1875 results about "Pool" patented technology

The invention discloses a compatible operation method for an Android camera HAL in a container based on memory access virtualization, and the method comprises the steps: taking a DMA-Buf memory heap of a Linux kernel host system as a target memory heap, taking an ION memory heap of an Android container system as a source memory heap, building a process exclusive memory management context, an FD cache table and a synchronous fence pool, and carrying out the execution of the process exclusive memory management context, the FD cache table and the synchronous fence pool; kernel registration and node binding of virtual ION equipment, pre-allocation of a first memory pool and access hook registration of kernel layer equipment are completed, and when an ION file descriptor is obtained in an HAL process, legality of a container process is verified, and context binding is initialized to the file descriptor; intercepting a memory allocation request of the HAL process, analyzing and adapting parameters, and preferentially multiplexing first memory pool resources to obtain an ION handle; when a data sharing request is processed, matching an FD cache or generating a new FD through an ION handle; when the HAL process releases the memory, resources are recycled according to a memory source, and when the HAL process exits, the context is cached or destroyed, so that cross-system memory operation compatibility is realized.

The invention relates to the technical field of page rendering, and discloses a progressive page rendering method and system based on streaming data, and the method comprises the steps: carrying out the data segmentation through obtaining a data stream, user interaction data and equipment performance parameters, and obtaining data blocks; then, performing cache management in combination with the data, and constructing a multi-level cache pool; thirdly, performing priority grading and sorting on the data blocks to form a rendering task queue; and according to the equipment performance parameters, optimizing a task sequence and obtaining an optimized task sequence. Next, combining the optimized task sequence and user interaction data, predicting data about to enter a viewport, and generating a viewport pre-rendering task; and finally, according to the viewport pre-rendering task, the multi-level cache pool and the equipment performance parameters, performing rendering strategy optimization to obtain a dynamically adjusted rendering task flow. The method can realize dynamic resource scheduling.

The invention provides a memory pooling method and system for model reasoning acceleration and a computer program product, and relates to the technical field of computers. The model is a separation pooling architecture, the separation pooling architecture comprises a pre-filling node pool, a decoding node pool and a CXL memory pool which are separated, and the memory pooling method for model reasoning acceleration comprises the steps that a reasoning request is allocated to a first pre-filling node in the pre-filling node pool based on a first scheduling strategy; the first pre-filling node processes the reasoning request to obtain a key value cache; storing the key value cache in a CXL memory pool; selecting a first decoding node in the decoding node pool based on a second scheduling strategy; the first decoding node obtains a key value cache from a CXL memory pool based on the reasoning request; and the first decoding node generates a reasoning result according to the obtained key value cache.

The invention discloses a park management method based on digital twinning, particularly relates to the field of park operation, and is used for solving the problem that a multi-source event is difficult to stably chain under the conditions of compensation delay and evidence gap and causes misalignment of disposal triggering. Comprising the following steps of: registering an event in a pool to generate an event number and writing the event number into a platform arrival moment; screening and mapping an anchor point event to a park object identifier and a process stage identifier to generate a management token; checking a stage sequence and an evidence item requirement set according to an event chain rule table in event chain construction and generating a conflict point list; the delay portrait library generates a credible mark when a source is generated and forms a reverse sequence violation spectrum; the digital twin object relation graph forms an influence domain span mark; the closed table is returned to obtain a disposal convergence guarantee level; the cost-sensitive decision tree outputs a disposal template identifier, and the disposal template table generates a management action instruction and a trigger voucher and records an instruction acceptance identifier; and returning, comparing and freezing the exception management token, and updating the event chain rule table and the delay portrait library.

The invention discloses a Direct3D rendering model compatible method based on a dynamic template pool, which comprises the following steps: establishing three types of mapping tables between D3D and Vulkan when compiling DXVK, constructing resource metadata, and creating a core, extended and temporary three-level template pool according to the mapping tables after starting; when the D3D application creates resources, metadata is initialized, parameters are verified, physical memories are allocated and grouped, resource groups are pre-verified, a batch binding command is generated, memory binding is completed, and the metadata is updated; when a resource view is created, a template is matched from a template pool, a handle is generated after instantiation, a resource handle is associated, and a descriptor is bound; when a rendering state is set and a rendering instruction is executed, the rendering state and the rendering instruction are respectively converted into a Vulkan related state and a Vulkan related instruction through a mapping table, a handle is bound after a PSO cache is inquired, a command buffer area is submitted to a GPU queue to execute drawing, and compatible operation of the D3D application on a platform supporting a Vulkan operating system is realized under the condition that the GPU does not support VKKHRmaintence5 and VKKHRmaintence6 extension.

