4845 results about "User needs" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention discloses a multi-round knowledge-guided question-answering method and system fusing a large language model and a knowledge graph. The method comprises the following steps: S1, constructing the knowledge graph; s2, performing retrieval and clarification; s3, extracting labels and classifying questions; s4, performing scene guidance and dynamic retrieval; and S5, answer generation and formatting output. The method aims at solving the fuzzy problem in complex policy and regulation questions and answers, and the accuracy of user demand analysis and the comprehensiveness of answers are improved. According to the method, a knowledge graph is taken as a core, and multi-path semantic analysis and problem guidance capabilities are provided by constructing a hierarchical structure covering first-level items, second-level items, scene categories and keyword nodes. In combination with strong semantic comprehension and generation capabilities of the pre-trained large language model, the system can dynamically track context information in user input, iteratively access related nodes along different paths of the knowledge graph, and gradually clarify user intentions.

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

The invention discloses a simulation model auxiliary system for a cigarette cut tobacco manufacturing workshop, an operation method, electronic equipment and a storage medium. Comprising a workshop simulation model mark management module, a workshop simulation basic knowledge extraction module, a workshop simulation multi-mode knowledge graph construction module, a workshop simulation knowledge interaction and problem diagnosis module and a workshop system quality evaluation module. According to the invention, the workshop simulation model and the multi-modal knowledge graph are associated through the simulation model marking and management module, and model recommendation of a dialogue system is supported; a basic knowledge extraction module is utilized to extract data from MES, ERP, PLM and process documents, a multi-modal knowledge graph is constructed, and the construction efficiency is improved; the knowledge interaction module analyzes user requirements and provides structured support through knowledge graph retrieval; the optimization suggestion and problem diagnosis unit is combined with various reasoning methods to analyze requirements and provide diagnosis and optimization suggestions; and the system quality evaluation module ensures the reasoning accuracy, integrity and practicability, and provides guarantee for simulation model optimization and decision making.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

The invention belongs to the technical field of e-commerce, and particularly relates to an e-commerce platform search recommendation and intelligent secretary integrated system, which comprises the following steps of: establishing an accurate user portrait by analyzing behavior data of a user and personalized preference of the user; through a basic search function, a commodity search function, a keyword matching function and an advanced function, a user is helped to quickly find a target commodity; personalized commodity recommendation is provided for the user based on collaborative filtering, and the user experience is improved; a virtual secretary function is provided, and a user is supported to perform natural language interaction; the behavior of the user on the platform is monitored and analyzed in real time, the demand and trend of the user are extracted, and searching and recommendation strategies are optimized; the system supports intelligent interaction of various input modes of voice, texts and pictures, searches commodities through voice or uploads pictures to search pictures through pictures, brings convenient, fast and personalized service experience for vast consumers, and also provides intelligent and efficient operation management for merchants.

The invention relates to an AI Agent agent based on a big language model collaborative knowledge graph, which comprises a natural language dialogue module, a big language model module, an RPA SOP process knowledge graph module and an RPA execution engine, and is characterized in that the natural language dialogue module receives a natural language and converts the natural language into a structured query statement; the big language model module interacts with the natural language dialogue module and the RPA SOP process knowledge graph module respectively, and the big language model module understands user requirements, retrieves a knowledge graph according to query statements and generates an RPA process; the RPA SOP process knowledge graph module stores standard SOP knowledge of vertical business of a specific industry, and provides professional knowledge and standard constraints for knowledge query and reasoning; the RPA execution engine executes the generated RPA process; the invention further comprises an agent method and a storage medium. The RPA process knowledge graph is utilized to enhance the knowledge and reasoning ability of the large language model, RPA process steps meeting requirements are generated, and the RPA process generation accuracy and efficiency are improved.

The invention relates to a PDF extraction method and system based on deep learning and layout analysis, and the method comprises the steps: obtaining a PDF file, carrying out the element recognition of a scanning version file and a text version file through an OCR technology and a deep learning model, and enabling the elements to comprise a text, a picture, a table and a formula; dividing a page of the PDF file into a plurality of logic blocks according to the identified elements, and determining a hierarchical relationship and a sequence; deleting elements without substantial help from the identified elements, and extracting the content of the document; performing page layout analysis according to the document content, and reordering and organizing each logic block to obtain a layout; document contents are classified and analyzed, and then classified storage is carried out; and according to user requirements, splicing the classified and stored document contents in a corresponding layout to generate a document in a target format. Compared with the prior art, efficient and accurate PDF extraction is achieved, and meanwhile the important structure and content of an original document are reserved.

