Method and apparatus for controlling an electric water heater

Abstract

The application relates to the technical field of Internet of Things, and discloses a method for controlling an electric water heater, which comprises the following steps: acquiring heart rate data and motion intensity values of a user in a set time period; determining motion intensity information according to the heart rate data and the motion intensity values of the user; and triggering the electric water heater to heat according to the motion intensity information. According to the method, the electric water heater is controlled to heat according to the motion intensity after the user exercises, so that the user can use hot water in time after exercising. The heart rate data and the motion intensity values of the user can be used to more accurately determine the motion intensity of the user, so that the electric water heater can be more accurately and timely controlled to heat. The application further discloses a device for controlling an electric water heater.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, for example to a method and apparatus for controlling an electric water heater. Background Technology

[0002] Water heaters are common household appliances, with electric water heaters being the most widely used type. Electric water heaters generally include storage-type and instantaneous types. Because instantaneous water heaters have high power consumption, require sophisticated wiring, and produce unstable water temperatures, most households choose storage-type water heaters. However, storage-type water heaters typically have a long heating time, resulting in a less than ideal user experience. Currently, storage-type water heaters are always in heating or heat preservation mode, allowing users to access hot water whenever needed. While this provides greater convenience, it is very energy-intensive. Therefore, users usually turn off the water heater when they are not home, but they often need to shower and rest after returning home from exercise.

[0003] In the process of implementing the embodiments of this disclosure, it was found that at least the following problems exist in the related technology: users have to wait a long time after returning home from exercise to turn on the storage-type electric water heater to use hot water, resulting in a poor user experience. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] This disclosure provides a method and apparatus for controlling an electric water heater so that users can use hot water more promptly after exercise.

[0006] In some embodiments, the method includes:

[0007] Obtain the user's heart rate data and exercise intensity values ​​within a specified time period;

[0008] The exercise intensity information is determined based on the user's heart rate data and exercise intensity value;

[0009] The electric water heater is triggered to heat up based on the exercise intensity information.

[0010] In some embodiments, the apparatus includes a processor and a memory storing program instructions, the processor being configured to perform the method for controlling an electric water heater as described above when executing the program instructions.

[0011] The method and apparatus for controlling an electric water heater provided in this disclosure can achieve the following technical effects: the water heater can be heated according to the intensity of exercise after the user has exercised, so that the user can use hot water promptly after exercise. By using the user's heart rate data and exercise intensity value, the user's exercise intensity can be more accurately determined, thereby enabling more accurate and timely control of the water heater's heating.

[0012] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0013] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0014] Figure 1 This is a schematic diagram of a method for controlling an electric water heater provided in an embodiment of this disclosure;

[0015] Figure 2 This is a schematic diagram of a device for controlling an electric water heater provided in an embodiment of this disclosure. Detailed Implementation

[0016] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0017] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0018] Unless otherwise stated, the term "multiple" means two or more.

[0019] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0020] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0021] Combination Figure 1 As shown in the embodiments of this disclosure, a method for controlling an electric water heater is provided, comprising:

[0022] Step S101: Obtain the user's heart rate data and exercise intensity value within a set time period;

[0023] Step S102: Determine exercise intensity information based on the user's heart rate data and exercise intensity value;

[0024] Step S103: Trigger the electric water heater to heat up based on the exercise intensity information.

[0025] The method for controlling an electric water heater provided in this disclosure controls the water heater's heating based on the user's exercise intensity after exercise, ensuring the user can use hot water promptly afterward. By using the user's heart rate data and exercise intensity values, the user's exercise intensity can be more accurately determined, thereby enabling more precise and timely control of the water heater's heating.

[0026] Optionally, after obtaining the user's heart rate data and exercise intensity value within a set time period, the method further includes: determining the scenario type based on the user's heart rate data and exercise intensity value; and if the scenario type includes an exercise scenario, obtaining exercise intensity information based on the user's heart rate data and exercise intensity value.

