151 results about "Cache optimization" patented technology

The invention provides an online map tile fast loading and caching optimization method, medium and system, and belongs to the technical field of map processing.The method includes the steps that a tile index database and an exchange priority matrix are established, a viewport moving trend vector is calculated according to user sliding operation, a future viewport range is predicted, and a preloaded tile list is screened; the method comprises the following steps: performing cache validity check on tiles, establishing a small change detection vector to judge whether updating is needed, establishing a tile hit rate statistical matrix, fusing a time decay factor and a spatial correlation factor to calculate a popularity score, and gathering high-popularity tiles in a quick access area by adopting a thermodynamics-imitating entropy reduction algorithm to realize cache ordering. And the next tile demand of the user is predicted, so that the technical problem that the overall loading response speed is reduced due to an overlong high-value tile access path caused by disordered distribution of cache resources in the online map tile loading process is solved.

The invention discloses a data production and application method based on index management, and the method comprises the steps: receiving an index query request containing a target index identifier, a dimension constraint condition and a time range parameter, carrying out the analysis and semantic verification of the target index identifier based on a global index asset library, and obtaining the index definition information; generating a standardized query statement according to the index definition information, and selecting an optimal data calculation engine to execute query; performing multi-level cache optimization and parallel computing acceleration on the query process to obtain an original data set; processing the original data set in real time according to a preset analysis model to generate structured index data containing trend analysis, anomaly detection or attribution inference results; and filtering and desensitizing the data based on a fine-grained permission control strategy, only returning contents in a permission range and recording an audit log. By means of the method, unified management, efficient query and intelligent analysis of the index data are achieved, the automation level and query performance of data production are improved, and meanwhile data safety and access controllability are guaranteed.

The invention discloses a multi-level cache optimization method and system based on data popularity self-adaptive adjustment, relates to the technical field of data storage, and aims to solve the problems that a cache mechanism in an existing desktop cloud system is static, initialization is slow, and cold and hot data are difficult to recognize. The adopted scheme comprises the steps that a three-level cache structure of a local cache L1, a distributed cache L2 and a database L3 is set; constructing a data popularity scoring model based on the access frequency, the time decay and the user priority; dividing the data into hot data, temperature data and cold data according to the popularity score, and dynamically determining migration and elimination strategies of the data in L1, L2 and L3; the hot data are loaded to the upper-level cache T in advance through a prediction mechanism, and container-level cache preheating is supported; a cache preheating strategy is integrated to a Kubernetes life cycle, and hot data loading and cold data degradation are achieved; visual strategy configuration and monitoring during operation are supported, and cache strategies are managed in real time. The method is suitable for an access optimization scene of a large-scale desktop cloud system.

A system and method for extending mobile-optimized multi-stage language model processing with federated persistent cognitive architecture. The system processes prompts through a first large language model to generate “thoughts,” which are cached and processed with the original prompt through a smaller language model. Building upon the three-tier thought caching, the system implements a federated multi-tier hierarchy with local device, domain-specific branch, and global collective caches. A federated cognitive orchestrator coordinates operations across multiple domain-specialized instances, managing thought routing, state synchronization, and cross-domain knowledge sharing while maintaining domain boundaries. During user inactivity, autonomous reasoning continues in cloud environments, generating insights from existing thoughts and interaction history. The system performs memory consolidation, thought cache optimization, and cross-domain pattern recognition without consuming mobile device resources, while maintaining privacy boundaries. This persistent cognitive architecture functions as an evolving reasoning partner rather than merely a responsive tool.

