4332 results about "Voice data" patented technology

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention relates to the technical field of large language models, and discloses a voice data interaction feedback control processing method based on a large language model. The method comprises the following steps: acquiring an industrial voice instruction through an AMR main controller, and obtaining a standardized vector through industrial lexicon matching and intention classification; inputting an industrial large language model for reasoning processing, and generating an AMR execution scheme; carrying out distributed coordination and task allocation on the AMR cluster to form a control instruction sequence; and the motion controller executes monitoring, processes exceptions and outputs a feedback strategy. Accurate classification and semantic understanding of complex industrial instructions are achieved, the processing capacity of professional knowledge in the industrial field is improved, and meanwhile high-precision real-time state monitoring is achieved.

The invention relates to dialect speech synthesis, in particular to a semi-supervised dialect emotion speech synthesis system based on hybrid experts, which comprises a text analysis module for preprocessing an input dialect text and generating a text representation vector through feature extraction and feature fusion; the mixed expert module is used for acquiring dialect acoustic features, rhythm features, emotional features and general acoustic features; the dynamic routing module is used for realizing intelligent cooperation among experts through a task-aware soft routing algorithm; the semi-supervised learning module is used for training supervised learning by using the marked dialect emotion voice data and training self-supervised learning by using the unmarked dialect emotion voice data; the acoustic parameter generation module is used for integrating the output of each expert to generate a complete acoustic parameter set; the neural vocoder is used for converting the acoustic parameter set into final dialect emotion voice; the method can effectively overcome the defect that the dialect emotional voice is difficult to accurately synthesize under the condition of lack of sample resources.

The invention discloses a mechanical arm control method, system and equipment for realizing a multi-modal universal operation task. The method comprises the following steps: acquiring multi-modal data of text, voice and vision; performing modal alignment on the multi-modal input data, encoding the multi-modal input data into a uniform input representation vector, and splicing the uniform input representation vector into a multi-modal instruction paragraph; retrieving a user information voice database based on voiceprint features of the voice instruction data to obtain task associated voice data; instruction information is input into the strategy large model to obtain an action plan; a post-processing task action planning input controller controls a mechanical arm, and a multi-mode input instruction is updated in real time to perceive the latest task state; and performing closed-loop control based on the initial target instruction information and the environment perception information until the task is completed. The task planning and error correction capacity of the mechanical arm is improved through closed-loop control and voice data retrieval based on the large strategy model, and the dynamic adaptive capacity, the autonomous error correction capacity and the multi-mode interaction capacity of the mechanical arm are improved.

The invention provides a multi-modal intelligent question-answering and recommendation system supporting emotional speech output, and relates to the technical field of visual question-answering, and the system specifically comprises a multi-modal question-answering module, a multi-modal recommendation module, a speech recognition module, and a speech synthesis module. Wherein the multi-mode question answering module converts an image input by a user into a natural language form, and final question answering is completed in combination with a comet knowledge base COMET; the voice recognition module is used for recognizing voice data input into the multi-mode intelligent question answering and recommending system and processing the voice data into a text form; the multi-modal recommendation module identifies whether modal missing exists in input data or not, generates representation of the missing modal, obtains vector representation of the user and the article through a graph convolutional neural network and learning according to an interaction matrix of the user and the article, carries out reliability calculation, and finally sends a multi-modal recommendation result to the user and the article. Sending back to the multi-mode question and answer module; and the voice synthesis module outputs the voice to the user.

The invention discloses a large-model complaint intention recognition method based on sentiment analysis, and the method comprises the steps: obtaining call voice data of a customer, and converting the call voice data into text data; preprocessing the text data, and removing noise and marking components; performing emotion feature analysis on the text data, extracting an emotion feature value, and generating an emotion vector; the emotion vectors and the text data are input into a large model together, and the large model combines the emotion feature values and context semantics to generate intention feature vectors; constructing an emotion-intention state vector, inputting the emotion-intention state vector into an asynchronous dominant actor commentator algorithm model, and generating a corresponding complaint intention probability value; judging a complaint intention probability, and generating risk early warning; and preferentially distributing high-risk customers and responding to customer demands. Through combination of voice data preprocessing, text semantic feature extraction, emotion intensity quantitative analysis and a multi-modal fusion algorithm and efficient risk assessment based on an asynchronous dominant actor reviewer model, the early warning and response capabilities of customer complaint risks are significantly improved.

