564 results about "Thread pool" patented technology

The invention provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The invention relates to the technical field of electric data processing, and discloses a system and a method for hybrid processing of mass offline data and mass real-time data, and the system comprises a queue management module which receives task requests, identifies and marks task types, distributes tasks to a stream processing queue or a batch processing queue, and sorts the tasks according to priorities; the task types comprise flow tasks and batch tasks; the thread pool management module establishes a thread pool, sequentially processes tasks according to priorities and collects real-time parameters; the calculation module is responsible for calculating task priorities and calculating task jitter factors based on real-time parameters; the scheduling decision module compares the task jitter factor with a reference value, determines a weight adjustment coefficient, corrects the priority and formulates a scheduling strategy; the alarm module judges whether to trigger an alarm. According to the invention, by introducing the dynamic priority scheduling and preemptive processing mechanism driven by the jitter factor, the real-time response capability and the resource utilization efficiency of the system are improved.

The invention belongs to the technical field of data transmission and processing, and discloses a zero-copy-based multi-thread high-throughput data stream channel separation method and system, and the method comprises the steps: directly writing a mixed data stream into a front-end buffer region configured as an annular structure through a data receiving module by adopting direct memory access; then, a multi-thread processing module dynamically allocates a plurality of processing threads from a thread pool to separate channel data in parallel, each thread adopts a zero copy algorithm based on pointer offset, positions the channel data in a memory, creates pointer reference and associates the channel data to a corresponding rear-end buffer area, and logic separation is achieved without physical copy; and finally, the data storage module efficiently writes the separated data into persistent storage in an asynchronous I / O mode. According to the method, zero-copy, multi-thread parallel and two-stage dynamic buffering strategies are combined, the data separation efficiency is remarkably improved, CPU occupation and memory bandwidth are greatly reduced, and the real-time performance and stability of high-throughput data processing are guaranteed.

The invention relates to the technical field of computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a method and device for dynamically adjusting threads of a thread pool, electronic equipment and a storage medium. The method comprises the steps that initialization setting is conducted on thread pool parameters through a management center of a target service system; when a thread pool parameter adjustment request is received, generating a first thread pool parameter according to the thread pool parameter adjustment request; receiving a configuration change event through a configuration change monitor, and analyzing the configuration change event to obtain a first thread pool parameter; positioning a thread pool configuration field of the distributed service instance according to a mapping relation between a program entity factory and a parameter cache; injecting the first thread pool parameter into a thread pool component instance of the distributed service instance; and calling the setting function of the distributed service instance, and resetting the corresponding configuration parameters in the thread pool component of the distributed service instance. The method improves the adaptability of the system to load change, and breaks through the system performance bottleneck.

The invention discloses a three-area three-line database automatic quality inspection evaluation method based on deep learning. The method comprises the following steps of S1, obtaining three-area three-line database data and performing preprocessing; s2, checking a directory structure and a file name of the original data set; s3, extracting spatial layer data from the hierarchical data set; s4, performing segmentation and attribute information extraction on various pattern spots in the layer data after spatial verification; s5, inputting the pattern spot relation characteristic data into an artificial fish swarm algorithm; s6, automatically comparing the pattern spot area and distribution data with a quality inspection standard preset in a three-area three-line database; and S7, performing parallel processing on an automatic comparison result through a multi-thread thread pool. According to the method, an artificial fish swarm algorithm, a relational attention network and a task allocation and thread pool management technology are combined, and three-area three-line database automatic quality inspection evaluation based on deep learning is realized.

The invention relates to a railway material purchasing asynchronous task processing method, device and system, and the method comprises the steps: constructing a six-layer message channel architecture of a purchasing business process, and executing an instruction of deploying a message agent node in an active-active mode for each channel based on the core message middleware of a Kafka cluster; the front-end gateway is instructed to package the purchase event into a binary message body with a transaction ID and a timestamp, and the binary message body is written into a persistent queue after priority labeling is carried out according to a task type label; processing the concurrent tasks by adopting a multi-stage feedback queue scheduling algorithm, and dynamically allocating computing resources based on a thread pool; a maximum retry threshold value is set according to an exponential backoff retry strategy, so that the failed task is automatically transferred into a dead message queue; and dynamically calculating an optimal message routing path based on the supplier portrait and the material classification model, and extracting the order to an exclusive processing queue of the optimal supplier according to Topic dynamic switching of the message queue. According to the invention, the coupling degree is reduced, the system performance is improved, and the fault tolerance is enhanced.

