker role switches within a structural unit. Its calculation method can be expressed as:

[0059] ,

[0060] in, Representing the The interaction intensity of each structural unit This is the total number of speaker switching events within this unit. This data can be directly obtained from the interaction role information. This represents the total time spent on the structural unit. A high interaction heat value indicates frequent question-and-answer sessions, confirmations, or discussions on the topic. The system then combines this interaction heat with key knowledge points from the semantic fusion results. When a key knowledge point is located within a structural unit with high interaction heat, the system highlights it, for example, by using bolding, highlighting, or special markings in the final document, to emphasize its central role and importance in the communication. To further enrich the document content, the system uses each key knowledge point as an index to query the dynamic knowledge network, retrieve and extract other directly related knowledge nodes as supplementary knowledge, and integrates this knowledge as related recommended content into the document. Finally, based on the industry document framework, the system populates the highlighted key knowledge points and their associated recommended content into the corresponding structural units, ultimately generating a structured document.

[0061] Optionally, the step of obtaining the structured documents generated in each session to form an aggregated document, and performing pattern mining based on the aggregated document and the dynamic knowledge network to generate user behavior patterns includes:

[0062] The structured documents generated in each session are retrieved to form an aggregated document;

[0063] Data on knowledge coverage, explanation logic, and interactive guidance dimensions are extracted from the aggregated documents to form multidimensional behavioral data.

[0064] By combining the multidimensional behavioral data and the dynamic knowledge network, behavioral analysis and evaluation are performed to obtain quantitative results.

[0065] User behavior patterns are generated based on the quantification results.

[0066] Specifically, the system first systematically aggregates all structured documents generated from all conversations within a specific user or scenario, forming an aggregated document for longitudinal analysis. This aggregated document is essentially a collection of structured data. Next, the system parses this aggregated document, extracting information from three orthogonal dimensions to construct multidimensional behavioral data. In the knowledge coverage dimension, the system traverses all documents, identifying all unique key knowledge points, forming a knowledge point set that reflects the breadth of knowledge involved in long-term communication. In the explanation logic dimension, the system analyzes the order of key knowledge points within each structured document, as well as the evolution sequence of topics across documents and conversations, thereby constructing a temporal path reflecting the user's explanation approach and content organization habits. In the interaction guidance dimension, the system extracts interaction heat data associated with each key knowledge point to measure the effectiveness of the user in evoking feedback and participation when explaining specific knowledge. After obtaining the multidimensional behavioral data, the system deeply integrates it with a dynamic knowledge network, conducting behavioral analysis and evaluation to obtain quantitative results. This process maps the user's actual behavioral performance onto an ideal knowledge model for measurement. For example, the evaluation of the knowledge coverage dimension is accomplished by calculating the ratio of the number of key knowledge points mentioned by the user to the total number of knowledge nodes under a certain topic in the dynamic knowledge network. The evaluation of the explanation logic dimension quantifies the logical coherence and systematicity by comparing the user's explanation sequence with the preset optimal or standard logical paths between knowledge nodes in the dynamic knowledge network using an algorithm. The evaluation of the interaction guidance dimension analyzes the distribution of interaction popularity across different levels or categories of knowledge nodes in the dynamic knowledge network to determine whether the user's interaction guidance accurately targets core knowledge points. These independent quantitative indicators are integrated into a comprehensive quantitative result. Finally, based on this quantitative result, the system uses statistical methods such as cluster analysis or applies predefined rule models to identify the user's stable tendencies and typical characteristics in terms of the breadth of knowledge explanation, logical organization ability, and interaction skills, ultimately summarizing and generating user behavior patterns that clearly describe their communication style and abilities.

[0067] Optionally, the step of combining the multidimensional behavioral data and the dynamic knowledge network to perform behavioral analysis and evaluation, and obtaining quantitative results, includes:

[0068] The knowledge coverage dimension data in the multidimensional behavioral data is compared with the knowledge nodes in the dynamic knowledge network to quantify the knowledge coverage rate.

[0069] The logical dimension data in the multidimensional behavioral data is fitted with the logical path between knowledge nodes in the dynamic knowledge network to evaluate logical coherence.

[0070] The interaction guidance dimension data in the multidimensional behavioral data is mapped to the interaction frequency of specific knowledge nodes in the dynamic knowledge network to analyze the effectiveness of the interaction;

[0071] By integrating the knowledge coverage, logical coherence, and interaction effectiveness, a quantitative behavioral outcome is formed.