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

The invention relates to the technical field of medical informatization, and discloses a method for generating a medical record report based on a doctor-patient dialogue, and the method comprises the following steps: multi-modal data collection: collecting the multi-modal data of the doctor-patient dialogue, including voice data, video data and environment data; multi-modal data preprocessing: preprocessing the collected data, including noise reduction and feature extraction of voice signals, key point extraction of video data and time synchronization of the multi-modal data; and semantic analysis: converting the voice signal into a text by using a voice recognition technology, carrying out semantic analysis on the text, and extracting key information. Through multi-modal data acquisition, semantic analysis, emotion modeling and knowledge graph reasoning technologies, the comprehensiveness, logicality and dynamic correction ability of medical record generation are improved, meanwhile, the defects of patient psychological state analysis in the prior art are overcome, information orderliness and doctor decision-making efficiency are remarkably improved through generation of the visual medical record report, and the medical record report generation method is suitable for popularization and application. And powerful support is provided for precise medical treatment.

The invention discloses a speech recognition and authentication method and system based on multi-modal features and dynamic evaluation in the technical field of speech recognition and authentication, and the method comprises the steps: collecting an original speech signal of a user through a microphone, and carrying out the preprocessing of the original speech signal, and obtaining the preprocessing speech data; and extracting feature data of the preprocessed voice data by adopting a multi-dimensional feature hierarchical extraction technology, and injecting a multi-source noise sample into an acoustic feature space of a voiceprint feature model based on an initial training stage established by the voiceprint feature model to construct an anti-noise mixed voiceprint map. Through integrating voiceprint, semantics, behavior characteristics and an environment adaptation mechanism, an authentication threshold is adjusted in real time according to dynamic risk assessment, meanwhile, a risk scoring model is utilized to calculate a comprehensive risk value, authentication modes of different levels are started according to risk scenes of different degrees, and two-factor authentication is forcibly implemented for high-risk scenes. And the authentication security and reliability can be obviously enhanced.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, cultural research and the like, and discloses a multi-modal abstract generation and output method, which comprises the following steps of: acquiring target domain data and cleaning to obtain structured data; extracting entities and association relationships among the entities from the structured data, and constructing a target domain knowledge graph; generating a text abstract containing entity associated information based on the target domain knowledge graph; matching the image data and the audio data corresponding to the text abstract, and integrating the text abstract, the image data and the audio data to generate a multi-modal abstract; and constructing a user portrait according to the user behavior data, and outputting the multi-modal abstract based on the user portrait. According to the method, extraction and analysis of entities and association relationships thereof are optimized through knowledge graph construction; through multi-modal fusion, associated display of texts, images and voices is realized; and through user portrait optimization output, the efficiency and adaptability of knowledge summarization are improved.

The invention discloses a construction method of a real-time streaming voice intelligent question and answer service system. The construction method comprises the following steps: receiving input voice data; calling an independent voice recognition service to convert the voice data into an input text; the method comprises the following steps: constructing cue words of a large language model based on an input text, initiating an LLM streaming request to an independent large language model service, and obtaining streaming text answers generated by LLM in real time; segmenting the flow type text answer in real time through a dynamic sentence segmentation device to generate a plurality of clauses; calling an independent speech synthesis service in parallel for each clause, and converting the text into an audio data block; and combining the audio data blocks into streaming audio data according to the generation sequence, and returning the streaming audio data to a client for playing in real time. According to the invention, by decoupling ASR, LLM and TTS services and combining the asynchronous streaming framework and the strategy of "wanting while speaking", the real-time performance and flexibility of the voice question-answering system are significantly improved.

The invention discloses a commerce and trade circulation supply chain optimization method based on multi-modal data fusion, and the method comprises the following steps: 1, multi-modal data collection: collecting the multi-modal data of the whole supply chain in real time through an Internet of Things device, an enterprise ERP system, a logistics tracking system and a third-party platform, the multi-modal data comprises text data, image data, voice data, time sequence data and spatial data. When the scheme is implemented, multi-source heterogeneous data of the Internet of Things, ERP and the like are integrated, so that the problem of an information island in a traditional supply chain is effectively solved; position coding and a cross-modal attention mechanism are introduced into the multi-modal fusion model, so that the data alignment precision is remarkably improved, for example, when'emergency order adding 'in a voice communication record is associated with a corresponding order image, the weight distribution accuracy is improved; the application of the block chain technology ensures the transparency and traceability of the supply chain decision.

