Ultraviolet device control method, medium, device and program product

Abstract

The invention relates to an ultraviolet device control method, a medium, a device and a program product. The method comprises the following steps: acquiring current reference data; inputting the current reference data into a pre-constructed ultraviolet equipment power determination model, and obtaining first reference power through a target search algorithm; obtaining second reference power according to the current reference data and historical reference data; determining target power of the ultraviolet equipment according to the first reference power and the second reference power; and controlling the operation of the ultraviolet equipment according to the target power. Therefore, on one hand, the reliability of the target power can be improved, the power of the ultraviolet equipment is adjusted in real time to adapt to current water quality parameters, and energy use is optimized as much as possible while it is ensured that the ultraviolet irradiation dose reaches the level required for killing target microorganisms; on the other hand, equipment loss can be reduced, and the service life of the ultraviolet equipment is prolonged.

Description

Technical Field

[0001] The present disclosure relates to the field of water treatment technology, and in particular, to an ultraviolet device control method, medium, device and program product. Background Art

[0002] In the field of water treatment, the use of ultraviolet light for sterilization is a common and effective disinfection method. Traditional ultraviolet lamps usually operate at a fixed power. However, this fixed power operation mode has many defects and shortcomings. On the one hand, factors such as impurities, microbial content, and water flow rate in the water will continue to change. Fixed-power ultraviolet lamps cannot adapt to these changes, resulting in poor sterilization effects when the water quality is poor or the microbial content is high, and the safety and hygiene of the water quality cannot be guaranteed; and when the water quality is good, it causes a waste of energy. On the other hand, as the use time increases, the lamp tube of the ultraviolet lamp will gradually age, and the intensity of the ultraviolet light it emits will gradually weaken. If it is still operated at the initial fixed power, it will inevitably affect the sterilization effect.

[0003] At present, how to accurately realize automatic adjustment of the power of ultraviolet equipment has become a technical problem that needs to be solved urgently in the field of water treatment. Summary of the invention

[0004] The purpose of the present disclosure is to provide a UV device control method, medium, device and program product to accurately control the power of the UV device, while ensuring that the UV radiation dose reaches the level required to kill target microorganisms, while optimizing energy use as much as possible.

[0005] In order to achieve the above-mentioned object, the present disclosure provides a first aspect of a method for controlling an ultraviolet device, comprising: Acquire current reference data, wherein the reference data includes more than one of water quality parameter turbidity, suspended matter mass concentration, organic matter content, water quality pH value, water flow velocity, ultraviolet equipment working time and ultraviolet equipment aging degree; Inputting the current reference data into a pre-built ultraviolet device power determination model and obtaining a first reference power through a target search algorithm; Obtaining a second reference power according to the current reference data and the historical reference data; determining a target power of the ultraviolet device according to the first reference power and the second reference power; The operation of the ultraviolet device is controlled according to the target power.

[0006] A second aspect of the present disclosure provides an ultraviolet device control device, comprising: An acquisition module, used to acquire current reference data, wherein the reference data includes more than one of water quality parameter turbidity, suspended matter mass concentration, organic matter content, water quality pH value, water flow velocity, ultraviolet equipment working time and ultraviolet equipment aging degree; A first determination module is used to input the current reference data into a pre-built ultraviolet device power determination model and obtain a first reference power through a target search algorithm; A second determination module, configured to obtain a second reference power according to the current reference data and the historical reference data; A third determination module, configured to determine a target power of the ultraviolet device according to the first reference power and the second reference power; A control module is used to control the operation of the ultraviolet device according to the target power.

[0007] A third aspect of the present disclosure provides an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute a computer program in a memory to implement the steps of the method provided in the first aspect of the present disclosure.

[0008] A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect of the present disclosure.

[0009] A fifth aspect of the present disclosure provides a computer program product, including a computer program, which implements the steps provided in the first aspect of the disclosure when executed by a processor.