[0027] Optionally, obtaining the user's exercise intensity value within a set time period includes: obtaining acceleration data and angular velocity data of the wearable device within the set time period; and obtaining the exercise intensity value based on the acceleration data and angular velocity data of the wearable device.

[0028] Optionally, the motion intensity value is obtained based on the acceleration data and angular velocity data of the wearable device, including: fusing the acceleration data and angular velocity data of the wearable device to obtain the motion intensity value.

[0029] calculate Obtain the acceleration weights.

[0030] calculate Obtain the angular velocity weights.

[0031] calculate The exercise intensity value is obtained.

[0032] Where δ is the acceleration weight, δ′ is the angular velocity weight, and R is the motion intensity value. J x J y Jz This refers to the x-axis, y-axis, and z-axis acceleration data output from the accelerometer of the wearable device. x T y T z The angular velocity data along the x, y, and z axes are collected from the gyroscope output of the wearable device. β is a preset constant, β>0. The above scheme fuses acceleration and angular velocity data based on the amplitude of motion data changes, providing support for motion scene recognition.

[0033] Optionally, the scenario type is determined based on the exercise intensity value and the user's heart rate data, including: obtaining sample points based on the exercise intensity value and the user's heart rate; clustering the sample points to obtain a first cluster set; and classifying each first cluster set to obtain the scenario type.

[0034] Optionally, the x-coordinate of the sample point is the exercise intensity value, and the y-coordinate of the sample point is the user's heart rate data; or, the x-coordinate of the sample point is the user's heart rate data, and the y-coordinate of the sample point is the exercise intensity value.

[0035] Optionally, the x-coordinate of the sample point is the normalized exercise intensity value, and the y-coordinate of the sample point is the normalized user's heart rate data; or, the x-coordinate of the sample point is the normalized user's heart rate data, and the y-coordinate of the sample point is the normalized exercise intensity value.

[0036] Optionally, calculate The normalized motion intensity value R′ is obtained, where R max R represents the maximum exercise intensity value. min This is the minimum exercise intensity value.

[0037] calculate The normalized heart rate data XL′ is obtained, where XL is the heart rate data collected from the user. max For maximum heart rate data, XL min This is the minimum heart rate data.

[0038] Optionally, clustering the sample points to obtain a first cluster set includes: determining core sample points; creating sample clusters for each core sample point and adding all first objects in the ∈-neighborhood of each core sample point to the corresponding candidate set; checking the ∈-neighborhood of all first objects in the ∈-neighborhood of each core sample point, and when the ∈-neighborhood of a first object contains at least MinPts second objects, then all second objects in the ∈-neighborhood of the first object are added to the candidate set corresponding to the first object; MinPts is a set threshold, and MinPts is a positive integer; iteratively adding first objects or second objects in the candidate set that do not belong to any cluster to the corresponding sample cluster until all sample clusters cannot be expanded, thus obtaining the first cluster set.

[0039] Optionally, determining the core sample point includes: setting a neighborhood parameter ∈; determining the ∈-neighborhood of the sample point based on the neighborhood parameter; and determining the core sample point based on the number of samples in the ∈-neighborhood of the sample point. When the number of samples in the ∈-neighborhood of the sample point is greater than a set threshold MinPts, the sample point is a core sample point, where MinPts is a positive integer.

[0040] Optionally, the scene type is obtained by classifying each first cluster set, including: selecting a sample point from each first cluster set and obtaining the scene type of the selected sample point; using the scene type of the selected sample point as the scene type of the corresponding first cluster set. Optionally, the corresponding scene type, such as exercise scene, rest scene, or walking scene, is matched from a preset database using the exercise intensity value and heart rate data of the selected sample point. This method only requires determining the scene type for each first cluster set once, resulting in high classification efficiency.

