1022 results about "Concurrency" patented technology

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

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

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

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

The invention discloses a resource scheduling method for dynamic rendering and related equipment, and relates to the technical field of dynamic rendering, and the method comprises the following steps: obtaining time sequence data of a user touch event; based on the time sequence data, predicting a user behavior state through a behavior prediction model, and determining a current resource demand level; dynamically adjusting a resource allocation strategy and a video memory management strategy of the graphic processing unit according to the resource demand level; and executing a rendering task based on the adjusted resource allocation strategy, and performing picture output by adopting a double-buffer architecture so as to reduce delay caused by time sequence mismatch. According to the method and the device, by establishing closed-loop control of a user behavior-resource demand-supply strategy, accurate scheduling and efficient utilization of resources are realized, the problems of time sequence mismatch, resource waste and jamming in the traditional technology are solved, the rendering efficiency and the user experience are improved, and technical support is provided for high-concurrency and low-delay scenes.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

The present invention discloses a modular AI-based system for knowledge instantiation and evidence synthesis, unifying five modules: Knowledge Instantiation, ingesting anchor knowledge via retrieval- augmented generation and neuromorphic prompting; Case-Based Agent Generation, orchestrating domain constraints, performance criteria, and sub-task decomposition; Secondary Epistemogenesis, spawning sub-agents with inherited heuristics; Workflow Orchestration and Evidence Synthesis, merging outputs, tracking metadata, and storing final artifacts; and Reinforcement Learning from Human Refinement, capturing feedback and edits. The method for output generation initiates by defining knowledge requirements, indexing public and proprietary knowledge, validating output structure, drafting content, and refining outputs via sub-agent spawning and human oversight. The system ensures domain alignment, concurrency control, and persistent versioning. By combining specialized knowledge sources, multi-agent concurrency, and iterative human feedback loops, it dynamically adapts to evolving knowledge requirements. This invention emphasizes reliability, traceability, and AI-driven outputs in regulated industries, delivering trustworthy outcomes anchored in expert knowledge.

The invention discloses a front-end task scheduling and event processing method and device, equipment and a storage medium, and relates to the field of distributed systems and network technologies, and the front-end task scheduling and event processing method comprises the steps of capturing events triggered by all sub-elements; processing the captured events into standard task objects in a unified format; adding the standard task objects into a scheduling queue according to a priority sequence, and performing de-duplication processing and task merging processing on all the standard task objects in the scheduling queue to obtain a cleaned scheduling queue; all the standard task objects are sorted again according to the priority in the multi-task priority queue; according to the priorities of the standard task objects in the multi-task priority queue, dynamically adjusting the resource allocation of each standard task object in the multi-task priority queue in combination with the current load of the system; and executing the standard task object allocated with the resources by combining an asynchronous execution mode with a user feedback mechanism. According to the method, the problems of task conflict and resource competition in high-concurrency interaction can be effectively solved.

The invention discloses a speech recognition streaming feature extraction and batch reasoning method for a high-concurrency artificial intelligence real-time dialogue system. According to the method, a heterogeneous computing architecture in which a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) cooperatively work is adopted, operations such as preprocessing, feature extraction and normalization of voice data are executed through the CPU, and batch feature reasoning is executed by utilizing the GPU; the method specifically comprises the following steps: after segmenting and preprocessing voice data at a CPU (Central Processing Unit) end, executing Mel filter bank feature extraction and cepstrum mean variance normalization to obtain a standardized acoustic feature vector; extracting feature vectors from the feature buffer area in batches at the GPU end, filling and aligning the feature vectors, and sending the feature vectors to a neural network model for parallel reasoning calculation; according to the method, streaming processing and batch reasoning in the speech recognition process are realized, and the concurrent processing capability and the real-time performance of the system are effectively improved.

The invention discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the system.

