1673 results about "Multithreading" patented technology

The invention relates to a multi-protocol transmission text data monitoring and warning method and system, and the method comprises the steps: generating a multi-source protocol transmission instance based on dynamic authorization and hardware security verification, and collecting and analyzing text data; a network connection state, a data backlog amount and sensor numerical value content parameters are monitored in real time through multiple threads, and a transmission state and content exception event queue is generated; learning a causal relationship among network congestion, equipment faults and alarm events by using a Bayesian network algorithm, calculating a root cause probability in combination with a dynamic weight distribution strategy, and generating a comprehensive alarm list of priority ranking; on the basis of user feedback data, protocol weights and alarm strategies are adaptively updated, abnormal early warning triggering, data snapshot binding and closed-loop optimization of alarm logs are achieved, and the problems that in a multi-protocol mixed transmission scene, safety adaptability is poor, the monitoring dimension is single, root cause analysis depends on static rules, and strategy updating lags are solved. And the real-time performance, the accuracy and the self-adaptability of data transmission of the industrial Internet of Things are improved.

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 provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The invention relates to the technical field of streaming media decoding, and discloses a streaming media decoding method and device for a network high-definition player and a storage medium, and the method comprises the steps: carrying out the multi-thread parallel collection and storage of real-time temperature data, network bandwidth jitter data and video code rate fluctuation data of a decoding chip in the network high-definition player; obtaining a streaming media dynamic feature data set; performing classification training to obtain an inter-frame correlation prediction model; constructing a multi-stage streaming media caching system based on the inter-frame correlation prediction model; monitoring a cache state, a network state and a chip state in the multi-stage streaming media cache system in real time to obtain a decoding event sequence; according to the decoding event sequence, the decoding parameter is adjusted in real time, and the streaming media decoding control instruction is obtained.The collaborative optimization of a network layer, a decoding layer and a display layer is achieved, the playing starting delay is reduced, and the overall stability of a network high-definition player is improved.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention provides a timestamp-based data change intelligent deduplication and message simplification method and system, and the method comprises the steps: capturing a change event of a database in real time, and generating a change record containing a timestamp; based on a preset timestamp comparison algorithm, performing timestamp analysis and comparison on the multiple change records of the same service entity; the change records are grouped according to the service keys, the latest change records are stored and dynamically updated through a hash table, the latest timestamp data under the same service key are reserved, and intelligent duplicate removal of redundant data is achieved; when the data volume exceeds a preset threshold value, a data fragmentation strategy is triggered, batch data is uniformly segmented into a plurality of subtasks, multi-thread parallel processing of fragmented data is achieved by dynamically allocating thread resources, and processing results are summarized; and carrying out merging processing on the deduplicated changed data, constructing a standardized message body, selecting immediate sending or delayed queue distribution according to a message priority, and controlling a network transmission load through a message batching mechanism.

The invention discloses a multi-stage task processing method and system based on an intelligent Agent model, and relates to the technical field of task processing, and the method comprises the steps: collecting heterogeneous data streams through a distributed sensor network, and generating a dynamic feature vector through a feature encoder inspired by quantum annealing; calculating a path expected utility value by using a Bayesian optimization algorithm, and projecting a high-order task space to a Kupman space; starting multi-thread asynchronous calculation, collecting execution state data in real time and constructing a causal graph model; the short-term execution logs are integrated through a neural Turing machine, edge computing nodes are called for distributed knowledge extraction, a multi-mode interpretable report is generated, and a long-term memory library is updated. According to the method, by starting multi-thread asynchronous calculation, the execution state data are collected in real time, the causal graph model is constructed, the execution result matrix with the confidence score is output, and the stability and reliability of task execution are improved.

The invention discloses a cross-platform GPU support-free real-time digital human rendering system and method, and relates to the technical field of digital human rendering, and the system comprises an offline modeling module which constructs a mouth base space model through principal component analysis based on video data of a target person; the real-time driving module is used for mapping real-time voice features into dynamic coefficients of the mouth base space model; the image synthesis module is used for reconstructing a mouth image according to the dynamic coefficient and fusing the mouth image with a pre-stored reference face image; the cross-platform adaptation layer is configured to realize real-time rendering without GPU dependence through compiling, memory mapping and multi-thread scheduling technologies; and the dynamic resource manager is used for optimizing resource occupation and ensuring stable operation in a multi-platform environment. According to the technical scheme, dependence on high-computing-power hardware and an ideal acoustic environment can be broken through, and a complete theoretical framework and an engineering implementation path are provided for large-scale popularization of a digital human technology in a lightweight terminal.

