458 results about "Distributed cache" patented technology

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention provides a wireless communication network method and system of a mobile first-aid station. The method comprises the following steps: monitoring and evaluating a surrounding environment in real time, selecting an optimal working frequency band, and adjusting a frequency band switching strategy to obtain a self-adaptive multi-frequency-band access mechanism; the bandwidth and the connection priority of the wireless communication network are dynamically adjusted, the task priority is evaluated, and a dynamic resource allocation strategy is generated; deploying a distributed cache and synchronization system, realizing balanced distribution and rapid positioning of data, and generating a distributed temporary data cache pool; establishing an intelligent routing algorithm of deep reinforcement learning, calculating an optimal transmission path from a source to a destination, and finally generating an intelligent routing planning scheme; a redundant path is preset on the path, and a redundant path planning scheme is generated according to a dynamic resource allocation strategy and an intelligent routing planning result. According to the technical scheme provided by the invention, the reliability and the response speed of the wireless communication network of the mobile first-aid station are improved.

The invention provides a multilayer nested query cache multiplexing method and system based on a dynamic materialization strategy, and relates to the technical field of cache multiplexing, and the method comprises the steps: constructing a query dependency graph, and carrying out dynamic materialization marking based on the ratio of query cost to materialization cost; generating result fingerprints containing query features, version information and timestamps for the materialized nodes, and storing query results in a distributed cache system with a layered architecture; when a new query is processed, efficient query result reuse is realized by verifying version information and dependency data of a cache result. According to the method, the query performance can be remarkably improved, the consumption of computing resources is reduced, and efficient caching and multiplexing of query results are realized.

The invention provides a flow limiting method and system based on a distributed cache strategy, belongs to the technical field of distributed flow limiting, and is applied to a distributed cache system which comprises a plurality of cache partitions. The method comprises the following steps: acquiring real-time traffic and historical traffic of each cache partition; inputting the real-time traffic and the historical traffic into a traffic prediction model to obtain predicted traffic at the next moment; and determining a flow limiting parameter of the cache partition based on the predicted flow, and limiting the flow at the next moment based on the flow limiting parameter. According to the method, the flow limiting parameters are automatically adjusted by using the machine learning technology, the data flow can be accurately controlled, the system overload is prevented, the resource waste caused by excessive limitation is avoided, and the flow management efficiency and effect of the distributed cache system are remarkably improved.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a data hierarchical storage and query processing method, which comprises the following steps of: dividing business data into offline data and real-time data, and respectively storing the offline data and the real-time data in an offline database and a real-time database; real-time data processing tasks are generated through a set time interval, and are distributed to a message queue according to priorities; the task processor obtains the task from the message queue, extracts real-time data and stores the real-time data in a distributed cache; the query request obtains data through the distributed cache, and if the data are not found, the data are searched from the real-time database and a result is returned. Through hierarchical storage, priority management and implementation of a cache mechanism on the data, the direct query pressure on the database is reduced, the response speed of the system in a high-concurrency scene is improved, and the real-time performance and stability of data query are ensured.

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.

The invention discloses a distributed computing task non-perception migration method and system under optical fiber network interruption. The distributed computing task non-perception migration method comprises the following steps: constructing a distributed task migration system and an output system architecture; collecting network state indexes, constructing a network topological graph, and analyzing the state of an optical fiber link for fault prediction; obtaining a calculation task operation state, establishing a sub-task mapping relation, constructing a directed dependency graph, and generating a state snapshot; analyzing resource requirements, reserving standby resources, and deploying a distributed cache system; analyzing a fault influence range, extracting an influenced calculation sub-graph, and selecting a migration target node; in a target node preloading environment, reconstructing an execution context, and redirecting a communication path; and setting a data change capture mechanism, synchronizing incremental data and executing consistency verification. According to the method, non-perception migration of the computing tasks is realized, task continuity and data consistency are guaranteed, and the reliability of the distributed computing system in an optical fiber network fault scene is improved.

