A job flow arrangement method and system for wide-area collaborative computing

Abstract

This invention discloses a job workflow orchestration method and system for wide-area collaborative computing. It obtains extended workflow definitions supporting dynamic task nodes; parses and instantiates the workflow, dynamically generating specific sub-tasks based on data characteristics; allocates appropriate computing resources to each task and generates execution plans based on real-time collected wide-area computing network resource status using a resource affinity matching algorithm; drives cross-center task execution according to the plan, and employs multi-level strategies for anomaly monitoring and handling; finally, it aggregates the status to generate a global view. The system includes modules for workflow management, intelligent orchestration, execution driving, and monitoring display. This invention solves the problems of rigid workflows, suboptimal resource matching, limited anomaly handling, and difficulty in template reuse faced by existing technologies in cross-domain collaborative scenarios, achieving efficient, flexible, and reliable collaborative orchestration of wide-area computing resources.

Description

Technical Field

[0001] This invention belongs to the field of high-performance computing and distributed computing technology, and in particular relates to a job workflow orchestration method and system for wide-area collaborative computing. Background Technology

[0002] Currently, mainstream workflow orchestration and management technologies have the following limitations: Static workflow engines (such as Apache Airflow) define task dependencies through directed acyclic graphs, but only support predefined static task nodes. They cannot dynamically generate task branches based on runtime data, making them unsuitable for business scenarios that require dynamic decision-making, such as weather simulation and drug screening.

[0003] Distributed task scheduling systems (such as Kubernetes Workflow) focus on resource allocation within a single data center or cluster. They lack a unified measurement and collaborative optimization mechanism for computing power and network resources across multiple heterogeneous supercomputing centers. This can easily lead to "data migration" problems when distributing tasks across domains, resulting in huge transmission overhead.

[0004] Supercomputing center dedicated scheduling software (such as IBM Platform LSF): typically adopts a tightly coupled architecture, the definition of job processes is strongly bound to the hardware environment and software stack of a specific center, process templates cannot be directly reused between different centers, and migration and collaboration costs are high.

[0005] In summary, existing technologies are insufficient to meet the needs of efficient, flexible, and reliable orchestration of complex, dynamic, and cross-center collaborative computing workflows in a wide-area computing network environment. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of the prior art and provide a job workflow orchestration method and system for wide-area collaborative computing, so as to solve technical problems such as inflexible static workflows, low efficiency of cross-domain collaboration, single exception handling and low degree of templateization.

[0007] In a first aspect, the present invention provides a job workflow orchestration method for wide-area collaborative computing, comprising: Obtain the target job flow defined using an extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on input data features. The target workflow is parsed and instantiated. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. Based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, appropriate computing resources are allocated to each subtask using a resource affinity matching algorithm, and a task execution plan is generated. Based on the task execution plan, the task is driven to execute in the corresponding computing center, and the execution process is monitored and handled through a multi-level exception response strategy. Summarize the execution status of each computing center to generate a global process view.

[0008] Secondly, the present invention provides a job scheduling system for wide-area collaborative computing, comprising: The acquisition module is configured to acquire the target job process defined by the extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on the characteristics of the input data. The parsing module is configured to parse and instantiate the target job process. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. The allocation module is configured to allocate appropriate computing resources to each subtask based on the task dependency graph and real-time resource status collected from multiple collaborative computing centers, using a resource affinity matching algorithm, and generate a task execution plan. The execution module is configured to drive task execution in the corresponding computing center based on the task execution plan, and to monitor and handle the execution process through a multi-level exception response strategy. The generation module is configured to summarize the execution status of each computing center and generate a global process view.

[0009] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the job workflow orchestration method for wide-area collaborative computing according to any embodiment of the present invention.

[0010] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the job workflow orchestration method for wide-area collaborative computing according to any embodiment of the present invention.

[0011] This application discloses a job workflow orchestration method and system for wide-area collaborative computing. The method involves obtaining extended workflow definitions that support dynamic task nodes; parsing and instantiating the workflow, dynamically generating specific sub-tasks based on data characteristics; allocating appropriate computing resources to each task and generating execution plans based on real-time collected wide-area computing network resource status using a resource affinity matching algorithm; driving cross-center task execution according to the plan, and employing multi-level strategies for anomaly monitoring and handling; and finally, a global view is generated by converging the status. The system includes modules for workflow management, intelligent orchestration, execution driving, and monitoring display. This invention solves the problems of rigid workflows, suboptimal resource matching, limited anomaly handling, and difficulty in template reuse faced by existing technologies in cross-domain collaborative scenarios, achieving efficient, flexible, and reliable collaborative orchestration of wide-area computing resources. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a job workflow orchestration method for wide-area collaborative computing, as provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a job workflow orchestration system for wide-area collaborative computing, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 The diagram shows a flowchart of a job workflow orchestration method for wide-area collaborative computing according to this application.