The invention relates to an intelligent routing model selection method, device and equipment and a computer readable medium. The method comprises the steps of performing model classification based on a model parameter scale and a model deployment mode; creating an intelligent router, extracting multi-dimensional key features in the user task request and generating a multi-dimensional key feature vector; performing multi-dimensional resource pool evaluation on each large model through the intelligent router to obtain a first evaluation result, and constructing a multi-dimensional model capability vector; determining a model candidate set based on the multi-dimensional key feature vector and each multi-dimensional model capability vector; if a plurality of candidate large models exist, multi-dimensional element scoring is carried out on each candidate large model to obtain respective second evaluation results, and elements are respectively matched with element weights which are dynamically adjusted based on user requirements; and constructing a target function, and selecting a target large model for response based on the target function, the decision constraint condition and each second evaluation result. The problems of low model selection flexibility and low data security are solved.

The invention provides an interactive question and answer task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including interactive question and answer tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an interactive question and answer agent, and different sub-agents are used for processing different types of sub-tasks; the interactive question-answering agent is controlled to present an interactive graphical user interface corresponding to the interactive question-answering task to the user, an answer result corresponding to the received feedback answer is presented, and the interactive graphical user interface comprises a question and an interactive answer area; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on an answer result output by the interactive question and answer agent, and the associated sub-target agent is a sub-target agent which depends on the answer result and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. Through application of the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost, especially for an interactive question and answer task in which a user needs to subjectively answer a proposed question and the effect is checked according to an answer result; the interactive questioning and answering agent provides the interactive graphical interface containing the question and the interactive answering area for the user, so that the task requirement of the user can be met more quickly in an interactive answering mode, and the task solving effect is improved.

Embodiments of the present disclosure provide a report generation method and system, an electronic device, and a storage medium. The report generation method comprises: acquiring requirement text information of a report to be generated in a target application scenario; performing requirement intent recognition on the requirement text information, to acquire a report parameter matching the report to be generated; on the basis of the target application scenario, selecting and arranging a preset intelligent agent, to obtain an intelligent agent call chain; on the basis of the report parameter, performing chain-type calling on the preset intelligent agent in the intelligent agent call chain, to generate a report matching the requirement text information. The present method can automatically and quickly generate a report matching a user requirement, effectively increasing report generation efficiency. Using the present method to generate a report does not require manual data acquisition and analysis; efficiency is higher, and costs are lower.

The invention relates to a task planning and scheduling method which is quicker, lighter and finer in task splitting based on a large model, and aims to improve the capability of processing complex tasks by calling a tool through the large model. According to the method, on the basis of a FastAPI framework, a Text-to-SQL technology, a local knowledge base, Function Calling and other core technologies are integrated, user intention understanding is completed through a large model, dynamic calling and fusion of an API interface, the knowledge base and a database are achieved on the basis of intention understanding, and the task planning, tool calling and decision support capacity of the large model in a complex scene is remarkably enhanced. By introducing a thinking chain technology of multi-level deep task decomposition, the system can decompose a complex task into a plurality of sub-tasks, an execution path is optimized, and the accuracy and execution efficiency of a multi-step reasoning task are improved. A user query tool calling module is researched and developed, user requirements can be accurately recognized and decomposed, proper tools are automatically selected and called for task processing, and efficient solving of complex problems and optimal configuration of resources are achieved.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

The invention discloses a method for an AI model maternal and infant assistant from question answering to accurate recommendation, belongs to the technical field of artificial intelligence, and comprises the steps of user demand input and analysis, user portrait construction and dynamic updating, commodity recommendation and optimization, feedback optimization and strategy adjustment. According to the method and the system, the change of user demands and interests can be flexibly coped with, the recommendation accuracy and the user satisfaction degree can be improved, accurate understanding and processing of complex demands are ensured, the real-time performance and accuracy of user portraits are ensured, the change of the user demands and interests can be flexibly coped with, and more accurate and personalized recommendation is realized. The recommendation accuracy and the user satisfaction are improved, and the user can understand what a certain commodity is recommended, so that the trust of the user to the recommendation system is enhanced.