[0010] In the above technical solution, the current reference data is obtained, wherein the reference data includes multiple of water quality parameters such as turbidity, suspended solids mass concentration, organic matter content, water quality pH value, water flow velocity, UV equipment working time and UV equipment aging degree; the current reference data is input into a pre-built UV equipment power determination model, and a first reference power is obtained through a target search algorithm; a second reference power is obtained based on the current reference data and historical reference data; a target power of the UV equipment is determined based on the first reference power and the second reference power; and the operation of the UV equipment is controlled based on the target power. In this way, on the one hand, the reliability of the target power can be improved, and by adjusting the power of the UV equipment in real time to adapt to the current water quality parameters, while ensuring that the UV irradiation dose reaches the level required to kill the target microorganisms, the energy use can be optimized as much as possible; on the other hand, the equipment loss can be reduced and the service life of the UV equipment can be extended.

[0011] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 is a flow chart of a method for controlling an ultraviolet device provided by an exemplary embodiment of the present disclosure.

[0013] Figure 2 It is a schematic diagram of the implementation process of the improved sparrow search algorithm provided by an exemplary embodiment of the present disclosure.

[0014] Figure 3 is a block diagram of an ultraviolet equipment control device provided by an exemplary embodiment of the present disclosure.

[0015] Figure 4 is a block diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0017] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0018] Figure 1 FIG. 1 is a flow chart of a method for controlling an ultraviolet device provided by an exemplary embodiment of the present disclosure. The method can be applied to a controller. Figure 1 As shown, the method may include S101 to S105.

[0019] S101, obtaining current reference data.

[0020] The reference data include multiple ones of water quality parameters such as turbidity, suspended matter mass concentration, organic matter content, water pH value, water flow velocity, UV equipment working time and UV equipment aging degree.

[0021] For example, the current reference coefficient can be obtained through a pre-set sensor. Specifically, the water quality parameter turbidity, suspended matter mass concentration, organic matter content and other data can be obtained through a pre-set sensor for detecting water quality parameters; the water flow rate can be obtained through a pre-set flow rate measurement sensor; the ultraviolet device working time and the degree of aging of the ultraviolet device can be obtained through an ultraviolet device detection device.

[0022] Among them, the water quality parameter turbidity can be positively correlated with the output power of the ultraviolet device. If the water quality parameter turbidity is high, it can be determined that there are more impurities in the water, which may affect the penetration and sterilization effect of ultraviolet rays. To ensure the sterilization ability, the output power of the external line device can be increased. The water flow rate can be positively correlated with the output power of the ultraviolet device to ensure the same ultraviolet irradiation dose when the water flow rate changes. The types and numbers of microorganisms in the water can be analyzed in real time through microbial detection equipment. If a pathogen with strong drug resistance or a large number is detected, the output power of the ultraviolet device can be controlled to increase to ensure effective killing. In addition, during the peak water consumption period, the output power of the ultraviolet device can be increased for areas with a large amount of water to meet higher sterilization requirements; and at night when the water consumption is small, the output power of the ultraviolet device can be appropriately reduced to save energy.

[0023] S102, inputting the current reference data into a pre-built ultraviolet device power determination model, and obtaining a first reference power through a target search algorithm.

[0024] In an optional embodiment, the ultraviolet device power determination model may include an objective function and constraints. The objective function is constructed with the minimum ultraviolet device power output value as the optimization goal; the constraints may include water quality parameter turbidity constraints, suspended matter mass concentration constraints, organic matter content constraints, water quality pH value constraints, water flow velocity constraints, ultraviolet device working time constraints, and ultraviolet device aging degree constraints.

[0025] S103, obtaining a second reference power according to current reference data and historical reference data.

[0026] For example, historical reference data can be obtained from a preset historical database, and then the scene with the highest similarity to the current reference data is determined from the historical reference data, that is, the historical water treatment scene with the highest similarity to the current water treatment scene is determined, and the operating power of the ultraviolet equipment under the historical water treatment scene is determined as the second reference power corresponding to the current reference data.

[0027] S104: Determine a target power of the ultraviolet device according to the first reference power and the second reference power.

[0028] For example, the first reference power and the second reference power may be weighted and summed to obtain the target power of the ultraviolet device. The weights corresponding to the first reference power and the second reference power may be preset based on actual needs. For example, the weights corresponding to the first reference power and the second reference power may both be set to 0.5. At this time, the average value of the first reference power and the second reference power may be determined as the target power of the ultraviolet device.

