Iot data point location usage tracking system, method, electronic device, and medium

Abstract

This application relates to the field of Internet of Things (IoT) technology, providing a system, method, electronic device, and medium for tracking the usage of IoT data points. The system includes: a business grouping module, used to assign unique identifiers to each business party on the IoT platform and bind the identifiers to the usage of data points; a data service module, including core service components providing data access or distribution; an event acquisition module, used to intercept data requests or distribution operations on corresponding usage paths, parse data point identifiers and business party identifiers, and encapsulate them as usage events to be reported to a central aggregation module; a central aggregation module, used to convert usage events into structured data and store them in a time-series database; and a statistical analysis module, used to perform statistical analysis and display the analysis results through a visual interface. This application can uniformly track and analyze the usage of specific data points originating from different business parties and through different service paths.

Description

Technical Field

[0001] This application belongs to the field of Internet of Things (IoT) technology, and in particular relates to an IoT data point usage tracking system, method, electronic device, and medium. Background Technology

[0002] IoT platforms support diverse application scenarios by accessing, storing, processing, and distributing data from a large number of connected devices. This data is typically represented in the form of data points, which represent specific measurements or states of physical entities. Accurately tracking the usage of these data points is crucial for platform optimization, cost management, and data value assessment. However, existing IoT platforms have complex and diverse data consumption paths, with different paths employing different technical protocols and data formats, resulting in fragmented and inconsistent usage information. This makes it difficult for platforms to uniformly track and analyze the use of specific data points by different business parties through different service paths, limiting their ability to optimize resource allocation and manage the data lifecycle. Summary of the Invention

[0003] In view of the shortcomings of the prior art, this application provides an IoT data point usage tracking system, method, electronic device and medium to solve the technical problem that the IoT platform cannot effectively collect data on the consumption status of data points due to the complex and diverse consumption paths in the related technologies.

[0004] In a first aspect, this application provides an IoT data point usage tracking system, comprising:

[0005] The business grouping module assigns unique identifiers to each business unit on the IoT platform and binds these identifiers to the usage of data points. The data service module includes core service components that provide data access or distribution, forming heterogeneous usage paths for the data points. The event acquisition module includes event adapters corresponding to the core service components. These adapters intercept data requests or distribution operations on the corresponding usage paths, parse the data point identifiers and business unit identifiers, and encapsulate them as usage events to be reported to the central aggregation module. The central aggregation module converts the reported usage events into structured data and stores them in a time-series database. The statistical analysis module accesses the time-series database, performs statistical analysis, and displays the analysis results through a visual interface.

[0006] In one embodiment of this application, the core service components include: an API gateway, a data push service, and a data dump service.

[0007] In one embodiment of this application, the event parsing adapter includes: an API gateway event adapter, used to intercept on-demand call requests, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module; a data push event adapter, used to intercept real-time push messages, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module; and a data dump event adapter, used to intercept data dump operations during the data dump process, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module.

[0008] In one embodiment of this application, the central aggregation module further includes: The data splitting module is used to break down usage events containing multiple data point identifiers into records with a granularity of a single data point; the transformation and storage module is used to convert each record into structured data and store it in a time-series database.

[0009] In one embodiment of this application, the usage event includes a context object of the data point, and the context object includes at least a tracking identifier field, a timestamp field, a data point identifier field, a business party identity identifier field, and a usage method field.

[0010] In one embodiment of this application, the data push service includes at least one of HTTP push, MQTT push, RocketMQ push, or Kafka push.

[0011] In one embodiment of this application, the statistical analysis includes at least one of: data point usage source distribution, usage trend report, and usage details.

[0012] Secondly, this application also provides a method for tracking the use of IoT data points, including: Assign a unique identity to each business unit on the IoT platform and bind the identity to the usage of data points; receive data requests or subscriptions initiated by business units, and after the data points are returned, use the corresponding event adapter to intercept the data request or sending operation, parse out the data point identifier and the business unit identity, and encapsulate them into data point usage events; convert the usage events into structured data and store them in a time-series database; perform statistical analysis on the usage of data points based on the records in the time-series database, and display the analysis results through a visualization interface.

[0013] Thirdly, this application also provides an electronic device, which includes: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to perform the steps of the above-described method.

[0014] Fourthly, the present invention also provides a computer-readable storage medium comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the computer-readable storage medium performs the steps of the above-described method.

