Temperature-adjustable instant electric water boiler

Abstract

The application provides a temperature-adjustable instant electric water boiler, relates to the technical field of electric water boilers, and through the pre-set water outlet temperature and water outlet quantity of a user, a water inlet valve is opened to add water with a volume greater than n volume units of the water outlet quantity into a water tank of the electric water boiler; the water inlet of the electric water boiler is heated, and the water inlet temperature before heating is obtained; the water inlet temperature, the water outlet temperature, the water outlet quantity and the pre-set heating duration are input into a pre-set machine learning model to output a corresponding optimal heating power; and the heating power of a heating assembly is adjusted according to the optimal power. The application solves the problem that the prior art is difficult to adaptively adjust the heating power of the water inlet of the electric water boiler according to the water outlet quantity and water outlet temperature required by the user, leading to unstable heating duration and failing to meet the instant heating requirement.

Description

Technical Field

[0001] This invention relates to the field of electric water heater technology, and in particular to an adjustable temperature instant electric water heater. Background Technology

[0002] In today's fast-paced life and work environment, instant electric water heaters, as the core device for obtaining hot water instantly, have become a core demand for users due to their "instant hot water" convenience. However, existing instant electric water heaters have revealed a key shortcoming when facing diverse user water needs: because they cannot dynamically adjust the heating power of the incoming water according to the user's set water volume and temperature, the heating time remains unstable.

[0003] When users require a large flow rate and a high water temperature, the fixed heating power is insufficient to meet the water's heat demand in a short time, forcing the heating process to be prolonged. Users have to wait a long time to obtain hot water that meets their requirements, directly deviating from the original design intention of "instant hot water". On the other hand, when users only need a small amount of hot water but set a high water temperature, the fixed power will cause the water temperature to exceed the target value in a short time due to excessive heat absorption per unit volume of water, which not only affects the user experience but also wastes energy. Summary of the Invention

[0004] The present invention provides an adjustable temperature instant electric water heater, which aims to solve the problem that the existing technology is difficult to adapt the heating power of the electric water heater to the user's needs for water volume and water temperature, resulting in unstable heating time and failure to meet the demand for instant hot water.

[0005] To achieve the above objectives, the present invention provides an adjustable temperature instant electric water heater. The electric water heater incorporates an electronic control system, which includes a water outlet setting module, a water inlet module, a heating module, and a power adjustment module. The water outlet setting module, water inlet module, heating module, and power adjustment module are communicatively connected.

[0006] The water outlet setting module is used by the user to preset the water temperature and water flow rate;

[0007] The water inlet module is used to open the water inlet valve and add water to the water tank of the electric water heater in a volume greater than n units of the water output according to the preset water output.

[0008] The heating module includes a main control heating unit, which is equipped with heating components for heating the water entering the electric water heater and simultaneously obtaining the water temperature before heating.

[0009] The power adjustment module is used to input the inlet water temperature, the preset outlet water temperature and flow rate, and the preset heating time into a preset machine learning model, output the corresponding optimal heating power, and adjust the heating power of the heating component according to the optimal power; the optimal heating power enables the heating component to heat the inlet water temperature in the water tank to the set outlet water temperature within the preset heating time.

[0010] Furthermore, the electronic control system also includes a steam control module and a water purification module. The steam control module is used to discharge water vapor separated from water vapor during the heating process. The water purification module includes a reverse osmosis water purification unit and a water quality detection unit. The reverse osmosis water purification unit is used to purify municipal tap water, and the water quality monitoring unit performs water quality testing on the purified tap water.

[0011] Furthermore, the water quality testing classifies the degree of water purification and displays the purity level using indicator lights.

[0012] Furthermore, the electronic control system also includes an antifreeze module, which is communicatively connected to the heating module and is used to control the heating module to heat the system pipeline to prevent the pipeline from freezing.

[0013] Furthermore, the preset machine learning model is configured as a random forest model, including:

[0014] Obtain a sample of heating operation data for electric water heaters from historical periods. The heating operation data includes water output, inlet water temperature, outlet water temperature, heating power, and heating time.

