Water heater control method, device and equipment and storage medium

Abstract

The invention provides a water heater control method and device, equipment and a storage medium. According to the method, the turbidity of water in an inner container of the water heater is obtained, if the turbidity is larger than or equal to a turbidity threshold value, a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition are obtained, the multivariate planning cleaning function comprises to-be-determined self-cleaning parameters, and the target constraint condition is determined according to the to-be-determined self-cleaning parameters. The target constraint condition comprises a value range of the self-cleaning parameter to be determined. And according to the multivariate planning cleaning function and the target constraint condition, determining self-cleaning parameters of the self-cleaning operation, and according to the self-cleaning parameters, carrying out self-cleaning on the inner container so as to reduce the turbidity of water in the inner container. According to the method, the accuracy of determining the self-cleaning parameters of the inner container can be improved, and the self-cleaning effect of the inner container is improved.

Description

Technical Field

[0001] The present invention relates to the field of water heaters, and in particular to a water heater control method, device, equipment and storage medium. Background Art

[0002] With the development of science and technology, the application scenarios of water heaters have become more and more extensive, which has facilitated the needs of users to use hot water. After the water heater has been used for a long time, the inner tank needs to be self-cleaned to maintain the cleanliness of the inner tank. At present, when the inner tank of the water heater is self-cleaned, it is usually necessary to adjust multiple parameters of the inner tank self-cleaning to clean the inner tank. However, the accuracy of the parameter adjustment of the inner tank self-cleaning of the water heater is currently low, resulting in poor effect of the inner tank self-cleaning.

[0003] Therefore, how to improve the accuracy of parameter adjustment for determining the self-cleaning of the water heater tank is an urgent problem to be solved. Summary of the invention

[0004] The present application provides a water heater control method, device, equipment and storage medium, which are used to solve the problem in the prior art of improving the accuracy of parameter adjustment for determining the self-cleaning of the inner tank of the water heater.

[0005] In a first aspect, the present application provides a water heater control method, comprising:

[0006] Obtaining the turbidity of water in the inner tank of the water heater;

[0007] If the turbidity is greater than or equal to the turbidity threshold, a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition are obtained; the multivariate planning cleaning function includes a self-cleaning parameter to be determined, and the target constraint condition includes a value range of the self-cleaning parameter to be determined;

[0008] Determining self-cleaning parameters of a self-cleaning operation according to the multivariate planning cleaning function and the target constraint condition;

[0009] The inner container is self-cleaned according to the self-cleaning parameters to reduce the turbidity of water in the inner container.

[0010] Optionally, the obtaining of the multivariate planning cleaning function corresponding to the turbidity and the target constraint condition includes:

[0011] Acquire a cleaning mode for inner tank self-cleaning, wherein the cleaning mode represents a cleaning preference of a user of the water heater;

[0012] A multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and a target constraint condition are obtained.

[0013] Optionally, the cleaning mode is an energy-saving mode, and the obtaining of a multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and a target constraint condition, include:

[0014] Determine a first multivariate planning cleaning function corresponding to the turbidity and the energy-saving mode according to the turbidity and the energy-saving mode, and a mapping relationship among the turbidity, the energy-saving mode, and the multivariate planning cleaning function;

[0015] According to the turbidity, determining a desired turbidity range;

[0016] determining a desired turbidity from the desired turbidity range;

[0017] According to the expected turbidity, a target constraint condition corresponding to the expected turbidity is determined.

[0018] Optionally, the cleaning mode is a fast cleaning mode, and the multivariate programming cleaning function corresponding to the turbidity and the cleaning mode is obtained according to the turbidity and the cleaning mode, and the target constraint condition includes:

[0019] Determine a second multivariate planning cleaning function corresponding to the turbidity and the fast cleaning mode according to the fast cleaning mode and the turbidity, and a mapping relationship among the turbidity, the fast cleaning mode, and the multivariate planning cleaning function;

[0020] The objective constraint of the second multivariate programming cleaning function is determined according to the cleaning cost and / or the expected turbidity.

[0021] Optionally, the cleaning mode is a deep cleaning mode, and the multivariate planning cleaning function corresponding to the turbidity and the cleaning mode is obtained according to the turbidity and the cleaning mode, and the target constraint condition includes:

[0022] Determine a third multivariate planning cleaning function corresponding to the turbidity and the deep cleaning mode according to the deep cleaning mode and the turbidity, and a mapping relationship among the turbidity, the deep cleaning mode, and the multivariate planning cleaning function;

[0023] The objective constraint condition of the third multivariate programming cleaning function is determined according to the cleaning cost and / or the cleaning time consumption.

[0024] Optionally, the step of obtaining the cleaning mode of the inner tank self-cleaning includes:

[0025] Obtaining user information of the water heater;

[0026] Determining the cleaning preference of the user according to the user information;

[0027] According to the cleaning preference, a cleaning mode for self-cleaning of the inner tank is obtained.

[0028] Optionally, after obtaining the cleaning mode of the inner tank self-cleaning according to the cleaning preference, the method further includes:

[0029] Sending a confirmation message of the cleaning mode to the electronic device of the user;

[0030] Receiving a confirmation result of the message to be confirmed returned by the electronic device of the user;

[0031] If the target cleaning mode included in the confirmation result is different from the cleaning mode, the target cleaning mode is sent to the server of the water heater to update the mapping relationship between the cleaning preference and the cleaning mode in the server.

[0032] In a second aspect, the present application provides a water heater control device, comprising:

[0033] An acquisition module, used for acquiring the turbidity of the water in the inner tank of the water heater; if the turbidity is greater than or equal to the turbidity threshold, acquiring a multivariate planning cleaning function corresponding to the turbidity, and a target constraint condition; the multivariate planning cleaning function includes a self-cleaning parameter to be determined, and the target constraint condition includes a value range of the self-cleaning parameter to be determined;

[0034] A processing module, configured to determine a self-cleaning parameter of a self-cleaning operation according to the multivariate planning cleaning function and the target constraint condition;

[0035] The control module is used to perform self-cleaning on the inner tank according to the self-cleaning parameters to reduce the turbidity of the water in the inner tank.