[0015] The beneficial effects of this technical solution are as follows: By assigning a unique identity to each business party and binding it to usage requests through the business grouping module, traceability across all business dimensions can be achieved; based on adapters set up on multiple heterogeneous consumption paths, data point identifiers are parsed from requests or configurations, avoiding the loss of usage information due to protocol differences and inconsistent data formats, ensuring the integrity and compatibility of the collection process; simultaneously, the reported usage events are converted into structured data and stored in a time-series database through the central aggregation module, enabling unified tracking and analysis of the use of specific data points from different business parties and through different service paths; based on the records in the time-series database, statistical analysis is performed and displayed through a visual interface, providing a multi-dimensional usage view, timely understanding of the distribution and changing trends of data point usage under each business party and consumption path, facilitating cost allocation, resource optimization, and data governance for platform operators.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a schematic diagram illustrating the architecture of an IoT data point usage tracking system, as shown in an exemplary embodiment of this application. Figure 2 This is an exemplary embodiment of the present application illustrating the architecture of another IoT data point usage tracking system; Figure 3 This is a schematic diagram illustrating the structure of a context object in an exemplary embodiment of this application; Figure 4 This is a schematic diagram of the architecture of another IoT data point usage tracking system, as illustrated in an exemplary embodiment of this application. Figure 5 This is a flowchart illustrating an exemplary embodiment of the present application of a method for tracking the use of IoT data points; Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0018] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0021] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the architecture of an IoT data point usage tracking system, as shown in an exemplary embodiment of this application. Figure 1 As shown, the system includes: a business grouping module 101, a data service module 102, an event acquisition module 103, a central aggregation module 104, and a statistical analysis module 105, with each module working collaboratively.

[0022] The business grouping module 101 is used to assign unique identifiers to each business party on the IoT platform and bind the identifiers to the usage of data points; the data service module 102 includes core service components that provide data access or distribution, which constitute heterogeneous usage paths for data points; the event acquisition module 103 includes event adapters corresponding to the core service components, which are used to intercept data requests or distribution operations on the corresponding usage paths, parse the data point identifiers and business party identifiers, and encapsulate them into usage events for reporting to the central aggregation module 104; the central aggregation module 104 is used to convert the reported usage events into structured data and store them in a time-series database; the statistical analysis module 105 is used to access the time-series database, perform statistical analysis, and display the analysis results through a visual interface.

[0023] In some embodiments, the core service components include: an Application Programming Interface (API) gateway, a data push service, and a data dump service.

[0024] In some embodiments, the event parsing adapter includes: an API gateway event adapter, used to intercept on-demand call requests, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module; a data push event adapter, used to intercept real-time push messages, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module; and a data dump event adapter, used to intercept data dump operations during the data dump process, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module.

[0025] In some embodiments, the central aggregation module further includes: The data splitting module is used to break down usage events containing multiple data point identifiers into records with a granularity of a single data point; the transformation and storage module is used to convert each record into structured data and store it in a time-series database.

[0026] In some embodiments, the use event includes a context object of the data point, which includes at least a tracking identifier field, a timestamp field, a data point identifier field, a business party identification field, and a usage method field.

[0027] Specifically, see Figure 3 The usage events of data points can be passed by a context object. A minimal context object includes at least a tracking identifier field, a timestamp field, a data point identifier field, a business party identification field, and a usage method field.

[0028] In addition, the context object can also include a span ID and a parent span ID.

[0029] In some embodiments, the data push service includes at least one of HTTP push, MQTT push, RocketMQ push, or Kafka push.

[0030] In some embodiments, statistical analysis includes at least one of: data point usage source distribution, usage trend report, and usage details.

[0031] See Figure 2 The core function of the business grouping module is to define and manage the identity identifiers of each business entity on the IoT platform. The business grouping module can be an independent service or a management function module of the platform. This implementation requires that when a business entity uses the services provided by the data service module, it must bind its registered identity identifier; otherwise, subsequent tracking of data point usage cannot be effectively linked to the specific business entity.

[0032] Binding can be achieved in several ways, such as associating an API key with an identity identifier, or specifying the corresponding identity identifier when configuring a data push subscription or data dump job.

[0033] The data service module is the part of the IoT platform that actually provides data access and distribution functions, forming multiple different paths or sources for the use of data points. Specifically, the data service module may include the following core service components: API gateway, data push service, and data dump service. The core function of the event acquisition module is to intercept requests or distribution operations at key nodes to capture events related to data point usage. To adapt to different types of core service components within the data service module, this module includes corresponding event parsing adapters. Specifically, these may include: an API gateway event adapter, a data push event adapter, and a data dump event adapter. The common key function of all adapters is to parse the accurate data point identifier and business party identity involved in the event, and then encapsulate this information, along with the event type and timestamp, into a standardized usage event report, which is then reported to the central aggregation module synchronously or asynchronously. Asynchronous reporting ensures that event acquisition does not significantly impact the performance of core data services. For data point usage events, context objects can be used for transmission, such as... Figure 3 As shown, a minimal context object can consist of a tracking ID, a location identifier, a timestamp, a business group identity identifier, and an identity identifier.