[0015] A random forest model was trained using water output, inlet water temperature, outlet water temperature, and heating time as explanatory variables and heating power as the response variable.

[0016] Furthermore, the heating assembly includes at least two independently temperature-controlled heating units, and the power adjustment module activates one or more heating units according to a preset water output volume; when the water output volume is less than a preset first volume threshold, only one heating unit is activated and adjusted to the optimal heating power; when the water output volume is greater than or equal to a preset second volume threshold, all heating units are activated; the second volume threshold is greater than the first volume threshold.

[0017] Furthermore, the random forest model is configured with a self-updating unit. After each heating cycle of the electric water heater, the self-updating unit collects the difference between the actual outlet water temperature and the preset outlet water temperature, the difference between the actual heating time and the preset heating time, and the corresponding model parameters to form sample data. When the sample data accumulates to a preset number, the self-updating unit iteratively optimizes the model parameters until the prediction error of the optimal heating power is within ±a%.

[0018] Furthermore, when the optimal heating power exceeds a preset power threshold, the power of the power threshold is used as the heating power of the heating component; simultaneously, the heating component is turned on in advance during the water intake process; wherein, the heating power when the heating component is turned on in advance is determined in the following manner:

[0019] Calculate the power difference between the optimal heating power and the power threshold;

[0020] Input the power difference and the preset heating time into a preset mapping table, and output the heating power when the heating component is turned on in advance.

[0021] The above technical solution has the following technical effects:

[0022] The system adds water to the electric water heater's tank by n volume units greater than the user-preset outlet water temperature and flow rate. It heats the incoming water while simultaneously acquiring the initial inlet water temperature. The inlet water temperature, outlet water temperature, flow rate, and preset heating time are input into a preset machine learning model, which outputs the optimal heating power. The heating power of the heating element is then adjusted based on this optimal power. This invention solves the problem in existing technologies where the heating power of the electric water heater's inlet water cannot be adaptively adjusted according to the user's requirements for water flow rate and temperature, resulting in unstable heating time and an inability to meet the demand for instant hot water.

[0023] In a further embodiment, the preset machine learning model is configured as a random forest model. This involves acquiring historical heating operation data samples from electric water heaters, including water flow rate, inlet water temperature, outlet water temperature, heating power, and heating time. The random forest model is trained using water flow rate, inlet water temperature, outlet water temperature, and heating time as explanatory variables, and heating power as the response variable. This invention quantifies the relationship between water flow rate, inlet water temperature, outlet water temperature, heating power, and heating time using a random forest model, thereby improving the accuracy of heating power control. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of an adjustable temperature instant electric water heater according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the process of constructing a random forest model according to an embodiment of the present invention. Detailed Implementation

[0026] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0027] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0028] Figure 1 This is a schematic diagram of the structure of an adjustable temperature instant electric water heater according to an embodiment of the present invention, as shown below. Figure 1 As shown, the electric water heater in this embodiment has a built-in electronic control system, which includes a water outlet setting module, a water inlet module, a heating module, and a power adjustment module for establishing communication connections.

[0029] The water outlet setting module is used by users to preset the water temperature and flow rate.

[0030] The water inlet module is used to open the water inlet valve and add water to the water tank of the electric water heater in a volume greater than n units of the water output according to the preset water output.

[0031] In this embodiment, the water outlet setting module is the core part of the user's interaction with the electric water heater. It allows users to pre-set their desired water temperature (e.g., 45℃ for making milk, 95℃ for brewing tea) and specific water volume (e.g., 200ml, 500ml), providing clear target parameters for subsequent heating. The water inlet module adds water to the tank according to the user's pre-set water volume by controlling the inlet valve. The added water volume will be n volume units more than the user's set water volume, for example, 50ml more. The purpose of this design is to reserve a certain margin to cope with the small amount of water loss during the heating process, such as evaporation and pipe residue, ensuring that the final actual water volume accurately matches the user's settings and avoiding insufficient water volume due to loss.

[0032] The heating module includes a main control heating unit, which is equipped with heating components to heat the water entering the electric water heater and simultaneously obtain the water temperature before heating.

