94 results about "Preference vector" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent agent architecture-based education large model autonomous teaching planning method and system. The method comprises the following steps: inputting a cognitive ability matrix, a knowledge mastery degree vector and a learning preference vector into an improved Bayesian knowledge tracking algorithm, and adding an agent parameter consistency constraint term and a teaching planning coordination factor to obtain a learning track state vector; based on the learning track state vector, multiple agents cooperatively generate a recommendation content identification sequence, a difficulty level sequence and a time distribution sequence; and monitoring the knowledge mastery degree vector variation, and reordering the recommended content sequence when the knowledge mastery degree vector variation exceeds a preset threshold value to generate a teaching sequence. According to the invention, the Bayesian knowledge tracking algorithm is improved, the agent parameter consistency constraint term and the teaching planning coordination factor are fused, and coordination and unification of multi-agent teaching decisions in a communication-free environment are realized.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

The invention discloses a big language model dialogue recommendation method based on multi-modal geographic information fusion and context modulation, which comprises the following steps of: firstly, coding and fusing a text address, geographic coordinates and numerical attributes to obtain a geographic context matrix; then intention modeling is carried out to generate user intention representation, then context feature modulation is utilized, dynamic modulation is carried out on the user intention representation by utilizing an asymmetric fusion mechanism, a context guiding signal containing geographical constraints is generated, and finally, the context guiding signal is spliced before the user intention representation. The big language model input to the parameter freezing obtains user preference vectors and calculates recommendation scores with the candidate positions, the candidate positions are sorted according to the recommendation scores, and the candidate positions with high scores are recommended to the user. According to the method, on the premise that internal parameters of the large language model are not changed, the geospatial reasoning ability is effectively given to the large language model, and the accuracy and interpretability of dialogue recommendation are remarkably improved.

The invention relates to the technical field of intelligent logistics and path optimization, and particularly discloses a multi-target vehicle path optimization method based on an enhanced self-attention mechanism. The method comprises the following steps: S1, constructing an enhanced self-attention encoder, and carrying out deep fusion encoding on input node features and preference vectors through a self-attention module integrating channel attention, SwiGLU activation and adaptive scaling; s2, constructing a path generation network based on a problem scale perception decoder, embedding problem scale information injection nodes, and generating a path sequence in an autoregression mode; and S3, based on a reinforcement learning framework, carrying out model training in combination with a random preference strategy, and generating a Pareto frontier approximate solution of a multi-target vehicle path problem. Through the collaborative design, the model expression ability, the generalization ability and the solving precision are improved, and an efficient and reliable solution is provided for the multi-target vehicle path problem.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The invention discloses a cross-domain AI knowledge aggregation method based on collaborative filtering. The method comprises the steps that S1, multi-source heterogeneous AI knowledge data and user behavior data are collected and preprocessed; s2, constructing a double-tower cross-domain embedded network, and outputting a cross-domain semantic fusion sequence; s3, modeling and analyzing user preferences through the improved Bi-GRU network, and generating user behavior preference vectors; s4, performing semantic diffusion and neighborhood reasoning on the cold start user, and complementing interest features; s5, adopting a double-tower recall structure and an XGBoost model to sort and generate a cross-domain recommendation list; s6, constructing a context rule base to execute context adaptability judgment, and generating a matched knowledge aggregation recommendation list; and S7, performing incremental learning according to user feedback information, and dynamically updating the double-tower cross-domain embedded network and the improved Bi-GRU network. According to the method, the knowledge matching precision, the cold start adaptability and the scene adaptation capability of cross-domain recommendation content are improved.

The invention discloses a personalized learning scheme recommendation method and system based on artificial intelligence, and relates to the technical field of intelligent recommendation, and the method comprises the steps: collecting a learning behavior track and a knowledge mastering progress, carrying out the normalization processing, and generating structural feature input; performing multi-dimensional feature mapping and weight training on the structured feature input by using a reinforcement learning model to generate a learning preference vector; matching the learning preference vector with the weighted feature interaction matrix, calculating an adaptive score, and generating a candidate resource set; and performing iterative fusion on the candidate resource set and the real-time interaction feedback vector to generate a feedback fusion adaptive score matrix, updating adaptive scores by adopting a dynamic adjustment method, forming an optimized resource sequence, converting the optimized resource sequence into a task execution path, and generating personalized learning scheme recommendation. According to the invention, the accuracy of learning resource matching and the timeliness of personalized recommendation are improved.

