Dynamic protocol adaptation method and device for microgrid heterogeneous equipment, and electronic device

Abstract

The application discloses a micro-grid heterogeneous device dynamic protocol adaptation method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a device original message; analyzing the device original message by a lightweight protocol analysis model to obtain a protocol feature; aggregating the protocol feature to obtain an optimal conversion rule; and converting the protocol corresponding to the device original message according to the optimal conversion rule. The micro-grid heterogeneous device dynamic protocol adaptation method provided in the application realizes low-delay, low-cost and high-security heterogeneous device communication through lightweight AI technology.

Description

Technical Field

[0001] This application relates to the fields of smart grid and industrial Internet of Things technology, specifically to a dynamic protocol adaptation method, device, electronic device and storage medium for heterogeneous devices in a microgrid. Background Technology

[0002] In photovoltaic microgrid systems (including photovoltaic inverters, battery management systems (BMS), charging piles, and other equipment), the communication protocols of equipment from different manufacturers differ. This difference leads to significant time delays (usually exceeding 500ms) during the conversion of heterogeneous communication protocols and conflicts in control strategies, affecting the overall energy utilization efficiency of the photovoltaic-storage-charging system.

[0003] In photovoltaic microgrid systems, there is a significant delay in the communication protocol conversion between heterogeneous devices. Existing technologies mainly include the following three solutions: (1) Time-Sensitive Networking (TSN) solution, but requires hardware upgrades, increasing the cost of a single project by 30%-50%, and has poor compatibility with old energy storage BMS equipment produced before 2015; (2) Edge computing + intelligent controller solution, currently mainly based on complex algorithms (such as Long Short-Term Memory (LSTM)), with inference latency greater than 100ms, and some edge devices are easily invaded, causing scheduling instructions to be tampered with; (3) Unified protocol stack (such as OPC UA over TSN) solution, but the protocol conversion latency still reaches 200ms, and there is insufficient support for non-standard protocols (such as private Modbus variants), and manual configuration of a single device takes up to 4 hours.

[0004] In the traditional solutions described above, the latency in edge computing solutions still exceeds 100ms, and they cannot dynamically adapt to non-standard protocols (such as proprietary Modbus variants). Even after relying on hardware upgrades (such as TSN networks) or manual rule configuration, drawbacks remain, including high transformation costs (increasing by 30%-50%), poor compatibility (insufficient support for older devices), and security risks (edge ​​devices are vulnerable to attacks). These solutions generally suffer from high transformation costs, insufficient real-time performance, and significant security risks, especially in scenarios involving older device access and dynamic protocol adaptation. Summary of the Invention

[0005] In view of the above problems, this application provides a dynamic protocol adaptation method, device, electronic device and storage medium for heterogeneous devices in a microgrid, which realizes low latency, low cost and high security communication between heterogeneous devices through lightweight AI technology.

[0006] Firstly, this application provides a dynamic protocol adaptation method for heterogeneous devices in a microgrid, including: Obtain the device's original message; The original messages of the device are parsed using a lightweight protocol parsing model to obtain protocol characteristics; By aggregating the aforementioned protocol features, the optimal conversion rule can be obtained; The protocol corresponding to the original message of the device is converted according to the optimal conversion rule.

[0007] In the technical solution of this application embodiment, a lightweight protocol parsing model is used to achieve real-time protocol parsing, serving as a protocol conversion hub to solve the latency problem of heterogeneous protocol parsing in AC / DC hybrid microgrids. By aggregating features of multiple devices through federated learning and combining reinforcement learning (RL) to generate optimal conversion rules, it supports automatic adaptation of mainstream protocols such as Modbus, OPC UA, and ProfiNet, as well as private protocol variants, thereby achieving performance improvement, further reducing the configuration time of conversion rules, greatly shortening the time for manual intervention, adapting to MCU-level chips, and significantly reducing the edge computing power requirements.

[0008] In some embodiments, the step of converting the protocol corresponding to the original device message according to the optimal conversion rule includes: converting the protocol corresponding to the original device message according to the optimal conversion rule based on a gVisor sandbox. The gVisor sandbox isolates the protocol conversion process, preventing data leakage and packet attacks, reducing the system's attack surface, and thus improving system security.

