Equipment fault detection method and device based on artificial immune network

Abstract

The embodiment of the invention discloses an equipment fault detection method and device based on an artificial immune network, electronic equipment and a storage medium, and the method comprises the steps: S101, obtaining an equipment data set with a first tag or a second tag from a historical database, and constructing a hyper-parameter combination and decision function; s102, fitting parameters in the decision function by using an equipment data set with a first label or a second label and a hyper-parameter combination to obtain a trained decision function; and S103, acquiring equipment data acquired by a sensor, inputting the equipment data into the trained decision function to obtain an equipment fault detection result, and sending a fault warning when the equipment has a fault. According to the embodiment of the invention, the operation state of the equipment is detected in real time by collecting and analyzing the data during the operation of the equipment, whether the current equipment has a fault during operation can be accurately judged, and the timeliness and accuracy of fault detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production and manufacturing, and in particular to an equipment fault detection method, device, electronic equipment and storage medium based on an artificial immune network. Background Art

[0002] Industrial equipment usually contains multiple sensors and multiple important features that need to be monitored, which plays a guiding role in judging equipment failure anomalies. The initial detection method mainly relies on manual visual inspection and experience judgment. In this method, personnel observe whether the equipment has a fault by observing the temperature, temperature, vibration amplitude, sound, etc. However, this method is easily affected by personnel experience and subjective judgment, and there is a great possibility of misjudgment and missed judgment, while consuming a lot of manpower costs. Summary of the invention

[0003] To solve the problems existing in the prior art, one or more embodiments of this specification describe a method, apparatus, electronic device and storage medium for detecting equipment faults based on an artificial immune network.

[0004] According to a first aspect, a method for detecting equipment faults based on an artificial immune network is provided, the method comprising:

[0005] S101, obtaining a device data set with a first label or a second label from a historical database, and constructing a hyperparameter combination and a decision function;

[0006] S102, fitting the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function;

[0007] S103, obtaining device data collected by sensors, inputting the device data into a trained decision function, obtaining device fault detection results, and issuing a fault warning when the device fails, wherein the device data collected by the sensors include time, temperature, pressure, and vibration amplitude.

[0008] Preferably, obtaining a device data set with a first label or a second label from a historical database includes: obtaining acquisition features from various sensors of the device, aligning them according to timestamps to obtain a device data set, and assigning a first label or a second label to each set of device data in the device data set.

[0009] Preferably, fitting the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain the trained decision function comprises:

[0010] S1011, performing encoding extraction operation on the hyperparameter combination to obtain an initial set;

[0011] S1012, performing anomaly detection on each element in the set and the device data set after training the decision function, and obtaining the fault detection accuracy of each element;

[0012] S1013, set the accuracy threshold and the number of iterations. If the accuracy of each element is less than the accuracy threshold or the number of iterations has not been reached, perform the next iteration, and execute S1012 after obtaining the next generation set.

[0013] Preferably, performing the next iteration to obtain the next generation set includes:

[0014] Select multiple elements in descending order of fault detection accuracy;

[0015] After performing mutation operations on multiple elements, they are merged with the selected multiple elements to obtain the next generation set.

[0016] Preferably, performing the next iteration to obtain the next generation set further comprises:

[0017] Set a similarity threshold. If the similarity between two elements is less than the similarity threshold, one of the two elements will be removed.

[0018] Preferably, the similarity between two elements is the Euclidean distance between the two elements.

[0019] Preferably, the number of elements selected in order from high to low according to the fault detection accuracy is calculated by the following formula: Where N c represents the number of elements selected from the previous generation collection, f i represents the fault detection accuracy of element i in the current set, f max 、f min represents the maximum and minimum fault detection accuracy in the current set, and α is the selection factor.

[0020] According to a second aspect, a device fault detection apparatus is provided, the apparatus comprising:

[0021] A preprocessing module, used to obtain a device data set with a first label or a second label from a historical database, and construct a hyperparameter combination and a decision function;

[0022] A fitting module, used to fit the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function;

[0023] The execution module is used to obtain the device data collected by the sensor, input the device data into the trained decision function, obtain the device fault detection result, and issue a fault warning when the device fails. The device data collected by the sensor includes time, temperature, pressure, and vibration amplitude.

