227 results about "Preemption" patented technology

The invention relates to the technical field of computing power resources, and discloses a flexible allocation monitoring system for computing power resources, which defines a clear resource use boundary for different types of tasks through a container-level QoS strategy of a resource isolation unit, and adjusts resource allocation in real time in combination with a dynamic partition management module, thereby avoiding resource waste in a traditional fixed allocation mode, and improving the resource allocation efficiency. The dynamic preemption unit accurately screens low-priority tasks for resource recovery through a multi-factor decision engine and a preemption cost evaluation algorithm, in 50 concurrent task scenes, the resource preemption response time is shortened to 20-35 ms and is improved by 50%-70% compared with 70-100 ms of Docker / K8s, the blocking duration of high-priority tasks is reduced to 15-25 ms from 80-120 ms, and the core service interruption risk is greatly reduced.

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The invention discloses a multi-task scheduling method, system and device based on resource queue management and a medium, and relates to the technical field of chip design simulation. The method mainly comprises the following steps: receiving a task creation request, generating a unique task identifier for a task according to a resource type identifier and task validity period information contained in the task creation request, and adding the task into a resource queue of a corresponding resource type; sorting and scheduling the tasks in the resource queue according to the task creation time, the task state and the task validity period; in a task execution process, according to a resource type required by the task, adopting a preemptive or polling resource allocation mode to obtain a target resource; after the task is executed, the task state is updated, the state of the completed task is allowed to roll back to the to-be-scheduled state under the preset condition, and the task enters the corresponding resource queue again to wait for scheduling. According to the method, the fairness of task scheduling, the resource utilization rate and the system robustness can be effectively improved.

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention provides a mechanical arm control system and control method based on an embedded virtual machine architecture. A real-time operating system and a non-real-time operating system are deployed on the same embedded board card; the non-real-time operating system transmits control words of all the drivers to a driver communication module deployed on the real-time operating system through a control queue based on the received control request, and the driver communication module transmits state data of all the drivers to the non-real-time operating system through a state queue; and the motion planning module deployed on the non-real-time operating system transmits the generated position data to the driver communication module through the position queue according to a preset control period, and issues the control word and the position data to the driver through the driver communication module. By the adoption of the method, the problems that a non-real-time operating system is interrupted, communication stability is poor due to multi-task resource preemption, control response is slow, state coverage is caused by jitter of the non-real-time operating system, and a mechanical arm is stuck due to position transmission jitter can be solved.

The embodiment of the invention discloses an interrupt processing method, a processor, a chip, a storage medium and a program product, and the method comprises the steps: determining the remaining processing time of an assembly line of a target task based on the state of the assembly line of the target task being processed in response to a to-be-processed interrupt signal; determining the real-time weight of the interrupt signal to be processed based on the interrupt type of the interrupt signal to be processed and the current working mode of a processor; the real-time weight is used for representing the urgency of the interrupt signal to be processed; determining a response score of the interrupt signal to be processed based on the remaining processing time of the pipeline and the real-time weight; the response score represents the response priority of the interrupt signal to be processed; and in response to the response score reaching a first threshold value, triggering interrupt preemption, and processing the interrupt signal to be processed.

The invention provides a multi-factor collaborative dynamic priority task scheduling method based on waiting time feedback, which is characterized by comprising the following steps of: initializing waiting time, task criticality and weight values of queue length, initializing a mixed task set and a task waiting queue, and initializing the task waiting queue into an empty set; entering a task waiting queue; updating the priorities of all tasks in the task waiting queue; sorting the tasks in the task waiting queue according to the priority; monitoring whether a new task arrives in real time; judging whether the newly arrived task meets a preemption condition or not; updating the historical average waiting time of the task; updating weight values of task waiting time and queue length; and judging whether all tasks in the mixed task set are scheduled. According to the method, long-term hunger of low-priority tasks is avoided, and the average waiting time of the tasks is shortened; the preemption threshold value is dynamically adjusted, and the context switching overhead is reduced; a dynamic weight mechanism is designed, and the self-adaptive capacity of the system is improved.

The invention discloses an unmanned aerial vehicle real-time task scheduling and resource management system, which belongs to the technical field of unmanned aerial vehicle computing resource management, and comprises a task load prediction module, a priority dynamic evaluation module, a resource elastic distribution module and a closed-loop feedback optimization module, the task load prediction module adopts an LSTM-GRU hybrid neural network to predict a resource demand peak value in advance and triggers a pre-scheduling signal; the priority dynamic evaluation module dynamically adjusts the task priority based on the multi-dimensional evaluation matrix; the resource elastic allocation module realizes elastic allocation through a reserved resource pool and a multi-level preemption mechanism; the closed-loop feedback optimization module monitors performance deviation and reversely adjusts parameters of each module, the four modules are deeply coupled to form a closed-loop cooperation mechanism, and intelligent task scheduling and dynamic resource optimization are achieved.