[0029] S105, controlling the operation of the ultraviolet device according to the target power.

[0030] For example, the ultraviolet device may include a power regulation circuit, which may include power semiconductor devices, such as MOSFET (metal oxide semiconductor field effect transistor) and IGBT (insulated gate bipolar transistor), to achieve smooth regulation of the output power of the ultraviolet device and reduce the impact of power fluctuations on the life of the ultraviolet device. For another example, multiple groups of ultraviolet lamps with different fixed powers may be installed in the ultraviolet device, and different ultraviolet lamps may be controlled to work according to different target powers.

[0031] In the above technical solution, the current reference data is obtained, wherein the reference data includes multiple of water quality parameters such as turbidity, suspended solids mass concentration, organic matter content, water quality pH value, water flow velocity, UV equipment working time and UV equipment aging degree; the current reference data is input into a pre-built UV equipment power determination model, and a first reference power is obtained through a target search algorithm; a second reference power is obtained based on the current reference data and historical reference data; a target power of the UV equipment is determined based on the first reference power and the second reference power; and the operation of the UV equipment is controlled based on the target power. In this way, on the one hand, the reliability of the target power can be improved, and by adjusting the power of the UV equipment in real time to adapt to the current water quality parameters, while ensuring that the UV irradiation dose reaches the level required to kill the target microorganisms, the energy use can be optimized as much as possible; on the other hand, the equipment loss can be reduced and the service life of the UV equipment can be extended.

[0032] In an alternative embodiment, the objective function for:

[0033] in, is the first weight, is the second weight, The third weight, The fourth weight, The fifth weight, The sixth weight, is the seventh weight;

[0034]

[0035] Turbidity is the water quality parameter, is the mass concentration of suspended matter, The organic matter content, The pH value of water quality, is the water flow velocity, Working hours and is the aging degree of UV equipment, is the fitting coefficient, where The value range is 1 to 29, and the fitting coefficient It is obtained by finding the minimum value of a multivariate function.

[0036] Correspondingly, the water quality parameter turbidity constraint is: ; The suspended matter mass concentration constraint is: ; The organic matter content is limited to: ; The water quality PH value is constrained to: ; The water flow velocity constraint is: ; The working time constraints of the UV equipment are: ; The aging degree of UV equipment is constrained as follows: .

[0037] In an optional embodiment, the target search algorithm is an improved sparrow search algorithm.

[0038] For example, the implementation process of the improved sparrow search algorithm is as follows: Figure 2 As shown: S201, set relevant parameters and use Bernoulli chaos to initialize the population.

[0039] S202, calculate the fitness value of each individual in the current population, the top ranked ones are discoverers, the bottom ranked ones are followers, and the sentinels are randomly generated in each iteration.

[0040] S203: Update the position according to the dynamic disturbance strategy.

[0041] S204, checking whether the updated position exceeds the upper and lower bounds of the variable, and if so, performing boundary adjustment.

[0042] S205, recalculating the fitness value and related parameters of each individual sparrow according to the updated position.

[0043] S206, updating the position and fitness value of the global optimal solution according to the fitness value.

[0044] S207, determine whether the maximum number of iterations has been reached or the termination condition has been met, if so, terminate the algorithm and execute step S208, otherwise return to step S203 to continue iterating.

[0045] S208, output the position and fitness value of the optimal solution.

[0046] In an optional embodiment, in S103, obtaining a second reference power according to current reference data and historical reference data includes: Determine a target number of historical reference data from the historical database that has the highest similarity to the current reference data; A second reference power is determined according to the historical power of the ultraviolet device corresponding to the historical reference data.

[0047] For example, the historical reference data in the historical database that is more similar to the current reference data can be determined by calculating the cosine similarity. The target number can be preset based on actual needs. For example, it can be set to 3, that is, 3 historical reference data with the highest similarity to the current reference data can be determined from the historical database. After that, the historical power of the ultraviolet equipment corresponding to the screened historical reference data can be weighted summed to obtain the second reference power. Among them, the higher the similarity, the higher the similarity corresponding to the historical power in the weighted summation process. In this way, the reliability of the determined second reference power can be effectively improved, thereby improving the accuracy of the target power.

