942 results about "Multithreading" patented technology

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 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 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.

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 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 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.

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 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 relates to the technical field of artificial intelligence chips, and provides a register resource management method and device, equipment and a storage medium, and the method comprises the steps: obtaining register resource information needed by an operator; based on the register resource information, target register addresses are searched from register resource pools of all the thread bundles, and the target register addresses are register addresses which are the same in address and are not used in the register resource pools of all the thread bundles; and allocating the target register address to the operator, so that each thread bundle shares the target register address when executing the operator. According to the method, through an intelligent register resource searching and distributing mechanism, independent application in the processing logic of each thread bundle is not needed, and only one-time application outside the logic of the thread bundle is needed, so that the requirements of different logic among multiple thread bundles and application of same address register resources can be met, and the expense of register resource application is reduced.

The invention discloses an electric energy data processing method, system and device based on a DSP and multi-thread parallel computing and a medium, and belongs to the technical field of electric energy data processing.The method comprises the steps that multi-source operation data is collected, a structured data set is constructed and written into a buffer structure, and a channel thread binding structure is generated; when the running state changes, activating the processing threads of each bound channel according to the channel thread binding structure to form a thread set; and extracting data from the current binding channel, generating a channel processing result, carrying out unified correction on a time label, constructing a state data matrix with a unified format, inputting the state data matrix into a preset evaluation model, executing operation state identification, and calculating an output result. According to the method, high-speed processing, time consistency fusion and intelligent risk identification of multi-source heterogeneous electric energy data are realized, and the method has high real-time performance and strong expansibility and is suitable for scenes such as dynamic monitoring of a power grid.

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 chip verification, in particular to a multi-thread-based DPI task processing method, electronic equipment and a medium, and the method comprises the following steps: S1, setting M DPI task processing containers {C1, C2,..., Cm,..., CM} and an upper management module DR; s2, the nth DPI task An is obtained, the DR allocates the An to the Pm of the corresponding target Cm, and the An with the dependency relationship is allocated to different Cm; s3, creating a thread in the Qm by the Rm in the Cm; and S4, the Rm allocates the DPI tasks in the Pm to the threads in the Qm, each thread processes one DPI task, and the threads in the Qm run the allocated DPI tasks in parallel. According to the invention, the processing efficiency of the DPI task in the chip development process is improved.

The invention provides an edge heterogeneous Internet of Things operation and maintenance chassis data processing method and system. The method comprises the following steps: firstly, according to the types of the multi-source heterogeneous terminals, formulating a unified data acquisition scheme, defining a unified acquired data format, respectively managing and executing a data acquisition task of each multi-source heterogeneous terminal by adopting an asynchronous multi-thread scheduling mechanism, and acquiring and generating corresponding acquired original data; and performing standardization processing on the collected original data in the normal sampling state to generate a multi-source collected data set. And then, according to the type of each multi-source heterogeneous terminal, for the corresponding multi-source acquisition data set, calculating a standardized anomaly score, performing weighted comprehensive processing, generating a comprehensive anomaly score, and determining an anomaly level according to the comprehensive anomaly score. And finally, correspondingly matching the exception level of the multi-source heterogeneous terminal with a preset local protection strategy, executing a local exception protection action based on a matching result, recording exception information and reporting exception alarm information to a remote center platform.

The invention provides a cross-platform video decoding method and device, computing equipment and a computer readable storage medium, and the method is combined with a factory mode to completely package bottom layer differences of all platform types, and an application layer developer does not need to care about the bottom layer platform differences. The optimal implementation can be automatically selected on different engines and platforms through a uniform API (Application Program Interface); during rendering, a single-frame memory management strategy is used, and only one frame which is currently displayed is reserved in a memory; and in the aspect of performance, the advantages of a multi-thread platform and the parallel computing characteristics of the GPU are fully utilized. According to the embodiment of the invention, through deep fusion of a cross-platform soft decoding architecture, single-frame memory optimization, multi-thread parallel, GPU acceleration and other technologies, perfect balance of cross-platform, high performance and low memory is realized, and the development efficiency of the whole project is improved.

The invention discloses a GPU accelerated multi-particle coupling Monte Carlo calculation method and device. The method comprises the following steps: asynchronously migrating geometric material parameters subjected to CPU serial processing and a basic database to a pre-allocated memory space in a GPU; operation parameters of the GPU are determined, and Monte Carlo simulation of the multi-particle coupling transportation process is realized in the GPU based on the operation parameters and the neutron nuclear reaction database; on the basis of an atom addition function of a CUDA parallel computing architecture, energy transfer data, obtained through Monte Carlo simulation, of particles and voxel grids in a multi-thread environment is efficiently summarized, energy deposition of the particles is determined, and a high-resolution three-dimensional energy deposition spatial distribution data set is generated. According to the method, high precision can be maintained, and meanwhile, the parallel computing capability of the GPU is fully utilized to realize multi-particle coupling transportation synchronous simulation, so that the computing efficiency of a complex radiation field is improved, and the dual requirements of different fields on high precision and real-time performance are met.