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 computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.

The invention discloses a production area cadmium and arsenic pollution collaborative prevention and control knowledge integration management method and system, and relates to the technical field of data integration management, in the method, a multi-thread parallel acquisition technology is used for acquiring massive network literatures related to cadmium and arsenic pollution, and core text data in the network literatures are extracted; the method comprises the following steps of: preprocessing the network literature, identifying and screening named entities to obtain a key sentence set, determining the category of the corresponding network literature based on the key sentence set by adopting a random forest algorithm, and clustering all the network literatures to obtain a network literature clustering result; and finally, constructing a cadmium and arsenic pollution knowledge graph and visually displaying the cadmium and arsenic pollution knowledge graph. According to the method, cadmium and arsenic pollution related data can be efficiently and automatically collected and subjected to standardization processing, the consistency and comparability of the data are ensured, meanwhile, a knowledge graph mode is adopted, multi-angle and multi-dimensional data analysis and display functions can be provided, and the requirements of users in different fields and different levels are met.

The invention relates to a world model construction method, in particular to a high-fidelity lightweight world model construction method for an end-to-end automatic driving test, which is used for constructing a high-fidelity world model and performing knowledge distillation on the world model to solve the problems of huge parameters, low reasoning efficiency and the like of the world model. Model parameters are reduced on the basis of reserving the world model generation capability, and the reasoning efficiency is improved; a CUDA operator is developed in a user-defined mode for the bottleneck part of world model calculation, video memory allocation is optimized, and the reasoning efficiency of a high-fidelity world model is improved based on a single-device multi-thread scheduling and multi-device cooperative calculation method. According to the method, the high-fidelity lightweight world model for the end-to-end automatic driving test can be constructed, the problems that an existing world model is low in multi-modal information alignment precision, poor in cross-view and cross-frame consistency, low in reasoning efficiency and the like are effectively solved, the confidence coefficient of the end-to-end automatic driving system test process is improved, and the test efficiency of the end-to-end automatic driving system is improved. And the testing efficiency of the end-to-end automatic driving system is greatly improved.

The invention discloses a multi-thread and LOD-based point cloud rendering method, and relates to the field of video rendering, and the method comprises the steps: dividing point cloud data into n data blocks; calculating an initial position of each data block in the point cloud file, and creating n threads; extracting m pieces of LOD point cloud data with different levels of detail from the octree of each data block; calculating the proportion # imgabs0 # of the size of the current point cloud in a rendering viewport and the distance # imgabs1 # from a viewpoint to a camera, and calculating the detail level # imgabs4 # of the current to-be-rendered LOD point cloud according to a preset viewport proportion threshold value # imgabs2 # and a preset sight distance threshold value # imgabs3 #; according to # imgabs5 #, selecting point cloud data of a corresponding detail level from the m pieces of LOD point cloud data; for each interactive operation event, parallel coordinate transformation and illumination calculation are carried out on the LOD point cloud data to be rendered currently in different threads; submitting a calculation result to a renderer for rendering; aiming at low rendering efficiency in the prior art, the method and the device can efficiently render large-scale point cloud data.

The invention discloses a high-concurrency data cache sorting method and system, and the method comprises the steps: carrying out the time synchronization of a system server and a subsystem server, building a first-level cache and a second-level cache, and setting a double-buffer queue for the second-level cache; a plurality of threads receive high-concurrency data and distribute different thread groups for processing, a local timestamp and a serial number are set to be combined into a primary key, the primary key and a data value v are written into a first-level cache, and an index is established. A thread pool 1 is started to read data from a first-level cache and write the data into a second-level cache, and corresponding processing including discarding repeated data, writing the data into different buffer queues and the like is carried out by judging conditions such as a sequence number and a data value v. Meanwhile, a thread pool 2 is started to read the data from the second-level cache double-buffering queue in sequence and delete the cache, and if a specific situation is encountered, a threshold value is waited for or next data processing is continued. And the index and the cache queue content can be expanded to realize processing in a plurality of protocol modes, so that the data access efficiency is improved, the data completeness can be realized, and the conditions of data disorder and dirty data cannot occur.

