Industrial data processing system and method, electronic equipment and medium
By combining a multi-layered storage architecture with a policy scheduling module, and using neural networks to predict access requests and dynamically adjust resources, the problems of access latency and fixed resource allocation in existing technologies are solved, achieving efficient data access and storage management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN GEYUAN TECH CO LTD
- Filing Date
- 2026-01-26
- Publication Date
- 2026-05-15
AI Technical Summary
Existing industrial data storage solutions cannot identify the sequential and periodic patterns in industrial production, resulting in the inability to proactively preload data that is about to be accessed, causing access delays. Furthermore, the fixed allocation of storage resources cannot be dynamically adjusted according to the real-time production status, affecting efficiency.
It adopts a multi-layer storage architecture, a statistical monitoring module, and a policy and scheduling module. It uses a neural network model to predict future access requests, proactively prefetches data to the cache layer, and dynamically adjusts resource allocation according to production load status.
It improved cache hit rate, reduced access latency, and enhanced data access response speed and overall storage system efficiency, especially in high-performance inference scenarios, freeing up 30%-50% of memory resources for detection tasks.
Smart Images
Figure CN122044482A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and more specifically, to an industrial data processing system, method, electronic device, and medium. Background Technology
[0002] With the development of Industry 4.0, intelligent manufacturing production lines widely adopt machine vision for quality inspection, generating massive amounts of image data. Each production line can produce hundreds of thousands of inspection images daily, with each image typically ranging from 10MB to 200MB in size. This data needs to be accessed frequently and in real time for critical tasks such as result verification, standard template comparison, and historical traceability, placing extremely high demands on the response speed of the storage system.
[0003] However, existing industrial data storage solutions have significant limitations. Their caching mechanisms (such as LRU and LFU) generally employ a passive response strategy, relying solely on historical access records for data eviction, lacking the ability to predict access patterns. Because they cannot identify the inherent sequential and periodic patterns in industrial production—for example, after material A is inspected, material B is usually processed next—the system cannot proactively preload data that is about to be accessed (such as a standard template image of material B). This results in data often not yet residing in the cache when a user initiates access, necessitating slow disk I / O operations, causing significant access latency and making it difficult to meet real-time requirements. Summary of the Invention
[0004] In view of the above, the purpose of this application is to provide an industrial data processing system, method, electronic device and medium, which aims to overcome at least one of the above-mentioned defects.
[0005] In a first aspect, this application provides an industrial data processing system, including: a multi-layer storage architecture, a statistical monitoring module, and a strategy and scheduling module; The multi-layered storage architecture is used to store industrial data, and the multi-layered storage architecture includes a cache layer, a disk cache layer, and a local file system. The statistical monitoring module is connected to the multi-layer storage architecture and is used to record data access to the multi-layer storage architecture to generate historical access logs. The strategy and scheduling module is connected to the multi-layer storage architecture and the statistical monitoring module, respectively. The strategy and scheduling module is used to schedule the processing of data access requests, predict industrial data for at least one future access request based on the historical access log, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
[0006] In one possible implementation, the strategy and scheduling module includes: The time-series prediction unit, connected to the statistical monitoring module, is used to output the predicted industrial data through a neural network model based on the historical access logs. The prefetch execution unit, connected to the timing prediction unit and the multi-layer storage architecture, is used to prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer before the next access request arrives.
[0007] In one possible implementation, it also includes: The status awareness and resource scheduling module is connected to the policy and scheduling module and the multi-layer storage architecture, and is used to adjust the resource allocation ratio between the cache layer and the disk cache layer according to the production load status.
[0008] In one possible implementation, the state awareness and resource scheduling module includes: The status awareness unit, connected to the statistical monitoring module, is used to monitor the production load status of the industrial vision inspection engine. The production load status includes the length of the task queue to be processed, the utilization rate of the computing unit, and the frequency of access requests. A resource scheduling unit, connected to the state awareness unit, is used to reduce the resource quota of the cache layer when the state awareness unit detects that the length of the queue of tasks to be processed is higher than a first threshold and the utilization rate of the computing unit is higher than a second threshold, and to increase the resource quota of the cache layer when the frequency of access requests is detected to be higher than a third threshold.