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

The invention provides a method and system for dynamically exporting Excel based on asynchronous streaming block data, and belongs to the technical field of large data volume export. Comprising the following steps: receiving a user export request, and generating a unique request ID; an export task is generated based on the export request, a task ID is returned, and an asynchronous response is made; submitting the task to a thread pool queue, and obtaining block data from a database in batches through streaming query; wherein the core thread count and the maximum thread count of the thread pool are adjusted based on a dynamic resource allocation method, and load change is smoothed in combination with a sliding window algorithm; writing the block data into an Excel file batch by batch by adopting a streaming writing technology; and after the task is completed, pushing a notification to the user side. According to the method, the rapidity, the availability and the safety during data export can be ensured when large data volume export is performed.

The invention discloses a cloud hard disk data backup and recovery method and device, electronic equipment and a storage medium, and relates to the technical field of cloud computing. A cloud hard disk is segmented into a plurality of segmentation blocks and a multi-thread parallel processing mode is adopted, a backup and recovery task of a large-capacity cloud hard disk is decomposed into a plurality of sub-tasks to be processed at the same time, and the data backup and recovery efficiency is improved. The operation time is greatly shortened, the number of threads in the thread pool is determined according to the number of the segmentation blocks and the current available computing resources, the utilization rate of the computing resources can be improved, cloud hard disk segmentation strategy parameters can be flexibly configured according to service requirements and cloud hard disk characteristics, data protection requirements in different scenes are met, and the user experience is improved. Consistency verification is carried out on the transmitted data, so that the integrity and consistency of the data in the backup and recovery process are ensured, the data loss risk is reduced, and the purposes of improving the backup and recovery efficiency of the cloud hard disk and enhancing the system reliability and the computing resource utilization rate are achieved.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

The invention relates to the technical field of data management, and discloses a dynamic partitioning and priority scheduling method based on time sequence data, which comprises the following steps: collecting time sequence characteristics of an industrial data source through an edge gateway and carrying out structured storage in Redis, the time sequence characteristics comprising collection frequency, historical peak time period and service priority; load indexes are calculated based on time sequence characteristics, and a distributed message system is driven to execute dynamic partition adjustment of pre-judgment type capacity expansion, hot spot precise splitting and low-peak intelligent capacity reduction; and full-link data priority scheduling is realized through production end route isolation, Broker end resource inclination and consumption end thread pool isolation. According to the invention, the dynamic adaptation of the distributed message system resource and the industrial time series data load is realized, the real-time performance of high-quality data is ensured, the integrity of data transmission and the system adaptability are improved, and the problems of poor load fluctuation adaptation, high-quality data delay and easy data loss due to faults in the prior art are effectively solved.

The invention provides an elastic thread pool management method and device based on load prediction and electronic equipment, and the method comprises the steps: obtaining a task submitted by a client, and routing the task to a corresponding queue according to the task feature of the task; wherein the I / O type task is routed to an asynchronous queue, and the CPU calculation type task is routed to a thread pool queue; monitoring and collecting thread index data of the thread pool queue in real time, and obtaining thread index data of a first preset time period based on a preset sliding window; inputting the thread index data of the first preset time period into a prediction model to obtain a predicted load value of a second preset time period; wherein the prediction model is a Holt-Winters model, and the prediction model is a Holt-Winters model; and performing thread adjustment based on the predicted load value and a preset load threshold value. The problems existing in an existing thread pool management scheme are solved.

The invention discloses an unmanned ship-oriented task priority scheduling and resource awareness management method, and belongs to the technical field of unmanned ship control. The method comprises the steps of obtaining a task request generated by an unmanned ship system, inputting the task request into a task classifier for task classification and priority allocation, performing real-time monitoring on resources of the unmanned ship system based on a resource perception and threshold grading model, judging an alarm interval where the resources of the current unmanned ship system are located, and sending the alarm interval to the unmanned ship system. Obtaining a target scheduling strategy; allocating each task in the task request to a corresponding thread pool for execution, dynamically adjusting the scheduling of each task according to a multi-thread pool cooperation and preemption mechanism, and dynamically optimizing the load and channel of the unmanned ship system based on a load balancing and channel selection mechanism; feedback information during execution of each task is collected, a target scheduling strategy is adjusted through an adaptive optimization mechanism, and scheduling and resource allocation of each task are updated in real time. The method improves the resource utilization rate of the unmanned ship system.