The invention relates to the technical field of customer services, in particular to an intelligent outbound call skill generation method, system and device using LLM and a medium, and the method comprises the steps: collecting user voice data and text dialogue data in real time, and converting the user voice data and the text dialogue data into a time sequence aligned text sequence matrix based on the user voice data and the text dialogue data; inputting the time sequence aligned text sequence matrix into a preset multi-mode sentiment analysis model, and calculating a user sentiment tendency value; extracting an intention keyword based on the text sequence matrix with the aligned time sequence, inputting the intention keyword into a preset intention recognition model, and outputting a purchase intention probability of the user; and according to the user emotion tendency value and the user purchase intention probability, calling LLM to generate a personalized outbound call, and outputting the personalized outbound call to a dialogue system. The method has the advantages that high-quality and dynamic-adaptability customer service is achieved, and user experience is remarkably improved.

The invention discloses a personalized digital human generation method based on a single video, and relates to the field of virtual digital human modeling and driving, and the method aims at the video and voice data of a target person, through introducing a multi-modal alignment constrained voice driving synchronization mechanism and combining semantic understanding and an emotion label expression generation model, a personalized digital human model is generated. High synchronization, nature and vividness of digital human facial expressions and voice contents are realized. According to the method, a self-supervised style consistency constraint is added in model training to ensure that the style of a generated character image is stable, and a 3D semantic mask is added in an image fusion stage to improve the fusion precision and realistic effect of a synthetic facial expression and a reference face. According to the method, the digital human can be rapidly cloned and driven to synthesize the expression through the voice only through a single video sample, the generation process is efficient, and the obtained digital human video has excellent sense of reality and interactivity.

The invention belongs to the technical field of vehicle state detection and early warning, and particularly relates to a vehicle state monitoring and early warning method and system.The monitoring and early warning method comprises the steps that a sensor obtains real-time data, an anomaly detection algorithm is applied, and an abnormal event is marked; calculating an information priority according to the abnormal severity and the driving scene, and distributing the information priority to a high-priority queue; multi-mode early warning is generated, and high-frequency sound and vibration are used during high-speed driving; extracting a voice prompt, and generating voice waveform data through a voice synthesis module; voice input of a driver is recognized, intention is analyzed, and abnormal information feedback is provided; according to the scene and the abnormal state, interactive output is optimized, and detailed information is displayed during low-speed congestion; dynamically adjusting the interface of the instrument panel, and amplifying the key area in case of abnormal severity; and integrating the image data and the voice data, generating a multi-mode signal, and transmitting the multi-mode signal after rendering processing. The vehicle abnormal information can be timely and accurately transmitted to a driver, and the driving safety is effectively improved.

A method of programming an industrial robot (100), composed of a robot manipulator (110) and a robot controller (120), comprises: recording movements of the robot manipulator during a kinesthetic programming session, for thereby obtaining a robot trajectory; capturing speech data while recording the movements of the robot manipulator; decomposing the recorded speech data into a plurality of phases; for each phase of the speech data, parsing the speech data into robot parameter values using a natural-language model (170), identifying a contemporaneous phase of the robot trajectory and annotating it with the robot parameter values; and, on the basis of the annotated robot trajectory, generating a robot program executable by the robot controller.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

The invention belongs to the field of voice noise reduction processing, and discloses an intelligent communication equipment voice noise reduction method and system based on artificial intelligence. Comprising intelligent communication equipment, and a microphone array is deployed on the intelligent communication equipment to record real-time noisy voice data of a user; inputting the recorded noisy voice data into the edge layer, and obtaining the voice data after noise reduction through real-time processing; the method comprises the following steps: pre-constructing a dialect voice database, adding a scene evolution layer, a voice change layer and a noise mixing layer in a conditional adversarial network cGAN to expand the dialect voice database, forming a data sample, and performing three-dimensional marking on each piece of voice data in the data sample; an input, output and multi-task learning mechanism is designed based on an end-to-end voice enhancement model, pre-training is carried out by using a data sample, and a shared encoder is arranged in the multi-task learning mechanism and is used for combining a task R1, a task R2 and a task R3 to realize voice noise reduction of intelligent communication equipment.