The invention relates to the technical field of artificial intelligence and natural language processing, and discloses a task processing method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the dynamic batch processing of an input text task, and obtaining a conventional batch and an ultra-long batch; detecting a task belonging to an ultra-long batch in the to-be-processed text task as a priority text task, and realizing preemptive scheduling of the current text task by the priority text task through a hardware interrupt mechanism; calculating the priority text task by adopting a local-global mixed attention mechanism; calculating the text tasks belonging to the conventional batch by adopting a first sparse rate, and generating a first historical key value pair; calculating a priority text task by adopting a second sparse rate, and generating a second historical key value pair; and caching the first historical key value pair in a first cache pool, and caching the second historical key value pair in a second cache pool. The method can be applied to the field of financial science and technology businesses, and efficient differentiation processing and computing resource optimization of super-long and conventional text tasks are achieved.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention provides a super-node system which comprises an intelligent computing resource pool and a general computing resource pool, the intelligent computing resource pool comprises a first in-pool switching module, a first inter-pool switching module and a plurality of GPUs, the general computing resource pool comprises a second in-pool switching module, a second inter-pool switching module and a plurality of CPUs, and decoupling of heterogeneous computing resources is achieved. The GPUs in the same intelligent computing resource pool and the different intelligent computing resource pools can communicate through the first in-pool switching module, the CPUs in the same general computing resource pool and the different general computing resource pools can communicate through the second in-pool switching module, and the system can provide intelligent computing and general computing resources at the same time. And the intelligent computing resource pool and the general computing resource pool can be expanded respectively, so that elastic matching of resources is realized, and the utilization rate is improved. Intelligent computing resources and general computing resources are pooled and deployed in different cabinets, deployment decoupling of heterogeneous resources is achieved, the single-cabinet GPU density of the intelligent computing resource cabinets is improved, the influence range of single-point faults is reduced, and maintenance is easy.

The invention discloses a geological exploration service management method and system based on cloud intelligence, and relates to the technical field of geological exploration management, and the system comprises a standard specification system, a base layer, a data layer, a platform layer, an application layer and a safety guarantee system. Data collaborative acquisition, processing and sharing are realized through a multi-level architecture design, project approval analysis, work layout, task distribution auditing and data management are realized, multi-specialty collaborative acquisition and safety management of a field data acquisition APP are supported, rapid mapping and standardized filing of an interior work data processing tool are supported, a standardized process is formed, and data processing efficiency is improved. The working efficiency and the data quality are improved through indoor and outdoor real-time collaboration, multi-source data verification, edge calculation and the like, the data safety is guaranteed, the team collaboration and achievement customization ability is enhanced, meanwhile, integrated data sharing is achieved through the cloud computing pool, and the efficiency of geological exploration work is improved.

The invention discloses a garbage collection method and device, equipment and a storage medium, and relates to the technical field of electrical digital data processing.The method comprises the steps that when metadata of a cache pool is updated, internal object abstracts corresponding to the metadata are put into a garbage collection queue; according to the water level condition of the data pool and the disk utilization rate of the object storage device of the data pool, the single-round execution task number of the garbage collection queue is determined; according to the number of single-round execution tasks of the garbage collection queue and the garbage collection priorities of the internal objects, the internal objects are selected to execute garbage collection, so that effective data in the internal objects are aggregated into new internal objects, and the internal objects subjected to garbage collection are deleted, namely, the situation that the water level of the current data pool is too high, and the garbage collection efficiency is improved is avoided. And the overlarge disk utilization rate of the object storage equipment is avoided, and the two are balanced, so that the influence on other businesses is avoided as far as possible on the basis of ensuring the normal recovery of the garbage.

The invention discloses a resume analysis method and system based on multiple large language models, and belongs to the technical field of large language models.The resume analysis method and system based on the multiple large language models.The resume analysis method and system based on the multiple large language models comprise the following specific steps that firstly, model parallel analysis is conducted, simultaneously inputting the data into at least two heterogeneous large language models through an application program interface; and each large language model independently performs information extraction and analysis according to the model structure and the training data of the large language model, and outputs a structured data result containing a plurality of preset fields. Through the multi-model parallel analysis and conflict re-judgment mechanism, the misjudgment risk of a single model is effectively reduced, the robustness of the whole system is improved, the resume analysis accuracy is remarkably improved, the model pool is automatically optimized and updated through the dynamic scoring mechanism, and the problem that the model is difficult to select and update is solved.

