Device and method for predicting appearance time of mould

Abstract

The present disclosure provides a first apparatus for predicting the occurrence time of mold, comprising: an information acquisition unit that obtains environmental parameters related to an environment in which the occurrence time of mold is to be predicted, the environmental parameters including temperature and humidity; and a processing unit that, on the basis of the environmental parameters, uses a machine learning model to predict a predicted time at which mold will appear within the environment.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and more particularly, to a device and method for predicting the occurrence of mold. Background Art

[0002] When in a humid indoor environment, mold may appear on the wall or furniture surface. For indoor environments where mold has appeared, the detection equipment in the prior art can only detect the severity of the mold.

[0003] However, for users in such indoor environments, mold often already exists when the detection device prompts them, which not only affects the aesthetics of the indoor environment but also the health of the users. Therefore, the detection devices in the prior art cannot meet the needs of users. Summary of the Invention

[0004] It is desirable to provide a device and method for predicting the time of mold occurrence, so that when mold is likely to occur in the detected environment, the user can be prompted before mold occurs in the environment, providing the user with an opportunity to effectively avoid the occurrence of mold, thereby significantly improving the user experience.

[0005] According to a first aspect of the present disclosure, a first apparatus for predicting the emergence of mold is provided. The apparatus includes: an information collection unit configured to obtain environmental parameters related to an environment for which the emergence of mold is to be predicted, wherein the environmental parameters include temperature and humidity; and a processing unit configured to use a machine learning model based on the environmental parameters to predict the expected time of emergence of mold in the environment.

[0006] According to a second aspect of the present disclosure, a first device for predicting the time of mold emergence is provided. The device includes: an information collection unit that obtains environmental parameters related to the environment for which the time of mold emergence is to be predicted, wherein the environmental parameters include temperature and humidity; a processing unit that uses a first machine learning model to generate preprocessed data specific to the environmental parameters based on the environmental parameters; and a sending unit that sends the preprocessed data to a second device, for the second device to use a second machine learning model to predict the time when mold will emerge in the environment based on the preprocessed data.

[0007] According to a third aspect of the present disclosure, a second device for predicting mold emergence time is provided. The device includes: a receiving unit that receives preprocessed data from a first device regarding environmental parameters related to an environment for which mold emergence time is to be predicted, the preprocessed data being generated by the first device using a first machine learning model based on the environmental parameters, wherein the environmental parameters include temperature and humidity; and a processing unit that uses a second machine learning model based on the preprocessed data to predict a predicted time when mold will emerge in the environment.

[0008] According to a fourth aspect of the present disclosure, a second device for predicting mold emergence time is provided. The device includes: a receiving unit that receives environmental parameters related to an environment for which mold emergence time is to be predicted from one or more devices, wherein the environmental parameters include temperature and humidity; and a processing unit that uses a machine learning model to predict a predicted time when mold will emerge in the environment based on the environmental parameters.

[0009] According to a fifth aspect of the present disclosure, a first device for predicting mold emergence time is provided. The device includes a mode switching unit configured to configure the first device to operate in a first mode or a second mode, wherein the first mode corresponds to the first device described in the first aspect, and the second mode corresponds to the first device described in the second aspect.

[0010] According to a sixth aspect of the present disclosure, a second device for predicting mold emergence time is provided. The device includes a mode switching unit configured to configure the second device to operate in a first mode or a second mode, wherein the first mode corresponds to the second device described in the third aspect, and the second mode corresponds to the second device described in the fourth aspect.

[0011] According to a seventh aspect of the present disclosure, a method for predicting the emergence time of mold is provided, comprising: a method performed by the apparatus according to any one of the first to sixth aspects.

[0012] According to an eighth aspect of the present disclosure, a device for predicting mold emergence time is provided, comprising: a memory; and a processor coupled to the memory and configured to execute the method performed by the device according to any one of the first to sixth aspects.

[0013] According to a ninth aspect of the present disclosure, a computer-readable medium for predicting mold emergence time is provided, which stores a computer program including instructions. When the instructions are executed by a processor, the processor is configured to execute the method performed by the apparatus according to any one of the first to sixth aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the embodiments of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like elements throughout the various drawings.