The embodiment of the invention discloses a speech recognition method and a related device, and at least relates to a speech recognition technology in artificial intelligence, speech data to be recognized are used as input data of a time delay neural network in an acoustic model, and an output layer of the time delay neural network comprises acoustic modeling units corresponding to a plurality of syllables respectively, so that the speech recognition efficiency is improved. And the syllable probability distribution corresponding to the voice frames included in the voice data can be obtained by taking the syllables as the recognition granularity through the time delay neural network. When syllable recognition is carried out through the output layer, auxiliary judgment can be carried out on the syllables to which the voice frames belong on the basis of pronunciation rules in combination with front and back syllable information of the voice frames, so that more accurate syllable probability distribution is output. Moreover, since the syllables are generally composed of one or more phonemes, the method has higher fault-tolerant capability, not only can more accurately determine the speech recognition result based on the probability distribution of the syllables, but also has low requirements for the quality of the speech data to be recognized, and effectively expands the application scenarios of the speech recognition technology.

The invention relates to a vehicle control method and device, equipment and a medium, and the method comprises the steps: obtaining voice data and video data of a target user in a vehicle, inputting the voice data and the video data into an instruction generation model, and obtaining a target instruction text outputted by the instruction generation model; and then the vehicle is controlled through the target instruction text. Wherein the instruction generation model is used for fusing audio features corresponding to the voice data and image features corresponding to the video data so as to generate a target instruction text, the audio features comprise a first text sub-feature and a first emotion sub-feature, and the image features comprise a second text sub-feature and a second emotion sub-feature; the first emotion sub-feature is used for representing at least one of the tone, intonation and volume of speaking of the target user, and the second emotion sub-feature is used for representing the facial expression and / or lip change of the target user. According to the invention, the accuracy of vehicle control is improved, and the driving experience of a user is improved.

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

The invention provides a model-based interaction method and system, wearable equipment and a storage medium, and belongs to the technical field of intelligent interaction.The method comprises the steps that in response to a received interaction instruction, voice data, a gesture image, eye movement data and an environment image are obtained based on the interaction instruction; extracting user intention features based on the voice data, the gesture image and the eye movement data, and determining scene type features based on the environment image; determining an interaction theme based on the interaction instruction, obtaining user historical interaction information associated with the interaction theme from a context memory database, and generating a context feature vector based on the user historical interaction information; and inputting the user intention feature, the scene type feature and the context feature vector into an intention recognition model based on quantum enhancement to obtain a user intention, and generating interaction response data based on the user intention. According to the invention, the accuracy of user intention recognition can be improved, and the intelligence of interaction is improved.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology and medical health, and discloses an environmental acoustics simulation voice generation method, device and equipment and a medium, and the method comprises the steps: obtaining and separating mixed voice data, and generating original voice content and original environmental acoustics information; converting the original voice content into first text information; determining a target environment acoustic tag in combination with the original environment acoustic tag, the first text information and the target geographical location information; acquiring target environmental acoustic information from a preset sound data set based on the tag, and adjusting the amplitude characteristic of the target environmental acoustic information to match the original environmental acoustic information; and synthesizing the adjusted target environment acoustic information and the original voice content into simulated voice data. According to the method, the target geographic position information is introduced to participate in acoustic feature determination and amplitude adjustment, so that the generated simulated voice data is more consistent in geographic semantics and acoustic performance, and the authenticity and concealment of voice environment disguise are effectively improved.

The invention relates to an audio data processing method and device, electronic equipment and a vehicle, and is applied to the technical field of computers. The audio data processing method comprises the following steps: acquiring target voice data of a user; obtaining at least one grammar element corresponding to the target voice data, and determining a grammar structure integrity score corresponding to the target voice data based on the at least one grammar element; determining an environment interference score corresponding to the target voice data; determining a confidence score corresponding to the target voice data based on the grammatical structure integrity score and the environmental interference score; and under the condition that the confidence score is greater than or equal to the target confidence threshold, determining to execute a target control instruction corresponding to the target voice data, thereby determining whether the target voice data is an instruction for vehicle control by combining the grammatical structure integrity of the voice data and the environmental interference degree. The accuracy of vehicle control and the interaction experience of the user and the vehicle are improved.

The invention provides an AI-driven personalized voice training and pronunciation correction system. The system firstly collects original voice data of a user in a voice training process, and extracts multi-dimensional voice feature vectors including pitch, speed, intonation, formant parameters and corresponding texts; based on the feature vector, recognizing a context tag where the current voice is located, and constructing a pronunciation feature vector of the user; the system further calls a standard pronunciation database according to the context tag, generates a target pronunciation feature vector under the corresponding context, and performs multi-dimensional comparison on the user pronunciation and the target pronunciation to obtain a difference parameter set containing pronunciation parts, speed and emotional differences; finally, the system generates correction suggestions including pronunciation action guidance, intonation adjustment prompts and semantic emotion enhancement instructions, and receives user feedback information to improve the personalized training effect; according to the invention, accurate pronunciation correction under context perception can be realized, and the personalized and intelligent level of voice training is enhanced.