[0034] The central aggregation module is responsible for receiving reported events from different usage sources synchronously or asynchronously, and storing the processed structured data in a time-series database. This structured data records detailed information about when, how, and by whom a specific data point was used.

[0035] In addition, for cases where the report may contain multiple data point identifiers, they can be split into multiple records to ensure that each record corresponds to a specific data point usage event.

[0036] In addition, time-series databases help to efficiently store and query large-scale event data with timestamps.

[0037] The statistical analysis module can analyze the usage of location data according to business needs. By accessing the time-series database managed by the central aggregation module, it can perform various statistical analysis operations. The analysis results are ultimately displayed to users through data visualization interfaces (such as web dashboards and reports) or provided to other systems through API interfaces.

[0038] To better understand this embodiment, the following explanation uses an API gateway and an API gateway event adapter as examples: See Figure 4 When a business entity initiates a data query request through the API gateway, the API gateway event adapter intercepts the request, extracts the API key from the request header, and converts the API key into a business entity identity based on the identity identifier and data point usage information pre-bound by the business group module. Then, it parses out one or more data point identifiers involved in the query, records the current timestamp as the usage time, encapsulates the above information into standardized usage events, places the encapsulated usage events into a synchronous or asynchronous queue, and has an independent thread pool responsible for reporting them to the central aggregation module. The reported usage events are then converted into structured data (passed by the data point's context object, which specifically includes tracking identifier fields, timestamp fields, data point identifier fields, business entity identity fields, and usage method fields) and stored in the time-series database. The statistical analysis module accesses the time-series database, performs at least one analysis of data point usage source distribution, usage trend reports, and usage details, and displays the analysis results through a visual interface.

[0039] The specific processing flow for push subscribers and external systems can be found in the API caller's processing flow, which will not be elaborated on here.

[0040] Furthermore, in some examples, the IoT platform may include multiple data points. These data points are consumed by business users through API gateways, HTTP push, MQTT push, and data dump services. When a business user queries a data point via an API, the API gateway records the request log, but it only knows which interface a certain IP called at what time. It cannot automatically extract the specific data point identifiers queried from the interface path, nor can it associate the query behavior with the specific business user's identity. When the message push service pushes data, the push service only knows that a message was sent to a certain topic, but the message body may only contain numerical values ​​and not data point names. Moreover, the association between the subscription relationship in the push configuration and the business user's identity is not perceived by the data collection system. When the data dump service is executed, the dump service only knows that an export task has been completed, exporting 1 million data points. However, it cannot know which data points these 1 million data points involve or how many times each data point was exported.

[0041] In this example, the business grouping module assigns a unique identity to all business users of the platform and requires that the identity be bound when using data services: for API calls, the API key is associated with the identity; for message push, the identity is specified in the subscription configuration; for data dump, the identity is bound in the job configuration.

[0042] Secondly, the event acquisition module sets up corresponding adapters on the three consumption paths: the API gateway event adapter intercepts each API request, extracts the API key from the request header and maps it to the business party's identity identifier, parses one or more location identifiers involved in this request from the URL path and query parameters, and encapsulates this information along with the timestamp into a standardized usage event; the message push event adapter intercepts the push process, obtains the business party's identity identifier and the list of subscribed locations from the subscription configuration, confirms the locations involved in this push, and encapsulates them into usage events; the data dump event adapter intercepts the ETL job startup, parses the business party's identity identifier and the complete list of locations from the job configuration, and generates an independent usage event for each location in the list.

[0043] All adapters can use an asynchronous reporting mechanism, returning immediately after parsing without blocking core service processes. Then, the central aggregation module receives these usage events from different paths, breaking them down into records at the individual point level. For batch dumping scenarios, tens of thousands of points in a single job are split into tens of thousands of independent records. Each record contains fields such as business party identity, point identifier, timestamp, and usage method (API query / message push / batch dump), and is stored in a time-series database.

[0044] Finally, the statistics module performs multi-dimensional analysis based on records in the time series database. It can query which locations a business unit used within a specific time period, the proportion of each usage method, the frequency and trend of each location's use, and display this information through a visual interface.