The invention provides an FPGA (Field Programmable Gate Array) multi-modal control method based on dynamic partial reconfiguration, which comprises the following steps that: a main body adopts a layered hardware architecture to realize multi-modal control, firstly, a system module of a whole system is divided, so that FPGA logic resources are divided into a static region and a reconfigurable region; wherein the static area is responsible for global control and a communication interface part, the reconfigurable area realizes a multi-mode function by loading different configurations, and the middle layer is used for deploying the dynamic part to reconfigure a controller and is responsible for time sequence coordination and bit stream management. Compared with the prior art, the method has the advantages that the mapping relation between the modal feature library and the hardware module is established, the priority preemptive reconfiguration strategy and the modal switching cost model are adopted, the real-time matching of the sensor data flow and the hardware architecture is achieved, the dynamic data path reconstruction technology supports multi-modal parallel processing, and the real-time matching of the sensor data flow and the hardware architecture is achieved. Meanwhile, time sequence constraint, resource occupancy rate and power consumption budget are considered.

The invention belongs to the technical field of computers, and particularly relates to a resource allocation method and device, equipment and a storage medium. The method comprises the following steps: S1, acquiring a target resource occupation task and real-time state data of a plurality of clusters; s2, calculating a cluster resource utilization rate based on the total resource configuration amount and the resource usage amount; calculating a residual resource proportion of the cluster based on the resource residual amount and the resource demand parameter; calculating a load sensitivity coefficient of the target resource occupation task based on the task priority, the resource demand parameter, an execution duration threshold and a task dependency identifier; and S3, screening the clusters according to preset screening conditions corresponding to different task guarantee levels, determining the clusters meeting the preset screening conditions as candidate clusters, monitoring in real time, triggering resource migration based on quantitative migration conditions, and forming a closed-loop process of data acquisition-index calculation-screening distribution-preemption coordination-monitoring migration.

The invention relates to the technical field of task scheduling, and discloses a scheduling control method and device for a multi-core real-time processing system and a computer storage medium, and the method comprises the steps: when a target process has a high-bandwidth operation request, executing a first bandwidth preemption operation based on a bandwidth lock mechanism, scheduling a target processor core corresponding to the target process to preempt a first shared resource required by the high-bandwidth operation request; the first shared resource comprises a CPU shared resource; based on a dynamic bandwidth preemption priority mechanism, executing a second bandwidth preemption operation according to the process priority and the task information of the target process so as to schedule a task execution unit of the target process to preempt a second shared resource; the second shared resources at least comprise global shared resources. Visibly, the scheduling reliability and accuracy between the processor cores and the main devices of the multi-core CPU in the multi-core real-time processing system can be improved, the problems of inner competition and outer competition are solved at the same time, and therefore the real-time performance of the system is improved.

The invention discloses a task preemption scheduling method, which relates to the field of task preemption scheduling, and comprises the following steps of: performing dynamic task priority analysis and queue topology reconstruction by acquiring task basic data to obtain a coupling task group set, performing resource satisfaction degree decision by acquiring node resource data to obtain a global resource satisfaction degree mark, and performing task preemption scheduling. Performing resource matching degree space-time prediction on the basis of the global resource satisfaction degree mark to obtain optimal preemption time, performing preemption decision on the optimal preemption time and the coupling task group set to obtain a total preemption loss value of the node, and performing resource fragment integration scheduling on the basis of the total preemption loss value of the node to obtain a final scheduling scheme. The node direct scheduling model is created based on the global resource satisfaction degree mark, the optimal node identifier is obtained, the resource fragments are integrated to construct the comprehensive fragment index of the multi-dimensional resources, the overall utilization rate of the resources is improved, the overall income of the system is improved, and the recovery cost and the time overhead of the system can be reduced.

