Energy consumption anomaly positioning method and device based on equipment image and dynamic threshold

Abstract

This application proposes a method, apparatus, device, and storage medium for energy consumption anomaly localization based on device profiling and dynamic thresholds. The method includes: acquiring real-time operating data and real-time energy consumption data of a target device; determining the real-time operating condition of the target device based on the real-time operating data; determining an energy consumption range based on the real-time operating condition and a pre-established energy consumption threshold model; in response to the real-time energy consumption data not falling within the energy consumption range, acquiring the time-series characteristics of the target device's energy consumption data; and determining the type of energy consumption anomaly based on the time-series characteristics and a pre-established device profiling. The device profiling is established based on the operating data and energy consumption data of the target device under different operating conditions. Through the technical solution of this application, different anomaly types can be accurately distinguished, improving the accuracy and precision of energy consumption anomaly identification.

Description

Technical Field

[0001] This application relates to the field of equipment energy consumption monitoring technology, and in particular to a method, device, equipment and storage medium for locating energy consumption anomalies based on equipment profiles and dynamic thresholds. Background Technology

[0002] With the development of industrialization and informatization, the number of various energy-consuming devices has surged, making energy consumption management an increasingly important means for enterprises to reduce costs and increase efficiency. In related technologies, energy consumption monitoring of equipment often relies on fixed thresholds or manual experience to determine whether equipment energy consumption is abnormal, such as setting power limits or energy consumption standards per unit time. However, fixed thresholds cannot adapt to dynamic conditions such as equipment operating status, load changes, and environmental factors, leading to high false alarm rates, high false negative rates, and difficulty in anomaly localization. Furthermore, existing methods mostly focus on overall energy consumption anomaly detection, lacking the ability to perform refined analysis on specific equipment and specific time periods. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Firstly, this application proposes a method for locating energy consumption anomalies based on device profiling and dynamic thresholds. The method includes: acquiring real-time operating data and real-time energy consumption data of a target device; determining the real-time operating condition of the target device based on the real-time operating data; determining an energy consumption range based on the real-time operating condition and a pre-established energy consumption threshold model; in response to the real-time energy consumption data not being within the energy consumption range, acquiring time-series features of the target device's energy consumption data; and determining the type of energy consumption anomaly based on the time-series features and a pre-established device profiling; wherein the device profiling is established based on the operating data and energy consumption data of the target device under different operating conditions.

[0005] In one implementation, the energy consumption threshold model is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device within a historical time period; based on the historical operating data, dividing the historical energy consumption data into multiple datasets, each dataset corresponding to a working condition; and for each dataset, using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval.

[0006] In one optional implementation, the step of using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval includes: using statistical methods or machine learning methods to calculate the dataset to obtain the corresponding energy consumption data interval.

[0007] In one implementation, the device profile is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device; aligning the historical operating data and historical energy consumption data by time; processing the time-aligned historical operating data and historical energy consumption data to obtain an operating-energy consumption sample dataset, and establishing the device profile based on the sample dataset.

[0008] In one optional implementation, determining the energy consumption anomaly type based on the time series features and a pre-established equipment profile includes: obtaining an energy consumption data sequence corresponding to the operating condition to which the first time series feature belongs based on the equipment profile; extracting features from the energy consumption data sequence to obtain a second time series feature; and determining the energy consumption anomaly type based on the feature differences between the first time series feature and the second time series feature.

[0009] Secondly, this application proposes an energy consumption anomaly location device based on device profile and dynamic threshold. The device includes: an acquisition module for acquiring real-time operating data and real-time energy consumption data of a target device; a first processing module for determining the real-time device condition of the target device based on the real-time operating data; a second processing module for determining an energy consumption range based on the real-time device condition and a pre-established energy consumption threshold model; a third processing module for acquiring time-series features of the target device's energy consumption data in response to the real-time energy consumption data not being within the energy consumption range; and a fourth processing module for determining the energy consumption anomaly type based on the time-series features and a pre-established device profile. The device profile is established based on the operating data and energy consumption data of the target device under different operating conditions.

[0010] In one implementation, the energy consumption threshold model is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device within a historical time period; based on the historical operating data, dividing the historical energy consumption data into multiple datasets, each dataset corresponding to a working condition; and for each dataset, using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval.

[0011] In one optional implementation, the step of using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval includes: using statistical methods or machine learning methods to calculate the dataset to obtain the corresponding energy consumption data interval.

