122 results about "Collaborative knowledge" patented technology

The invention relates to the technical field of knowledge maps, and discloses a semantic association and logic rule inference method based on a knowledge map, and the method comprises the steps: obtaining a cross-functional department first data set, and constructing a cross-department knowledge map; extracting index nodes from the cross-department knowledge graph, identifying semantic conflict indexes, generating an index semantic conflict list, and constructing the cross-department knowledge graph after semantic mapping based on the index semantic conflict list; obtaining a second data set of a cross-functional department, and obtaining a fused cross-department knowledge graph and a semantic association degree matrix based on the second data set and the cross-department knowledge graph after semantic mapping; dynamically adjusting the weight of each node and edge of the fused cross-department knowledge graph according to the semantic association degree matrix, and generating a cross-department collaborative knowledge graph; generating a cross-department collaborative decision suggestion based on the cross-department collaborative knowledge graph; according to the invention, the efficiency and quality of cross-department collaborative decision-making are significantly improved.

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

The invention discloses a sparse reward environment-oriented collaborative knowledge fusion reinforcement learning method, and relates to the field of collaborative knowledge fusion reinforcement learning methods. By constructing a lightweight collaborative knowledge fusion model and a dynamic reward remodeling mechanism, the problems of low intelligent agent exploration efficiency and difficulty in strategy convergence in a sparse reward environment are solved. The method comprises the following steps: constructing a reinforcement learning framework comprising a policy network and a value network; designing an action space mutation supervision mechanism and a lightweight collaborative knowledge fusion model, and generating a smooth substitution action when a strategy is detected to be unstable; and a reward function is designed in combination with the task target and the dynamic constraint, and reward remodeling is realized by activating rewards through sub-target potential energy difference and knowledge fusion. According to the method, effective intermediate feedback can be provided for the agents in the sparse reward environment, the exploration efficiency is improved, the convergence time is shortened, the stability and cross-scene migration ability of the strategy are enhanced, and an effective solution is provided for sparse reward scenes such as robot control and multi-agent game.

The invention provides a multi-model collaborative knowledge graph construction method, system and device and a storage medium, and the method comprises the steps that a routing engine receives a knowledge graph construction request, matches a preset rule base according to target domain parameters, and generates a node assembly sequence; the scheduling engine constructs a task execution directed acyclic graph according to the task execution directed acyclic graph; the execution engine preprocesses the original document according to the graph to generate a structured document block set with metadata; calling a pre-training model in the small model resource pool to perform entity and relation extraction to form a preliminary entity set and an association relation set; attribute completion and implicit relation reasoning are carried out on the preliminary entity set, and a completion entity attribute set and a newly-added relation set are generated; performing entity alignment on the preliminary entity set and the complemented entity attribute set by the large model to obtain a fused entity set; and performing conflict resolution on the incidence relation set and the newly-added relation set to obtain a fusion relation set. And storing the fusion entity set and the fusion relationship set to a knowledge graph database.

The invention relates to the technical field of federated learning, and discloses a federated learning method and system based on multiple agents and knowledge distillation, and a medium, and the method comprises the steps: S1, training a local model; s2, knowledge distillation based on decoupling; s3, decentralized knowledge sharing based on a block chain: packaging the standardized distillation knowledge fragments extracted in S2 and metadata thereof into block chain transactions, submitting the block chain transactions to a block chain network, verifying the legality of the transactions, and writing the transactions into a block chain account book to realize decentralized distribution; s4, performing multi-agent collaborative knowledge management based on a large language model, and updating a student model; and S5, repeatedly executing the steps S1 to S4 until a training termination condition is met. According to the method, personalized selection, adaptive filtering and efficient sharing of knowledge in the federated learning process are realized, the client drift phenomenon caused by data non-independent identical distribution characteristics is effectively relieved, the convergence speed, the overall performance and the personalized level of the model in a heterogeneous data environment are remarkably improved, and the communication overhead between clients is optimized.