The invention relates to the technical field of data transmission, and discloses an OpenHarmony-based data transmission method and platform, and the method comprises the steps: carrying out the automatic discovery of network equipment through employing a distributed soft bus technology, and obtaining an authenticated equipment list; constructing an initial dynamic routing table and updating the initial dynamic routing table to obtain an optimized routing table; fragmenting the data to be transmitted and performing redundant coding to obtain a coded transmission unit; business is divided into real-time and non-real-time categories, a state space is constructed, and a business migration strategy is formulated; performing parallel transmission on the encoded transmission units, and dynamically adjusting a data distribution proportion; distributed cache nodes are deployed in a network, data transmission is optimized, efficient transmission and recovery of data are realized, and automatic discovery and security authentication of equipment, dynamic routing optimization, intelligent data fragmentation and coding, resource scheduling of service awareness, parallel transmission of multiple network interfaces and efficient utilization of distributed cache can be realized.

The invention discloses an AI model distribution and deployment system and method oriented to cloud edge collaboration, belongs to the technical field of artificial intelligence and edge computing crossing, and aims to solve the problems of low AI model distribution efficiency and poor deployment adaptability in a cloud edge collaboration scene. The method comprises the steps of collecting multi-dimensional attribute data of edge nodes and converting the data into multi-level capability tags, and triggering tag differentiation updating according to data change amplitude; constructing a model demand hierarchical description framework by relying on capability labels, converting distribution demands into quantitative query conditions, and adjusting and screening a target edge node set through a three-level progressive matching mechanism in combination with dynamic weight; coding data blocks corresponding to redundancy are generated based on the network state of a target node, adaptive cache nodes are screened by using a distributed cache mechanism, the data blocks are pre-stored, and a cache network is established and transmitted to the target node; according to the invention, efficient distribution and accurate deployment of the AI model in the cloud edge collaborative environment are realized, and the model transmission reliability and the node adaptability are improved.

The invention discloses a distributed flow limiting method and system, electronic equipment and a storage medium, and relates to the field of flow control. In the method, the initial token number of a token bucket is determined according to the maximum concurrent request number of service instances, the token bucket of the service instances is created according to the initial token number, and requests from a client are distributed to each currently started first target service instance according to a load balancing algorithm. Detecting whether a token can be obtained from an associated token bucket of the first target service instance; when the token can be obtained from the associated token bucket, the request is processed through the first target service instance, the number of tokens in the associated token bucket is updated after processing is completed, and the updated state information of the associated token bucket is synchronized to a distributed cache; and adjusting the current limiting parameter according to the state information of all token buckets in the distributed cache. By implementing the technical scheme provided by the invention, the intelligent control of the flow is realized, and the stability and the high efficiency of the system are ensured.

The invention provides a drug identification information efficient query method and system based on distributed cache. The method comprises the following steps: firstly, receiving and analyzing a drug identifier query request to generate a dynamic cache routing key, then positioning a target cache node, then matching a cache data block corresponding to a drug unique code in the target cache node, if matching succeeds, extracting a drug full life cycle record in the cache data block, and sending the drug full life cycle record to the target cache node; if the matching fails, initiating a cross-node retrieval request to the drug block chain database to return drug source data, splitting the drug source data into a core attribute set and an extended track set, and finally executing a dynamic combination operation on the core attribute set and the extended track set to generate a drug tracing result set and returning the drug tracing result set to the user terminal; according to the technical scheme provided by the invention, the query fluctuation defect of a single storage architecture is eliminated from the source, so that the whole system simultaneously meets three drug traceability core requirements of high concurrency, cross-mechanism and tamper-proofing.

A hotspot data elimination and maintenance method in a distributed cache system comprises the steps that the access frequency, the time locality score, the service weight and the real-time data popularity value of data are acquired, and the dynamic priority score of the data is generated; determining failure data based on the dynamic priority score; dividing the data into hot spot area data, temperature spot area data and cold spot area data, when the cache space is insufficient, preferentially eliminating the data with the lowest dynamic priority score and the retention benefit value lower than a preset threshold value in the cold spot area data, and if the cache space is still insufficient, performing data elimination on the temperature spot area data according to the latest access time of the temperature spot area data; packaging the cold point area elimination data, the warm point area elimination data and the failure data into a final transaction message; and broadcasting the final transaction message through a preset protocol, and performing distributed consistency maintenance operation on the final transaction message through the receiving node. According to the invention, intelligent and efficient cache data elimination and consistency maintenance management can be realized.