[0036] In a third aspect, the present application provides an electronic device, comprising: a processor, a communication interface, and a memory; the processor is communicatively connected to the communication interface and the memory respectively;

[0037] The memory stores computer-executable instructions;

[0038] The communication interface communicates and interacts with external devices;

[0039] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the water heater control method as described in any one of the first aspects.

[0041] In a fifth aspect, the present application provides a computer program product, which, when executed by a processor, is used to implement the water heater control method as described in any one of the first aspects.

[0042] The water heater control method, device, equipment and storage medium provided in the present application obtain the turbidity of the water in the inner tank of the water heater. If the turbidity is greater than or equal to the turbidity threshold, the multivariate planning cleaning function corresponding to the turbidity and the target constraint conditions are obtained. According to the multivariate planning cleaning function and the target constraint conditions, the self-cleaning parameters of the self-cleaning operation are determined, and according to the self-cleaning parameters, the inner tank is self-cleaned to reduce the turbidity of the water in the inner tank. This method can fully consider the mutual influence between the parameters that affect the self-cleaning effect, such as the water quality, water temperature, and detergent dosage of the water in the inner tank, and construct a multivariate planning cleaning function including the above parameters. The accuracy of determining the inner tank self-cleaning parameters is improved by multivariate planning, so that the inner tank is self-cleaned by more accurate inner tank self-cleaning parameters, thereby improving the cleaning effect of the inner tank self-cleaning. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description are used to explain the principles of the present application:

[0044] Figure 1 It is a structural schematic diagram of an existing electric water heater;

[0045] Figure 2 A schematic flow chart of a water heater control method provided in an embodiment of the present application;

[0046] Figure 3 A schematic flow chart of another water heater control method provided in an embodiment of the present application;

[0047] Figure 4 A flow chart of another water heater control method provided in an embodiment of the present application;

[0048] Figure 5 A flow chart of another water heater control method provided in an embodiment of the present application;

[0049] Figure 6 A flow chart of another water heater control method provided in an embodiment of the present application;

[0050] Figure 7 A flow chart of another water heater control method provided in an embodiment of the present application;

[0051] Figure 8 It is a structural schematic diagram of a water heater control device provided in an embodiment of the present application;

[0052] Fig. 9 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0053] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0055] First, to facilitate understanding, the scenarios involved in this application are introduced in detail.

[0056] Taking electric water heater as an example, Figure 1 FIG. 1 is a schematic diagram of the structure of an existing electric water heater. Figure 1 As shown, the electric water heater includes: an inner tank, a water inlet, a water outlet, a water purification and sterilization module, a magnesium rod, a heating rod, and a temperature measuring tube.

[0057] Among them, the inner tank is used to store water. When cold water enters the inner tank from the water purification port, the electric water heater obtains hot water stored in the inner tank by heating the cold water in the inner tank. When the user uses the electric water heater, the electric water heater discharges the hot water in the inner tank through the water outlet to meet the user's hot water use needs. The water purification and sterilization module is used to purify and sterilize the water discharged from the water outlet to improve the cleanliness of the water discharged by the electric water heater. The heating rod is used to heat the water in the inner tank, the magnesium rod is used to prevent the metal inner tank from being corroded and to soften the water quality of the water in the inner tank, and the temperature measuring tube is used to determine the temperature of the water in the inner tank.

[0058] However, when the cumulative number of times and time of use of the electric water heater increases, if the quality of the cold water entering the water inlet is poor, the dirt in the water will gradually settle in the inner tank of the water heater. Even if the hot water discharged from the water outlet can be purified and sterilized when the water is discharged, the problem of dirt in the inner tank is still not solved. The water quality of the water stored in the inner tank will become worse and worse as the dirt increases. When the water quality in the inner tank becomes worse and worse, it may also exceed the water purification and sterilization processing capacity of the water purification and sterilization module, resulting in more dirt and bacteria in the water discharged from the water outlet, thereby endangering the health of the user. Therefore, how to improve the cleanliness of the water quality in the inner tank of the water heater is an urgent problem to be solved.

[0059] At present, electric water heaters can drive the water in the inner tank to form strong convection through water pumps or impellers, etc., so as to replace the dirt deposited at the bottom of the inner tank with the convection water and discharge it from the drain outlet. However, during the self-cleaning process, it is usually necessary to adjust multiple inner tank self-cleaning parameters of the electric water heater, and perform targeted inner tank self-cleaning operations according to different cleaning conditions. The current inner tank self-cleaning parameters are usually preset or adjusted by the user. These methods make it difficult to accurately set the inner tank self-cleaning parameters, resulting in poor inner tank self-cleaning effects and failure to meet user needs.

[0060] In view of this, the present application provides a water heater control method, which can accurately determine the self-cleaning parameters of the inner tank of the water heater through a multivariate planning cleaning function corresponding to the current turbidity in the water heater, and the constraints corresponding to the function through multivariate linear programming, and perform self-cleaning operations corresponding to the self-cleaning parameters on the inner tank of the water heater according to the self-cleaning parameters, thereby improving the self-cleaning effect of the inner tank of the water heater.

[0061] The execution subject of the present application may be a water heater, or a processing chip of the water heater. If the execution subject is the water heater, the water heater may execute the method of the present application based on its own processing; if the execution subject is the processing chip of the water heater, the processing chip may execute the method of the present application by executing a program or code that implements the method. The water heater may be, for example, an electric water heater, a solar water heater, or other water heater with an inner tank, and the present application does not limit this. The execution subject of the present application may also be a cloud platform or server that is communicatively connected to the water heater, and the water heater obtains the required information or data through interaction with the cloud platform or server for judgment and processing.

