128 results about "Smart Cache" patented technology

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

The invention discloses a memory access optimization method based on intelligent cache management, and relates to the technical field of computer storage. According to the method, spatial-temporal characteristics and semantic association data of memory access requests are collected in real time, a dynamic heat matrix is constructed, and a multi-dimensional access rule is fused to improve modeling precision. And inputting the dynamic popularity matrix into a hybrid prediction model, predicting a future access probability by using a time convolutional network, analyzing a competition relationship between data blocks through a graph attention network, generating a conflict pre-judgment weight and a corrected popularity ranking, and effectively reducing the cache jitter risk. On the basis of popularity ranking and conflict weight, a fragmented reinforcement learning algorithm is adopted to divide logic sub-regions, a differential reward function is designed to dynamically decide cache operation, and performance and energy efficiency requirements are balanced; and finally, through an online learning mechanism, combining real-time feedback to dynamically adjust a prediction model weight and strategy parameters, forming a closed-loop optimization link, and realizing adaptive stability under long-term load fluctuation.

The invention discloses an industrial scene-oriented data acquisition system and an industrial scene-oriented data acquisition method. According to the invention, through the multi-level dynamic optimization design, the data acquisition efficiency and the system reliability in a complex environment are significantly improved. The system dynamically allocates thread resources based on the real-time communication state of the equipment, realizes stable throughput under a high-concurrency scene in combination with core binding and polling scheduling strategies, and ensures that millisecond-level response is still maintained when 10000-level equipment is accessed. The memory preloading and protocol template technology eliminates jitter during operation, the hierarchical resource isolation mechanism provides deterministic guarantee for key control instructions, and interruption of the production process due to data delay or resource competition is avoided. The data flow delay is further reduced through multi-protocol efficient analysis and intelligent cache management, seamless compatibility of heterogeneous equipment in a hybrid networking scene is supported, and the strict requirements of continuous production industries such as steel and chemical engineering for real-time performance and stability are met.

The invention discloses a hybrid expert model reasoning method based on cooperation of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and belongs to the field of deep learning. According to the method, a CPU-GPU computing framework of a hybrid expert model is constructed, heterogeneous computing resource loads are effectively balanced, and the hardware utilization rate is remarkably increased; an intelligent cache management mechanism based on dynamic priority scores is provided, high-demand experts are reserved preferentially, and the transmission overhead caused by cache missing is reduced; through pipeline parallel design for separating calculation and transmission tasks, CPU calculation and PCIe transmission are overlapped in the GPU execution period, and delay is effectively hidden. In addition, in combination with a multi-layer expert activation prediction prospective prefetching mechanism, the expert cache hit rate is improved. The method is compatible with hybrid expert models of different scales and structures, and stable and efficient reasoning acceleration is realized on a resource-limited heterogeneous platform.

The invention relates to the technical field of audio signal processing, and discloses a high-sampling-rate audio analysis optimization method and system, a storage medium and equipment, and the method comprises the steps: upgrading an FFTW (Fast Fourier Transform) library, and building an optimized memory management mechanism; carrying out adaptive framing processing on the input audio data, and optimizing the data access efficiency by adopting a multi-stage cache system; the calculation precision of the FFT fast Fourier transform is dynamically adjusted, and the calculation of the FFT fast Fourier transform is optimized by pre-calculating and caching an FFTW plan; performing clock synchronization optimization and jitter elimination on the COAX digital audio signal; establishing a multi-dimensional result cache matrix and an intelligent multiplexing decision tree, and reducing repeated operation through intelligent cache prediction and similarity calculation; system performance indexes are monitored in real time, adaptive parameter adjustment is realized based on an optimization objective function, audio quality and system stability are monitored in combination with subjective and objective evaluation mechanisms, repeated operation is remarkably reduced, and calculation resource allocation is optimized on the premise of ensuring sound quality.