[0048] In an optional embodiment, the ultraviolet device control method provided by the present disclosure may also include: If the quality of newly supplied water is changed or the operating conditions of the water treatment equipment are changed, the power determination model of the ultraviolet equipment is adjusted through an adaptive control algorithm.

[0049] For example, the adaptive control algorithm can be pre-set based on actual needs. If the quality of newly supplied water is being processed, or the operating conditions of the water treatment equipment change, it can be determined that the ultraviolet power output by the current ultraviolet device may not be well adapted to the current scene. By adjusting the ultraviolet device power determination model through the adaptive control algorithm, the adaptability of the ultraviolet device power determination model to the current scene can be improved, and the reliability of the determined first reference power can be effectively improved, thereby improving the accuracy of the target power.

[0050] In an optional embodiment, the ultraviolet device control method provided by the present disclosure may also include: According to the historical power of the UV device and the current reference data, the predicted value of the reference data is determined through the Gated Recurrent Unit (GRU) deep learning algorithm based on multi-head attention; Based on the predicted value, determine the predicted power of the UV equipment.

[0051] For example, based on the historical data of UV equipment power and the current values ​​of water quality and water flow, the GRU deep learning algorithm based on multi-head attention can be used to predict the changes in water quality and water flow in the future, and the output power of the UV equipment can be adjusted in advance to avoid a delay in the sterilization effect.

[0052] In an optional embodiment, when a system failure occurs, the cause of the failure can be quickly located through the fault diagnosis function of the controller and the data record of the sensor. Common failures may include sensor failure, controller communication abnormality, power regulation module failure, etc. For different failures, corresponding maintenance or replacement measures are taken.

[0053] Based on the same inventive concept, the present disclosure also provides an ultraviolet equipment control device. Figure 3 FIG. 3 is a block diagram of an ultraviolet device control device 300 provided by an exemplary embodiment of the present disclosure. Figure 3 , the ultraviolet device control device 300 may include: An acquisition module 301 is used to acquire current reference data, wherein the reference data includes more than one of water quality parameter turbidity, suspended matter mass concentration, organic matter content, water quality pH value, water flow velocity, ultraviolet device working time and ultraviolet device aging degree; A first determination module 302 is used to input the current reference data into a pre-built ultraviolet device power determination model and obtain a first reference power through a target search algorithm; A second determination module 303, configured to obtain a second reference power according to the current reference data and the historical reference data; A third determination module 304 is used to determine the target power of the ultraviolet device according to the first reference power and the second reference power; The control module 305 is used to control the operation of the ultraviolet device according to the target power.

[0054] In this way, on the one hand, the reliability of the target power can be improved by adjusting the power of the UV equipment in real time to adapt to the current water quality parameters, while ensuring that the UV irradiation dose reaches the level required to kill the target microorganisms, while optimizing energy use as much as possible; on the other hand, it can reduce equipment losses and extend the service life of the UV equipment.

[0055] Optionally, the ultraviolet equipment power determination model includes an objective function and constraints, the objective function is constructed with the minimum ultraviolet equipment power output value as the optimization target; the constraints include multiple ones of water quality parameter turbidity constraint, suspended matter mass concentration constraint, organic matter content constraint, water quality pH value constraint, water flow velocity constraint, ultraviolet equipment working time constraint and ultraviolet equipment aging degree constraint.

[0056] Optionally, the objective function for:

[0057] in, is the first weight, is the second weight, The third weight, The fourth weight, The fifth weight, The sixth weight, is the seventh weight;

[0058]

[0059] Turbidity is the water quality parameter, is the mass concentration of suspended matter, The organic matter content, The pH value of water quality, is the water flow velocity, Working hours and is the aging degree of UV equipment, is the fitting coefficient, where The value range is 1 to 29, and the fitting coefficient It is obtained by finding the minimum value of a multivariate function; The water quality parameter turbidity constraint is: ; The suspended matter mass concentration constraint is: ; The organic matter content is constrained to: ; The water quality pH value is constrained to: ; The water flow velocity constraint is: ; The working time constraints of the ultraviolet equipment are: ; The aging degree of the ultraviolet equipment is constrained as follows: .

