Temperature adjustment time estimation method based on adaptive learning

Abstract

The invention discloses a temperature adjustment time estimation method based on adaptive learning, and the method comprises the steps: obtaining basic parameters of a liquid to be subjected to temperature adjustment, and calculating the total heat based on the basic parameters; acquiring a preset heat conductivity coefficient, and calculating initial estimated time according to the preset heat conductivity coefficient and the total heat; the current water temperature is detected again after a preset time interval so as to update the basic parameters, residual heat is calculated based on the updated basic parameters, and the preset heat conductivity coefficient is updated so as to obtain a target heat dissipation coefficient; and calculating target estimated time according to the residual heat and the target heat dissipation coefficient. The heating time and the refrigerating time can be estimated, and the use experience of a user is improved.

Description

Technical Field

[0001] This invention relates to the field of heating and cooling technology, and in particular to a method for predicting temperature adjustment time based on adaptive learning. Background Technology

[0002] Current temperature control devices, such as water dispensers, can heat or cool liquids to a specified temperature to meet user needs. However, most current temperature control devices cannot predict the required heating or cooling time, thus reducing the user experience. Summary of the Invention

[0003] This invention provides a method for predicting temperature control time based on adaptive learning, which aims to solve the problem that current temperature control devices cannot predict heating and cooling times.

[0004] In a first aspect, embodiments of the present invention provide a method for predicting temperature adjustment time based on adaptive learning, the method comprising: Obtain the basic parameters of the liquid to be heated, and calculate the total heat based on the basic parameters; Obtain the preset thermal conductivity and calculate the initial estimated time based on the preset thermal conductivity and the total heat. After a preset time interval, the current water temperature is re-detected to update the basic parameters, and the remaining heat is calculated based on the updated basic parameters, and the preset thermal conductivity is updated to obtain the target heat dissipation coefficient. The target estimated time is calculated based on the remaining heat and the target heat dissipation coefficient.

[0005] Secondly, embodiments of the present invention also provide a temperature control device, wherein the temperature control device is configured with the temperature control time estimation method based on adaptive learning as described in any of the above claims.

[0006] This invention provides a method and apparatus for predicting temperature adjustment time based on adaptive learning. The method includes: acquiring basic parameters of the liquid to be temperature-adjusted, and calculating the total heat based on the basic parameters; acquiring a preset thermal conductivity, and calculating an initial estimated time based on the preset thermal conductivity and the total heat; re-detecting the current water temperature after a preset time interval to update the basic parameters, and calculating the remaining heat and updating the preset thermal conductivity based on the updated basic parameters to obtain a target heat dissipation coefficient; and calculating the target estimated time based on the remaining heat and the target heat dissipation coefficient. This invention can calculate the total heat based on the basic parameters of the liquid to be temperature-adjusted, and then calculate the initial estimated time based on the preset thermal conductivity and the total heat. Furthermore, after a period of temperature adjustment, the current water temperature can be re-detected to update the basic parameters, and the remaining heat and the target heat dissipation coefficient can be calculated. Then, the target estimated time can be calculated based on the remaining heat and the target heat dissipation coefficient. This not only predicts heating and cooling times but also improves the accuracy of the prediction. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart illustrating the temperature adjustment time prediction method based on adaptive learning provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the first sub-process of the temperature adjustment time prediction method based on adaptive learning provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the second sub-process of the temperature adjustment time prediction method based on adaptive learning provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the third sub-process of the temperature adjustment time prediction method based on adaptive learning provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the fourth sub-process of the temperature adjustment time prediction method based on adaptive learning provided in the embodiments of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] It should be understood that, when used in this specification and the appended claims, the terms “comprising” and “including” indicate the presence of the described features, integrals, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, operations, elements, components and / or collections thereof.

[0011] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0012] Please see Figure 1 , Figure 1 This is a flowchart illustrating the temperature control time estimation method based on adaptive learning provided in this embodiment of the invention. This method can be applied to temperature control devices, such as water dispensers, to estimate the heating and cooling times. Figure 1 As shown, the method includes steps S110 to S140.

[0013] S110: Obtain the basic parameters of the liquid to be conditioned, and calculate the total heat based on the basic parameters.

[0014] In this embodiment of the invention, the temperature-regulating device can be a water dispenser, and the liquid to be temperature-regulated can be water or other solutions, such as coffee. Taking a water dispenser as an example, when the water dispenser needs to heat or cool, it can obtain the basic parameters of the water and then calculate the total heat based on these parameters. The basic parameters include, but are not limited to, the target temperature, the real-time temperature, the ambient temperature, the real-time power, the specific heat capacity of the water, and the mass of the water. The total heat refers to the heat absorbed (in a heating scenario) or the heat released (in a cooling scenario) by the water to be temperature-regulated during the process of reaching the target temperature from the initial temperature. That is, when the water dispenser needs to heat, the total heat is the heat that the water needs to absorb; when the water dispenser needs to cool, the total heat is the heat that the water needs to release.

