2533 results about "User needs" patented technology

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.

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 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 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 relates to the technical field of customer service quality inspection, in particular to an online customer service intelligent quality inspection system and method based on artificial intelligence. And automatically extracting a user demand keyword, an emotion expression keyword and a potential violation term keyword, and generating a structured keyword sequence. Performing emotion analysis on each dialogue round through a Transform model, and accurately outputting a customer emotion classification and an intensity score; and semantic correlation of the context is carried out through a neural network model. And automatically identifying the content of each round of dialogue and counting illegal verbal skills. Furthermore, the customer emotion value, the context coherence score and the occurrence frequency of violation verbal skills are input into a quality inspection scoring model, a comprehensive quality inspection score is automatically calculated, and whether the service is qualified or not is judged according to the comprehensive quality inspection score, so that the dynamic evaluation of the service quality is realized, the quality inspection efficiency is improved, and the customer experience is truly reflected.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention discloses a scheduling system of a content distribution network based on edge computing, and relates to the technical field of computer networks. An edge resource sensing module; an NLP semantic analysis module; reinforcing a learning scheduling engine; a semantic and resource linkage module; and a federation collaboration module. According to the scheduling system of the content distribution network based on edge computing, semantic understanding is carried out on a request of a user to analyze a deep intention of the user, edge node multi-dimensional state sensing data and a semantic label vector are integrated, a scheduling action is generated based on a node state and the user intention, and the scheduling effect is improved. Therefore, each edge node is fully utilized while user requirements are met, the edge nodes have caching, computing and scheduling capabilities, the limitation of surface feature matching of a traditional CDN is broken through, technical upgrading from traditional file type recognition to intention understanding and from a fixed rule to dynamic intelligence is realized, and finally, the resource efficiency and service quality of the CDN are improved.

The invention relates to the technical field of art education data analysis, and discloses an AI-enabled data-driven art education resource recommendation engine, which is characterized in that user behaviors, resource metadata and context environment data are acquired through a multi-source data acquisition module; the user portrait construction module constructs a multi-dimensional user portrait by using a deep clustering algorithm and an attention mechanism; the resource feature extraction module extracts resource features by means of a graph neural network; the privacy protection processing module uses differential privacy and federated learning to protect data privacy; and the dynamic recommendation generation module generates a personalized recommendation list based on reinforcement learning. The recommendation engine can accurately recommend art education resources meeting user requirements, data privacy is guaranteed, resource association is mined, recommendation strategies are optimized, the quality and effect of art education resource recommendation are effectively improved, and personalized learning requirements of users are met.

Techniques disclosed integrate generative AI assistant plugins with a large language model (LLM) to enhance conversational interactions. The techniques include receiving a user's input and retrieving relevant text passages based on a query derived from this input. A complex LLM prompt is generated, including these passages, descriptions of candidate plugins, and the user's input. This prompt is sent to an LLM service, which selects the most suitable plugin for the user's needs. Following this, a query is sent to the chosen plugin, and its response is used to craft the agent's reply to the user. The techniques emphasize dynamic selection and integration of specialized plugins based on real-time user input, leveraging LLM capabilities to interpret and recommend the best plugin response. This approach ensures tailored, informed interactions by providing responses that are both relevant and enriched with specialized plugin knowledge or functionality.

The invention relates to the technical field of intelligent analysis, and particularly discloses an indoor decoration intelligent analysis system and method based on user demands and spatial data, which utilizes an artificial intelligence technology based on deep learning to analyze and understand decoration demand information of a user and a plan graph of a house. Semantic features of user decoration demands and structure size features of a house are captured, and a decoration material list and quotation thereof are intelligently generated based on semantic interaction features between the user decoration demands and the structure size of the house. Therefore, the user can quickly obtain the decoration scheme meeting the requirements of the user and know the decoration cost in advance, the decoration efficiency is improved, and the time and the cost of the owner are saved.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

The invention relates to the technical field of interaction based on visual earphones, and discloses an interaction method and device based on a visual earphone, computer equipment and a medium, and the method comprises the steps: judging whether a user wearing the visual earphone sends an interaction instruction or not; analyzing content in the interaction instruction to determine a user demand; in response to the user demand, controlling a camera to scan current content to obtain a scanned picture; performing data processing on the scanned picture based on the user demand to obtain a processing result; and feeding back the processing result to the user. The method has the beneficial effects that the user demand is determined through the interaction instruction, and intelligent and personalized services can be provided according to the user demand. Through the camera integrated with the earphone, a user can obtain environment information in real time without depending on external equipment, and obtain instant feedback through the earphone, so that the practicability and interaction experience of the intelligent wearable equipment in a complex environment are greatly improved.