The invention provides a large language model long text reasoning acceleration method and device based on a speculative key value cache sparse technology, a medium, a terminal and a program product, the method is applied to electronic equipment comprising a GPU and a CPU, and the method comprises the steps that an index list of most important historical tokens is generated based on a distillation language model according to an obtained context sequence; comparing the index list generated at the current moment with the index list at the previous moment, and calculating to obtain a difference set part; asynchronously prefetching the key value cache of the difference set part from the CPU to the GPU; according to the asynchronously prefetched key value cache, performing parallel execution based on a large language model to generate a new token; obtaining a new sequence length according to the generated new token, and judging whether the new sequence length exceeds a preset threshold value or not; and if the threshold value is exceeded, executing unloading operation. According to the method, the performance and the stability of processing long text reasoning by the large language model can be improved, and key value cache optimization in the long context reasoning process is ensured to be always effective.

The invention discloses a cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention discloses a big language model key value cache optimization method based on a cooperative game, which comprises the following steps: modeling all attention heads of a big language model as participants in a cooperative game model according to a reasoning task to be performed, and calculating cooperative contribution scores of different attention heads to the reasoning task; normalizing the collaborative contribution score of each attention head, allocating a preset total shared key value cache budget to the corresponding attention head in proportion according to the normalized score, and ensuring that all the attention heads reserve a basic local window cache; the key values in each attention head are subjected to importance sorting, and a plurality of most important key values are reserved according to the key value cache budget of the attention head, so that compression optimization of key value cache is realized. According to the method, the cooperative game theory is applied to large language model key value cache allocation optimization, so that the utilization rate of hardware computing resources is improved while the reasoning accuracy is ensured.

The invention discloses an input data sharing and cache optimization method in matrix multiplication calculation. The method comprises the steps of 1, segmenting and distributing an input data matrix and a weight matrix according to the number N of calculation cores on a chip; 2, sequentially connecting the plurality of calculation cores end to end to form a data transmission annular structure; step 3, calculating the distributed matrix multiplication by each calculation core, and transmitting the current input sub-matrix of the calculation core to the next calculation core; step 4, performing matrix multiplication operation on the transmitted input sub-matrix and the weight sub-matrix in the next calculation kernel; and 5, iterating transmission and calculation of the input sub-matrixes, and carrying out N rounds of matrix multiplication of the input sub-matrixes and the weight sub-matrixes to complete the whole operation process. The invention further discloses a system for implementing the method, and the system has wide application value.

The invention relates to the field of database reading, and particularly discloses an efficient database reading method based on multi-level cache optimization and dynamic index fragment.The efficient database reading method comprises the steps that high-frequency query data is accurately recognized through a sliding window algorithm, a dynamic cache loading and preheating strategy is implemented, query delay is effectively shortened, and the database burden is relieved; intelligent distribution of index fragments is realized by applying a consistent Hash algorithm, and the query efficiency is greatly improved in cooperation with distributed query routing and transaction processing optimization; the query load balancing ensures stable and efficient operation of the system in a high-concurrency scene by means of weighted polling and a minimum connection strategy in combination with real-time monitoring and an elastic capacity expansion and contraction mechanism. According to the method, the database access efficiency and the dynamic adaptive capacity are remarkably improved, and the method is particularly suitable for a large-scale distributed data processing environment.

The invention relates to a KV cache optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: calculating a key vector and a value vector corresponding to each element in a text input sequence input into a large language model; through a multi-head potential attention mechanism, performing low-rank joint compression on the key vector and the value vector to obtain a potential vector, and storing the potential vector in a KV cache space; based on a scaling law, determining an optimal compression dimension, regenerating an adaptive potential vector and updating a KV cache space; for the same text input sequence, generating corresponding query vectors, and grouping the query vectors according to a preset grouping rule; calculating a semantic association weight between each group and the correspondingly called potential vector, and taking the semantic association weight as a group attention calculation result; in the reasoning process, potential vectors and grouping attention calculation results are calculated to calculate attention weights. By adopting the method, the storage requirement of the KV cache can be further reduced.