[0015] See Figure 2 In some embodiments, such as in the embodiments of the present invention, the basic parameters include ambient temperature, target temperature, real-time temperature, and real-time power, and step S110 further includes steps S111-S112.

[0016] S111, acquire the target temperature and the real-time temperature, and calculate the temperature change based on the target temperature and the real-time temperature; S112, Substitute the temperature change into the preset heat calculation formula to calculate the total heat, wherein the preset heat calculation formula is: Q = C×M×ΔT Where Q is the total heat, C is the specific heat capacity of the liquid to be conditioned, M is the mass of the liquid to be conditioned, and ΔT is the temperature change.

[0017] In this embodiment of the invention, the target temperature is a set temperature. For example, in a heating scenario, if the target temperature is 30°C, it means that the water needs to be heated from its current temperature to 30°C. In a cooling scenario, if the target temperature is 5°C, it means that the water needs to be cooled from its current temperature to 5°C. The real-time temperature is the current temperature of the water, which can be obtained in real time through a temperature sensor.

[0018] After obtaining the target temperature and the real-time temperature, the difference between the target temperature and the real-time temperature can be calculated to obtain the temperature change. For example, in a cooling scenario, if the real-time temperature is 25℃ and the target temperature is 10℃, the temperature change is 15℃. In a heating scenario, if the real-time temperature is 20℃ and the target temperature is 50℃, the temperature change is 30℃.

[0019] After calculating the temperature change, you can substitute it into a preset heat calculation formula to calculate the total heat. For example, if the specific heat capacity of water is 4.2 kJ / (kg·℃) and the mass of water is 1 kg, then the total heat in a heating scenario is: Q=C×M×ΔT=4.2kJ / (kg・℃)×1kg×30℃=126kJ That is, in a heating scenario, the amount of heat required to heat water from 20°C to 50°C is 126 kJ.

[0020] The total heat in a cooling scenario is: Q=C×M×ΔT=4.2kJ / (kg・℃)×1kg×15℃=63kJ That is, in a refrigeration scenario, the amount of heat required to cool water from 25°C to 10°C is 63kJ.

[0021] S120, obtain the preset thermal conductivity, and calculate the initial estimated time based on the preset thermal conductivity and the total heat.

[0022] In this embodiment of the invention, the preset thermal conductivity refers to the theoretical initial value of the heat dissipation-related empirical coefficient K, calculated experimentally before the device leaves the factory. This reference coefficient is retrieved from the device's memory parameters (such as FLASH storage) when the water heating / cooling time is estimated for the first time. A larger preset thermal conductivity value results in a longer heating / cooling time; a preset thermal conductivity of 0 is close to the ideal state. It is understood that a preset thermal conductivity value can be used when the device first operates in heating or cooling mode, and this value will change accordingly with increased usage. After obtaining the preset thermal conductivity value, an initial estimated time can be calculated based on the preset thermal conductivity value and the total heat. This initial estimated time is a static estimated time, which is the time estimated when the device first starts operating in cooling or heating mode.

[0023] See Figure 3 In some embodiments, such as in the embodiments of the present invention, step S120 further includes steps S121-S122.

[0024] S121, calculate the average temperature difference based on the ambient temperature and the real-time temperature, and calculate the product of the average temperature difference and the preset thermal conductivity to calculate the impact power; S122, substitute the total heat, the impact power, and the real-time power into the first preset calculation formula or the second preset calculation formula to calculate the initial estimated time, wherein the first preset calculation formula is used to calculate the initial estimated time in the cooling mode, and the second preset calculation formula is used to calculate the initial estimated time in the heating mode.

[0025] The first preset calculation formula is: t = Q / (P + O) Where t is the initial estimated time, Q is the total heat, P is the real-time power, and O is the influencing power.

[0026] The first preset calculation formula is: t = Q / (P - O) Where t is the initial estimated time, Q is the total heat, P is the real-time power, and O is the influencing power.

[0027] In this embodiment of the invention, under a cooling environment, the average temperature difference can be calculated using the following formula: Average temperature difference = Tambient - (Tinitial + Tgoal) / 2 Where Tambient is the ambient temperature, Tinitial is the real-time temperature, and Tgoal is the target temperature. For example, if the ambient temperature is 25℃, the real-time temperature is 25℃, and the target temperature is 10℃, then the average temperature difference is 7.5℃.

[0028] In a heating environment, the average temperature difference can be calculated using the following formula: Average temperature difference = (Tinitial + Tgoal) / 2 - Tambient Where Tambient is the ambient temperature, Tinitial is the real-time temperature, and Tgoal is the target temperature. For example, if the ambient temperature is 25℃, the real-time temperature is 20℃, and the target temperature is 50℃, then the average temperature difference is 10℃.

[0029] After calculating the average temperature difference, the product of the average temperature difference and the preset thermal conductivity can be calculated to calculate the impact power. For example, if the preset thermal conductivity is 0.05 kJ / (s·℃), then the impact power is 0.05 kJ / (s·℃) × 10℃ = 0.5 kJ / s.