The invention relates to the field of heterogeneous computing resource management, in particular to a CPU (Central Processing Unit), GPU (Graphic Processing Unit) and NPU (Network Processing Unit) resource allocation method and system of a training and reckoning all-in-one machine. The invention discloses a CPU, GPU and NPU resource allocation system of a training and reckoning all-in-one machine. The system comprises a schedulable resource analysis module, a load analysis module and a resource allocation module. According to the method, the reference parameters and the real-time dynamic indexes of heterogeneous hardware are fused, so that deep perception and efficient quantification of computing power resources are realized; on the basis of an NPU temperature attenuation experiment, calibrating a computing power loss coefficient, and dynamically correcting available weight values of a CPU, a GPU and an NPU in combination with suitability analysis of a GPU stream processor utilization rate and a task batch processing demand and collaborative efficiency evaluation of a CPU dominant frequency and an IPC value; and the accuracy of resource availability prediction is improved, so that the system can still guarantee the stability of computing power output in complex environments such as high temperature and high concurrency.

The invention discloses a performance pressure test system and method applied to a large model streaming interface, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to comprehensively evaluate the performance of the large model streaming interface in a high-concurrency scene. Comprising a test data management module used for constructing a test data set; the concurrent access simulation module is used for dynamically configuring concurrent access parameters and simulating and generating a concurrent access scene of a plurality of users; the performance monitoring module is used for monitoring and recording the large-model streaming interface in the concurrent access scene; the fault-tolerant processing module is used for analyzing and processing the abnormal condition of the large model interface in the concurrent access scene based on a predefined abnormal processing strategy and forming an error log file; the post-processing module is used for calculating a performance index based on the monitoring data; and the result output module is used for constructing a test result file based on the error log file and the performance index and outputting the test result file.

The invention relates to the technical field of data analysis and calculation, in particular to a distributed scheduling method based on tasks, nodes and link resources, which is applied to a solution for task scheduling and management in a large-scale distributed system. The method is particularly suitable for cloud computing, big data processing and distributed storage systems which need to process mass data and high-concurrency requests. And dynamically adjusting resource allocation by using real-time monitoring data and a prediction algorithm in combination with task priorities and resource requirements. Compared with traditional static resource allocation, a dynamic resource allocation mechanism is adopted, resource allocation can be intelligently adjusted in real time according to task requirements and node resource conditions, and therefore the resource utilization rate and the task execution efficiency are improved.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention discloses a large language model reasoning method based on an unloading pipeline, and the method comprises the steps: obtaining an optimal unloading reasoning strategy through calculation according to the obtained model structure information and model configuration information of a large language model, and the hardware specification information and system operation load information of reasoning equipment; and then tasks in the optimal unloading reasoning strategy are scheduled through a fine-grained pipeline to output an unloading reasoning result of the large language model. According to the method, reasoning tasks are automatically configured according to input large language model information and a system hardware environment, and hardware resource use and reasoning performance are optimized. For deployment of a large language model on local equipment, transmission optimization is carried out for unloading using a solid state disk, so that the data transmission speed is increased. Meanwhile, fine-grained assembly line task scheduling is carried out for unloading reasoning, and the reasoning concurrency and the model throughput can be remarkably improved through unloading reasoning of an assembly line model.

The present application discloses a compute-in-memory chip, an instruction scheduling method, and a related apparatus. The compute-in-memory chip comprises an instruction memory, an instruction scheduler, and at least one compute-in-memory memory; each compute-in-memory memory comprises at least one storage array; the instruction memory is used for acquiring a first tensor instruction to be executed; and the instruction scheduler is used for scheduling, on the basis of the association relationship between the first tensor instruction and a second tensor instruction and the state of a target storage array needing to be operated for executing the first tensor instruction, the first tensor instruction to the compute-in-memory memory to which the target storage array belongs so that the compute-in-memory memory executes the first tensor instruction. According to embodiments of the present application, diversified compute-in-memory computing can be supported, efficient out-of-order execution scheduling of a tensor instruction set is achieved on the basis of the compute-in-memory chip, and the requirements of compute-in-memory technology for high concurrency and high throughput rate are met.

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

The invention discloses a real-time simulation performance evaluation method based on an ARMv8 architecture multi-core processor, and particularly relates to the technical field of multi-core performance evaluation. Comprising the following steps that a concurrent monitoring module is arranged, and an access instruction sequence and memory barrier instruction information of a processing core are collected; analyzing an address cross relation and time sequence dependence between the access instructions, and generating an instruction rearrangement risk access group; concurrency consistency analysis is carried out on the risk access group, race conditions are identified, and a synchronization mode and memory barrier instruction distribution are recorded; high-intensity concurrent operation is injected into the simulation environment, read-write operation of the shared variables is continuously tracked, and an instruction execution sequence is analyzed; the matching degree under different simulation load conditions is counted, a real-time simulation performance evaluation index for the weak memory model is generated, and the potential consistency problem caused by instruction rearrangement can be accurately recognized.

