609 results about "Cold start" patented technology

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

The invention provides a model training and information replying method and device, a storage medium and a program product, and relates to the technical field of computers. The method comprises the steps of performing continuous pre-training on a base model based on a first training sample to obtain a basic model; performing cold start supervision fine tuning training on the basic model based on the second training sample to obtain a first supervision fine tuning model; performing multiple reasoning based on the third training sample, the target information and the to-be-trained model to obtain a reasoning result; in the Mth reasoning process, the target information comprises information obtained after a target tool determined by previous M-1 reasoning is called; optimizing the to-be-trained model based on the reasoning result to obtain a first reinforcement learning model; and based on the general recognition data and reasoning data output by the first reinforcement learning model, carrying out general recognition alignment training to obtain a target model. According to the method, the target tool can be called to obtain the required target information, so that the information output by the large model is more comprehensive.

The invention discloses a large language model semantic enhancement collaborative filtering recommendation method based on comparative learning, and belongs to the field of recommendation systems.The method comprises the steps that a user-item bigraph is constructed, user-item semantic information collaborative information is provided through GNN, user-item semantic information is extracted through cue words, a deterministic topology view enhancement strategy is adopted, and user-item semantic information collaborative filtering recommendation is achieved. According to the method, a semantic neighbor extension view is generated, a semantic neighbor reconstruction view is generated, collaboration and semantic information alignment are performed, double-view structure alignment is performed, and a loss function is integrally trained, so that the accuracy and the cold start capability of a recommendation system are remarkably improved through collaborative graph structure learning and semantic enhancement.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to the technical field of text generation, provides an NL2SQL method and system based on thinking reasoning, and aims to enable a model to interact with a database environment by introducing a reinforcement learning mechanism to obtain reward signal feedback. The system comprises a table field recall component and a structured query language generation component. The table field recall component fills table field information into a preset template to construct a structured text, then vectorization processing is carried out, the structured text is stored into an elastic search engine, a relevant table is searched according to user query, and finally specific fields are screened through a field selector model. And the structured query language generation component receives the screening result, generates a structured query language in combination with user query, and performs retrieval replacement on a filtering value. In the aspect of training, the method combines supervised fine-tuning cold start and reinforcement learning reasoning training, and the model performs reasoning and thinking according to an input context through trial and error learning. In the reinforcement learning training process, a reward function iteration optimization model based on task execution result correctness is adopted.

The invention discloses an electric power detection method and system for multi-modal enhanced fine tuning, and relates to the technical field of vision-driven intelligent electric power inspection, and the method comprises the steps: 1, obtaining an electric power inspection image, and marking the image; 2, inputting the marked image into the initial vision-language model, and carrying out cold start supervision fine tuning to train the initial vision-language model to obtain a model subjected to supervision fine tuning; 3, performing reinforced fine tuning on the model subjected to supervised fine tuning, and updating model parameters to obtain a PowerGPT-R1 model; and 4, evaluating the performance of the PowerGPT-R1 model based on the test set, and verifying the reliability of power detection. According to the method, the detection precision is remarkably improved under the condition of few samples, meanwhile, the multi-modal reasoning capability which a visual perception model does not have is shown, and the method has important application value in autonomous inspection of the infrastructure of the smart power grid.

The invention discloses an academic group mining and directional recommendation system for regional natural fund, and relates to the technical field of computers. The academic group mining and directional recommendation system for the regional natural fund comprises a data management system, a knowledge graph module, a mixed recommendation algorithm module, an AI robot module and a user interaction interface module, and a multi-dimensional heterogeneous relation graph, an innovative cold start solution and a lightweight graph recommendation framework are constructed. Accurate mining, intelligent recommendation and efficient management of scientific researchers and projects are realized. According to the method, potential interests and cross-domain association requirements of users can be deeply captured, the problem of data sparsity in a cold start stage is effectively solved, and rapid deployment and real-time recommendation are realized through lightweight design. And powerful technical support and decision basis are provided for reasonable distribution and scientific research and innovation of regional natural funds.