[0012] In one implementation, the device profile is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device; aligning the historical operating data and historical energy consumption data by time; processing the time-aligned historical operating data and historical energy consumption data to obtain an operating-energy consumption sample dataset, and establishing the device profile based on the sample dataset.

[0013] In an optional implementation, the fourth processing module may be used to: obtain an energy consumption data sequence corresponding to the operating condition to which the first time-series feature belongs, based on the device profile; extract features from the energy consumption data sequence to obtain a second time-series feature; and determine the energy consumption anomaly type based on the feature difference between the first time-series feature and the second time-series feature.

[0014] Thirdly, this application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0015] Fourthly, this application proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect.

[0016] Fifthly, this application proposes a program product comprising at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0017] The energy consumption anomaly localization method, apparatus, device, and storage medium provided in this application, based on equipment profiling and dynamic thresholds, can identify the real-time operating conditions of the target equipment based on its real-time operating data. It then matches the standard energy consumption range corresponding to the real-time operating conditions with a pre-built energy consumption threshold model. If the energy consumption exceeds this range, it extracts the time-series features of the equipment energy consumption and combines these features with the benchmark time-series features of the equipment profiling built from multi-condition operating data and energy consumption data to accurately identify the type of energy consumption anomaly. This solution combines dynamic threshold determination of operating conditions with time-series comparison of the equipment profiling, enabling precise differentiation of different anomaly types and improving the accuracy and precision of energy consumption anomaly identification.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0020] Figure 1 This is a flowchart illustrating a method for locating energy consumption anomalies based on device profiling and dynamic thresholds, provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of an energy consumption anomaly location device based on device profiling and dynamic thresholds provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following describes, with reference to the accompanying drawings, a method and apparatus for locating energy consumption anomalies based on device profiling and dynamic thresholds, according to embodiments of this application.

[0023] Figure 1 This is a flowchart illustrating a method for locating energy consumption anomalies based on device profiling and dynamic thresholds, provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps: S1: Obtain real-time operating data and real-time energy consumption data of the target device.

[0024] The aforementioned operational data includes at least one of the following: rated power, operating time, load rate, ambient temperature, and production cycle time; energy consumption data includes at least one of the following: instantaneous power, cumulative electrical energy, energy consumption per unit product, and energy consumption during operating hours.

[0025] In some embodiments, the acquired real-time operating data and real-time energy consumption data can be aligned before subsequent steps are performed.

[0026] S2: Determine the real-time operating status of the target device based on real-time operating data.

[0027] For example, different operating conditions can be pre-configured with corresponding operating data ranges, so that matching can be performed based on real-time operating data to determine the target data range, and the operating conditions corresponding to the target data range can be used as the real-time equipment operating conditions.

[0028] S3: Determine the energy consumption range based on real-time equipment operating conditions and a pre-established energy consumption threshold model.

[0029] For example, a target operating condition is matched with the real-time equipment operating condition of the target device in a pre-established energy consumption threshold model, and the normal energy consumption upper and lower limits corresponding to the target operating condition are obtained as the target energy consumption range for energy consumption anomaly determination. The aforementioned normal energy consumption upper and lower limits are calculated based on historical operation and energy consumption data, and the energy consumption threshold model includes normal energy consumption upper and lower limits corresponding to different operating conditions.

[0030] S4: In response to real-time energy consumption data not being within the energy consumption range, obtain the time series characteristics of the target device's energy consumption data.

[0031] For example, when it is determined that the real-time energy consumption data of the target device is not within the energy consumption range corresponding to the real-time operating condition, the determination time is used as the time reference, and continuous energy consumption time series data of a preset duration are extracted before and after. Then, the features of the time series data are extracted to obtain the energy consumption time series features.

[0032] Among them, the aforementioned time-series characteristics include at least one of the following: the degree of deviation of energy consumption amplitude, the characteristic of continuous over-limit, and the pattern of fluctuation rhythm.

[0033] S5: Based on time series characteristics and pre-established device profiles, determine the type of energy consumption anomaly.

[0034] Among them, the equipment profile is established based on the operating data and energy consumption data of the target equipment under different operating conditions.

[0035] For example, based on the equipment operating conditions corresponding to the abnormal period, the standard energy consumption time series features under real-time operating conditions are obtained from the pre-established equipment profile as a benchmark. The extracted energy consumption time series features are compared with the benchmark features, and the corresponding energy consumption anomaly type is determined based on the difference in features between the two.