[0009] In one possible implementation, a storage management module is also included, the storage management module comprising: The first storage processing unit is connected to the multi-layer storage architecture and an external interface for receiving industrial data, and is used to store the industrial data into the multi-layer storage architecture in response to a storage request. The data lookup and read unit is connected to the strategy and scheduling module and the multi-layer storage architecture. In response to the read scheduling of the strategy and scheduling module, it sequentially searches for target industrial data in the cache layer, the disk cache layer, and the local file system and returns it to the requester.
[0010] In one possible implementation, the historical access log includes the access timestamp of each access request, the access source, and the material identifier of the industrial data corresponding to each access request.
[0011] In one possible implementation, the data lookup and read unit is further configured to migrate industrial data in the cache layer to the disk cache layer according to the access time order when the capacity of the cache layer exceeds a threshold.
[0012] Secondly, this application provides an industrial data processing method, including: A multi-layered storage architecture is used to store industrial data, and the multi-layered storage architecture includes a cache layer, a disk cache layer, and a local file system; The statistics monitoring module is connected to the multi-layer storage architecture and is used to record data access to the multi-layer storage architecture to generate historical access logs; The strategy and scheduling module is connected to the multi-layer storage architecture and the statistical monitoring module, respectively. The strategy and scheduling module is used to schedule the processing of data access requests, predict industrial data for at least one future access request based on the historical access log, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
[0013] Thirdly, this application also provides an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method described above are performed.
[0014] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.
[0015] This application provides an industrial data processing system, method, electronic device, and medium. The method includes: a multi-layer storage architecture, a statistical monitoring module, and a strategy and scheduling module. The multi-layer storage architecture stores industrial data and includes a cache layer, a disk cache layer, and a local file system. The statistical monitoring module is connected to the multi-layer storage architecture and records data access to the multi-layer storage architecture to generate historical access logs. The strategy and scheduling module is connected to both the multi-layer storage architecture and the statistical monitoring module. The strategy and scheduling module schedules the processing of data access requests and predicts the predicted industrial data for at least one future access request based on the historical access logs, prefetching the predicted industrial data from the disk cache layer or the local file system to the cache layer. This application improves the data cache hit rate and access response speed.
[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is one of the structural schematic diagrams of the industrial data processing system provided in the embodiments of this application; Figure 2 This is a second schematic diagram of the structure of the industrial data processing system provided in the embodiments of this application; Figure 3 A flowchart illustrating the industrial data processing method provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.
[0020] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of data processing technology.
[0021] With the deepening development of Industry 4.0, intelligent manufacturing production lines widely adopt high-resolution machine vision for online quality inspection, generating massive amounts of image data. In a typical system, a single production line can generate hundreds of thousands of inspection images per day, with each image typically ranging from 10MB to 200MB in size. These images not only require long-term storage but also need to support real-time, high-frequency access for critical scenarios such as immediate verification of inspection results, rapid comparison with standard templates, and production history traceability. This places extremely high demands on the response speed and throughput capacity of the storage system.
[0022] However, existing industrial data storage solutions have significant shortcomings. First, commonly used caching strategies (such as LRU and LFU) are passive response modes, relying solely on historical data for data eviction. They cannot learn or predict the inherent sequential access patterns in the production line (such as "material A → material B"), resulting in the inability to proactively preload upcoming data and causing access delays. Second, storage resources (such as memory and disk cache) are typically allocated in a fixed ratio, failing to dynamically adjust according to real-time production status (such as peak detection task periods and peak external access request periods), easily leading to resource mismatch and impacting overall efficiency. Third, for images already persistently stored in the local file system, existing solutions still perform unnecessary copying to add them to the cache, additionally consuming valuable memory and I / O bandwidth.
[0023] Based on this, embodiments of this application provide an industrial data processing system, method, electronic device, and medium, aiming to overcome at least one of the above-mentioned defects.