[0041] Optionally, obtaining exercise intensity information based on exercise intensity values ​​and user heart rate data includes: clustering sample points in the exercise scenario to obtain a second cluster set; and classifying each second cluster set to obtain exercise intensity information. Optionally, the x-axis of the sample point is the exercise intensity value, and the y-axis of the sample point is the user's heart rate data; or, the x-axis of the sample point is the user's heart rate data, and the y-axis of the sample point is the exercise intensity value. Optionally, the x-axis of the sample point is the normalized exercise intensity value, and the y-axis of the sample point is the normalized user's heart rate data; or, the x-axis of the sample point is the normalized user's heart rate data, and the y-axis of the sample point is the normalized exercise intensity value.

[0042] Optionally, the sample points in the motion scene are clustered to obtain a second cluster set, including: taking k sample points in the motion scene as first centroids; calculating the distance between each sample point in the motion scene and each first centroid, dividing the sample points in the motion scene according to the distance to each first centroid to obtain a second cluster set, where k is a positive integer; calculating the second centroid of each second cluster set; stopping the iteration when the distance between the second centroid and the first centroid in the corresponding second cluster set is less than a set threshold.

[0043] Optionally, the distance between each sample point and each first centroid is calculated, including: calculating... The distance d between the m-th sample point and the first centroid is obtained. m Among them, R m XL represents the motion intensity value of the m-th sample point. m For the heart rate data of the m-th sample point, YZX n Let YZY be the x-coordinate of the nth first centroid. n Let be the ordinate of the nth first centroid. m is a positive integer, n is an integer and 1≤n≤k. If the distance from the mth sample point to the nth first centroid is the shortest, then the mth sample point belongs to the cluster set of the nth first centroid.

[0044] Optionally, the distance between each sample point and each first centroid is calculated, including: calculating... The distance d between the m-th sample point and the first centroid is obtained. m Among them, R m ' is the normalized motion intensity value of the m-th sample point, XL m ′ represents the normalized heart rate data for the m-th sample point.

[0045] In some embodiments, if k is 2, then two first centroids are selected. The sample points are divided according to the distance between the sample points and each first centroid. The sample points closer to the first first centroid belong to the first first centroid set, and the sample points closer to the second first centroid belong to the second first centroid set. This results in two cluster sets, representing high-intensity motion and low-intensity motion, respectively.

[0046] Optionally, after dividing the second cluster set, the method for controlling the electric water heater further includes: recalculating the second centroid of each second cluster set; stopping the iteration when the distance between the second centroid and the first centroid of the second cluster set is less than a set threshold.

[0047] Optionally, the second centroid of each cluster set is recalculated, including:

[0048] calculate The second centroid μ′ is obtained n ;

[0049] Among them, c n Let x be the nth cluster set. a For c n The a-th sample point, |c n |The nth cluster set c n The number of sample points within the range, where a is a positive integer.

[0050] When the distance between the newly calculated centroids of each cluster set and the original centroids of the second cluster set is less than a set threshold, it indicates that the positions of the recalculated centroids have not changed significantly and are approaching stability, i.e., convergence. The algorithm is considered to have achieved the desired clustering result and terminates. If the change in distance between the newly calculated centroids and the original centroids is greater than or equal to the set threshold, the distance between each sample point and each first centroid is recalculated, and then the second cluster set is created. The second centroids are then calculated again until the distance between the newly calculated centroids and the original centroids of the second cluster set is less than the set threshold, indicating that the clustering has achieved the desired result.

[0051] Optionally, the motion intensity information is obtained by classifying each second cluster set, including: selecting a sample point from each second cluster set and obtaining the motion intensity value of the selected sample point; obtaining the motion intensity type corresponding to the selected sample point based on the motion intensity value; obtaining the motion intensity information of the corresponding second cluster set based on the motion intensity type; the motion intensity information includes the motion intensity type and the number of sample points corresponding to the motion intensity type.

[0052] Optionally, if the type of any sample point is determined, the corresponding second cluster set is labeled with that type, or all sample points within that set are labeled. Optionally, if any sample point is randomly selected from any second cluster set, and its corresponding motion intensity value is greater than or equal to a set threshold, the second cluster set corresponding to that sample point is labeled as high motion intensity; if the corresponding motion intensity value is less than the set threshold, the second cluster set corresponding to that sample point is labeled as low motion intensity. This method only requires determining the type of each cluster set once, meaning the number of determinations is the same as the number of cluster sets. Compared to existing technologies that determine the type of each sample point, this significantly saves computation and improves classification efficiency.

