18results about How to "Realize intelligent recommendation" patented technology

According to the welding process parameter recommendation method using the knowledge graph to retrieve the enhanced large model, the welding process knowledge graph is constructed, multi-source heterogeneous welding knowledge is stored in the graph, and effective management and reuse of the welding knowledge are achieved; meanwhile, through user feedback and verification, the welding process knowledge graph can be continuously updated and perfected, and continuous accumulation and iterative optimization of knowledge are achieved; the welding process knowledge graph is used as an external knowledge base to retrieve and enhance the large model, and accurate and comprehensive welding process related knowledge can be provided for the large model to serve as a reasoning basis, so that the illusion problem of the large model is effectively relieved; according to the method, intelligent recommendation of the welding process parameters is realized, personalized analysis can be carried out according to specific requirements of the user, welding process parameter suggestions meeting user expectations are quickly generated, the efficiency of welding process parameter recommendation is effectively improved, and the problems of strong expert experience dependence and relatively high cost in process parameter recommendation are solved.

The invention discloses an intelligent interactive thematic map generation system and method based on a large language model. Comprising a large language model module, a thematic map template library, an intelligent data source management module, an intelligent cartographic engine, an interactive optimization module, a user interaction configuration module, a configuration library, an external service interface module, a model data quality verification module, a thematic map personalized correction module and the like. A user interactively selects a template, specifies a data source and describes drawing requirements through a natural language, LLM drives an intelligent drawing engine to generate a thematic map, fine adjustment and real-time optimization of the user are supported, and user-friendly, efficient and flexible thematic map making is achieved. Meanwhile, the system has the functions of data quality verification, personalized configuration, interactive history learning and the like, a powerful tool is provided for non-professional users to quickly customize and finely adjust thematic maps, and the intelligent level and adaptability of thematic map making are greatly improved.

The invention belongs to the technical field of rail transit vehicles, and discloses a rail transit vehicle fault intelligent diagnosis and maintenance service recommendation system based on a knowledge graph, and the service recommendation system is composed of a rail transit vehicle fault detection module, a knowledge graph construction and updating module, and a maintenance service recommendation module. According to the method, efficient, accurate and real-time monitoring and fault diagnosis of the vehicle state are realized through a fault detection algorithm, powerful data support is provided for intelligent fault diagnosis through a constructed comprehensive, dynamic and highly integrated rail transit vehicle knowledge graph, a reliable knowledge basis is provided for maintenance service recommendation, and the reliability of fault diagnosis is improved. The method greatly improves the safety and maintenance efficiency of the rail transit system, accurately generates and recommends an optimal maintenance service scheme through a maintenance service recommendation algorithm, achieves the intelligent recommendation of the maintenance service, improves the maintenance efficiency to the maximum degree, and reduces the operation cost.

The invention discloses a consumer automatic locking method based on automobile 4S store intelligent sales. The consumer automatic locking method comprises the steps that a 4S store controller is combined with a face camera and a face recognition and proofreading module to carry out face collection and locking on a consumer; an entrance guard worker terminal builds and supplements consumer files entering the 4S store; consumption data are supplemented and shared on a consumer terminal by a consumer; the stay area of the consumer in the 4S store is monitored by adopting an area identification module and an area monitoring camera; and the server is used for formulating consumer purchase track information. The method has the advantages that through the steps, the identity of the consumer is automatically recognized and locked, the purchase intention of the consumer is quickly known, and intelligent recommendation and automobile sales are achieved.

The invention relates to the technical field of storage of tobacco powder borers, and particularly discloses a method for killing stored tobacco powder borers by feeding adult cotesia testis in batches, which comprises the following steps of: adding bee sweet water into a feeding box of host larvae of the cotesia testis for feeding; meanwhile, the temperature, the humidity and the bee sugar water liquid level in the feeding box are subjected to time sequence analysis by adopting a data processing technology based on deep learning, so that the time sequence consumption trend of the bee sugar water liquid level and a time sequence correlation mode of the temperature and the humidity in the box are extracted; the dynamic association relationship between bee sugar water consumption and the environmental condition in the box is disclosed by carrying out interactive analysis on the time sequence association characteristics of the temperature and humidity in the box and the time sequence consumption characteristics of the bee sugar water, so that the intelligent recommendation of the bee sugar water flow under the current environmental condition is realized. By means of the mode, optimal control over bee sugar water supply can be achieved, the breeding conditions of the adult cotesia gigas can be improved, and therefore the survival rate of the cotesia gigas and the quality of the adult cotesia gigas are improved, and the biological control effect is enhanced.

