1137 results about "User needs" patented technology

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 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 particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the field of data retrieval, and particularly provides a dynamic retrieval enhancement generation method and system based on a knowledge graph, a terminal and a medium, and the method comprises the following steps: extracting a structured triple from multi-source heterogeneous data through a large language model, and constructing a global knowledge graph by means of an entity linking technology; integrating a real-time data stream interface, and dynamically updating graph nodes and attributes based on an event-driven mechanism; adopting a RotatE model to respectively encode the entity and the relationship to a complex number space, and fusing to generate a mixed vector to construct an efficient index; after user query is received, topic nodes are positioned through semantic analysis, related entities are retrieved through mixed indexes, and multi-hop reasoning is executed along a relation path to generate reasoning sub-graphs and extended contexts; and finally, generating structured text answers by using a large language model, and adaptively outputting multi-modal results such as texts, charts and the like according to user requirements. According to the method, the knowledge updating timeliness, the complex query reasoning capability and the retrieval precision are effectively improved.

The invention discloses a scene adaptive recommendation method in smart home based on multi-modal data, and relates to the technical field of multi-modal data processing. According to the method, firstly, scene classification is carried out based on multi-mode sensing data for a smart home scene, a current life scene of a user is accurately recognized, meanwhile, an equipment linkage state is analyzed in combination with a classification result, whether equipment operation is matched with a scene requirement or not is judged, and then a scene mode is recognized according to a linkage analysis result to generate preliminary recommendation; whether the dynamic weight space relation model of the user is updated or not is judged, finally, the recommendation effect is quantified according to the weight adjustment judgment result, the recommendation strategy is optimized through two-way feedback, the adaptability to the user requirement is improved, and the user experience is improved. A full-process closed loop of multi-modal data classification, equipment linkage analysis, mode recognition recommendation, weight dynamic updating and effect quantitative feedback in a smart home scene is realized, and the problem that the intelligent degree of scene recommendation in smart home is not high in a user behavior dynamic change scene is effectively solved.

The invention relates to an engineering construction stage user multi-dimensional demand analysis system and method, and the system comprises a multi-modal semantic model construction module which integrates a multi-source knowledge base, and extracts triple description statements from the provisions of the multi-source knowledge base to construct an engineering object ontology knowledge graph; the demand structured guidance and intelligent analysis engine module dynamically identifies an engineering stage and a user role, guides a user to perform text demand input, and performs semantic completion and standardization processing; the demand conflict detection and coordination correction module is used for detecting conflicts in user demands and generating a conflict influence analysis report and a coordination processing scheme; the intelligent drawing and dynamic optimization module is used for automatically drawing according to the triple obtained through demand analysis; and the demand analysis visualization and flow closed-loop module is used for generating an executable parameterized demand document and an association chart, and completing demand-design-verification engineering closed-loop management, so that intelligent analysis and standardization of user multi-dimensional demands are realized, and the system has the advantages of high efficiency, accuracy and intelligence.

Disclosed is a system and method for grounded query generation over heterogeneous data sources. The method includes receiving a user input corresponding to a user requirement from at least one user, extracting a plurality of sub-models corresponding to the received user input, creating a context of the received user input based on the extracted plurality of sub-models, generating an executable query in a specific query language based on the context using a Large Language Model (LLM) by processing the context and the user input within a structured prompt, validating the generated executable query using a multi-stage validation process, generating an LLM response for the received user input based on results of the validation, and outputting the generated LLM response on a user interface of a user device.

The invention provides a long-range visual question and answer and multi-modal reasoning task agent construction method. For input images and natural language questions, generating answers through orderly calling of an external tool set; the construction process of the intelligent agent is divided into three stages: a global planning stage: analyzing user requirements through a global planning navigator, and screening tool subsets; in the tool autonomous execution stage, visual details are supplemented, background knowledge is retrieved and accurate operation is carried out in the multi-step process by surrounding three types of tools through a tool autonomous executor, and a returned tool result is used for assisting the next step of decision making; in the response synthesis stage, the reasoning process is combed through a model response synthesizer, and user-friendly and clear-format responses are sorted; and finally, the model response synthesis module extracts and obtains answers from the reasoning track and outputs the answers. The problems of global planning insufficiency and visual context forgetting generally existing in complex task reasoning of an existing multi-mode large language model are solved.