A device debugging method includes according to a first thread to be created and a breakpoint set, determining that the first thread contains a corresponding breakpoint; injecting a hook function into the first thread, and creating a first thread and executing in a debugging mode; if the hook function is invoked, determining whether a triggering condition of the breakpoint is satisfied; if not, directly exiting the hook function; if so, stopping the code running of the first thread, waiting, and executing a debug instruction. Therefore, the device is integrated with both functionalities of debugging and actual execution due to the breakpoint set for debugging configured on the device, the actual result of each debugging can be observed in real time, and the hook functions of different threads operate independently and do not interfere, thus multithread debugging can be achieved, and debugging efficiency is improved.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention discloses a mobile terminal offline bidirectional data synchronization method and system, and relates to the technical field of data synchronization processing, and the method comprises the following steps: initializing a whole table synchronization mechanism, and establishing a file mapping structure; a user performs data operation in an offline state, records data operation details and stores the data operation details in a local cache; when the network connection is recovered, a whole table synchronization mechanism is started, data compression and partitioning are performed, a synchronization request is sent to a server, and segmented data transmission is performed; after receiving the data, the server performs data storage and management by using a file mapping mechanism; detecting potential conflicts by utilizing a multi-version parallel management mechanism, carrying out conflict resolution, merging data with local data after the conflict resolution, and confirming that synchronization is completed to a server; after it is confirmed that all data are synchronized, local and global timestamps are updated, and meanwhile, the optimal thread count is dynamically determined by adopting a multi-thread technology. According to the invention, the efficiency, reliability and accuracy of data synchronization of the mobile terminal are remarkably improved.

A system that includes at least one multi-threaded processor forms a multitude of converged thread sub-groups from a main thread group, wherein each thread sub-group includes a common code block. A loop is configured to jump to a different target address of a branch instruction in an order determined by a priority configured for each different target address.

The invention relates to the technical field of mixing processing, and discloses a multi-thread processing method for intelligently optimizing working parameters of a mixer. Comprising the following steps: obtaining initial stirring time according to a proportional relation, mixing to-be-mixed materials according to the initial stirring time to obtain an initial mixture, obtaining particle characteristics according to a real-time surface image, and calculating a mixing influence degree value based on real-time detection data, inputting the proportional relation, the particle characteristics and the mixing influence degree value into a pre-constructed dose generation model to obtain an antistatic dose, and determining a mixing time interval and a mixing rotating speed interval according to the proportional relation and the antistatic dose, determining an optimal mixing time and an optimal mixing rotating speed based on the mixing time interval, the mixing rotating speed interval and an optimization algorithm; the combination of the mixing time and the mixing rotating speed after optimization can reduce the generation of static electricity to the greatest extent, thereby avoiding the agglomeration or non-uniformity of the mixture, and further improving the overall quality and consistency of the product.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

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 the technical field of surveying and mapping data processing, and discloses a surveying and mapping data processing method based on cloud computing, and the method comprises the steps: obtaining topographic elevation, topographic slope and environmental parameters, carrying out the multi-source fusion and feature extraction based on the data, obtaining a preliminary feature set, carrying out the dynamic correction and integrity verification of the preliminary feature set, and obtaining a final feature set; generating a correction feature set; the method comprises the following steps: obtaining an optimized feature set through residual analysis, dividing high-frequency feature cache, constructing a partition feature set, carrying out synchronization verification on partition data, obtaining a consistency feature set, executing multi-thread integration, forming a global integration feature set, completing abnormal region reconstruction and optimization, generating a high-precision feature set, and finally, carrying out parallel task decomposition and accelerated integration to obtain a high-precision feature set. And outputting high-precision surveying and mapping data. The method solves the problem that the scheduling precision is limited under multi-source complex data.