[0015] Figure 1 is a block diagram of a first apparatus 100 for predicting mold occurrence time according to the present disclosure.

[0016] Figure 2 is a block diagram of another first apparatus 200 for predicting mold emergence time according to the present disclosure.

[0017] Figure 3 is a block diagram of a second apparatus 300 for predicting mold emergence time according to the present disclosure.

[0018] Figure 4 is a block diagram of another second apparatus 400 for predicting mold emergence time according to the present disclosure.

[0019] Figure 5 is a block diagram of an apparatus 500 for predicting mold emergence time according to the present disclosure. DETAILED DESCRIPTION

[0020] The subject matter described herein will now be discussed with reference to various embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein and are not intended to limit the scope, applicability, or examples set forth in the claims. The functions and arrangements of the elements discussed may be varied without departing from the scope of the claims. Various embodiments may omit, replace, or add various processes or components as needed.

[0021] In daily life, when the temperature and humidity in an environment reach certain conditions, mold may grow on the surfaces of objects within such an environment. The presence of mold not only damages the surfaces of objects but also has adverse effects on the health of people within the environment. Existing mold detection devices can detect the severity of mold in an environment after it has already appeared. However, even if users clean the environment according to the instructions of these mold detection devices, mold will still appear when the environment is once again exposed to temperature and humidity conditions conducive to mold growth, and mold growth cannot be fundamentally prevented.

[0022] The devices 100, 200, 300, 400, and 500 proposed in the present disclosure use artificial intelligence technology to predict the time when mold may appear in the detected environment. The devices 100, 200, 300, 400, and 500 can be chips, working modules, or other devices with the functions described in the present disclosure. For example, but not limitation, they can be temperature sensing units, humidity sensing units, air pressure detection units, mold detection units, or other similar units with various levels of intelligence; or they can be devices that include these units. The devices 100, 200, 300, 400, and 500 can be installed in various electrical appliances (for example, air conditioners, air purifiers, thermometers, hygrometers, sweeping robots, fresh air systems, etc.), or they can be used independently.

[0023] By using the device for predicting the occurrence time of mold proposed in the present disclosure, the user is no longer prompted until mold has appeared. Instead, the user is prompted in advance before mold appears. This allows the user to have the opportunity to change the conditions of the detected environment, thereby effectively avoiding the occurrence of mold. This fundamentally avoids the occurrence of mold and can significantly improve the user experience.

[0024] In addition, for some environments where the appearance of mold is avoided by setting the temperature and humidity, the adjustment of the temperature and humidity of the environment can be made more targeted by using the device for predicting the appearance time of mold proposed in the present disclosure. For example, based on the time when mold may appear given by the device for predicting the appearance time of mold proposed in the present disclosure, the user can adaptively select the working gear of the device for setting the temperature and humidity of the environment. If the user knows through the predicted time that the environment is not yet in a state where mold is prone to grow, the device for setting the temperature and humidity of the environment can be operated at a relatively low working gear, without having to operate these devices at a relatively high working gear all the time, which helps to achieve the purpose of energy conservation and environmental protection, and helps to extend the service life of these devices by reducing the consumption caused by the frequent startup of these devices.

[0025] Figure 1 1 is a block diagram of a first device 100 for predicting mold emergence according to the present disclosure. Whether incorporated into the aforementioned appliance or used independently, the first device 100 is placed in the environment for which mold emergence is to be predicted. The environment for which mold emergence is to be predicted can be any indoor environment, such as a living room, cabinet, storage box, restroom, warehouse, garage, basement, truck compartment, or other environment where mold prevention is desired. The first device 100 may include an information collection unit 102 and a processing unit 104. The information collection unit 102 and the processing unit 104 may be implemented in hardware or software.

[0026] The information collection unit 102 can obtain environmental parameters related to the environment where the mold emergence time is to be predicted from any sensing device in the prior art. The environmental parameters can be the temperature and humidity of the environment, where the humidity can be absolute humidity or relative humidity.