The invention provides a meteorological-based online question and answer service agent construction method and device, and relates to the field of agents, and the method comprises the steps: constructing a multi-dimensional knowledge base based on meteorological and industry knowledge data, and carrying out the real-time retrieval of a retrieval enhancement generation model RAG, and dynamically updating the knowledge data stored in the multi-dimensional knowledge base; constructing a standardized tool library based on weather and industry tools; obtaining user dialogue data, identifying a user demand real intention of a current problem in the user dialogue data, performing semantic screening on tools in the standardized tool library by utilizing an Embedding model according to the user demand real intention, and determining a target tool matched with the user demand real intention in the standardized tool library according to a semantic screening result; and based on the knowledge data, utilizing a TODOLIST planning mechanism to perform dynamic answer path planning for a current question in the user dialogue data through a to-be-called target tool to obtain an answer planning path.

The invention discloses an AI automatic editing method and related equipment, and the method comprises the steps: obtaining a video prototype template selected by a user, prompt information inputted by the user, and original materials uploaded by the user through a user interaction interface, and calling a language large model to generate a structured video script; performing association degree matching on the structured script and the original material through a target detection model, and screening the original material according to a matching result and a quality scoring model to obtain an original video clip; performing background music matching according to the video script to obtain background music, generating voice dubbing and a flower-character chartlet according to the video script, generating video subtitles according to the voice dubbing, performing time axis alignment on each video media element, and synthesizing into a finished video. According to the embodiment of the invention, full-process automatic processing from script generation to video synthesis can be realized, and a high-quality finished video meeting user requirements can be efficiently output. The method can be widely applied to the technical field of automatic editing.

The invention discloses an interpretable pushing method and system based on a cognitive large model and an industry knowledge graph, and the method comprises the steps: carrying out the semantic understanding of the natural language input of a user, and generating a structured intention containing a target entity, a result type, a relation type and a time constraint; constructing a sub-graph related to the target entity in the industry knowledge graph, searching a candidate evidence chain meeting the time constraint according to the entity category, the relationship type and the time identifier, and forming a structured description attached with a source literature identifier; under the condition that only evidence chain fact information is limited to be used, the interpretation adaptation degree of the evidence chain and the intention is calculated through a cognitive large model, a target evidence chain is selected, corresponding interpretation text and conclusion information are generated, and a structured push result is formed through combination and output to a user terminal; according to the method, traceable association of the push conclusion, the evidence chain and the literature source is realized, and the interpretability of the push result based on the industry knowledge graph and the matching degree to the user demand are improved.

The invention provides a product scheme PPT automatic generation method, system and device and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that product demand information is collected, an NLP tool is called for entity recognition and relation extraction, and a demand feature vector is generated; based on a MySQL database, retrieval is performed according to the demand feature vector, a structured record set is returned, and an associated document ID is obtained; based on a Milvus vector database, retrieval is carried out according to the demand feature vectors, and an unstructured document fragment set is returned; filtering the unstructured document fragment set according to the document ID, retrieving the unstructured document fragment set by adopting a mixed strategy of a BM25 algorithm and semantic retrieval, comprehensively sequencing retrieval results through a weighted scoring mechanism, and generating a final content fragment set; and calling a PPT template according to user requirements, and filling the final content fragment set into the PPT template to generate a PPT draft file.