[0072] Specifically, firstly, in terms of knowledge coverage, the system extracts the set of key knowledge points mentioned by users in each conversation from multi-dimensional behavioral data and compares them with all preset core knowledge nodes under a specific topic or domain in the dynamic knowledge network. The knowledge coverage rate is quantified by calculating the proportion of the number of knowledge points mentioned by the user to the total number of knowledge nodes in that domain. This process can be represented as:

[0073] ,

[0074] in, Represents knowledge coverage. It is the number of unique key knowledge points actually covered by users, calculated from aggregated documents. The first dimension is the total number of knowledge nodes that should be covered within a specific domain as defined in the dynamic knowledge network. Secondly, in terms of explanation logic, the system extracts the temporal sequence of key knowledge points explained by users from the multidimensional behavioral data and fits it to the pre-defined logical paths between knowledge nodes representing optimal explanation practices in the dynamic knowledge network. This fitting process uses a sequence alignment algorithm to evaluate the similarity or deviation between the user's actual explanation path and the ideal path, thereby quantifying its logical coherence. The sequence alignment algorithm can employ dynamic programming algorithms such as the Smith-Waterman algorithm. Finally, in terms of interaction guidance, the system maps the interaction heat value corresponding to each key knowledge point recorded in the multidimensional behavioral data to the corresponding knowledge node in the dynamic knowledge network. The system analyzes whether high interaction heat is concentrated on knowledge nodes marked as key or difficult points in the network, thereby analyzing the effectiveness of user-guided interaction. After completing the independent quantification of these three dimensions, the system integrates the obtained knowledge coverage, logical coherence evaluation score, and interaction effectiveness analysis results into a multi-dimensional quantitative behavioral result, which comprehensively reflects the user's communication performance.

[0075] Optionally, the step of obtaining a benchmark practice model from the dynamic knowledge network to define idealization criteria, comparing the user behavior pattern with the benchmark practice model, and generating personalized optimization suggestions includes:

[0076] Obtain benchmark practice models for defining idealized standards from the dynamic knowledge network;

[0077] Calculate the gap between the user behavior pattern and the benchmark practice model in the dimensions of knowledge coverage, explanation logic, and interactive guidance, and generate gap analysis results;

[0078] Construct a rule template library for converting the gap analysis results into natural language strategies;

[0079] Based on the gap analysis results and the rule template library, personalized optimization suggestions are generated.

[0080] Specifically, the system first extracts a benchmark practice model from the dynamic knowledge network to define idealized standards. This model is abstracted from communication data or authoritative guidelines from leading industry experts. It defines the optimal breadth of knowledge coverage, logical sequence of explanation, and interactive guidance strategies for specific communication scenarios in the form of quantitative indicators and ideal paths. Subsequently, the system performs a dimension-by-dimensional quantitative comparison between the user behavior patterns generated in the preceding steps and this benchmark practice model to generate gap analysis results. The comparison results are as follows: Figure 2 As shown. In terms of knowledge coverage, the system calculates the difference between the user's actual knowledge coverage and the ideal coverage set by the benchmark model; in terms of explanation logic, it quantifies the degree of deviation by comparing the user's explanation path with the optimal logical path in the benchmark model; and in terms of interactive guidance, it compares the gap between the interaction intensity generated by the user at key knowledge nodes and the interaction level expected by the benchmark model. This gap calculation can be summarized as follows:

[0081] ,

[0082] in, Indicates a certain dimension The difference value on, It is the ideal indicator of the benchmark practice model in this dimension. This represents the actual quantitative result of user behavior patterns in this dimension. To transform these numerical gap analysis results into easily understandable and executable natural language strategies, the system pre-builds a rule template library. This library contains a series of conditional rules and corresponding text templates. For example, when the gap in the knowledge coverage dimension exceeds a certain threshold, the "knowledge point supplementation suggestion" template is triggered. Finally, the system inputs the gap analysis results into the rule template library for matching. Based on the matched rules, it selects the appropriate text template and fills the preset spaces in the template with the specific numerical values ​​and related knowledge points from the gap analysis results, generating personalized optimization suggestions that include specific problem diagnoses and executable improvement measures.

[0083] Optionally, the method further includes:

[0084] The transcribed text is subjected to part-of-speech tagging and phrase extraction to obtain a candidate concept set;

[0085] Calculate the similarity between the candidate concepts in the candidate concept set and the knowledge nodes in the dynamic knowledge network, and select the candidate concepts with similarity less than a preset similarity threshold to construct a new knowledge point set;

[0086] Based on the new set of knowledge points, new knowledge nodes are added to the dynamic knowledge network, and the dynamic knowledge network is updated.