The invention provides an asynchronous-to-synchronous method and device, equipment, a storage medium and a product, and the method comprises the steps: responding to a first call of a first thread to an asynchronous interface, generating a first call identifier corresponding to the first call, responding to successful call of the first call, blocking the first thread, and generating a second call identifier corresponding to the first call; the first thread is used for executing synchronous processing logic; when the asynchronous interface stores a first asynchronous processing result to a buffer storage library, a second thread is triggered to obtain the first asynchronous processing result in the buffer storage library according to the first call identifier, and the first asynchronous processing result comprises an asynchronous processing result of the asynchronous interface on the first call; and the second thread sends the first asynchronous processing result to the first thread to wake up the first thread to continue execution. According to the method provided by the invention, excessive consumption of system resources and system crash risks in a high-concurrency scene can be avoided, and the stability of the system is improved.

The invention provides a service capability calling method and system in application software. The method comprises the following steps of: monitoring real-time temperature data and load state data of each available service node in a server cluster in real time, and combining a preset load balancing strategy and a virtual machine dynamic migration technology to ensure the stable operation of the server cluster in a high-concurrency scene. And when it is detected that the real-time temperature of the available server node exceeds the preset temperature threshold value or the capacity of the server node reaches the capacity upper limit, the service capability of the available server node is migrated to the standby node, and service interruption is avoided. Meanwhile, the real-time temperature data, the load state data and the execution frequency of the migration operation are subjected to correlation analysis, and a load balancing strategy and a migration trigger threshold value are dynamically optimized, so that service continuity is maintained, and the temperature fluctuation range of hardware resources is controlled. According to the technical scheme provided by the invention, continuous response of service requests and temperature stability of hardware resources in a high-concurrency scene can be realized.

The invention discloses a big language model reasoning performance evaluation and optimization method, electronic equipment and a storage medium. The method comprises the following steps: initializing a test environment; dynamically generating concurrent user requests; collecting performance data based on the concurrent user requests; performing real-time monitoring and reporting based on the performance data; and generating a performance evaluation report based on the performance data. Through the above steps, the method can comprehensively evaluate the reasoning performance of the LLM in a high-concurrency scene, and ensures the optimal performance of the system in different hardware environments.

The invention provides a heat supply digital twinborn intelligent regulation and control method, and relates to the technical field of intelligent regulation and control, and the method comprises the steps: executing millisecond-level high-concurrency numerical simulation according to a dynamic digital twinborn body, so as to obtain a simulation result; based on the simulation result, coupling the electricity price fluctuation data and the weather prediction data, performing future heat supply system multi-working-condition deduction, and dynamically optimizing a heat source unit start-stop combination and pipe network valve opening strategy to obtain a deduction result and an optimization strategy; generating an equipment regulation and control instruction set according to the deduction result and the optimization strategy; and an equipment regulation and control instruction set is issued to an edge computing node, a variable-frequency circulating pump and an electric control valve executing mechanism are controlled, and dynamic closed-loop regulation of the flow and the pressure of the heat supply pipe network is achieved. According to the invention, the pipe network flow and pressure regulation response time can be shortened to a second level.

The invention relates to the technical field of computers, and discloses a multi-mode audio and video synchronous processing method and system based on a distributed architecture. The method comprises the following steps: generating a unique logic sequence identifier consisting of a device code, a media type and a frame number for each frame of audio and video data; distributing the frame carrying the identifier to a distributed node for processing and adding a local timestamp; and the sink node reconstructs an original frame sequence according to the identifier, calculates a synchronous offset by combining high-precision clock calibration, and realizes dynamic reordering and output through a double-buffer structure and a self-adaptive drift compensation algorithm. The system comprises a source end acquisition module, an identifier generation module, a task scheduling module, a distributed processing cluster module, a convergence synchronization module and a synchronous output module. According to the scheme, the system effectively guarantees the audio and video synchronization precision in a high-concurrency heterogeneous environment, the synchronization error is controlled within 20 milliseconds, and the playing quality and the user experience of scenes such as live broadcast and cloud games are remarkably improved.