The invention discloses a three-normal-form automatic modeling method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing a structured domain knowledge base, and injecting three-normal-form design experience for an RAG framework; business semantics are clarified in real time in combination with user demand input and multi-round dialogue interaction of model questions; a double-stage AI recommendation engine is adopted to match user requirements and knowledge base semantic vectors, a high-precision model change scheme is generated, automatic normal form optimization is conducted on a recommendation scheme, DDL scripts, ER diagrams, data migration schemes and the like conforming to the normal form are dynamically output, integration is conducted in a sandbox environment, and finally closed-loop automatic modeling of'requirement-design-verification 'is achieved. The method is used for solving the problems that traditional data modeling is low in efficiency and high in error rate and cost rate, the problem that model knowledge updating needs full-amount or increment fine tuning, and the cost is high, and the problem that business semantic understanding is insufficient in three-normal-form modeling is solved.

The invention provides a risk detection method, a server, a medium and equipment. In the method, a server firstly obtains multi-modal data such as a text, a consumption time sequence, a network relationship and the like of a user, performs time sequence alignment and feature extraction, and then constructs a behavior logic connection relation graph based on association of the time sequence and content in the multi-modal data, and the logic connection relation graph can intuitively present a logic direction among user behavior nodes. And then, the server identifies a behavior chain starting point related to the user demand as a candidate reason on the basis of the logic connection relation graph, and performs confidence scoring on the candidate reason in combination with a preset loan reason knowledge graph so as to deduce the real reason of the user demand. And finally, the server combines the risk feature corresponding to the inference reason and the user portrait feature into an enhanced feature vector, and a quantitative risk score and a risk level are obtained through operation of a preset evaluation function. According to the method, more accurate detection of the user risk is realized, and the accuracy of risk detection is effectively improved.

The invention belongs to the field of electronic design automation, and discloses a multi-agent radio frequency circuit netlist generation method and system based on a large language model, and the method comprises the steps: preparing a data set, and constructing a knowledge base; the method comprises the following steps: receiving and analyzing a natural language demand of a user, combining a cue word project and an RAG mechanism, carrying out chained reasoning, introducing a radio frequency design rule, and converting the information into structured parameter information; combining RAG knowledge base retrieval and new topology generation, and fusing PDK process constraints to obtain candidate topologies and a simulation netlist; a simulation tool is called to execute parameter analysis, iterative optimization is conducted on parameters in combination with a closed-loop feedback mechanism, and a final netlist meeting user requirements is obtained. Automatic decomposition, parallel cooperation and high-speed iteration of design tasks are achieved through multi-agent cooperation and in combination with the radio frequency knowledge graph and the RAG; introducing physical constraints to enable the generated topological structure and parameters to meet the physical constraints; and a closed-loop simulation feedback mechanism is fused, so that the dependence on manpower is reduced.

The invention relates to the technical field of data compliance. According to the AI-driven data compliance rule intelligent method, device and equipment and the medium, the method comprises the steps that natural language input of a user is obtained, and user demand description is obtained; performing multi-dimensional semantic analysis on the user demand description through a pre-trained large language model, and generating a structured semantic framework comprising an intention vector, an entity relation graph and a constraint condition set; performing compliance correction on the structured semantic framework to generate an optimized semantic framework; performing logic conflict detection and performance simulation test on the domain-specific language rule to generate an executable compliance rule; and deploying the executable compliance rule to the target data governance platform so as to achieve the technical effects of reducing the learning threshold and the manual error rate generated by the domain specific language rule, improving the semantic adaptation capability in a complex business scene and optimizing the operation stability of the rule on the data governance platform.

The invention discloses an intelligent knowledge management system for a clean energy project, and the system comprises the steps: collecting multi-source heterogeneous data from the clean energy project, and converting the multi-source heterogeneous data into structured knowledge input data; performing knowledge extraction on the structured knowledge input data, and aligning and fusing knowledge extracted from different data sources to construct a structured knowledge base; performing data processing on the structured knowledge base through a time-aware graph attention network T-GAT and a graph structure causal model SCM to construct a dynamic time sequence knowledge graph; according to user requirements and scenes, a decision result and a knowledge recommendation result are obtained through an integrated causal-driven multi-objective optimization algorithm and a dynamic time sequence knowledge graph, and user feedback data are output; and processing the user feedback data by fusing Temporal Graph Network and an online near-end strategy optimization Online PPO algorithm, and carrying out dynamic optimization and adaptive adjustment on the knowledge graph, the rule base and the decision model according to the user feedback data.