The invention belongs to the technical field of cache prefetching optimization of a firmware layer of a computer storage system, and particularly provides a memory control method, a storage device, a medium and equipment.The instantaneous flow is effectively filtered and persistent hotspot data is accurately recognized through a three-layer structure of combining a first-stage hotspot filtering module with a window cache, a view queue and a protection queue; the second-stage continuous access filtering module dynamically judges a continuous access mode by means of mean-variance statistics of an annular buffer area, and accidental continuous triggering of prefetching is avoided; and the third-stage random access judgment module prohibits invalid prefetching and effectively distinguishes access modes. The pre-fetching bandwidth is limited through the token bucket algorithm, the global priority queue of the skip list is combined, it is ensured that the priority of the write task is higher than that of the pre-fetching task, pre-fetching and foreground I / O resources are isolated, delay jitter control over foreground key I / O can be achieved during full-load pre-fetching, and the problem that delay soaring is caused when pre-fetching preempts the resources is solved.

The invention relates to an automatic driving simulation test system based on a local cluster. According to the system, a local structured file is used as a database, a plurality of common computers are built into a distributed computing cluster through an SSH protocol, and a Web front end is integrated to provide a user interface. The core executes a priority scheduling algorithm fused with a dynamic adjustment and preemption mechanism through an intelligent scheduling and control module, and drives a simulation software API (Application Program Interface) to realize full-process automation from task submission, scheduling, distributed parallel execution, state monitoring to result collection and report generation. The invention further relates to a corresponding method and device, a processor and a computer readable storage medium. According to the automatic driving simulation test system, method and device based on the local cluster, the processor and the storage medium, the problems that an existing simulation test cloud platform is high in cost and complex in deployment are effectively solved, and an efficient, low-cost and high-flexibility test solution is provided for automatic driving research and development.

The invention discloses a financial scene-oriented model reasoning method and device, equipment and a storage medium, and relates to the field of model reasoning. According to the extracted semantic information, the model structure and the system bandwidth resources, determining a delay estimated value for reasoning the current task; performing score evaluation on the current task based on the task priority information and the delay estimated value, and updating the task queue and the KV cache sharing degree of the queue in combination with the SLO information; based on the KV cache sharing degree and the queue task number, the task queues form task batches, and the task batches are sent into a GPU video memory for model calculation; and when the GPU computing resources are occupied by the low-priority tasks in the task batch, acquiring SLO information of not executing the high-priority tasks, and calculating the preemption state of executing the high-priority tasks through a profit-cost quantification mechanism. According to the method and the device, the task delay, the priority and the KV cache sharing degree are dynamically evaluated, and the benefit-cost quantitative preemption mechanism is combined, so that the method and the device have the advantages of improving the high-priority task response speed and the overall efficiency of the system.

The invention provides a process GPU-oriented real-time fault tolerance and task migration system and method, and relates to the technical field of distributed computation.The method comprises the steps that a training task state in a GPU video memory is captured, difference data is generated through an incremental difference algorithm, and the difference data is stored in a multi-level storage system after being subjected to lightweight compression; meanwhile, GPU node health indexes are collected in real time, node states are analyzed through a time sequence prediction model, and a migration process is triggered when fault risks are predicted or preemption early warning is received; during migration, the task is paused, a final increment check point is generated, check point data is quickly transmitted to a target healthy GPU node through an RDMA network, a complete context state is reconstructed, task execution is recovered, and real-time fault tolerance and seamless migration of the GPU task are achieved. According to the method, the continuous operation reliability and the resource utilization efficiency of the GPU cluster in the long-time training task are improved.

Provided are a collective communication method and apparatus. The method is applied to a distributed computing system, and comprises: acquiring a task to be computed, wherein the task to be computed comprises a first-type parallel computing task and a second-type parallel computing task that have no dependency relationship; with respect to the first-type parallel computing task and the second-type parallel computing task, respectively creating a first-type communication domain and a second-type communication domain, wherein the first-type communication domain and the second-type communication domain at least comprise two identical computing devices, and each of the first-type communication domain and the second-type communication domain has an independent communication buffer and an independent communication connection; and concurrently executing a collective communication operation for the first-type communication domain and a collective communication operation for the second-type communication domain. The collective communication method provided in the present application can create an independent communication buffer and an independent communication connection for each of a plurality of communication domains that can be concurrently executed, thereby realizing concurrent execution of the plurality of communication domains, avoiding communication connection preemption, and improving the execution efficiency of a distributed computing task.