The invention relates to a mixed expert multi-model task processing method and system based on an AI Agent scene. The method comprises the following steps: constructing a knowledge block set for unstructured data and semi-structured data; constructing an associated document vector set of topN similarity; user input and a reverse decoding document are spliced, and user tasks are divided by using LLM to generate subtask sets; constructing a task allocation data set # imgabs0 # based on the scoring model and the expert model pool; and the dependency aggregation # imgabs1 # for generating the subtask answers is used as a final user task result. According to the method and the system, fine-grained division and route selection of the model pool are carried out on user tasks, so that the accuracy, diversity and authenticity of answers required by users can be effectively improved, and the multi-model parallel utilization rate of an Agent system is enhanced.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses a distributed training scheduling and communication optimization method and system of a multi-modal large model on a domestic computing power platform. The method comprises the following steps: virtualizing a heterogeneous computing unit of a preset platform into a virtual device pool, and fusing first-order gradient of a multi-modal sample and Hessian matrix information based on quantitative perception training to generate a sample sensitivity grading atlas; virtual device pool attributes and the sensitivity grading atlas are used as input, an optimal hybrid parallel configuration scheme is automatically generated through a configuration search algorithm, and a parallel combination mode, resource mapping and a high-sensitivity sample scheduling strategy are defined; a distributed training code of an integrated communication optimization strategy is automatically generated according to a configuration scheme, pipeline parallel communication and data parallel gradient synchronization constraint are executed in a topology adjacent equipment subset, and a hierarchical aggregation mechanism is adopted; and dynamically screening a core training set and scheduling a calculation task to complete distributed training. According to the method, efficient cooperative training of the multi-modal large model on the domestic computing power platform is realized.

The invention discloses a video stream concurrent access front-end dynamic scheduling control system and method based on a digital twinborn scene, and relates to the technical field of computer software and network communication, the system comprises three core modules: a self-adaptive request queue module manages UE rendering resources based on a session token pool, distributes tokens through preemptive scheduling, and sends the tokens to a server; in combination with a GPU frame rate feedback dynamic adjustment strategy, queuing through a Promise asynchronous queue when the request fails; the front-end fusing and backoff retry module monitors the connection failure rate, when the connection failure rate reaches a threshold value, fusing is triggered, retry is delayed by adopting an exponential backoff strategy, and a semi-open state tends to recover; and the local cache collaboration module intercepts the request through Service Worker, caches the latest video clip, pushes the local content when the request is delayed, and ensures the freshness through ETag verification. The method and the device are used for efficiently managing loading and interaction of multiple users on real-time rendering pictures at a browser end.

Provided are a multi-agent construction method and apparatus, a task processing method and apparatus, a computing device, a storage medium, a computer program product, and a chip. The multi-agent construction method comprises: splitting a task into a plurality of sub-tasks; determining, from an agent pool, agents respectively matching the plurality of sub-tasks, wherein the agent pool comprises a plurality of agents, and the agents matching the sub-tasks can process the sub-tasks by calling at least one of tools and other agents which are configured by the agents; and constructing a multi-agent system on the basis of the agents respectively matching the plurality of sub-tasks, wherein the multi-agent system is used for processing the task.

The invention discloses an MQTT message transmission optimization method and system. The method comprises the steps of analyzing a theme, extracting a device type, a data feature and a geographic position triple, calculating a hash value, and mapping a device to a specified Broker fragment cluster node to generate a fragment mapping table; processing the equipment data in the edge domain in the fragment mapping table through an edge calculation layer, removing invalid data according to a preset rule, merging the equipment data in the same fragment node, and embedding a fragment node ID for an aggregation message generated after merging; identifying a fragment node ID and routing to a target fragment node, positioning a corresponding shared memory pool, and writing the message into the shared memory pool; and responding to a direct access request of the client, so that the client directly accesses the data from the shared memory pool through the user mode network stack. According to the method, the problem of uneven load is effectively solved, multiple times of state switching in the data transmission process is avoided, and the MQTT message transmission efficiency is improved while the transmission cost is reduced.

The invention relates to the technical field of printing task scheduling, in particular to a printing task scheduling method and system based on a main control chip and a terminal, and the method comprises the steps of initial task classification, dynamic priority adjustment, task queue recombination, resource allocation execution, termination mechanism triggering and the like. The high-priority task response speed is optimized through system load monitoring and a waiting time coefficient, and a real-time task pool and an elastic buffer pool are divided to improve the scheduling efficiency. According to the method, the real-time task pool and the elastic buffer pool are divided through an algorithm of'dynamic memory pool partitioning + task life cycle binding ', and the memory blocks are pre-allocated according to the task types, so that the real-time performance and the scheduling efficiency of the high-concurrency printing tasks are improved, rapid response of high-priority tasks can be realized, delay of key tasks is reduced, and the efficiency of the high-concurrency printing tasks is improved. The resource utilization rate and the scheduling fairness are improved, and the method is suitable for high-concurrency printing scenes.