The invention discloses an industrial Internet of Things edge computing unloading method and system based on a distributed deep neural network, and the method comprises the following steps: firstly, introducing a feature matrix of an attention mechanism optimization task, transmitting the optimized task features to a multi-layer deep neural network DNN, and generating an unloading decision set in parallel; allocating resources for each task; after the to-be-unloaded task is preliminarily selected, computing resources of the high-priority task are preferentially guaranteed in the resource allocation stage according to the priority and deadline of the task. For tasks with relatively large calculation amount or relatively high priorities, decision storage is performed by using a cache strategy to optimize unloading and resource allocation processes of subsequent similar tasks, so that efficient and low-delay task unloading in a large-scale and multi-task environment is realized. Meanwhile, a distributed cache updating mechanism is utilized in each unloading time slot, the unloading decision precision is continuously optimized, and the unloading efficiency and the overall stability of the system are improved.

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

The invention relates to the field of data processing, and discloses a cloud computing-based data processing method, which comprises the following steps of: constructing a reference count management micro-service cluster at a cloud end, receiving a reference count request carrying a count type through an API (Application Program Interface) gateway, and analyzing the reference count request into a structured operation instruction; reading a first state identifier of the referenced object and a second state identifier of the reference object from the distributed cache system according to the metadata snapshot; according to the method, through dynamic scheduling of a reference counting management micro-service cluster and a container arrangement engine, efficient allocation and scheduling of computing resources can be realized, maximization of resource utilization is ensured, resource waste is avoided, and high-speed query capability of a distributed cache system and a memory database is utilized; the state identification of the object can be retrieved at a millisecond level, the system response speed is improved, and the performance requirement under a high-concurrency scene is met.

The present application relates to the technical field of middleware, and in particular to a cache data semi-synchronization method, comprising comprises the following steps: writing and running a distributed cache proxy layer service using a preset programming language, receiving a read / write command request from an application client, parsing a Key consistent hashing value and sending same to a rear-end Redis sharding node. A master instance parses a message packet, marks a write operation and records same in an AOF file. Upon write to disk, the master instance sends an AOF file transfer command to a slave instance node, and the slave instance acknowledges reception. When receiving a reception response, the master instance updates an acknowledgment offset of the slave node and responds to the client. When network delay is high, data of a master node and a slave node is marked as "asynchronous", and it is automatically downgraded to asynchronous replication. According to the present application, by means of a self-developed Redis kernel, an asynchronous ack mechanism is used to optimize redis to achieve master-slave semi-synchronization, thereby enhancing data reliability of a single point of failure, and eliminating the influence introduced by a native Redis asynchronous replication mechanism such as data delay and data consistency.

The invention relates to the technical field of data transmission, and discloses a flight data transmission method and system based on wireless communication and a storage medium. The method comprises the following steps: carrying out edge calculation noise reduction and data fusion processing on multi-source sensing flight data to obtain preprocessed transmission data; performing distributed cache node fragmentation and data synchronization processing on the preprocessed transmission data to obtain multi-source communication data; performing WiFi, 4G / 5G and satellite communication resource bidding and bandwidth allocation processing on the multi-source communication data to obtain priority network data; performing hierarchical network coding and transmission redundancy processing on the priority network data to obtain coded transmission data; and carrying out cloud edge cooperative decoding and data recovery processing on the coded transmission data to obtain transmission flight data. According to the invention, reliable transmission of flight data in a complex flight environment is realized, and the technical problems that an existing single communication mode is liable to be interfered and data transmission is unstable are solved.