[0024] According to a third aspect, there is provided an electronic device, comprising a processor and a memory;

[0025] The processor is connected to the memory;

[0026] The memory is used to store executable program code;

[0027] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0028] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0029] The beneficial effects of the present invention are:

[0030] 1. The method and device provided in the embodiments of this specification collect data during device operation for analysis, detect the device operation status in real time, and can accurately determine whether the current device operation is faulty, thereby improving the timeliness and accuracy of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a flowchart of a method for detecting equipment faults based on an artificial immune network in the specific implementation of this specification;

[0033] Figure 2 It is a structural schematic diagram of a device fault detection device in the specific implementation of this specification;

[0034] Figure 3 It is a structural schematic diagram of an electronic device in the specific implementation of this specification; DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0036] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0037] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0038] See also Figure 1 , Figure 1 : is a flow chart of a device fault detection method provided in an embodiment of the present application. In an embodiment of the present application, the method includes:

[0039] S101, obtaining a device data set with a first label or a second label from a historical database, and constructing a hyperparameter combination and a decision function;

[0040] S102, fitting the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function;

[0041] S103, obtaining device data collected by sensors, inputting the device data into a trained decision function, obtaining device fault detection results, and issuing a fault warning when a device fails.

[0042] The execution entity of this application can be a cloud server.

[0043] In the embodiment of this specification, the cloud server first obtains a device data set with a label from the historical database. The device data set contains multiple groups of device data. The labels include a first label indicating "normal" and a second label indicating "abnormal". Each group of device data is assigned a first label or a second label to construct a hyperparameter combination and a decision function. The device data set with the first label or the second label and the hyperparameter combination are used to fit the parameters in the decision function, and then a trained decision function is obtained. After the decision function is trained, the cloud server obtains the sensor acquisition device data, and then inputs the device data into the trained decision function to obtain the device fault detection result, and issues a fault warning to notify the operator when the device fails. Compared with the prior art, personnel observe whether the device has a fault by observing the temperature, temperature, vibration amplitude, sound, etc. of the device. In this application, the data collected during the operation of the device is analyzed to detect the operation status of the device in real time, and it can accurately determine whether the current device operation is faulty, thereby improving the timeliness and accuracy of fault detection.

[0044] In one possible implementation mode, the device data collected by the sensor includes time, temperature, pressure, vibration amplitude, etc.

[0045] In one possible implementation, obtaining a device data set with a first label or a second label includes: obtaining collected features from various sensors of the device, aligning them according to timestamps to form a device data set with unified time, labeling each set of device data in the aligned device data set, assigning each set of device data in the device data set with a first label or a second label, and dividing the device data in the device data set into two parts: a training set and a test set, wherein the training set is used to fit the decision function, and the test set is used to test the fitted decision function. The collected feature data sets are aligned according to timestamps to ensure that the time corresponding to each feature is consistent, thereby ensuring the accuracy of the trained decision function.

[0046] In one possible implementation, since the device has only two results: failure or no failure, a support vector machine can be used to fit the decision function. The decision function is where α i represents the Lagrange multiplier, k(x i ,x) is the kernel function, which is described as

[0047] In one possible implementation, the hyperparameter combination includes a first hyperparameter and a second hyperparameter. The parameters in the decision function are fitted using a device data set with a first label or a second label and the hyperparameter combination to obtain a trained decision function. The process is as follows: an initial first hyperparameter and an initial second hyperparameter are set. Specifically, the first hyperparameter is a penalty factor, and the second hyperparameter is a kernel function. After encoding the initial first hyperparameter and the initial second hyperparameter, they are randomly extracted from the encoding interval to form an initial set. The cardinality of the initial set is N, that is, the set includes N elements, each element contains two parameters, and each element represents a set of hyperparameter combinations. After training the decision function for each element in the set and the device data set, perform anomaly detection to obtain the accuracy of each element, set the accuracy threshold and the number of iterations. If the accuracy of each element is less than the accuracy threshold or the number of iterations has not been reached, perform the next iteration to obtain the next generation set and continue to train the decision function for each element in the set and the device data set, perform anomaly detection, and make a judgment after obtaining the accuracy of each element. If the accuracy of an element is greater than the accuracy threshold, output the decision function trained using the element and the device data set. If the maximum number of iterations is met, output the decision function trained using the element and the device data set with the highest accuracy under the current number of iterations.