The invention provides a data cache optimization method, device and equipment for a vehicle-specification MCU and a storage medium, and the method comprises the steps: building a multi-target cache decision through predicting the future data access probability distribution, and giving consideration to the real-time performance and the memory utilization rate. A cache is divided into logic partitions with different priorities, and the sizes of the partitions are dynamically adjusted, so that a self-adaptive cache layout is formed. In combination with access probability distribution and a partitioning strategy, a dynamic pre-fetching threshold value is set, data pre-fetching is executed, and elimination and updating are performed in a task execution period, so that the caching efficiency and stability of a vehicle gauge MCU in a high-temperature and strong-vibration environment are improved, and the method is suitable for a vehicle-mounted control system with strict requirements on safety and performance.

The invention provides an efficient query acceleration and cache optimization device for data knitting, which comprises a semantic partitioning component used for performing dynamic partitioning on a multi-source heterogeneous data set based on multi-source heterogeneous semantic metadata and embedding cross-domain association identifiers of data knitting to generate a semantic partitioning index table; the query intention mapping component is used for executing intention extraction on a query request initiated by a user to generate a query intention representation, and establishing a mapping relationship of the query intention representation to-be-queried target data partitions according to the semantic partition index table so as to generate a query-partition mapping table; the cache dynamic sorting component is used for generating a cache priority sorting table according to the query-partition mapping table and historical query frequency statistical data; and the scheduling and query execution component is used for optimizing the cache mechanism of the to-be-queried target data partition based on the cache priority sorting table and a preset proportion threshold value. According to the method, the time consumption of cache calling and source data repeated loading in the later query period is reduced.

The invention relates to the technical field of information management methods, and discloses a data query management method based on dynamic cache optimization. Comprising the steps of constructing a dynamic aging adjustment engine, and automatically adjusting cache time based on data change frequency; real-time monitoring is carried out through a database trigger and log analysis; the method comprises the following steps: acquiring a historical query record, predicting query types and time possibly initiated by a user in the future through a machine learning model, before the query time is predicted, loading predicted query type data to a cache, dynamically marking hotspot data according to an access frequency, preferentially allocating cache resources to the hotspot data, and prolonging the time efficiency; constructing a multi-level cache architecture based on a local cache and a distributed cache, and acquiring data by adopting a cache degradation strategy; an enhanced Kafka message processing mechanism is introduced; the method has the advantages of improving data query efficiency and ensuring data consistency.

The invention discloses a cache optimization method for a cell-free multiple-input-output environment based on a VPPO algorithm, and relates to the technical field of edge cache optimization. Multiple independent environment instances are generated and run in parallel; training the strategy network based on the plurality of independent environment instances and the value network to obtain a trained strategy network; and inputting the cache proportion of the current edge server into the trained strategy network to obtain the adjustment of the cache proportion of the edge server. A complex dynamic scene is simulated through multi-environment parallel interaction, multi-scene data are collected in parallel, and the adaptability and decision-making ability of the model to dynamic changes are improved. According to the method, a non-Markov environment is adapted by using Retract advantage estimation, and the problem of deviation of traditional advantage estimation in such scenes is solved by truncation weight and recursive calculation and processing of the condition that state transition depends on history, so that the advantage function calculation is more accurate, a foundation is laid for strategy optimization, and the performance of a model in a complex dependency relationship scene is improved.

The invention relates to a distributed storage cache hotspot prediction method and system based on an access mode. The method comprises the following steps: acquiring an access log to obtain original data; the gradient change of the access frequency between the adjacent data blocks is analyzed in real time to divide the hot spot range and identify continuous hot spot data blocks; constructing a time sequence based on the historical access record, and pre-judging an access hotspot in a future time period by applying a time sequence trend prediction algorithm; constructing a multi-dimensional feature vector and inputting the multi-dimensional feature vector into a machine learning prediction model to output a hotspot probability of the corresponding data object in a future time period; according to the hotspot probability and a popularity threshold value, scheduling the data predicted as the hotspot into a cache, and allocating corresponding cache levels or storage paths for the data objects with different popularity at the same time; and periodically updating the prediction model and the popularity statistics to obtain dynamic prediction and cache optimization of the hotspot data. According to the invention, the prediction of the hotspot data is realized, and the hit rate and the system performance of the distributed cache are improved.