The invention discloses an article recommendation method and device, a storage medium and electronic equipment. Relates to the field of artificial intelligence, in particular to the field of recommendation systems. Based on the initial implicit collaborative knowledge graph, constructing a plurality of embedded representations, the plurality of embedded representations including an account embedded representation, an article embedded representation and an entity embedded representation; based on the multiple embedded representations, an adaptive implicit collaborative knowledge graph is constructed, and the adaptive implicit collaborative knowledge graph is used for representing potential interests of the account on the articles and implicit preferences and associations between the articles and the entities; based on the self-adaptive implicit collaborative knowledge graph, an article recommendation model is adopted to obtain an article recommendation result corresponding to a target account, and the target account is an account in the self-adaptive implicit collaborative knowledge graph. The object recommendation method and device solve the technical problem that the object recommendation effect is poor due to large sparsity of interaction data and a knowledge graph.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention provides an information recommendation method and device based on collaborative sampling of a knowledge graph and an adjacent graph, and relates to the technical field of information recommendation. According to the method, the triple is constructed by obtaining the historical interaction data of the user, and the triple is fused with the project attribute graph to construct the collaborative knowledge graph; performing expression alignment through projection transformation to obtain initial embedding expression; performing multi-hop neighbor sampling through biased random walk to obtain a multi-hop neighbor set; determining a neighbor which is most similar to the embedding representation of the positive sample item as an anchor neighbor; then linear interpolation is carried out, and the embedding representation of the difficult negative sample is synthesized; on the basis of the multi-head graph attention network, training by adopting a positive sample and a synthesized difficult negative sample, and aggregating to obtain an updated embedded representation; and calculating an interaction probability based on the updated embedded representation, and generating a recommended item for the user. According to the method, the problems of user-project interaction sparsity and false negative example interference are effectively solved, and stronger semantic comprehension and generalization ability are provided for a recommendation system.

The invention provides a production and teaching cooperation practical training method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time operation data set of industrial equipment and a practical training case data set of a production and teaching cooperation knowledge base, and then constructing the digital twinning of the industrial equipment based on the real-time operation data; the method comprises the following steps: analyzing training case data by using a large language model to generate a multi-modal training interaction feature set, performing collaborative feature mapping on a digital twin and the interaction feature set to obtain a virtual-real fusion training scene feature set, and dynamically optimizing the virtual-real fusion training scene feature set through a preset training strategy generation model to obtain a virtual-real fusion training scene feature set. According to the method and the system, a personalized production and teaching collaborative practical training scheme is generated, finally, the personalized production and teaching collaborative practical training scheme is deployed to digital twinborn triggering real-time interactive training, and a production and teaching collaborative knowledge base is updated according to operation feedback data of trainees, so that efficient and personalized production and teaching collaborative practical training is realized, and the practical training effect is improved.

The invention relates to the technical field of mechanical arm optimization learning, and discloses a sparse reward environment optimization learning method and system based on demonstration data enhancement, and the method comprises the steps: obtaining expert demonstration data of a grabbing task of a mechanical arm, generating an enhanced demonstration sample through track segmentation time sequence interpolation and state space neighborhood extension, and carrying out the reconstruction of the enhanced demonstration sample; constructing a demonstration experience playback buffer area; building a reinforcement learning framework containing a strategy network and a value network, and learning an optimal grabbing strategy by utilizing reinforcement demonstration; designing a lightweight collaborative knowledge fusion model to monitor an action change rate, and outputting a smooth action when the action change rate exceeds a threshold value; and a reward function containing a task target and dynamic constraints is constructed, knowledge fusion rewards are superposed when the fusion model is activated, and reward sparsity compensation and strategy optimization are realized. According to the method, the executing stability and safety of the mechanical arm are improved, and the parameter updating accuracy and the training convergence speed are improved.

The invention provides a team cooperation training method and system based on scene reproduction and multi-agent cooperation, and relates to the technical field of team cooperation, and the method comprises the steps: building an interaction scene through building a team cooperation knowledge base, training an agent initial behavior mode through deep reinforcement learning, and building a knowledge distillation enhanced multi-agent cooperation learning network. Anti-fact reasoning is adopted to analyze cooperation behavior influence, a capability evolution track is dynamically tracked, and a cooperation optimization scheme is output. According to the method, the team cooperation efficiency can be effectively improved, cooperation short boards are accurately identified, and continuous optimization of team capability is realized.

