800 results about "Large scale data" patented technology

The invention discloses an RAG enhanced Text-to-SQL (Structured Query Language) query method and system for a large-scale database environment. The method comprises the following steps: constructing a vector database; based on the original query of the user, generating query enhancement description aiming at Schema recall and query enhancement description aiming at SQL (Structured Query Language) by utilizing LLM (Logistics Language Model); carrying out Schema recall and historical question and answer pair recall operations in a double-way parallel manner by utilizing an RAG technology and relying on the constructed vector database; based on query enhancement description, recalled Schema and historical question and answer pairs, generating an SQL query statement by using LLM in combination with RAG, and realizing Text-to-SQL conversion; the generated SQL query statement is subjected to post-processing optimization through a database interface, the post-processing optimization comprises grammar verification, performance optimization and error correction, the SQL query statement is combined for execution and feedback, efficient, accurate and reliable natural language query is achieved, and continuous optimization can be conducted through user feedback.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention discloses a distillation SAE and dynamic integration converter steelmaking carbon temperature soft measurement method, which comprises the following steps of: training a teacher model on a large-scale data set of a conventional production working condition, and compressing the model by adopting a knowledge distillation technology; then, a small amount of data from unconventional production working conditions is used for fine tuning to obtain a plurality of expert-type student stacking auto-encoder models SAE; in the prediction stage, a to-be-tested sample is mapped to respective feature space through a plurality of SAEs, and the posterior probability of the sample is calculated in each feature space based on a Gaussian distribution model; and finally, respectively inputting a to-be-measured sample into a plurality of corresponding regression devices to obtain a predicted value, and carrying out weighted fusion on the output of each regression device according to a posterior probability to realize dynamic integration selection of the soft measurement model. The method can effectively adapt to complex data distribution of multi-working-condition changes in converter steelmaking, the carbon temperature prediction precision under the unconventional working condition is improved, and the method has high robustness and engineering practicability.

The invention relates to the technical field of data processing, in particular to a computer feature extraction processing system and method fusing multi-source heterogeneous data, and the system comprises a structure correlation mapping module, a density mutation recognition module, a disturbance path analysis module, a path logic matching module and a risk logic output module. According to the method, by optimizing a data processing mode, important features in data can be accurately recognized, manual intervention and experience dependence are reduced, large-scale data analysis and processing efficiency is improved, the accuracy of a data model is enhanced, and by deeply analyzing the fluctuation amplitude and the change trend of the data, potential associated areas and abrupt change grids can be accurately positioned; key disturbance information is effectively identified, path logic matching can reveal a risk behavior mode in data evolution, potential early warning events are marked in time, the efficient processing capacity of the model for a complex data mode is ensured, and particularly, the problems of low efficiency and insufficient precision in a traditional scheme are solved in the face of large-scale data.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.

The present application relates to the technical field of large-scale data processing, and particularly relates to a parallel computing method and system suitable for large-scale data processing. The system comprises a task management unit, a distributed load balancing module, an elastic expansion architecture, an intelligent communication optimization module, and a resource monitoring unit, wherein the task management unit divides large-scale data into a plurality of sub-tasks by means of a task decomposer, and distributes the sub-tasks to computing nodes by means of a task scheduler and a priority distributor; the distributed load balancing module achieves global load balancing by means of a load sensing unit, a dynamic adjustment unit and a balance optimization unit; the elastic expansion architecture dynamically adjusts system resources by means of a node manager, a resource pool controller and an expansion decision-making device; the intelligent communication optimization module optimizes inter-node communication by means of a communication path planning unit, a bandwidth distribution unit and a delay compensation unit; and the resource monitoring unit monitors the system performance in real time by means of a performance collector, a state analyzer and an anomaly detector.