[0027] Furthermore, since the first device 100 can predict when mold will appear in an actual environment, the richer the parameters related to the environment, the more accurate the prediction results, provided the processing power of the first device 100 can handle it. Therefore, the environmental parameters may optionally include at least one of the following: ventilation volume, light intensity, air pressure, building structure type, building material type, or indoor cleanliness. The ventilation volume, light intensity, and air pressure may be derived from any sensing device in the prior art that can provide corresponding data; the building structure type, building material type, and indoor cleanliness may be derived from data input by a user through a user interface capable of directly or indirectly interacting with the first device 100.

[0028] Processing unit 104 may include a pre-trained machine learning model. Based on the aforementioned environmental parameters, processing unit 104 uses the machine learning model to predict the expected time of mold emergence in the environment. The machine learning model is trained using the aforementioned environmental parameters. The machine learning model may be any machine learning model in the field of artificial intelligence that can achieve the inventive objectives of the present disclosure.

[0029] As an example, the machine learning model used in the present disclosure to predict mold growth is shown below.

[0030] For the case where the environmental parameters include ventilation rate, light intensity, air pressure value, building structure type, building material type, or indoor cleanliness, the machine learning model for predicting mold growth is

[0031]

[0032] Formula (1) expresses the mold growth index M as a differential equation, which corresponds to the regression equation in the machine learning model. The M value calculated using the model represented by formula (1) starts from the initial value M(t=0)>0 and increases monotonically to the maximum value M ∞ Where T represents temperature, φ represents humidity, k(T,φ) represents the mold growth rate, M represents the mold growth index, M ∞ Indicates the maximum mold growth value.

[0033] For the case where the environmental parameters include two or more of ventilation volume, light intensity, air pressure, building structure type, building material type, or indoor cleanliness, the machine learning model for predicting mold growth is improved based on formula (1) as follows:

[0034]

[0035] Among them, C mat Indicates the coefficient corresponding to the environmental parameters, C mat ={C1,C2,…,C N}, C1, C2, ..., C N are the coefficients corresponding to one of the above environmental parameters, and Represents a prediction of when mold will appear in the environment.

[0036] It should be noted that the above formula is only illustrative and not restrictive. The machine learning model used in the present disclosure can be any machine learning model in the art that can achieve the above functions.

[0037] Furthermore, the machine learning model can also be a deep learning model. This deep learning model can be any deep learning model in the field of artificial intelligence that can achieve the inventive objectives of the present disclosure. By using a pre-trained deep learning model to predict the time of mold emergence, the accuracy of the prediction can be improved.

[0038] Optionally, the first device 100 may further include a sending unit 106. The sending unit 106 may be a unit implemented in hardware or in software. The sending unit 106 may send the estimated time predicted by the processing unit 104 to the second device. The second device may be a device connected to one or more first devices 100 by wireless or wired means, which may be a master device in a smart home system, or any control device capable of achieving the inventive purpose of the present disclosure. The second device may receive the estimated times predicted by one or more first devices 100 respectively; and based on these estimated times, determine the time when mold will appear in the environment according to a pre-set algorithm. By having the second device finally determine the time when mold will appear in the environment based on one or more estimated times provided by one or more first devices 100, the accuracy of the prediction results can be effectively improved.

[0039] Optionally, the first device 100 may further include a receiving unit 108. The receiving unit 108 may be implemented in hardware or software. Because the feedback mechanism helps optimize the accuracy of the first device 100's prediction of the time of mold emergence, the first device 100 may receive feedback data related to the actual situation of mold in the environment through the receiving unit 108. This feedback data may come from user input provided through a user interface capable of directly or indirectly interacting with the first device 100, from other conventional sensing devices connected to the first device 100 for measuring the degree of mold presence, or from other devices capable of providing actual situation of mold in the environment. After receiving the feedback data, the receiving unit 108 provides the feedback data to the processing unit 104. The processing unit 104 may update the machine learning model it uses based on the feedback data, thereby effectively improving the accuracy of its prediction of the time of mold emergence.