[0087] Specifically, the first step is to perform deep linguistic analysis on the transcribed text generated from a single conversation. The system uses a natural language processing toolkit to perform part-of-speech tagging on the transcribed text, assigning a grammatical role to each word, such as noun or verb. Based on this, phrase extraction techniques based on rules or statistical models are used to identify and extract phrases with independent semantics from the text, especially noun phrases, forming a candidate concept set. This set contains all potential technical terms or new concepts mentioned in the dialogue. Next, to identify truly new knowledge, the system calculates the semantic similarity between each candidate concept in this set and all existing knowledge nodes in the dynamic knowledge network. This process is achieved by converting candidate concepts and knowledge nodes into high-dimensional vectors using a pre-trained word vector model, and then calculating their cosine similarity. The calculation method can be expressed as follows:

[0088] ,

[0089] in, This represents the maximum similarity between a candidate concept and all nodes in the knowledge network. It is the semantic vector of the current candidate concept. It is the semantic vector of the nth knowledge node in the dynamic knowledge network. This represents the total set of nodes in the knowledge network. The system will... Compare with a preset similarity threshold, if If the value is below this threshold, it indicates that the candidate concept is semantically far removed from all known knowledge points. Therefore, the system determines it as a new knowledge point and adds it to the new knowledge point set. Finally, the system iterates through this new knowledge point set, creating a new knowledge node for each new knowledge point in the dynamic knowledge network. Based on the context of the new knowledge point in the original transcribed text, it analyzes its relationship with surrounding existing knowledge nodes, establishing preliminary association edges, thus completing the incremental update of the dynamic knowledge network.

[0090] Optionally, the method further includes:

[0091] Based on the interaction heat within different structural units of the industry document framework and the multi-dimensional contextual information, the attention level of knowledge nodes corresponding to the interaction heat in the dynamic knowledge network is calculated, and knowledge node change parameters are generated.

[0092] Based on the changes in the knowledge node parameters, the weights of the corresponding knowledge nodes in the dynamic knowledge network are adjusted.

[0093] Specifically, the system first utilizes the industry document framework generated in previous steps and the interaction intensity within each structural unit. For each session, the system identifies key knowledge points mentioned in structural units with high interaction intensity. Areas with high interaction intensity typically indicate intensive two-way communication such as Q&A, clarification, and objections; therefore, the knowledge points involved are considered to be of high user attention, difficult to understand, or controversial. The system directly uses this interaction intensity value as the attention level of the corresponding knowledge node, which constitutes the knowledge node change parameter. Subsequently, the system dynamically adjusts the weights of the corresponding knowledge nodes in the dynamic knowledge network based on this parameter. Each knowledge node in the network has an initial weight, representing its importance in the theoretical framework. When a knowledge node is discussed with high attention in an actual session, the system increases its weight; conversely, nodes that are not mentioned for a long time or appear in low-interaction areas may have their weights moderately reduced. The weight update process employs a smooth update mechanism to ensure network stability. This adjustment process can be represented by the following formula:

[0094] ,

[0095] in, It is the new weight of knowledge node n after this update. It is the old weight before the update. It is an update rate coefficient between 0 and 1, used to control the degree of impact of a single session on the overall weight system. This is the attention level of knowledge node n calculated in this session, and this value is directly derived from the interaction popularity of the structural unit to which the node belongs. If a knowledge node is not mentioned in this session, then its attention level is calculated accordingly. The value is 0. By iteratively executing this process, the weight distribution of the dynamic knowledge network will gradually approximate the knowledge importance distribution in real business scenarios.

[0096] Based on the same inventive concept, such as Figure 3 As shown, the present invention also provides a speech content semantic analysis system for microphones that integrates an industry knowledge base, characterized in that the system includes:

[0097] The context analysis module is used to acquire the original speech stream collected by the microphone, perform speech-to-text transcription on the original speech stream to generate transcribed text, and extract acoustic context information and interaction role information based on the original speech stream and the transcribed text, and combine the acoustic context information and the interaction role information into multi-dimensional context information.

[0098] The semantic fusion module is used to construct a dynamic knowledge network through a preset industry knowledge base, input the transcribed text and the multi-dimensional contextual information into the dynamic knowledge network for semantic fusion processing, and generate a semantic fusion result containing key knowledge points.

[0099] The document generation module is used to construct an industry document framework based on the industry knowledge base, and to generate structured documents using the semantic fusion results and the industry document framework.

[0100] The pattern mining module is used to obtain the structured documents generated in each session to form an aggregated document, and to perform pattern mining based on the aggregated document and the dynamic knowledge network to generate user behavior patterns.

[0101] The optimization suggestion generation module is used to obtain a benchmark practice model for defining idealization standards from the dynamic knowledge network, compare the user behavior pattern with the benchmark practice model, and generate personalized optimization suggestions.