[0024] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of an industrial data processing system provided in an embodiment of this application. Figure 1 As shown in the figure, the industrial data processing system provided in this application embodiment includes: a multi-layer storage architecture 101, a statistical monitoring module 102, and a strategy and scheduling module 103.
[0025] The multi-tier storage architecture 101 is used to store industrial data. The multi-tier storage architecture 101 includes a cache layer, a disk cache layer, and a local file system.
[0026] Specifically, the multi-layered storage architecture 101 forms the foundation of the system's data storage, comprising a cache layer, a disk cache layer, and a local file system. The cache layer stores the most frequently accessed hot data to provide extreme read speeds; the disk cache layer, as a secondary cache, stores less frequently accessed warm data or data moved from the cache layer; and the local file system is the final persistent storage location for all data.
[0027] For data that is already fully stored in the local file system, the system can save only its path index without copying the data entity, which lays the foundation for subsequent "zero-copy" access.
[0028] The statistical monitoring module 102 is connected to the multi-layer storage architecture 101 and is used to record data access to the multi-layer storage architecture 101 to generate historical access logs.
[0029] Here, the statistics monitoring module 102 records the timestamp of each access, the source of the request (e.g., whether it comes from the real-time detection program on the production line or the query interface on the backend), and the key identifier of the accessed data (e.g., material ID), thereby generating a continuous historical access log.
[0030] The strategy and scheduling module 103 is connected to the multi-layer storage architecture 101 and the statistics monitoring module 102 respectively. The strategy and scheduling module 103 is used to schedule the processing of data access requests, predict the predicted industrial data of at least one future access request based on historical access logs, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
[0031] In this embodiment, the strategy and scheduling module 103 includes a timing prediction unit and a prefetch execution unit.
[0032] The time-series prediction unit is connected to the statistical monitoring module 102 and is used to output predicted industrial data based on historical access logs through a neural network model.
[0033] The prefetch execution unit is connected to the timing prediction unit and the multi-tier storage architecture 101 to prefetch predicted industrial data from the disk cache layer or the local file system to the cache layer before the next access request arrives.
[0034] On the one hand, the strategy and scheduling module 103 schedules and responds to real-time data access requests; on the other hand, it drives forward-looking data preloading.
[0035] Specifically, when processing a current access request for a specific target data, the strategy and scheduling module 103, while coordinating data retrieval, sends the historical access logs generated by the statistics monitoring module 102 to its internal time-series prediction unit. The time-series prediction unit uses a lightweight long short-term memory (LSTM) neural network model to learn the periodic patterns of material access on a specific industrial production line by analyzing the logs (e.g., "material A is always followed by material B"), and predicts one or more data objects that are most likely to be requested next.
[0036] Subsequently, the prefetching execution unit, which works in conjunction with the prediction unit, will take immediate action to proactively retrieve the predicted data from the slower disk cache layer or local file system and pre-store it in the cache layer before the predicted future access actually occurs. If the predicted data is already in the cache layer, no action will be taken.
[0037] This means that when subsequent user requests actually arrive, the required data is already ready in high-speed memory, thereby significantly reducing access latency from the traditional disk I / O level (about 50 milliseconds) to the memory access level (within 5 milliseconds).
[0038] In the embodiments of this application, such as Figure 2 As shown, Figure 2 This is a second schematic diagram of the structure of the industrial data processing system provided in the embodiments of this application. The industrial data processing system also includes: a status awareness and resource scheduling module 104 and a storage management module 105.
[0039] The state awareness and resource scheduling module 104 is connected to the policy and scheduling module 103 and the multi-layer storage architecture 101, and is used to adjust the resource allocation ratio between the cache layer and the disk cache layer according to the production load status.
[0040] The state awareness and resource scheduling module 104 includes a state awareness unit and a resource scheduling unit.
[0041] The status awareness unit is connected to the statistical monitoring module 102 to monitor the production load status of the industrial vision inspection engine. The production load status includes the length of the task queue to be processed, the utilization rate of the computing unit, and the frequency of access requests.