The invention discloses an inference type large model first token performance test method and device and a medium, belongs to the technical field of software development, and aims to solve the technical problem of how to truly simulate a system load in a high-concurrency environment and accurately evaluate the processing performance of a first token of a large model. Based on a first token performance test plug-in, data interaction is realized through an OkHttpClient client by adopting a streaming transmission protocol, and the method specifically comprises the following steps: capturing a receiving timestamp of a first token in an interface response stream, taking the receiving timestamp of the first token as a measurement index of first token response time, immediately terminating current stream transmission, and concurrently triggering multiple test requests through a multi-thread mechanism, the system load in a high-concurrency environment is truly simulated, and the first token processing performance of a large model is accurately evaluated.

The invention relates to the technical field of multi-thread rendering pipeline optimization, in particular to a WebGPU-oriented multi-thread rendering pipeline optimization system and method.In the system, a thread task dynamic balance adjusting module is used for dynamically adjusting thread task distribution according to a real-time tracking result of an atomic counter, and when it is detected that a thread task fails, a thread task dynamic balance adjusting module is used for dynamically adjusting thread task distribution according to the real-time tracking result of the atomic counter; and triggering an error isolation and local rollback mechanism. According to the method, a dynamic task dividing and conquering mode is adopted, rendering pipelines can be effectively split and effectively distributed to a plurality of WebWorker threads for execution, full-link parallelization of the rendering pipelines is achieved, the affinity constraint of the WebGPU threads is broken through to a certain degree, and the utilization rate of a CPU is obviously increased; and in combination with atomic state collaboration and task state linkage analysis modes, dynamic adjustment of thread tasks is realized, and the influence of load imbalance is effectively reduced.

The invention provides a method, a device and equipment for realizing a high-concurrency extensible hash table (hash table) in a multi-thread environment and a medium. The implementation content of the method comprises two parts: 1) realizing high concurrency by a hash table fine-grained lock: realizing by adding the fine-grained lock to the hash table, locking each bucket of a bucket array of the hash table, and improving the concurrency of accessing the hash table in a multi-thread environment; 2) expandability of the hash table double-bucket array structure: two bucket arrays are used, the original bucket array is used for storing hash table elements in a non-expansion state, the expansion bucket array is used for storing newly added hash table elements and elements migrated from the original bucket array in an expansion state, the expansion bucket array is used as the original bucket array in an expansion state after migration is completed, and the expansion bucket array is used as the original bucket array; according to the method, the problem of realizing the high-concurrency extensible hash table in the multi-thread environment is solved, the high-concurrency access of the hash table in the multi-thread environment is ensured, the expandability of the hash table in the multi-thread environment is also ensured, the high concurrency and the expandability are carried out in parallel without conflict, and the hash table can adapt to the change of the data scale.

The invention relates to a data processing device and method, a processor and a chip, and relates to the technical field of computers, the data processing device comprises a processing module and a driving module; the processing module obtains address information of each thread in a first instruction when judging that the instruction to be processed is the first instruction, the first instruction is a multi-thread parallel loading instruction, and the address information of different threads is mutually independent; the driving module determines a parallel loading result according to address information of each thread in the first instruction, the address information is used for determining an off-chip address and an on-chip address of each thread, and the thread is used for loading data at a position indicated by the off-chip address to a position indicated by the on-chip address. According to the embodiment of the invention, multi-thread parallel execution of different data accesses can be realized, and the calculation efficiency is remarkably improved.

The invention discloses a test method and system for starting and stopping trusted proxy services of DCS (Distributed Control System) upper computers in batches, belonging to the technical field of industrial control, comprising the following steps: running a pre-configured executable program by using a multi-thread mechanism, reading a protocol file in the pre-configured executable program, and remotely connecting to each DCS upper computer in batches based on the protocol file; printing a process of connecting to each DCS upper computer and executing a command at a terminal in real time, and if a failure occurs, immediately returning failure reasons such as connection timeout and incorrect reply of a connected target host; after the operation of the pre-configured executable program is finished, returning the number of threads and a prompt that the operation of the threads is finished; and based on the real-time printing result and the returned prompt, checking the online state of each DCS upper computer, and verifying whether the batch online and offline functions take effect or not. According to the method, the trusted proxy service of the DCS upper computer is started and stopped in batches by one key, so that a large batch of equipment can be online and offline at the same time at one time.