The invention relates to a data recommendation method which is suitable for a video recommendation scene. The method comprises the following steps: acquiring an article knowledge graph, an interaction graph between an object and an article, and modal content of the article; fusing the article knowledge graph and the interaction graph to obtain a collaborative knowledge graph, determining an article relation graph based on modal content of the article, and constructing an object co-occurrence graph based on the interaction graph; heterogeneous graph learning is carried out based on the interaction graph and the collaborative knowledge graph, and object features and article features are obtained; performing isomorphic composition learning based on the object co-occurrence graph and the object features to obtain a target object representation, and performing isomorphic composition learning based on the object relation graph and the object features to obtain a target object representation; and according to the target object representation of the target object and the target object representation of each object, performing object recommendation on the target object. By adopting the method, the recommendation performance of the strict cold start article can be improved on the premise of keeping the recommendation performance of the hot start article.

The invention relates to the technical field of knowledge recommendation, and discloses a knowledge recommendation method and system based on cross-modal comparison and Riemannian hyperbolic geometry, and the method comprises the steps: obtaining a user and project interaction bipartite graph; obtaining a basic knowledge graph; combining the user and project interaction bipartite graph with the basic knowledge graph to obtain a collaborative knowledge graph; multi-modal features are set for each item in the collaborative knowledge graph, and the multi-modal features comprise visual modal features and text modal features; inputting the collaborative knowledge graph and the multi-modal features of the items into the trained knowledge recommendation model, and recommending the items to the user through the trained knowledge recommendation model; according to the trained knowledge recommendation model, potential information among different modals is further mined by using cross-modal comparative learning, and an extra supervision signal is introduced for a recommendation task. Movie items or game items that the user is interested in can be recommended to the user.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention discloses a collaborative distillation personalized federal learning method for multi-user semantic communication, and belongs to the technical field of artificial intelligence and wireless communication. The semantic consistency in a task group is ensured through a clustering method, meanwhile, personalized training is performed on a semantic encoder in the task group, the adaptability of a semantic communication model is improved in combination with a partial parameter sharing strategy, and finally, global semantic knowledge is shared through inter-group collaborative knowledge distillation; the method comprises the following specific steps: clustering and grouping clients participating in training, constructing a hierarchical semantic communication model, performing hierarchical personalized federated learning training in a task group, and performing knowledge sharing based on collaborative distillation to obtain a semantic communication model of collaborative distillation personalized federated learning. The method can accurately reflect preferences, and is high in individuation performance, high in semantic recovery accuracy, high in semantic communication model generalization ability, high in data processing heterogeneity and high in task diversity.

The invention discloses an interpretable sequence recommendation method fusing time perception and path reasoning. The method comprises the following steps: 1) constructing a time sequence collaborative knowledge graph; 2) node representation learning is carried out on the time sequence collaborative knowledge graph through TransE; 3) designing a time sensing system, updating an article vector at the current moment through a path and an article attention mechanism, and generating an article prediction vector in combination with a user vector; 4) designing a path reasoning system, performing correlation path reasoning by using a reinforcement learning framework, and obtaining a multi-path vector through path representation learning; and 5) model training: training the time sensing system and the path reasoning system to obtain an article prediction result. According to the method, double systems are introduced to simulate the human reasoning process, and the accuracy and interpretability of the recommendation result are improved by learning the time dependence characteristics of the user behaviors in the time sequence collaborative knowledge graph.

The present invention relates to an AI Agent intelligent body based on a large language model collaborative knowledge graph, which includes a natural language dialogue module, a large language model module, an RPA SOP process knowledge graph module and an RPA execution engine, wherein the natural language dialogue module receives natural language and converts it into a structured query statement; the large language model module interacts with the natural language dialogue module and the RPA SOP process knowledge graph module respectively, the large language model module understands user needs, retrieves the knowledge graph according to the query statement and generates the RPA process; the RPA SOP process knowledge graph module stores standard SOP knowledge of vertical businesses in a specific industry, provides professional knowledge and normative constraints, and provides knowledge query and reasoning; the RPA execution engine executes the generated RPA process; and also includes an agent method and a storage medium. The present invention utilizes the RPA process knowledge graph to enhance the knowledge and reasoning capabilities of the large language model, generates RPA process steps that meet the requirements, and improves the accuracy and efficiency of RPA process generation.