The invention relates to the field of communication, and discloses an intelligent traffic scheduling optimization method, device and equipment and a storage medium, and the method is used for 5G network intelligent traffic scheduling optimization. The method comprises the following steps: receiving a service request, analyzing the service request to obtain a 5G standard service type, an application scene and a 5G network slice identifier, then obtaining an SLA parameter corresponding to the service request and a candidate link required by service transmission, and converting the SLA parameter into an SLA preference vector; acquiring global data of the candidate link, and predicting by using the network state prediction model to obtain a predicted network state of the candidate link; the SLA preference vector is used as optimization target guidance, the predicted network state is used as a constraint condition, and a global optimization routing strategy is calculated through a multi-target optimization algorithm; and generating a control message packet based on the global optimization routing strategy, issuing the control message packet to the network forwarding equipment, and adjusting parameters of the network state prediction model and the multi-objective optimization algorithm based on the actual performance data after execution.

The invention provides a resource recommendation method and system based on a large model, and the method comprises the steps: S1, obtaining the historical behavior information and the current query information of a user, and converting the historical behavior information and the current query information of the user into a user preference vector and a user demand vector; s2, based on the user preference vector and the user demand vector, using a language large model to extract a keyword set vector; s3, extracting a candidate resource set from a resource library by using a vector similarity retrieval method according to the user preference vector, the keyword set vector and the user demand vector; s4, reordering the candidate resource set by adopting a bootstrap strategy to obtain a candidate resource sequence; and S5, updating the user preference vector according to the first N items of the candidate resource sequence, then repeating the steps S2-S5 until the maximum number of iterations, and then recommending the first N items of resources. According to the method, the problems of poor generalization ability and insufficient adaptation in the prior art are solved.

The application discloses a kind of based on key value memory network's complex electromechanical system design knowledge active recommendation method.First, the scene feature semantic information extraction of software platform log file in the design process of complex electromechanical system is carried out, and scene ontology is established based on scene feature semantic information, and system scene knowledge base is formed by scene ontology and original knowledge base;Then, the scene maximum frequent sequence is used to describe the scene sequence feature similarity between designers;Again, the knowledge item interaction sequence of all designers is learned, and the knowledge item sequence preference vector corresponding to all designers is obtained;Further, input to key value memory network, and the initial design knowledge active recommendation sequence is obtained;Finally, after knowledge item selection, the final design knowledge active recommendation sequence of each designer is obtained.The application obtains design knowledge active recommendation sequence more in line with the needs of designers, and then improves the efficiency of complex electromechanical system design.

The invention relates to resource allocation in a wireless communication network. The present disclosure proposes a network device for efficient multi-target radio resource allocation for user equipment. The network device includes a preference module and a policy module. The preference module is configured to determine a global preference vector, where the global preference vector describes an overall weight of each network performance metric in a set of network performance metrics for one or more user devices, and provide the global preference vector to the policy module. And the strategy module is used for acquiring the global preference vector from the preference module and making a resource allocation decision based on the global preference vector.

The application discloses an intelligent agent generated interactive system and method applied to hospital operation management, relates to the technical field of hospital operation management, and captures each interactive operation of a user for an operation entity from a hospital heterogeneous system and records each operation as an atomic interactive event; analyzes the atomic interactive event to generate a user long-term preference vector and stores the user long-term preference vector; when a natural language query is received, a target operation entity identifier and an analysis index are analyzed; the analyzed index is matched with a candidate analysis model, an execution model is generated, and an index analysis result set is output; the data type and the dimension quantity of the index analysis result set are analyzed, and a basic UI component requirement is generated; the UI component requirement and a UI component preference are fused and decided, a target UI component selection is generated, and a final interactive interface is rendered and generated; and the frequency distribution is updated according to the user interaction. Personalized decision is supported, and the use experience of the user is improved.

The application discloses a real-time dynamic packing optimization method and system for high hybrid SKU, relates to the technical field of logistics management, and comprises the following steps: acquiring a loading task and a current packing state; analyzing field-specific language business rules to generate executable constraint functions, graph construction metadata and arbitration metadata; constructing constraint dependency graphs, geometric graphs and heterogeneous state graphs; updating the heterogeneous state graph when an order is changed or a container is changed, and determining an influence domain based on logical association and geometric adjacency; inputting the updated heterogeneous state graph into a constraint-aware graph neural network to output a priority score and a position preference vector; generating an action set in the influence domain, pruning and locally re-optimizing to obtain an updated packing scheme. The scheme realizes rule structured expression, incremental solution of the influence domain and local rapid repair, reduces the dynamic rearrangement calculation amount and response time delay, and improves the packing compliance, stability and loading rate.