[0040] Figure 2 is a block diagram of another first apparatus 200 for predicting mold emergence time according to the present disclosure. Figure 3 This is a block diagram of a second device 300 for predicting the appearance time of mold according to the present disclosure. The first device 200 is a further improvement on the first device 100. The same functions as the first device 100 can be found in the detailed description of the first device 100 and will not be repeated here. Figure 1 A further improvement based on the second device in Figure 1 For the same functions as the second device in the embodiment, please refer to the detailed description of the second device, which will not be repeated here.

[0041] The first device 200 may include an information collection unit 202, a processing unit 204, and a sending unit 206. The function of the information collection unit 202 may refer to the detailed description of the information collection unit 102 in the first device 100. The second device 300 may include a receiving unit 302 and a processing unit 304. The functions of the first device 200 and the second device 300 are similar. Figure 1 The difference between the first device 100 and the second device described in the embodiment is that the first device 200 does not predict the expected time when mold will appear in the environment. Instead, the first device 200 uses the first machine learning model on it to pre-process the collected environmental data and sends the pre-processed environmental data to the second device 300. Furthermore, the second device 300 uses the second machine learning model on it to predict the expected time when mold will appear in the environment based on the pre-processed environmental data received from the first device 200.

[0042] Specifically, the processing unit 204 of the first device 200 may include a pre-trained first machine learning model. The processing unit 204 uses the first machine learning model to generate pre-processed data for the above-mentioned environmental parameters. The pre-processed data may include data from any intermediate stage in the process of predicting the expected time when mold will appear in the environment. The processing unit 204 provides the pre-processed data to the sending unit 206, and the sending unit 206 sends the pre-processed data to the second device 300. After receiving the pre-processed data, the receiving unit 302 of the second device 300 provides the pre-processed data to the processing unit 304. The processing unit 304 includes a pre-trained second machine learning model. Based on the pre-processed data, the processing unit 304 uses the second machine learning model to predict the expected time when mold will appear in the environment. The first machine learning model and the second machine learning model can be any machine learning model in the field of artificial intelligence that can achieve the inventive objectives of the present disclosure.

[0043] Furthermore, the first machine learning model and the second machine learning model may also be deep learning models. These deep learning models may be any deep learning model in the field of artificial intelligence that can achieve the inventive objectives of this disclosure. By using a pre-trained deep learning model to predict the time of mold emergence, the accuracy of the prediction can be improved.

[0044] In addition, with Figure 1 The difference between the technical solution described in Figure 2 and Figure 3 In the technical solution described in

[15] , feedback data related to the actual conditions of mold in the environment is provided to the receiving unit 302 of the second device 300. The receiving unit 302 of the second device 300 provides the feedback data to the processing unit 304, which updates the second machine learning model based on the feedback data, thereby effectively improving the accuracy of the prediction.

[0045] In another embodiment, Figure 1 The technical solution executed by the first device 100 in the embodiment may be set to the first mode of the first device. Figure 2 The technical solution implemented by the first device 200 in the embodiment can be set to the second mode of the first device. The first device may include a mode switching unit to enable the first device to switch between the first mode and the second mode, that is, the first device can operate in the first mode or the second mode. The mode switching unit can be a unit implemented by hardware or a unit implemented by software.

[0046] Figure 4is a block diagram of another second device 400 for predicting the appearance time of mold according to the present disclosure. The second device 400 is Figure 1 The second device and Figure 2 The second device 300 is further improved based on Figure 1 The second device and Figure 2 The same functions as the second device 300 can be found in the detailed description of the second device, which will not be repeated here.

[0047] The second device 400 may include a receiving unit 402 and a processing unit 404. Figure 1 The second device described in Figure 2 The difference between the second device 300 and the second device 300 is that it receives environmental parameters from one or more devices via a receiving unit 402, which then provides the received environmental parameters to a processing unit 404. Processing unit 404 may include a pre-trained machine learning model. Based on the received environmental parameters, processing unit 404 uses the machine learning model to predict the expected time when mold will appear in the environment. In other words, the functions of processing unit 404 of second device 400 can be found in the detailed description of processing unit 104 of first device 100 and will not be repeated here.