[0102] Example 1

[0103] To verify the feasibility and application value of this invention, it was applied to a high school physics teaching scenario. This embodiment focuses on the teaching of the "Newton's Laws of Motion" unit by physics teacher Mr. Wang, over a period of one semester. By deploying a high-fidelity microphone array in the classroom to continuously collect classroom audio data, the aim is to transform the raw, unstructured teaching process into knowledge assets and provide teachers with data-driven, personalized teaching optimization suggestions, ultimately achieving a complete closed loop from data analysis to empowering teaching capabilities.

[0104] In a 45-minute lesson on "Newton's Second Law," the system first acquired the raw speech stream captured by a microphone and then used a speech recognition engine with an integrated physics lexicon containing terms such as "vector," "instantaneous acceleration," and "net external force" to generate transcribed text. Simultaneously, the system performed acoustic context analysis on the raw speech stream, extracting the following information: When Teacher Wang explained the core formula "F=ma," his volume was 5 decibels higher than average, forming a volume accent; when explaining the difficult point of "the vector nature of force," related phrases were repeated three times in the lesson, forming a key repetition interval; after posing the thought-provoking question, "If mass is not constant, how should the formula be modified?", there was an unusual pause of up to 8 seconds, giving students ample time to think. Figure 4 As shown. Simultaneously, the system used speaker log technology to analyze interaction roles, successfully distinguishing between Teacher Wang's explanation and questions from five different students, and marking 12 interaction moments. This acoustic contextual information and interaction role information were combined into multi-dimensional contextual information aligned with the transcribed text timestamps.

[0105] The system pre-constructs a dynamic knowledge network using a pre-set industry knowledge base—the high school physics education knowledge base—which includes curriculum standards, multiple versions of mainstream textbooks, past college entrance examination questions, and excellent lesson plans. "Newton's Second Law" is the core knowledge node, logically linked to nodes such as "inertia," "acceleration," and "composition and decomposition of forces." The system inputs the transcribed text and multi-dimensional contextual information into this network for semantic fusion processing. Using the multi-dimensional contextual information, the system automatically locates potentially emphasized content in the transcribed text, such as the explanation paragraph "F=ma," corresponding to volume emphasis points, and the explanation paragraph "the vector nature of force," corresponding to repeated explanations. Subsequently, these contents are semantically matched and anchored within the dynamic knowledge network, successfully activating the corresponding knowledge nodes and confirming them as key knowledge points for the lesson. Finally, all confirmed key knowledge points, along with their related knowledge within the network, are integrated to form a semantic fusion result that includes core formulas, key concepts, analysis of difficulties, and teacher-student Q&A.

[0106] Based on the standard lesson plan structure defined in the educational knowledge base, the system constructs an industry document framework for "Teacher Professions," including structural units such as teaching objectives, new lesson introduction, concept explanation, example analysis, and class summary. The system fills this framework with semantic fusion results, generating a structured document—the intelligent lecture notes for this lesson. Specifically, the system analyzes interaction popularity by combining interactive role information, finding that teacher-student interaction is most frequent in the "Example Analysis" unit. Therefore, the key knowledge point in this unit, "Acceleration Analysis under Variable Force Conditions," is highlighted, for example, by bolding and highlighting. Simultaneously, the system queries the dynamic knowledge network and automatically recommends supplementary knowledge related to this knowledge point, such as "Preliminary Applications of Calculus in Physics," and attaches it as related recommended content at the end of the lecture notes. After the lecture notes are automatically generated, the system further extracts its core logic and generates a mind map for this lesson with one click, which can be in Xmind format, intuitively displaying a "learning path diagram" from basic definitions to complex applications.

[0107] After a one-month teaching cycle, the system aggregated the structured documents generated from each session, covering everything from "Newton's First Law" to "Weight Gain and Weightlessness." Based on this aggregated document and the dynamic knowledge network, the system performed pattern mining, extracting multidimensional behavioral data from three dimensions. First, regarding knowledge coverage, statistics showed that Teacher Wang covered 95% of the knowledge points in the curriculum standards, but omitted some information on "non-inertial frames of reference." Second, regarding explanation logic, analysis of the order of knowledge point explanations revealed a 98% fit between the logical path and the recommended optimal teaching path in the knowledge network, demonstrating excellent logical coherence. Finally, regarding interactive guidance, the average interaction frequency was lower than the baseline model during concept explanations, but 20% higher during exercise sessions. Combining these quantitative results, the system generated Teacher Wang's user behavior pattern: clear logic and comprehensive knowledge coverage, but room for improvement in interactive guidance during theoretical teaching.