The invention relates to the technical field of underground pipe gallery asset management, and discloses a BIM-driven underground pipe gallery asset dynamic management and emergency decision-making platform, and the platform comprises a dynamic topology mapping module which achieves the dynamic grid density adjustment of a BIM model through monitoring the change rate of environment parameters, and generates a logic topology network synchronized with a physical pipe gallery structure in real time; the asset state reverse derivation unit is used for reversely deducing asset degradation based on environmental data by using a physical rule library dynamically loaded in a time-sharing domain; the logic rule self-triggering engine constructs a multi-level response rule chain containing space-time prediction and optimizes an emergency plan; and the edge-cloud collaborative architecture synchronously realizes real-time decision-making of low bandwidth dependence through intelligent cache and asynchronous increment. When dynamic topological mapping is driven by the change rate of environmental parameters, the real-time state of the underground pipe gallery can be represented more finely, and a high-reliability data basis is provided for subsequent accurate analysis and decision making.

The invention provides a meta-universe scene distributed two-stage cache optimization method combining MADDPG and GCN, and the method comprises the steps: dividing a meta-universe scene in the horizontal and vertical directions, obtaining a preset number of sub-scenes, and carrying out the integration of resources needed by the rendering of each sub-scene; the sub-scenes are compressed through an edge network, and the similarity of user tracks is calculated and obtained through a fast dynamic time warping algorithm; based on the similarity of different track fragments, sub-scene popularity ranking is obtained by weighting, summarizing and sorting the number of access times of a GPU server, sub-scenes predicted to be frequently accessed are requested and cached in advance according to the popularity ranking of the sub-scenes, and a two-stage cache replacement framework combining GCN and MADDPG is adopted to adjust a cache strategy of the server. According to the method, intelligent cache distribution and adjustment can be realized according to sub-scene popularity prediction, the cache hit rate is improved, and the delay response of a user request is reduced.

A method for managing data items includes: obtaining a query log; initiating performance of a first recommendation cycle (RC) by analyzing the query log to generate a recommendation list (RL) including at least a data item with a high-level cache reward; providing the RL to an administrator via a graphical user interface (GUI); providing an administrator-confirmed recommendation list (ACRL) to a cache engine, in which the ACRL is received from the administrator in response to the RL being provided to the administrator; receiving a refreshment policy of the data item included in the ACRL from the administrator; storing, based on the ACRL, the data item as a cached data item in a storage device.

The invention relates to the field of front-end page display, and discloses an intelligent resource loading method and device and a storage medium, and the method comprises the following steps: obtaining user browsing behavior data; inputting the user browsing behavior data into a pre-trained machine learning model to obtain a predicted browsing behavior of the user; resource loading is carried out based on the browsing behavior predicted by the user, the preloading strategy, the delay loading strategy and the condition loading strategy; after the resources are loaded, the resources are cached, and according to the real-time state of the user, the same resources are loaded by quickly applying the cache during the next browsing. According to the method, the user experience can be improved, the resource utilization is optimized, the page performance is improved, the flexibility is enhanced, and offline browsing and intelligent cache management are supported.

A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.

The invention relates to the technical field of distributed computing and networks, in particular to a cloud computing and edge computing collaborative system integration architecture and a collaborative computing method. The method comprises the following steps: an edge processing cache unit filters and caches received original data based on a self-adaptive data filtering strategy and an intelligent cache strategy; the cloud management cooperation unit constructs a time delay energy consumption optimization objective function based on the current cloud node and edge node states, solves the time delay energy consumption optimization objective function by using a multi-objective optimization algorithm, and dynamically cooperates task scheduling and resource allocation; the heterogeneous hardware acceleration unit adopts a hierarchical accelerator scheduling strategy to calculate an optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources. According to the cloud computing and edge computing collaborative system integration architecture and the collaborative computing method, efficient collaborative scheduling between cloud computing and edge computing is realized by combining intelligent matching of a multi-objective optimization algorithm and a heterogeneous accelerator.