The invention discloses a knowledge question and answer rapid processing system based on artificial intelligence. The system comprises a knowledge graph construction module, a semantic entity analysis module, a user demand analysis module, a question and answer generation and interaction module, a back-end service interface module, a user feedback and optimization module and a security and privacy protection module. The knowledge graph construction module is used for constructing a knowledge graph; the semantic entity analysis module can identify semantic entities in questions of a user; the user demand analysis module can analyze the questioning demand of the user; the question and answer generation and interaction module is used for generating answers according to the recognized semantic entities and user requirements; the back-end service interface module provides an interface to realize load balancing and automatic capacity expansion and contraction; the user feedback and optimization module collects user evaluation and optimizes the model according to feedback; and the security and privacy protection module is used for protecting the privacy of user data and the security of the system. The user intention and entity information can be accurately recognized, and the response speed of the question answering system is remarkably improved.

The invention discloses an industrial knowledge graph completion and adaptive retrieval method based on a large language model, and belongs to the technical field of computers, an incomplete knowledge graph is divided into a plurality of sub-graphs, and the knowledge graph is completed through negative sample filtering, neighborhood information pruning, information merging, question and answer template mapping, knowledge graph completion large model construction and LoRA fine tuning. The self-adaptive retrieval query module is used for receiving user problems, task decomposition, entity exploration, relation exploration, updating sub-graphs, reasoning paths and sub-target states in the memory sub-module, evaluating the performance and outputting a query result, and in the reflection and self-correction process, when information is insufficient, the self-adaptive retrieval query module is used for performing self-adaptive retrieval query. And the LLM evaluates whether the exploration direction needs to be adjusted or the reasoning path needs to be corrected based on the current information, and finally outputs an answer meeting the user demand according to the retrieved information. According to the method, the capability of a large language model is effectively combined, and the efficiency and the accuracy of knowledge graph completion are improved through adaptive retrieval and an reflection mechanism.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, cultural transmission and the like, and discloses a voice generation method based on multi-modal fusion, which comprises the following steps: collecting audio data to extract timbre features, and training a field feature timbre generation model; analyzing text semantic recognition emotion information, adjusting speech synthesis parameters, combining personalized information to construct a parameter mapping table, fusing to generate a synthesis control parameter sequence, aligning the synthesis control parameter sequence with character labels, visual elements and background music data, driving a domain feature timbre generation model, and generating synthesis data of synchronous speech, text, vision and music. Domain timbres are generated through timbre feature training, speech expression is optimized by combining semantic analysis and emotion recognition, user requirements are matched based on personalized information, and time alignment is performed by fusing text, vision and music data, so that the synthesized speech has domain features, emotion adaptability and personalization, and the speech quality is improved. And the voice immersion and the information transmission capability are improved.

The invention discloses a personalized recommendation system based on semantic analysis, and relates to the technical field of personalized recommendation systems.The personalized recommendation system comprises a data acquisition module, a dynamic situational semantic modeling module and a recommendation engine module, the data acquisition module transmits acquired user behavior data and situational data to the dynamic situational semantic modeling module, and the dynamic situational semantic modeling module transmits the acquired user behavior data and situational data to the recommendation engine module; the dynamic situational semantic modeling module extracts sequence features of user behavior data through a behavior context analyzer, generates context embedding through a situational semantic embedding network, completes cross-modal semantic alignment of multi-modal data through a multi-modal fusion layer, and generates multi-level semantic embedding features. According to the method, the dynamic situational semantic modeling module is designed, the semantic alignment function of the situation and the behavior is achieved, the problem that a traditional recommendation system is insufficient in situation data utilization is solved, the adaptability of the recommendation result to the dynamic situation is enhanced, and the method is suitable for dynamically changing user demand scenes.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.