The invention discloses a product personalized recommendation method and system based on multi-source data fusion. The method comprises the following steps: acquiring multi-dimensional behavior data of a user, associating financial product attributes with user spatio-temporal characteristics to construct a product association map, fusing multi-source semantic information, and performing noise elimination to generate a unified semantic vector; after reaching the standard, extracting time sequence interaction information to construct a deep customization preference vector, analyzing a sub-graph semantic coincidence degree to obtain semantic analysis strength, and generating a cross-scene association path identification potential preference point set; screening the core preference subsets to obtain a recommendation list, and matching the user motivation components to determine a final recommendation result. According to the method, accurate personalized recommendation driven by multi-source data can be realized, and high-quality recommendation requirements of deep customization and cross-scene adaptation in a complex scene are met.

The application provides an information recommendation method, a training method and device of a research report information scoring prediction model, equipment and a storage medium. It relates to the technical field of machine learning. The method comprises: obtaining a behavior sequence of a target object, and dividing the behavior sequence into multiple session information; performing encoding processing on each session information to obtain a first feature vector of each session information; performing behavior-to-behavior time distance feature extraction on multiple first feature vectors of multiple session information to obtain a second feature vector; performing real intention spatial distance feature extraction on the second feature vector based on a multi-head self-attention mechanism to obtain a preference vector of the target object; and obtaining a research report information recommendation list of the target object from at least one candidate research report information according to the preference vector, and recommending the research report information recommendation list to the target object. The method can improve the accuracy of the recommendation result.

The present application relates to the technical field of artificial intelligence and recommendation system, in particular to a kind of dynamic interaction perception and multilayer semantic compression generative recommendation method, comprising the following steps: obtaining the original attribute information of article and carrying out semantic extension, generating enhanced article description text;The historical interaction sequence of user is obtained, and the historical interaction sequence is carried out time feature extraction, multi-factor weighting and gate memory state update, to generate the dynamic preference vector of user;The historical interaction sequence is divided into multiple blocks, and the block-level abstract of each block is generated using a large language model and recursively compressed to obtain a global preference abstract;Question and answer samples are constructed, and a large language model is trained based on the question and answer samples to obtain the trained large language model.The present application improves the modeling capability of the recommendation system for user interest evolution through dynamic interaction perception and multilayer semantic compression, reduces long sequence input redundancy, improves the inference efficiency of the large model, and enhances the accuracy and interpretability of the recommendation results.

The invention relates to a personalized design generation method and system based on a user portrait, equipment and a medium. The method comprises the following steps: analyzing historical interaction data of a target user to generate a structured user preference vector; constructing a dual-path evaluation model, and generating an initial design image through a conditional diffusion model based on the structured user preference vector and the design task text description; inputting the initial design image into the double-path evaluation model to obtain a corresponding subjective matching degree score and a corresponding objective quality score, and calculating a comprehensive reward score by combining the calculated fusion weight coefficient; with maximization of the comprehensive reward score as a target, the conditional diffusion model is updated, and a personalized design generation model is obtained; and generating a personalized design image through the personalized design generation model and the design request of the target user. By adopting the method, the core problem that subjective preference and objective quality are difficult to quantitatively align and dynamically balance during personalized generation in the prior art can be effectively solved.

The invention relates to a multi-target vehicle path optimization method based on preference perception expert mixed deep reinforcement learning, and belongs to the field of path optimization. The method comprises the following steps: inputting a problem instance and a preference vector to an encoder; the encoder outputs a node embedding set through a multi-head attention mechanism; the node embedding set is input into an adapter, a decoder is modulated according to the preference vector, adaptive features are obtained, and the adaptive features are used for each time step in the decoder; the state information of the decoder is represented by a context vector, and at each time step of the decoder, the inference nodes are modulated according to the adaptive features and the score level until all the nodes are accessed, and the selection probability distribution is obtained. The problem that efficient and extensible real-time reasoning and generalization ability is difficult to achieve on the premise of guaranteeing performance due to the fact that an effective zero sample preference generalization mechanism is lacked, preference modeling is rough, parameter efficiency is low and a training strategy is unstable is solved.

The invention discloses a project transaction pushing method and system based on artificial intelligence, and belongs to the technical field of semantic modeling and graph neural networks. Project information submitted by at least one project party is collected, and text description of the project information is converted into semantic vectors; historical investment records and preference information of multiple capital parties are collected, and a multi-dimensional semantic preference vector is constructed; inputting the preference vector into a pre-training graph neural network model, and performing mapping and enhancement on a transverse semantic association relationship between the items; short-term interest features are extracted in combination with the current active state, the recent behavior and the fund flow condition of the capital party, and the short-term interest features are fused with long-term preferences to generate a dynamic weight vector; performing weighted sorting on the items based on the priority score and the dynamic weight, and outputting a personalized candidate item set; according to the method, the accuracy, timeliness and individuation degree of project matching are improved, and the method has good popularization value.