The invention provides an efficient AI model management and safe loading system and method oriented to a Serverless framework, and aims to solve the high concurrency performance bottleneck and potential safety hazards of AI model loading in a Serverless environment. According to the system, an AI model management module is introduced, efficient semantic cutting is carried out on an uploaded AI model, and fine-grained encryption model blocks are generated; a high-performance P2P network is constructed by utilizing a Serverless cluster, and distributed storage and fast routing of model blocks are realized; a function instance loads a model block in a high-concurrency mode through a P2P network, a multi-stage intelligent cache is constructed locally, and dynamic caching and prefetching are achieved. According to the method, the model loading parallelism degree is improved by adopting a heuristic cutting algorithm, lightweight model updating is realized by utilizing incremental updating transactions, and the loading overhead is minimized. According to the method, the AI model loading performance and efficiency in the Serverless scene are effectively improved, meanwhile, the flexibility and safety of model management are considered, and the method has remarkable technical innovation and practical value.

The invention discloses a video monitoring system based on multi-link redundant transmission and intelligent supplementary transmission, relates to the technical field of communication monitoring, and solves the problems that key frames are lost due to insufficient network link stability, transmission delay is uncontrollable due to bandwidth resource allocation rigidness, and the dependency relationship between complex systems is difficult to optimize in real time. The video monitoring system comprises a link transmission management module, an edge intelligent cache module, a dynamic supplementary transmission control module and an intelligent storage management module, the problem of network disconnection is solved through a camera device in combination with redundant backup of a cache time model, and meanwhile, automatic extraction and complementation are achieved through a protocol, and seamless connection is achieved; through memory redundancy two-stage storage, the hard disk writing speed is improved, and the blocking probability of video data is reduced.

The invention provides a real-time data distribution and intelligent caching method and system based on edge computing, and belongs to the technical field of computers, and the method comprises the steps: 1, collecting an original data stream of terminal equipment, converting the original data stream into a structured data format, and generating a feature vector based on the structured data; 2, outputting a first shunting signal and a second shunting signal through a pre-trained shunting decision model according to the feature vector; 3, extracting a feature identifier based on the first shunt signal, obtaining the feature identifier and a corresponding historical access record, and determining a final priority score of the corresponding data in the edge cache; 4, managing an edge cache space according to the priority score, and generating a cache update record; and 5, performing security processing on the structured data, generating a global cache distribution topology, and synchronizing the global cache distribution topology to an edge node. And redundant storage and cloud load are effectively reduced.

The invention relates to the technical field of artificial intelligence and automatic testing, and particularly discloses an AI-based automatic testing method and device. The method comprises the following steps: capturing and recording complete execution historical information in a process of executing a test task by an AI agent, and serializing and storing the complete execution historical information as reusable cache data; before the test is executed, the validity of the cache is judged through a multi-dimensional verification mechanism including data integrity, timeliness, functionality and the like; when the cache verification is passed, a result is quickly obtained based on the cache data playback test process; when cache verification is not passed or execution fails, triggering an AI agent to perform new reasoning and steps to generate and update cache data; and meanwhile, a caching strategy is dynamically adjusted according to test case changes and resource conditions. The device comprises a test executor, an AI agent manager, an intelligent cache manager and a dynamic cache management module, and all the modules work cooperatively to improve the test efficiency, reduce the calling cost of a large language model and enhance the stability of a test result. The method is suitable for Web / APP automatic testing, cross-platform testing, CI / CD process testing and other scenes, the execution efficiency can be remarkably improved, the cost is reduced, and resource utilization is optimized.

