535 results about "Cross lingual" patented technology

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention provides a minority language professional question and answer method based on mixed retrieval and retrieval enhancement generation, which comprises the following steps: S1, constructing a multi-language knowledge constructing a vector index database and a term knowledge graph by using a multi-language model according to a related minority language document; s2, multi-layer mixed retrieval: cross-language document recall is realized through a multi-layer mixed retrieval module, and the multi-layer mixed retrieval module is composed of keyword retrieval, semantic retrieval and vector retrieval; s3, answer generation: performing answer generation through an adaptive multi-language model by using a retrieval enhancement generation module, and introducing rule constraint decoding and a dynamic attention mechanism in the generation stage to improve the professionality and accuracy of the answer; and S4, self-adaptive optimization and knowledge updating: through a user feedback reinforcement learning module, optimizing the model based on user error correction data and supervising updating of the knowledge base. According to the method, professional questions and answers of the minority language can be realized based on a small amount of professional data of the minority language, the recall rate of the document of the minority language is improved, and the generation quality is improved.

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention relates to the technical field of subtitle translation, in particular to a multi-modal time sequence alignment AI video translation method and system, and the method comprises the steps: 1, carrying out the multi-modal analysis of a to-be-translated video, and obtaining audio separation data, voiceprint feature data and visual time sequence data; 2, performing cross-language translation and context optimization on the basis of the voice of the audio separation data to generate a target language text, and synthesizing target language voice retaining the original voice color in combination with the voiceprint feature data and the target language text; generating a mouth shape animation matched with the target language voice based on the lip key point data and the limb action time sequence data; and step 3, performing four-dimensional alignment on the target language voice, the translated text, the mouth shape animation and the limb action sequence through a cross-modal time sequence encoder, and dynamically adjusting the layout of the bilingual subtitles to adapt to a video picture. According to the method and the device, multi-mode synchronization can be taken into consideration during video translation, so that the body actions such as voice, subtitles and mouth shapes are kept aligned.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a cross-language voice migration synthesis method, device, equipment and medium. And training an acoustic model by adopting a hierarchical self-adaptive fine tuning strategy, fusing the phoneme sequence for reasoning and the tone mark to generate a representation sequence, and finally synthesizing a target language speech signal. According to the method, a sharing and separation parallel cross-language modeling structure is constructed, the accuracy of phoneme and tone modeling in a low-resource language is effectively improved, the target language acoustic model has higher generalization ability and migration efficiency in combination with a multi-stage self-adaptive fine tuning and training data enhancement strategy, and the method is suitable for being applied to the field of multi-language modeling. And finally, synchronous improvement of the voice naturalness and the tone fidelity is realized.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention belongs to the technical field of speech translation, and particularly relates to a video speech automatic translation method based on a pre-training model, and the method comprises the following steps: 1, preprocessing video and audio data; step 2, voice recognition and language detection; step 3, machine translation and text post-processing; step 4, speech synthesis and audio mixing; step 5, synchronizing video processing and subtitles; step 6, quality control and multi-dimensional evaluation; 7, carrying out model iteration and data closed loop; and step 8, system deployment and engineering implementation. Through deep fusion of efficient transfer learning of the pre-training model and the multi-modal technology, a high-precision, low-cost and easy-to-expand video speech translation solution is constructed, the time and labor cost of globalized content production is greatly reduced, the cross-language communication efficiency is improved, immersive multi-language experience is provided, and the method is suitable for popularization and application. And a data-driven continuous optimization mechanism is established, so that the system performance is improved along with the increase of the use scale.

The invention discloses a Tibetan language multi-dialect real-time semantic conversion method based on a cross-language BERT model, and the method comprises the following steps: S1, collecting original text corpora of each dialect of the Tibetan language, and constructing a standardized training corpus set; s2, performing parameter initialization on the mBERT model, and preliminarily training the mBERT model; s3, constructing a semantic modeling model, and performing fine adjustment on the semantic modeling model; s4, receiving to-be-converted text input, and encoding; s5, obtaining an intermediate semantic representation vector of the text through a semantic coding sub-module; s6, inputting the intermediate semantic representation vector into a semantic generation sub-module, and generating target text output; and S7, executing syntactic consistency correction and language fluency correction. According to the method, mBERT modeling and an adversarial optimization mechanism are fused, real-time semantic consistency conversion of multiple dialects of the Tibetan language is achieved, and the method has the advantages of being high in accuracy, high in robustness and low in response delay.