The invention discloses a real model consistency review system and method based on a large language model, according to the scheme, through integration of multi-source heterogeneous data, standardized preprocessing of real scene cloud or images is completed, and through combination of deep learning recognition and multi-scale feature analysis, accurate correspondence and intelligent comparison of a real model and a BIM component are achieved; performing semantic reasoning and logic verification by using a large language model, automatically detecting differences such as dimensional deviation, positioning deviation, missing and redundant components and the like, and generating structured difference data; furthermore, difference information is visually presented through graphical annotation and natural language description, and batch screening and multi-scale interaction are supported. Based on a difference analysis result, a standardized review report is automatically compiled, and statistical analysis, change trend tracking and multi-format export functions are provided. According to the scheme, the consistency review efficiency and reliability in a large-scale data environment can be guaranteed, and the quality management level of building construction and operation and maintenance stages can be effectively improved.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

The invention relates to the technical field of personalized recommendation, in particular to a nonlinear enhanced decoupling type contrast hypergraph learning method for next POI recommendation. The method comprises the following steps: S1, constructing a multi-view decoupling hypergraph; s2, optimizing the multi-view decoupling hypergraph constructed in the step A to obtain a nonlinear hypergraph convolutional network; s3, based on self-adaptive fusion of user representation, learning and fusing the user preferences under the multiple views obtained in the step S2; and S4, realizing comparative learning and self-supervised learning based on an Encoder module and a GRACE module. According to the nonlinear enhanced decoupling type contrast hypergraph learning method for the next POI recommendation, by introducing ReLU and residual connection, the linear limitation of a traditional hypergraph layer is broken through, the model is endowed with higher expression ability, a KNN adjacency matrix sparse strategy is adopted, the calculation efficiency is remarkably improved on the premise that the precision is guaranteed, and the method is suitable for popularization and application. And meanwhile, batch processing InfoNCE and GRACE modules are creatively applied, so that robust cross-view collaborative learning on a large-scale data set is realized.

The invention discloses an intelligent substation configuration file checking method and system based on artificial intelligence, and relates to the field of artificial intelligence, and the method comprises the steps: responding to an input signal of a to-be-checked configuration file, extracting n character strings of the to-be-checked configuration file, carrying out the semantic analysis of each character string, and obtaining a semantic analysis result, n is an integer greater than 1; key features of the analysis result are extracted from the semantic analysis result, feature learning is conducted on each key feature through a preset feature learning algorithm, and high-level feature representation of each key feature is obtained; combining all high-level feature representations of the analysis result to obtain a feature set; and inputting the feature set into an artificial intelligence model to obtain a proofreading result of the to-be-checked configuration file and a preset reference configuration file. According to the method and the device, the problem of huge resource consumption caused by a traditional checking mode can be solved in the face of large-scale data or complex comparison requirements.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention discloses a semi-supervised learning method based on a self-adaptive threshold value, belongs to the field of radio communication, and aims to extract general semantic features of signals through comparative learning so as to improve generalization of downstream modulation identification tasks. A self-adaptive threshold mechanism is introduced to generate a high-quality pseudo tag, and confirmation deviation is reduced through additional category information, so that the robustness of the model is improved; besides, a hierarchical encoder capable of learning Fourier filtering is designed to capture multi-scale semantic features of large-scale long-sequence signals, improve the calculation efficiency of a model and research and verify an algorithm based on a plurality of large-scale data sets, so that the accuracy of automatic modulation recognition is remarkably improved, and particularly, under the condition of less label data, the accuracy of automatic modulation recognition is greatly improved. Compared with a mainstream method, the method has higher generalization ability and robustness, and can effectively cope with signal diversity and noise interference in a real communication environment.

The invention relates to the technical field of high-concurrency data processing, and discloses a storage and calculation integrated data scheduling system and method for a high-concurrency scene, and the system comprises a storage and calculation fusion architecture module, a distributed cache module, a dynamic fragmentation module, a parallel calculation module, a preloading module and a resource scheduling center. The method corresponds to the system. According to the method, by deeply fusing data storage and calculation and utilizing a distributed caching technology and a data preloading mechanism, I / O bottleneck is effectively reduced, dynamic data fragmentation and parallel calculation are supported, and the method is particularly suitable for large-scale data analysis scenes.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes such as financial science and technology and medical health, and discloses a routing decision making method, device and equipment for data processing and a medium, and the method comprises the steps: constructing a label strategy SQL training set, and carrying out the supervision training of the SQL training set, and obtaining a trained language model; receiving a to-be-processed SQL task, generating a structured feature vector, generating a routing strategy through a reinforcement learning module, and determining a processing engine of the task; after the execution path identifier is analyzed, the task is distributed to a corresponding engine through message middleware; and generating a feedback data set based on the task execution index and the execution path identifier, and updating the strategy parameter based on the data set. According to the method, the label strategy SQL training set is constructed, decision optimization is carried out in combination with the reinforcement learning module, the optimal processing engine of the calculation task can be judged, calculation overhead and resource waste are reduced, and particularly the calculation performance is improved in a large-scale data processing scene.