[0108] Finally, the system retrieves a benchmark practice model composed of teaching data from top-tier teachers from the dynamic knowledge network. By comparing Teacher Wang's user behavior patterns with this model, the system calculates the gap analysis results for each dimension. For example, the gap in the knowledge coverage dimension is reflected in the lack of "non-inertial" knowledge points; the gap in the interaction guidance dimension is manifested in the low frequency of interaction in theoretical classes. Based on these gaps, the system uses a rule template library to generate personalized optimization suggestions and outputs a detailed post-class optimization report. The report includes the following suggestions: "Structural recommendations: In the lesson on 'Summary of Newton's Laws of Motion,' it is recommended to supplement the introduction with the concept of 'non-inertial frames of reference' and its differences from inertial frames of reference. Methodological recommendations: When explaining abstract theories such as 'inertia,' try introducing fun real-life experiments or using group discussions to increase student participation. Pace recommendations: Data shows that your average speaking speed is 210 words / minute, slightly higher than the baseline model's 180 words / minute. It is recommended to slow down your speaking speed appropriately when explaining difficult points and increase pauses for questions." Through this closed-loop feedback, this invention transforms each teaching record into a precise teaching diagnosis and growth opportunity, effectively promoting teachers' professional development.

[0109] Example 2

[0110] To verify the feasibility and application value of this invention in the legal services field, it was applied to the commercial litigation department of a law firm. This embodiment focuses on a "software development contract breach" case handled by Attorney Li, covering the entire process from the initial client interview to pre-trial preparation. By deploying high-fidelity microphones in the conference room and lawyer's office and integrating the law firm's document management system, the aim is to transform case information into structured, analyzable legal knowledge assets, and to provide lawyers with data-driven suggestions for optimizing litigation strategies, ultimately achieving a complete closed loop from information processing to decision empowerment.

[0111] In the early stages of the case, Attorney Li conducted a two-hour interview with the CEO of Client Company A. The system first acquired the raw audio stream captured by a microphone and then used a speech recognition engine with an integrated legal terminology database containing terms such as "delivery milestones," "acceptance criteria," and "intellectual property ownership" to generate a high-precision transcribed text. Through multi-source voice recognition technology, the system automatically labeled each line of dialogue with the interaction role information of "Attorney Li" or "Client CEO." Simultaneously, Attorney Li uploaded relevant electronic evidence to the system, including emails exchanged between the parties, project management records, and the text of the relevant "Software Development Contract." The system integrated the transcribed text with this written evidence, linking it using timestamps and entity recognition technology to create a multi-dimensional contextual information system that included both oral statements and written documents, along with roles and temporal context.

[0112] The system pre-constructs a dynamic knowledge network using a pre-set industry knowledge base, namely the "Commercial Contract Law Knowledge Base," which includes relevant legal provisions, guiding cases, industry-specific transaction practices, and authoritative legal treatises. The system inputs the aforementioned multi-dimensional contextual information into this network and performs semantic fusion processing. Using Natural Language Processing (NLP) technology, the system automatically extracts legal elements from the contract text and interview records, identifying Company A as the plaintiff and Company B as the defendant; extracts key nodes such as "contract signing date," "agreement delivery date," and "date of first objection," constructing a case timeline; and marks the disputed issue of "whether the software functions conform to the technical specifications in Appendix A of the contract" as the main focus of the dispute. These extracted and confirmed key legal elements are anchored to knowledge nodes such as "contract breach" and "defective delivery quality" in the dynamic knowledge network, forming the semantic fusion result of this case.

[0113] Based on the standardized litigation document format defined in the legal knowledge base, the system has constructed an industry document framework for "civil complaints." Utilizing semantic fusion results, the system automatically populates the framework with case facts, party information, points of contention, and preliminary legal basis, generating a structured document—a draft complaint—containing sections such as "facts and reasons" and "claims," ​​thus shortening document drafting time. Furthermore, to clearly demonstrate litigation strategies, the system visualizes the case's argumentation logic, generating a legal logic diagram. This diagram, with Company B as the central node, branches into multiple chains of argumentation. One chain links the evidence of "system crash logs of the delivered software" to the legal claim of "breach of contract quality assurance clauses," and then to "Guiding Case No. XX" supporting this claim. Another chain combines the interview testimony of the CEO of Client Company A with email records, both pointing to the factual finding that "the defendant failed to fulfill its obligation to promptly repair the issue."

[0114] During the six-month period that Attorney Li handled this case and other similar contract disputes, the system retrieved the structured documents generated from each session to form an aggregated document. Based on this aggregated document and dynamic knowledge network, the system performed pattern mining on Attorney Li's professional habits and established his personal user behavior pattern. Analysis revealed that, in terms of knowledge coverage, Attorney Li cited the legal application of "anticipatory breach" more frequently than average in contract breach cases, but his knowledge of "rules for the examination of electronic evidence" was slightly insufficient. In terms of argumentation logic, his argumentation style favored the comparative case method, exhibiting rigorous logic, but occasionally made oversights when constructing complex chains of evidence involving multiple causal relationships. In terms of risk identification, he was extremely sensitive to "limitation of liability" clauses when reviewing contracts, but his attention to the risk point of "jurisdiction objection" needs improvement.