The invention relates to the technical field of vocabulary data, in particular to a construction method of a bilingual vocabulary data knowledge graph. The method comprises the following steps: acquiring a vocabulary database; performing data vocabulary preprocessing on the vocabulary database to obtain unstructured vocabulary data; performing nested relationship extraction according to the unstructured vocabulary data to obtain semi-structured vocabulary data; performing label analysis on the semi-structured vocabulary data to obtain initial vocabulary data; performing context coding on the initial vocabulary data by using a preset BERT model to obtain a vocabulary embedding vector; therefore, by introducing a multi-step collaborative mechanism of context coding, cross-language alignment and graph semantic mapping, the technical problems of incomplete structure, semantic disjunction, poor language transformation ability and the like of a traditional vocabulary construction method are solved, and the automatic construction ability and expression depth of a multi-language semantic knowledge system are improved.

The invention discloses a multi-language object illusion relieving method based on a cross-language attention mode, solves the problem of how to relieve multi-language object illusion during detection of a large visual language model under non-English questions, and belongs to the technical field of multi-mode questions and answers. The method comprises the following steps: identifying a cross-modal attention head set of which a visual language model shows obviously different behaviors for English and a target language when same semantic questions in different languages are processed; constructing image description queries of English and target languages for the same image, respectively inputting the image description queries into respective visual language models for reasoning, obtaining attention output under the English and target languages, and taking an average difference between the attention output as a language migration vector of the target languages; and in the reasoning process of the target language question, intervening the attention heads in the attention head set by using the language migration vector, so that the visual understanding ability of the visual language model under the non-English question is closer to the English question.

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

The invention relates to the technical field of cross-language information processing, and provides a cross-language text fusion intelligent alignment method and system.The cross-language text fusion intelligent alignment method comprises the steps that a hierarchical alignment model is constructed through preprocessing and label recognition of multi-coding-type texts, deep semantic feature extraction and labeling of a multi-language pre-training model and text semantic and format information analysis; transform is taken as a core, cross-language semantic association is enhanced through a multi-head attention mechanism, character-level and paragraph-level format collaboration is realized through label weight allocation and condition constraint, the format alignment accuracy is obviously improved in a multi-language mixed typesetting scene, and the multi-language mixed typesetting efficiency is improved. The analysis capability of the model on the structured information can be extended to layout relation processing of texts, images and tables, so that the comprehensive alignment efficiency in a multi-modal fusion scene is remarkably improved. The accuracy of text and label fusion is guaranteed, the problem of confusion of messy codes and labels is avoided, and high-precision cross-language text alignment from semantics to formats, from single mode to multiple modes and from semantics to formats is achieved.

The invention relates to the technical field of natural language processing, and discloses a cross-domain vocabulary logical reasoning and extension method. According to the method, a multi-source cross-domain text data stream is received, cross-language alignment and semantic fusion are performed through a pre-trained semantic decoupling model, an adaptive semantic extension threshold model and a causal reasoning framework are constructed, logic interruption nodes are identified, a compensation strategy is configured, and collaborative parameters are iteratively optimized by using a distributed semantic collaborative learning framework. Multi-source data can be effectively integrated, semantic barriers can be broken, the vocabulary expansion range and mode can be accurately controlled, logic transmission coherence and accuracy are ensured, efficient collaboration of multi-language agents is achieved, algorithm efficiency and semantic accuracy are improved, the problem of semantic instability is monitored in real time, the problem of semantic instability is solved, and powerful support is provided for cross-domain natural language processing.

The invention discloses a multi-language emotion recognition method based on semantic comprehension, which comprises the following steps: realizing cross-language emotion analysis through a three-stage innovation framework, and separating semantic and emotion features by adopting improved relative position coding; the method comprises the following steps of: constructing an anchor point vector by utilizing a Hofstep culture dimension, and realizing culture sensitive vector space alignment through a loss function; the emotional features are dynamically adjusted in combination with the power distance and personal difference, and the cross-culture adaptability is enhanced in cooperation with an emotional intensity quantification formula. In a SemEval-2023 task, the accuracy of the system is 21.6% ahead of the baseline at 87.3%, the recognition rate of the small language reaches 78.2%, and the culture misjudgment rate is reduced by 43%. And through a multi-language parallel corpus and a mixed data enhancement strategy, the reasoning speed is increased by three times while the model parameter quantity is reduced by 40%, and an efficient solution is provided for a cross-culture NLP task.