The invention provides a blood relationship analysis method based on a data dependency relationship, electronic equipment and a medium, and relates to the technical field of data blood relationship analys.The method comprises the steps that an SQL statement vector corresponding to each SQL statement is generated according to all table names and field names corresponding to each SQL statement; obtaining an SQL statement vector list A; all SQL statement vectors in A are clustered to obtain a cluster list B; analyzing each SQL statement in the Bj to extract a corresponding table name, a field name and an operation type, and establishing a field-level blood relationship graph Xj between the table names and the field names of the SQL statements in the Bj; establishing a cross-cluster blood relationship mapping table QR corresponding to the B; fusing the clusters in the B to obtain a global blood relationship analysis graph QW; displaying the QW in a layered interactive view; according to the method, the bottlenecks of the traditional blood relationship analysis method in accuracy, efficiency and availability are solved, and efficient and reliable technical support is provided for large-scale data management.

The embodiment of the invention provides a data vectorization acceleration method and system, and the method comprises the steps: writing an optimizer rule into an optimizer, and carrying out the vectorization acceleration according to the optimizer rule, respectively fusing respective original operators in the first operator rule and the second operator rule with respective matched replacement operators of the first operator rule and the second operator rule to obtain a vectorized fusion operator; and converting the vectorization fusion operator into a vectorization execution operator according to the conversion logic, so that the vectorization execution operator calls a vectorization execution engine through a local interface JNI bridge to accelerate data vectorization. Therefore, through the embodiment of the invention, the problem that the calculation efficiency is relatively low in a large-scale data processing scene due to the fact that an existing Flink framework cannot fully utilize an instruction set and vectorization hardware resources in a batch processing mode can be solved.

The invention discloses a geographic space data search method, a geographic space data transaction method and a related device. The search method comprises the following steps: acquiring a search range; according to the search range and a pre-constructed hybrid tree index, searching a data set A intersecting with the search range from all data sets corresponding to the hybrid tree index; wherein the hybrid tree index is composed of a ball tree index and a KD tree index; and performing multi-dimensional similarity calculation on the search range and the data set A, and determining a search result according to a multi-dimensional similarity calculation result. According to the search method, the mixed tree index composed of the ball tree and the KD tree is adopted for first-stage search, the node access frequency during search is reduced through the complementary advantages of the two tree structures, and the search efficiency is remarkably improved when high-dimensional and large-scale data are processed; and multi-dimensional similarity search is further performed based on the search result of the first stage, so that complex search requirements are met.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence data annotation and cue word automatic construction engine system, which comprises a data annotation module for firstly carrying out preliminary annotation on data based on a pre-training model, then automatically annotating a data sample through an active learning algorithm, and meanwhile, monitoring the quality of annotated data in real time; and the cue word automatic construction module generates cue words based on task analysis, optimizes the cue words by using a reinforcement learning technology, and performs classified storage and management on the generated cue words. By adopting the semi-automatic labeling and active learning labeling functions, not only can the workload be greatly reduced, but also the unnecessary labeling work can be reduced, so that the labeling efficiency is remarkably improved, the labeling time is shortened, the large-scale data labeling requirement is met, and the problem of efficiency bottleneck caused by slow manual labeling is solved.

The invention relates to a large language model long thinking chain verification method and device based on an inference process abstract. The method comprises the following steps: obtaining a to-be-verified inference thinking chain; abstracting the obtained reasoning thinking chain to obtain a linear path containing a key reasoning link; and gradually executing verification on the linear path by adopting a checker, judging whether each step is correct or not, positioning the first wrong step, regarding the subsequent steps as invalid, and not independently verifying. Compared with the prior art, the method has the advantages that an accurate and efficient verification scheme is provided, the preciseness of process verification is guaranteed, the verification and labeling cost is remarkably reduced, and large-scale data set construction and model training can be supported.

