354 results about "Preemption" patented technology

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention discloses a heterogeneous AI computing power resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous AI computing power resource pool, wherein the heterogeneous AI computing power resource pool integrates the computing resources of a plurality of heterogeneous AI acceleration chips; obtaining a scheduling demand of the AI task, wherein the scheduling demand comprises a task type, a resource request quantity, a priority identifier and a task group association relationship; generating a multi-dimensional scheduling strategy according to task requirements, wherein the scheduling strategy comprises a priority scheduling rule, an affinity scheduling rule and a resource preemption rule; based on a multi-dimensional scheduling strategy, the AI tasks are dynamically allocated to target computing power nodes of the heterogeneous AI computing power resource pool, and the task execution state and the resource utilization rate are monitored in real time; and dynamically adjusting computing resource allocation according to the resource utilization rate. Through the heterogeneous AI computing power resource pool, the resource utilization rate is remarkably improved, dynamic resource allocation is realized through a multi-dimensional scheduling strategy, and meanwhile, a communication path is optimized through an affinity scheduling strategy, so that the problem of task starvation caused by resource fragmentation is avoided.

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

The invention discloses a dynamic data scheduling method and system of a computing power cluster, and belongs to the field of computer science and technical field, and the method comprises the following steps: reading node resources of the computing power cluster, synchronously obtaining a task scale, initializing a dynamic granularity distribution network, and determining splitting granularity; task splitting is carried out according to the splitting granularity, and task priorities are configured; performing load and task execution end prediction on each node in the computing power cluster through the prediction model; performing resource adaptation optimization of the M sub-tasks according to the prediction result; establishing a dynamic optimization channel, performing dynamic optimization on an initial allocation result, and performing task preemption optimization based on task priorities; and performing dynamic data scheduling management of the computing power cluster. According to the method and the device, the technical problems of resource waste, low task execution efficiency and slow system response caused by inflexible resource scheduling and unreasonable task allocation of the computing power cluster under the condition of dynamic load and task scale change in the prior art are solved.

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 electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

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 discloses a modular reclosing circuit breaker protection system based on intelligent electrical cloud coordination, particularly relates to the technical field of intelligent power distribution network relay protection, and is used for solving the problems that fault diagnosis is delayed and a protection strategy cannot be generated in a fault clearing time window due to resource competition in a cloud coordination protection system. A dynamic preemption priority is generated based on power grid topology vulnerability and associated circuit breaker collaborative requirements by monitoring the load state of cloud computing node resources in real time; credibility attenuation control is carried out in combination with a fault current waveform distortion rate; when the resource preemption necessity exceeds a threshold value, freezing a periodic task thread and releasing computing resources, and opening up a high-priority communication channel at the same time; sending a fault recording compression instruction to a target circuit breaker terminal to obtain an edge preprocessing feature vector; and protection strategy optimization is executed in the released resources to generate reclosing parameters, and the reclosing parameters are issued and executed through a high-priority channel, so that quick response of a fault diagnosis task is realized.

The disclosure herein describes scheduling execution of artificial intelligence (AI) workloads in a cloud infrastructure platform. A global scheduler receives AI workloads associated with resource ticket values. The scheduler distributes the AI workloads to nodes based on balancing resource ticket values. Local schedulers of the nodes schedule AI workloads on resources based on the resource ticket values of the AI workloads. Based on scheduling the AI workloads, coordinator services of the local schedulers execute the distributed AI workloads on the infrastructure resources of the nodes. The disclosure further describes scheduling AI workloads based on priority tiers. A scheduler receives AI workloads, and each AI workload is associated with a priority tier indicative of a preemption priority while being executed. The AI workloads are scheduled for execution on a distributed set of nodes based on the priority tiers and then execute based on the scheduling.

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 relates to the technical field of network resource scheduling, and provides a heterogeneous network intelligent slice resource scheduling method and device, electronic equipment and a medium. The method comprises the following steps: performing three-dimensional joint modeling on heterogeneous network physical resources to obtain a dynamic resource state tensor, and performing a dynamic resource allocation decision by combining a deep reinforcement learning model with a service demand matrix to obtain an initial slice resource allocation matrix; and performing mixed integer linear programming through a preemption decision model in combination with the URLLC service request queue to obtain a resource allocation sequence, and performing occupancy rate updating on the dynamic resource state tensor according to the resource allocation sequence to obtain ground network resource data, and carrying out resource fusion in combination with the satellite beam capacity data to obtain a space-ground integrated resource pool tensor, thereby carrying out traffic prediction in combination with historical traffic data through a long-short memory network to obtain a bandwidth pre-adjustment strategy. Through organic integration of technologies such as mixed integer linear programming and traffic prediction, the overall network resource utilization rate is improved.