The present invention discloses a data management method and system based on a knowledge graph, including: obtaining historical record data of various fields and standardizing it to obtain standardized data corresponding to each field; constructing a knowledge graph of the corresponding field based on the standardized data corresponding to each field to obtain a standard knowledge graph library; obtaining data to be processed and determining the field information of the data to be processed; querying the standard knowledge graph library based on the field information of the data to be processed to determine a target knowledge graph of the field corresponding to the data to be processed; managing the data to be processed based on the target knowledge graph; constructing knowledge graphs for different fields, and determining the target knowledge graph based on the field of the data to be processed, so as to more accurately classify and manage the data to be processed and improve the efficiency and accuracy of data management.

The application discloses a semiconductor thin film deposition process intelligent recommendation system and method based on a knowledge graph, belongs to the technical field of semiconductor manufacturing processes, and comprises the following: a semiconductor thin film deposition process knowledge graph construction module; a prompt word template generation module: used for receiving process requirements and constraint conditions input by a user, performing semantic analysis on the process requirements and constraint conditions, filling the analysis results into a preset prompt word template, and outputting standardized instructions meeting the calling requirements of a large language model; and a large language model reasoning module: used for receiving the standardized instructions, performing semantic retrieval and logical reasoning on the standardized instructions within the constraint range of the semiconductor thin film deposition process knowledge graph, generating a plurality of candidate thin film deposition process schemes meeting process constraint conditions based on entity relationships and causal associations in the semiconductor thin film deposition process knowledge graph; and the application constructs a complete process recommendation solution and exhibits significant advantages in multiple aspects.

The invention discloses a scenic spot motion balance car management method and device based on cloud computing and a medium, and relates to the technical field of cloud computing, and the method comprises the steps: constructing an enhanced scenic spot rule base containing road sections and parking rules, calculating the dynamic type of a region where a motion balance car is located based on the enhanced scenic spot rule base, and determining the dynamic type of the motion balance car. According to the dynamic type, access control of the user leasing request is carried out; acquiring running data of the moving balance car in real time to perform car returning intention judgment, matching a recommended parking area through a judgment result, and calculating a congestion index of the recommended parking area on the basis of in-out event counting in combination with virtual calibration and parking state recognition; and screening recommended parking areas by using the congestion index, generating a control strategy, and issuing and pushing the control strategy. Through geo-fence event calibration, multi-dimensional congestion assessment and SVDD health diagnosis, dynamic closed loop of scenic spot motion balance car management is realized, and scenic spot operation management safety, resource utilization rate and user satisfaction are significantly improved.

The invention discloses a lease and purchase management system and method with a determined matching relation, and relates to the field of mechanical equipment software lease management systems.The system provided by the invention can construct and optimize a two-dimensional mechanical equipment combination matrix and generate a user feature matrix and an article feature matrix through matrix decomposition, so that equipment feature vectors are dynamically extracted. The system also calculates the similarity between the devices through the implicit matching coefficient, so as to generate a recommendation list with high matching device combination, thereby realizing the intelligent recommendation of the matching lease.

The present application discloses a digital security chip processing system and method, which uses deep learning-based artificial intelligence technology to perform semantic analysis on the security protection demand information of the digital power grid system, mines the contextual semantic features of the security protection demand information, and extracts a set of type labels of security chips from the database, and performs cross-domain query matching between the security protection demand features and the security chip type feature set to achieve intelligent recommendation of security chips, and then performs node deployment of the digital power grid system and encryption processing of target data based on the selected security chip. In this way, the efficiency and accuracy of security chip selection can be effectively improved, the complexity of manual configuration can be reduced, and intelligent matching between security protection requirements and security chip types can be achieved.

The invention discloses a knowledge graph embedding method and system for intelligent knowledge recommendation. A complex intelligent knowledge recommendation framework is built based on knowledge graph embedding and deep reinforcement learning technologies; on the basis of the built complex knowledge intelligent recommendation framework, a complex knowledge graph model is built in combination with complex knowledge characteristics; a TransEx knowledge graph embedding method is constructed by fusing an attention mechanism and a complex value embedding technology, entity nodes and relationships in a knowledge graph are converted into a vector sequence, and the structure and semantic relationships of the graph are reserved.

The invention discloses an attention mechanism fusion-based return and supplement electric quantity calculation method of a Bi-LSTM model. The method comprises the following steps of: judging a metering fault time interval; the method comprises the following steps of: judging an optimal trend change point of starting and ending of a metering fault by using a change point detection algorithm according to trend change of data characteristics acquired by different meters in a time period when the metering equipment is mounted in a metering equipment fault time period last time, so as to realize intelligent recommendation of a metering fault interval; dividing a metering fault time interval; calculating metering fault retreating and supplementing electric quantity; and generating a metering fault electricity quantity returning and supplementing report. According to the method, the efficiency of returning and supplementing electric quantity calculation is improved, manual query and calculation links are reduced, and the personnel cost is reduced. And data values are fully released by utilizing a digital intelligence means, the timeliness of the return and supplement electric quantity calculation is enhanced, and the working process efficiency is effectively improved through a refined return and supplement calculation process.