The invention discloses a method and a device for constructing a multilevel power field knowledge base, relates to the field of artificial intelligence, and mainly aims to solve the problem that the field knowledge base constructed based on an existing method is poor in effect of improving the application reliability of a large language model. Comprising the steps of performing text image separation on original data to obtain a domain knowledge original text and a domain knowledge original image; performing adaptive semantic text block segmentation on the original text to construct a semantic text block knowledge base; performing dual-channel collaborative knowledge extraction on the text blocks, fusing extraction results, and constructing a knowledge graph knowledge base; performing adaptive text description on the domain knowledge original image to generate a constructed image and a text description knowledge base; vector conversion and mapping alignment are carried out on the domain knowledge text blocks, the knowledge graph triple, the domain knowledge original image and a combination of corresponding text descriptions to construct a semantic vector knowledge base; and constructing a multi-level domain knowledge base based on the plurality of knowledge bases.

The invention provides a tourism route recommendation system based on tourism consumer portraits and psychological models, the system collects user behavior data through a data acquisition and preprocessing module, and constructs a user and scenic spot interaction information graph, and meanwhile, a scenic spot knowledge graph construction module integrates scenic spot information from top to bottom to form a structured knowledge base, so that the scenic spot information is recommended to the tourism consumer portraits and psychological models. The collaborative knowledge graph construction module combines user behaviors with scenic spot knowledge to provide a basis for the deep learning model and knowledge graph application, the deep learning model application module utilizes a GRU neural network to mine deep features of users and scenic spots, the knowledge graph application module optimizes recommendation results through a TransR model and an attention mechanism, and finally, the recommendation results are recommended to the users and scenic spots. The route planning module plans the travel route according to the information and displays the information to the user through the visualization module, and the system can improve the accuracy and individuation degree of travel route recommendation and improve the user experience.

The invention discloses an innovative resource collaborative matching method and system oriented to distributed operation, and relates to the field of data processing recommendation, and the method comprises the steps: carrying out the standardization processing of a heterogeneous resource portrait description text through a large language model, constructing and forming a resource attribute knowledge graph, generating extension description information of the resources in the target industrial chain through the large language model, and constructing and forming an industrial chain knowledge graph; performing semantic alignment and hidden associated information mining on the fused graph by using a pre-trained SACN model, and constructing a resource collaborative knowledge graph representing resource attributes and collaborative logic in an industrial chain; and performing semantic fusion on the original description text representation of the resource and the resource collaborative knowledge graph representation based on a large language model, generating comprehensive vector representation through multi-modal fusion, and outputting a resource matching recommendation result by adopting a collaborative filtering recommendation model. According to the method, the cross-organization and cross-industry-chain innovative resource collaborative knowledge graph is constructed, instant and clear demands of users are accurately responded, and the resource allocation efficiency is improved.

The invention discloses a multi-source feature collaborative knowledge mining and semantic association method, and belongs to the technical field of full-life-cycle knowledge processing of railway track engineering. According to the method, field-related documents are screened, an element range and a processing sequence are limited in combination with a knowledge graph, the documents are customized to generate a term candidate set, term statistical weights are calculated based on a full-life-cycle corpus, dynamic semantic vectors are generated by utilizing a field self-adaptive pre-training model, and composite concept representation is obtained through fusion of an attention mechanism. And linking the knowledge graph to construct a heterogeneous graph, mining association through a graph attention network, analyzing, querying and verifying sequential logic, and then outputting structured knowledge. According to the method, the problems of railway track engineering data islands and cross-stage semantic segmentation can be solved, low-frequency key terms are accurately recognized, the result interpretability is enhanced, applications such as design optimization and construction management and control are directly supported, and the method is adaptive to multiple engineering professions and high in practicability.