The invention relates to a toxic text collection method and system based on retrieval enhancement generation, and the method comprises the steps: firstly obtaining target text data through a crawling platform, manually constructing a small-scale initial data set through a cold start mode, and injecting the initial data set into a knowledge base as basic data; and performing semantic retrieval on each batch of target texts, obtaining the first k texts with semantic similarity from the knowledge base, reasoning by adopting a plurality of large language models through thinking chain reasoning in combination with the texts, generating a toxic label, and labeling the target texts to be labeled in the current batch. And injecting the labeled target text into a knowledge base, continuously collecting and labeling data in an iteration mode, and continuously optimizing the performance of a retriever through an incremental learning mechanism in the iteration process so as to realize toxic text collection. According to the method, large-scale, fine-grained and high-consistency toxic text tagging corpora can be efficiently accumulated, and the problems of high tagging cost, inconsistent quality and poor expansibility in traditional toxic text data collection are remarkably relieved.

The invention discloses a federal learning-based cross-device brain grain model training system. The system comprises a terminal device layer, an edge coordination layer and a central server layer, the terminal equipment layer is responsible for collecting original electroencephalogram data and executing local lightweight training; the edge coordination layer is used for aggregating model updates of a plurality of terminal devices in the jurisdiction to generate a regionalized sub-model; according to the central server layer, a central server performs secondary optimization on each regional model to generate a global unified model; the encrypted global model and strategy configuration are pushed to all edge coordination layers; and the edge coordination layer pushes the optimized model and strategy to terminal equipment. Through a three-layer federated architecture and multi-dimensional optimization, the system efficiency is remarkably improved: the terminal equipment only uploads an encrypted model increment, and source data protection is realized in combination with differential privacy and homomorphic encryption; federal transfer learning shortens the cold start time of new equipment.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.

The invention discloses an intelligent recommendation system and algorithm based on a knowledge graph, and relates to the technical field of information recommendation. The intelligent recommendation system based on the knowledge graph and the dynamic updating method thereof are achieved mainly by taking dynamic updating of the knowledge graph, multi-modal information fusion, cold start problem solving and recommendation algorithm optimization as the core, and the purpose is to establish and maintain the association relation graph between the user and the article and combine real-time data analysis to achieve the intelligent recommendation system based on the knowledge graph and the dynamic updating method of the intelligent recommendation system based on the knowledge graph. And the recommendation algorithm is continuously optimized to improve the accuracy and timeliness of recommendation.

The invention discloses a reinforcement learning unmanned ship path control method based on double-track regulation and control random network distillation. The method comprises the operation steps that an unmanned ship builds a path tracking simulation environment and a kinetic model; the unmanned ship builds a core algorithm flexible action evaluation algorithm framework; the unmanned ship deploys a priority experience playback pool based on quality and success guidance; an uncertainty perception and risk perception mechanism is introduced into the unmanned ship; the unmanned ship builds a success rate-based reward attenuation and cold start module, and the unmanned ship calculates a total reward and designs a reward softening mechanism to smooth the total reward; the unmanned ship imports hyper-parameters of all the modules, starts training circulation in a simulation environment, and dynamically adjusts exploration intensity and the like; according to the method, uncertainty and risk indexes are introduced, the exploration intensity of the intelligent agent is controlled, the intelligent agent is prevented from making dangerous actions, and the robustness is improved; a priority experience playback pool based on quality and success guidance is introduced, high-quality samples are better played back, and strategy convergence is accelerated.

The invention provides a cold start acceleration method and device, electronic equipment and a medium, which can be widely applied to the technical field of computers, and the cold start acceleration method comprises the following steps: obtaining call information of a target function, and preprocessing the call information to obtain call characteristics; inputting the calling features into an online preheating model, predicting a function calling interval through a first preheating module in the online preheating model to obtain predicted calling time, and predicting the number of containers required for calling through a second preheating module in the online preheating model to obtain the number of predicted containers; determining a prediction decision according to the prediction calling time and the prediction container number; and deploying the containers according to the prediction decision, and creating a first number of preheated containers before the prediction call time to cope with a call request of the target function. The problem of request response delay caused by cold start in related technologies can be relieved, the request response speed is increased, and the user experience is improved.

