749 results about "Computational Technique" patented technology

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention discloses a digital twin three-dimensional scene modeling method based on a webGPU, and relates to the technical field of three-dimensional scene modeling and graphic computing, multi-source sensing data are asynchronously sampled from sparse point cloud, a video texture sequence, a scene semantic tag graph and a structure boundary tuple, and a six-dimensional structure unit group is generated through a normalization operator; constructing a structural unit atlas with nodes representing component entities and edges representing constraint relations, and introducing a tension balance mechanism and multi-scale constraints to generate a modeling path prior model; constructing a graph calculation and graph rendering dual-channel assembly line in the WebGPU, and executing parallel texture mapping and boundary fitting operation; a dynamic sensing module is used for capturing scene disturbance and driving atlas response, and incremental reconstruction of the model is achieved; and finally, mapping the model to a Web terminal, and supporting microscopic semantic query and multi-layer data linkage. According to the method, the response speed, semantic consistency and structural adaptability of three-dimensional modeling in a complex environment are improved.

This document generally describes methods and systems for generating digital reconstructions of tissue from humans or other species. The method can, for example, include receiving, at a computing system, image data of a tissue sample, where one or more sections of the tissue sample are stained with hematoxylin and eosin (H&E), registering the image data to generate registered image data, identifying tissue subtypes based on application of a machine learning model to the registered image data, annotating the identified tissue subtypes to generate annotated image data, and determining a digital volume of the tissue sample in three dimensional (3D) space based on the annotated image data. The disclosed technology can provide for single-cell analysis and other analysis of tissue samples, such as early detection of cancer in human tissue samples.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention discloses a display color adjustment method and system, and the method comprises the steps: collecting the spectral data of ambient light in real time through a multispectral sensor according to the environment where a display is located; according to the visual preference of the user, utilizing a visual response model to extract visual features of the user; according to the environment light field dynamic data set and the user visual preference data set, color parameters of a display are adjusted through a dynamic optimization algorithm, and optimized color parameters are generated; according to the optimized color parameters, color presentation of the display is adjusted in real time through a color management engine, and the color management engine ensures low delay and high precision of color adjustment through a streaming computing technology and a distributed architecture to generate a final display effect. According to the embodiment of the invention, the method can adapt to the change of ambient light in real time, considers the visual preference of a user, and achieves the efficient and precise color adjustment.

The invention discloses a three-dimensional medical image visual language model pre-training method for reconstructing a hybrid strategy, which belongs to the technical field of medical image calculation and comprises the steps of constructing a medical image text pair data set, a language text mask reconstruction strategy, a visual image mask reconstruction strategy, a semantic perception fusion strategy and multi-task joint learning. According to the method, the large language model is finely adjusted, the diagnosis and attribute information in the medical report is extracted by using the finely-adjusted large language model, the efficient prompt is generated, and the large language model has relatively high generalization ability, so that the cost of manual annotation is greatly saved; according to the semantic perception fusion strategy, the text feature obtained by the text encoder and the image feature obtained by the image encoder are combined to obtain the new text feature, so that the text perceives the diagnosis and attribute information of the image in advance, the alignment of the image and the text in the embedding space is further optimized, and the pre-training efficiency is improved.