[0033] The power adjustment module is used to input the inlet water temperature, the preset outlet water temperature and flow rate, and the preset heating time into a preset machine learning model, output the corresponding optimal heating power, and adjust the heating power of the heating component according to the optimal power; in one specific implementation, the optimal heating power enables the heating component to heat the inlet water temperature in the water tank to the set outlet water temperature within the preset heating time.

[0034] Figure 2This is a schematic diagram of the process of constructing a random forest model according to an embodiment of the present invention, as shown below. Figure 2 As shown, in one specific implementation, the preset machine learning model is configured as a random forest model, including:

[0035] Obtain historical data samples of electric water heater heating operation, including water output, inlet water temperature, outlet water temperature, heating power, and heating time;

[0036] A random forest model was trained using water output, inlet water temperature, outlet water temperature, and heating time as explanatory variables and heating power as the response variable.

[0037] In this embodiment, a power adjustment module takes the inlet water temperature, preset outlet water temperature, water flow rate, and preset heating time as input parameters, and inputs them into a machine learning model preset as a random forest model. The random forest model is trained based on a large number of historical heating operation data samples of electric water heaters. These samples cover information such as water flow rate, inlet water temperature, outlet water temperature, heating power, and heating time. By outputting the corresponding optimal heating power through this model, precise control of the heating power is achieved, ensuring that the heating component stably heats the inlet water in the water tank to the set outlet water temperature within the preset heating time. This effectively solves the problem in the prior art that it is difficult to adaptively adjust the heating power according to the user's demand for water flow rate and outlet water temperature, resulting in unstable heating time and inability to meet the demand for instant hot water.

[0038] Random Forest is a machine learning algorithm based on ensemble learning. Its core consists of multiple independent decision trees, and its design allows it to efficiently handle multi-parameter fitting problems in electric water heater heating scenarios. During the training phase, the model generates multiple different training subsets from historical heating data of the electric water heater through random sampling (including sample randomness and feature randomness). Each subset corresponds to a decision tree. For example, samples of water output, inlet water temperature, outlet water temperature, heating time, and heating power are randomly selected from historical data, and different feature combinations are assigned to each tree (e.g., some trees emphasize the correlation between water output and temperature difference, while others strengthen the mapping between heating time and power), thus dispersing the risk of overfitting from a single tree.

[0039] During the prediction phase, when new parameters (inlet water temperature, preset outlet water temperature, outlet water flow rate, and preset heating duration) are input, all decision trees independently calculate the corresponding predicted heating power. The model then integrates these results by averaging them to output the final optimal heating power. This multi-tree collaborative mechanism retains the decision trees' ability to capture nonlinear relationships (accurately fitting the complex correlation between parameters such as water flow rate and temperature difference and power), while reducing the prediction bias of a single tree through randomness, making the model more robust when facing data noise (such as occasional water temperature sensor errors).

[0040] Meanwhile, the adaptability of random forests to high-dimensional inputs allows them to process multiple variables affecting heating simultaneously without the need for manual selection of core features, making them particularly suitable for scenarios with dynamic changes in multiple parameters, such as electric water heaters. After training with historical heating data, the model can quickly learn the power adjustment patterns under different water demand conditions. For example, it can automatically identify potential patterns where high power is required for large water volume and high temperature difference, and low power is required for small water volume and low temperature difference, ultimately achieving accurate prediction of the optimal heating power and providing reliable algorithmic support for the dynamic power adjustment of electric water heaters.

[0041] In one specific implementation, the electrical control system further includes a steam control module and a water purification module. The steam control module is used to discharge water vapor separated from water vapor during the heating process. The water purification module includes a reverse osmosis water purification unit and a water quality monitoring unit. The reverse osmosis water purification unit is used to purify municipal tap water, and the water quality monitoring unit tests the water quality of the purified tap water. The steam control module discharges water vapor separated from water vapor during heating to prevent steam accumulation from affecting equipment operation. The water purification module consists of a reverse osmosis water purification unit and a water quality monitoring unit. The former purifies municipal tap water, and the latter tests the quality of the purified water to ensure that the water source is qualified. Both optimize equipment performance from the perspectives of operational safety and water quality assurance, respectively.