The invention provides a code pattern data efficient processing system and method based on real-time compression and intelligent cache. The code pattern data efficient processing system comprises a code pattern processor, a compression module, a multi-level cache structure, a cache controller and a multi-channel interface. And the compression module is connected with the code pattern processor and is used for performing segmented compression processing on the pseudo-random code pattern data generated by the code pattern processor to obtain compressed data. The multi-level cache structure is connected with the compression module, and the cache controller is connected with the multi-level cache structure and the compression module and used for monitoring the compression state of the compression module and the occupation state of the multi-level cache structure in real time, dynamically adjusting a cache allocation strategy and sending a scheduling instruction to the multi-channel interface; and the multi-channel interface is connected with the cache controller and the multi-stage cache structure and is used for reading the compressed data from the multi-stage cache structure according to the scheduling instruction and executing multi-channel load balancing transmission. The problem that in the prior art, compression and caching are difficult to collaboratively optimize is effectively solved.

The invention provides a game performance optimization method and device based on dynamic scheduling and a storage medium. The method comprises the following steps: dynamically adjusting a byte code compiling strategy based on operation data, and reducing the execution priority of a byte code compiling task when monitoring that a multi-person voice team function is started and a CPU load reaches a preset condition; performing dynamic scheduling on allocation of the CPU cores based on the operation data, allocating high-priority tasks to the CPU cores with loads lower than a preset load threshold for execution, and allocating other tasks to the remaining CPU cores; carrying out scheduling and intelligent cache management on the resource file according to the operation data; and when monitoring that the network bandwidth or the network delay reaches a preset condition, adjusting a multi-person voice data transmission strategy based on the operation data. According to the method and the device, dynamic scheduling can be performed according to the real-time state during game running, tasks and resources are flexibly allocated, CPU occupation is reduced, and game running lagging is avoided.

The embodiment of the invention provides a data processing method and system based on cloud edge collaboration. According to the method, a collaborative scheduling module is utilized, and processing tasks can be dynamically scheduled according to data processing requirements and network states. In addition, an intelligent cache module is adopted, a cache mechanism is established between the cloud processing module and the edge computing module, and a distributed loading module is utilized to divide and distribute a large data set to realize parallel processing. By means of the mode, dynamic adjustment and optimization of collaborative scheduling, intelligent caching and distributed loading strategies of the data can be achieved, cloud computing and edge computing are combined, a cloud-edge collaborative architecture is formed, and the data processing efficiency can be improved.

The invention provides a multi-model adaptive reasoning system and reasoning method, and is applied to the technical field of artificial intelligence. The system comprises four core modules: a user side is responsible for receiving and forwarding a request; the routing layer module serves as a decision-making center, analyzes request types and intelligently distributes the request types to the reasoning layer; the reasoning layer module executes a specific reasoning task and returns a result; the cache layer module stores a high-frequency request result to improve response efficiency; the implementation process of the method is as follows: submitting a request by a user, analyzing and distributing by a routing layer, processing and caching a result by a reasoning layer, and regularly optimizing a model based on cached data. According to the system, through a dynamic routing and intelligent caching mechanism, the consumption of computing resources is effectively reduced, and the response speed and the processing efficiency of the artificial intelligence service are improved.

The invention relates to the technical field of computer storage, and discloses a real-time search cache replacement method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional search data in the system, carrying out the data preprocessing of the multi-dimensional search data, building an LSTM (Long Short-Term Memory) network model, and carrying out the real-time search cache replacement through the LSTM network model. Using an improved whale optimization algorithm IWOA to perform parameter optimization on the LSTM long short-term memory network model, designing a multi-target reward function based on a PPO algorithm, using the multi-target reward function to optimize a current cache state and weight parameters of an online prediction model, inputting multi-dimensional feature real-time data into the prediction model to perform training, and obtaining a prediction result; meanwhile, the dynamic weight of the model is calculated, and target adjustment of the cache replacement rule is carried out based on an incremental data set. The anti-interference capability and the long-period learning efficiency of the cache system are obviously enhanced, and an intelligent cache solution with low delay and high resource utilization rate is provided for a high-concurrency scene.