The invention relates to the field of traffic storage optimization and traffic data compression, in particular to a multithreading-combined lossless compression method for fine-grained traffic, which comprises the following steps of: acquiring a traffic data file, extracting a header triple based on the traffic data file, generating an identifier according to the header triple, and transmitting the identifier to a server; aggregating the traffic data files with the same identifier to obtain data streams, and generating a sorted data stream size table according to the data volume of the data streams; creating threads according to system computing power, distributing the data flow to the thread with the minimum thread load according to the data flow size table and the thread load condition, and outputting a data flow distribution scheme; and performing fine-grained characterization and serialization on each data stream to obtain an integer sequence and a byte sequence, selecting a compression processing method according to redundancy characteristics of the integer sequence and the byte sequence, obtaining a redundancy-eliminated sequence, and writing the redundancy-eliminated sequence into a compressed file. The invention aims to provide high compression efficiency to reduce the storage cost while ensuring the data precision.

The invention discloses a dual-channel adaptive network stability testing method and system, and belongs to the technical field of computer network testing. The system adopts a dual-channel asynchronous communication architecture in which a control channel and a data channel are physically separated, instructions and state information are transmitted through an independent TCP control channel, and high-speed test data streams are transmitted through a UDP or TCP data channel. The method comprises the following steps: dynamically and adaptively adjusting a timeout parameter based on real-time network delay detection; nanosecond constant interval packet sending control is realized by using a high-precision monotonic clock source and busy waiting compensation mechanism; a packet loss detection, positioning and ACK confirmation mechanism based on the serial number is realized in an application layer; and real-time statistical display and idle detection automatic termination are realized through multi-thread parallel processing. According to the invention, the precision, the stability and the adaptive capability of network testing are remarkably improved, and the method is suitable for long-time stability testing in various scenes such as a high-speed Ethernet, a satellite link, an industrial Internet of Things gateway and the like.

The invention provides a satellite-borne routing telemetering and remote control method based on a multi-thread framework, which comprises the following steps: a satellite-borne routing telemetering and remote control system adopts the multi-thread framework to realize parallel processing: a telemetering acquisition thread is used for acquiring system telemetering data by taking per second as a period and storing the telemetering data into a memory according to an EPDU (Extended Protocol Data Unit) coding format; the remote control execution thread is used for circularly monitoring a task queue, analyzing and executing an instruction when a remote control instruction task exists in the queue, and feeding back an execution state to the telemetry data storage memory; the time synchronization thread is used for continuously monitoring the PPS and performing microsecond-level calibration on the system time in combination with the received time broadcast frame; the main thread is used for monitoring GPIO interrupt events in real time. According to the invention, the EPDU data coding format and CAN protocol split data transmission are supported, the reliability is improved through hardware redundancy, software redundancy and a data verification mechanism, and the technical problems of on-orbit real-time control and high fault tolerance requirements of satellite-borne routing equipment are solved.

The embodiment of the invention provides a vector file data processing method, a vector file data processing system and related equipment, which are used for improving the lattice processing efficiency of vector files. The method in the embodiment comprises the following steps: reading a Gerber file, and sorting all primitives according to a drawing sequence; boundary fusion processing is conducted on the adjacent primitives, and the boundary fusion processing comprises the steps that whether boundaries between the adjacent primitives coincide or not is judged, and if yes, the boundaries are merged to generate new primitives; and partitioning the primitives subjected to boundary fusion processing to form a plurality of data blocks, and performing multi-thread line scanning processing on the primitives in the data blocks to obtain a dot matrix file.

The invention discloses a robot control optimization method based on deep reinforcement learning, and the method comprises the steps: firstly initializing a control system, constructing an experience playback buffer pool, and setting an Actor network strategy function, a Critic network weight, a hyper-parameter, and a target network parameter; then, the robot generates an action according to the Actor network, collects environment feedback after executing the action, and stores the state transition tuple into a buffer pool; next, a small batch of data is sampled, a time difference error is calculated, and a network parameter is updated accordingly to minimize a value estimation deviation, synchronously optimize the network parameter, maximize an expected value of a state-action value function, and improve control performance. According to the method, a multi-thread architecture and a deep reinforcement learning technology are fused, the real-time performance, stability and generalization ability of robot control are improved, the problem of algorithm convergence in a non-stationary environment is effectively solved, the cost of adjustment and calculation is reduced, and the robustness of long-term stable operation and deployment feasibility in a complex scene are enhanced.