The invention relates to a knowledge management method and system based on AI intelligent recommendation, and belongs to the technical field of artificial intelligence and knowledge management. The method comprises the steps of obtaining file data of a user side and preprocessing the file data through a preprocessing model to obtain preprocessed data; according to the preprocessed data, feature data and relational data are obtained through calculation of a joint extraction model, and a knowledge graph is constructed; creating metadata and an identifier, performing data association to obtain associated data, and storing the associated data into a graph structure database; obtaining search semantic data, calculating correlation among the knowledge maps, and performing database search according to the correlation to obtain a search result; according to the retrieval semantic data, the graph structure database is retrieved through the intelligent recommendation model to obtain recommendation data, and the recommendation data is transmitted to a user side recommendation interface. And intelligent knowledge recommendation and knowledge management of the enterprise knowledge management system are realized.

The present invention discloses an antibiotic combination recommendation model and its application. The model is constructed through the following training process: S1, obtaining different antibiotic combination historical data sets, and extracting the historical compatibility frequency of any antibiotic combination in the historical data set, as well as the historical effectiveness after the combination is used; S2, based on the historical compatibility frequency of any of the aforementioned antibiotic combinations and its historical effectiveness, determining the recommended type of the antibiotic combination combination; S3, using the molecular fingerprint information of each antibiotic in the aforementioned antibiotic combination as input data, and its recommended type as output data, and performing classification learning through a fully connected deep neural network to obtain the antibiotic combination recommendation model. The model realizes intelligent recommendation of antibiotic combination by combining drug feature information with clinical drug feature information, thereby improving the safety and effectiveness of patients' combination medication.

The invention discloses an archive data management system and method in a distributed environment, and relates to the technical field of data analysis. Paper archives are converted into electronic archives by scanning instruments, an electronic archive system is established, and after user registration and identity authentication, query conditions are input to generate query records, historical query records are obtained, recommendation parameters are collected and recommendation scores are calculated, abnormal recommendation records are determined, user permissions are assigned, user information in abnormal recommendation records is collected, user personal scores are calculated, user levels are determined, abnormal frequencies of levels are calculated, abnormal user levels are determined, training cycles are set, recommendation success rates of abnormal user levels in a certain training time period are calculated, characteristic time periods of abnormal user levels are determined, and real-time recommendation success rates in characteristic time periods of abnormal user levels are calculated. If the rate is lower than a recommendation success rate threshold, management personnel are reminded to optimize recommendation methods and improve recommendation accuracy.

This invention relates to the fields of graphics rendering and real-time interactive technology, and particularly to a Canvas-based intelligent seat selection method. This Canvas-based intelligent seat selection method first loads the stadium structure in SVG format, and then pre-renders repeating elements as bitmaps for cache reuse using a Canvas rendering engine. Next, it uses a second-order Bézier curve to fit the arc-shaped area and calculates the seat coordinates based on the curvature of the stands. It employs a Redis Bitmap storage scheme to compress the seat status into a binary bitmap, and uses WebSocket to achieve incremental status synchronization. Based on a collaborative filtering algorithm, it generates seat recommendations combined with user historical behavior. After the user selects a seat, the Bitmap is pushed via WebSocket and updated to the client. This Canvas-based intelligent seat selection method not only innovatively uses a collaborative filtering algorithm to achieve intelligent seat recommendations based on user historical behavior, but also reduces user operation latency, reduces server load, and solves the lag problem of traditional seat selection systems when rendering seating charts in large sports stadiums.

The invention discloses a data recommendation method and system based on an electric power engineering project knowledge graph, and the method comprises the steps: carrying out the structural processing of isomerized data in an electric power engineering project, obtaining the structural data and entity information, and constructing a structural database; the method comprises the following steps: capturing key elements of structured data, and combining mass text data of an electric power engineering project to construct an electric power engineering project knowledge graph with the structured data and associated information thereof; compared with single data storage and query, the electric power engineering project knowledge graph constructed by the method can reveal the deep semantic relationship and hidden association between the data. The understandability of the data is enhanced, the integrity and accuracy of the knowledge base are further improved through the reasoning algorithm and the large language model, and dynamic and intelligent knowledge management and expansion are achieved.

This invention provides a medical device supply chain recommendation method and system based on a large language model, belonging to the field of model-assisted technology. The method includes acquiring demand descriptions and converting them into structured vectors, constructing a domain knowledge graph to perform multi-hop reasoning to identify collaborative paths, generating adversarial disturbance analysis to reconstruct failure modes and the supply chain, establishing a risk propagation graph to identify trigger points and generate hierarchical prevention and control strategies. This method can effectively improve the accuracy of intelligent recommendations for the medical device supply chain, enhance the supply chain's robustness and risk response capabilities.