[0042] The resource scheduling unit is connected to the state awareness unit. When the state awareness unit detects that the length of the queue of tasks to be processed is higher than the first threshold and the utilization rate of the computing unit is higher than the second threshold, the resource quota of the cache layer is reduced, and when the frequency of access requests is detected to be higher than the third threshold, the resource quota of the cache layer is increased.
[0043] Specifically, the state-aware unit continuously monitors key system performance metrics, including the length of the backlog of unprocessed tasks in the industrial vision inspection engine, the utilization rate of computing units (CPU / GPU), and the frequency of data access requests from external sources. Based on these metrics, it can determine the current production load mode, such as "inference-first" (heavy computing tasks) or "access-first" (data query-intensive).
[0044] The resource scheduling unit dynamically adjusts the resource allocation ratio between the cache layer and the disk cache layer based on the judgment results of the state awareness unit. For example, in "inference-first" mode, the system reduces the resource quota allocated to the cache, freeing up more memory for the visual inspection algorithm, thereby improving the inspection throughput of the production line; conversely, in "access-first" mode, the cache resources are increased to ensure the response speed of data queries. This dynamic scheduling mechanism completely changes the drawback of fixed resource allocation in traditional solutions.
[0045] The storage management module 105 includes: a first storage processing unit and a data search and read unit.
[0046] The first storage processing unit is connected to the multi-layer storage architecture 101 and an external interface for receiving industrial data, and is used to store industrial data into the multi-layer storage architecture 101 in response to storage requests.
[0047] The data lookup and read unit is connected to the strategy and scheduling module 103 and the multi-layer storage architecture 101. In response to the read scheduling of the strategy and scheduling module 103, it sequentially searches for the target industrial data in the cache layer, disk cache layer, and local file system and returns it to the requester.
[0048] The data lookup and read unit is also used to migrate industrial data in the cache layer to the disk cache layer according to the access time order when the capacity of the cache layer exceeds the threshold.
[0049] Specifically, the storage management module 105 is the executor of all storage instructions in the system, and the first storage processing unit is responsible for handling data storage requests. When a storage request is received, the unit will make an intelligent judgment: if the request already contains a local file path and there is no need to copy data (such as an existing standard template image), the first storage processing unit will only establish a lightweight reference to the path in the multi-tier storage architecture 101; otherwise, the first storage processing unit will directly store the data entity into the cache layer.
[0050] The data lookup and read unit is responsible for handling data read requests. It follows a hierarchical lookup strategy: first, it searches in the cache layer; if not found, it moves to the disk cache layer; if still not found, it finally reads the raw data directly from the local file system using an established reference. Furthermore, this unit is responsible for maintaining the balance of the storage architecture. When the amount of data in the cache layer exceeds a set threshold, the data lookup and read unit will migrate some data to the disk cache layer according to a predetermined strategy (such as based on the order of access time), thereby ensuring the continuous availability of the cache space.
[0051] Compared to existing technologies, this application, based on time-series learning of access patterns in industrial production lines, enables the system to proactively preload data that is about to be accessed, increasing the cache hit rate from 40% to over 85% and reducing the access latency for hot data from 50ms to less than 5ms. Simultaneously, the system can perceive the production load status in real time and dynamically adjust storage resource allocation. In high-performance inference scenarios, it can release 30%-50% of memory resources for the inference engine, thereby increasing the throughput of detection tasks by over 20%. Furthermore, by employing a zero-copy local file reference mechanism, redundant copying of data already existing in local files is avoided, effectively reducing memory usage and disk I / O overhead.
[0052] Based on the same inventive concept, this application also provides an industrial data processing method corresponding to the industrial data processing system. Since the principle of the device in this application is similar to that of the industrial data processing system described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.
[0053] Please see Figure 3 , Figure 3 A flowchart of an industrial data processing method provided in this application embodiment includes: S201, a multi-layered storage architecture is used to store industrial data. The multi-layered storage architecture includes a cache layer, a disk cache layer, and a local file system.