[0062] The following takes the water heater as an example, and describes the technical solution of the embodiment of the present application in detail in combination with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0063] Figure 2 A schematic diagram of a water heater control method provided in an embodiment of the present application. Figure 2 As shown, the method includes:

[0064] S201. Obtain the turbidity of water in the inner tank of the water heater.

[0065] The turbidity of the water in the inner tank of the water heater can be obtained by a turbidity sensor deployed in the water heater. The turbidity sensor can be a turbidity sensor that measures the turbidity of water by any method such as transmission method, scattering method, and transmitted light ratio method, and the present application does not limit this. The turbidity sensor can be deployed at any position in the inner tank, for example, it can be deployed at the bottom of the inner tank, the side of the inner tank, etc. Preferably, since the dirt in the water tends to settle to the bottom of the inner tank, the turbidity sensor can be deployed at the bottom of the inner tank, thereby improving the accuracy of the detected turbidity.

[0066] S202: If the turbidity is greater than or equal to the turbidity threshold, obtain a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition.

[0067] Among them, the multivariate planning cleaning function includes self-cleaning parameters to be determined, and the target constraint condition includes the value range of the self-cleaning parameters to be determined. The self-cleaning parameters may include, for example, water temperature, water flow, detergent dosage, cleaning time, water quality in the inner tank, etc. The inner tank self-cleaning parameters can be determined according to actual needs to determine which parameters are included, and this application does not limit this.

[0068] The multivariate planning cleaning function may be, for example, any one of a function characterizing the cost of a self-cleaning operation, a function of a self-cleaning operation duration, a function of a self-cleaning operation effect, etc. For any multivariate planning cleaning function, its corresponding target constraint condition may include other multivariate planning cleaning functions, and / or the value range of the self-cleaning parameter in the multivariate planning cleaning function.

[0069] For example, taking the multivariate planning cleaning function as a function to characterize the effect of the self-cleaning operation, assuming that the turbidity is 50 NTU, the self-cleaning parameters include water temperature, water flow, detergent dosage, and cleaning time, then the cleaning function corresponding to the turbidity can be expressed as the following formula (1):

[0070] C=a1T+a2q v +a3V+a4t+b (1)

[0071] Where C is the expected turbidity; T is the water temperature, a1 is the parameter of water temperature; q v is the water inlet flow rate, a2 is the parameter of the water inlet flow rate, and the water inlet flow rate is determined according to the aforementioned water inlet speed; V is the detergent dosage, a3 is the parameter of the detergent dosage; t is the cleaning time, a4 is the parameter of the cleaning time, and b is a constant term.

[0072] For the above formula (1), according to the actual needs of the water heater (for example, cleaning ability, inner tank size, etc.), a multivariate linear regression analysis can be performed on the above content to obtain the specific values ​​of the above coefficients a1 to a4. For example, according to the actual needs, the least squares method (LSM) and other fitting methods can be used to fit and obtain the specific values ​​of the above coefficients. This application only provides the specific content of the cleaning function of the following formula (2) based on a possible actual need as an example:

[0073] C=-0.132T-3.407q v -0.495V-0.003t+27.611 (2)

[0074] It should be understood that how to perform multiple linear regression according to actual needs can refer to the existing technology, and this application does not limit this.

[0075] Correspondingly, the objective constraints of the multivariate planning cleaning function may include: the value range of T, q v The value range of , the value range of V, the value range of t, and the constraint condition that characterizes the cost of the self-cleaning operation can also be determined by the cost data of the electricity used by the water heater and the cost data of the detergent. For example, the cost data of the electricity used by the water heater can be determined by the power consumption of driving the water pump for the cleaning time t and the power consumption of heating to the water temperature T, as well as the power consumption cost data; the cost data of the detergent can be determined by the detergent dosage V and the cost data of the unit detergent, and the constraint condition that characterizes the cost of the self-cleaning operation can be determined by controlling the range of the total cost data of the constraint condition, for example, the total cost data can be a fixed value, or it can be within a preset cost data range. Exemplarily, the total cost data of each self-cleaning operation can be determined as a fixed value according to actual needs, that is, the above-mentioned T, q need to be adjusted. v , V, t and other self-cleaning parameters so that the current self-cleaning operation is equal to the total cost data.

[0076] A possible implementation method is that the multivariate planning cleaning function corresponding to the turbidity and the target constraint condition are preset in the water heater, and any one of the multivariate planning cleaning function and the target constraint condition mentioned above can be selected as the preset multivariate planning cleaning function and the target constraint condition.

[0077] Another possible implementation is to divide the cleaning mode into different modes according to the cleaning preference of the user, and different cleaning modes correspond to different types of multivariate planning cleaning functions and target constraints. Before obtaining the multivariate planning cleaning function and the target constraint, the cleaning mode is obtained to obtain the corresponding multivariate planning cleaning function and the target constraint.

[0078] S203: Determine the self-cleaning parameters of the self-cleaning operation according to the multivariate planning cleaning function and the target constraint conditions.

[0079] Among them, the self-cleaning parameter may include, for example, at least one of the parameters such as cleaning time, detergent dosage, water inlet speed, and water temperature. The self-cleaning parameter may be determined according to the self-cleaning method of the inner tank of the water heater. For example, when a detergent is used for self-cleaning of the inner tank, the self-cleaning parameter may include the detergent dosage. Different detergent dosages have different cleaning effects and cleaning efficiencies. When the inner tank is self-cleaned by forming a vortex flow in the inner tank through the inlet and outlet water, the self-cleaning parameter may include the water inlet speed. The faster the water inlet speed, the better the self-cleaning effect of the inner tank. In addition, increasing the cleaning time and / or increasing the water temperature can also significantly improve the self-cleaning effect of the inner tank.

[0080] When the turbidity of the water in the inner tank is different and the expected turbidity is different, the self-cleaning capabilities required by the water heater are also different. Therefore, different self-cleaning parameters can be used to achieve different self-cleaning capabilities to cope with different turbidity conditions.