The invention discloses a cross-language application program vulnerability mining method based on static analysis, which adopts a nested cross-programming language pointer analysis method and a micro-service cross-programming language taint tracking method to effectively solve the limitation of processing nested cross-language control flow and micro-service cross-language data flow. A nested language semantic boundary is accurately positioned by constructing a nested byte code, and a data stream is completely tracked by utilizing an interface relay technology, so that the detection precision is improved. According to the method, part of multi-end data streams and complex control streams of cross-programming language applications can be uniformly processed, and the analysis process in a cross-programming language environment is simplified; by nesting cross-programming language control flow diagram construction and double-end / multi-end data flow diagram construction, a unified analysis framework is constructed, seamless cooperation of static analysis among different languages is achieved, the analysis cost is reduced, the analysis efficiency is improved, efficient and accurate vulnerability mining is achieved in a complex cross-programming language application program, and the method is suitable for application and popularization. And the reliability of cross-language vulnerability detection is improved.

The invention discloses an archive arrangement system and method capable of achieving multilingual conversion, and relates to the technical field of archive arrangement. The system comprises a multilingual archive database, a semantic analysis module, a retrieval matching module and a user interaction interface; according to the method, a multi-language association retrieval system based on semantic understanding is constructed, a semantic deep mining algorithm, a cross-language semantic association algorithm and a semantic association degree algorithm are applied, the effect of accurately matching semantic similar contents in files of different languages is achieved, and automatic index updating and incremental updating algorithms are designed for a multi-language file database, so that the accuracy of the multi-language file database is improved. According to the method, the effect of efficient file storage and query is achieved, complexity and resource waste of index updating during new file input or information change are avoided, the effect of extracting semantic features more accurately is achieved by customizing preprocessing processes for different language texts and combining a word vector generation algorithm based on semantic context, and the efficiency of file storage and query is improved. And the accuracy and efficiency of multi-language archive arrangement are improved.

The invention provides an interface communication method, device, medium and equipment adaptive to a swan-monk system, and the method comprises the steps: deploying an interface adaptation layer adaptive to a universal underlying library in a client of the swan-monk system; the method comprises the following steps: acquiring a callback instruction (including a callback function identifier and parameter data which need to be executed by a client of the swan-mong system) issued by a server on the basis of a service request of the client of the swan-mong system, and determining a corresponding target callback function reference from a method mapping table on the basis of the callback function identifier in the callback instruction; the method mapping table comprises a plurality of registered callback functions, and the registered callback functions form key value pairs through callback function identifiers and callback function references; and based on the parameter data and the reference of the target callback function, initiating cross-language calling of the target function of the general underlying library so as to complete execution of the callback instruction and realize processing of the service request. According to the scheme, cross-language communication of the client of the swan-mong system can be realized, and seamless connection between the swan-mong system and the existing general underlying library is realized.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention discloses a multilingual work order processing method and device based on a dynamic LoRA network, computer equipment and a storage medium, and the method comprises the steps: receiving multilingual voice input, and converting the voice input into a source language text in real time; identifying a feature vector of the source language text; calculating the correlation with a plurality of LoRA networks to be selected according to the feature vectors; determining an activation probability of each to-be-selected LoRA network based on the correlation, and dynamically activating a target LoRA network through a pre-configured gating network according to the activation probability; fusing the target LoRA network into a basic large language model to generate a fusion model; and performing cross-language translation and work order content generation on the source language text by using the fusion model, and outputting a standard work order of a target language so as to distribute the work order to a corresponding processing module according to the work order content. By dynamically activating the proper LoRA network, the efficiency and accuracy of multilingual work order processing are improved.

The invention provides a voice synthesis method and system based on VITS improvement, and the method comprises the steps: optimizing a text encoder of a VITS model, introducing a large language model, and enabling the emotion, intention and speaking style of an input text to be captured when the text is encoded; a random disturbance item is introduced when the Q value is dynamically planned and solved, the alignment flexibility in the initial training stage is improved, meanwhile, monotonicity constraint is strictly kept, and it is avoided that a suboptimal solution is obtained through convergence too early; a ConvNeXt module is used as a basic backbone network of a decoder, and ISTFT is utilized to efficiently reconstruct a time domain signal, so that waveform up-sampling is realized, redundant calculation of traditional transpose convolution is avoided, and reasoning is accelerated. According to the method, the reasoning speed, the emotion expression ability and the style control flexibility of speech synthesis can be effectively improved, a new solution is provided for cross-language diversified speech synthesis, and a reference is provided for the more efficient and more intelligent development of the speech synthesis technology.