Techniques for host fleet management in a cloud provider network are described. Forecast data including a forecasted demand for virtual machines in each capacity pool of a set of capacity pools is obtained. A mathematical optimizer application is executed to generate a first optimal fleet plan, the mathematical optimizer application having an objective function to minimize a number of new host computer systems to add to the set of host computer systems to satisfy the forecasted demand for each capacity pool, the first optimal fleet plan includes an identification of a set of hardware types and, for each hardware type in the set, a quantity of new host computer systems of the hardware type. A plurality of new host computer systems is deployed, based on the first optimal fleet plan, for a hardware type in the set of hardware types into the set of host computer systems.

The embodiment of the invention discloses a task pool priority adjustment method and device, equipment and a medium, and is applied to a specified system, the task pool priority adjustment method comprises the steps that the priority of each task queue in a task pool is defined according to service requirements and task characteristics, and each task queue comprises a plurality of tasks; calling the tasks in each task queue in the task pool based on a preset task scheduling algorithm; acquiring a real-time load parameter of the specified system; and if the real-time load parameter exceeds a preset threshold value, adjusting the tasks in each task queue.

The invention belongs to the technical field of blockchains, and discloses a blockchain-based node computing power resource allocation method and a storage medium, and the method comprises the steps: collecting the load data of each chain in real time through a resource propagation chain control point mechanism as a node computing power resource, screening idle resources through a smart contract, and injecting the idle resources into a hierarchical resource pool, generating a cross-chain shared pool and a metadata index; the smart contract receives each chain resource request, obtains a two-dimensional priority, generates a scheduling sequence, synchronously arbitrates resource competition, and generates a resource allocation instruction; dividing a high-computing-power verification node and a low-computing-power verification node according to node computing power, dynamically matching an encryption strategy, generating a decryption key fragment, and forming an encrypted transmission packet; a target chain consensus mechanism is adapted, a resource request format is converted, a corresponding verification strength is matched, resource transfer is executed, and a handover voucher is generated; and the intelligent contract monitors the resource use condition, rewards points on the active shared nodes, deducts contribution degrees from the hidden resource nodes, recycles resources, and updates the quota of the cross-chain shared pool.

The invention discloses a configurable system for externally providing multi-source interface data, which comprises a flow arrangement engine, a data processing engine and a data processing engine, and is characterized in that the flow arrangement engine comprises a visual node arrangement interface, a dynamic input parameter module and a flow variable pool; through version isolation, new version configuration is automatically generated when a user modifies a process, an old version continuously processes a stock request until the stock request is completed, and a new request is automatically routed to the new version, so that service switching is non-aware, and the risk of service interruption caused by server stopping and restarting in a traditional scheme is eliminated; free combination of heterogeneous data source nodes through a graphical interface and dynamic adjustment of an execution sequence are supported, the solidification problem of a traditional hard coding process is solved, a process variable pool temporarily stores an intermediate result of each node for a subsequent node to reference through a preset format, the limitation that a multi-step result cannot be reused in a traditional scheme is solved, and flexible series connection of heterogeneous data sources is achieved; each configuration version has an independent process variable pool, an intermediate variable generated by an old version only serves a self task, a variable pool of a new version is completely isolated, data cross contamination during parallel of multiple versions is avoided, and result consistency is ensured.

Thermal output aware workload placement is disclosed. When a scheduler receives a workload to be deployed in computing resources such as a pool of servers, thermal data that may include fan related data and power consumption data is used to select one of the servers for the workload. The thermal data is used to select the server that reduces or minimizes the thermal output of the server and / or the computing resources.

The invention discloses a database connection pool management method and device, equipment and a storage medium, and relates to the technical field of database management, the method comprises the following steps: creating a corresponding cluster connection pool for each cluster according to cluster parameter information and cluster configuration information, the cluster connection pool comprising a plurality of connection clusters, the connection cluster comprises a plurality of target connections, and the target connections carry first tags and timers; creating a database cluster pool according to the corresponding relationship between the cluster identifier and the cluster connection pool; and managing the target connection based on the database cluster pool, the first tag and the timer. The database connection resources are managed in a unified manner through the two-stage pool, so that the overhead of connection establishment and disconnection is reduced, and the processing efficiency is improved. And assigning a label to each connection, and upgrading the database connection pool into unequal stateful connections. And the timer is used for marking and tracking the use condition of each connection, so that the life cycle of the connection can be managed, and the management efficiency of the connection pool is improved.