[0048] Furthermore, the machine learning in processing unit 404 can be a deep learning model. This deep learning model can be any deep learning model in the field of artificial intelligence that can achieve the inventive objectives of this disclosure. By using a pre-trained deep learning model to predict the time of mold emergence, the accuracy of the prediction can be improved.

[0049] and Figure 1 The technical solution described in is different and similar to Figure 3 The second device 300 and the second device 400 described in the embodiment receive feedback data related to the actual situation of mold in the environment and update the machine learning model used by them, thereby effectively improving the accuracy of prediction.

[0050] In another embodiment, Figure 3 The technical solution executed by the second device 300 in the embodiment may be set to the first mode of the second device. Figure 4 The technical solution implemented by the second device 400 in the embodiment can be set to the second mode of the second device. The second device can include a mode switching unit to enable the second device to switch between the first mode and the second mode, that is, the second device can operate in the first mode or the second mode. The mode switching unit can be a unit implemented by hardware or a unit implemented by software.

[0051] Figure 5 is a block diagram of an apparatus 500 for predicting mold emergence time according to the present disclosure.

[0052] The apparatus 500 includes a processor 504 connected to an internal communication bus 502, the processor 504 being configured to execute instructions in a memory 506 to implement the system by combining Figure 1-Figure 4 The method described above is performed by a device for predicting the emergence of mold. Examples of processor 504 include a central processing unit (CPU), a microcontroller, and the like. Memory 506 suitable for tangibly embodying computer program instructions and data includes various forms of memory, such as EPROM, EEPROM, and flash memory devices. Device 500 may also include an input interface 508 and an output interface 510. Input interface 508 is used to receive input signals and data. Output interface 510 is used to transmit output signals and data.

[0053] The computer program may include instructions executable by a computer for causing the processor 504 of the apparatus 500 to execute the instructions in conjunction with the computer program. Figure 1-Figure 4 The method described herein can be performed by a device for predicting mold emergence. The program can be recorded on any data storage medium, including a memory. For example, the program can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or a combination thereof. The process / method steps described herein can be performed by a programmable processor executing program instructions to perform the method, steps, or operations by operating on input data and generating output.

[0054] For example, the memory may include but is not limited to Random Access Memory (RAM), Read-Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Static Random Access Memory (SRAM), hard disk, flash memory, etc.

[0055] In another embodiment, the method for predicting the occurrence of mold includes combining Figure 1-Figure 4 The method described is performed by a device for predicting the occurrence of mold.

[0056] In another embodiment, a computer readable medium for predicting mold emergence time stores a computer program including instructions that, when executed by a processor, configure the processor to perform a process performed by combining Figure 1-Figure 4The method described is performed by a device for predicting the occurrence of mold.

[0057] The foregoing description describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] Not all steps and units in the above processes and system structure diagrams are required, and some steps or units may be omitted according to actual needs. The device structure described in the above embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, some units may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0059] The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, these technologies can be implemented without these specific details. In some instances, in order to avoid obscuring the concepts of the described embodiments, well-known structures and devices are shown in block diagram form.

[0060] The above describes in detail the optional implementation methods of the embodiments of the present disclosure in conjunction with the accompanying drawings. However, the embodiments of the present disclosure are not limited to the specific details of the above implementation methods. Within the technical concept of the embodiments of the present disclosure, various modifications can be made to the technical solutions of the embodiments of the present disclosure, and these modifications all fall within the protection scope of the embodiments of the present disclosure.

[0061] The foregoing description of the present disclosure is provided to enable any person skilled in the art to implement or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is intended to be consistent with the widest range of principles and novel features disclosed herein.

Claims

1. A first device for predicting the emergence time of mold, comprising: an information collection unit for obtaining environmental parameters related to the environment in which the mold emergence time is to be predicted, wherein the environmental parameters include temperature and humidity; and A processing unit uses a machine learning model to predict an estimated time when mold will appear in the environment based on the environmental parameters.

2. The first device according to claim 1, further comprising: A sending unit sends the predicted time to a second device, so that the second device predicts the time when mold will appear in the environment based on the predicted time from one or more devices.