The invention provides a neuromorphic visual target tracking method and system based on image processing, and relates to the technical field of neuromorphic calculation, and the method comprises the steps: fusing the input of an event camera and a conventional image sensor, constructing a combined input tensor, and introducing a multi-scale convolution and synaptic event driving mechanism. Quick response and stable feature extraction of a high-speed moving target are realized, and coding block mistaken deletion and tracking loss caused by quick movement of the target are effectively avoided, so that system delay is reduced. Meanwhile, in combination with significance entropy difference evaluation and a dynamic brightness enhancement mechanism, the target judgment accuracy under low illumination and complex backgrounds is improved; redundant noise blocks are screened out through a significance weight function and confidence calculation, the redundancy calculation burden is relieved, and the lightweight characteristic of the system is guaranteed. A memory trajectory tensor and dynamic template adjustment mechanism based on a recurrent neural network is further introduced, time sequence consistency verification and self-adaptive updating are achieved, and the stability and robustness of the tracking process are enhanced.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention discloses a power grid energy storage capacity demand determination method and system based on multiple time scales, relates to the technical field of power grid energy storage capacity demand calculation, and aims to solve the problem of inaccurate energy storage capacity demand calculation. By generating multiple scenes, quantitatively screening key scenes and incorporating various uncertain factors, energy storage capacity calculation focuses on high-influence scenes, the coping capacity of the scheme to actual risks is enhanced, decision scientificity is improved, ultra-short-term to long-term multi-time scales are divided, core contradictions of all the scales are captured in a targeted mode, limitation of a single scale is avoided, and energy storage capacity calculation efficiency is improved. According to the method, capacity requirements and equipment distribution are integrated, operation rules are defined, visual documents are generated, multi-scale energy storage cooperative operation is achieved, the stability of a power grid is guaranteed, meanwhile, cost is reduced, scheme landing performance and operation efficiency are improved, multi-time-scale power grid state prediction is carried out based on a dynamic database, and historical rules and real-time data are combined, so that the power grid state prediction efficiency is improved. And the prediction coordination is ensured through multi-dimensional verification.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention discloses a lightweight ViT based on image feature cutting and cloud edge knowledge distillation, belongs to the technical field of computer vision and edge computing, is used for efficiently deploying a ViT model on an edge device with limited computing power, and comprises the steps of designing a lightweight ViT Block and a sub-sampling Block. The method comprises the following steps: designing an image feature cutting module, collecting images by edge equipment, screening highly uncertain samples in the images as a knowledge distillation data set, and transmitting the knowledge distillation data set and local model parameters to a cloud end. A ViT model is deployed for the edge equipment with limited resources and weaker performance to carry out an image processing task, and the ViT is subjected to lightweight processing; therefore, the Flops of the model is reduced while the accuracy of the model is ensured.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention discloses an information operation and maintenance monitoring system, which belongs to the technical field of information operation and maintenance monitoring and comprises a heterogeneous data acquisition layer, a streaming processing engine, an intelligent analysis center, a closed-loop control module, a visual platform and a safety protection system. Physical equipment, virtualized resources and a cloud native environment can be fully covered, the breadth and depth of data acquisition are ensured, the adaptive sampling and edge computing technology improves the efficiency and real-time performance of data acquisition, the data transmission delay and the central processing pressure are reduced, a streaming processing engine and an intelligent analysis center are combined with various algorithms and models, and the real-time performance of data acquisition is improved. Millisecond-level data processing and anomaly detection are realized, the accuracy and timeliness of fault identification are improved, the root cause positioning and fault prediction functions help operation and maintenance personnel to quickly position the root cause of a problem and formulate preventive measures, and the fault occurrence probability and the influence range are reduced.

The invention relates to the technical field of mobile positioning calculation, in particular to high-precision positioning mobile terminal equipment for augmented reality inspection of power pipelines, which comprises a PDA (personal digital assistant) handheld terminal, a camera is fixedly connected to one side of the PDA handheld terminal, a display is arranged at one end, far away from the camera, of the PDA handheld terminal, and an electronic compass, a gyroscope and a processing system are arranged in the PDA handheld terminal; the processing system comprises an image acquisition module, a calibration module, a positioning module, an inspection data acquisition module, a GIS (Geographic Information System) data request module, a data standardization processing module, an SAR (Synthetic Aperture Radar) space construction module, a reality rendering enhancement module and a display module. And the GIS two-dimensional spatial data of the electric power pipeline are converted into visual augmented reality SAR data, so that the maintenance difficulty of the urban cable pipeline data is reduced, and the efficiency of cross-department business cooperation in an electric power enterprise is improved.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention provides a ship video concentration and intelligent retrieval method and system based on edge nodes, and belongs to the technical field of edge computing, and the method comprises the steps: S1, collecting ship video data; s2, an edge node generates a concentrated video and metadata through an improved Gaussian mixture model, an improved DeepSORT algorithm and a dynamic concentration proportion adjustment algorithm; s3, synchronously concentrating the index information of the videos and the metadata by each edge node through a publishing-subscribing mode; s4, training a lightweight intelligent retrieval model through a knowledge distillation algorithm; s5, calling the lightweight intelligent retrieval model to match the corresponding concentrated video clip, and returning a retrieval result; and S6, collecting feedback information of the user on the retrieval result, and performing incremental optimization on the lightweight intelligent retrieval model through an elastic weight consolidation algorithm. According to the invention, efficient monitoring management is realized through edge localization processing, cross-node cooperation and closed-loop optimization architecture, and the actual requirements of ship safety monitoring and operation management are met.