The invention belongs to the technical field of large language models, and particularly relates to a large language model KV cache compression method based on interlayer fusion. The method comprises the following steps: inputting a to-be-processed text into a large language model to obtain a KV cache; the compression ratio of each layer of the large model is calculated, and preliminary SVD compression is carried out on KV cache of different layers of the large model; the KV cache subjected to preliminary compression is subjected to block division, and a plurality of blocks are obtained; calculating the attention score of each block; s blocks with the highest attention score are selected, and SVD reconstruction is carried out on the selected blocks; constructing a cache pool to store the reconstructed S blocks; dynamically updating the cache pool; splicing the S blocks in the cache pool to obtain a KV cache, and completing the compression of the KV cache; according to the method, the model is ensured to maintain stable prediction accuracy under diversified task scenes such as text generation and question and answer systems, and balance between storage efficiency and performance is realized.

A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.

The invention discloses a storage space management method and device, a storage medium, electronic equipment and a program product, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining a plurality of physical addresses of a plurality of physical storage equipment on a server corresponding to different nodes in a distributed storage system; a plurality of physical addresses are connected in series to generate address space pools corresponding to different servers, and linked lists corresponding to the address space pools are determined; under the condition of determining that the target object initiates the logical volume creation request, determining a target number of logical addresses from the address space pool according to the linked list, and recording a mapping relationship among the logical volume, the logical addresses and a target physical address associated with the logical addresses; the storage space associated with the logical volume is managed based on the mapping relation, the storage space at least comprises two physical storage devices, and the problem that when an existing distributed storage system carries out data storage, metadata access delay is caused by data striping is solved.

The embodiment of the invention provides a model reasoning data caching method and device for a caching system and a storage medium, and the method comprises the steps: configuring a first virtual address mapped to a high-performance memory of the caching system for a data caching pool, and generating a first reasoning data according to the data size of the first reasoning data generated by a target reasoning model; determining a target memory space of the high-performance memory mapped by the first virtual address, and enabling the data volume of the first reasoning data to be in direct proportion to the memory space volume of the target memory space mapped by the first virtual address, thereby creating a dynamic data buffer pool of the target reasoning model, and then caching the first reasoning data based on the target memory space corresponding to the dynamic data buffer pool, thereby realizing on-demand allocation of the high-performance memory in the cache system, and improving the utilization efficiency and the use flexibility of the high-performance memory in the cache system.

The invention provides a server GPU computing power allocation method and system and a server, belongs to the field of server resource management, and solves the problems of low utilization rate and inflexible decision of traditional allocation resources. The method comprises the steps that a central agent constructs a directed weighted game diagram of tasks and a GPU cluster, and a global allocation strategy is solved based on Nash equilibrium; a local agent distributes tasks to a specific GPU through reinforcement learning, intelligent migration is achieved in combination with a dynamic threshold value and anomaly detection, and a strategy is iteratively optimized through federal learning. The system comprises a central agent, a local agent, a GPU cluster and an experience pool module, and a server carries the system execution method. According to the scheme, through double-layer agent cooperation and multi-algorithm fusion, the task cluster adaptation relation is quantified, the allocation strategy is dynamically adjusted, the resource utilization rate and the task processing efficiency are remarkably improved, and the adaptivity and reliability in a complex scene are enhanced.

The embodiment of the invention discloses a multi-core heterogeneous processor-oriented computing pool event-driven scheduling method and device. One specific embodiment of the method comprises the following steps: performing task node decoupling on an original task flow chart to obtain a virtual operator subset and a subtask information set; determining a candidate processor corresponding to each virtual operator according to mapping configuration representing an execution relationship between the operator and an execution processor; determining operator execution sequence information corresponding to each virtual operator according to a task node dependency relationship corresponding to the sub-task information set; for each virtual operator in the virtual operator set, executing a heterogeneous resource allocation step to serve as a virtual operator execution carrier; and through an event-driven mechanism and the operator execution sequence information, scheduling a virtual operator execution carrier to perform sequential execution of each sub-task to obtain a task execution result. According to the embodiment, the parallel efficiency when the multi-core heterogeneous processor processes the graph calculation task is improved, and the scheduling delay is reduced.