[0054] S202, the statistics monitoring module is connected to the multi-layer storage architecture and is used to record data access to the multi-layer storage architecture to generate historical access logs.
[0055] S203 and the strategy and scheduling module are connected to the multi-layer storage architecture and the statistics monitoring module, respectively. The strategy and scheduling module is used to schedule the processing of data access requests, predict industrial data for at least one future access request based on historical access logs, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
[0056] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.
[0057] The memory 320 stores machine-readable instructions that can be executed by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, the steps of the method described above can be performed. For specific implementation details, please refer to the method embodiment, which will not be repeated here.
[0058] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it can execute the steps of the method described above. For specific implementation details, please refer to the method embodiments, which will not be repeated here.
[0059] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0060] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0061] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0062] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0063] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0064] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, 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 this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. An industrial data processing system, characterized in that, include: Multi-layer storage architecture, statistical monitoring module, and policy and scheduling module; The multi-layered storage architecture is used to store industrial data, and the multi-layered storage architecture includes a cache layer, a disk cache layer, and a local file system. The statistical monitoring module is connected to the multi-layer storage architecture and is used to record data access to the multi-layer storage architecture to generate historical access logs. The strategy and scheduling module is connected to the multi-layer storage architecture and the statistical monitoring module, respectively. The strategy and scheduling module is used to schedule the processing of data access requests, predict industrial data for at least one future access request based on the historical access log, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
2. The system according to claim 1, characterized in that, The strategy and scheduling module includes: The time-series prediction unit, connected to the statistical monitoring module, is used to output the predicted industrial data through a neural network model based on the historical access logs. The prefetch execution unit, connected to the timing prediction unit and the multi-layer storage architecture, is used to prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer before the next access request arrives.
3. The system according to claim 1, characterized in that, Also includes: The status awareness and resource scheduling module is connected to the policy and scheduling module and the multi-layer storage architecture, and is used to adjust the resource allocation ratio between the cache layer and the disk cache layer according to the production load status.
4. The system according to claim 3, characterized in that, The state awareness and resource scheduling module includes: The status awareness unit, connected to the statistical monitoring module, is used to monitor the production load status of the industrial vision inspection engine. The production load status includes the length of the task queue to be processed, the utilization rate of the computing unit, and the frequency of access requests. A resource scheduling unit, connected to the state awareness unit, is used to reduce the resource quota of the cache layer when the state awareness unit detects that the length of the queue of tasks to be processed is higher than a first threshold and the utilization rate of the computing unit is higher than a second threshold, and to increase the resource quota of the cache layer when the frequency of access requests is detected to be higher than a third threshold.
5. The system according to claim 1, characterized in that, It also includes a storage management module, which includes: The first storage processing unit is connected to the multi-layer storage architecture and an external interface for receiving industrial data, and is used to store the industrial data into the multi-layer storage architecture in response to a storage request. The data lookup and read unit is connected to the strategy and scheduling module and the multi-layer storage architecture. In response to the read scheduling of the strategy and scheduling module, it sequentially searches for target industrial data in the cache layer, the disk cache layer, and the local file system and returns it to the requester.
6. The system according to claim 1, characterized in that, The historical access log includes the access timestamp, access source, and material identifier of the industrial data corresponding to each access request.
7. The system according to claim 5, characterized in that, The data lookup and read unit is also used to migrate industrial data in the cache layer to the disk cache layer according to the access time order when the capacity of the cache layer exceeds the threshold.
8. An industrial data processing method, characterized in that, include: A multi-layered storage architecture is used to store industrial data, and the multi-layered storage architecture includes a cache layer, a disk cache layer, and a local file system; The statistics monitoring module is connected to the multi-layer storage architecture and is used to record data access to the multi-layer storage architecture to generate historical access logs; The strategy and scheduling module is connected to the multi-layer storage architecture and the statistical monitoring module, respectively. The strategy and scheduling module is used to schedule the processing of data access requests, predict industrial data for at least one future access request based on the historical access log, and prefetch the predicted industrial data from the disk cache layer or the local file system to the cache layer.
9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in claim 8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in claim 8.