The invention discloses a light guide plate dot density detection method, system and device and a storage medium, and relates to the technical field of light guide plate dot density detection, and the method comprises the following steps: converting a historical light guide plate image into a historical light guide plate binary image based on a graying processing method and a binary processing method; obtaining a standard dot area and a dot area range based on the historical light guide plate binary image; obtaining a detection normal fitting area and a detection incomplete fitting area based on the standard dot area, the dot area range and the detection light guide plate binary image; calculating and obtaining the dot density of the detection light guide plate based on the detection normal fitting area and the detection incomplete fitting area; the method is used for solving the problem that errors are generated in dot density calculation due to the fact that edge incomplete dots are not analyzed and calculated in an existing light guide plate dot density calculation technology.

The invention discloses an emotion analysis method, system and device based on multi-modal information and a large language model and a medium, belongs to the technical field of natural language processing and multi-modal calculation, and aims at solving the technical problem of how to overcome the defects of data distribution difference, modal credibility deviation and low generated data quality in the prior art. According to the technical scheme, the method comprises the following steps: collecting multi-modal data; multi-modal data preprocessing and feature extraction: preprocessing the text data, the image data and the audio data respectively and extracting corresponding features; feature alignment: mapping features of different modes of texts, images and audios to a unified potential space by adopting a cross-modal alignment algorithm, and eliminating semantic gaps among the modes; weight distribution: adopting a dynamic weight distribution mechanism to adaptively and dynamically distribute weights of different modes; performing cross-domain attribute-level sentiment analysis; evaluating the credibility; and calibrating the confidence coefficient.

The invention discloses a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention discloses an integrated information automatic supervision system and method for an edge device, and relates to the technical field of edge computing, an edge node management module of the system accesses the edge device, generates basic attributes and carries out containerized application deployment; the real-time monitoring and diagnosis module is used for collecting equipment operation data, identifying abnormity, positioning a fault type and triggering an alarm; the automatic strategy generation module is responsible for generating a customized repair strategy; the MESH communication and repair execution module constructs an edge device ad hoc network, realizes networking and offline strategy transmission and executes a repair instruction; the solution knowledge base module stores equipment fault types, historical repair cases and strategy templates, and updates strategy matching logic; the application deployment management module packages application programs through container mirror images, and distributes the application programs to edge nodes in batches according to scene templates; and the centralized management and control module visually displays the equipment state, the fault alarm and the strategy execution record, and provides manual intervention and global strategy configuration.

The invention relates to the technical field of concentration calculation, in particular to a water suspended matter concentration calculation method based on multi-wavelength detection, which comprises the following steps of: adopting a multi-spectral measurement method, utilizing light beams with different wavelengths to penetrate through a water body, and identifying particle size distribution and morphological characteristics of suspended matter particles; separating and correcting the turbidity contribution of the particles with different particle sizes through a least square fitting algorithm; decomposing the measured optical signal into a basic scattering component, a particle size dependent component and a background signal component by adopting a multi-level signal decoupling algorithm; and in combination with data after particle characteristic correction and optical signal decoupling, constructing a suspended matter concentration calculation model based on particle dynamics, calculating the water suspended matter concentration by using the dynamic model, and outputting suspended matter concentration data. According to the method, dynamic modeling is carried out on the concentration of the suspended matter, a dynamic equilibrium equation is solved through numerical values, and the concentration change of the suspended matter in time and depth is accurately described.

The invention provides a parking lot license plate detection method and system based on exclusive learning, and belongs to the technical field of computer vision and edge computing. The method comprises the following steps: deploying a lightweight detection main model at each entrance and exit edge device to carry out real-time detection and screen low-confidence difficult case samples; after the sample is uploaded, the central processing end generates a high-quality pseudo label by using a high-performance teacher model, and trains an exclusive student model by using a mixed loss function fusing detection loss, distillation loss and difficult case concentration loss; and finally, calculating a weight difference value and quantizing to generate a lightweight incremental update package, and directionally pushing the lightweight incremental update package to edge equipment to complete non-perception hot update. The system correspondingly comprises an edge processing unit and a central processing unit. According to the method, the problem that a general model cannot adapt to a multi-gateway differentiated scene is solved, unification of personalized accurate optimization and efficient lightweight updating of the model is realized, and the accuracy and reliability of license plate detection in a complex scene are remarkably improved.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