[0081] By solving the multivariate programming cleaning function and the target constraint conditions, the multivariate programming cleaning function and the values ​​of the self-cleaning parameters included in the target constraint conditions are obtained. The solution method can refer to the solution method of multivariate linear programming in the prior art, and this application will not go into details.

[0082] S204: Perform self-cleaning on the inner tank according to the self-cleaning parameters to reduce the turbidity of the water in the inner tank.

[0083] The method of self-cleaning the inner tank can be to make the water in the inner tank form a vortex flow, replace the dirt deposited at the bottom of the inner tank into the flowing water flow, and flush the impurities and dirt on the magnesium rod in the inner tank of the water heater, replace the flushed dirt into the water flow, and then discharge the water with dirt from the inner tank through the drain port, so as to achieve the purpose of reducing the turbidity of the water in the inner tank. The method of self-cleaning the inner tank can also be to add a detergent to the water in the inner tank to clean the inner wall and the magnesium rod of the inner tank, and then discharge the water with dirt from the inner tank through the drain port, so as to achieve the purpose of reducing the turbidity of the water in the inner tank. The method of self-cleaning the inner tank can also be to use a cleaning device deployed in the inner tank to clean the inner wall of the inner tank by physical friction. This application does not limit which specific method is adopted, or which methods are mixed.

[0084] The water heater controls the subsequent inner tank self-cleaning operation according to the value of the self-cleaning parameter. For example, the amount of detergent required for self-cleaning the inner tank is determined according to the amount of detergent used; the speed of water inflow during self-cleaning of the inner tank is determined according to the water inflow speed to affect the intensity of the vortex flow of water in the inner tank, thereby adjusting the self-cleaning ability, etc.

[0085] The method provided in the embodiment of the present application obtains the turbidity of the water in the inner tank of the water heater. If the turbidity is greater than or equal to the turbidity threshold, a multivariate planning cleaning function corresponding to the turbidity is obtained, as well as a target constraint condition. According to the multivariate planning cleaning function and the target constraint condition, the self-cleaning parameters of the self-cleaning operation are determined, and according to the self-cleaning parameters, the inner tank is self-cleaned to reduce the turbidity of the water in the inner tank. This method can fully consider the mutual influence between the parameters that affect the self-cleaning effect, such as the water quality, water temperature, and detergent dosage of the water in the inner tank, and construct a multivariate planning cleaning function including the above parameters. The accuracy of determining the inner tank self-cleaning parameters is improved by multivariate planning, so that the inner tank is self-cleaned by more accurate inner tank self-cleaning parameters, thereby improving the cleaning effect of the inner tank self-cleaning.

[0086] Next, taking different cleaning modes corresponding to different types of multivariate planning cleaning functions and target constraints as an example, how to obtain the multivariate planning cleaning function corresponding to the turbidity and the target constraints in the aforementioned step S202 is described in detail.

[0087] Figure 3 A flow chart of another water heater control method provided in an embodiment of the present application. Figure 3 As shown, the aforementioned step S202 may include:

[0088] S301, obtaining a cleaning mode for self-cleaning of the inner tank.

[0089] The cleaning mode represents the cleaning preference of the user of the water heater. The cleaning preference may include, for example, a preference for cleaning cost data, a preference for cleaning time, a preference for cleaning effect, etc. For different cleaning preferences, the user's cleaning expectations are also different. For example, some users hope that the cost of each cleaning is lower, or the cleaning time is shorter, or the cleaning effect is better, etc., which correspond to different cleaning preferences.

[0090] In a possible implementation, the cleaning mode may be set by the user, for example, when the user uses the water heater for the first time or performs self-cleaning of the water heater for the first time, by operating the water heater or by a terminal device connected to the water heater. Alternatively, the user may be prompted to set the cleaning mode for the inner tank self-cleaning each time the water heater performs self-cleaning of the inner tank.

[0091] In another possible implementation, the cleaning mode may be obtained based on the user information of the water heater user. For example, the corresponding relationship between the user information and the cleaning mode is obtained from a server connected to the water heater, and the cleaning mode is obtained based on the user information and the corresponding relationship. The user information may be stored in the water heater, or in an application corresponding to the water heater on the user's terminal device, or in a server connected to the water heater, etc.

[0092] S302: Obtain a multivariate planning cleaning function corresponding to turbidity and cleaning mode, as well as target constraints.

[0093] According to the turbidity, the cleaning mode, and the mapping relationship among the turbidity, the cleaning mode, the multivariate planning cleaning function, and the target constraint condition, and the turbidity and the cleaning mode, the multivariate planning cleaning function and the target constraint condition are obtained. The mapping relationship is pre-stored in the water heater or in the server of the water heater.

[0094] For example, multiple groups of multivariate planning cleaning functions and target constraints corresponding to the turbidity may be obtained according to the turbidity, and the multivariate planning cleaning functions and target constraints corresponding to the cleaning mode may be obtained from the multiple groups of multivariate planning cleaning functions and target constraints according to the cleaning mode. Alternatively, multiple groups of multivariate planning cleaning functions and target constraints corresponding to the cleaning mode may be obtained according to the cleaning mode, and the multivariate planning cleaning functions and target constraints corresponding to the turbidity may be obtained from the multiple groups of multivariate planning cleaning functions and target constraints according to the turbidity.

[0095] Below, taking the cleaning modes of energy-saving mode, fast cleaning mode and deep cleaning mode as examples, how to obtain the multivariate planning cleaning function corresponding to the turbidity and cleaning mode, as well as the target constraint conditions are described in detail.

[0096] A: Cleaning mode is energy-saving mode.

[0097] Figure 4 A flow chart of another water heater control method provided in an embodiment of the present application. Figure 4 As shown, the aforementioned step S302 may include:

[0098] S401 : Determine a first multivariate planning cleaning function corresponding to the turbidity and the energy-saving mode according to the turbidity and the energy-saving mode, and a mapping relationship among the turbidity, the energy-saving mode, and the multivariate planning cleaning function.