[0036] Among them, the above-mentioned abnormal energy consumption types include at least one of the following: persistently high, instantaneous shock, and periodic fluctuation.

[0037] In some embodiments, the above energy consumption threshold model is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device within a historical period; dividing the historical energy consumption data into multiple datasets based on the historical operating data, with each dataset corresponding to a working condition; and calculating the corresponding energy consumption data range for each dataset using a preset interval calculation algorithm.

[0038] For example, historical operating data and historical energy consumption data of the target device within a historical period are obtained. Based on the operating condition dimension of the historical operating data, the historical energy consumption data is divided into multiple datasets, each dataset corresponding to a specific operating condition. Then, a preset interval calculation algorithm is used to calculate the energy consumption data interval corresponding to each operating condition.

[0039] In one optional implementation, a preset interval calculation algorithm is used to calculate the corresponding energy consumption data interval, including: using statistical methods or machine learning methods to calculate the dataset to obtain the corresponding energy consumption data interval.

[0040] As an example, the mean and standard deviation are calculated based on the energy consumption value distribution within the dataset, and the energy consumption data interval is determined by adding or subtracting a set multiple from the standard deviation of the mean.

[0041] As another example, the distribution pattern of energy consumption data can be fitted by algorithms such as quantile regression, and the upper and lower limits of energy consumption at the corresponding confidence level can be output to obtain the corresponding energy consumption data range under this working condition.

[0042] In some embodiments, a device profile is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device; aligning the historical operating data and historical energy consumption data by time; processing the time-aligned historical operating data and historical energy consumption data to obtain an operating-energy consumption sample dataset, and establishing a device profile based on the sample dataset.

[0043] For example, first, acquire the historical operating data and historical energy consumption data of the target device, align and match the two types of data according to a unified time dimension to ensure that the device operating status and the corresponding energy consumption value correspond one-to-one at the same time node, then preprocess the time-aligned historical operating data and historical energy consumption data to obtain an operating-energy consumption sample dataset, and finally establish a device profile that can characterize the standard energy consumption of the device under different operating conditions based on this sample dataset.

[0044] In one optional implementation, the energy consumption anomaly type is determined based on time series features and a pre-established equipment profile, including: obtaining an energy consumption data sequence corresponding to the operating condition of the first time series feature based on the equipment profile; extracting features from the energy consumption data sequence to obtain a second time series feature; and determining the energy consumption anomaly type based on the feature differences between the first time series feature and the second time series feature.

[0045] By implementing the embodiments of this application, the real-time operating conditions of the target device can be identified based on its real-time operating data. A pre-built energy consumption threshold model is then used to match the standard energy consumption range corresponding to the real-time operating conditions. If the energy consumption exceeds this range, the time-series features of the device's energy consumption are extracted. These features are then compared with the baseline time-series features of the device profile built from multi-condition operating data and energy consumption data to accurately identify the type of energy consumption anomaly. This solution combines dynamic threshold determination of operating conditions with time-series comparison of the device profile, enabling precise differentiation of different anomaly types and improving the accuracy and precision of energy consumption anomaly identification.

[0046] Please see Figure 2 , Figure 2This is a schematic diagram of a power consumption anomaly location device based on device profiling and dynamic thresholds, provided in an embodiment of this application. Figure 2 As shown, the device 200 includes: an acquisition module 201 for acquiring real-time operating data and real-time energy consumption data of the target device; a first processing module 202 for determining the real-time device operating condition of the target device based on the real-time operating data; a second processing module 203 for determining the energy consumption range based on the real-time device operating condition and a pre-established energy consumption threshold model; a third processing module 204 for acquiring the time series characteristics of the target device's energy consumption data in response to the real-time energy consumption data not being within the energy consumption range; and a fourth processing module 205 for determining the energy consumption anomaly type based on the time series characteristics and a pre-established device profile; wherein the device profile is established based on the operating data and energy consumption data of the target device under different operating conditions.

[0047] In one implementation, the energy consumption threshold model is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device within a historical period; based on the historical operating data, dividing the historical energy consumption data into multiple datasets, each dataset corresponding to a working condition; and for each dataset, using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval.

[0048] In one optional implementation, a preset interval calculation algorithm is used to calculate the corresponding energy consumption data interval, including: using statistical methods or machine learning methods to calculate the dataset to obtain the corresponding energy consumption data interval.

[0049] In one implementation, the device profile is established through the following steps: acquiring historical operating data and historical energy consumption data of the target device; aligning the historical operating data and historical energy consumption data by time; processing the time-aligned historical operating data and historical energy consumption data to obtain an operating-energy consumption sample dataset, and establishing the device profile based on the sample dataset.