The application discloses a kind of AIGC content generation and GEO optimization distribution method and system based on knowledge graph constraint.The core of the method is to change the AIGC content generation process from "unconstrained free generation" to "knowledge graph hard constraint generation", and change the content distribution from "passive waiting for inclusion" to "active reverse optimization distribution".The method first establishes the brand exclusive text business travel structured knowledge graph, and uses the hard facts such as merchant coordinates, joint IP and product SKU as mandatory boundary prompt words to constrain the text generation of large language model;At the same time, through the reverse analysis of the recommendation preference of mainstream generative AI engine by automatic probe, the preference vector matrix is formed to guide corpus generation;Then, through the full-automatic distribution platform, the corpus is fed to the whole network in batches through multiple channels;Finally, through the inclusion tracking probe and feedback closed-loop mechanism, the dynamic rectification of strategy is realized.The application realizes the generative engine optimization (GEO) for large model for the first time at the system level, effectively eliminates AI content illusion, realizes more than 80% automation of core operation process, and significantly reduces the cost of enterprise customer acquisition.

This invention relates to the field of content generation technology, and particularly to a method, apparatus, device, and storage medium for generating interactive content in museums. The method parses multimodal information to obtain scene recognition information, matches it with a spatial semantic map to obtain exhibit categories, dynamically updates interest preference vectors based on exhibit categories and acquired multimodal behavioral data to generate dynamic visitor profiles, generates multimodal guided tour content through association reasoning based on exhibit categories, and obtains a sharing index based on multimodal behavioral data and dynamic visitor profiles using a sharing willingness assessment model. If the index exceeds a sharing threshold, materials are selected to generate content to be shared. This method solves the problems of fragmented interactive experience and inefficient sharing generation in existing guided tour modes, reduces the manpower and content operation costs of museum guides, provides technical support for upgrading the museum visitor experience and efficiently disseminating culture, and is applicable to intelligent guided tours and interactive content generation in various cultural and museum venues.

This invention discloses a personalized recommendation system for reading software based on big data analysis, belonging to the field of computer software technology. Addressing the problems raised in previous technologies—that merely characterizing interests makes it difficult to depict the cognitive complexity users can process, that scene constraints are not transformed into quantitative requirements for cognitive load attributes and are not calculated in conjunction with user ability profiles, and that it is difficult to distinguish whether abnormal feedback stems from ability transfer or scene constraint distortion—this invention proposes the following solution, including data collection: the client SDK collects real-time environmental context data of the user's deep behavioral sequence during the reading process. This invention avoids recommending content that is short in form but cognitively overloaded, can distinguish whether abnormal feedback originates from user ability transfer or scene constraint definition deviation, and performs incremental calibration on the reading ability preference vector and scene constraint vector respectively, reducing the candidate size of the ranking model, reducing the amount of data transmission between the server and the client, and improving the overall system operating efficiency.

The invention relates to the technical field of user privacy data protection, in particular to a user privacy data protection method for a whole-store intelligent management platform, and the method comprises the steps: locally collecting song requesting records of a user at a box end, and calculating a privacy sensitivity coefficient for each song requesting record; the method comprises the following steps: aggregating song requesting records of a user according to song style dimensions, constructing an original preference vector, calculating an average sensitivity coefficient of each style dimension, determining a privacy budget parameter, adding Laplacian noise to each style dimension, and finally obtaining a user preference vector; calculating the similarity between the users to obtain a neighbor set; and determining the privacy score of each user, and encrypting the on-demand record of each user. The method and the device aim at better protecting the privacy data of the user.

The application provides a personalized music automatic generation system and method fusing multi-modal emotion perception, relates to the technical field of personalized music automatic generation, collects multi-modal perception data of a target user, performs emotion cross-modal extraction on the multi-modal perception data to obtain an emotion state vector; determines a music preference vector based on historical music preference data, performs expected tendency on music preference of the target user according to the emotion state vector and the music preference vector to obtain each music expectation value of the target user under a current emotion state; predicts a current expected music type of the target user according to each music expectation value and a music matching threshold to obtain a music response degree of the target user for each music type; and generates a personalized music recommendation table according to each music response degree. The application can fuse the current emotion state of the user and the historical preference data, realize real-time dynamic adaptation driven by emotion, and improve the accuracy and timeliness of personalized music recommendation.