The invention discloses an intelligent customer service method for adaptively adjusting an interaction strategy by using reinforcement learning, and the method comprises the following steps: S1, obtaining a natural language text inputted by a user, and generating a multi-dimensional state vector; s2, grouping the users by using a real-time dynamic clustering algorithm, and updating a clustering result; s3, generating an action space in a specific state, and enriching the action space in combination with a rule-based strategy and a learning-based strategy; s4, aggregating neighbor node features through a graph convolutional network, and extracting local features; s5, weighting adjacent node features by using a graph attention network, and dynamically allocating attention weights; s6, carrying out interaction strategy optimization by adopting a spiral ladder type deep exploration network, and optimizing action selection; and S7, combining online learning with an experience playback mechanism, continuously updating an interaction strategy, and making a quick response to dynamically changing user demands. According to the method, reinforcement learning, the graph neural network and the sentiment analysis technology are combined, and intelligent customer service interaction strategy self-adaptive adjustment and optimization are achieved.

The present invention relates to the field of voice interaction analysis technology, and in particular to a voice interaction analysis method and system for an intelligent voice robot. The method comprises the following steps: improving the clarity of user interaction voice through multi-scale noise decomposition and filtering enhancement, performing granular segmentation and long-distance semantic analysis on clear voice, generating a semantic structure, performing multi-level semantic recognition based on the structure, extracting specific meanings and making preliminary responses, generating personalized semantic graphs through independent graph structure mapping, predicting user interaction dialogues, building an advance response engine based on dialogue templates, preparing to respond to user needs, embedding the engine into an intelligent voice robot, and realizing efficient voice interaction analysis. The present invention realizes a more accurate and efficient voice interaction analysis method for an intelligent voice robot.

The invention relates to the technical field of content pushing, and discloses a personalized content real-time pushing method based on a user portrait. The method comprises the steps of collecting a user behavior data set and historical interaction records, extracting real-time behavior characteristics and generating a behavior dynamic sequence; identifying behavior delay features and converting the behavior delay features into interest feedback lag; determining a dynamic influence weight in combination with an interaction relationship between the static attribute characteristics of the user and the real-time behavior characteristics; predicting the interest tendency of the user in real time by using the hysteresis and the dynamic influence weight, and generating a fluctuation deviation value; and when the deviation value exceeds a preset threshold value, triggering dynamic updating of the user portrait and adjusting a pushing strategy. According to the method, the user behavior dynamic change is captured, the behavior feedback hysteresis is considered, and the feature influence weight is dynamically adjusted, so that the user interest is accurately predicted in real time, the user portrait and the pushing strategy can be flexibly updated, the individuation and timeliness of content pushing are improved, and the real-time requirement of the user is met.

The invention discloses an information consultation service system and method based on a cloud computing platform, and the method comprises the steps: accessing a user request through a plurality of channels, analyzing multi-modal data through a natural language processing technology, and carrying out the analysis in combination with the historical data of a user; data formats are unified through data cleaning and standardization tools, the data formats are stored in a cloud computing platform, and user requirements are understood through knowledge maps and semantic analysis; providing related consultation contents based on the user behavior data by using deep learning and machine learning recommendation algorithms; a / B testing and performance analysis are carried out regularly, and the system and the model are optimized; safety is guaranteed by adopting TLS and AES encryption and multi-factor authentication, the performance is monitored in real time, the response type design is adapted to different devices, and user feedback is collected to improve the experience; according to the information consultation service system and method based on the cloud computing platform, the accuracy and adaptability of the AI model are improved through reinforcement learning and deep learning technologies, and more accurate consultation services are provided for users.

The invention provides a scientific and technological achievement intelligent matching and transaction recommendation method and system based on a knowledge graph, and relates to the technical field of science and technology, and the method comprises the steps: extracting a technical gene sequence based on a scientific and technological achievement data set, generating a technical state vector, constructing a technical association intensity matrix and a technical iteration path graph, and calculating a technical potential energy distribution field; and forming an initial knowledge graph. And monitoring the fluctuation of the knowledge graph state feature set through the intelligent sensor, and dynamically optimizing the knowledge graph according to the fluctuation condition. Converting technical requirements of a user into a query state, performing state similarity calculation with the knowledge graph, identifying an optimal technical unit combination, predicting a state evolution trajectory, evaluating a regional adaptation degree, and generating a technical implementation scheme. By dynamically optimizing the knowledge graph and accurately matching the user demands, the efficiency and accuracy of scientific and technological achievement matching and transaction recommendation can be effectively improved, and scientific and technological achievement conversion is promoted.