The invention discloses a federal edge communication and calculation optimization method, system and device based on task prediction and a medium, and belongs to the technical field of edge intelligent collaborative optimization, and the method comprises the steps: collecting historical task data, carrying out the modeling of a communication and calculation process, building a multi-dimensional task feature modeling mechanism, extracting the heterogeneous features of the communication and calculation process, and carrying out the calculation of the communication and calculation process. Performing task demand prediction and load perception to obtain a prediction result; and establishing an integer programming model, performing approximate solution through a heuristic algorithm to complete service cache optimization, performing calculation unloading and resource joint allocation, calculating key performance indexes, performing periodic acquisition, and performing dynamic adjustment on a prediction result. According to the method, an efficient cache strategy is generated by adopting a heuristic algorithm, the defects of single resource allocation, decision lag and lack of global coordination in a traditional method are overcome through joint optimization of resource allocation and a real-time performance monitoring feedback mechanism, and the system response efficiency and the resource utilization rate are improved while the task success rate and reliability are guaranteed.

The invention relates to the field of artificial intelligence, the technical scheme can be applied to the field of financial science and technology / medical health, and discloses a long context key value cache optimization method, device, equipment and medium, and the method comprises the steps: carrying out the global analysis of the hidden state of an input sequence through embedding a lightweight attention gating module in each layer of a Transform architecture, and carrying out the global analysis of the hidden state of the input sequence; outputting a binary decision signal to dynamically select a key token key value state needing to be reserved; a key value cache pool is managed according to the signal, and a corresponding attention mask is constructed to ensure that the self-attention calculation is only focused on the retained context. According to the method, the problems of high key value cache memory occupation and large reasoning delay in long sequence processing are effectively solved, the reasoning efficiency is remarkably improved, the resource consumption is reduced, and meanwhile, the original performance and the output accuracy of the model in the long text understanding task are guaranteed.

The invention relates to the technical field of automatic control, in particular to a control system optimization problem compiling method, device and system and a computer readable storage medium, and the method comprises the steps: obtaining a to-be-compiled optimization problem, and recognizing the structure information and feature information of the to-be-compiled optimization problem; obtaining parameter information of the embedded hardware, and matching and optimizing a corresponding numerical solution algorithm in combination with the structure and the feature information to obtain an optimized numerical solution algorithm; converting the optimized algorithm, structure information and parameter information into a first embedded execution code without dynamic memory allocation; performing memory and cache optimization on the memory layout of the first embedded execution code to generate a second embedded execution code; compiling the second embedded execution code into a binary file adaptive to embedded hardware, and deploying the binary file; the environment parameters are obtained and transmitted into embedded hardware, the target control solution is obtained by operating the binary file and solving based on the environment parameters, and the calculation efficiency and hardware compatibility in an embedded scene are improved.

The invention discloses a large model content generation acceleration method and system based on error optimization cache, and the method comprises the steps: extracting priori knowledge, and obtaining the output of an attention layer and a multi-layer perceptron layer of each module in a sampling process of a large model; a cache module needing to be optimized is judged based on the error size and the relative position of the module in the sampling process; and for the cache module which is judged to be required to be optimized, introducing trend information in the priori knowledge to perform cache optimization. According to the method, the trend of priori knowledge is mapped to the sampling data, so that the stability and the quality of a generated result are kept while the calculation efficiency brought by cache is enjoyed.

The invention relates to the field of databases, and particularly provides a time sequence database high-availability master-slave replication method and device based on WAL, and the method comprises the following steps: S1, replacing a traditional three-node architecture with a master-slave dual-node system; s2, performing log cache optimization; s3, carrying out batch WAL transmission and dynamic control; and S4, recovering the consistency verification table and the breakpoint. Compared with the prior art, the method has the advantages that the problems of node redundancy, IO bottleneck and consistency of a traditional scheme can be solved through batch WAL transmission, dynamic cache optimization and strong consistency verification.