The invention relates to the technical field of industrial internet of things and mobile edge computing crossing, in particular to a computing task unloading and service caching collaborative optimization method, which comprises the following steps of: acquiring an IIOT equipment state, channel quality and edge server resources in real time by constructing a dynamic network awareness architecture, and analyzing a task DAG topological structure; designing a service cache delay compensation mechanism, parallelizing a cache process and task execution, predicting future required services based on a task dependency relationship, and calculating a delay compensation factor; a deep reinforcement learning algorithm based on Transform is adopted, and an unloading decision is generated in combination with a hierarchical attention mechanism and an improved DDQN network; tasks are dynamically allocated to the local, the edge or the cloud through a three-level unloading decision tree, task completion time is minimized, a resource constraint modeling and task preemption mechanism is further included, and the system response capability and the resource utilization rate are improved.

An example method may include determining whether a preemption flag associated with a first input / output (I / O) handling thread is equal to a first value indicating that preemption of the first I / O queue handling thread is forthcoming, wherein the first I / O queue handling thread is executing on a first processor, the first I / O queue handling thread is associated with a first set of one or more queue identifiers, and each queue identifier identifies a queue being handled by the first I / O queue handling thread, and, responsive to determining that the preemption flag is equal to the first value, transferring the first set of one or more queue identifiers to a second I / O queue handling thread executing on a second processor. Transferring the first set of queue identifiers may include removing the one or more queue identifiers from the first set.

The invention provides a dynamic predictive job scheduling method, which comprises the following steps of: acquiring a configuration file of a to-be-scheduled EDA (Electronic Design Automation) task by a scheduling central system, and extracting job characteristics of the to-be-scheduled EDA task; acquiring real-time monitoring data of the current running task from the computing node cluster; the prediction engine predicts the to-be-scheduled EDA task and the current running task according to the job characteristics and the real-time monitoring data to obtain first prediction data of the to-be-scheduled EDA task and second prediction data of the current running task, and the first prediction data and the second prediction data are fed back to the scheduling center system; the scheduling center system sends the first prediction data, the second prediction data and the real-time monitoring data to a decision engine; the decision engine determines a resource preemption strategy of the to-be-scheduled EDA task and feeds back the resource preemption strategy to the scheduling center system; the scheduling center system determines the execution sequence of the EDA tasks to be scheduled and sends the execution sequence to an execution engine; and the execution engine performs resource allocation on the to-be-scheduled EDA task.

The invention belongs to the technical field of automotive electronics AUTOSAR, and discloses an AUTOSAR multi-task-oriented nested lock management and real-time priority scheduling control system, a detection and prevention module establishes a dynamic resource allocation diagram by taking tasks and locks as nodes, real-time traversal is performed in combination with a depth-first search algorithm, and a task-lock-task circulation path is accurately identified; when a potential deadlock is detected, preferentially releasing a low optimal lock occupied by a low-priority task through priority arbitration, and triggering an overtime release mechanism when the priorities are the same; a scene of accessing multiple layers of shared resources by multi-task nesting under an AutoSar framework is adapted, so that the system is ensured to stably run in a complex concurrent scene, and function failure caused by deadlock is avoided; the priority scheduling module establishes a scheduling rule in combination with dual priorities of tasks and locks, optimizes a nested priority inheritance mechanism, temporarily improves the priority when a low-optimal task occupies a lock required by a high-optimal task, and configures a preemption right for the high-optimal lock.

The embodiment of the invention provides a GPU computing power scheduling method and device, equipment, a medium and a program product, and relates to the technical field of computing power scheduling, and the method comprises the steps: dividing a computing unit and video memory resources of a GPU into a plurality of dynamically adjustable partitions according to a real-time task demand; calculating the priority weight of each task; preemptive scheduling and cooperative scheduling are combined, resource preemption is carried out on the first priority task, and time division multiplexing is carried out on the non-preemptive task; executing a dynamic voltage frequency adjustment (DVFS) and task merging strategy; through dynamic resource partitioning, dynamic priority evaluation, a hybrid scheduling strategy and an energy efficiency optimization algorithm, the GPU resource utilization rate and the energy efficiency ratio in a multi-task scene are remarkably improved, and meanwhile, energy consumption is reduced by combining a DVFS and a task merging strategy.

The invention relates to the technical field of overwater construction, and discloses a hinge sinking row unit hoisting path optimization and construction intelligent scheduling method, which comprises the following steps: S1, initializing and preparing a system, digitally modeling a construction environment, tasks and resources, instantiating a task agent and a market scheduler, and calculating a construction contribution value; s2, hoisting path optimization and intelligent construction scheduling are carried out, a market scheduler issues resource time slots, a task agent executes path planning and constructs a bidding vector, and comprehensive risks are considered in the path planning; the market scheduler calculates a net contribution value based on the construction contribution value and the opportunity cost, and selects a scheme with the maximum net contribution value to issue an instruction; and S3, monitoring and dynamic intervention are executed, and when preset interruption conditions such as high-priority task preemption or environment safety interruption are met, the current task is interrupted, and scheduling circulation is restarted. According to the method, the resource utilization efficiency, the path security and the system dynamic response capability are improved.