[0050] In an optional implementation, the fourth processing module 205 can be used to: obtain an energy consumption data sequence corresponding to the operating condition to which the first time-series feature belongs based on the equipment profile; extract features from the energy consumption data sequence to obtain a second time-series feature; and determine the energy consumption anomaly type based on the feature difference between the first time-series feature and the second time-series feature.

[0051] The apparatus described in this application can identify the real-time operating conditions of a target device based on its real-time operational data. It then matches the standard energy consumption range corresponding to the real-time operating conditions using a pre-built energy consumption threshold model. If the energy consumption exceeds this range, the device's energy consumption time-series features are extracted. These features are then compared with the baseline time-series features of the device profile built from multi-condition operating data and energy consumption data to accurately identify the type of energy consumption anomaly. This solution combines dynamic threshold determination of operating conditions with time-series comparison of the device profile, enabling precise differentiation of different anomaly types and improving the accuracy and precision of energy consumption anomaly identification.

[0052] It should be noted that the foregoing explanation of the embodiment of the energy consumption anomaly location method based on device profile and dynamic threshold also applies to the energy consumption anomaly location device based on device profile and dynamic threshold in this embodiment, and will not be repeated here.

[0053] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 302 communicatively connected to the processor 301; the memory 302 stores computer execution instructions; the processor 301 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0054] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0055] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0056] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0057] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0058] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0059] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0060] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0061] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0062] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0064] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0065] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0066] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0067] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for locating energy consumption anomalies based on device profiling and dynamic thresholds, characterized in that, include: Acquire real-time operating data and real-time energy consumption data of the target device; The real-time operating status of the target device is determined based on the real-time operating data. Based on the real-time equipment operating conditions and the pre-established energy consumption threshold model, the energy consumption range is determined; In response to the real-time energy consumption data not being within the energy consumption range, the time series characteristics of the target device's energy consumption data are obtained; Based on the time series characteristics and the pre-established equipment profile, the type of energy consumption anomaly is determined; wherein, the equipment profile is established based on the operating data and energy consumption data of the target equipment under different operating conditions.

2. The method according to claim 1, characterized in that, The energy consumption threshold model is established through the following steps: Obtain the historical operating data and historical energy consumption data of the target device within the historical time period; Based on the historical operating data, the historical energy consumption data is divided into multiple datasets, and each dataset corresponds to a working condition; For each dataset, a preset interval calculation algorithm is used to calculate the corresponding energy consumption data interval.

3. The method according to claim 2, characterized in that, The step of using a preset interval calculation algorithm to calculate the corresponding energy consumption data interval includes: The dataset is calculated using statistical or machine learning methods to obtain the corresponding energy consumption data range.

4. The method according to claim 1, characterized in that, The device profile is established through the following steps: Obtain the historical operating data and historical energy consumption data of the target device; Time alignment is performed on the historical operating data and historical energy consumption data; The historical operating data and historical energy consumption data after time alignment are processed to obtain an operating-energy consumption sample dataset, and the device profile is established based on the sample dataset.

5. The method according to claim 4, characterized in that, The determination of energy consumption anomaly types based on the time series features and pre-established device profiles includes: Based on the device profile, obtain the energy consumption data sequence corresponding to the operating condition to which the first time-series feature belongs; Feature extraction is performed on the energy consumption data sequence to obtain a second time-series feature; Based on the feature differences between the first time-series feature and the second time-series feature, the energy consumption anomaly type is determined.

6. A device for locating energy consumption anomalies based on device profiling and dynamic thresholds, characterized in that, include: The acquisition module is used to acquire real-time operating data and real-time energy consumption data of the target device. The first processing module is used to determine the real-time operating status of the target device based on the real-time operating data. The second processing module is used to determine the energy consumption range based on the real-time equipment operating conditions and the pre-established energy consumption threshold model. The third processing module is used to obtain the time series characteristics of the target device's energy consumption data in response to the real-time energy consumption data not being within the energy consumption range. The fourth processing module is used to determine the type of energy consumption anomaly based on the time series features and the pre-established device profile; wherein the device profile is established based on the operating data and energy consumption data of the target device under different operating conditions.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

8. A storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method of any one of claims 1 to 5.

9. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the program or instructions is executed by an electronic device, it implements the steps of the method according to any one of claims 1 to 5.

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