The invention discloses a document generation system and method based on multi-agent collaboration, and the method comprises the steps: understanding and extracting the high-level semantic information of a text input by a user, and dynamically generating a problem to guide the user to define a demand, so as to form a detailed document demand; recognizing the theme and key elements of the document from the detailed document requirements; the optimal sequence of task execution is determined based on a shortest path algorithm, potential conflicts in task execution are predicted and solved through parallel processing and a search algorithm, and optimized task planning is formed through feedback circulation; generating a document outline according to user requirements, retrieving associated contents of document chapters so as to write chapter contents, and performing grammar and format verification to form a preliminary document; defining a document quality evaluation index according to user requirements and industry standards, performing document quality evaluation on the preliminary document, and feeding back the document quality evaluation result to the user; and generating a complete final document and presenting the complete final document to the user. According to the invention, the document generation quality is improved.

The invention discloses an interactive sand table demonstration system based on artificial intelligence, and relates to the technical field of intelligent interactive sand table demonstration. Comprising a multi-mode perception module, a semantic intention recognition module, a three-dimensional simulation module, a decision feedback module and a visualization module. Accurate recognition and semantic analysis of a user instruction are realized by fusing various input modes such as voice, gestures and images; based on a knowledge graph and a deep learning model, user demands can be intelligently understood, and a three-dimensional sand table is driven to carry out dynamic interactive demonstration. The situation adaptation module automatically adjusts the sand table scene according to the environment and the demonstration progress, and the reality sense and immersion sense of demonstration are improved; the decision feedback module generates demonstration results and strategy suggestions in real time, and supports multi-dimensional and intelligent interactive experience; the system is widely applied to the fields of emergency drilling, military deduction, urban planning, educational training and the like, and the intelligent level and the operation convenience of sand table demonstration are remarkably improved.

The present invention relates to the technical field of chemical and plastics product recommendation, provides a chemical and plastics product personalized recommendation method based on deep reinforcement learning, comprising: S1 receiving a search condition of a user; S2, constructing a user state vector; S3, obtaining a candidate product set on the basis of the user state vector and the search condition; S4, using a pre-trained deep reinforcement learning model to sort candidate products, and generating a recommendation result; S5, displaying the recommendation result to the user, and acquiring user feedback; and S6, determining whether a recommendation stop condition is met, if yes, stopping recommending, and outputting a final recommendation result, otherwise, updating parameters of the deep reinforcement learning model and the user state vector on the basis of the user feedback, and returning to step S3. The present invention can improve the correlation, diversity and timeliness of the recommendation result, better meet the personalized needs of a user in the chemical and plastics industry, and can continuously learn and adapt to changes in user needs.

The invention discloses a structured remote sensing report generation system, method and device based on a syntax tree and a medium, the system comprises a user input layer, a core processing layer and an output and verification layer, the system receives user demand parameters and input data, automatically analyzes the demand parameters and standardizes the input data; generating a syntax tree structure conforming to industry specifications, and driving the large model to obtain a first draft of a report under the constraint of the syntax tree; and performing automatic verification and manual intervention correction on the first draft of the report to form a professional remote sensing report. According to the method, the multi-dimensional syntax tree architecture is constructed, and the hierarchical syntax analysis algorithm and the domain knowledge graph are combined, so that semantic association of the multi-modal remote sensing data and accurate mapping of terminologies are realized, automatic generation of a long document report meeting remote sensing geographic industry specifications is supported, the structured degree and logic continuity of text generation are improved, and the text generation efficiency is improved. The method can be widely applied to the fields of geological monitoring analysis, environment evaluation report generation and the like.

The invention relates to a retrieval optimization method and a retrieval optimization device. The method comprises the steps of performing plan making processing based on a pre-acquired user problem to obtain a plan list; performing retrieval processing according to the plan list and a pre-established knowledge base, and performing iterative processing according to retrieval process data in the retrieval process to obtain a preliminary retrieval result; and performing uncertainty evaluation processing on the preliminary retrieval result, and determining a target retrieval result according to an uncertainty evaluation result and the preliminary retrieval result. According to the method, the plan list containing the progressive relation retrieval tasks is formulated based on the user problems, and user requirements can be gradually and deeply mined. Iterative processing is carried out according to retrieval process data in the retrieval process, and the retrieval strategy can be dynamically adjusted according to the real-time retrieval condition. And performing uncertainty evaluation on the preliminary retrieval result, and determining a target retrieval result, so that fuzzy or unreliable information in the retrieval process can be effectively identified and processed.