The application discloses a granular ball-based graph learning classification method under multi-view data and a related device. The application obtains a multi-view data node set and generates a multi-view shared representation; based on the multi-view shared representation, the multi-view data node set is divided into a plurality of granular balls; based on the granular balls and an expert network, a routing preference vector of a node is obtained; based on the multi-view shared representation, a feature routing vector of the node is obtained; based on the routing preference vector and the feature routing vector, a routing weight of the node to each expert is obtained; through a plurality of expert networks, the multi-view shared representation is transformed to obtain a preliminary embedding representation of the node to each expert, and based on the routing weight of the node to each expert and the preliminary embedding representation of the expert, an embedding representation of the node is obtained; a prediction classification result of the multi-view data node is obtained according to the embedding representation of the node; and the application realizes accurate capture of fine-grained local structures in multi-source heterogeneous data, and significantly improves the representation ability and classification performance of the node.

The application relates to an AI large model-based multi-modal learning resource intelligent recommendation method and system. The method comprises the following steps: obtaining an encrypted state feature vector generated by an original resource feature extraction and confusion coding of a client, constructing a heterogeneous interaction graph, enhancing the heterogeneous interaction graph through a guided diffusion model, and obtaining an aligned multi-modal resource representation, an updated user interest representation and a user modal preference vector through cross-modal contrast learning; constructing a knowledge graph, updating resource weights from bottom to top based on real-time behaviors, adjusting knowledge point weights from top to bottom combined with course objectives, outputting an evolved graph through cognitive load regularization, performing graph convolution propagation calculation on the graph to obtain a preference score, generating an encrypted recommendation list through cognitive load reordering, and outputting a final recommendation result through client decryption. By using the method, data privacy and copyright safety can be ensured, user interest, modal preference and cognitive level can be accurately adapted, and personalized learning resource intelligent recommendation can be realized.

The invention discloses an AI-based cultural activity adaptive planning method and system, and relates to the technical field of information intelligent processing and activity planning, and the method comprises the following steps: obtaining user behavior data from a plurality of campus digital platforms in real time, a dynamic short-term interest vector and a dynamic long-term preference vector are generated through processing of a time sequence model and a graph neural network, and a fusion user portrait set is formed through fusion of a learnable fusion mechanism; through multi-source data real-time acquisition and time sequence model and graph neural network processing, a learnable fusion mechanism is utilized to construct a fusion user portrait, so that a cultural activity planning scheme is established on the basis of continuously perceived real group preference, a traditional subjective mode depending on static experience is changed, and the user experience is improved. Objective and dynamic planning strategies are further realized; a planning scheme is structured into a planning element combination space with spatialization and dimensionalization, and multiple optimization targets are defined and model quantification processing is carried out.

The application discloses a click rate prediction method fusing multi-view features, comprising the following contents: constructing a training sample and generating a unified model input representation; calculating the preference score of each category of users in the category space and learning the category weight distribution, and obtaining a group preference vector representation by weighted aggregation; context encoding and sequence position identification generation are performed on the historical behavior sequence vector sequence, and a dynamic interest vector representation is obtained by sequence convergence; the historical behavior is mapped into an equidistant bucket sequence according to the timestamp, and a bucket vector representation is formed, and a periodic preference vector representation is obtained by weighted aggregation; the above multiple vector representations are adaptively fused to obtain a unified user interest vector representation; a joint feature representation is constructed, and after being input into a prediction network model, a click probability is obtained; the three vector representations constructed by the application simultaneously depict the stable preference structure, short-term behavior evolution and long-term repetition law of users, and the multi-scale expression ability and prediction performance of user interest are improved.

Multi-device cooperative AI personalized exercise planning generation system. The present application belongs to the technical field of intelligent fitness and health management, and specifically relates to a multi-device cooperative AI personalized exercise planning generation method, which comprises the following steps: S1: obtaining a user basic parameter set and a current dynamic physiological parameter set; S2: performing preprocessing and feature extraction based on the basic parameter set and the current dynamic physiological parameter set, generating a multi-dimensional derived feature vector and a standardized user vector through a feature engineering module; S3: performing feature fusion using the standardized user vector and the multi-dimensional derived feature vector to generate a multi-dimensional user state portrait feature sequence; and S4: performing parallel analysis and decision on the multi-dimensional user state portrait feature sequence using a multi-source hybrid decision model to generate an exercise type preference vector. The present application can realize highly personalized, scientific and safe dynamic exercise planning which can be continuously executed across devices by fusing multi-source heterogeneous data, performing parallel multi-source hybrid decision, dynamically weighted fusion and exercise science knowledge graph mapping.