[0009] In some embodiments, after converting the protocol corresponding to the original device message according to the optimal conversion rule, the process includes: updating the main protocol library according to the optimal conversion rule to obtain an updated main protocol library; and optimizing the lightweight protocol parsing model according to the updated main protocol library. The optimization of the lightweight protocol parsing model includes: hot updates and version rollback. Incremental update triggering: when an edge node detects a new protocol feature (such as a private Modbus variant), it obtains an incremental update package (controlled within 5KB) through a federated learning framework; dynamic loading: the updated data is loaded through the corresponding interface without restarting the entire system, reducing model switching latency; version rollback: a built-in dual model storage area is used, and if the new model verification fails (e.g., accuracy drops >5%), it automatically rolls back to the previous stable version.

[0010] In some embodiments, updating the main protocol library according to the optimal conversion rule includes: updating the main protocol library using a MicroVM incremental update mode according to the optimal conversion rule. In this embodiment, the main protocol library update adopts the MicroVM incremental update method. The lightweight MicroVM update strategy enables dynamic loading of the WASM module, which can adapt to unstable network environments, does not require system restart, has a model switching latency of less than 50ms, and can also adapt to unstable network scenarios.

[0011] In some embodiments, the lightweight protocol parsing model is a TinyML model based on MobileNetV3. The TinyML model based on MobileNetV3 parses the original device messages in real time, extracts protocol features, and through model optimization, significantly reduces the size of the protocol parsing model, thereby lowering model inference latency.

[0012] Secondly, this application provides a dynamic protocol adaptation device for heterogeneous microgrid devices, comprising: The acquisition module is used to acquire the original messages from the device. The lightweight protocol sniffing module is used to parse the original packets of the device using a lightweight protocol parsing model to obtain protocol characteristics; The dynamic rule generation engine module is used to aggregate the protocol features to obtain the optimal conversion rules; The conversion module is used to convert the protocol corresponding to the original message of the device according to the optimal conversion rule.

[0013] In the technical solution of this application embodiment, a lightweight protocol parsing model is used to achieve real-time protocol parsing, serving as a protocol conversion hub to solve the latency problem of heterogeneous protocol parsing in AC / DC hybrid microgrids. By aggregating features of multiple devices through federated learning and combining reinforcement learning (RL) to generate optimal conversion rules, it supports automatic adaptation of mainstream protocols such as Modbus, OPC UA, and ProfiNet, as well as private protocol variants, thereby achieving performance improvement, further reducing the configuration time of conversion rules, greatly shortening the time for manual intervention, adapting to MCU-level chips, and significantly reducing the edge computing power requirements.

[0014] In some embodiments, the device further includes: an optimization module, configured to update the main protocol library according to the optimal conversion rule to obtain the updated main protocol library; and optimize the lightweight protocol parsing model according to the updated main protocol library. The optimization of the lightweight protocol parsing model includes: hot updates and version rollback. Incremental update triggering: when an edge node detects a new protocol feature (such as a private Modbus variant), it obtains an incremental update package (controlled within 5KB) through a federated learning framework; dynamic loading: the updated data is loaded through the corresponding interface without restarting the entire system, reducing model switching latency; version rollback: a built-in dual model storage area is provided, and if the new model verification fails (e.g., accuracy drops >5%), it automatically rolls back to the previous stable version.

[0015] In some embodiments, the optimization module is further configured to update the main protocol library according to the optimal conversion rules using a MicroVM incremental update mode. In this embodiment, the main protocol library update adopts a MicroVM incremental update method, which realizes dynamic loading of the WASM module through a lightweight MicroVM update strategy. This can adapt to unstable network environments, does not require system restart, has a model switching latency of less than 50ms, and can also adapt to unstable network scenarios.

[0016] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the method described in the first aspect of the embodiment.

[0017] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the method described in the first aspect of the embodiment.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 is a schematic diagram of the photovoltaic microgrid system architecture of some embodiments of this application; Figure 2This is a first flowchart of a dynamic protocol adaptation method for heterogeneous microgrid devices according to some embodiments of this application; Figure 3 is a schematic diagram of the structure of a dynamic protocol adaptation device for microgrid heterogeneous devices according to some embodiments of this application; Figure 4 is a schematic diagram of the structure of an electronic device according to some embodiments of this application. Detailed Implementation

[0020] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0022] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0025] In photovoltaic microgrid systems (including photovoltaic inverters, battery management systems (BMS), charging piles, and other equipment), the communication protocols of equipment from different manufacturers differ. This difference leads to significant time delays (usually exceeding 500ms) during the conversion of heterogeneous communication protocols and conflicts in control strategies, affecting the overall energy utilization efficiency of the photovoltaic-storage-charging system.