[0099] The first multivariate planning cleaning function can achieve energy saving by controlling the total cost of the cleaning operation. Exemplarily, the first multivariate planning cleaning function may include cost data for heating water in the inner tank during the self-cleaning process, cost data for using detergent dosage, and cost data for driving a water pump.

[0100] The cost data of heating the water in the inner tank can be expressed as follows:

[0101] m1=Wt1P1×2.78×10 -7 (3)

[0102] Among them, m1 is the cost data of heating water in the inner tank, W is the power of the heating device, t1 is the heating time, P1 is the cost data per 1 kWh, 2.78×10 -7 is the conversion unit between degree and joule. Assume that the change of water temperature and heating time is linear, that is, assume T = kt1 + T0, k is the slope of the linear change, and T0 is the initial water temperature. That is, formula (3) can be rewritten as:

[0103]

[0104] The cost data of the detergent dosage can be expressed as follows:

[0105] m2=V×P2 (5)

[0106] Among them, m2 is the cost data of the dosage of detergent used, V is the dosage of detergent used, and P2 is the cost data per milliliter of detergent.

[0107] The cost data of driving the water pump can be expressed as follows (6):

[0108] m3=W1P1t2×2.78×10 -7 (6)

[0109] Among them, m3 is the cost data of driving the water pump, t2 is the driving time of the water pump, and W1 is the power of the water pump. ρ is the density of water in the inner tank driven by the water pump, g is the acceleration of gravity, H is the head, q v is the water inlet flow rate, so the above formula (6) can be changed to

[0110]

[0111] Therefore, the total cost m of the cleaning operation can be determined based on the sum of m1, m2, and m3. The first multivariate planning cleaning function is

[0112] min{m=m1+m2+m3} (8)

[0113] The mapping relationship among the turbidity, the energy-saving mode and the multivariate planning cleaning function is pre-stored in the water heater. After the turbidity and the energy-saving mode are determined, the corresponding first multivariate planning cleaning function can be further obtained according to the mapping relationship.

[0114] S402: Determine the expected turbidity range according to the turbidity.

[0115] In order to ensure the cleaning effect of the water heater during self-cleaning, the water heater can be set with an expected turbidity range corresponding to different turbidities to ensure that the self-cleaning operation at least reaches the expected turbidity range and improve the cleanliness of the water. Different turbidities can correspond to different expected turbidity ranges or the same expected turbidity range.

[0116] When different turbidities correspond to different expected turbidity ranges, the expected turbidity range can be obtained according to the mapping relationship between the turbidity and the expected turbidity range, as well as the turbidity.

[0117] S403: Determine the expected turbidity from the expected turbidity range.

[0118] A possible implementation method may be to determine the expected turbidity from the expected turbidity range according to the expected turbidity determined by the user last time. For example, if the expected turbidity determined by the user last time is within the expected turbidity range, the expected turbidity determined by the user last time may be used as the expected turbidity for this time. If the expected turbidity determined by the user last time is not within the expected turbidity range, the expected turbidity may be determined from the expected turbidity range according to the expected turbidity determined by the user last time and the position of the expected turbidity range last time. For example, if the expected turbidity range last time is 2NTU to 10NTU, the expected turbidity determined by the user last time is 6NTU, and the expected turbidity range this time is 2NTU to 8NTU, then the expected turbidity this time is 5NTU.

[0119] Another possible implementation is to send a prompt message of the expected turbidity range and determine the expected turbidity based on the user's feedback. For example, the prompt message is sent to the user's terminal device, the user selects an expected turbidity within the expected turbidity range, and the expected turbidity is fed back to the water heater through the terminal device.

[0120] S404: Determine target constraint conditions corresponding to the expected turbidity according to the expected turbidity.

[0121] The target constraint condition corresponding to the desired turbidity can be a function as shown in the above formula (2): C = -0.132T-3.407q v -0.495V-0.003t+27.611. If C in the function is replaced by the expected turbidity, the constraint condition corresponding to the expected turbidity can be obtained.

[0122] Optionally, the duration of the self-cleaning operation can be controlled by a similar method, such as controlling the value range of the total duration of the self-cleaning operation, or separately controlling the value range of the duration t1 of heating the water temperature and the duration t2 of driving the water pump, etc. Furthermore, the value range of the detergent dosage V can also be controlled to control the cleaning effect of the water heater.

[0123] The target constraint condition is formed by at least one of the above constraints, and the value range of the self-cleaning parameter in the first multivariate planning cleaning function is controlled. The value of the self-cleaning parameter that meets the first multivariate planning cleaning function and the target constraint condition is obtained through the multivariate planning solution method.

[0124] B: The cleaning mode is the quick cleaning mode.

[0125] Figure 5 A flow chart of another water heater control method provided in an embodiment of the present application. Figure 5 As shown, the aforementioned step S302 may include:

[0126] S501 . Determine a second multivariate planning cleaning function corresponding to the turbidity and the fast cleaning mode according to the fast cleaning mode and the turbidity, and the mapping relationship among the turbidity, the fast cleaning mode, and the multivariate planning cleaning function.

[0127] The second multivariate planning cleaning function can achieve fast cleaning by controlling the total duration of the cleaning operation. Exemplarily, the second multivariate planning cleaning function may include the duration of heating the water in the inner tank, the duration of driving the water pump, the duration of draining water from the sewage outlet, etc. during the self-cleaning process.

[0128] For example, taking the second multivariate planning cleaning function as an example, which may include the duration of heating the water in the inner tank and the duration of driving the water pump during the self-cleaning process, the second multivariate planning cleaning function may be shown in the following formula (9):

[0129]

[0130] According to the quick cleaning mode and turbidity, as well as the mapping relationship among turbidity, quick cleaning mode and multivariate planning cleaning function, the method of determining the second multivariate planning cleaning function corresponding to turbidity and quick cleaning mode is similar to the aforementioned energy-saving mode and will not be repeated here.