The invention discloses a distributed cache optimization method and device, a storage medium and a product, and relates to the technical field of distributed caches, and the method comprises the steps: determining the overall distribution type of a data access mode through a low-precision sorter; the overall distribution types are classified through a high-precision sorter; and determining an allocation strategy of the cache resources through the cache allocator according to the distribution parameter combination under each overall distribution type, and performing optimal allocation of the cache resources according to the allocation strategy. According to the method, the overall distribution type of data access is identified through the low-precision sorter, so that the consumption of computing resources is reduced; the distributed parameters are refined through the high-precision sorter, the accuracy of data feature capture is improved, the hierarchical processing mode not only improves the accuracy and efficiency of decision making, but also can adapt to different access modes and maintain efficient cache management, and the problem that various existing algorithms are insufficient in universality in specific scenes is solved.

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 method and a system for improving data interaction efficiency of a hyper-converged architecture, relates to the technical field of hyper-converged architectures, and aims to solve the problems of network bottleneck and storage delay of data transmission among different virtual machines or containers in the conventional hyper-converged architecture. According to the scheme, a data transmission optimization module needs to be introduced into a hyper-converged architecture, so that the data access speed and the network efficiency are improved through data prefetching and an intelligent routing technology; the distributed cache module is used for relieving the pressure of a single node and ensuring accurate synchronization of data through data locality optimization and cache consistency management; the parallel data processing module is used for improving the data processing efficiency through task parallelization and data fragmentation technologies; and the self-adaptive load balancing module is used for dynamically adjusting resource allocation according to the system load and the resource use condition so as to realize the balance of calculation, storage and network load. The method reduces the delay, improves the throughput, and improves the overall performance and resource utilization rate of the system.

The invention discloses a large language model reasoning method based on a distributed KV cache pool. According to the method, a platform receives requests which are sent by a large number of users and need a large language model service, the requests of the users and machines in a cluster are modeled correspondingly, and then corresponding strategies are used for processing. In addition, the use condition of machine resources in the cluster is also considered, and machines with more idle resources are preferentially considered. In this way, interference caused by resource competition is reduced to a certain extent. Meanwhile, by abstracting memories of numerous NPU cards into a distributed KV cache pool, elastic expansion and contraction are facilitated during request processing. Through the method, an efficient cluster based on a large language model can be constructed, and a corresponding platform can better understand the demands and intentions of users so as to provide more timely and personalized services.

The embodiment of the invention discloses a multi-level caching method and device for a distributed database and a medium, relates to the technical field of database management, and is used for solving the problem that the current dynamic collaborative management effect is poor. The method comprises the following steps: monitoring a configuration change event of a configuration center based on a long polling mechanism to obtain configuration update data; issuing a configuration rule parameter corresponding to the configuration updating data to determine a current caching strategy, and performing caching hierarchy switching based on the current caching strategy to realize caching of the current changed data; monitoring the cache to determine whether the current micro-service cluster receives a cache failure instruction or not; if yes, operation indexes of the distributed relational database are received in real time, and whether database exception exists or not is determined, so that the current caching strategy is backtracked and updated; and asynchronously synchronizing the current changed data to the distributed cache database based on the backtracking updated cache strategy.

The invention discloses a data migration method and device, computer equipment and a storage medium, and realizes a data migration processing mode oriented to an unstable network environment by combining neural network prediction, decision tree anomaly recognition and a distributed cache mechanism. Therefore, the data transmission stability and the migration task completion rate in the cross-network environment are obviously improved. Firstly, a pre-trained neural network is utilized to carry out deep prediction on a real-time network state, and a potential performance reduction trend is identified in advance, so that a migration process can actively adjust a path or a strategy before a risk occurs. And secondly, by performing decision tree identification on a data packet sending sequence, the system can position a potential interruption point based on fine-grained time sequence characteristics, and quantify the risk in an interpretable manner, so that migration control can be accurate to a specific data packet level. Finally, under the condition that the risk is unacceptable, the system automatically activates a distributed cache mechanism, and the durability and the restorability of the data are guaranteed in a redundant copy storage mode.