The invention discloses an Internet of Vehicles secure unloading and cache optimization method based on a graph neural network, relates to the field of intelligent traffic and edge computing, and aims to solve the problems of low resource scheduling efficiency, insufficient privacy protection and task and cache processing separation of the Internet of Vehicles. The method comprises the following steps: S1, constructing an Internet of Vehicles system model, a communication model comprising a vehicle-roadside unit-macro base station architecture, an orthogonal frequency division multiple access and dedicated short-range communication protocol, and a calculation model comprising local processing and partial remote unloading; s2, Gaussian noise is introduced to realize differential privacy protection, and asynchronous federated learning is combined with a long-short-term memory network to predict task popularity; and S3, constructing a dynamic heterogeneous graph, extracting spatio-temporal features by means of a graph neural network, inputting a near-end strategy optimization algorithm, and jointly optimizing an unloading target, a proportion and a caching strategy. According to the method, the collaborative improvement of privacy security, low system total time delay, high cache hit rate and resource utilization rate is realized, and the method adapts to the dynamic topology and complex task requirements of the Internet of Vehicles.

The invention discloses a hierarchical cache optimization method and system based on preloading, and relates to the technical field of computer system structures and parallel computing. According to the method, a thread pool is divided into a calculation execution unit and a preloading management unit; when the state of the calculation execution unit meets a preset preloading triggering condition, triggering a preloading management unit to start preloading of the next batch of data; and the preloading management unit selectively preloads data in the multi-layer cache system according to the target characteristics. According to the method, efficient data preloading is realized in a layered cache system through thread pool division; a hardware architecture does not need to be modified, calculation and preloading operation are dynamically coordinated through the thread pool, and the problem that the resource utilization rate of a calculation architecture based on a layered cache system is insufficient is solved.

The invention provides an edge cloud cache configuration method and device based on tensor complementation, and relates to the technical field of containerization deployment in an edge cloud network, the method comprises the steps of decomposing mirror image layer prefetching into prediction of mirror image layer cache and mirror image layer pre-scheduling, combining tensor complementation, an iTransform model and a CP decomposition algorithm, and after the tensor is complemented, obtaining an edge cloud cache configuration result. The iTransform model captures non-stationary dependence among dimensions of tensors through an inverted time sequence attention mechanism of the iTransform model while keeping high efficiency of parameters, steady inference on current time information is realized, so that an accurate mirror image layer prefetching task is supported, a CP decomposition algorithm performs tensor reconstruction on each selectable tensor to calculate a score of each selectable tensor, and the score of each selectable tensor is calculated to obtain a pre-fetching task of the mirror image layer. And finally, taking the selectable tensor with the highest score as a target tensor. Based on the method, efficient prefetching and cache optimization of the AI micro-service mirror image layer can be realized, so that challenges caused by resource limitation in an edge cloud environment are effectively relieved.

The invention discloses a data compression and cache optimization method and system for an electric power internet of things edge node. The method comprises the steps of performing normalization adaptation on electric power internet of things edge node data to obtain unified structure data; performing silent report suppression and window deduplication on the unified structure data to obtain redundancy suppression data; inputting the redundancy suppression data into a compression controller for compression to obtain compressed data; and performing layered caching on the compressed data. According to the method, systematic advantages are formed in the aspects of bandwidth, time delay, reliability and operability and maintainability through self-adaptive compressor selection according to point locations, hierarchical caching, congestion self-adaptive transmission, consistency and idempotence and complete decoupling, and the method is particularly adaptive to the characteristics of multi-source, strong real-time, weak network and high-consistency constraint of the electric power Internet of Things.