[0016] like Figure 1 As shown, the job workflow orchestration method for wide-area collaborative computing specifically includes the following steps: Step S101: Obtain the target job flow defined using an extended description model. The extended description model supports static task nodes and dynamic task nodes. The dynamic task nodes are configured with dynamic triggering conditions based on input data features.

[0017] In this step, the core metadata used to uniquely identify the process; It includes at least a set of task node definitions for static task nodes, dynamic task nodes, and a decision gateway, wherein the triggering conditions for the dynamic task nodes are defined by script expressions; A directed graph structure that describes the logical dependencies between nodes, supporting sequential flow, parallel gateways, and exclusive gateways; A serialization format definition used for process template storage and distribution.

[0018] Step S102: The target job process is parsed and instantiated. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph.

[0019] In this step, a logic verification algorithm based on topology sorting is used to check whether there are circular dependencies or isolated nodes in the target job process; When the verification is successful and a dynamic task node is encountered, the predefined task generation function is called to generate one or more sub-task nodes based on the current input data.

[0020] Step S103: Based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, allocate appropriate computing resources to each subtask using a resource affinity matching algorithm, and generate a task execution plan.

[0021] In this step, a task resource requirement vector and a computing center resource status vector are constructed. The vectors contain standardized parameters in at least three dimensions: computing performance, storage bandwidth, and network latency. Calculate the similarity between the task vector and the resource status vector of each computing center; Tasks are scheduled to computing centers where the similarity exceeds a preset threshold.

[0022] It should be noted that the similarity between the task vector and the resource state vectors of each computing center is calculated using the cosine similarity formula, specifically: Similarity (T, C) = (T_cpu * C_cpu + T_mem * C_mem + T_net * C_net) / (||T|| * ||C||), Where T represents the task resource requirement vector, C represents the computing center resource status vector, T_cpu and C_cpu represent computing dimension parameters, T_mem and C_mem represent storage dimension parameters, and T_net and C_net represent network dimension parameters.

[0023] Generating a task execution plan also includes: When allocating resources for subtasks involving cross-center data transmission, the optimal data transmission path is calculated based on an improved shortest path algorithm that comprehensively evaluates factors such as link bandwidth, real-time load, and data compression rate.

[0024] Step S104: Based on the task execution plan, drive the task execution in the corresponding computing center, and monitor and process the execution process through a multi-level exception response strategy.

[0025] In this step, the multi-level anomaly response strategy includes: The first level of response involves automatically retrying a task if it fails, including an exponential backoff mechanism. The second-level response, in response to resource unavailability or performance failure, triggers dynamic resource re-matching and task rescheduling based on the latest resource status. The third-level response, in response to process logic errors, suspends the process and notifies the user, supporting manual intervention and resuming from breakpoint.

[0026] Step S105: Summarize the execution status of each computing center and generate a global process view.

[0027] In one specific embodiment, the method includes the following steps: S1: Process definition and submission.

[0028] Users or application systems define their computational job flows based on the extended process description model proposed in this invention. This model extends the standard BPMN 2.0 specification, adding support for "dynamic task nodes." A process definition includes: Core metadata: process ID, version, creator, description, etc.

[0029] Task node library: includes static task nodes (executes deterministic code), dynamic task nodes (containing trigger conditions defined by JavaScript and other scripts, such as ${input.size>threshold}), decision gateway, etc.

[0030] Logical relationship graph: Defines the dependencies between nodes in the form of a directed graph, supporting sequential, parallel, and branching.

[0031] Resource template: Describes the CPU, memory, software environment, etc. required for the task, decoupling it from the environment.

[0032] Once defined, the process is serialized in JSON or XML format and submitted to the orchestration system.

[0033] S2: Dynamic parsing and instantiation.

[0034] The orchestration system's workflow management module receives the workflow definition. First, it performs logical verification, using a topological sorting algorithm to check for circular dependencies or isolated nodes. After successful verification, instantiation begins. When a "dynamic task node" is encountered, the system executes the predefined "task generation function" within that node. This function performs calculations based on the actual input data for the current step (such as file size and data characteristics).

[0035] Example: In a climate simulation workflow, a dynamic node can be configured as follows: "If the initial field data resolution for region A is higher than 1 km, generate a 'Refined Simulation of Region A' subtask; otherwise, skip." After the system reads the actual data, it dynamically creates or does not create the subtask based on the conditions. Once all dynamic nodes are instantiated, a fully deterministic, executable task dependency graph is formed.