The invention discloses a task scheduling method and system based on a heterogeneous pulse neural network on a GPU (Graphics Processing Unit), which are used for modeling a scheduling problem into a reward maximization task with constraints so as to optimize resource allocation and parameter adjustment. According to the priority-based multi-preemptive scheduling framework provided by the invention, the heterogeneous SNN tasks on the GPU are dynamically managed by adopting dynamic priority calculation, a self-adaptive time segmentation mechanism and an overhead perception optimization strategy. The effectiveness of the method is verified through a large number of simulation experiments, and the performance under different complexity proportions, arrival modes, load levels and time slice configurations is tested. Results show that the priority-based multi-preemptive scheduling framework provided by the invention is superior to the existing scheduling algorithm in the aspects of energy efficiency, throughput and resource utilization rate. The work promotes the practical application of the SNN workload in a service computing environment, and promotes the development of a more efficient and extensible neuromorphic computing solution.

The invention discloses a PreemptRT-based KVM (Keyboard Video Mouse) real-time optimization method, which comprises the following steps of: S1, virtualization layer fine-grained preemption optimization: reconstructing a KVM virtualization layer lock mechanism and a processing flow, realizing fine-grained preemption through lock replacement, compression of a critical zone and insertion of a preemption point, and shortening a preemption window; s2, interrupting a real-time forwarding architecture: optimizing an interrupt forwarding path through double-layer interrupt priority mapping, direct injection architecture and interrupt isolation optimization; step S3, optimizing vCPU scheduling real-time performance: improving vCPU scheduling certainty by configuring a real-time scheduling strategy, strong affinity binding and a high-precision awakening means; and S4, memory virtualization low delay: optimizing memory virtualization delay by combining large page pre-allocation and locking, memory lock and TLB refresh optimization and DMA lock granularity optimization.

This invention relates to a method for elastic scheduling of AI computing resources based on business awareness, comprising the following steps: predefining a business criticality level BK, and mapping the business criticality level BK to a corresponding weight coefficient V. bk The system automatically identifies the current iteration cycle stage of a task and predefines the corresponding dynamic weight coefficient C for the iteration cycle. It calculates and generates a dynamically changing priority score PF using a calculation function. It calculates and allocates the elastic quota FHQ for each business project, obtaining a total quota AHQ. When an urgent task is received, resource preemption assessment is performed. This invention employs a business-aware method, system, device, processor, and its computer-readable storage medium for elastic scheduling of AI computing resources. This achieves deep integration of business and technology, improves resource utilization, effectively suppresses users' false reporting of resource demands, thereby enhancing the overall utilization of the cluster; reduces preemption losses, avoids waste of computing resources, and maximizes the effective computing efficiency of the cluster.

The invention provides a vocational education system for multi-module collaborative management based on edge computing, and relates to the field of education management. Comprising a dynamic task state machine module for monitoring multi-dimensional parameters such as task progress, network delay, equipment load and security risk in real time and triggering a permission migration instruction; the authority fragmentation edge cache module is used for disassembling the authority into atomization units (APU) and storing the APUs in edge nodes in a blocking manner, and verifying the integrity through a Hash authority fingerprint chain; the conflict resolution protocol module dynamically generates a priority weight based on task remaining time, records an operation log chain and executes local rollback; the edge node cluster module dynamically switches calculation, routing and storage roles to optimize resource allocation; the system adapts to different security level tasks through a dynamic blocking strategy and a recombination threshold value, priority preemption and parallel transmission are adopted to reduce delay, and an encryption timestamp and a forward hash pointer are utilized to guarantee operation traceability.

The invention discloses a parallel module communication technology based on a serial port bus, and belongs to the technical field of communication. According to the real-time serial port bus, a plurality of independent modules are connected in parallel and communicate and interact with a host, firstly, the modules are made to know own allocated IDs through hardware automatic address allocation, and then the host determines the types and basic information of the modules at the corresponding positions through initial interactive communication; through a polling technology based on time slices, sequential data interaction between a host and a plurality of modules is realized, and meanwhile, high real-time performance is realized through a module preemption communication technology. The system is simple in structure, high in module independence, high in communication real-time performance, clear and easy to use in control logic, and suitable for being used as a short-distance universal parallel application technology of a serial port bus.