The technology introduces 3D Moments, a new computational photography effect. As input a pair of near-duplicate photos is taken (FIG. 1A), i.e., photos of moving subjects from similar viewpoints, which may be very common in people's photo collections. As output, the system produces a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D (FIG. 1B). To achieve this effect, the scene is represented as a pair of feature-based layered depth images augmented with scene flow (306). This representation enables motion interpolation along with independent control of the camera viewpoint. The system produces photorealistic space-time videos with motion parallax and scene dynamics (322), while plausibly recovering regions occluded in the original views. Experimentation demonstrating superior performance over baselines on public benchmarks and in-the-wild photos.

The invention relates to the technical field of radar signal processing and quantum computing, in particular to a vehicle-mounted radar signal interference noise reduction method based on a quantum convolution region proposal network. According to the technical scheme, the vehicle-mounted radar signal interference noise reduction method based on the quantum convolution region proposal network can efficiently extract features in a vehicle-mounted radar signal by combining the parallel processing advantages of the quantum convolution region proposal network and quantum calculation, accurately identify and suppress various complex interference signals, and improve the noise reduction performance of the vehicle-mounted radar signal. Self-adaptive processing can be carried out according to the characteristics of different interference signals, it is ensured that the system can cope with variable interference sources, the parallel processing capacity of quantum computing is utilized, rapid and accurate feature extraction and interference suppression can be achieved in the signal processing process, and compared with a traditional high-cost laser radar or visual image recognition system, the system has the advantage that the system is more accurate in feature extraction and interference suppression. On the basis of the radar signal processing method and the advantage of low power consumption of quantum calculation, lower hardware cost and energy consumption can be realized.

The invention discloses a defect-free neutral atom array preparation method and system based on dynamic optical tweezers rearrangement, and belongs to the technical field of quantum computing. The invention discloses a defect-free neutral atom array preparation method and system based on dynamic optical tweezers rearrangement. A static optical tweezers array is generated through a spatial light modulator to capture initial atoms, an optimal rearrangement path is planned by adopting a Hungary algorithm, and dynamic optical tweezers are generated through an acousto-optic deflector to implement atom grabbing, moving and releasing operation. According to the method, a weighted Gerchberg-Saxton algorithm is innovatively combined with real-time image feedback, so that the precise rearrangement of the atom array is realized. Important experimental technical support is provided for quantum calculation and simulation, and the application progress of the neutral atom array in the fields of quantum error correction, fault-tolerant quantum calculation, quantum simulation and the like is effectively promoted.

The invention provides a resource optimization method and system for a router, a gateway and a camera in the technical field of edge computing. The method comprises the following steps: S1, training a network state prediction model and deploying the network state prediction model to edge computing equipment; s2, the edge computing device collects network index data based on a dynamic weight data collection mechanism and caches the network index data to a time sequence database; s3, inputting the network index data into the network state prediction model to obtain a network state prediction result; s4, generating a network resource optimization strategy based on the network state prediction result, verifying the network resource optimization strategy based on the digital twin technology, and executing the network resource optimization strategy; and S5, performing rolling updating on the network index data cached in the time sequence database based on the access times and the final access time. The method has the advantages that the flexibility and rationality of network resource configuration are greatly improved, and then the system performance and efficiency are greatly improved.

The invention discloses a large model fine tuning method and device based on LoRA, equipment and a medium, and belongs to the technical field of quantum computing, the method comprises the following steps: decomposing target data by using a tensor decomposition technology to obtain a result representing that the target data passes through a first low-rank matrix as first data, the target data being training data of a target large model, and the training data being training data of the target large model; the first low-rank matrix is represented by a core tensor used for decomposing the target data; based on the first data, obtaining second data through a quantum neural network used for extracting high-dimensional features; based on the second data, a tensor decomposition technology is utilized to obtain a result representing the target data passing through a first low-rank matrix and a second low-rank matrix, the result serves as third data, and the second low-rank matrix is represented by a core tensor utilized by current decomposition; and respectively updating the core tensor representing the first low-rank matrix and the core tensor representing the second low-rank matrix by using the third data. By applying the embodiment of the invention, more efficient model fine tuning can be realized.