The invention discloses a thinking chain-based power distribution and utilization fault research, judgment and analysis method and system. The method comprises the following steps: acquiring a user demand, analyzing the user demand, and matching in a preset power distribution and utilization business process template library; designing a specific task execution path and a subtask sequence, and calling a pre-constructed model library and a knowledge library to execute corresponding tasks so as to obtain an execution result; wherein multi-modal knowledge in the field of power distribution and utilization operation analysis is stored in the knowledge base, and index construction and retrieval of the multi-modal knowledge are realized; models for realizing fault research and judgment and fault analysis based on a plurality of business scenes in the field of power distribution and utilization operation analysis are stored in the model library; and integrating and forming explainable and traceable power distribution and utilization operation analysis output results based on execution results. According to the method, an advanced artificial intelligence method is introduced into the service of the power distribution and utilization field, and meanwhile power distribution and utilization fault research, judgment and analysis which are easy to use and high in interpretation are achieved.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is used for determining recommended customers adapted to insurance products. Firstly, an advanced natural language processing model is utilized to accurately analyze a user intention, and key semantic information is extracted; secondly, user requirements are accurately mapped to specific industries and business scenes through semantic mapping rules; newest market data and policy information are obtained in real time through a networking search interface, and timeliness and comprehensiveness of the data are guaranteed. Based on a preset report template, the system automatically integrates the upstream and downstream structures and policy dynamics of the industrial chain, and generates a structured and visual market insight report. And finally, extracting high-value customers meeting business requirements from the report through a customer portrait matching algorithm, and forming an accurate recommendation customer set in combination with multi-dimensional feature evaluation. According to the invention, precise product recommendation and high-value customer mining based on natural language input of the user are realized.

The invention relates to the technical field of internet advertisement marketing, in particular to an internet advertisement marketing method and system based on artificial intelligence, and the method comprises the steps: integrating the browsing history, demographic information and behavior preference data of a user, generating a dynamic user portrait, and predicting a user demand; generating personalized advertisement creative content based on the user portrait and the predicted user demand; performing multi-dimensional automatic violation review on the advertisement creative content, and performing originality verification on the advertisement creative content passing the violation review; based on the dynamic user portraits and the advertisement putting targets, advertisement creative contents passing violation review and originality verification are accurately pushed; according to the method, the advertisement can be automatically examined before the advertisement is put, so that the examination efficiency is improved, the compliance risk caused by omission due to manual detection and the brand reputation loss caused by the compliance risk are reduced, and the advertisement marketing effect can be improved.

The invention discloses an automatic design and optimization system for a hydraulic speed control loop, which takes a large language model intelligent body as a core, realizes automatic design and optimization of the hydraulic speed control loop through semantic comprehension, knowledge retrieval and parameter optimization, and specifically comprises an LLM intelligent body, a data processing module and a data processing module, the analysis module is used for analyzing natural language input containing design requirements and performance targets of a user, generating a loop structure in combination with a local knowledge base and calling an optimization algorithm; the local knowledge base comprises a basic hydraulic loop, a key empirical relationship and a typical structure template which are artificially extracted; and the simulation and optimization module is used for executing a swarm intelligence optimization algorithm to optimize and adjust parameters of the loop structure. The invention constructs a hydraulic speed control loop automatic design and optimization system with semantic understanding, automatic generation and adaptive optimization capabilities, and aims to realize full-process intelligent design from user demand input to loop structure generation and performance optimization.