The application discloses a kind of blockchain data traceability query optimization methods, by introducing the method of cache optimization, utilize cache to reduce the number of disk IO in traceability, improve the efficiency of traceability search, while multi-level cache structure is designed, the problem that cache hit rate is not high under the condition that the memory resource of full node is limited is solved, that is, while giving consideration to the consumption of memory resource in the improvement of cache hit rate, reduce the burden of full node.A kind of blockchain data traceability query optimization method first by the node in network to full node of blockchain initiates traceability query request, then in full node query cache, finally carries out consistency check to full node, realizes blockchain data traceability query.The application improves the traceability query efficiency of blockchain system, query credibility, with certain practicality.

The application discloses a CAD drawing loading method and system combined with cache optimization, and relates to the technical field of graphic cache optimization.The method comprises the following steps: reading a target CAD drawing for identification; dynamically distributing tile data to multiple cache areas; performing real-time monitoring to generate a first monitoring data set, performing cache adjustment to generate a first cache parameter; performing data unloading to the disk cache area to generate a second cache parameter; performing interactive operation on the drawing to load the drawing in the memory cache area, and triggering the second cache parameter to perform asynchronous loading in the disk cache area according to the loading result.The application solves the technical problems of low cache management efficiency, long loading waiting time, unsmooth drawing display, and poor user interactive experience in the prior art CAD drawing loading, achieves efficient cache management of the CAD drawing loading, effectively reduces the loading waiting time, and improves the smoothness of the drawing display and the user interactive experience.

The application provides a read cache optimization method and device in an AI model training scene, comprising: during AI model training, setting each Epoch reading training set data process as two rounds of iterations; in the first round of iteration, each Mini-Batch queries S*N items of data to the cache in random order; S items of data are read from the data in the cache after the query for the current Mini-Batch, and the remaining data is marked; after all the training set data is traversed once, the first round of iteration ends, the second round of iteration starts, and the marked data in the first round of iteration is traversed; each Mini-Batch queries S items of data to the cache, the data is directly read in the cache, or the data is read from a remote storage node. The application improves the read cache hit rate when loading the training set, thereby reducing the time delay of reading data, improving the utilization rate of GPU and other scarce computing power devices, saving computing power, and improving the model training efficiency.

The invention provides a Spring Session performance optimization method based on partition cache, and relates to the technical field of session management and cache optimization in a distributed system, and the method comprises the steps: storing a mapping relation between a session ID and a final access timestamp by designing concurrent hash mapping caches C1 and C2 which are alternately used, and identifying an active cache by using a current cache label; configuring a timed task to periodically scan the cache, judging a session state according to the critical zone time, and dynamically updating or cleaning cache data; a session object obtained by a thread local variable cache Redis is introduced in Http request processing, so that repeated access is avoided; and a Spring session filter and a warehouse are rewritten to reconstruct session access logic, so that the dependence on Redis is effectively reduced, and the concurrency performance and response efficiency of the system are improved.

The invention provides an HTTP protocol multi-interface arrangement, combination and analysis method and device, and the method comprises the steps: sorting and calling target interfaces needing to be called based on an execution strategy selected by a user according to a business demand, and the execution strategy comprises a calling strategy and / or a caching strategy; the number of the target interfaces is at least one; the method comprises the following steps: when an execution strategy aiming at a target interface is received, determining a corresponding calling path, a calling parameter and a cache identifier according to the execution strategy and identity information of the target interface; determining a calling sequence of the target interface according to a preset arrangement rule; according to the calling sequence, calling the target interface according to the calling path, the calling parameter and the cache identifier; and after calling is completed, outputting an execution result, analyzing the execution result, and outputting an analysis result. And calling interfaces are dynamically sorted based on a strategy, and the execution efficiency and flexibility are improved in combination with cache optimization.

The present application relates to the field of computer graphics animation, and more particular to a system and method for geometric cache optimization. One aspect of the present invention relates to a method for optimizing a geometric data cache. The method comprises importing geometric data into a Universal Scene Description framework, applying an advanced compression algorithm to the geometric data cache, indexing the compressed geometric data, selecting a portion of the compressed geometric data for decompression, and decompressing the compressed geometric data.