The invention discloses a long-tail recommendation algorithm based on semantic enhancement and mixed attention. The algorithm comprises the following steps: fusing a user-article interaction graph and a knowledge graph to obtain a collaborative knowledge graph; on the collaborative knowledge graph, extracting new long-tail neighborhoods for the user and the article respectively by utilizing rich semantic information; long-tail proximity is gathered to the user and the article through relation attention, and long-tail embedding of the user and the article is obtained; further aggregating the obtained long tail embedding of the user and the article and the original proximity of the user and the article through self-attention to obtain a final embedding vector of the user and the article; and performing dot product on the final embedded vectors of the user and the article to obtain a prediction score of the user on the article, and recommending the first N articles with the highest prediction score to the user. According to the method, the long-tail recommendation performance can be improved on the basis of maintaining the recommendation precision.

The invention relates to the technical field of knowledge tracking, and discloses a graph enhanced double-memory collaborative knowledge tracking model based on an ACT-R cognitive architecture. Comprising a static knowledge structure coding module based on hypergraph projection, a batch-level dynamic learning track construction and coding module, a cross-graph gating fusion mechanism, a sequence modeling module and an expert hybrid prediction module. According to the method, long-term stable structured semantic association between concepts in declarative memory is modeled through a static knowledge structure diagram, a dynamic learning trajectory diagram based on batch reconstruction is designed to accurately capture an evolution rule of a behavior sequence in programmed memory, and on the basis, a cross-diagram gating fusion mechanism and a hybrid expert mechanism are introduced, so that the evolution rule of the behavior sequence in the programmed memory is accurately captured. And self-adaptive fusion and multi-path decision of double-graph features are realized.

The invention discloses a construction safety risk intelligent control system and method based on a knowledge graph, and the system comprises a data collection layer which is used for collecting construction model data, process parameters, real-time monitoring data and historical risk event data of a construction site; the multi-library collaborative knowledge graph layer is used for constructing a knowledge graph based on the data acquired by the data acquisition layer; the intelligent analysis processing layer is used for analyzing the association network of the scheme, the risk and the hidden danger and generating risk early warning information, an optimized construction scheme and a quantitative evaluation report; and the visual decision-making layer is used for receiving and displaying the risk early warning information, the optimized construction scheme and the quantitative evaluation report which are generated by the intelligent analysis processing layer. According to the method, deep and cross-professional coupling risks can be mined, automatic and quantitative optimization of the construction scheme is realized, the dynamic response capability to field changes is enhanced, and the scientificity and efficiency of power transmission construction risk management and control are remarkably improved.

The invention relates to the technical field of plant management, and discloses a chaenomeles speciosa remote management and data modeling method based on a cloud collaborative architecture. The method comprises the following steps: firstly, receiving begonia growth environment data collected by terminal equipment, and carrying out standardized preprocessing and multi-source data fusion analysis to obtain a growth feature entity candidate list and a non-feature data fragment; after a first to-be-modeled data entity is extracted from the candidate list, a plurality of matched candidate data entity entries are extracted from the cloud collaborative knowledge base, the candidate data entity entries and the non-feature data fragments are input into a collaborative disambiguation model, semantic-level collaborative aggregation analysis is carried out with the non-feature data fragments as contexts, semantic aggregation coding features are obtained and decoded, and the semantic aggregation coding features are obtained; and obtaining a disambiguation data entity. And circularly executing the steps to obtain a disambiguation data entity list, and further generating a remote management strategy. The method can improve data processing accuracy and management pertinence, and is suitable for large-scale and distributed planting scenes of begonia.