The invention relates to the technical field of computing resource scheduling, and discloses a heterogeneous multi-task computing power dynamic scheduling method and system.The method comprises the steps that in response to multiple computing task requirements of a user, the priority of computing tasks is analyzed and sorted, and a task priority sequence is obtained; classifying the heterogeneous computing resources according to the resource states of the heterogeneous computing resources, and respectively placing different categories of computing resources in corresponding resource pools to obtain multiple categories of resource pools; in combination with the task priority sequence and the multi-class resource pool, allocating computing resources of a corresponding class to each task through a preset resource matching mechanism; in the task calculation process, when the calculation resources of the first task are insufficient, the calculation resources of the second task are preempted through a preset resource preemption mechanism, the priority of the second task is improved after resource preemption, and the calculation resources are redistributed; according to the method and the device, the corresponding computing resources can be allocated in combination with the task priorities, and the resources are dynamically scheduled when the computing resource gap appears.

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 relates to the field of intelligent control, in particular to an intelligent washing control method and system based on multi-modal data, and the method comprises the steps: carrying out the time sequence alignment and feature fusion of a running state parameter set and a communication demand parameter set, and obtaining a multi-modal communication feature matrix; analyzing the coupling relationship between the dehydration stage identifier and the real-time energy consumption value of the washing equipment through a preset conflict prediction model, dynamically calculating the channel preemption priority of each washing equipment according to an instruction execution time window, and generating a clustering type communication resource scheduling sequence in combination with the priority of a control instruction; obtaining the number of instruction conflicts identified by each washing device in the dehydration stage, and constructing a load balancing optimization function with the instantaneous circuit load; and when the output value of the load balancing optimization function is minimized, obtaining a dehydration peak shifting control instruction. The problems of communication quality reduction and control delay during cooperative control of a plurality of washing devices are solved, and the timeliness and safety of control of the washing devices are improved.

A computer-implemented method of distributed management of locks includes: receiving, by a plurality of servers, a plurality of concurrent requests; performing lock preemption on the distributed lock using a synchronization mechanism; only one server uses a synchronization mechanism to obtain the distributed lock; updating the lock value of the distributed lock by only one server; and executing the business logic. Lock preemption is performed on the distributed lock by the respective server, including comparing a current memory value of the distributed lock to an expected value. The method further comprises the steps that if the current memory value is matched with the expected value, only one server modifies the current memory value into an updated value; writing the updated value into a memory of the distributed lock; and if the execution of the service logic fails, recovering the memory value of the distributed lock to the current memory value.

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 discloses a scheduling method for heterogeneous GPU resource pooling and dynamic computing power preemption, and relates to the technical field of data process.The method includes the steps that priority coefficient evaluation is conducted on a computing task, a computing power resource value and a computing priority required by the computing task are obtained, and the computing power resource value and the computing priority required by the computing task are calculated according to the computing power resource value required by the computing task; the method comprises the following steps: configuring driving programs of different heterogeneous resources, performing resource abstraction on virtual heterogeneous resources to generate a tree-shaped resource pool, and performing hierarchical matching on corresponding resource types from a root resource pool of the tree-shaped resource pool according to a computing power resource value required by a computing task and a computing priority; meanwhile, task related information can be analyzed according to the calculation task, the trend of the calculation task can be predicted, the estimated calculation task expansion amount is obtained, the estimated calculation power resource value needed in the next task requirement is calculated through the calculation task expansion amount, the resource expansion trend is evaluated in combination with the calculation power resource value needed by the calculation task, and the calculation efficiency is improved. And the tree-shaped resource pool is re-divided according to the resource expansion trend.

The invention relates to a fine-grained dynamic unit task scheduling strategy, aims to solve the problem of real-time scheduling of DAG (directed acyclic graph) tasks on a multi-core platform, and belongs to the field of real-time embedded system task scheduling. Static priority distribution is adopted in an existing priority preemptive scheduling method, the parallel execution degree of tasks is limited, and response time is possibly increased. According to the method, the DAG vertex is divided into a plurality of unit nodes, the independent priority is allocated to each unit node, execution is performed with the shorter worst case execution time (WCET), the priority is dynamically adjusted, the limitation of the static priority is broken through, the task parallelism degree is improved, and the calculation limit of the worst response time (WCRT) is optimized. Meanwhile, a unit node priority distribution algorithm is designed, and the task scheduling efficiency is further improved.