[0131] S502: Determine target constraints of a second multivariate programming cleaning function according to the cleaning cost and / or the expected turbidity.

[0132] The cleaning cost and / or the expected turbidity can be determined according to actual needs, for example, set by the user. Substituting the cleaning cost into m in the aforementioned first multivariate programming cleaning function, the constraint condition corresponding to the cleaning cost can be obtained. Substituting the expected turbidity into the aforementioned formula (2), the constraint condition corresponding to the cleaning effect can be obtained.

[0133] According to the constraints corresponding to the above-mentioned cleaning costs, and / or the constraints corresponding to the cleaning effects, and / or the value range of any self-cleaning parameters, the target constraints of the second multivariate planning cleaning function can be formed to control the value range of the self-cleaning parameters in the second multivariate planning cleaning function, and through the multivariate planning solution method, the values ​​of the self-cleaning parameters that meet the second multivariate planning cleaning function and the target constraints can be obtained.

[0134] C: Cleaning mode is deep cleaning mode.

[0135] According to the deep cleaning mode and turbidity, and the mapping relationship among turbidity, the deep cleaning mode, and the multivariate planning cleaning function, a third multivariate planning cleaning function corresponding to the turbidity and the deep cleaning mode is determined.

[0136] In this implementation, the second multivariate planning cleaning function can achieve a better cleaning effect by controlling the expected turbidity of the cleaning operation. The lower the expected turbidity, the better the cleaning effect. Exemplarily, the third multivariate planning cleaning function is obtained based on the aforementioned formula (2), that is, the third multivariate planning cleaning function can be shown in the following formula (10):

[0137] min{C}(10)

[0138] Here, C can refer to the above formula (2).

[0139] According to the cleaning cost and / or the cleaning time, the objective constraint condition of the third multivariate programming cleaning function is determined.

[0140] The cleaning cost and / or cleaning time can be determined according to actual needs, for example, set by the user. Substituting the cleaning cost into the aforementioned m, the constraint condition corresponding to the cleaning cost can be obtained. Substituting the cleaning time into the aforementioned t, the constraint condition corresponding to the cleaning effect can be obtained.

[0141] According to the constraints corresponding to the above-mentioned cleaning costs, and / or the constraints corresponding to the cleaning time, and / or the value range of any self-cleaning parameters, the target constraints of the third multivariate planning cleaning function can be formed to control the value range of the self-cleaning parameters in the third multivariate planning cleaning function, and through the multivariate planning solution method, the values ​​of the self-cleaning parameters that meet the third multivariate planning cleaning function and the target constraints are obtained.

[0142] Optionally, based on the above-mentioned three implementations A, B, and C, the multivariate planning cleaning function may also include multiple functions. Exemplary, for example, it may include a first multivariate planning cleaning function and a second multivariate planning cleaning function, that is, it is necessary to simultaneously solve the self-cleaning parameters corresponding to the optimal cleaning operation cost data and the shortest cleaning time. Through the corresponding target constraints (the range of values ​​of the self-cleaning parameters), the values ​​of the self-cleaning parameters that meet the multiple multivariate planning cleaning functions and the corresponding target constraints can be solved under this implementation.

[0143] It should be understood that the present application only uses the example that the multivariate planning cleaning function includes two functions for illustration, and the present application does not limit the number of functions included in the multivariate planning cleaning function, and the number of functions can be set according to actual needs.

[0144] The method provided in the embodiment of the present application obtains the cleaning mode of the inner tank self-cleaning that characterizes the cleaning preference of the user of the water heater, obtains the multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and the target constraint conditions, and obtains the corresponding self-cleaning parameters through different multivariate planning cleaning functions corresponding to different cleaning modes and linear programming solution of the target constraint conditions, thereby improving the accuracy of the inner tank self-cleaning parameters, and further improving the effect of the inner tank self-cleaning based on more accurate inner tank self-cleaning parameters.

[0145] Next, taking the acquisition of the cleaning mode according to the user information of the water heater as an example, how to acquire the cleaning mode of the inner tank self-cleaning in the aforementioned step S301 is described in detail.

[0146] Figure 6 A flow chart of another water heater control method provided in an embodiment of the present application. Figure 6 As shown, the aforementioned step S301 may include:

[0147] S601. Obtain user information of the water heater.

[0148] The user information may include the user's age, gender, etc., and may also include the user's cleaning preferences. The user information may be pre-stored in the water heater when the user first uses the water heater, or uploaded to a server of the water heater, etc.

[0149] The water heater may obtain the user information from the storage medium of the water heater or from the server before the first self-cleaning of the inner tank. Alternatively, the water heater may obtain the user information from the storage medium of the water heater or from the server before each self-cleaning of the inner tank.

[0150] S602: Determine the user's cleaning preference based on the user information.

[0151] In one possible implementation, the user type is determined based on the user's age and / or gender information, and the user's cleaning preference is determined based on the mapping relationship between the user type and the cleaning preference and the user type. The mapping relationship can be determined by the water heater server after performing big data analysis based on a large amount of user information. The big data analysis process can refer to any big data analysis method of user preferences in the prior art, and this application will not elaborate on this.

[0152] In another possible implementation, the user information includes the user's cleaning preference, and the cleaning preference is extracted from the user information.

[0153] S603: Obtain a cleaning mode for self-cleaning of the inner tank according to the cleaning preference.

[0154] Different cleaning preferences may correspond to different cleaning modes, such as a cleaning preference for a better cleaning effect, or a lower cleaning cost, or a shorter cleaning time. Furthermore, a specific value of the cleaning preference may be set to adjust the intensity of the cleaning mode corresponding to the cleaning preference.

[0155] The cleaning mode may be determined according to a mapping relationship between the cleaning preference and the cleaning mode, and the cleaning preference of the user.