The invention relates to a multi-modal interaction-oriented automatic operation backtracking and digital person continuous talking control method and system, and the system comprises an operation snapshot module which records operation nodes through a semantic anchor point marking technology, such as an interface XPath page number and a PPT page number; the interruption detection module is used for capturing a user interruption instruction in real time; the progress prediction engine dynamically adjusts a follow-up demonstration path based on a program; and the continuous talk controller is used for coordinating RPA operation execution, digital human rendering and voice synthesis through a three-level priority thread pool. The problems of inaccurate operation flow backtracking and multi-modal thread conflict after demonstration interruption are solved, the breakpoint continuous talk error is obviously lower than that of a traditional scheme, and the method is suitable for PPT demonstration, cockpit explanation, business system training, course explanation and 3D modeling guidance scenes.

The invention discloses an RDMA asynchronous communication optimization method and system based on a lock-free queue, a medium and a processor, and relates to the technical field of remote communication. The method comprises the following steps: constructing a lock-free annular buffer area at an edge node, registering the lock-free annular buffer area as an RDMA memory area, and configuring a queue and a thread pool; the collection thread concurrently writes data, and the RDMA submission thread asynchronously completes remote transmission based on a buffer area state; and the queue processing thread performs closed-loop linkage transmission and buffer area states through'batch polling-result analysis-state synchronization-exception processing ', and dynamically adjusts the buffer area and queue parameter optimization performance at the same time. The problems that an existing RDMA scheme is poor in thread cooperation, weak in resource adaptation, not timely in state linkage and the like are solved, transmission delay and CPU overhead are greatly reduced, communication stability and the resource utilization rate are improved, and the method is suitable for edge computing and other high-concurrency data interaction scenes.

The invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, and relates to the technical field of data processing, and the method comprises the following steps: receiving a client request, obtaining a data generation template, constructing a data dependency graph to analyze a field dependency relationship, and determining a field generation sequence by adopting a recursive generation strategy and depth-first search; data validity verification is executed, a data generation thread pool is created by adopting an adaptive load balancing mechanism based on the data dependency graph, and asynchronous processing is realized through double buffer queues, so that the data generation efficiency is improved, the data logic integrity is ensured, and the system resource consumption is reduced.

The invention provides an automatic fault-tolerant propulsion method and system for task execution abnormity and a storage medium, and relates to the technical field of distributed system fault-tolerant control, and the method comprises the steps: task dynamic arrangement: arranging tasks into a serial, parallel or dependent execution mode according to a preset dynamic rule, and realizing task trigger execution through an asynchronous thread pool; performing process monitoring, adding a monitoring annotation to an execution method of each task by using an AOP tangent, and capturing a task execution state in real time; performing abnormal grading processing, and executing a strategy according to a pre-configured three-level processing channel; spreading a dependency state, and automatically updating an associated task state based on a dependency relationship among the tasks: when an upstream task fails, pausing a downstream dependency task and marking the downstream dependency task as a waiting state, and continuing to execute a non-dependency task; and performing global state statistics, calculating a global state in real time through a task state algorithm, if all tasks are completed, marking the tasks as completed, if abnormal tasks exist, marking the tasks as abnormal, and triggering root cause analysis.

The invention relates to a business big data processing system and method based on double timed tasks, electronic equipment, a computer readable medium and a computer program product. The method comprises the steps of obtaining business big data from a database through a first timed task; creating a thread pool according to a time window of the first timed task, wherein the thread pool comprises a plurality of threads; processing the business big data through threads in the thread pool; the processing result is updated to the database; obtaining business big data meeting a preset condition from the database through the second timed task; and processing the business big data according to the time window of the second timed task. The real-time performance of data processing and the utilization efficiency of system resources can be remarkably improved, meanwhile, load balancing is achieved through task dispersion, stable operation of the system in a high-load scene is ensured, the system safety is ensured, and the system is suitable for various service scene requirements.

The invention discloses a large model training dynamic memory management method based on a multilayer hierarchical cache mechanism, and relates to the technical field of computer system architecture and storage. The method is managed on the basis of a dynamic heterogeneous memory unloading system, and comprises the following steps of: in an initial stage of a training task, constructing a user state access path, establishing a TensorID-LBA mapping table, and performing hierarchical caching and threshold presetting; in the training task running stage, the access popularity of each tensor is evaluated in real time; when occupation of the GPU video memory cache layer exceeds a threshold value or cold data is detected, a dynamic migration strategy is triggered, and the low-frequency tensor is unloaded to a CPU memory cache layer or an SSD storage layer; when the tensor access popularity is improved, automatically migrating back to a high-performance layer through a prediction mechanism; in the process, tensor self-adaptive migration is executed by a user mode I / O thread pool. According to the method, the high-performance I / O stack is constructed in the user mode, and the adaptive tensor unloading and hierarchical caching mechanism of the multi-layer heterogeneous memory is combined, so that the universal deployment of the system is realized.