[0045] In this way, by assigning a unique identity to each business party through the business grouping module and binding it to usage requests, full-scenario business dimension traceability can be achieved. Based on adapters set up on multiple heterogeneous consumption paths, data point identifiers are parsed from requests or configurations, which can avoid the loss of usage information due to protocol differences and inconsistent data formats, ensuring the integrity and compatibility of the collection process. At the same time, the reported usage events are converted into structured data and stored in a time-series database through the central aggregation module, which can uniformly track and analyze the usage of specific data points from different business parties and through different service paths. Statistical analysis based on the records in the time-series database and displayed through a visualization interface can provide a multi-dimensional usage view, enabling timely understanding of the distribution and changing trends of data point usage under each business party and each consumption path, which is convenient for platform operators to allocate costs, optimize resources, and govern data.

[0046] Figure 5 This is a flowchart illustrating an exemplary embodiment of the IoT data point usage tracking method. Figure 5 As shown, this exemplary method for tracking the use of IoT data points includes: S501 assigns a unique identifier to each business unit on the IoT platform and binds the identifier to the usage of data points; Specifically, for API calls, an identity identifier can be bound in the pre-application authentication section; for data subscriptions, an identity identifier can be bound in the subscription information section; and for data dumping, an identity identifier can be bound in the dumping task node. S502 receives data requests or subscriptions initiated by the business party. After the data point is returned, it uses the corresponding event adapter to intercept the data request or sending operation, parses the data point identifier and the business party identity identifier, and encapsulates them into a data point usage event. S503 converts the events into structured data and stores it in a time-series database; S504 performs statistical analysis on the usage of data points based on records in the time series database and displays the analysis results through a visual interface.

[0047] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the methods provided in the above embodiments.

[0048] Figure 6 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0049] like Figure 6 As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on a program stored in Read-Only Memory (ROM) 602 or a program loaded from Storage Section 608 into Random Access Memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An Input / Output (I / O) interface 605 is also connected to the bus 605.

[0050] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0051] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs various functions defined in the system of this application.

[0052] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0053] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0054] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0055] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0056] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the various embodiments above.

[0057] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the steps of this application.

Claims

1. A usage tracking system for Internet of Things (IoT) data points, characterized in that, include: The business grouping module is used to assign a unique identity to each business party on the IoT platform and bind the identity to the usage of data points; The data service module includes core service components that provide data access or distribution, and these core service components constitute the heterogeneous usage paths of the data points. The event acquisition module includes an event adapter corresponding to the core service component. The event adapter is used to intercept data requests or distribution operations on the corresponding usage path, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module. The central aggregation module is used to convert reported usage events into structured data and store them in a time-series database; The statistical analysis module is used to access the time series database, perform statistical analysis, and display the analysis results through a visual interface.

2. The system according to claim 1, characterized in that, The core service components include: API gateway, data push service, and data dump service.

3. The system according to claim 2, characterized in that, The event parsing adapter includes: The API gateway event adapter is used to intercept on-demand call requests, parse data point identifiers and business party identity identifiers, and encapsulate them into event reports to the central aggregation module. The data push event adapter is used to intercept real-time push messages, parse data point identifiers and business party identity identifiers, and encapsulate them into event reports to the central aggregation module. The data dump event adapter is used to intercept data dump operations during the data dump process, parse data point identifiers and business party identity identifiers, and encapsulate them as usage events to be reported to the central aggregation module.

4. The system according to claim 1, characterized in that, The central aggregation module also includes: The data splitting module is used to break down usage events containing multiple data point identifiers into records with a granularity of a single data point. The transformation and storage module is used to convert each record into structured data and store it in a time-series database.

5. The system according to claim 4, characterized in that, The usage event includes a context object for the data point, which includes at least a tracking identifier field, a timestamp field, a data point identifier field, a business party identification field, and a usage method field.

6. The system according to claim 2, characterized in that, The data push service includes at least one of the following: HTTP push, MQTT push, RocketMQ push, or Kafka push.

7. The system according to claim 1, characterized in that, The statistical analysis includes at least one of the following: data point usage source distribution, usage trend report, and usage details.

8. A method for tracking the use of IoT data points, applied to the IoT data point usage tracking system as described in any one of claims 1-7, characterized in that, include: Assign a unique identifier to each business unit on the IoT platform and bind the identifier to the usage of data points; Receive data requests or subscriptions initiated by the business party. After the data points are returned, use the corresponding event adapter to intercept the data request or sending operation, parse the data point identifier and the business party identity identifier, and encapsulate them into the data point usage event. The events will be converted into structured data and stored in a time-series database; Based on the records in the time series database, statistical analysis is performed on the usage of data points, and the analysis results are displayed through a visualization interface.

9. An electronic device, characterized in that, include: One or more processors and a memory, the memory storing a computer program that, when executed by the one or more processors, causes the device to perform the steps of the method as described in claim 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by one or more processors, causes the device to perform the steps of the method as described in claim 8.

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