[0156] In addition, when the user's cleaning preference is determined by the user information and the mapping relationship between the user information and the cleaning preference in the server, the method of the present application can also collect the user's cleaning preference for use in correcting the mapping relationship in the server.

[0157] Figure 7 A flow chart of another water heater control method provided in an embodiment of the present application. Figure 7 As shown, after obtaining the cleaning mode of the inner tank self-cleaning according to the cleaning preference, the method may further include:

[0158] S701, sending a message to be confirmed in the cleaning mode to the user's electronic device.

[0159] After the water heater determines the cleaning mode according to the user information, the confirmation message of the cleaning mode is sent to the user's electronic device, which may be, for example, the user's mobile phone, computer, smart home central control device, etc. The sending method may be, for example, application push, SMS push, mini program push, etc.

[0160] S702: Receive a confirmation result of the message to be confirmed returned by the user's electronic device.

[0161] The confirmation result includes the cleaning mode selected by the user, such as energy-saving mode, quick cleaning mode, etc. Furthermore, it may also include the intensity of the cleaning mode, such as the specific value range of the cleaning time corresponding to the quick cleaning mode, etc.

[0162] If the confirmation result returned by the user's electronic device is not received, the inner tank can be self-cleaned in the cleaning mode by default after a preset time. If the confirmation result returned by the user's electronic device is received, the inner tank can be self-cleaned in the cleaning mode in the confirmation result.

[0163] S703: If the target cleaning mode included in the confirmation result is different from the cleaning mode, the target cleaning mode is sent to the server of the water heater to update the mapping relationship between the cleaning preference and the cleaning mode in the server.

[0164] If the target cleaning mode included in the confirmation result is different from the cleaning mode, it indicates that the cleaning mode does not meet the needs of the user and needs to be adjusted to the target cleaning mode for self-cleaning of the inner tank. In this case, it also indicates that the mapping relationship between the cleaning preference and the cleaning mode in the server may be inaccurate and may need to be adjusted according to the user's confirmation result.

[0165] The water heater sends the target cleaning mode to the server of the water heater, and the server can determine whether the mapping relationship between the cleaning preference and the cleaning mode needs to be modified based on the confirmation results of similar users with similar user information to the user.

[0166] The method provided by the present application obtains user information of the water heater, determines the user's cleaning preference according to the user information, and determines the cleaning mode of the water heater's inner tank self-cleaning according to the cleaning preference and the mapping relationship between the cleaning preference and the cleaning mode. In addition, it is also possible to confirm whether the mapping relationship between the cleaning preference and the cleaning mode needs to be updated and corrected based on the user's confirmation result of the cleaning mode, thereby improving the intelligence of determining the cleaning mode and improving the accuracy of determining the cleaning mode.

[0167] Figure 8 This is a schematic diagram of the structure of a water heater control device provided in an embodiment of the present application. Figure 8 As shown, the device may include: an acquisition module 11, a processing module 12, and a control module 13. In a possible implementation, the device may also include: a transceiver module 14.

[0168] The acquisition module 11 is used to obtain the turbidity of the water in the inner tank of the water heater. If the turbidity is greater than or equal to the turbidity threshold, a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition are obtained. The multivariate planning cleaning function includes a self-cleaning parameter to be determined, and the target constraint condition includes a value range of the self-cleaning parameter to be determined.

[0169] The processing module 12 is used to determine the self-cleaning parameters of the self-cleaning operation according to the multivariate planning cleaning function and the target constraint condition.

[0170] The control module 13 is used to perform self-cleaning on the inner tank according to the self-cleaning parameters to reduce the turbidity of the water in the inner tank.

[0171] In a possible implementation, the acquisition module 11 is specifically used to acquire a cleaning mode for self-cleaning the inner tank, acquire a multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and a target constraint condition, wherein the cleaning mode represents a cleaning preference of a user of the water heater.

[0172] In this implementation, if the cleaning mode is the energy-saving mode, the acquisition module 11 is specifically used to determine the first multivariate planning cleaning function corresponding to the turbidity and the energy-saving mode according to the turbidity and the energy-saving mode, and the mapping relationship between the turbidity, the energy-saving mode, and the multivariate planning cleaning function. The processing module 12 is specifically used to determine the expected turbidity range according to the turbidity. Determine the expected turbidity from the expected turbidity range. Determine the target constraint condition corresponding to the expected turbidity according to the expected turbidity.

[0173] If the cleaning mode is a fast cleaning mode, the acquisition module 11 is specifically used to determine a second multivariate planning cleaning function corresponding to the turbidity and the fast cleaning mode according to the fast cleaning mode and the turbidity, and the mapping relationship among the turbidity, the fast cleaning mode, and the multivariate planning cleaning function. According to the cleaning cost and / or the expected turbidity, the target constraint condition of the second multivariate planning cleaning function is determined.

[0174] If the cleaning mode is a deep cleaning mode, the acquisition module 11 is specifically used to determine a third multivariate planning cleaning function corresponding to the turbidity and the deep cleaning mode according to the deep cleaning mode and the turbidity, and the mapping relationship among the turbidity, the deep cleaning mode, and the multivariate planning cleaning function. According to the cleaning cost and / or the cleaning time, the target constraint condition of the third multivariate planning cleaning function is determined.

[0175] In any of the above implementations, optionally, the acquisition module 11 is specifically used to acquire user information of the water heater. The processing module 12 is specifically used to determine the user's cleaning preference according to the user information. According to the cleaning preference, the cleaning mode of the inner tank self-cleaning is acquired.

[0176] Optionally, the transceiver module 14 is used to send a to-be-confirmed message of the cleaning mode to the user's electronic device after the acquisition module 11 acquires the cleaning mode of the inner tank self-cleaning according to the cleaning preference. Receive the confirmation result of the to-be-confirmed message returned by the user's electronic device. If the target cleaning mode included in the confirmation result is different from the cleaning mode, the target cleaning mode is sent to the server of the water heater to update the mapping relationship between the cleaning preference and the cleaning mode in the server.