[0042] In one specific implementation, the degree of water purification is graded during water quality testing, and the purity level is displayed using indicator lights.

[0043] In one specific implementation, the electronic control system also includes an antifreeze module, which is communicatively connected to the heating module and used to control the heating module to supply the system piping to prevent the piping from freezing, thereby ensuring that the electric water heater can operate normally under low temperature conditions.

[0044] In this embodiment, an independent antifreeze module is installed in the electrical control system. This module establishes a real-time communication connection with the heating module of the electric water heater. The antifreeze module monitors the system pipeline and surrounding environment temperature in real time through a built-in temperature sensor (or an ambient temperature detection component of an associated device). When the detected temperature is lower than a preset antifreeze threshold, such as approaching or falling below the freezing point, the antifreeze module sends a start command to the heating module, activating the heating module's heating capacity to heat the system pipeline. For example, it controls the heating component to activate the antifreeze heating function to maintain the pipeline temperature above the freezing point.

[0045] Meanwhile, the antifreeze module dynamically adjusts the heating intensity based on real-time temperature: when the temperature is far below the threshold, it controls the heating module to output higher power for rapid heating; when the temperature rises back to a safe range, it reduces the heating power or stops heating to avoid energy waste. Through direct linkage with the heating module, antifreeze can be achieved using existing heating resources without the need for an additional independent heating device. This simplifies the structure and ensures that the pipes do not freeze in low-temperature environments, guaranteeing smooth water flow and normal operation of the electric water heater.

[0046] In one specific implementation, the heating component includes at least two independently temperature-controlled heating units. The power adjustment module activates one or more heating units based on a pre-set water flow rate. When the water flow rate is less than a preset first volume threshold, only one heating unit is activated and adjusted to its optimal heating power. When the water flow rate is greater than or equal to a preset second volume threshold, all heating units are activated. The second volume threshold is greater than the first volume threshold. Through the tiered activation and deactivation of multiple heating units and power adaptation, dynamic matching of heating capacity with different water flow rates is achieved: single-unit operation reduces energy waste and improves the equipment's economy when the water flow rate is low; full-unit activation provides sufficient heating power when the water flow rate is high, ensuring the stability of heating duration. This design effectively solves the problem of excessive or insufficient power that easily occurs in a single heating unit when the water flow rate changes, comprehensively optimizes the equipment's performance under different water usage scenarios, and improves operational reliability and energy utilization efficiency.

[0047] In one specific implementation, the random forest model is configured with a self-updating unit. After each heating cycle, this unit collects the difference between the actual and preset water temperature, the difference between the actual and preset heating times, and the corresponding model parameters to form sample data. When the sample data accumulates to a preset number, the self-updating unit iteratively optimizes the model parameters until the prediction error of the optimal heating power is within ±a%, for example, ±2%. This design allows the model to continuously learn from deviations in actual equipment operation, dynamically correcting the prediction logic and avoiding a decrease in model accuracy due to equipment aging, environmental changes, or other factors. This ensures the long-term accuracy of heating power regulation, guaranteeing that the electric water heater can stably meet the demand for instant hot water during long-term use.

[0048] In one specific implementation, when the optimal heating power exceeds a preset power threshold, the power of the power threshold is used as the heating power of the heating element; simultaneously, the heating element is turned on in advance during the water intake process; wherein, the heating power when the heating element is turned on in advance is determined in the following way:

[0049] Calculate the power difference between the optimal heating power and the power threshold;

[0050] Input the power difference and the preset heating time into the preset mapping table, and output the heating power when the heating component is turned on in advance.

[0051] In this embodiment, a dual control strategy is adopted to address the situation where the optimal heating power exceeds a preset power threshold: when the optimal heating power calculated by the model exceeds the power threshold, the actual power of the heating component is first limited within the threshold to avoid equipment overload, excessive energy consumption, or safety risks; simultaneously, the heating component is preheated during the water intake process. The determination of the preheating power requires first calculating the difference between the optimal power and the threshold, and then inputting this difference and the preset heating duration into a preset mapping table, which outputs the corresponding value. This mapping table is a pre-constructed correspondence table based on a large amount of experimental data or historical operating experience. It records the required preheating power data under different combinations of power differences (i.e., the amount of power corresponding to the heat loss due to power limitation) and different heating durations, ensuring that the heat supplemented during the preheating stage can accurately compensate for the heat gap caused by power limitation.