Systems and methods for optimizing thread allocation in a model serving system include estimating a batch size for inference requests. An optimal configuration is then determined that defines a number of inference instances, a number of threads per inference instance, and a sub-batch size per inference instance for processing a batch of inference requests of the batch size using intra-operator parallelism that minimizes average per-batch latency. The optimal configuration is determined with reference to a plurality of predetermined model profiles that define single-inference average batch latencies for different combinations of thread counts and batch sizes, the predetermined model profiles being used as input to a dynamic programming algorithm that identifies optimal configurations that minimize the average per-batch latency.

The invention discloses a cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention relates to the technical field of message pushing, in particular to an SaaS (Software as a Service) intelligent concurrent pushing system which comprises a tenant flow monitoring module, a resource binding module, a lock-free object writing module, a handle layout construction module and a cache prefetching delivery module. According to the method, the high-frequency business object is identified by monitoring the flow characteristics of the tenants, and the complete Slab page is dynamically applied and bound to the thread exclusive local allocation buffer area, so that physical isolation of bottom layer resources is realized to eliminate global lock competition and context switching overhead in a multi-thread environment; lock-free memory writing is executed based on a privatized pointer, the object construction efficiency is greatly improved, a compact storage structure in which a bitmap is adjacent to a connection handle is constructed, a hardware cache prefetching mechanism is triggered by utilizing bitmap reading, connection data is loaded to a CPU cache line in advance, and the connection data is stored in the cache line. And the cache miss rate is reduced and the concurrent delivery performance of massive messages is remarkably improved by utilizing a memory locality principle.

The invention provides a multi-thread beam debugging method and device and an artificial intelligence chip, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining an expected thread beam to trigger debugging based on a comparison result of instruction information of executing an original program by each thread beam and instruction information stored in a target register group, and a thread beam identifier of each thread beam; sending debugging trigger information to a processor to enable the processor to send debugging enabling information to each thread bundle, and controlling the unexpected thread bundle to wait for the expected thread bundle to be debugged; based on the debugging enabling information, controlling the expected thread bundle to execute the debugging program based on an instruction address of the debugging program stored in the target register group; and sending debugging ending information to the processor to enable the processor to send debugging stopping information to each thread bundle, and controlling each thread bundle to continue to execute the original program. According to the method and the device provided by the invention, the debugging aiming at the specific thread beam can be realized in the multi-thread beam parallel computing process.

The invention relates to a distributed storage access method initiated by a GPU (Graphics Processing Unit) in an AI (Artificial Intelligence) scene. The method comprises the following steps: S100: constructing a software cache module and an I / O (Input / Output) queue module in a GPU memory; s200, the GPU thread queries the target data through the software cache module, reads the target data if the target data is hit, and enters the next step if the target data is not hit; s300, when the I / O request is not hit, the repeated I / O requests initiated by multiple threads are automatically combined to generate a single effective I / O request, and the request is written into an I / O queue module; s400, the user mode distributed storage client monitors the written submission queue, reads the target data and then transmits the target data to the software cache module, and a completion queue of the I / O queue module is updated; and S500, the GPU thread polls the updated completion queue, and updates the reference count of the software cache module after reading the data. The main purpose of the invention is to realize high-throughput and low-delay data transmission without the participation of a CPU (Central Processing Unit).

The invention belongs to the technical field of information technology, and particularly relates to an intelligent task allocation method based on a multi-thread group, which comprises the following steps of: 1, initializing a thread pool, and setting proper initial size, maximum size and queue capacity; 2, realizing a monitor for monitoring the response time, the CPU occupancy rate, the memory occupancy rate and the queue size of the system in real time; 3, realizing an evaluator, defining an evaluation algorithm and a threshold value, and regularly evaluating the load condition of the system; 4, implementing an adjuster, and dynamically adjusting the size of the thread pool according to an evaluation result and a preset strategy; 5, in the system operation process, the system state is continuously monitored and evaluated, the size of a thread pool is dynamically adjusted to adapt to load changes, system self-detection and self-adjustment are carried out, the resource utilization rate is improved, the system processing capacity is improved, operation and maintenance intervention is reduced, a delivery interface is graphical, and real-time checking is carried out.