[0048] In one embodiment, performing the next iteration to obtain the next generation set includes: when performing the next iteration, setting the affinity parameter and the mutation parameter, and selecting N from the previous generation set according to the affinity parameter in descending order of accuracy. c elements, according to the mutation parameter N c elements and perform mutation operations on them. c The next generation set is then merged to form the next generation set. The next generation set is trained continuously and iterated when the accuracy of each element is less than the accuracy threshold or the number of iterations has not been reached.

[0049] Specifically, p i ‘’ =p i +N(0,σ I ), where N c represents the number of elements selected from the previous generation collection, p i ‘’ Represents the mutated element, p i represents the element before mutation, α is the selection factor, β is the maximum mutation factor, α, β and ε can be set separately according to the actual situation, f i represents the fault detection accuracy of element i in the current set, f max 、f min Indicates the maximum and minimum fault detection accuracy in the current set.

[0050] From the above, we can see that in N c When a mutation operation is performed on N elements, the following may occur: c The elements in the training set are exactly the same. The same elements will lead to redundancy of training resources. Therefore, a similarity threshold is set. If the similarity between two elements is less than the similarity threshold, one of the two elements is selected and removed.

[0051] Furthermore, the similarity between two elements is represented by the Euclidean distance between the two elements.

[0052] The following will be combined with the attached Figure 2 , the device fault detection device provided in the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 2 The device fault detection device shown is used to implement the present application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0053] See also Figure 2 , Figure 2 Schematic diagram of the structure of the device fault detection device provided in the embodiment of the present application. Figure 2 As shown, the device comprises:

[0054] A preprocessing module 201 is used to obtain a device data set with a first label or a second label from a historical database and construct a hyperparameter combination and a decision function;

[0055] A fitting module 202, used to fit the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function;

[0056] The execution module 203 is used to obtain the device data collected by the sensor, input the device data into the trained decision function, obtain the device fault detection result, and issue a fault warning when the device fails. The device data collected by the sensor includes time, temperature, pressure, and vibration amplitude.

[0057] In one possible implementation, the preprocessing module 201 is specifically used to: obtain the device data set with the first label or the second label from the historical database, including: obtain the acquisition features from various sensors of the device, align them according to the timestamp, obtain the device data set, and assign the first label or the second label to each set of device data in the device data set

[0058] In one embodiment, the training module 202 is specifically used for:

[0059] S1011, performing encoding extraction operation on the hyperparameter combination to obtain an initial set;

[0060] S1012, performing anomaly detection on each element in the set and the device data set after training the decision function, and obtaining the fault detection accuracy of each element;

[0061] S1013, set the accuracy threshold and the number of iterations. If the accuracy of each element is less than the accuracy threshold or the number of iterations has not been reached, perform the next iteration, and execute S1012 after obtaining the next generation set.

[0062] In one embodiment, the training module 202 is specifically used for:

[0063] Select multiple elements in descending order of fault detection accuracy;

[0064] After performing mutation operations on multiple elements, they are merged with the selected multiple elements to obtain the next generation set.

[0065] In one embodiment, the training module 202 is specifically used for:

[0066] Set a similarity threshold. If the similarity between two elements is less than the similarity threshold, one of the two elements will be removed.

[0067] In one possible implementation, the training module 202 is specifically configured to: the similarity between two elements is the Euclidean distance between the two elements.

[0068] In one possible implementation, the training module 202 is specifically used to: select multiple elements in order from high to low according to the fault detection accuracy, and the number of elements is calculated by the following formula: Where N c represents the number of elements selected from the previous generation collection, f i represents the fault detection accuracy of element i in the current set, f max 、f min represents the maximum and minimum fault detection accuracy in the current set, and α is the selection factor.

[0069] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (Field-Programmable Gate Array, FPGA), an integrated circuit (Integrated Circuit, IC), etc.