The invention provides a construction method of a business element knowledge base and a local data analysis method based on the business element knowledge base. The business meta-knowledge base comprises table structure definitions for a plurality of business scenes, business rule documents and analysis templates. The local data analysis method based on the business meta-knowledge base comprises the following steps: receiving a user demand; generating a prompt instruction based on the user demand and the business meta-knowledge related to the user demand; sending the prompt instruction to the large language model, and receiving a structured data operation statement which is returned by the large language model and is generated based on the prompt instruction; and executing the structured data operation statement locally to obtain an answer to the user demand. According to the method, the business meta-knowledge base is constructed, then interaction between the local data and the large language model is achieved through the business meta-knowledge base, efficient data analysis is achieved through the large language model, and meanwhile the privacy security of the local data can be protected.

The invention relates to the technical field of electric power intelligent interaction, and discloses an AI large language model intelligent interaction system oriented to the electric power industry. The system comprises a multi-source data acquisition module for acquiring power industry multi-dimensional data from a plurality of heterogeneous data sources and integrating the power industry multi-dimensional data into a data warehouse; the semantic feature extraction module performs semantic analysis and feature extraction on the integrated data warehouse to obtain a semantic feature matrix; the industry knowledge graph construction module is used for constructing a power industry knowledge graph based on the semantic feature matrix; the user query analysis module is used for analyzing user query and extracting query feature vectors; the core semantic screening module is used for screening a core semantic sequence based on the semantic feature matrix and the query feature vector; and the intelligent response generation module generates an intelligent response based on the core semantic sequence and the power industry knowledge graph. The system can deeply understand user demands, generates professional and accurate responses, and is suitable for diversified intelligent interaction scenes in the power industry.

The invention discloses a holographic communication system-oriented multi-dimensional interaction quality perception and resource optimization scheduling method, which comprises the following steps of: firstly, constructing a system containing objective performance and subjective experience indexes according to a scene, acquiring network, holographic and user layer data, training a subjective experience representation model, and estimating the subjective experience indexes in real time; estimating indexes at the current moment and the next moment, constructing a design vector, and fusing physical, semantic and cognitive layer multi-source knowledge to obtain an enhanced input vector; reasoning the dynamic weight of the index through a utility function and a fuzzy rule, and calculating an interaction quality comprehensive score; screening indexes to be optimized, converting the indexes to be optimized into a multi-objective optimization problem by taking a comprehensive score reaching the standard as a constraint, and searching a compromise optimal scheme; and finally, converting the scheme into a resource configuration scheme to realize self-adaptive scheduling of network and terminal resources. Accurate mapping of objective indexes and subjective experience is realized, scenes and user requirements are dynamically adapted, and immersion experience and communication performance of holographic communication are improved.

The invention discloses a multi-source data driven intelligent charging stop resource matching method and system and a medium, and relates to the technical field of charging stop resource matching, and the method comprises the steps: respectively executing the multi-source data collection of a user side, a resource side and an environment side, and building a multi-source data set; mapping the multi-source data set to a multi-dimensional tensor space; weighting each dimension in the multi-dimensional tensor space through a space-time dynamic coding mechanism to form a dynamic tensor representation reflecting a real-time state and a future prediction state; constructing a user intention-resource state coupling tensor; calculating a dynamic matching potential index; and establishing an intelligent matching result. According to the method and the device, the technical problems of insufficient matching accuracy and low efficiency caused by insufficient fusion of multi-source data in charging stop resource matching and lack of dynamic adaptation and space-time scheduling consideration in the prior art are solved, and the technical effect of improving the matching accuracy and efficiency of the charging stop resource and the user demand is achieved.