The invention discloses a multi-dimensional data asynchronous calculation method and system based on a dynamic dependency graph, and relates to the field of data processing. The method comprises the following steps: constructing a metadata, logic and instance three-layer separation storage model; analyzing the reference relationship to construct a directed acyclic graph, and generating a calculation priority; monitoring data change, and executing asynchronous serialization calculation through a message queue based on priority; the associated document is automatically updated based on anchor mapping. According to the method, the calculation deadlock and the performance bottleneck of large-scale data in the Web environment are solved, logic decoupling and dynamic expansion are realized, the final consistency of the data is guaranteed, and the high-concurrency throughput and the stability are remarkably improved.

The invention discloses a teenager psychological sub-health intelligent early warning system based on multi-source heterogeneous data fusion, and the system comprises a data collection layer which collects the behavior, physiological and social three-dimensional data of teenagers through the multi-source channels of campus cards, wearable devices, social media and questionnaires, and builds an original data pool through cleaning, denoising and standardization; the data fusion layer is used for integrating multi-source data by adopting weighted average and Kalman filtering, mining psychological sub-health key indexes in combination with a feature selection and extraction technology, and forming a three-dimensional psychological portrait; the deep learning layer is used for constructing a multi-modal fusion early warning model based on a Transform architecture, and carrying out real-time prediction and dynamic tracking of psychological sub-health risks through large-scale data training and cross validation optimization; and the intelligent early warning layer is used for visually displaying an early warning result, integrating three-party linkage of a management end, a teacher end and a parent end, providing 24-hour online intervention by a built-in AI psychological counseling module, and automatically transferring high-risk cases to professional psychological consultants.

The invention discloses a spacecraft complex service damage automatic identification method based on multi-modal data fusion, and the method comprises the steps: S1, obtaining a real infrared thermal image data set S1 or a real vibration data set S2 for different spacecraft complex service damage test pieces; s2, performing expansion processing on the S1 and the S2 to obtain an infrared data set Sir and a vibration data set Svib, and combining the Svib and the Sir to obtain a multi-modal data set S; s3, constructing a complex service damage identification model, wherein the complex service damage identification model takes the labeled multi-modal data set S as a training sample; and S5, fusing prediction results through a fusion decision module in the complex service damage identification model so as to complete comprehensive judgment of the complex service damage state. According to the spacecraft complex service damage automatic identification method based on multi-modal data fusion, the number of data sets is remarkably increased under the condition that test piece samples are limited, and the requirement of deep learning model training for large-scale data is met.

The invention relates to a duplication name index creation and reconstruction method and device based on openGauss, and aims to solve the compatibility problem caused by index naming conflicts when heterogeneous databases such as MySQL migrate to openGauss. According to the method, a duplication index function switch is controlled through a GUC parameter ENABLEDUPLICATEINDEXNAME, when the duplication index function switch is started, a user table index name is rewritten (an affiliated table OID and an ASCII 0x1F invisible separator are added), and a unique system storage name is generated and written into a pgclass system table and other system tables; a REINDEX mechanism is provided to realize smooth transition of new and old index formats; and transparent analysis of user operations (such as INDEX HINT) is supported. According to the method, on the premise that the global uniqueness constraint of the openGauss index is not damaged, the MySQL cross-table duplicate-name index is compatible, the migration efficiency and the system compatibility are improved, the labor cost is reduced through automatic rewriting, parameter control and other mechanisms, and the accuracy and stability of large-scale database migration are guaranteed.