[0026] To address the aforementioned technical issues, this application provides a dynamic protocol adaptation method for heterogeneous devices in a microgrid. A lightweight protocol parsing model enables real-time protocol parsing, serving as a protocol conversion hub to resolve the latency issue in heterogeneous protocol parsing for AC / DC hybrid microgrids. By aggregating features from multiple devices through federated learning and combining reinforcement learning (RL) to generate optimal conversion rules, it supports automatic adaptation to mainstream protocols such as Modbus, OPC UA, and ProfiNet, as well as proprietary protocol variants, thereby improving performance. This further reduces the configuration time for conversion rules, significantly shortens manual intervention time, and is compatible with MCU-level chips, greatly reducing edge computing power requirements.

[0027] In this embodiment, the architecture diagram applied to the photovoltaic microgrid is shown in Figure 1. The photovoltaic microgrid system mainly includes power generation units (photovoltaic modules), energy storage systems, charging facilities (charging piles), and a smart distribution network. The smart distribution network includes smart inverters, an AC power grid, an energy management system, and a transmission network (5G, BeiDou). The method of this invention is mainly deployed between the DC bus and the microgrid energy management system (EMS), i.e., at the communication gateway layer, directly connecting photovoltaic inverters, energy storage BMS, charging piles, and other equipment. It uses the TinyML model to achieve real-time protocol parsing, serving as a protocol conversion hub to solve the protocol heterogeneity parsing delay problem in AC / DC hybrid microgrids.

[0028] like Figure 2 As shown, the dynamic protocol adaptation method for heterogeneous devices in the microgrid includes: S201, Obtain the original device message; S202 uses a lightweight protocol parsing model to parse the original messages of the device and obtain protocol characteristics; S203, aggregation protocol features, to obtain the optimal conversion rule; S204, convert the protocol corresponding to the original message of the device according to the optimal conversion rule.

[0029] In S202, applied to the data acquisition layer, the TinyML model based on MobileNetV3 is used to parse the original device messages in real time and extract protocol features (such as Modbus function code 0x03 and OPC UA UA-EXP header field). Through model optimization, the size of the protocol parsing model is reduced to less than 1MB and the inference latency is reduced to less than 50ms.

[0030] In some embodiments, the lightweight protocol parsing model is a TinyML model based on MobileNetV3. The TinyML model based on MobileNetV3 parses the device's original packets in real time, extracts protocol features, and through model optimization, significantly reduces the size of the protocol parsing model, lowering model inference latency.

[0031] In some embodiments, an STM32H7 series MCU is used as the hardware carrier to integrate a lightweight TinyML inference model (model size compressed to less than 50KB), supporting direct connection with devices such as photovoltaic inverters and energy storage BMS via RS-485 / CAN interfaces. During initial deployment, the protocol library (containing 200+ protocol templates) is upgraded remotely via OTA, supporting dynamic loading of private Modbus variant protocols without manual intervention. In this embodiment, when using a TinyML model based on MobileNetV3, a generative adversarial network (GAN) is used to simulate anomalous protocol data to enhance model robustness, ensuring high protocol recognition accuracy even in noisy environments. A dataset is constructed, training data samples are used to obtain an AI-based dynamic protocol adaptation model. Simultaneously, knowledge distillation technology is used to compress the model to adapt to MCU storage limitations. The knowledge distillation model compression algorithm significantly reduces model parameters, enabling operation on an 8-bit MCU (power consumption <1W), significantly reducing the computing resource requirements of edge devices.

[0032] In S203, multi-device features are aggregated through federated learning, and optimal conversion rules are generated by combining them with reinforcement learning (RL). Federated learning aggregates multi-device protocol features, and reinforcement learning (RL) optimizes the conversion strategy, supporting automatic adaptation to mainstream protocols such as Modbus, OPCUA, and ProfiNet, as well as proprietary protocol variants.

[0033] In practice, the optimal communication path (such as CAN bus -> Ethernet) is dynamically selected based on the adaptive routing table (which supports priority scheduling) through link quality monitoring, which can solve the problem of multi-protocol concurrency conflict (throughput is increased by 3 times); in addition, the optimal communication path (such as Modbus RTU / CAN bus) is automatically selected according to the device type (photovoltaic / energy storage / charging pile) to improve data transmission efficiency.