The invention relates to the technical field of communication, and provides a short message content compliance generation method and system fusing a semantic large model. The method comprises the following steps: carrying out coarse identification on short message content to be processed by a service provider to obtain a short message type and a user demand; inputting the short message content into a semantic large model for deep semantic analysis, and outputting short message semantic analysis information; performing multi-dimensional compliance comparison on the semantic analysis information of the short message according to the type of the short message and a user demand, and identifying risk description content; and optimizing and adjusting the risk description content according to the short message type and the user demand and the semantic analysis information of the risk content, and generating target short message content. The technical problem that short message content generation in communication service depends on a template, and compliance risks cannot be dynamically identified and avoided according to user requirements is solved, and deep semantic analysis and multi-dimensional risk comparison through a semantic large model are realized; and the intelligent level and compliance guarantee capability of short message content generation in the communication service are improved.

The invention discloses an intelligent supply and demand recommendation matching system based on scientific and technological achievement conversion, and relates to the technical field of resource services, the system comprises an information input module, an information management module, a supply and demand matching module, a pricing prediction module and a conversion recommendation module, user input information is subjected to distributed storage and classified labeling, and a knowledge graph is constructed; after preliminary supply and demand matching is carried out, semantic path reasoning is used for calculating the matching degree and sorting is carried out, a reinforcement learning model is adopted, the transaction price is dynamically predicted based on the transaction frequency, the industry permeability and the technical life cycle, based on a multi-factor evaluation algorithm, the matching degree, the transaction price satisfaction degree and the historical transaction rate are integrated, and a personalized recommendation list is generated; the problems that in the existing scientific and technological achievement conversion process, information of a recommendation matching system is scattered, opaque and single in evaluation dimension, and deep semantic association and user demand diversity are neglected are solved, and accurate and efficient matching services are provided for a scientific and technological achievement provider and a technical demander.

The invention relates to a mixed expert multi-model task processing method and system based on an AI Agent scene. The method comprises the following steps: constructing a knowledge block set for unstructured data and semi-structured data; constructing an associated document vector set of topN similarity; user input and a reverse decoding document are spliced, and user tasks are divided by using LLM to generate subtask sets; constructing a task allocation data set # imgabs0 # based on the scoring model and the expert model pool; and the dependency aggregation # imgabs1 # for generating the subtask answers is used as a final user task result. According to the method and the system, fine-grained division and route selection of the model pool are carried out on user tasks, so that the accuracy, diversity and authenticity of answers required by users can be effectively improved, and the multi-model parallel utilization rate of an Agent system is enhanced.

The application provides a virtual commodity supply chain scheduling system and method based on artificial intelligence, relates to the technical field of computers, and is applied to a commodity supply system, which comprises a plurality of supply warehouses; the scheduling system comprises: a data acquisition module for acquiring user behavior data, external supply data and inventory state data of each supply warehouse; an intelligent decision module for obtaining user demand prediction data according to the user behavior data based on a preset prediction model; determining the corresponding risk level according to each inventory state data, and obtaining the corresponding weight adjustment coefficient based on each risk level; and a decision execution module for performing inventory supply scheduling on each supply warehouse according to the user demand prediction data and each weight adjustment coefficient. The application collects various types of data through the data acquisition module and performs deep analysis and processing by using the intelligent decision module, realizes accurate prediction of user demand and automatic intelligent evaluation of inventory risk, and reduces the uncertainty of manual intervention.

The invention discloses an electronic commerce precision marketing intelligent system and method fusing AI personalized recommendation. The system comprises a multi-source data acquisition module, a data preprocessing module, a user portrait construction module, a real-time behavior monitoring module, a recommendation algorithm module, a marketing scheme generation module, a marketing effect evaluation module and a feedback optimization module. According to the method, the comprehensive user portrait is constructed by integrating the multi-dimensional user data, and the mixed recommendation algorithm is applied, so that the user requirements can be deeply mined, accurate personalized recommendation is realized, and the matching degree between the recommendation result and the actual requirements of the user is improved; by adopting the real-time behavior monitoring module and the recommendation algorithm module, the recommendation strategy can be updated in time according to the real-time behavior of the user, so that the recommendation better fits the current shopping intention of the user; by adopting the marketing scheme generation module, diversified marketing schemes can be generated, an effective marketing effect evaluation and feedback optimization mechanism is combined, marketing activities can be continuously optimized, and the marketing efficiency and income of merchants are improved.