[0052] Power threshold limits ensure the safety and stability of equipment operation; while the preheating power determined by the mapping table can accurately match the heat demand. By preheating in advance to supplement heat, it can ensure that even if the power is limited, the water can still be heated to the target temperature within the preset time. This solves the safety and energy consumption problems under high power demand, while maintaining heating efficiency and user experience, thus balancing the contradiction between power limitation and heating effect.

[0053] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. An adjustable temperature instant electric water heater, characterized in that, The electric water heater is equipped with an electronic control system, which includes a water outlet setting module, a water inlet module, a heating module, and a power adjustment module. These modules are communicatively connected. The water outlet setting module is used by the user to preset the water temperature and water flow rate; The water inlet module is used to open the water inlet valve and add water to the water tank of the electric water heater in a volume greater than n units of the water output according to the preset water output. The heating module includes a main control heating unit, which is equipped with heating components for heating the water entering the electric water heater and simultaneously obtaining the water temperature before heating. The power adjustment module is used to input the inlet water temperature, the preset outlet water temperature and flow rate, and the preset heating time into a preset machine learning model, output the corresponding optimal heating power, and adjust the heating power of the heating component according to the optimal heating power; the optimal heating power enables the heating component to heat the inlet water temperature in the water tank to the set outlet water temperature within the preset heating time. The preset machine learning model is configured as a random forest model, including: Obtain a sample of heating operation data for electric water heaters from historical periods. The heating operation data includes water output, inlet water temperature, outlet water temperature, heating power, and heating time. A random forest model was trained using water output, inlet water temperature, outlet water temperature, and heating time as explanatory variables and heating power as the response variable.

2. The adjustable temperature instant electric water heater according to claim 1, characterized in that, The electrical control system also includes a steam control module and a water purification module. The steam control module is used to discharge water vapor separated from water vapor during the heating process. The water purification module includes a reverse osmosis water purification unit and a water quality detection unit. The reverse osmosis water purification unit is used to purify municipal tap water, and the water quality detection unit performs water quality testing on the purified tap water.

3. The adjustable temperature instant electric water heater according to claim 2, characterized in that, The water quality testing classifies the degree of water purification and displays the purity level using indicator lights.

4. The adjustable temperature instant electric water heater according to claim 2, characterized in that, The electronic control system also includes an antifreeze module, which is communicatively connected to the heating module and is used to control the heating module to heat the system pipeline to prevent the pipeline from freezing.

5. The adjustable temperature instant electric water heater according to claim 1, characterized in that, The heating assembly includes at least two independently temperature-controlled heating units. The power adjustment module activates one or more heating units according to the preset water output. When the water output is less than a preset first volume threshold, only one heating unit is activated and adjusted to the optimal heating power. When the water output is greater than or equal to a preset second volume threshold, all heating units are activated. The second volume threshold is greater than the first volume threshold.

6. The adjustable temperature instant electric water heater according to claim 1, characterized in that, The random forest model is configured with a self-updating unit. After each heating cycle of the electric water heater, the self-updating unit collects the difference between the actual outlet water temperature and the preset outlet water temperature, the difference between the actual heating time and the preset heating time, and the corresponding model parameters to form sample data. When the sample data accumulates to a preset number, the self-updating unit iteratively optimizes the model parameters until the prediction error of the optimal heating power is within ±a%.

7. The adjustable temperature instant electric water heater according to claim 1, characterized in that, When the optimal heating power exceeds a preset power threshold, the power of the power threshold is used as the heating power of the heating component; simultaneously, the heating component is turned on in advance during the water intake process; wherein, the heating power when the heating component is turned on in advance is determined in the following way: Calculate the power difference between the optimal heating power and the power threshold; Input the power difference and the preset heating time into a preset mapping table, and output the heating power when the heating component is turned on in advance.

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