[0034] In S204, message parsing latency is controlled within 10ms through real-time protocol conversion. Priority queues are used to manage concurrent requests from multiple devices to ensure that critical data (such as energy storage SOC values) are transmitted first. MCU computing resources are dynamically allocated, with protocol identification tasks occupying ≤30% of the CPU, and the remaining resources are used for security sandbox operation (such as preventing injection attack detection).

[0035] In the technical solution of this application embodiment, a lightweight protocol parsing model is used to achieve real-time protocol parsing, serving as a protocol conversion hub to solve the latency problem of heterogeneous protocol parsing in AC / DC hybrid microgrids. By aggregating features of multiple devices through federated learning and combining reinforcement learning (RL) to generate optimal conversion rules, it supports automatic adaptation of mainstream protocols such as Modbus, OPC UA, and ProfiNet, as well as private protocol variants, thereby achieving performance improvement, further reducing the configuration time of conversion rules, greatly shortening the time for manual intervention, adapting to MCU-level chips, and significantly reducing the edge computing power requirements.

[0036] In some embodiments, S204 further includes: converting the protocol corresponding to the original device message according to the optimal conversion rules based on the gVisor sandbox. The gVisor sandbox isolates the protocol conversion process, preventing data leakage and packet attacks, reducing the system's attack surface, and thus improving system security. The protocol parsing model is compiled into an ELF file, and the gVisor sandbox isolation mechanism decouples the model from the underlying hardware, avoiding the risk of direct memory access. Furthermore, the sandbox isolation technology can block malicious packet injection (reducing the attack success rate to below 0.1%), and ASLR (Automatic System Randomization) defends against code injection attacks; simultaneously, two-way digital certificate authentication prevents device replacement, and non-volatile storage ensures fault self-recovery (rebooting within 200ms).

[0037] In some embodiments, after S204, the process includes: updating the main protocol library according to the optimal conversion rules to obtain the updated main protocol library; and optimizing the lightweight protocol parsing model based on the updated main protocol library.

[0038] In specific implementation, optimizations to the lightweight protocol parsing model include: hot updates and version rollback. Incremental update triggering: When an edge node detects a new protocol feature (such as a private Modbus variant), it obtains an incremental update package (controlled within 5KB) through the federated learning framework; Dynamic loading: Updated data is loaded through the corresponding interface without restarting the entire system, reducing model switching latency; Version rollback: A built-in dual model storage area automatically rolls back to the previous stable version if the new model fails to validate (e.g., accuracy drops >5%).

[0039] In some embodiments, updating the main protocol library according to the optimal conversion rules includes: updating the main protocol library using a MicroVM incremental update mode according to the optimal conversion rules. In this embodiment, the main protocol library update adopts the MicroVM incremental update method. The lightweight MicroVM update strategy enables dynamic loading of the WASM module, which can adapt to unstable network environments, requires no system restart, has a model switching latency of less than 50ms, and can adapt to unstable network scenarios. Furthermore, an OTA full update with verification is used during the initial deployment to ensure the integrity of the initial protocol library.

[0040] Automatic adaptation of non-standard protocols: The federated learning framework collaboratively updates protocol conversion rules, supporting 200+ protocol templates and private variants (such as non-standard Modbus versions). This reduces traditional manual configuration time from hours / devices to minutes, significantly lowering the access cost for older devices.

[0041] Figure 3 shows a schematic diagram of the structure of a dynamic protocol adaptation device for heterogeneous microgrid devices provided in this application. It should be understood that this device corresponds to the method embodiment performed in Figure 1 and is capable of performing the steps involved in the aforementioned method. The specific functions of this device can be found in the description above, and detailed descriptions are omitted here to avoid repetition. The device includes at least one software functional module that can be stored in memory or embedded in the device's operating system (OS) in the form of software or firmware.

[0042] The device includes: The acquisition module 301 is used to acquire the original messages from the device; The lightweight protocol sniffing module 302 is used to parse the original packets of the device through a lightweight protocol parsing model to obtain protocol characteristics; The dynamic rule generation engine module 303 is used to aggregate protocol features to obtain the optimal conversion rules; The conversion module 304 is used to convert the protocol corresponding to the original message of the device according to the optimal conversion rules.