[0177] The water heater control device provided in the embodiment of the present application can execute the water heater control method in the above method embodiment. Its implementation principle and technical effect are similar, and this application will not repeat them here.

[0178] Fig. 9 The structure diagram of an electronic device provided in an embodiment of the present application. The electronic device is used to execute the water heater control method mentioned above, for example, it can be any of the water heaters with inner tanks mentioned above. Fig. 9 As shown, the electronic device 900 may include: at least one processor 901 , a memory 902 , and a communication interface 903 .

[0179] The memory 902 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer operation instructions.

[0180] The memory 902 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0181] The processor 901 is used to execute the computer-executable instructions stored in the memory 902 to implement the method described in the above method embodiment. The processor 901 may be a CPU, or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0182] The processor 901 can communicate and interact with external devices through the communication interface 903, and the external device can be, for example, the user's terminal device mentioned above. In a specific implementation, if the communication interface 903, the memory 902 and the processor 901 are implemented independently, the communication interface 903, the memory 902 and the processor 901 can be connected to each other through a bus and complete the communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0183] Optionally, in a specific implementation, if the communication interface 903, the memory 902 and the processor 901 are integrated on a chip, the communication interface 903, the memory 902 and the processor 901 can communicate through an internal interface.

[0184] The present application also provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. Specifically, the computer-readable storage medium stores program instructions, and the program instructions are used for the methods in the above embodiments.

[0185] The present application also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a computing device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the computing device implements the action of the water heater control device.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A water heater control method, characterized in that: The method comprises: Obtaining the turbidity of water in the inner tank of the water heater; If the turbidity is greater than or equal to the turbidity threshold, a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition are obtained; the multivariate planning cleaning function includes a self-cleaning parameter to be determined, and the target constraint condition includes a value range of the self-cleaning parameter to be determined; Determining self-cleaning parameters of a self-cleaning operation according to the multivariate planning cleaning function and the target constraint condition; The inner container is self-cleaned according to the self-cleaning parameters to reduce the turbidity of water in the inner container.

2. The method according to claim 1, characterized in that: The obtaining of the multivariate planning cleaning function corresponding to the turbidity and the target constraint condition comprises: Acquire a cleaning mode for inner tank self-cleaning, wherein the cleaning mode represents a cleaning preference of a user of the water heater; A multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and a target constraint condition are obtained.

3. The method according to claim 2, characterized in that The cleaning mode is an energy-saving mode, and the obtaining of a multivariate planning cleaning function corresponding to the turbidity and the cleaning mode, and target constraint conditions, include: Determine a first multivariate planning cleaning function corresponding to the turbidity and the energy-saving mode according to the turbidity and the energy-saving mode, and a mapping relationship among the turbidity, the energy-saving mode, and the multivariate planning cleaning function; According to the turbidity, determining a desired turbidity range; determining a desired turbidity from the desired turbidity range; According to the expected turbidity, a target constraint condition corresponding to the expected turbidity is determined.

4. The method according to claim 2, characterized in that: The cleaning mode is a fast cleaning mode, and the multivariate programming cleaning function corresponding to the turbidity and the cleaning mode is obtained according to the turbidity and the cleaning mode, and the target constraint condition includes: Determine a second multivariate planning cleaning function corresponding to the turbidity and the fast cleaning mode according to the fast cleaning mode and the turbidity, and a mapping relationship among the turbidity, the fast cleaning mode, and the multivariate planning cleaning function; The objective constraint of the second multivariate programming cleaning function is determined according to the cleaning cost and / or the expected turbidity.

5. The method according to claim 2, characterized in that: The cleaning mode is a deep cleaning mode, and the multivariate programming cleaning function corresponding to the turbidity and the cleaning mode is obtained according to the turbidity and the cleaning mode, and the target constraint condition includes: Determine a third multivariate planning cleaning function corresponding to the turbidity and the deep cleaning mode according to the deep cleaning mode and the turbidity, and a mapping relationship among the turbidity, the deep cleaning mode, and the multivariate planning cleaning function; The objective constraint condition of the third multivariate programming cleaning function is determined according to the cleaning cost and / or the cleaning time consumption.

6. The method according to any one of claims 2 to 5, characterized in that: The step of obtaining the cleaning mode of the inner tank self-cleaning comprises: Obtaining user information of the water heater; Determining the cleaning preference of the user according to the user information; According to the cleaning preference, a cleaning mode for self-cleaning of the inner tank is obtained.

7. The method according to claim 6, characterized in that After obtaining the cleaning mode of the inner tank self-cleaning according to the cleaning preference, the method further includes: Sending a confirmation message of the cleaning mode to the electronic device of the user; Receiving a confirmation result of the message to be confirmed returned by the electronic device of the user; If the target cleaning mode included in the confirmation result is different from the cleaning mode, the target cleaning mode is sent to the server of the water heater to update the mapping relationship between the cleaning preference and the cleaning mode in the server.

8. A water heater control device, characterized in that: include: An acquisition module, used for acquiring the turbidity of water in the inner tank of the water heater; If the turbidity is greater than or equal to the turbidity threshold, then obtaining a multivariate planning cleaning function corresponding to the turbidity and a target constraint condition; The multivariate planning cleaning function includes a self-cleaning parameter to be determined, and the target constraint condition includes a value range of the self-cleaning parameter to be determined; A processing module, configured to determine a self-cleaning parameter of a self-cleaning operation according to the multivariate planning cleaning function and the target constraint condition; The control module is used to perform self-cleaning on the inner tank according to the self-cleaning parameters to reduce the turbidity of the water in the inner tank.

9. An electronic device, characterized in that: include: A processor, a communication interface, and a memory; the processor is communicatively connected to the communication interface and the memory respectively; The memory stores computer-executable instructions; The communication interface communicates and interacts with external devices; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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