[0053] When the number of sample points corresponding to high exercise intensity reaches a set threshold within a set time period, the water heater is triggered to heat up after the set time; or, when the number of sample points corresponding to high exercise intensity reaches a set threshold within a set time period, the water heater is triggered to heat up directly. This application fuses acceleration and angular velocity data based on the amplitude of changes in exercise data. Based on this fusion result and the user's heart rate data, it can accurately determine the user's current scenario at a certain time period. In particular, it avoids the inaccuracy of judging whether a user is in an exercise scenario simply by relying on changes in the user's exercise data. For example, when a user wears a fitness tracker, hand shaking will cause significant fluctuations in the exercise data detected by the wearable device, but the user may not actually be in an exercise state. At the same time, when the user is in an exercise scenario, the intensity of the exercise is also considered, further improving the rationality of the water heater's linkage. The technical solution of this application clusters user's exercise and heart rate data using two different methods. The exercise scenario clustering method can discover clusters of arbitrary shapes and identify outliers during clustering. Since user movement habits and exercise behaviors are often unpredictable, and the data characteristics collected from different types of exercise vary greatly, this method can effectively determine whether a user is exercising. The exercise intensity clustering method, when clearly distinguishing between high and low intensity exercise, can clearly and quickly divide sample points, thereby rapidly determining whether the user has reached the intensity required to trigger the water heater. This application, by determining the scenario before determining the intensity, avoids misinterpreting non-exercise actions as exercise or user activity, making the water heater control more accurate. It avoids situations where exercise data, such as frequent hand waving, may be prominent but the user doesn't sweat, leading to false triggering of the water heater, thus improving the user experience.

[0054] Combination Figure 2 As shown, this disclosure provides a device for controlling an electric water heater, including a processor 100 and a memory 101 storing program instructions. Optionally, the device may further include a communication interface 102 and a bus 103. The processor 100, communication interface 102, and memory 101 can communicate with each other via the bus 103. The communication interface 102 can be used for information transmission. The processor 100 can call the program instructions in the memory 101 to execute the method for controlling the electric water heater described in the above embodiment.

[0055] Furthermore, the program instructions in the aforementioned memory 101 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0056] The memory 101, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 100 executes functional applications and data processing by running the program instructions / modules stored in the memory 101, that is, it implements the method for controlling the electric water heater in the above embodiments.

[0057] The memory 101 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 101 may include high-speed random access memory and may also include non-volatile memory.

[0058] The device for controlling an electric water heater provided in this disclosure can determine whether a user is in a specific activity scenario and control the heating of the water heater based on the intensity of their exercise, so that the user can use hot water promptly after exercise. In particular, by using acceleration and angular velocity data from wearable devices within a set time period, it is possible to more accurately determine whether a user is in an exercise scenario. Furthermore, by using the user's heart rate data and exercise intensity values, it is possible to more accurately judge the user's exercise intensity, thereby enabling more precise control of the water heater's heating.

[0059] This disclosure provides an apparatus that includes the aforementioned device for controlling an electric water heater. Optionally, the apparatus may be a computer, a smart gateway, a smartphone, a server, or an electric water heater.

[0060] In some embodiments, when the device is an electric water heater, the electric water heater is bound to a smart mobile terminal such as a smartphone or smart bracelet. The smart mobile terminal sends data such as the user's heart rate data, the acceleration data and angular velocity data of the wearable device within a set time period to the electric water heater.

[0061] In some embodiments, when the device is a server, computer, or smart gateway, it receives data such as the user's heart rate data, the wearable device's acceleration data, and the angular velocity data within a set time period from a smart mobile terminal such as a smartphone or smart bracelet, and sends control commands to the electric water heater to trigger its heating.

