216 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 provides a task disassembly and multi-agent arrangement execution system and method based on a large language model, and belongs to the technical field of computers, and the system comprises an instruction analysis module, a DAG construction module, a scheduling execution module and a cache optimization module. Analysis is carried out according to the dependency relationship between the tasks, a task execution DAG is automatically constructed, and concurrent calling is carried out on the sub-modules without dependency; by caching an authentication result, a context reasoning result and the like, a universal module is executed in advance, the result is reused, and repeated calculation is reduced; the maintainability and the expandability of the system are improved through graph structure visualization and node element information injection; and performing context analysis and scheduling optimization in combination with the reasoning ability of the language model to realize an intelligent decision execution path.

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 discloses a database query execution method and device based on dynamic query compiling cache optimization, and belongs to the field of database management systems. Receiving a query statement input by a user and constructing an abstract syntax tree; generating a corresponding identifier according to the abstract syntax tree, searching a matched machine code, and loading and executing the matched machine code which can be reused to obtain an execution result; generating a corresponding executable plan tree for the query statement input by the user without finding the matched machine code or the query statement input by the user with the matched machine code incapable of being reused, generating and optimizing the machine code through dynamic compiling, obtaining the optimized machine code, and loading and executing the optimized machine code to obtain an execution result; and then an execution result is sent to the user, and machine codes in the cache are cleaned regularly. According to the method, whether the machine codes in the cache are multiplexed or not is accurately determined, so that unnecessary compilation overhead is reduced, and query execution efficiency is improved.

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 multi-model dynamic image analysis system, and relates to the technical field of artificial intelligence. The system adopts a heterogeneous computing architecture and comprises a preprocessing model, a multi-modal analysis model, an anisotropic correction model, a large language model, a task knowledge base embedding model and a KV cache management system. An incongruous task reconstruction mechanism based on hardware isomerism is constructed, a large language reasoning model is nested for analysis, and efficient reuse of computing resources is achieved by using a cross-model KV cache optimization technology. The system is provided with multiple graphics cards and a double-channel NUMA, and a multi-mode large language model carries out unsupervised or weak supervised training on a target detection model in the operation of the system. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced display card in a full-load mode is innovatively designed, and three-section real-time streaming enhanced output is achieved. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced graphics card in a full-load mode is innovatively designed.

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 embodiment of the invention provides a cache optimization method in large model reasoning, which comprises the following steps: in a pre-filling stage, cache operation is carried out layer by layer for a plurality of attention layers in a large model, and the cache operation for any ith layer comprises the step of acquiring a target attention matrix of the ith layer. And determining a first index value and a second index value according to the distribution of the row data and the column data of the target attention matrix. And determining an ith preference score corresponding to the ith layer according to the first index value and the second index value. And according to the ith preference score, determining a target cache region allocated for the ith layer in the total cache region, and storing attention data of a target character in the input text in the target cache region. And according to the ith preference score, updating a previous cache region of each layer before the ith layer, and updating attention data of characters stored in the previous cache region.

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 relates to the technical field of project cost consultation management, and discloses a project cost consultation management method and system, and the method comprises the steps: building a material price database which is dynamically updated in real time, obtaining market material price data from the Internet, and updating the material price database; adding the material price database into the data warehouse set, executing a cluster cache strategy, and optimizing the data warehouse set by using cache; executing a load balancing strategy on the server network of the transmission data warehouse set; acquiring data features of the cost project, executing an intelligent matching strategy, and acquiring an optimal cost model of the cost project; calculating the cost of the cost project by using the optimal cost model; if the cost is higher than the cost threshold, executing a budget project adjustment strategy, and adjusting budget distribution of sub-projects of the cost project; and executing the block chain recording strategy, and storing the operation record in the log record. And the reliability and accuracy of project cost management are improved.

The embodiment of the invention provides a distributed function scheduling optimization method and device under a server-free architecture, and the method employs a dynamic priority scheduling strategy, dynamically adjusts the priority of a function task according to a predefined priority rule and real-time monitoring data, guarantees the priority processing of a key task, and improves the scheduling efficiency. And the system response speed and the resource utilization efficiency are improved. A distributed cache is adopted to store function tasks, execute environment snapshots and a dependency library, a preheating mechanism is supported, cold start delay is reduced, and function task execution efficiency is improved. Through a monitoring feedback mechanism, index data related to system operation are collected in real time, and according to the index data, a priority and a caching strategy are dynamically adjusted, so that adaptive optimization is realized, and the stability and robustness of the system are improved. And through dynamic priority scheduling and cache optimization, the system throughput and the resource utilization rate are improved.

The invention discloses a distributed high-concurrency real-time industrial data pushing method and system, relates to the technical field of high-concurrency data processing, and solves the problems that dynamic adjustment of traditional buried point logic is difficult, and decoupling with service data cannot be carried out. The industrial data pushing system comprises a gateway management and control layer, a message queue layer, a cache optimization layer, a data processing layer, a pushing service layer and a monitoring operation and maintenance layer, asynchronous decoupling is carried out on the client and the server through the message queue layer, and priority transmission and flow peak clipping are carried out on original industrial data; performing anomaly prediction on the original industrial data through a data processing layer according to an anomaly detection rule to obtain anomaly alarm data and an out-of-order anomaly event; according to the invention, the push service layer carries out connection management and abnormal alarm data and out-of-order abnormal event push on the client according to a scheduling push mechanism, and carries out data burying point judgment on a client request, thereby improving the dynamic flexibility of data burying points, and solving the problems of industrial data analysis and resource configuration optimization.