The invention relates to the technical field of intelligent matching, and particularly discloses a personalized decoration scheme design auxiliary system and method based on big data, and the system employs a big data technology and an artificial intelligence algorithm to analyze the decoration demands of a user, captures the semantic feature expression of the decoration demands of the user, and carries out the design of a personalized decoration scheme. Meanwhile, the alternative decoration schemes are extracted from the decoration case library, whether the alternative decoration schemes are matched with the user requirements or not is judged by measuring the semantic similarity features between the user decoration requirements and the alternative decoration schemes, and therefore the alternative decoration schemes meeting the user requirements are returned. In this way, the efficiency of decoration design can be improved, meanwhile, the personalized requirements of users are met, and the innovativeness and practicability of decoration design are improved.

The invention relates to a new media AI marketing content creation method and device, equipment and a medium. The method comprises the steps of obtaining user demand configuration, and obtaining a creation intention vector through natural language analysis; based on the platform characteristic knowledge base, extracting a corresponding structure specification and a propagation mechanism according to the target platform, and encoding to obtain a platform characteristic vector; obtaining historical content interaction data corresponding to the audience group, and generating a user-content interaction vector by adopting collaborative filtering and a label similarity algorithm in combination with the content keyword; and calling an artificial intelligence large model, and performing content generation according to the creation intention vector, the platform feature vector and the user-content interaction vector to obtain content creation data. By adopting the method, the goal of automatic, high-quality and personalized new media marketing content creation in a multi-platform environment can be realized by means of natural language analysis, knowledge structure extraction, large model generation constraint and the like.

The invention relates to the technical field of live broadcast interaction, in particular to an intelligent live broadcast interaction method and system based on an AI virtual human. The method comprises the following steps: identifying a real-time live broadcast interaction data stream, carrying out intelligent bullet screen filtering processing and multi-modal user interaction perception, and constructing a multi-modal user interaction perception map; key bullet screen extraction is carried out based on the multi-modal interaction perception map, and user demand prediction is carried out, so that user interaction demand features are generated; performing deep semantic space mapping on the user interaction demand features, and then performing reverse live broadcast semantic blank analysis to obtain demand blank covering information; user behavior dynamic analysis is carried out based on the multi-mode user interaction perception map, real-time interaction emotion global evolution fitting is carried out, and a real-time interaction emotion map is constructed. Through deep understanding and analysis of user demands, the emotion matching degree and the interaction content satisfaction degree of user interaction are improved.

The invention discloses a multi-modal interactive fusion virtual reality emotion computing system, which comprises a multi-modal interactive fusion virtual reality emotion computing device, a multi-modal interactive fusion virtual reality emotion computing device and a multi-modal interactive fusion virtual reality emotion computing device, the virtual reality emotion computing device based on multi-modal interaction fusion further comprises a distributed multi-modal sensing device, a user adaptation interaction module, an interaction feedback module, an emotion extraction module, a multi-modal fusion module and a self-adaptation emotion computing model. The multi-modal fusion module is used for carrying out full-dimensional monitoring and intelligent decision making on a complex scene, the user adaptive interaction module can realize accurate identification and response to user requirements, and the multi-modal fusion module is used for making up for information limitation of a single modal, so that effective interaction and accurate identification can be realized on the whole.

The invention discloses a data governance method and device based on an MCP protocol and a medium, and the method comprises the steps: obtaining a language instruction of a user through a data center regulation and control MCP client, determining a data governance demand according to the language instruction, and determining an obtaining request according to the data governance demand; and sending the acquisition request to the MCP server to acquire the data resource through the MCP server, and performing data processing according to the data resource. The data governance process is started through the natural language instruction, user operation is facilitated, and the use threshold is lowered. And the data governance demand and the acquisition request are accurately determined through the analysis instruction, so that the governance pertinence is improved. And resources are obtained and processed by means of the MCP server, so that efficient data management is realized. According to the process, user requirements and data acquisition and processing links are integrated, so that data management is more convenient, accurate and efficient.

The invention discloses a multi-split cross-platform distributed monitoring system state data acquisition method, which belongs to the field of system operation and maintenance and data monitoring, and comprises the following steps: acquiring node data through a data acquisition module arranged on a distributed monitoring system node, and introducing a self-adaptive acquisition strategy into the data acquisition module, adaptively adjusting the node data acquisition frequency according to the resource utilization condition; a node data monitoring strategy is formulated according to user requirements, and the monitoring strategy is optimized by using a hybrid monitoring algorithm; a Delta compression technology is utilized to compress node data to obtain a compressed data packet, the compressed data packet is transmitted, and a multi-level transmission optimization mechanism is introduced in the transmission process of the compressed data packet. According to the method, the technologies such as message bus publishing and subscribing, a customized data acquisition strategy and data flow optimization are integrated, so that consistent state monitoring can be realized among different platforms and hardware architectures, and the efficiency and reliability of system operation and maintenance are greatly improved.