The invention discloses a self-adaptive pushing method and system based on user behavior dynamic analysis, relates to the technical field of intelligent information pushing and user behavior modeling, and is used for solving the problems that user interest description is not accurate, multi-modal data is difficult to fuse, and the pushing matching degree is low. Collecting behavior data of a user on multiple platforms, and cleaning the data; then, multi-modal interest features of the user are extracted, and interest state vectors are constructed; time sequence modeling is carried out on user interest evolution, a stability scoring mechanism is constructed by introducing cold start density and interest drift frequency, and dynamic compensation and classification adjustment are carried out on different stability behavior sequences. The behavior stage of the user is identified by combining the network topology change of the user behavior, a staged pushing strategy is formulated, and the fine matching and opportunity control of the pushed content are realized. And finally, a feedback closed loop is constructed, the model is continuously optimized, and the accuracy and adaptability of pushing are improved.

The invention provides an article recommendation method and device, and the method comprises the steps: determining a target word from a search word set; obtaining external platform domain data corresponding to the target word, matching the external platform domain data with the internal platform domain data, and determining the matching similarity between the external platform domain data and the internal platform domain data; and taking the internal platform domain data and the external platform domain data of which the matching similarity exceeds a first threshold value as target internal platform domain data and target external platform domain data, and constructing product word data based on the target internal platform domain data and the target external platform domain data so as to perform article recommendation based on the product word data. According to the embodiment, the hotspot trend can be responded in time, and the cold start period of the article related to the hotspot trend is shortened, so that the timeliness and accuracy of article recommendation are improved, and the user experience is improved.

The invention relates to the technical field of visual identification, and discloses an annotation, evaluation and learning integrated visual identification platform, which comprises a data acquisition module, an annotation enhancement module, a detection model training module, a dynamic evaluation module, an active learning module and an optimization closed loop module. Through integration of data acquisition, annotation enhancement, detection model training, dynamic evaluation, active learning, closed loop optimization and other modules, full-process collaboration is realized based on an improved YOLOv8 algorithm, and the annotation efficiency and quality are improved through auxiliary annotation, quality verification and cross-modal annotation association functions of the annotation enhancement module. The ICR algorithm and the sample screening strategy of the active learning module solve the cold start problem and reduce the labeling workload, and the dynamic evaluation module realizes model interpretability quantification through a class activation thermodynamic diagram, a MobileSAM segmentation mask, N-IoU, N-Recall and other indexes, so that an efficient and accurate one-stop solution is provided for a visual identification task.

The invention discloses a large video model training method and a related device, and relates to the technical field of video recognition, and the method comprises the steps: collecting a training video data frame to obtain an image frame, inputting a preset prompt word, a user question and the image frame into a large image model, and obtaining a thinking chain and a question answer. Performing cold start on the video large model based on the thinking chain and the question answer to enable the video large model to have thinking chain output capability; and combining training video data and questions to generate space and time disordered data and thinking chain data. And inputting the three types of data into the model to obtain corresponding outputs, calculating the accuracy of each output, obtaining space and time accuracy reward values, and training the model through a group relative strategy optimization algorithm in combination with a thinking chain consistency reward value to obtain an inference video large model. According to the method, the trained video large model can have thinking reasoning capability based on thinking chain implementation.