Disclosed are a vehicle-road cooperative control architecture system based on data-mechanism coupled modeling, and a construction method. To address the challenges of traditional mechanism-based modeling for autonomous driving, a multi-agent system modeling method driven by the fusion of data and mechanisms, and a federated reinforcement learning-based vehicle-road cooperative swarm optimization method are provided, establishing a vehicle decision-making model parameter updating technique based on multi-dimensional experience sharing, and thereby resolving interpretability and generalization challenges of purely data-driven models. A rule-based driving safety field is built to achieve rule-guided data-driven training; a secondary planning-control framework based on an intelligent chassis is constructed, providing state quantity input based on chassis feedback, and resolving problems related to purely data-driven models such as questionable reliability, reliance on large-scale data, and a lack of transparency and interpretability in the decision-making process; quantitative comfort metrics are constructed to filter for an optimal strategy for the current environment, and a balance between sample efficiency and model robustness is achieved by synthesizing shared models benefiting from different environments.

The invention discloses a database cutting and storing method and system based on a single data source, and the method comprises the following steps: automatically generating an optimal partitioning strategy through a machine learning algorithm according to the characteristics and query mode of the single data source; according to the load condition of the system, the partition distribution is automatically adjusted, and the data volume and load balance among the partitions are ensured; a distributed index mechanism and a cache mechanism are introduced, and the performance of cross-partition query is optimized; the number of partitions is dynamically adjusted according to service requirements, and data is automatically migrated to adapt to a new partition strategy to achieve elastic expansion; and the system dynamically adjusts and stores the received single data source through an intelligent storage strategy. According to the database cutting and storing method and system based on the single data source, the query performance is improved, the number of partitions is dynamically adjusted according to service requirements, the system expansibility is enhanced, manual intervention is reduced, the maintenance cost is reduced, and the method and system are suitable for large-scale data storage and query scenes.

The invention discloses a multi-level privacy protection data sharing method and system based on a block chain. The method comprises the following steps: S1, collecting and uploading original data; generating an encryption key by adopting a dynamic DNA coding sequence and combining an AES key expansion method, encrypting the collected data by utilizing the generated encryption key to obtain an encrypted file, and storing the encrypted file in a local storage library; s2, uploading the encrypted file in the step S1 to a distributed hash table of an IPFS distributed storage system through an SHA-256 hash function, and verifying and confirming a block by adopting a dynamic hybrid consensus mechanism; s3, accessing the block chain by the user through the smart contract, and executing space-time double-attenuation permission control; and S4, after the permission verification is passed, a decryption key is provided for the user, and the user performs data decryption by using an AES decryption algorithm and DNA reverse coding. According to the method, more accurate authority management can be provided, the security and compliance of data access are improved, and the method is suitable for management of large-scale data.

The invention provides a rail transit remote monitoring method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the fault causal reasoning and fault propagation simulation of an equipment fault event based on artificial intelligence when an equipment abnormal event occurs in a rail transit scene through remote monitoring; and performing multi-modal early warning based on a fault causal reasoning result and a fault propagation simulation result. According to the rail transit remote monitoring method and system based on artificial intelligence, fault causal reasoning and fault propagation simulation are carried out on the equipment fault event based on artificial intelligence, multi-modal early warning is carried out based on the fault causal reasoning result and the fault propagation simulation result, and the reliability of the system is improved. The system has sufficient monitoring efficiency to a great extent when facing sudden faults, complex fault modes and large-scale data analysis in a rail transit scene, and monitoring accuracy and response timeliness are improved.

The invention relates to an underwater visible light signal recovery method based on transfer learning, and aims to solve the problems of signal noise, distortion and communication performance reduction caused by disturbance of water turbidity, flow velocity, ambient light intensity and the like on an underwater visible light communication (UVLC) system. In combination with transfer learning and a generative adversarial network (GAN), data are acquired through a self-developed hardware platform, background data are generated through MATLAB simulation, simulation and real image features are fused by using a conditional generative adversarial network (cGAN) to generate simulation data, signal recovery capability is trained by using a U-NetGAN model, and finally the system is deployed at a receiving end. Compared with the prior art, the scheme overcomes the problems that traditional modulation is poor in adaptability under disturbance of water turbidity, flow velocity and the like and is difficult to deal with a complex underwater environment; the defects that a deep learning method depends on large-scale data and cross-scene generalization is weak are overcome. Through the combination of transfer learning and GAN, the UVLC system communication performance is significantly optimized, the delay is low, the complexity is low, and actual deployment requirements are better met.