[0115] Finally, the system retrieves a benchmark practice model composed of successful case data from senior litigation lawyers from a dynamic knowledge network. By comparing Attorney Li's user behavior patterns with this model, the system calculates the gap analysis results for each dimension. Based on this, the system uses a built-in rule template library to generate personalized optimization suggestions, which are presented in the form of instant reminders and case review reports. For example, when Attorney Li is reviewing a new contract, the system pops up a prompt: "Compliance suggestion: The dispute resolution clause of this contract selects the court where the defendant is located as the jurisdiction. According to your historical case data, this may increase future litigation costs. It is recommended to change it to the plaintiff's location or arbitration. Argument optimization suggestion: For this case, in addition to the breach of contract evidence you have prepared, you can consider introducing a third-party software evaluation report as evidence to strengthen the argument that 'the delivery has a fundamental defect,' referring to the successful experience of the 'YY case.' Knowledge supplement suggestion: The system has detected that the cases you have recently handled involve a lot of electronic evidence and has pushed the latest judicial interpretation of the 'Several Provisions on Evidence in Civil Litigation' and related interpretation articles to you." In this way, this invention is not only a document assistant but also a legal strategic partner, providing continuous empowerment for lawyers' professional growth and improving case success rates.

[0116] Example 3

[0117] To verify the feasibility and application value of this invention in the medical field, it was applied to the cardiology outpatient department of a hospital. This embodiment focuses on a follow-up visit by Dr. Zhang to a patient with hypertension and chest tightness. The aim is to intelligently integrate independent systems such as voice consultation, electronic medical records (EHR), laboratory testing (LIS), and picture archiving (PACS) to form a complete closed loop for diagnostic decision support and medical quality control, transforming diagnostic information into structured knowledge assets that can be continuously optimized.

[0118] After the patient enters the consultation room, the system first acquires the raw speech stream captured by the microphone. Then, using a speech recognition engine integrating ICD-10 coding and a clinical medical terminology database containing terms such as "atypical angina," "beta-blockers," and "statins," it generates high-precision transcribed text and automatically labels the interaction roles of "Dr. Zhang" and "patient." Simultaneously, the system retrieves the patient's previous electronic medical record (EHR) in real time via an interface, automatically obtaining their history of "5-year history of hypertension, long-term use of metoprolol," and last week's "routine blood lipid test results." The voice-text from this consultation is integrated with historical medical records and test data, aligned based on timestamps, forming a multi-dimensional contextual information system containing the current complaint and historical data, laying the foundation for subsequent analysis.

[0119] The system pre-constructs a dynamic medical knowledge network using a pre-set industry knowledge base, namely the "Evidence-Based Medicine and Clinical Guidelines Knowledge Base," which includes the American Heart Association (AHA / ACC) hypertension guidelines, the FDA drug interaction database, and massive amounts of anonymized medical record data. The system inputs the aforementioned multidimensional contextual information into this network and performs semantic fusion processing. When Dr. Zhang mentions "considering adding rosuvastatin 10mg, once nightly" during the consultation, the system matches "rosuvastatin" with the patient's long-term medication "metoprolol" in the knowledge network in real time. Through drug interaction model analysis, it confirms there are no contraindications and provides a "appropriate medication" prompt on the interface. Furthermore, through semantic analysis of the consultation voice, the system automatically anchors key descriptions such as "chest tightness after exertion, lasting about three minutes, relieved by rest" to the "exertional angina" knowledge node in the knowledge network, providing potential directions for the doctor's diagnosis.

[0120] After completing the consultation, Dr. Zhang verbally described the findings of the physical examination and ordered an electrocardiogram (ECG). The system automatically parsed the verbal description, such as "heart rate 75 bpm, regular rhythm, clear breath sounds bilaterally," and filled it into the Physical Examination (PE) section of the standardized SOAP medical record template. Minutes later, the ECG report completed by the patient in the examination department was generated through the Hospital Information System (HIS). This invention's system captured the report in real time and automatically read the key field: "ST segment depression of 0.1 mV in leads V3-V5." This information was immediately pushed back to Dr. Zhang's work interface, triggering a secondary comparison. The system correlated the patient's complaint of "chest tightness," the physical examination findings, and the ECG finding of "ST segment depression," confirming a high degree of consistency. It automatically generated a pending diagnosis of "suspected coronary atherosclerotic heart disease" in the "Preliminary Diagnosis" section of the medical record, recommended a corresponding treatment plan, and finally generated a structured document, i.e., a draft outpatient medical record.