A collaborative knowledge enhancement method for multi-behavior recommendation combines cascade correlation learning and parallel interaction enhancement through a collaborative data fusion module, captures a hierarchical high-order relationship between behaviors by using a graph neural network and residual connection, and independently extracts each behavior feature to avoid high-frequency behavior interference. Multi-stream representation is fused through a projection mechanism; sharing and unique information are separated based on a decoupling projection mechanism to generate behavior exclusive expert representation, and multi-behavior data are integrated in combination with a gating aggregation strategy to relieve gradient conflicts and negative migration problems; and finally, a prediction target behavior interaction probability is output through fusion features and an expert network, and a recommendation result is optimized. According to the method, the problems of unbalanced behavior distribution and multi-task interference are effectively solved, the target behavior prediction precision is enhanced while the low-frequency behavior representation capability is improved, and personalized recommendation and model stability are considered.

The invention belongs to the field of fire-fighting data processing, and particularly relates to an AI intelligent data processing method for the fire-fighting field, and the method comprises the steps: firstly, building a fire-fighting multi-association knowledge graph through collecting fire-fighting multi-field and multi-source data; secondly, analyzing semantic information and a scene background input by a user by adopting a configured multi-input intention reasoning model, and determining a main-collaborative knowledge sub-graph; 3, constructing a multi-graph joint reasoning model, fusing the main-collaborative knowledge sub-graph and semantic information input by the user, generating a user fire-fighting demand plan set, and continuously optimizing the model performance by constructing an optimal reasoning search path and a reasoning approximation loss function; and finally, according to real-time user input and scene feature space, a user-customized fire-fighting demand plan is quickly generated by using the trained multi-graph joint reasoning model, and the information is fed back to the fire-fighting multi-association knowledge graph, so that continuous updating and accumulation of knowledge are realized, and powerful support is provided for intelligent decision making in the fire-fighting field.

The invention relates to the field of knowledge tracking, and discloses a time-guided attention and mixed expert collaborative knowledge tracking method and system, and the method comprises the steps: obtaining a historical answer interaction sequence of a student; constructing and training a knowledge tracking model; and inputting the historical answer interaction sequence into the knowledge tracking model to complete prediction of the answer condition of the student. According to the method, the individuation of knowledge tracking and the time sequence modeling capability are effectively improved through a time-guided attention and mixed expert cooperation mechanism. Wherein the time-guided attention module is combined with multi-scale forgetting bias, so that stable modeling for long-term dependence is enhanced; and the hybrid expert module dynamically activates an adaptive expert according to the learning rhythm of the student, thereby realizing adaptive capture of heterogeneous learning behaviors. The method does not need to depend on complex external semantic information, can achieve high-quality prediction only based on basic interaction data, and remarkably improves the robustness and prediction accuracy of irregular time intervals.

The invention provides a handheld bank personalized search recommendation method and system, electronic equipment and a storage medium, and is suitable for a handheld bank APP to obtain a pre-constructed collaborative knowledge graph; obtaining a historical user query, and extracting a historical query suggestion set related to the historical user query from the collaborative knowledge graph; forming a plurality of training triads according to each user in the user set, the historical user query and a historical query suggestion set related to the historical user query, and training a to-be-trained personalized search recommendation model according to a scoring function learned by a training set constructed by each training triad to obtain the personalized search recommendation model; and when a query currently input by a target user in the search session of the handheld bank APP is detected, calculating the recommendation degree between the query and each entity in the handheld bank APP by using the personalized search recommendation model, screening out to-be-recommended entities from the entities according to the recommendation degree of each entity, and outputting each to-be-recommended entity.

The invention discloses a multi-model collaborative knowledge distillation optimization method based on edge computing. The method comprises the following steps that a plurality of edge devices construct local models respectively and train the local models through collected local data, and the edge devices upload the trained models to a server; real-time performance evaluation is carried out on local models on the edge devices, whether the performance evaluation result meets preset conditions or not is judged, if not, the local models are updated, and the model updating comprises the following steps that 1, a multi-model collaborative distillation strategy is adopted, and knowledge exchange is carried out on the local models on the different edge devices; 2) carrying out data heterogeneity and imbalance processing; 3) carrying out resource adaptive scheduling; (4) the server carries out local model aggregation and issues model updating content in the iteration process to the edge device; if yes, the local model does not need to be updated in the iteration. According to the invention, the performance of multiple models on the edge device can be improved, the communication overhead is reduced, and the data privacy is ensured.