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 discloses a label-based digital equipment model dynamic creation method and system, and relates to the field of complex data integration and equipment management technology application in the power industry, and the method comprises the following steps: predefining a general data label model, dynamically configuring a query model, and establishing a database; and constructing a digital equipment data entity and designing a technical process of parallel computing and asynchronous processing. Data standardization is realized, and the query efficiency is improved; through a dynamic configuration mechanism, the system can automatically generate the query model, and the flexibility is improved; through the data entity construction method, different business data can be associated through a standardized model, and the data consistency is improved; according to the method, parallel computing and cache optimization technologies are utilized, and data query and processing are accelerated; according to the method, an incremental updating mechanism is adopted, so that the real-time performance and consistency of data are ensured; according to the method, the creation efficiency, query performance and system flexibility of the equipment model can be remarkably improved, and better technical support is provided for digital transformation of the power industry.

The invention discloses a task scheduling and cache optimization method in a storage and calculation collaborative edge computing network, and aims to optimize the service delay cost of a user and the cache deployment cost of a service provider. The method comprises the following steps: modeling a reusable task scheduling and cache optimization problem in a storage-computing collaborative edge computing network, and decoupling the reusable task scheduling and cache optimization problem into two sub-problems: a task scheduling problem for minimizing service delay cost, and providing a dynamic task scheduling algorithm based on a merging mechanism for the task scheduling problem. The invention provides a calculation result caching algorithm combining dynamic planning and greedy, and further provides a forward-looking pre-caching strategy for solving the problem of minimizing the service caching cost. The task calculation scheduling and result caching method with reusability is provided for space-time correlation of task requests in a storage and calculation collaborative edge calculation network, strict performance guarantee is provided, the method has wide applicability, and the method is suitable for large-scale popularization and application. And challenges of resource limitation and high real-time requirements in a complex calculation scene can be efficiently dealt with.

According to the shared LLC cache optimization management method based on the multi-core processor, exclusive shared LLC is divided for virtual machines with specific requirements, especially virtual machines with high real-time requirements, the memory access bandwidth of the virtual machines is dynamically adjusted, and meanwhile memory access operations of other virtual machines with low priorities are blocked, so that the memory access efficiency of the virtual machines is improved. And the real-time performance of the memory access response of the virtual machine with specific requirements is ensured.

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 computer storage systems, and provides a cache optimization method and device for a power data system, computer equipment, a readable storage medium and a program product. The method comprises the following steps: determining target cache data in a fixed period access scene according to a historical access rule of a power data system; according to a historical access rule of the power data system, predicting the current cache time of the target cache data; according to the current cache time of the target cache data, prolonging the retention period of the target cache data in the cache region; and when a data generation request related to target cache data is received, reading the target cache data from the cache region, and generating data according to the target cache data. By adopting the method, the loading time of the target cache data can be shortened, and the data processing efficiency of the power data system can be remarkably improved.

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 discloses a design space exploration method for a cache hierarchical structure in a multi-core particle system. The method aims at optimizing the cache subsystem in the multi-core particle system, and the system performance is improved by reasonably configuring the cache hierarchical structure and the interconnection network topology between the core particles. The method comprises the following specific steps: 1) modeling a cache miss rate and network delay: modeling the cache miss rate and the network delay as a function of a cache hierarchical structure and interchip interconnection network parameters; 2) optimization problem definition: defining an optimization objective and minimizing concurrency average storage access time (C-AMAT) under the constraint of cost and power consumption; and 3) solving by using a double-layer optimization algorithm: respectively optimizing the cache subsystem and the interconnection network between the chip grains through the double-layer optimization algorithm. The method provides an effective solution for cache optimization of the multi-core particle system, and has a wide application prospect.

The invention relates to the field of big data stream processing, and provides a cache optimization method based on traffic behavior characteristics, and the method comprises the steps: recording an access timestamp sequence of a target stream in real time; constructing a periodic detection model based on an adjacent interval change trend of the timestamp sequence, and calculating a periodic stability parameter of the target flow through the periodic detection model; dividing a time axis into a plurality of non-overlapping sliding windows, and generating a cross-window activity evaluation parameter by counting the access frequency of the target stream in each window; according to the periodic stability parameter and the active window coverage, screening out a target flow which simultaneously meets the periodic stability and cross-window activity characteristics through a joint judgment condition; distributing a dynamic priority for the target flow and constructing a fuzzy matching cache key; and based on a fuzzy matching cache key, distributing target streams meeting periodic stability and cross-window activity characteristics to a multi-level cache structure according to priorities, and triggering a prefetching mechanism to realize cross-cache-layer linkage loading and resource scheduling.

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.