The invention provides an event processing method, system and equipment based on Internet of Things equipment and a medium, and belongs to the technical field of Internet of Things. The method comprises the following steps: receiving an Internet of Things equipment message through an MQTT Broker, identifying an event type according to a theme, and distributing the event type to a corresponding thread pool; concurrently analyzing the JSON load in each thread pool, and converting the JSON load into a structured data object; caching the data in a batch processing queue in a classified manner, and triggering batch processing based on a queue state; extracting batch data, generating an optimized SQL statement, executing writing, and processing exceptions; screening data based on a routing rule and asynchronously forwarding the data to message middleware; meanwhile, processing parameters are dynamically adjusted according to the equipment state data and the system load. According to the invention, efficient processing of Internet of Things equipment events is realized, and the system throughput, the resource utilization rate and the data processing real-time performance are improved.

The invention discloses a domestic database data real-time synchronization method and system in a heterogeneous environment, belongs to the technical field of databases, and aims to solve the technical problem of how to realize a low-intrusion and high-compatibility real-time synchronization scheme. Comprising the steps of establishing a log monitoring service of a source database master-slave replication protocol; sending an instruction to a source database through a log monitoring service to obtain a binary log stream, identifying an event in a log, extracting table structure metadata, converting binary data into a JSON structured data change message, and broadcasting the JSON structured data change message to the downstream; the downstream obtains data change messages in a set mode, grouping is carried out with indication as a unit, corresponding execution statements are generated according to SQL grammar rules of a target database, and corresponding execution tasks are generated in each group; and concurrently executing the execution tasks of each group by utilizing the thread pool, executing the execution statements in the same group of tasks according to a sequence, and if the statements which fail to execute exist, marking the corresponding group to be failed.

The invention relates to the technical field of meteorological data visualization, in particular to a meteorological data visualization method and device, electronic equipment and a storage medium. According to the meteorological data visualization method and device, firstly, a lightweight multi-resolution data organization unit is established based on a spatial index, a server-side pre-rendering slice is replaced, the storage pressure is reduced, and the data updating flexibility is improved; secondly, a dynamic thread pool and a load balancing mechanism are adopted, data processing tasks are intelligently decomposed and executed in parallel, client computing resources are fully utilized, the processing efficiency is remarkably improved, then coordinate conversion, interpolation and graph drawing are completed in a working thread through an off-screen rendering engine, calculation and rendering decoupling is achieved, and the system performance is improved. And finally, an incremental updating mechanism and a dirty marking technology are introduced, only local redrawing is carried out on a change area, and a cache multiplexing mechanism is combined, so that global rendering overhead is effectively avoided, and real-time, smooth and high-interactivity visualization of mass meteorological data is realized at a Web end.

The invention provides an online migration management method and device for database hotspot data and a medium, and belongs to the technical field of database migration. The method comprises the following steps: generating a hotspot object list based on hotspot statistical information collected in a preset time period; and according to the hot object list, determining a corresponding online migration task, and allocating the online migration task to a migration thread pool, so that the migration thread pool executes the online migration task based on preset migration strategies corresponding to different granularity levels. In response to an execution instruction of the online migration task, generating a corresponding hotspot object access mapping table, and configuring a preset access agent module to call the hotspot object access mapping table after performing access interception on a client request; and after a data access request from a client is received, determining a target access object corresponding to the data access request based on a preset access agent module, and forwarding the target access object to a corresponding data physical address. Through the method, the hotspot data management efficiency and the service continuity are improved.

The invention provides a concurrent scheduling and fault-tolerant optimization method and a concurrent scheduling and fault-tolerant optimization system for distributed jobs of a Spark application layer. The method comprises the following steps: acquiring a scheduling parameter, and analyzing the parameter to generate a configuration object; dividing data in a starting and ending date range according to business logic based on the configuration object, and generating a discrete data fragment list; based on the discrete data fragment list and the concurrent thread count, creating a thread pool, packaging each discrete data fragment into an independent task, and storing the independent task into a to-be-processed fragment queue; obtaining discrete data fragments through a thread pool working thread, calling SparkSession to execute service processing logic, and submitting a job to a cluster to obtain an execution result; performing judgment processing on the execution result, and recording a task processing state; based on all processing states, an execution report is generated. According to the method, the execution efficiency and the resource utilization rate of the Spark application are remarkably improved.