[0070] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0071] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one central processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0072] The communication bus 302 is used to realize the connection and communication between these components.

[0073] The user interface 303 may include a display screen (Display) and a camera (Camera), and the optional user interface 303 may also include a standard wired interface and a wireless interface.

[0074] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0075] Among them, the central processing unit 301 may include one or more processing cores. The central processing unit 301 uses various interfaces and lines to connect various parts of the entire electronic device 300, and executes various functions and processes data of the terminal 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the central processing unit 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The central processing unit 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the central processing unit 301, and it can be implemented separately through a chip.

[0076] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned central processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0077] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the central processor 301 can be used to call the application stored in the memory 305 and specifically perform the following operations:

[0078] S101, obtaining a device data set with a first label or a second label from a historical database, and constructing a hyperparameter combination and a decision function;

[0079] S102, fitting the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function;

[0080] S103, obtaining device data collected by sensors, inputting the device data into a trained decision function, obtaining device fault detection results, and issuing a fault warning when the device fails, wherein the device data collected by the sensors include time, temperature, pressure, and vibration amplitude.

[0081] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0082] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0083] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0084] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0087] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0088] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct the relevant hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0089] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A device fault detection method based on artificial immune network, characterized in that: The method comprises: S101, obtaining a device data set with a first label or a second label from a historical database, and constructing a hyperparameter combination and a decision function; S102, fitting the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function; S103, obtaining device data collected by sensors, inputting the device data into a trained decision function, obtaining device fault detection results, and issuing a fault warning when the device fails, wherein the device data collected by the sensors include time, temperature, pressure, and vibration amplitude.

2. The device fault detection method based on artificial immune network according to claim 1 is characterized in that: The obtaining of the device data set with the first label or the second label from the historical database includes: obtaining acquisition features from various sensors of the device, aligning them according to timestamps to obtain the device data set, and assigning the first label or the second label to each set of device data in the device data set.

3. The device fault detection method based on artificial immune network according to claim 1 is characterized in that: The method of fitting the parameters in the decision function by combining the device data set with the first label or the second label with the hyperparameter to obtain the trained decision function comprises: S1011, performing encoding extraction operation on the hyperparameter combination to obtain an initial set; S1012, performing anomaly detection on each element in the set and the device data set after training the decision function, and obtaining the fault detection accuracy of each element; S1013, set the accuracy threshold and the number of iterations. If the accuracy of each element is less than the accuracy threshold or the number of iterations has not been reached, perform the next iteration, and execute S1012 after obtaining the next generation set.

4. The device fault detection method based on artificial immune network according to claim 3 is characterized in that: The next iteration is performed to obtain the next generation set including: Select multiple elements in descending order of fault detection accuracy; After performing mutation operations on multiple elements, they are merged with multiple selected elements to obtain the next generation set.

5. The device fault detection method based on artificial immune network according to claim 4 is characterized in that: The performing of the next iteration to obtain the next generation set further includes: Set a similarity threshold. If the similarity between two elements is less than the similarity threshold, one of the two elements will be removed.

6. The method for detecting equipment faults based on artificial immune network according to claim 5, characterized in that: The similarity between two elements is the Euclidean distance between the two elements.

7. The device fault detection method based on artificial immune network according to claim 4 is characterized in that: The number of elements selected in order from high to low according to the fault detection accuracy is calculated by the following formula: Where N c represents the number of elements selected from the previous generation collection, f i represents the fault detection accuracy of element i in the current set, f max 、f min represents the maximum and minimum fault detection accuracy in the current set, and α is the selection factor.

8. An equipment fault detection device, characterized in that: The device comprises: A preprocessing module, used to obtain a device data set with a first label or a second label from a historical database, and construct a hyperparameter combination and a decision function; A fitting module, used to fit the parameters in the decision function using the device data set with the first label or the second label and the hyperparameter combination to obtain a trained decision function; The execution module is used to obtain the device data collected by the sensor, input the device data into the trained decision function, obtain the device fault detection result, and issue a fault warning when the device fails. The device data collected by the sensor includes time, temperature, pressure, and vibration amplitude.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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