The invention discloses a multi-perception interaction method and system based on virtual reality. The method comprises the following steps: creating a virtual scene and an intelligent agent based on an interaction demand, and constructing a perception model of the virtual scene and the intelligent agent; the intelligent agent perceives visual, auditory and tactile environments, extracts perceived feature time sequence data, divides instructions according to the perceived feature time sequence data, and calculates instruction priorities. And generating a decision network according to a preset knowledge graph, and forming an instruction chain in combination with the instruction priority, so that the intelligent agent obtains a feedback strategy. A dynamic weight value is distributed to a feedback strategy, a virtual scene is updated through fusion of an attention mechanism and a perception model, an interaction mode is obtained, meanwhile, physical and psychological parameters of a user are recorded in real time, abnormal fluctuation segments are recognized, and emotional tendency of the user is learned from corresponding interaction, so that the interaction mode is continuously optimized. According to the method, the interaction experience can be improved, the intelligent agent can better meet the user requirement, the interaction effectiveness and adaptability are enhanced, and meanwhile good interpretability is achieved.

The invention relates to the technical field of artificial intelligence, in particular to an AI intelligent customer obtaining processing method and system, and the method comprises the following steps: collecting interaction data of a user and a platform, summarizing click, browsing duration and frequency information, extracting a user activity mode through an RNN and an LSTM network, analyzing the interest and behavior mode of the user, and generating a user behavior feature library. According to the method, the client acquisition process is optimized through data analysis and behavior prediction, the method does not depend on a traditional marketing means, user data are accurately analyzed through a sequence model, deep understanding of user behaviors is achieved, the accuracy of market segmentation is improved through refined analysis, it is ensured that marketing activities are matched with a target client group, and the marketing efficiency is improved. According to the method, the conversion rate is remarkably improved, the marketing means can be rapidly adjusted by means of intention recognition and real-time data analysis, synchronization with user requirements is ensured, the customer extension cost of the entity store is reduced by continuously integrating customer feedback and marketing data, and the problem that an entity store operator is difficult to obtain customers is solved.

The invention discloses a data isolation and privacy protection method and system for a data security large model, and relates to the technical field of artificial intelligence, the method comprises the following steps: setting an independent storage area for each enterprise in an external knowledge base in advance, and transmitting encrypted data to the corresponding storage area based on an enterprise identifier, setting corresponding access authority for the encrypted data of each storage area; judging whether the user identity information and the user access permission information are compliant or not, extracting a keyword corresponding to the user demand information under the compliant condition, and performing similarity retrieval in an external knowledge base based on the keyword to obtain corpus data with the highest similarity with the keyword; the corpus data and the user requirements are jointly input into the large data security model to generate the data security document, and the problems that in the prior art, data security isolation and privacy protection cannot be achieved, and security and compliance use of data in the whole process cannot be guaranteed are solved.

The invention provides a Web application fuzzy testing method based on a multi-modal large model assisted web crawler, which comprises the following steps of: collecting page information of a Web application program by using an LLM (Logical Link Model), extracting associated information among Web pages, and constructing a state machine according to the associated information among the Web pages to guide a sequence of crawling the Web pages; performing screenshot on the Web page by using the MLLM, obtaining HTML (Hypertext Markup Language) content of the Web page, and generating a natural language description of a task so as to describe the task needing to be completed by a user on the page and an operation required for completing the task; and sequentially converting the natural language description into test codes by using LLM, judging whether the test codes successfully complete a task or not according to the state change of the Web page before and after execution, and if the test codes successfully complete the task, generating corresponding form content and an operation sequence to generate an effective HTTP request. The method has the advantages of simple implementation method, low cost, high test efficiency, high coverage rate and the like.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention discloses a soothing lamp intelligent control method and system based on voice interaction, and relates to the technical field of data encryption, and the method comprises the steps: receiving a voice command inputted by a user through a voice interaction device, awakening a control system, and starting a voice emotion recognition unit; the voice emotion recognition unit is used for performing emotion analysis on the voice command, extracting emotion characteristics and judging real-time emotion of the user; on the basis of continuous voice input of a user, voice signals are collected regularly, the emotional state is updated dynamically, and light setting is optimized in combination with environmental data; by recording historical voice interaction data and environment data of a user, an emotion mode and light preference of the user are learned, and light setting is continuously optimized. Light setting is continuously optimized through a self-learning mechanism, it is ensured that the system can conduct self-adaptive adjustment along with changes of user requirements, and light control is more intelligent and personalized.