931 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 embodiment of the invention provides a thread pool operation state monitoring method and device, and the method comprises the steps: dividing the fluctuation range of the ratio of the number of active threads to the number of core threads into a high-load interval, a normal interval and a low-load interval, and calculating the optimization coefficient of the core threads based on the interval distribution characteristics; calculating the variance of the ratio of the total thread count to the maximum thread count, and extracting the adjustment coefficient of the maximum thread through the fluctuation characteristics of a variance curve; taking a difference value between the task growth rate and the processing rate as a calibration factor of the queue capacity; respectively comparing the evaluation results of the three parameters with a preset reasonable interval boundary to generate a parameter rationality score; counting the performance improvement effect of each preset adjustment strategy from a historical adjustment record; calculating a current adjustment step length based on the reference strategy; recording the adjustment suggestions and the calculation basis thereof into a version library, and setting corresponding effective conditions; according to the application, the efficiency and accuracy of monitoring the running state of the thread pool can be improved.

The invention discloses a multi-time-sequence three-dimensional visualization expression method for flood routing, and the method comprises the steps: analyzing NetCDF data of a two-dimensional hydrodynamic model, building an element index library and a space library, and storing the indexes of water level, water depth, flow velocity and the like and the space information of an irregular triangular mesh. And generating an association relationship between the grid points and the element indexes, and creating a time sequence cache attribute table and a spatial data set. And reading three-dimensional point space data and element index data, constructing a three-dimensional space grid, and performing range cutting by using a two-dimensional surface data set. And generating a time sequence service conforming to a three-dimensional model tile data format by adopting a thread pool technology. And loading a three-dimensional service and a terrain service at a browser end, carrying out color and height rendering based on the element values, and dynamically displaying the flood process. And a timer is arranged to realize three-dimensional visual playing moment by moment. The method has the advantages that the dynamic display capability of flood routing is effectively improved, and efficient three-dimensional visual rendering and real-time playing are supported.

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 embodiment of the invention discloses a task processing method and system based on multi-thread fusion management. According to the technical scheme provided by the embodiment of the invention, under the condition that the newly triggered system task is received, the associated attribute information of the system task is acquired, and the associated attribute information identifies the thread pool to which the system task belongs; the system task is added to a target thread pool based on the associated attribute information, the target thread pool comprises a native thread pool and a plurality of user-defined thread pools, the user-defined thread pools are created corresponding to different associated attribute information in advance, and the user-defined thread pools are obtained by modifying calling class codes of the native thread pool; and processing the system task based on a task thread pre-constructed by the target thread pool. By the adoption of the technical means, efficient and reasonable thread scheduling and task processing can be carried out, the thread pool is prevented from preempting resources, processor resources are reasonably utilized, then the system jamming problem is reduced, and the use experience of a user is improved.

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.

The invention discloses an emergency fire fighting question answering method and system based on the AI large language model technology and a storage medium, and relates to the technical field of artificial intelligence, the emergency fire fighting question answering system comprises a front-end module, an AI preprocessing thread pool, a knowledge base module and a large model calculation engine module, the AI preprocessing thread pool receives a service request sent by the front-end module, and sends the service request to the AI preprocessing thread pool; different task threads are distributed, a knowledge base of a corresponding region category is automatically called according to the region category input by a service request to perform content similarity retrieval, knowledge content data obtained through retrieval and the corresponding service request are sorted and then uniformly sent to the large model calculation engine module supporting parallel processing to perform analysis and calculation, and the large model calculation engine module is used for performing analysis and calculation on the knowledge content data and the corresponding service request. By using the technical means of the invention, the user does not need to think what the keyword of the current question is, and can completely organize the question, directly ask the question, automatically query the local professional knowledge base and give professional and accurate answers according to the actual situation of the site, so that real barrier-free communication is realized.

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 embodiment of the invention provides a thread pool dynamic adjustment method and device based on a configuration center. The method comprises the steps that thread pool parameters are initialized, historical parameter versions are sorted through effective time, the credibility of each parameter source is calculated based on the change effect of different parameter sources, and three weighted change proportions are summed to obtain a risk value; analyzing the corresponding relationship between the effective time of each parameter in the historical version library and the system load to establish an optimal time window of parameter change, when the risk value is lower than a threshold value and is in the optimal time window, determining an adjustment step length according to the credibility of a parameter source, continuously collecting performance indexes and associating the performance indexes to corresponding parameter versions, when the performance index is degraded, recording and positioning the last stable parameter value and the last stable effective time through the version library, and executing rollback according to the last stable parameter value and the last stable effective time; according to the method, the efficiency and accuracy of dynamic adjustment of the thread pool can be effectively improved.

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.

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 provides a pathological specimen auxiliary diagnosis system based on multi-thread control and task scheduling, and the system comprises a distributed lock management module which is used for employing a distributed lock to judge the operation state of a resource needed by a task based on a task initiation request, and distributing the lock and starting task execution when the needed resource is not locked; the timed task scheduling module is used for regularly scanning the task queue and distributing periodic tasks and delayed tasks in the task queue to a thread pool based on a scanning result; the multi-thread task execution module is used for performing multi-thread task scheduling and control on the periodic tasks and the delayed tasks according to a priority mechanism on the basis of the distribution result, and performing data processing on the periodic tasks and the delayed tasks on the basis of a multi-thread task scheduling and control result; and the loop operation management module is used for releasing the lock after the execution is finished, and notifying the idle thread to continuously process the to-be-processed task in the task queue based on a release result. And the efficiency and the effect of auxiliary diagnosis of the pathological specimen are ensured.