3. The first device according to claim 1, further comprising: A receiving unit receives feedback data related to actual conditions regarding mold in the environment.

4. The first device according to claim 3, wherein: The processing unit updates the machine learning model based on the feedback data.

5. The first device according to any one of claims 1 to 4, wherein: The environmental parameters further include at least one of the following: ventilation volume, light intensity, air pressure value, building structure type, building material type, or indoor cleanliness.

6. The first device according to any one of claims 1 to 4, wherein: The machine learning model includes a deep learning model.

7. A first device for predicting the emergence time of mold, comprising: an information collection unit for obtaining environmental parameters related to the environment in which the mold emergence time is to be predicted, wherein the environmental parameters include temperature and humidity; a processing unit, which generates pre-processed data for the environmental parameters using a first machine learning model based on the environmental parameters; and A sending unit sends the pre-processed data to a second device, so that the second device uses a second machine learning model to predict the time when mold will appear in the environment based on the pre-processed data.

8. The first device according to claim 7, wherein: The environmental parameters further include at least one of the following: ventilation volume, light intensity, air pressure value, building structure type, building material type, or indoor cleanliness.

9. The first device according to claim 7, wherein: At least one of the first machine learning model or the second machine learning model comprises a deep learning model.

10. A second device for predicting the emergence of mold, comprising: a receiving unit configured to receive pre-processed data of environmental parameters related to an environment for which a mold emergence time is to be predicted from a first device, the pre-processed data being generated by the first device using a first machine learning model based on the environmental parameters, wherein the environmental parameters include temperature and humidity; and A processing unit uses a second machine learning model to predict an estimated time when mold will appear in the environment based on the pre-processed data. 11 . The second device according to claim 10 , wherein the receiving unit further receives feedback data related to actual conditions of mold in the environment.

12. The second device according to claim 11, wherein The processing unit updates the second machine learning model based on the feedback data.

13. The second device according to any one of claims 10 to 12, wherein: The environmental parameters further include at least one of the following: ventilation volume, light intensity, air pressure value, building structure type, building material type, or indoor cleanliness.

14. The second device according to any one of claims 10 to 12, wherein: At least one of the first machine learning model or the second machine learning model comprises a deep learning model.

15. A second device for predicting the emergence of mold, comprising: a receiving unit configured to receive environmental parameters related to an environment for which a mold emergence time is to be predicted from one or more devices, wherein the environmental parameters include temperature and humidity; and A processing unit uses a machine learning model to predict an estimated time when mold will appear in the environment based on the environmental parameters. 16 . The second device according to claim 15 , wherein the receiving unit further receives feedback data related to actual conditions regarding mold in the environment.

17. The second device according to claim 16, wherein: The processing unit updates the machine learning model based on the feedback data.

18. The second device according to any one of claims 15 to 17, wherein: The environmental parameters further include at least one of the following: ventilation volume, light intensity, air pressure value, building structure type, building material type, or indoor cleanliness.

19. The second device according to any one of claims 15 to 17, wherein: The machine learning model includes a deep learning model.

20. A first device for predicting the emergence of mold, comprising: A mode switching unit configured to configure the first device to operate in a first mode or a second mode, wherein the first mode corresponds to the first device according to claims 1-6, and the second mode corresponds to the first device according to claims 7-9.

21. A second device for predicting the emergence of mold, comprising: A mode switching unit configured to configure the second device to operate in a first mode or a second mode, wherein the first mode corresponds to the second device according to claims 10-14, and the second mode corresponds to the second device according to claims 15-19.

22. A method for predicting the emergence of mold, comprising the method performed by the device according to any one of claims 1 to 21.

23. A device for predicting the emergence of mold, comprising: Memory; as well as A processor is coupled to the memory and configured to execute the method performed by the apparatus according to any one of claims 1-21.

24. A computer-readable medium for predicting mold emergence time, the medium storing a computer program comprising instructions, which, when executed by a processor, configure the processor to execute the method performed by the apparatus according to any one of claims 1 to 21.

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