[0121] After three months of operation, the system aggregated the structured documents generated from each session, covering the complete medical records of hundreds of cardiovascular disease patients treated by Dr. Zhang. Based on this aggregated document and dynamic medical knowledge network, the system performed pattern mining on Dr. Zhang's clinical behavior and established his personal user behavior pattern. Analysis revealed the following: In terms of diagnostic logic, Dr. Zhang's adherence to the AHA / ACC hypertension guidelines was as high as 98%, demonstrating rigorous diagnostic logic; in terms of medication habits, data showed that he preferred statins in 95% of patients with hyperlipidemia, but when faced with patients with complex combined medications, the frequency of querying drug interactions was slightly lower than the baseline model. In terms of medical record quality, his medical record completeness rate reached 99%, but the average word count of the "patient education" section was relatively low.

[0122] Finally, the system retrieves a benchmark practice model from the dynamic knowledge network, composed of anonymized clinical data from senior experts within the hospital. By comparing Dr. Zhang's user behavior patterns with this model, the system calculates the gap analysis results. Based on this, the system utilizes a rule template library to generate personalized optimization suggestions, empowering doctors, patients, and the hospital. For doctors, the system pushes a weekly report: "Your adherence to treatment guidelines this week remains at a high level. To further reduce potential medication risks, it is recommended to use the 'one-click drug interaction' check function when prescribing new medications to patients using multiple medications. The system has automatically pushed the latest 'Expert Consensus on the Combined Use of Beta-Blockers and Statins' to you." For patients, the system automatically generates an easy-to-understand "Plain Language Medical Record Summary" and a "Treatment Plan Mind Map" containing medication times, dosages, and follow-up reminders from the current outpatient medical record, and pushes it to the patient through the hospital's app. For hospitals, the system regularly generates medical quality reports for departments, quantitatively displaying core indicators such as "grade A medical record rate" and "guideline compliance rate," providing data-driven decision-making basis for hospitals' refined management and continuous quality improvement.

[0123] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any method of indirect connection is applicable to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0124] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for semantic analysis of voice content from a microphone pen that integrates an industry knowledge base, characterized in that, The method includes: The system acquires the original speech stream collected by the microphone, performs speech-to-text transcription on the original speech stream to generate transcribed text, and extracts acoustic context information and interactive role information based on the original speech stream and the transcribed text, and combines the acoustic context information and the interactive role information into multi-dimensional context information. A dynamic knowledge network is constructed by building a pre-set industry knowledge base. The transcribed text and the multi-dimensional contextual information are input into the dynamic knowledge network for semantic fusion processing to generate a semantic fusion result containing key knowledge points. An industry document framework is constructed based on the industry knowledge base, and structured documents are generated using the semantic fusion results and the industry document framework. The structured documents generated in each session are obtained to form an aggregated document. Based on the aggregated document and the dynamic knowledge network, pattern mining is performed to generate user behavior patterns. A benchmark practice model for defining idealized standards is obtained from the dynamic knowledge network. The user behavior pattern is compared with the benchmark practice model to generate personalized optimization suggestions.

2. The method for semantic analysis of voice content using a microphone pen that integrates an industry knowledge base, as described in claim 1, is characterized in that... The step of combining the acoustic context information and the interactive role information into multidimensional context information includes: Acquire the original speech stream captured by the microphone pen, and perform speech-to-text transcription on the original speech stream to generate transcribed text; Acoustic context analysis is performed on the original speech stream to extract speech rate spikes, volume stress points, and abnormal pause intervals, generating acoustic context information. Based on the transcribed text and the original speech stream, interactive role analysis is performed to distinguish different speakers and mark interaction moments, thereby generating interactive role information. The acoustic context information and the interactive role information are combined to obtain multidimensional context information.

3. The method for semantic analysis of voice content using a microphone pen that integrates an industry knowledge base, as described in claim 2, is characterized in that... The generated semantic fusion result containing key knowledge points includes: Industry data is acquired through a pre-set industry knowledge base, and a dynamic knowledge network is constructed by analyzing the industry data. The acoustic context information in the multidimensional context information is used to locate potential emphasized content in the transcribed text; The potential emphasized content is matched and anchored in the dynamic knowledge network to activate the corresponding associated knowledge nodes and confirm that the potential emphasized content is a key knowledge point; Integrate all the potential emphasized content identified as key knowledge points and the corresponding associated knowledge nodes activated in the dynamic knowledge network to form a semantic fusion result.