[0036] S3: Cross-domain intelligent orchestration.

[0037] The intelligent orchestration module runs a "resource affinity matching algorithm" based on the task dependency graph generated by S2 and the real-time resource status (via resource metric units) collected from participating supercomputing centers A, B, and C. This algorithm includes: Vectorization: The requirements of each subtask (such as 10 TFLOPS of computing power and high-speed storage) and the available resources of each computing center are quantified into a vector containing three dimensions: computing, storage, and network (bandwidth and latency).

[0038] Similarity calculation: The cosine similarity formula is used to calculate the matching degree between the task vector and each central resource vector.

[0039] Decision-making and optimization: For each task, the optimal center with a matching degree exceeding a threshold (e.g., 0.7) is selected. Simultaneously, for tasks involving cross-center data migration, the path optimization unit uses an improved Dijkstra algorithm based on real-time network conditions to select transmission paths with sufficient bandwidth and low latency. Finally, a detailed task execution plan is output, specifying when and where each task will be executed, and how data transmission will be routed.

[0040] S4: Flexible execution and exception handling.

[0041] The execution driver module breaks down the task execution plan into specific operation instructions and distributes them to the "execution agents" deployed in various computing centers. The agents are responsible for pulling standard container images, preparing data, and starting computation. The monitoring system tracks the task status in real time. In the event of an anomaly, a multi-level anomaly response strategy is activated: Level 1 Response (Automatic Retry): For transient failures such as short-term network timeouts, the system will automatically retry the task up to 3 times, with the retry interval increasing exponentially.

[0042] Level 2 Response (Dynamic Rescheduling): If a computing center node fails, causing a task to fail, the anomaly management center will notify the intelligent orchestration module. Based on the latest global resource view, the orchestration module will reallocate suitable resources from other centers to the task and ensure that relevant data is accessible.

[0043] Level 3 Response (Manual Intervention): If the process logic itself is faulty, the system pauses the process, highlights the fault node and cause analysis on the monitoring display interface, and waits for the user's decision (such as resuming the process after modifying parameters).

[0044] S5: Global monitoring and visualization.

[0045] The monitoring and display module continuously collects status and performance data from the agents at each center and visualizes it on a unified dashboard. Users can see key indicators such as global process progress, resource utilization at each center, task duration, and data transmission volume, gaining a comprehensive understanding of the job execution status.

[0046] In summary, the method presented in this application significantly improves technical performance by introducing an extended process description model that supports dynamic task generation, an intelligent scheduling algorithm based on multi-dimensional resource vector affinity matching, and a hierarchical anomaly response mechanism. Specifically, this method significantly enhances the flexibility and automation of process description, enabling dynamic generation and adjustment of computational task branches based on real-time data characteristics, effectively adapting to complex and ever-changing business scenarios. Simultaneously, through collaborative orchestration optimization oriented towards computing power networks, it drastically reduces cross-center data transmission overhead and optimizes global resource utilization, thereby significantly improving the execution efficiency of wide-area collaborative tasks. Furthermore, the system enhances its adaptive handling capability for various operational anomalies through intelligent multi-level anomaly response strategies, significantly improving the overall robustness and reliability of complex processes in heterogeneous cross-domain environments. Finally, thanks to the decoupled design of process logic, task implementation, and hardware environment, it achieves a high degree of template-based and standardized computational job processes, greatly promoting low-cost and rapid reuse and migration of processes across different computing centers, providing a solid technical foundation for building an efficient and flexible wide-area computing power collaborative ecosystem.

[0047] Please see Figure 2The diagram shows a structural block diagram of a job workflow orchestration system for wide-area collaborative computing according to this application.

[0048] like Figure 2 As shown, the job scheduling system 200 includes an acquisition module 210, a parsing module 220, an allocation module 230, an execution module 240, and a generation module 250.

[0049] The acquisition module 210 is configured to acquire the target job process defined by the extended description model, wherein the extended description model supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on the characteristics of the input data. The parsing module 220 is configured to parse and instantiate the target job process. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. The allocation module 230 is configured to allocate appropriate computing resources to each subtask based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, and generate a task execution plan. The execution module 240 is configured to drive task execution in the corresponding computing center based on the task execution plan, and to monitor and process the execution process through a multi-level exception response strategy. Module 250 is configured to summarize the execution status of each computing center and generate a global process view.

[0050] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0051] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the job flow orchestration method for wide-area collaborative computing in any of the above method embodiments. In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: Obtain the target job flow defined using an extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on input data features. The target workflow is parsed and instantiated. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. Based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, appropriate computing resources are allocated to each subtask using a resource affinity matching algorithm, and a task execution plan is generated. Based on the task execution plan, the task is driven to execute in the corresponding computing center, and the execution process is monitored and handled through a multi-level exception response strategy. Summarize the execution status of each computing center to generate a global process view.