The present disclosure relates to computer-implemented methods, software, and systems for controlling the execution of concurrent threads executing tasks in a database management system. A first task is executed at a first thread from a pool of threads for execution of tasks at the database management system. It can be identified that the execution of the first task is paused and that the first task is in a sleep mode. In response to identifying that the first task is in an awake mode after the sleep mode, it can be determined whether a current number of threads in the pool that are currently processing tasks in parallel is below an allowed number of threads. It can be determined that the allowed number of threads has been reached and a waiting status can be assigned to the first task at the first thread.

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 discloses an input and output request processing method and device and electronic equipment, and belongs to the technical field of electronic equipment. The method is applied to the electronic equipment, a first execution environment of the electronic equipment comprises a first working thread pool, and the first working thread pool communicates with a first application running in a second execution environment of the electronic equipment through a first task queue and a first result queue. The first working thread pool comprises a plurality of working threads used for processing IO requests in the first task queue. The method comprises the following steps: working threads in a first working thread pool respectively obtain IO requests from a first task queue; and for a first IO request obtained by the first working thread in the first working thread pool, the first working thread executes the first IO request, and writes an obtained first request result into a first result queue. Through the method, the efficiency of processing the IO request through the first execution environment by the first application in the second execution environment of the electronic equipment can be improved.

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 provides a search task processing method, electronic equipment and a storage medium, and relates to the technical field of task processing, the method concurrently executes a core sub-task and all edge sub-tasks based on a thread pool technology, when a return result of the core sub-task is received, a time point at the moment is recorded as a key update time point, and the key update time point is updated; and copying the initial return result list at the moment as a first return result list, when the first return result is NULL, indicating that the edge sub-task corresponding to the first return result is not completed, obtaining candidate waiting duration corresponding to the identifier of the uncompleted sub-task, and taking the maximum candidate waiting duration as target waiting duration, and when the time point obtained by adding the key update time point and the target waiting duration arrives, all the initial return results in the initial return result list at the moment are integrated to obtain the final return result corresponding to the search task, so that the overall search time is shortened, and the overall search efficiency is improved.

The invention discloses an ROS2 hybrid scheduling actuator system. The system comprises a registry, two ready callback sets, two schedulers and two thread pools, the two dispatchers are respectively a real-time dispatcher and a fair dispatcher; the real-time scheduler is responsible for scheduling callback instances of the key chain; the fair scheduler is responsible for scheduling callback instances of the common chain; the two thread pools comprise a special thread and a shared thread, and the special thread specially runs a real-time scheduler; the shared thread not only can run a real-time scheduler, but also can run a fair scheduler; the shared thread comprises two execution modes: (1) real-time scheduler priority: the thread operates the real-time scheduler preferentially, and only when the priority queue of the real-time scheduler is empty, the fair scheduler operates; and (2) priority of the fair scheduler: firstly operating the fair scheduler by the thread, and operating the real-time scheduler when the vector set of the fair scheduler is empty.

The invention relates to the technical field of vehicles, and discloses a vehicle-mounted process resource management method and system, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: creating at least one configuration file which is used for defining a resource limitation parameter of each vehicle-mounted application process; calling a Cgroup interface, creating a corresponding Cgroup for each process, and setting a corresponding resource limit; in response to initialization of the vehicle-mounted application process, dynamically creating and managing a thread pool according to the resource limiting parameters; the configuration thread attribute setting interface is used for dynamically configuring the scheduling strategy, the priority and the affinity of the thread by the vehicle-mounted application process according to task requirements; configuring a thread monitoring mechanism for monitoring the execution time and state of each thread and recovering the corresponding thread; and configuring a cross-platform API (Application Program Interface) for uniformly calling interfaces related to process resource management and thread management. According to the method, excessive occupation and waste of resources are avoided, the thread execution efficiency and the response speed are improved, and the development difficulty and the maintenance cost are reduced.

The invention discloses a thread task processing method, a storage medium, electronic equipment and a program product, and relates to the technical field of computers.The method comprises the steps that in response to a to-be-processed thread task triggered by a user, current hardware resource data of a client side are obtained, and the hardware resource data comprise hardware configuration data and resource use data of the client side; identifying a target category corresponding to the to-be-processed thread task; determining a target parameter prediction model corresponding to the to-be-processed thread task from the plurality of parameter prediction models based on the target category, inputting the hardware resource data into the target parameter prediction model, and predicting target parameters needing to be configured when a thread pool in the client processes the to-be-processed thread task through the target parameter prediction model; adjusting parameters of the thread pool into target parameters, and processing the to-be-processed thread task based on the adjusted thread pool; according to the invention, the resource utilization rate of the system can be optimized.

The invention relates to a service orchestration method based on a multi-tenant micro-service architecture, in particular to the field of distributed systems, comprising the steps of creating a thread pool and defining a private final pipeline, creating a pipeline builder and appointing each stage in the pipeline, generating a pipeline execution class, and initializing a pipeline processing overall business process. The method comprises the following steps: establishing a pipeline object, packaging into different processors, creating a pipeline object, effectively processing concurrent tasks, carrying out reasonable processing under an overtime condition, avoiding waste and blockage of system resources, adding each processor into a pipeline and inserting an interceptor into the pipeline, enhancing expandability and flexibility of a system, and improving system performance. The output results of the processors are combined into a final result, exception handling and rollback operation are carried out, the steps of the whole business process are divided into a plurality of sub-tasks, parallel execution and serial execution sub-task groups are formed, the execution sequence is determined, and the response speed and the concurrent processing capacity of the system are improved.

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.