[0043] In the technical solution of this application embodiment, a lightweight protocol parsing model is used to achieve real-time protocol parsing, serving as a protocol conversion hub to solve the latency problem of heterogeneous protocol parsing in AC / DC hybrid microgrids. By aggregating features of multiple devices through federated learning and combining reinforcement learning (RL) to generate optimal conversion rules, it supports automatic adaptation of mainstream protocols such as Modbus, OPC UA, and ProfiNet, as well as private protocol variants, thereby achieving performance improvement, further reducing the configuration time of conversion rules, greatly shortening the time for manual intervention, adapting to MCU-level chips, and significantly reducing the edge computing power requirements.

[0044] In some embodiments, the device further includes: an optimization module (not shown in the figure), used to update the main protocol library according to the optimal conversion rules to obtain the updated main protocol library; and to optimize the lightweight protocol parsing model according to the updated main protocol library. The optimization of the lightweight protocol parsing model includes: hot updates and version rollback. Incremental update triggering: when an edge node detects a new protocol feature (such as a private Modbus variant), it obtains the model incremental update package (controlled within 5KB) through the federated learning framework; dynamic loading: the updated data is loaded through the corresponding interface without restarting the entire system, reducing model switching latency; version rollback: a built-in dual model storage area is provided, and if the new model verification fails (e.g., accuracy drops >5%), it automatically rolls back to the previous stable version.

[0045] In some embodiments, the optimization module is further configured to update the main protocol library using a MicroVM incremental update mode according to the optimal conversion rules. In this embodiment, the main protocol library update adopts a MicroVM incremental update method, which realizes dynamic loading of the WASM module through a lightweight MicroVM update strategy. This can adapt to unstable network environments, does not require system restart, has a model switching latency of less than 50ms, and can also adapt to unstable network scenarios.

[0046] According to some embodiments of this application, as shown in FIG4, this application provides an electronic device 400, including: a processor 401 and a memory 402. The processor 401 and the memory 402 are interconnected and communicate with each other through a communication bus 403 and / or other forms of connection mechanism (not shown). The memory 402 stores a computer program executable by the processor 401. When the computing device is running, the processor 401 executes the computer program to perform the method executed by the external terminal in any optional implementation.

[0047] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the method in any of the aforementioned optional implementations.

[0048] The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0049] This application provides a computer program product that, when run on a computer, causes the computer to perform a method in any of the optional implementations.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application 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 or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A dynamic protocol adaptation method for heterogeneous devices in a microgrid, characterized in that, include: Obtain the device's original message; The original messages of the device are parsed using a lightweight protocol parsing model to obtain protocol characteristics; By aggregating the aforementioned protocol features, the optimal conversion rule can be obtained; The protocol corresponding to the original message of the device is converted according to the optimal conversion rule.

2. The method according to claim 1, characterized in that, The step of converting the protocol corresponding to the original message of the device according to the optimal conversion rule includes: Based on the gVisor sandbox, the protocol corresponding to the original message of the device is converted according to the optimal conversion rule.

3. The method according to claim 1 or 2, characterized in that, After converting the protocol corresponding to the original message of the device according to the optimal conversion rule, the process includes: The main protocol library is updated according to the optimal conversion rule to obtain the updated main protocol library; The lightweight protocol parsing model is optimized based on the updated master protocol library.

4. The method according to claim 3, characterized in that, The step of updating the main protocol library according to the optimal conversion rule includes: The main protocol library is updated using the MicroVM incremental update mode according to the optimal conversion rules.

5. The method according to claim 1, characterized in that, The lightweight protocol parsing model is the TinyML model based on MobileNetV3.

6. A dynamic protocol adaptation device for heterogeneous devices in a microgrid, characterized in that, include: The acquisition module is used to acquire the original messages from the device. The lightweight protocol sniffing module is used to parse the original packets of the device using a lightweight protocol parsing model to obtain protocol characteristics; The dynamic rule generation engine module is used to aggregate the protocol features to obtain the optimal conversion rules; The conversion module is used to convert the protocol corresponding to the original message of the device according to the optimal conversion rule.

7. The apparatus according to claim 6, characterized in that, The device further includes: The optimization module is used to update the main protocol library according to the optimal conversion rule to obtain the updated main protocol library; and to optimize the lightweight protocol parsing model according to the updated main protocol library.

8. The apparatus according to claim 7, characterized in that, The optimization module is also used to update the main protocol library according to the optimal conversion rules using the MicroVM incremental update mode.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

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