The invention discloses a speech recognition streaming feature extraction and batch reasoning method for a high-concurrency artificial intelligence real-time dialogue system. According to the method, a heterogeneous computing architecture in which a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) cooperatively work is adopted, operations such as preprocessing, feature extraction and normalization of voice data are executed through the CPU, and batch feature reasoning is executed by utilizing the GPU; the method specifically comprises the following steps: after segmenting and preprocessing voice data at a CPU (Central Processing Unit) end, executing Mel filter bank feature extraction and cepstrum mean variance normalization to obtain a standardized acoustic feature vector; extracting feature vectors from the feature buffer area in batches at the GPU end, filling and aligning the feature vectors, and sending the feature vectors to a neural network model for parallel reasoning calculation; according to the method, streaming processing and batch reasoning in the speech recognition process are realized, and the concurrent processing capability and the real-time performance of the system are effectively improved.

The invention discloses a text and voice-based multi-modal electric power customer emotion analysis method and system, and the method comprises the steps: collecting customer service text data and customer service voice data, and carrying out the preprocessing; inputting the preprocessed customer service text data into a BERT-based text emotion recognition model to extract features of a text data modal; inputting the preprocessed customer service voice data into a voice emotion recognition model based on Transform, and extracting features of a voice data modal by using a multi-head self-attention mechanism; based on a cross-modal attention mechanism, performing multi-modal fusion on the features of the text and voice data modals to realize alignment and splicing of the features of the text and voice data modals; and marking the emotion polarity and emotion intensity output by the classification model through a conditional random field (CRF) layer according to the aligned and spliced features of the text and voice data modes. According to the method, the emotion can be effectively labeled, so that the emotion tendency can be more accurately judged.

The invention discloses an intelligent psychological state assessment method and system based on multi-modal alignment, and relates to the technical field of psychological assessment, and the method comprises the following steps: S1, constructing a training sample set of a psychological state assessment model; s2, preprocessing video data, voice data and text data in the training data sample set; s3, performing alignment and fusion by using multi-modal cross attention, taking the text data as a main modal, and taking the video data and the voice data as assistance of the text data to obtain multi-modal fusion features for psychological state analysis; s4, training by using the aligned and fused multi-modal fusion features to obtain a psychological state analysis model; and S5, obtaining a multi-dimensional psychological analysis result of the testee through the psychological state analysis model obtained through training. According to the method, the multi-modal deep features of the teenagers are extracted by pre-training the large model, and the feature expression ability of the data is improved by using the modal alignment technology.

The invention relates to a user psychological state monitoring method and system based on voice and semantic recognition, and the method comprises the steps: firstly obtaining voice data and corresponding text data of a user, and respectively extracting a voice spectrum feature vector and a text feature vector; then, inputting the two features into a cross-modal joint coding network model, and aligning a speech spectrum with a text embedding space by using a multi-head attention mechanism to obtain a joint feature; thirdly, confidence coefficients of the joint features are extracted through a Bayesian network model, weighted features are obtained, a Gaussian mixture model is used for fitting an emotion fluctuation trend, and an emotion distribution probability is calculated; the confidence coefficient weight is dynamically adjusted according to the emotion distribution probability, new weighted features are obtained, and after time sequence alignment processing, local information is extracted by using a convolutional neural network and the features are fused; and finally, calculating a classification probability through a softmax function, and determining a psychological state classification result of the user.

The invention discloses a multi-language knowledge graph construction method, system and device and a medium. The method comprises the steps of obtaining text data, voice data and image data; performing standardization processing on the text data, the voice data and the image data to obtain multi-source heterogeneous data after format unification; mapping the multi-source heterogeneous data with the unified format into a unified semantic representation space, and performing semantic annotation to generate a corresponding semantic annotation result; the embedding representations of different languages are aligned based on a comparative learning technology, the embedding of multiple languages is mapped to a unified semantic space, and a semantic annotation result is updated; and constructing a knowledge graph based on the updated semantic annotation result, and updating the knowledge graph in real time based on an incremental updating algorithm. According to the technical scheme, precise analysis of complex semantics and efficient annotation of unseen knowledge can be achieved, dynamic real-time updating of the knowledge graph is achieved, and the response speed and knowledge updating efficiency of a system are remarkably improved.