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 reasoning-based multi-language security protection framework, a medium and equipment, and belongs to the technical field of artificial intelligence. According to the framework, cross-language knowledge migration and interpretability enhancement are realized in a mode of combining thinking chain reasoning and constraint alignment optimization; comprises: an SFT-based cold start module configured to perform knowledge distillation on a basic large language model through supervised fine tuning so as to endow the model with a preliminary reasoning ability for a safety protection task; the reasoning training module based on the GRPO is configured to be capable of improving normalization, accuracy and diversity of a model reasoning chain and enhancing interpretability; and the CAO-based cross-language alignment module is configured to realize knowledge migration from a high-resource language to a low-resource language and avoid performance reduction of the high-resource language. The method can solve the problems that an existing method mainly depends on a classifier lacking interpretability, the performance of a low-resource language safety fence is insufficient, and the performance of the low-resource language safety fence is poor.

The invention discloses an intelligent verification method for multi-dimensional scene perception, and the method comprises the steps: obtaining the field service data, edge calculation environment information and network state information of a power supply station, and constructing a multi-dimensional scene feature vector; based on the multi-dimensional scene feature vector, constructing a meta-confidence model by adopting a feature progressive strategy, and based on the meta-confidence model, generating initial confidence, decision suggestions and decision context information of an edge calculation result; a cloud verification result is received, decision context information is combined, a time sequence memory module and an error propagation graph are constructed, and a verification feedback optimization instruction and predictive feedback buffer data are generated; and updating the meta-confidence model based on the verification feedback optimization instruction and the predictive feedback buffer data, and executing a final decision in combination with the initial confidence and the decision suggestion to generate a service execution report. The problem of cold start and the problem of low efficiency of asynchronous verification are solved through a feature progressive structure and a predictive feedback buffer mechanism.

The invention discloses an object resource recommendation method and device, a storage medium and electronic equipment. The method comprises the steps that based on object resources issued by a target platform and candidate user accounts stored in a cold start pool corresponding to the target platform, a target local relation graph matched with the target platform is constructed, and the target local relation graph comprises an evaluation relation used for indicating the candidate user accounts and the object resources; a recommendation coefficient between each candidate user account and each object resource is determined by using the target local relation graph and a global relation graph, and the global relation graph is a relation graph jointly constructed based on the target local relation graph and a reference local relation graph matched with a reference platform except the target platform; and according to the recommendation coefficient, determining a target object resource to be recommended for each candidate user account. According to the resource recommendation method and device, the technical problem that the resource recommendation accuracy is low in a resource recommendation method provided by the related technology is solved.

A dynamic preference perception online course recommendation method based on a Hawkes process and a TimeXer mechanism comprises the steps that user data and course data are acquired, and the data comprise user attributes, historical interaction sequences and course attributes; generating a context semantic embedding vector by adopting a pre-trained BERT language model; projecting an embedded vector to a unit hyperspherical space by using a spherical Gaussian model, and measuring similarity by using a spherical distance so as to relieve the problems of data sparsity and cold start; a user dynamic preference model integrating a Hawkes process and a TimeXer attention mechanism is constructed, the Hawkes process calculates event occurrence intensity to capture self-excitation characteristics, the TimeXer attention mechanism is adjusted through the intensity information, and weighted user representation reflecting preference dynamic changes is generated; and according to the similarity between the weighted user representation and the projected course embedding vector, calculating a recommendation score, and generating a personalized online course recommendation result. According to the method, the accuracy of online course recommendation is improved by accurately capturing the dynamic preference of the user and effectively processing the sparse cold start problem.

The invention discloses an electric vehicle charging load generation method and system. The method comprises the steps that a one-dimensional time sequence signal is converted into a heterogeneous feature token containing local, global and guiding information through mixed token processing; constructing a decomposition type diffusion model based on the token, and directly generating three core components of trend, seasonality and residual error of the time sequence; optimizing a model training process by adopting a composite loss function and a warm start mechanism so as to solve the cold start problem of model training; and performing high-quality fusion reconstruction on each component through a weighted overlap-add method, and outputting a final charging load curve. According to the method, highly real and diversified charging load data can be generated, and powerful data support is provided for power grid planning and dispatching in a structure interpretable mode.