The invention relates to the technical field of storage optimization, in particular to a storage deduplication collaborative optimization method based on a multi-dimensional feature perception cache replacement model, which comprises the following steps of: S1, adopting a clustering center fingerprint as a key field of a primary index, and storing a clustering feature vector and a subordinate index access path; s2, each cluster member fingerprint forms a secondary index entry including complete fingerprint features and storage meta-information; and S3, the first-level index adopts a memory residence design. According to the storage deduplication collaborative optimization method based on the multi-dimensional feature perception cache replacement model, a mixed feature weight evaluation strategy is adopted, a dynamic priority queue management mechanism is designed, intelligent replacement of cache items is achieved, secondary indexes are optimized, the misjudgment rate is controlled to be within 0.1%, and by means of fine-grained feature perception and intelligent cache management, the storage deduplication collaborative optimization of the multi-dimensional feature perception cache replacement model is achieved. The problem of I / O bottleneck in a large-scale de-duplication scene is effectively relieved, and the performance advantage of the system is remarkably improved compared with a baseline system especially in a mixed working load environment.

The invention provides an intelligent caching method and device based on a self-adaptive caching strategy, and the method comprises the steps: collecting an access event flow of each caching key in real time through a sensing probe embedded in a caching client, the event flow comprising Key, operation type, timestamp, request source, hit state and response time; running a streaming processing engine at a client based on an access event stream, and calculating indexes such as instantaneous QPS, continuous miss times and short-term access frequency by adopting a sliding time window; a calculation result is input into a rule engine for online matching, a decision instruction for a single Key is generated, and the decision instruction comprises distributed mutual exclusion lock addition, local TTL dynamic adjustment and read-write mode switching; a decision instruction is directly executed in a client memory through a strategy executor, and nanosecond strategy effectiveness is achieved; and finally, collecting system feedback data after strategy execution, inputting the system feedback data into a machine learning module for online learning, and dynamically optimizing a rule threshold value or recommending a new strategy.

The invention discloses a dynamic component loading and intelligent cache management method, and relates to the technical field of front-end development. Comprising the steps that 1, all available components are registered and managed in a unified mode through a component registration center, 2, component codes are dynamically loaded on a current page according to operation of a user and page configuration, and 3, intelligent cache management is conducted on the component codes according to the use frequency and updating time of the components. And 4, performing communication between the components by using the event bus, and transmitting data or sending events between the components through the event bus.

The invention discloses a power grid data collaborative editing method and system, and relates to the technical field of power grid information communication, and the method comprises the steps: collecting and analyzing historical operation data of a user, and designing a caching mechanism according to the historical operation data; determining a cache priority score through the cache mechanism, and performing data caching and preloading according to the priority score; and performing power grid data collaborative editing based on the cache mechanism and the operation conversion algorithm, and processing conflicts occurring in the collaborative editing process. By establishing the centralized cloud data center and adopting the distributed database technology, timely updating and rapid processing of data are ensured. A data compression algorithm and an intelligent caching mechanism of the client greatly reduce local storage requirements, and meanwhile, the data access speed is increased. The system also considers the relevance of the data and the actual use frequency of the user, and dynamically adjusts the cache priority. The real-time collaborative editing function ensures the data consistency when multiple users edit at the same time based on an operation conversion algorithm.

The present invention proposes a "cloud-edge-end" collaborative caching method for a metaverse scene, the method comprising: dividing the metaverse scene into several sub-scenes, and organizing and compressing the resources required for rendering each sub-scene to obtain a sub-scene compression package; using a fast dynamic time warping algorithm to calculate the similarity between different trajectory segments in each sub-scene; based on the similarity between different trajectory segments, using a multi-GPU distributed streaming computing system to match the segment with the smallest similarity in real time and obtain a corresponding number of recommended predicted sub-scene trajectory sequences; distributing a number of recommended predicted sub-scene trajectory sequences to a number of edge servers to calculate the popularity of the sub-scene, and using a cache replacement framework consisting of states, actions and rewards to adjust the edge server cache strategy. The present invention can realize intelligent cache allocation and adjustment according to the sub-scene popularity prediction, improve the cache hit rate, and reduce the delayed response of user requests.