[0052] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of a job orchestration system for wide-area collaborative computing. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the job orchestration system for wide-area collaborative computing via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0053] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the job workflow orchestration method for wide-area collaborative computing described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the job workflow orchestration system for wide-area collaborative computing. The output device 340 may include a display screen or other display device.

[0054] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0055] In one implementation, the above-described electronic device is applied to a job workflow orchestration system for wide-area collaborative computing, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Obtain the target job flow defined using an extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on input data features. The target workflow is parsed and instantiated. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. Based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, appropriate computing resources are allocated to each subtask using a resource affinity matching algorithm, and a task execution plan is generated. Based on the task execution plan, the task is driven to execute in the corresponding computing center, and the execution process is monitored and handled through a multi-level exception response strategy. Summarize the execution status of each computing center to generate a global process view.

[0056] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A job workflow orchestration method for wide-area collaborative computing, characterized in that, include: Obtain the target job flow defined using an extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on input data features. The target workflow is parsed and instantiated. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. Based on the task dependency graph and the real-time resource status collected from multiple collaborative computing centers, appropriate computing resources are allocated to each subtask using a resource affinity matching algorithm, and a task execution plan is generated. Based on the task execution plan, the task is driven to execute in the corresponding computing center, and the execution process is monitored and handled through a multi-level exception response strategy. Summarize the execution status of each computing center to generate a global process view.

2. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The extended description model is based on the business process modeling standard and includes: Core metadata used in the unique identification process; It includes at least a set of task node definitions for static task nodes, dynamic task nodes, and a decision gateway, wherein the triggering conditions for the dynamic task nodes are defined by script expressions; A directed graph structure that describes the logical dependencies between nodes, supporting sequential flow, parallel gateways, and exclusive gateways; A serialization format definition used for process template storage and distribution.

3. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The process of parsing and instantiating the target workflow includes: A topology-based logical verification algorithm is used to check whether there are circular dependencies or isolated nodes in the target job process; When the verification is successful and a dynamic task node is encountered, the predefined task generation function is called to generate one or more sub-task nodes based on the current input data.

4. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The resource affinity matching algorithm includes: Construct a task resource requirement vector and a computing center resource status vector, wherein the vectors contain standardized parameters in at least three dimensions: computing performance, storage bandwidth, and network latency. Calculate the similarity between the task vector and the resource status vector of each computing center; Tasks are scheduled to computing centers where the similarity exceeds a preset threshold.

5. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The similarity between the computation task vector and the resource status vector of each computing center is calculated using the cosine similarity formula, specifically: Similarity (T, C) = (T_cpu * C_cpu + T_mem * C_mem + T_net * C_net) / (||T|| *||C||), Where T represents the task resource requirement vector, C represents the computing center resource status vector, T_cpu and C_cpu represent computing dimension parameters, T_mem and C_mem represent storage dimension parameters, and T_net and C_net represent network dimension parameters.

6. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The multi-level anomaly response strategy includes: The first level of response involves automatically retrying a task if it fails, including an exponential backoff mechanism. The second-level response, in response to resource unavailability or performance failure, triggers dynamic resource re-matching and task rescheduling based on the latest resource status. The third-level response, in response to process logic errors, suspends the process and notifies the user, supporting manual intervention and resuming from breakpoint.

7. The job workflow orchestration method for wide-area collaborative computing according to claim 1, characterized in that, The generated task execution plan also includes: When allocating resources for subtasks involving cross-center data transmission, the optimal data transmission path is calculated based on an improved shortest path algorithm that comprehensively evaluates factors such as link bandwidth, real-time load, and data compression rate.

8. A job workflow orchestration system for wide-area collaborative computing, characterized in that, include: The acquisition module is configured to acquire the target job process defined by the extended description model, which supports static task nodes and dynamic task nodes, and the dynamic task nodes are configured with dynamic triggering conditions based on the characteristics of the input data. The parsing module is configured to parse and instantiate the target job process. When a dynamic task node is parsed, a specific sub-task is generated based on the dynamic triggering conditions and real-time input data to form the final task dependency graph. The allocation module is configured to allocate appropriate computing resources to each subtask based on the task dependency graph and real-time resource status collected from multiple collaborative computing centers, using a resource affinity matching algorithm, and generate a task execution plan. The execution module is configured to drive task execution in the corresponding computing center based on the task execution plan, and to monitor and handle the execution process through a multi-level exception response strategy. The generation module is configured to summarize the execution status of each computing center and generate a global process view.

9. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 7.

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