The invention discloses an efficient graph comparison learning method for social recommendation. As an emerging self-supervised learning normal form, graph contrast learning is excellent in response to data sparseness and cold start due to the fact that the graph contrast learning can effectively capture similarity and heterogeneity characteristics in a graph structure, although the learning normal form achieves a good effect in a recommendation system, the graph contrast learning can be used for solving the problems of data sparseness and cold start. However, the method still faces three defects: (1) average neighbor aggregation and a non-adaptive representation reading mechanism are adopted in a message propagation process, and high-quality node representation is difficult to learn; (2) a visual angle is enhanced by depending on a random disturbance generation graph during intervention of comparative learning, which may destroy the inherent structure of graph data and further weaken the accuracy of the model; and (3) equally treating all observation samples during parameter optimization, and neglecting the difference influence of positive samples in different training stages. Specifically, aiming at the problems, the invention provides an efficient graph contrast learning method (EGCL for short). The method comprises the following steps: firstly, designing a graph adaptive propagation module, improving an information propagation rule of a graph neural network by referring to a thermonuclear thought and an attention mechanism, and realizing differentiated aggregation of neighbor nodes by adopting a learnable weight distribution strategy; secondly, designing a double contrast learning normal form which does not need graph enhancement, and realizing mutual promotion of node characterization through intra-domain contrast learning (inter-CL) and inter-domain contrast learning (inter-CL); and finally, introducing a sample weight adaptive efficient optimization algorithm, converting the training process into a double-layer optimization problem, and adaptively adjusting the contribution degree of each sample to model optimization in different stages.

The invention belongs to the technical field of knowledge maps, and discloses a next interest point recommendation method based on a cross-regional city space knowledge map. According to the method, on the basis of the geographic space knowledge graph and the user preference knowledge graph, a cross-regional relationship modeling mechanism is introduced, the spatial proximity and the reachability between the regions are comprehensively considered, and more comprehensive description of the cross-regional behavior of the user is realized. According to the method, a recommendation framework combining a geographic module and a sequence module is designed, spatial dependence between interest points can be captured, a high-order sequence mode in user sign-in behaviors can be mined, and recommendation accuracy and diversity are improved. Geographic representation and sequence representation are fused through a consistency learning framework, the robustness and generalization ability of the model can be enhanced, and the model can still keep stable performance in sparse data and cold start scenes. According to the method, the defects of insufficient region boundary perception, recommendation result centralization, poor cross-region prediction adaptability and the like of an existing method are overcome.

The invention discloses a class preloading method and device for Java micro-service cold start acceleration, and belongs to the technical field of cloud computing and Java virtual machine optimization. The method comprises the steps of class dependency analysis based on a historical call chain, hierarchical class preloading execution and shared memory snapshot management. The device comprises a class analysis engine, a preloading decision maker and a memory snapshot management and JVM optimization adaptation module. Through intelligent analysis of class popularity, hierarchical preloading and combination of a cross-JVM instance memory sharing technology, the Java micro-service cold start time is reduced from average 4.2 s to 0.8 s, memory occupation is reduced by 45%, compatibility with a standard JVM and a mainstream framework is kept, and the method is suitable for financial transaction, real-time recommendation and other micro-service applications in a Serverless scene.

The invention discloses a graph database query acceleration method and system. The method comprises the following steps: constructing panoramic query observation data through non-intrusive log interception and structured processing; the query statement is analyzed into an abstract syntax tree, semantic normalization processing is carried out, and a unique hash value is generated; predicting a hotspot query template and high-frequency parameters thereof based on a popularity value score model, and asynchronously preloading a result to a cache; dynamically calculating adaptive survival time for each cache item according to the data change frequency and the access popularity; and constructing a multi-level cache architecture comprising a local cache, a distributed cache and a database built-in cache, and performing intelligent routing and collaborative backfilling based on a hash value. According to the method, intelligent and transparent acceleration of graph database query is realized, the query performance is remarkably improved, the database load is reduced, and the problems of redundancy, high cold start delay and difficulty in balancing cache consistency caused by grammar difference in traditional cache are solved.