4. The method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base as described in claim 3, characterized in that, The step of constructing an industry document framework based on the industry knowledge base and generating structured documents using the semantic fusion results and the industry document framework includes: Based on the industry knowledge base, define the document structure and construct an industry document framework; By combining the interaction role information in the multidimensional contextual information, the interaction popularity within different structural units of the industry document framework is analyzed. Based on the interaction popularity and the key knowledge points in the semantic fusion results, the key knowledge points are highlighted. The dynamic knowledge network is queried to obtain supplementary knowledge related to the key knowledge points, and the supplementary knowledge is used as associated recommended content. Based on the highlighted key knowledge points and the associated recommended content, a structured document is constructed.

5. The method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base as described in claim 4, characterized in that, The process of obtaining the structured documents generated in each session to form an aggregated document, and performing pattern mining based on the aggregated document and the dynamic knowledge network to generate user behavior patterns includes: The structured documents generated in each session are retrieved to form an aggregated document; Data on knowledge coverage, explanation logic, and interactive guidance dimensions are extracted from the aggregated documents to form multidimensional behavioral data. By combining the multidimensional behavioral data and the dynamic knowledge network, behavioral analysis and evaluation are performed to obtain quantitative results. User behavior patterns are generated based on the quantification results.

6. The method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base as described in claim 5, characterized in that, The behavioral analysis and evaluation, which combines the multidimensional behavioral data and the dynamic knowledge network, yields quantitative results including: The knowledge coverage dimension data in the multidimensional behavioral data is compared with the knowledge nodes in the dynamic knowledge network to quantify the knowledge coverage rate. The logical dimension data in the multidimensional behavioral data is fitted with the logical path between knowledge nodes in the dynamic knowledge network to evaluate logical coherence. The interaction guidance dimension data in the multidimensional behavioral data is mapped to the interaction frequency of specific knowledge nodes in the dynamic knowledge network to analyze the effectiveness of the interaction; By integrating the knowledge coverage, logical coherence, and interaction effectiveness, a quantitative behavioral outcome is formed.

7. The method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base as described in claim 5, characterized in that, The step of obtaining a benchmark practice model from the dynamic knowledge network to define idealization standards, comparing the user behavior pattern with the benchmark practice model, and generating personalized optimization suggestions includes: Obtain benchmark practice models for defining idealized standards from the dynamic knowledge network; Calculate the gap between the user behavior pattern and the benchmark practice model in the dimensions of knowledge coverage, explanation logic, and interactive guidance, and generate gap analysis results; Construct a rule template library for converting the gap analysis results into natural language strategies; Based on the gap analysis results and the rule template library, personalized optimization suggestions are generated.

8. The method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base as described in claim 7, characterized in that, The method further includes: The transcribed text is subjected to part-of-speech tagging and phrase extraction to obtain a candidate concept set; Calculate the similarity between the candidate concepts in the candidate concept set and the knowledge nodes in the dynamic knowledge network, and select the candidate concepts with similarity less than a preset similarity threshold to construct a new knowledge point set; Based on the new set of knowledge points, new knowledge nodes are added to the dynamic knowledge network, and the dynamic knowledge network is updated.

9. A method for semantic analysis of voice content using a microphone pen integrated with an industry knowledge base, as described in claim 8, is characterized in that... The method further includes: Based on the interaction heat within different structural units of the industry document framework and the multi-dimensional contextual information, the attention level of knowledge nodes corresponding to the interaction heat in the dynamic knowledge network is calculated, and knowledge node change parameters are generated. Based on the changes in the knowledge node parameters, the weights of the corresponding knowledge nodes in the dynamic knowledge network are adjusted.

10. A speech content semantic analysis system for a microphone pen that integrates an industry knowledge base, characterized in that, The system includes: The context analysis module is used to acquire the original speech stream collected by the microphone, perform speech-to-text transcription on the original speech stream to generate transcribed text, and extract acoustic context information and interaction role information based on the original speech stream and the transcribed text, and combine the acoustic context information and the interaction role information into multi-dimensional context information. The semantic fusion module is used to construct a dynamic knowledge network through a preset industry knowledge base, input the transcribed text and the multi-dimensional contextual information into the dynamic knowledge network for semantic fusion processing, and generate a semantic fusion result containing key knowledge points. The document generation module is used to construct an industry document framework based on the industry knowledge base, and to generate structured documents using the semantic fusion results and the industry document framework. The pattern mining module is used to obtain the structured documents generated in each session to form an aggregated document, and to perform pattern mining based on the aggregated document and the dynamic knowledge network to generate user behavior patterns. The optimization suggestion generation module is used to obtain a benchmark practice model for defining idealization standards from the dynamic knowledge network, compare the user behavior pattern with the benchmark practice model, and generate personalized optimization suggestions.

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