The invention relates to the technical field of model training learning, in particular to a training method of an intention recognition model for understanding user requirements in an intext software scene, which comprises the following steps of: traversing paths to determine levels and establish variable-dimension projection mapping, generating semantic orthogonal conflict samples, obtaining variable-dimension features by utilizing matrix transformation, and obtaining an intention recognition model for understanding user requirements in an intext software scene. And quantizing the brother node overlapping degree to apply orthogonal constraint, updating the weight by minimizing the positive sample distance and maximizing the conflict sample distance, and constructing an intext software intention recognition optimization model. According to the method, a mapping mechanism of a hierarchical position and a variable-dimension projection matrix is established through software architecture topology, a semantic orthogonal logic conflict sample is constructed, brother node feature branch orthogonal constraint is introduced, and feature representation of parallel architecture branches is forcibly separated in a vector space; the logic conflict sample spacing is maximized, semantic overlapping of similar function nodes is eliminated, and a decision boundary with high distinction degree is constructed to accurately analyze a complex nested instruction.

The invention relates to the technical field of big data and is applied to the fields of finance and medical health. The invention discloses a visual data integration method and device, computer equipment and a storage medium. The method comprises the following steps: determining entities, attributes and relationships based on business requirements, and forming document metadata; on the basis of the document metadata, generating an interface prototype according to user demand research and competitive product analysis results; generating a graphic component for operating the interface prototype and realizing a dragging function to obtain a data model; and storing the data model in a selected storage medium, and recording version information for a terminal to backtrack and compare historical version information. By implementing the method provided by the invention, the development efficiency can be remarkably improved, and meanwhile, the consistency of data and the expansibility of the system are ensured, so that non-professional developers can also efficiently participate in the development and maintenance of the application program, the technical threshold is greatly reduced, and complex business requirements are met.

The invention relates to the technical field of data mining and product demand analysis, and discloses a user demand mining method based on feature fusion density perception, a terminal and a medium. The method comprises the following steps: collecting product rejection data, extracting a semantic feature vector from a preprocessed data text, obtaining a user behavior sequence by using a session identifier in meta-information to generate a behavior feature vector, and generating a situation feature vector based on time and environment attributes in the meta-information; calculating the attention weight of each dimension feature, and performing weighted fusion on the semantic feature vector, the behavior feature vector and the situation feature vector by using the attention weight to generate a multi-dimensional feature representation vector; clustering the multi-dimensional feature representation vectors to obtain a plurality of clusters; and feature portrait construction and value evaluation are carried out on the cluster, a demand description document is generated, and optimization algorithm parameters are fed back according to a verification result. According to the method, the efficiency and the quality of mining potential user requirements from neglected rejection data can be remarkably improved.

The invention discloses a legal service intelligent searching and pushing system based on user requirements, particularly relates to the field of information processing, and is used for solving the problems of missing evidence binding and insufficient version anchoring in existing retrieval and touch. Executing structured positioning according to the retrieval intention to generate a fragment anchor identifier, querying a leatherwise table by the fragment anchor identifier to complete version coincidence judgment and paraphrasing closing judgment to generate an effective fragment pointing and rebounding entry, reading a touch node to bind and generate touch content, embedding the touch content into the rebounding entry, recovering a rebounding drop point, checking consistency and correcting a record, and performing rebounding. And a closed evidence chain of retrieval, judgment, reaching and checking is realized.

The invention belongs to the technical field of intelligent user retrieval, and particularly discloses and provides an intelligent user retrieval platform based on campus services, which remarkably improves the joint effect of dynamic user portraits and semantic intention enhancement through retrieval accuracy, can capture user demands and situation changes in real time, and improves user experience. In combination with accurate matching of knowledge graph multi-hop reasoning on resources, the proportion of invalid retrieval results is reduced, and the intention recognition accuracy and the resource matching precision are improved; the environment context variable is greatly optimized through scene adaptability, and the real-time position information is introduced, so that the service recommendation is more suitable for the current scene, and the user requirements under diversified campus situations can be met; the resource position can be visually positioned through the map, and the preference service can be quickly found through personalized sorting, so that the service acquisition path is shortened, the operation complexity is reduced, and the use satisfaction of the user is improved.