The invention discloses an intelligent resource scheduling method and system based on task characteristics and system state perception, and relates to an operating system kernel optimization technology. According to the method, resource demand types, importance labels and time sensitivity of tasks are captured in real time through an eBPF technology, system bottlenecks are recognized by combining / proc and sysfs file system data, a dynamic scheduling strategy is generated by adopting an improved Q-Learning model, and resource allocation is executed through a schemed * function and a cgroup v2 interface. The problems that a traditional scheduler is poor in self-adaptive capacity and resources are mismatched are solved, the actually-measured resource utilization rate is increased to 85%-92%, and the key task delay standard reaching rate gt is achieved; the method is suitable for edge computing, cloud servers and other high-concurrency scenes.

The invention provides a heterogeneous reasoning computing power scheduling method and device and a storage medium, and the method comprises the steps: receiving a reasoning request, splitting the reasoning request, and obtaining at least one reasoning task; according to the coding length of each reasoning task, scheduling the reasoning tasks to a long task queue or a short task queue; when it is monitored that reasoning tasks with to-be-processed task states exist in the long task queue and the short task queue, the working state of the reasoning service of the first processor is judged based on the number of tasks in processing of the first processor in the long task queue and the short task queue and the maximum concurrency of the first processor; the maximum concurrency of the first processor is obtained based on the current conditions of the long task queue and the short task queue; and on the basis of the working state of the first processor reasoning service, the reasoning task to be processed is scheduled to the first processor reasoning service or the second processor reasoning service so as to maximize and reasonably utilize the mixed computing power resource of the heterogeneous processor.

The invention belongs to the field of query optimization, and particularly relates to a fragmentation storage and query optimization method and system for a high-concurrency database, and the method comprises the steps: analyzing business features through a preset decision tree model, and selecting an optimal fragmentation key, constructing a fragmentation rule engine and initializing a database in combination with hash, range and list fragmentation rules and a fragmentation splitting-merging strategy; generating a query abstract syntax tree by using an ANTLR4 analyzer, and detecting whether a preset Cube is hit or not to directly return a result; if not, dynamically routing to a target fragment list based on a fragment key field, load balancing and a failover strategy, accelerating data retrieval in combination with a high-frequency index, and combining fragment-level results through a T-TopK algorithm; according to the method, the self-adaptive matching of the fragmentation strategy and the service requirement, the calculation push-down of the query process and the result optimization are realized, and the low delay and the high availability in a high-concurrency scene are ensured.

The invention provides a task scheduling method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the following steps: adjusting the current priority of each task to be scheduled in a main queue according to the current resource load; under the condition that the priority adjustment frequency corresponding to the to-be-scheduled task reaches a preset frequency and the duration of the to-be-scheduled task entering the main queue exceeds a first preset duration, the to-be-scheduled task is stored in a retention queue as a retention task; for the detention tasks with overlong waiting time in the detention queue, the detention tasks are added into the main queue again after the priorities are adjusted, so that the tasks are scheduled to be dequeued and executed according to the dynamically adjusted priority sequence, the tasks with high priorities can be executed as soon as possible, and the situation that the tasks with low priorities cannot be processed for a long time can be avoided; the efficiency and the fairness of task processing are considered under the condition of high concurrency.

The invention discloses a method and a device for optimizing dynamic task scheduling resources of a distributed system. The method comprises the following steps of: monitoring a task dependency relationship and hardware resource state data in real time; a dependency release amount R representing the downstream task activation capability is generated based on the dependency relationship; generating an index A representing the resource utilization efficiency based on the resource state; a scheduling strategy is dynamically selected according to the load state: when the load is low, the task with the maximum R is preferentially allocated to improve the CPU parallelism degree, when the load is stable, allocation is carried out according to R and A weighted values to balance resource utilization, and when the load is high, the task with the maximum A is preferentially allocated to reduce the memory pressure; and finally driving task execution and updating a resource state. By dynamically sensing the task topological relation and the resource state, adaptive scheduling under load fluctuation is realized, the collaborative utilization efficiency of CPU and memory resources is effectively improved, and the problem of performance bottleneck of a traditional scheme under complex dependence and high-concurrency scenes is solved.

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.