Techniques are provided to enable mid-thread (instruction level) preemption in a graphics processor without requiring software intervention by a graphics driver associated with the graphics processor. Hardware based mid-thread preemption is facilitated using the thread dispatch hardware of the graphics processor to trigger execution of a kernel program by the graphics processor that saves the thread state of preempted processing resources and facilitates the subsequent restoration of that thread state to the same or a different set of processing resources.

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 invention discloses a task scheduling method based on dynamic weight, and belongs to the technical field of workflow engines and distributed computing. The method comprises the steps of establishing a to-be-allocated task set; calculating a dynamic priority weight; establishing a task queue; sequentially processing the tasks according to a task queue sequence; establishing a calculation node set of all available processing tasks, and screening out an optimal calculation node corresponding to a to-be-processed task in the task queue; judging whether the to-be-processed task is partitioned or not; establishing a task preemption condition until the to-be-processed task is processed; and repeating the steps until all tasks are processed. The method can be applied to cloud computing, intelligent manufacturing and distributed system task scheduling scenes, and the resource utilization rate and task completion timeliness are remarkably improved.

A system and method for avoiding problems associated with locks and preemption. After interrupting a rollback section of user code, the kernel causes a roll back” to the first instruction in the section. After the interrupt, when control is returned to the interrupted user-level thread, execution resumes at the beginning of the rollback section, not the point where the interrupt occurred. After interrupting a rollforward section of the present invention, the kernel causes execution to “roll forward” through the section to the last instruction. That is, the kernel essentially suppresses interrupts within a rollforward section, delaying the servicing of any interrupt until the section completes. Spinlocks employing the techniques of the present invention have none of the disadvantages of traditional locks: they formally fit the definition of “lock-free” code.

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 present disclosure provides a data monitoring and sending method and apparatus, an electronic device, and a vehicle. Real-time monitoring of anomalous events can be implemented by means of the state of the vehicle, and then target anomalous events satisfying a trigger condition are determined, so that signals can be sent in time, improving the sending efficiency. Then, when a plurality of target anomalous events are present, a transmission order of the target anomalous events is determined by means of preemption arbitration, avoiding disordered preemption of data sending channels by the target anomalous events, avoiding excessive resource occupation and resource waste, and ensuring that signals can be smoothly sent. Finally, after signal ordering is completed, events to be sent are sent. By means of preemption arbitration and signal arrangement, data of a plurality of events can be sent using a small quantity of CAN sending resources. The use of an event preemption policy can ensure that data of the target anomalous event having the highest priority is preferentially sent, efficiently completing collection and sending of data, and reducing waste of resources.

The invention discloses a multi-master-station synchronization method and device and a storage medium. The multi-master-station synchronization method comprises the following steps: setting priority for each master station process; the preset master station generates a time synchronization message and sends the time synchronization message to a network, when the receiving master station receives the time synchronization message, hardware interruption is triggered, the receiving master station executes a time synchronization task, and the receiving master station calculates time deviation according to the time synchronization message and adjusts a local clock; the preset master station updates the data of the shared data area and sends a data updating notification to the receiving master station; and receiving local data updated by the master station based on the data updating notification. According to the method, on the basis of hardware interruption and a preemption mechanism of an operating system, a time synchronization task is endowed with stable execution capability in a complex system environment, and the time synchronization task can always preferentially acquire system resources in the case of complex conditions such as high load and network fluctuation, so that the accuracy and continuity of time synchronization are ensured.

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 embodiment of the invention provides a data management method and device, a storage medium and computer equipment, and the method comprises the steps: periodically receiving congestion control transmission type data, and carrying out the statistics of the buffer occupation information of the congestion control transmission type data; if so, integrating current period related data information to obtain current period integrated data, and inputting the current period integrated data into a prediction model in combination with historical period integrated data to obtain a predicted target data volume; and if the target occupancy reaches a larger second occupancy threshold, obtaining the current total occupancy of the buffer area and the occupancy of each type of current data. And finally, integrating the target data volume, the current total occupancy and the current data occupancy, scientifically managing buffer data, optimizing resource utilization, preventing mutual preemption of different types of data, improving data transmission efficiency, and ensuring stable and efficient operation of the network so as to cope with network congestion challenges and meet diversified data transmission requirements.