[0060] Optionally, the second determining module 303 is configured to obtain the second reference power in the following manner: Determine from the historical database a target number of historical reference data having the highest similarity to the current reference data; The second reference power is determined according to the historical power of the ultraviolet device corresponding to the historical reference data.

[0061] Optionally, the first determination module 302 is further configured to adjust the ultraviolet device power determination model through an adaptive control algorithm if the quality of newly supplied water or the operating conditions of the water treatment equipment change.

[0062] Optionally, the ultraviolet device control device 300 further includes: A fourth determination module is used to determine the predicted value of the reference data according to the historical power of the ultraviolet device and the current reference data through a GRU deep learning algorithm based on multi-head attention; The fifth determination module is used to determine the predicted power of the ultraviolet device according to the predicted value.

[0063] Optionally, the target search algorithm is an improved sparrow search algorithm.

[0064] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0065] Figure 4 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 , a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0066] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned ultraviolet device control method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. For example, these data may include instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0067] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned ultraviolet device control method.

[0068] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned ultraviolet device control method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned ultraviolet device control method.

[0069] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing the above-mentioned ultraviolet device control method when executed by the programmable device.

[0070] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0071] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0072] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for controlling ultraviolet equipment, characterized in that: include: Acquire current reference data, wherein the reference data includes more than one of water quality parameter turbidity, suspended matter mass concentration, organic matter content, water quality pH value, water flow velocity, ultraviolet equipment working time and ultraviolet equipment aging degree; Inputting the current reference data into a pre-built ultraviolet device power determination model and obtaining a first reference power through a target search algorithm; Obtaining a second reference power according to the current reference data and the historical reference data; determining a target power of the ultraviolet device according to the first reference power and the second reference power; The operation of the ultraviolet device is controlled according to the target power.

2. The method according to claim 1, characterized in that The ultraviolet equipment power determination model includes an objective function and constraints, wherein the objective function is constructed with the minimum ultraviolet equipment power output value as the optimization target; the constraints include multiple ones of water quality parameter turbidity constraint, suspended matter mass concentration constraint, organic matter content constraint, water quality pH value constraint, water flow velocity constraint, ultraviolet equipment working time constraint and ultraviolet equipment aging degree constraint.

3. The method according to claim 2, characterized in that The objective function for: in, is the first weight, is the second weight, The third weight, The fourth weight, The fifth weight, The sixth weight, is the seventh weight; Turbidity is the water quality parameter. is the mass concentration of suspended matter, The organic matter content, The pH value of water quality, is the water flow velocity, Working hours and is the aging degree of UV equipment, is the fitting coefficient, where The value range is 1 to 29, and the fitting coefficient It is obtained by finding the minimum value of a multivariate function; The water quality parameter turbidity constraint is: ; The suspended matter mass concentration constraint is: ; The organic matter content is constrained to: ; The water quality pH value is constrained to: ; The water flow velocity constraint is: ; The working time constraints of the ultraviolet equipment are: ; The aging degree of the ultraviolet equipment is constrained as follows: .

4. The method according to claim 1, characterized in that: The obtaining a second reference power according to the current reference data and the historical reference data includes: Determine from the historical database a target number of historical reference data having the highest similarity to the current reference data; The second reference power is determined according to the historical power of the ultraviolet device corresponding to the historical reference data.

5. The method according to claim 1, characterized in that The method further comprises: If the quality of newly supplied water or the operating conditions of the water treatment equipment change, the ultraviolet equipment power determination model is adjusted through an adaptive control algorithm.

6. The method according to claim 1, characterized in that The method further comprises: Determine the predicted value of the reference data by using a GRU deep learning algorithm based on multi-head attention according to the historical power of the ultraviolet device and the current reference data; The predicted power of the ultraviolet device is determined according to the predicted value.

7. The method according to claim 1, characterized in that The target search algorithm is an improved sparrow search algorithm.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images