The invention discloses an intelligent retrieval and hierarchical authorization utilization system and method for archive resources, and belongs to the field of intelligent retrieval. Natural language query of a user is received, the language type of the user is recognized, a semantic vector is generated in combination with a multi-language natural language processing model, and then the semantic vector is matched with each archive semantic representation in an archive database; for the situation related to cross-language matching, a cross-language similarity offset rate and a language context consistency score are further calculated, the semantic generalization ability is divided into a high level, a middle level and a low level according to a preset weight model, and for the middle level situation, a misjudgment risk assessment mechanism is introduced to judge whether additional authorization is needed or not; and when the file is low in level and the languages are inconsistent, the user is directly prohibited from accessing the high-security-level file, so that the information leakage risk caused by semantic generalization is effectively prevented, and the high-sensitivity file is safely and accurately controlled in a multi-language environment.

The invention discloses a data model establishment method based on a large language model and a local knowledge base. The method comprises the following steps: S1, collecting parallel corpora through a distributed crawler technology; s2, converting data in a non-text format into data in a text format; s3, cleaning and preprocessing all the collected data; s4, denoising, segmenting and aligning the content to construct a parallel corpus; s5, storing the processed parallel corpora and terminology codes in a vector database; s6, constructing a retriever, and retrieving related information from the vector database; and S7, combining the retrieved related parallel corpora with the to-be-translated content in combination with the footstone model to realize high-quality translation generation. According to the method, the flexibility and practicability of the system are greatly improved, the method is suitable for various application scenes such as received text processing and manuscript translation, cross-language communication is more convenient and efficient, and the method has extremely high practical value and wide application prospects.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention relates to the technical field of artificial intelligence, provides a multi-language task execution method, device and equipment and a medium, is applied to financial and medical health care service scenes, and can acquire text data and visual action data of multiple languages according to an execution instruction and perform preprocessing to realize standardized processing of multi-modal data; constructing a cross-language word vector semantic space based on adversarial training according to the multi-language features to realize preliminary word vector alignment; performing multi-language semantic alignment on the multi-language features according to a cross-language word vector semantic space, further breaking language barriers, and realizing depth mapping and alignment among different language semantics; a multi-language culture knowledge graph is constructed, so that culture knowledge is introduced, and the culture perception ability is improved; fusion is carried out through a gating fusion mechanism, fusion features with language attributes and cultural attributes can be obtained, and therefore multi-language tasks can be executed more accurately by integrating multi-language information and cultural knowledge.

The present invention relates to a communication system and method designed for patients in clinical settings who face challenges using standard communication devices. This system includes a mobile application and associated devices that establish a secure, asynchronous communication channel between clinicians, patients, and their support networks. Authorized users can record and send voice messages and other media, which are automatically played on the patient's device without requiring active operation. The system is tailored for ease of use, accommodating patients with severe physical or cognitive limitations through hands-free operation and simple commands. It adapts to the patient's condition, pausing messages during rest or medical procedures and resuming when appropriate. The system also features cross-linguistic messaging, translating and synthesizing voice messages across languages while maintaining the speaker's voice. Additional features include support for rich media, automatic transcription, and AI-driven content analysis. Secure, encrypted channels ensure privacy, complying with healthcare regulations. This invention enhances emotional connection and support for isolated patients, acting as a virtual visitation tool.

The invention is applicable to the technical field of multi-modal entity alignment, and provides a multi-modal entity alignment method which comprises the following steps: acquiring knowledge graph structure feature embedding by utilizing a GAT model, and calculating importance weights of nodes and neighbor nodes thereof through a multi-head attention mechanism; obtaining entity name feature embedding in the knowledge graph through a BGE-M3 model, and obtaining entity description information feature embedding in the knowledge graph through a PairWiseBERT model; capturing context information of the entity in different knowledge maps by calculating out-degree and in-degree of the relationship in the relationship triad; adopting a dynamic cross-modal weighting module to generate dynamic entity-level weights for different modals; and combining cross-view comparative learning fused with a temperature mechanism with dynamic temperature control to realize entity alignment. According to the method, the fusion effect of multi-modal data is enhanced, the accuracy of cross-modal entity alignment is improved, and adaptive feature fusion optimization is more suitable for complex cross-language and heterogeneous alignment problems.

The invention relates to a speech translation method, device and equipment of an end cloud translation system and a medium, and relates to the technical field of intelligent speech translations, the end cloud translation system comprises an audio receiving end and a cloud, the method is applied to the audio receiving end, and the method comprises the following steps: obtaining a first language audio of the audio receiving end; sending the first language audio to a cloud; and receiving and playing a second language audio, wherein the second language audio is translated by the cloud according to the first language audio and then is sent out. The method can be widely applied to cross-border conference scenes, and the cross-language communication efficiency and experience are improved.