The invention relates to the technical field of data management, and provides a non-intrusive thread pool dynamic management method and system.The method comprises the steps that S1, in the application program loading stage of a virtual machine, a loading request of a thread pool class is intercepted through a Java Agent; s2, performing byte code enhancement processing on the thread pool class; s3, dynamic management during operation: S31, when the configuration monitoring service monitors configuration change, initiating adjustment of related thread pool parameters, and S32, when a monitoring reporting period is reached, reporting the collected indexes; and S33, a blocking queue type set in the thread pool parameters is a bounded queue, and corresponding capacity expansion and capacity reduction operations are performed according to a self-adaptive queue capacity adjustment algorithm in combination with the acquired monitoring indexes. The method has the core advantages of zero transformation cost, fast dynamic response and strong framework compatibility, and provides a thread pool optimization capability which is ready to use when the box is opened for a high-concurrency system.

The invention provides a message sorting method and device, electronic equipment and a program product, original messages are obtained through a distribution thread pool, the messages of the same business process are distributed to the same to-be-sorted queue, a working thread of the sorting thread pool reads the messages and stores the messages in a thread local cache set, the sendability is judged based on the dependency relationship of the messages in the business process, and the message sorting efficiency is improved. The messages meeting the conditions are sent to the subscription queue, and the messages not meeting the conditions are reserved in the cache. According to the mechanism, the problem of disorder of messages of the same service caused by multiple switches and network delay of message middleware is effectively solved, and data inconsistency and service abnormity are avoided; unified sorting logic replaces repeated development of subscribers, so that the system complexity is reduced; through thread pool division, local cache storage and a cache strategy of messages which do not meet conditions, the interaction overhead is reduced, the message orderliness and low delay requirements are considered in a high-frequency transaction scene, and the processing throughput, the real-time performance and the resource utilization rate under high concurrency are improved.

The micro-service processing system provided by the embodiment of the invention comprises an event identification module which is used for defining a business domain event type and registering an event attribute structure; the event bus module constructs a distributed communication network containing a dual-channel message queue, and the event bus module implements partition routing according to event types; the event processing module is composed of an event producer and a consumer which are executed asynchronously, the event producer packages service actions into a standardized event data packet, and the consumer processes subscription events through a thread pool; the event storage module adopts a hierarchical log database for persistence of an event sequence and supports reestablishment of a service state according to a timestamp; and the security control module is used for implementing service authentication and event content encryption on a transmission layer. Through event bus dual-channel design, sender box transaction binding and hierarchical storage optimization, millisecond-level event processing delay is realized while the reliability of distributed transactions is ensured, and second-level state recovery can be realized based on a complete event log when a service fault occurs.

A communication-middleware-implemented architecture system and control method applicable to an automatic parking planning and control algorithm. By using an SOA concept to perform service-oriented division on data conversion logic, and by optimizing the real-time performance of software by means of EventLoop and dynamic thread pool technology, a high-performance general-purpose middleware solution compatible with a plurality of vendor perception solutions is implemented to ensure the decoupling of an automatic parking algorithm from upstream perception data, such that a vendor can freely access, in the form of a standard part, middleware designed in the present application, thereby greatly shortening a vehicle model adaptation period and reducing the mass production costs for automatic parking.

The invention is suitable for the technical field of vehicle diagnosis, and provides a vehicle diagnosis system and method, and the system comprises a processor which is connected with a plurality of electronic control units (ECUs) of a vehicle and a vehicle diagnosis device, and is used for analyzing a diagnosis configuration file in a preset folder under the condition that the vehicle diagnosis device establishes communication connection with the plurality of ECUs, and sending the diagnosis configuration file to the vehicle diagnosis device; and obtaining diagnosis configuration data of the plurality of ECUs. According to diagnosis business process parameters in the diagnosis configuration data, creating a corresponding software channel for each ECU, and packaging all configuration parameters of each software channel in corresponding straight-through structure examples; and managing straight-through structure instances of all software channels through a pointer array and a thread channel mapping table in the global management structure body. And the server is also used for receiving the global channel configuration parameters through the universal thread pool and matching a corresponding working thread for each software channel. According to the system, synchronous diagnosis of a plurality of ECU systems is realized by establishing a parallel diagnosis architecture, and the efficiency of vehicle diagnosis is improved.

The invention discloses an AI story picture book generation method and device based on parallel processing and a terminal. The method comprises the steps of obtaining a story generation request; calling an AI large model to automatically generate a complete story text according to the story generation request; extracting the first-segment text content of the complete story text, starting an independent thread, and performing AI story picture book generation processing on the first-segment text content of the complete story text; when the AI story picture book of the first text content is generated and processed, the AI story picture book is timely returned to the front end for playing; meanwhile, creating a thread pool to dynamically allocate processing tasks of subsequent paragraphs, and sequentially performing dynamic AI story picture book generation processing on text contents of the paragraphs behind the complete story text; and when the playing of the AI story picture book of the first segment of text content is completed, sequentially playing the AI story picture books of the subsequent segments.