[0062] In some embodiments, when the device is a smartphone, it receives data such as the user's heart rate data, the wearable device's acceleration data, and the angular velocity data within a set time period obtained by the smart bracelet, and sends control commands to the electric water heater to trigger its heating.

[0063] The device provided in this disclosure can determine whether a user is in a specific activity scenario and control the heating of the electric water heater based on the intensity of their exercise, so that the user can use hot water promptly after exercise. In particular, by using acceleration and angular velocity data from wearable devices within a set time period, it is possible to more accurately determine whether a user is in an exercise scenario. Furthermore, by using the user's heart rate data and exercise intensity values, it is possible to more accurately judge the user's exercise intensity, thereby enabling more precise control of the electric water heater's heating.

[0064] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for…

[0065] This disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the method described above.

[0066] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0067] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0068] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0069] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0070] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure 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.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for controlling an electric water heater, characterized in that, include: Obtain the user's heart rate data within a specified time period; Acquire the acceleration and angular velocity data of the wearable device within the specified time period; calculate The exercise intensity value is obtained; where, For acceleration weights, As the weight for angular velocity, This represents the exercise intensity value. These are the x-axis, y-axis, and z-axis acceleration data output from the accelerometer of the wearable device, respectively. These are the angular velocity data of the x-axis, y-axis, and z-axis output by the gyroscope of the wearable device, respectively. The scenario type is determined based on the user's heart rate data and exercise intensity value; When the scene type includes a motion scene, the sample points in the motion scene are clustered to obtain a second cluster set; If a sample point is randomly selected from any second cluster set, and its corresponding motion intensity value is greater than or equal to a set threshold, the second cluster set corresponding to the sample point is marked as high motion intensity; if the corresponding motion intensity value is less than the set threshold, the second cluster set corresponding to the sample point is marked as low motion intensity. When the number of sample points corresponding to high exercise intensity reaches a set threshold within a set time period, the electric water heater will be triggered to heat up after the set time; or, when the number of sample points corresponding to high exercise intensity reaches a set threshold within a set time period, the electric water heater will be triggered to heat up directly.

2. The method according to claim 1, characterized in that, Clustering the sample points within the aforementioned motion scene yields a second cluster set, including: Pick The sample points in the aforementioned motion scene are used as the first centroid; Calculate the distance between each sample point in each motion scene and each first centroid, and divide the sample points in the motion scene according to the distance to each first centroid to obtain the second cluster set. It is a positive integer; Calculate the second centroid of each of the second cluster sets; The iteration stops when the distance between the second centroid and the corresponding first centroid is less than a set threshold.

3. The method according to claim 1, characterized in that, The scenario type is determined based on the user's heart rate data and exercise intensity value, including: Sample points are obtained based on the exercise intensity value and the user's heart rate; The sample points are clustered to obtain the first cluster set; The scene type is obtained by classifying each of the first cluster sets.

4. The method according to claim 3, characterized in that, Clustering the sample points yields a first cluster set, including: Identify core sample points; Create sample clusters for each of the core sample points, and group the core sample points... - All first objects in the neighborhood are added to the corresponding candidate set; Check each of the core sample points. -All first objects in the neighborhood - Neighborhood, when the first object -If the neighborhood contains at least MinPts second objects, then the first object - All second objects in the neighborhood are added to the candidate set corresponding to the first object; MinPts is a set threshold, and MinPts is a positive integer; Iteratively add the first or second object from the candidate set that does not belong to any cluster to the corresponding sample cluster until all sample clusters cannot be expanded, thus obtaining the first cluster set.

5. The method according to claim 3, characterized in that, The scene types are obtained by classifying each of the first cluster sets, including: Select a sample point from each of the first cluster sets and obtain the scene type of the selected sample point; The scene type of the selected sample points is used as the scene type of the corresponding first cluster set.

6. A device for controlling an electric water heater, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when executing the program instructions, perform the method for controlling an electric water heater as described in any one of claims 1 to 5.

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