The invention discloses an authority management method and system for double-token decoupling and dynamic token mapping, and relates to the technical field of information security. According to the method, a user identity is verified in a unified identity management system; receiving the user identity identifier in the target subsystem and generating a local permission token based on the permission model; in the token mapping service, when it is detected that a mapping relation is not established, the user identity identifier, the target subsystem identifier and the time slice identifier are combined according to a preset sequence, a one-time random factor is inserted, and a mapping key is generated through two-time abstract calculation of different secure hash functions and key parameters; determining a unified expiration time based on a smaller value of the validity period of the double tokens, generating a mapping value by using the exclusive key of the target subsystem in an encryption manner, and storing the mapping value, the mapping key and the expiration time in a distributed cache in an association manner; and the validity of the identity token is verified and the corresponding local permission token is obtained during user access, so that cross-system security access is realized, permission data migration is reduced, and compatibility and security are improved.

The invention provides a cloud vehicle data caching method, which is applied to a multi-layer storage architecture, and the multi-layer storage architecture comprises a memory queue layer, a distributed cache layer and a persistent storage layer. The caching method comprises the following steps: acquiring cloud vehicle data; caching the cloud vehicle data to a memory queue layer, obtaining a first cache capacity and a first cache duration of the memory queue layer, and when the first cache capacity exceeds a preset first capacity threshold or the first cache duration exceeds a preset first time interval, transferring the cloud vehicle data to a distributed cache layer; and acquiring a second cache capacity and a second cache duration of the distributed cache layer in real time, and when the second cache capacity exceeds a preset second capacity threshold or the second cache duration exceeds a preset second time interval, transferring the remaining cloud vehicle data to a persistent storage layer. According to the caching method provided by the invention, the memory resource utilization rate can be improved, the vehicle data caching efficiency is improved, and the fluency of the caching process is ensured.

The invention provides a multi-tenant multi-user login method based on enhanced OAuth2, which relates to the technical field of network security, and comprises the following steps: obtaining a key threshold through a tenant identifier, and executing OAuth2 authentication to obtain an access token; tenant fingerprint information is generated through zero-knowledge proof and homomorphic encryption, and a tenant verification token is generated through secure multi-party calculation; constructing a reinforcement learning access controller in combination with the historical access data, and generating and verifying an authority list; and finally forming a session identifier and storing the session identifier in a distributed cache. According to the invention, the authentication security is improved, cross-tenant fine-grained authority management is realized, and unauthorized access is effectively prevented.

The invention provides a distributed cache consistency system, method and device, a storage medium and a product, and is applied to a CXL switch, and a connection physical device comprises a first cache device and a CXL memory protocol-based memory extension device comprising a second cache device and a memory device. The system is composed of a virtualization aggregation layer and a cache consistency control module, the virtualization aggregation layer aggregates physical equipment as logic equipment, and the cache consistency control module intercepts operation requests, performs protocol conversion and queries directories to determine cache line states and equipment where the cache line states are located and feeds back target data. According to the scheme, the problems of delay, expansibility, communication efficiency and resource waste of the existing centralized architecture are solved. A distributed system is deployed on a CXL switch, so that processing skips and steps are reduced, and delay is reduced; a distributed architecture is adopted to avoid resource bottlenecks and improve expansibility; inter-device communication intermediate links are reduced, and the communication efficiency is improved; intermediate nodes of a data transmission path are reduced, transmission overhead is reduced, resource utilization rate is improved, and resource waste is reduced.

The invention discloses a distributed caching method and system and an acceleration method for large model reasoning service. The system comprises a confidential virtual machine cluster, a zero-trust distributed caching system and a FUSE client. The zero-trust distributed cache system comprises a data strategy service, a data engine plug-in, a distributed file system and a trusted access service. And the FUSE client is deployed at the confidential container end, and establishes secure connection with the trusted access service based on the remote proof and the identity legality confirmation proof of the corresponding confidential container so as to provide a plaintext data access service for the corresponding confidential container. According to the scheme, a distributed cache implementation scheme is provided for the confidential computing container cluster, and the problem of performance loss caused by repeatedly reading, encrypting and decrypting the same data from an external storage system is avoided while the data security is guaranteed by using the large-scale confidential container cluster; and the storage access efficiency in scenes such as model training and reasoning is improved.