Embodiments disclosed herein relate to systems and methods for providing a smart cache. In embodiments, a variable time to live (TTL) may be calculated and associated with data as it is stored in a cache. The variable TTL may be calculated based upon reputation and / or category information related to the source of the data. The reputation and / or category information may include TTL modifiers for adjusting the TTL for data from a particular data source that is stored in the cache. In further embodiments, a feedback method may be employed to update reputation and / or category information for a particular data source.

The invention provides a multi-level distribution network path planning method and device, electronic equipment and a storage medium, and in the method, the total travel cost minimization under multiple scenes is taken as a target, and a resource redistribution mechanism and a cache acceleration technology are fused, so that the path planning efficiency is improved. The defect that adaptability, optimality and calculation efficiency of a solution cannot be considered in a traditional method is effectively overcome. Specifically, the method significantly improves the robustness of the final optimization path scheme for demand fluctuation, so that the final optimization path scheme can adapt to various actual scenes; meanwhile, through staged optimization and intelligent caching, the efficiency of the solving process is greatly improved, the consumption of computing resources is reduced, and the quality of a final solution is effectively improved, so that the final solution is closer to global optimum. Finally, the technical effect of quickly and automatically generating a high-quality and executable final optimization path scheme in a complex and uncertain environment is realized.

The present invention relates to the field of data processing technology, and in particular to an intelligent cache management method and system based on user needs. The method includes the steps of: S1, concurrent environment determination and cache mode switching; S2, interaction complexity identification; S3, sensitive field identification; S4, cache duration regulation; S5, execution cache; the present invention switches the cache mode through real-time concurrent environment determination, identifies the interaction complexity of user input behavior to further subdivide the cache resource usage intensity, divides the input fields into sensitivity and priority levels, and dynamically adjusts the cache duration by priority level and call frequency. While improving the flexible adaptability of the method, the present invention ensures the cache stability of highly sensitive and high-priority fields, realizes the reasonable allocation of cache space and information accuracy assurance, and is suitable for personalized intelligent form filling guidance under high concurrency.

The application discloses a multi-model cooperative alarm log noise reduction and attack threat research and judgment method, and the specific process is as follows: firstly, the data preprocessing is carried out on the multi-source heterogeneous security alarm log, then the two-stage reasoning is carried out by using the cascade classification model based on the light gradient boosting machine, and the attack state is identified; for the high-priority alarm screened out, the context-containing prompt word is constructed, the large language model is called for deep research and judgment, and the intelligent cache mechanism based on the feature hash is introduced, so that the similar attack mode is quickly and accurately identified, and finally, the end-to-end security research and judgment report is output in combination with the automatic flow processing and breakpoint continuation mechanism. The application solves the problems of high alarm false alarm rate and low research and judgment efficiency in the current security operation.

The invention belongs to the field of data storage, and particularly relates to an integrated circuit big data storage optimization system which comprises a data control module, a storage unit module, a data caching module, an error detection and correction module, a data compression module, an address mapping module and a power management module. And the storage unit module is used for controlling and managing the read-write operation of the data, and is used for determining the structural design of the storage unit and monitoring the state of the storage unit. According to the integrated circuit big data storage optimization system, the storage capacity is greatly improved by adopting a novel nonvolatile memory technology and an optimized storage unit structure and material, and meanwhile, the read-write speed of data is remarkably improved by adopting an intelligent cache replacement strategy, a multi-level cache structure, a cache prefetching technology and the like, so that the storage efficiency of the integrated circuit big data is improved. In this way, in a big data storage scene, a large amount of data can be quickly stored and read, and the high requirement of the modern information technology for data processing is met.