[0030] The first preset calculation formula is used to calculate the initial estimated time in cooling mode, and the second preset calculation formula is used to calculate the initial estimated time in heating mode. For example, in a cooling scenario, the affected power is 0.03 × 7.5 = 0.225 kJ / s. If the real-time power is 0.8 kJ / s, then the initial estimated time is: t = Q / (P+O) = 63 kJ / (0.8+0.225) kJ / s ≈ 61.5 seconds In a heating scenario, the power impact is 0.5 kJ / s. If the real-time power is 0.8 kJ / s, then the initial estimated time is: t = Q / (PO) = 126 kJ / (1.5 kJ / s - 0.5 kJ / s) = 126 seconds S130, after a preset time interval, re-detect the current water temperature to update the basic parameters, and calculate the remaining heat and update the preset thermal conductivity based on the updated basic parameters to obtain the target heat dissipation coefficient.

[0031] In this embodiment of the invention, the preset time can be an empirical value, for example, updated every 60 seconds. After the device heats for 60 seconds, the current water temperature is re-detected to update the real-time temperature. Then, based on the updated real-time temperature, the remaining heat is calculated, and the preset thermal conductivity is updated to obtain the target thermal conductivity. It is understood that step S130 can be repeated multiple times, and it can be adjusted according to the initial estimated time. For example, if the initial estimated time is 5 minutes, step S130 can be repeated every 60 seconds; if the initial estimated time is 2 minutes, step S130 can be executed only once.

[0032] See Figure 4 In some embodiments, such as in the embodiments of the present invention, step S130 further includes steps S131-S132.

[0033] S131, calculate the remaining temperature difference based on the target temperature and the updated real-time temperature; S132, calculate the remaining heat based on the remaining temperature difference.

[0034] In this embodiment of the invention, the difference between the target temperature and the updated real-time temperature can be calculated to obtain the remaining temperature difference, and then the remaining heat can be calculated based on the remaining temperature difference. That is, the remaining temperature difference can be used as a low temperature change quantity and substituted into the preset heat calculation formula to calculate the remaining heat.

[0035] See Figure 5 In some embodiments, such as in the embodiments of the present invention, step S130 further includes steps S133-S134.

[0036] S133, obtain the current temperature to update the real-time temperature, and calculate the temperature change rate based on the updated real-time temperature and the target temperature; S134, Substitute the temperature change rate into the third preset calculation formula to calculate the target heat dissipation coefficient.

[0037] In this embodiment of the invention, the difference between the updated real-time temperature and the target temperature can be calculated, and then the difference can be divided by a preset interval time to calculate the temperature change rate. The temperature change rate is then substituted into a third preset calculation formula or a fourth preset calculation formula to calculate the target heat dissipation coefficient. The third preset calculation formula is: Where k is the target heat dissipation coefficient, P is the real-time power, dT / dt is the temperature change rate, C is the specific heat capacity, M is the mass, and S is the average temperature difference.

[0038] For example, in a cooling environment, the ambient temperature is 25℃, the real-time temperature is 12℃, the target temperature is 10℃, the average temperature difference is 6.5℃, the real-time power is 0.8 kJ / s, and the temperature change rate is 0.217℃ / s. Then, k can be calculated to be 0.017 kJ / (s・℃).

[0039] After calculating the target thermal conductivity, the target thermal conductivity can be saved and directly called up during the next cooling or heating cycle. The target thermal conductivity can also be updated based on the data from the next heating or cooling cycle.

[0040] In some embodiments, such as in the embodiments of the present invention, the following steps are included after step S134: The target heat dissipation coefficient is substituted into a preset smoothing adjustment formula to obtain the adjusted target heat dissipation coefficient.

[0041] In this embodiment of the invention, the preset smoothing adjustment formula is: k1 = α·k0 - (1-α)k Where k1 is the adjusted target heat dissipation coefficient, k0 is the preset heat dissipation coefficient, k is the target heat dissipation coefficient, and α is a smoothing factor (e.g., 0.9). If k0 is 0.03 and k is 0.017, then: K1=0.9×0.03+0.1×0.017≈0.0287kJ / (s·℃) S140, calculate the target estimated time based on the remaining heat and the target heat dissipation coefficient.

[0042] Under heating conditions, assuming the water mass M is 1 kg, the specific heat capacity is 4.2 kJ / (kg·℃), the initial real-time temperature Tinitial is 20℃, the ambient temperature Tambient is 25℃, the target temperature Tgoal is 50℃, the heating power P is 1500 W (1.5 kJ / s), and the preset target thermal conductivity k_old is 0.05 kJ / (s·℃), then: Calculate the water temperature change: ΔT = Tgoal - Tinitial = 50℃ - 20℃ = 30℃ Calculate the total heat required for water: Q = C × M × ΔT = 4.2 kJ / (kg・℃) × 1 kg × 30℃ = 126 kJ Calculate the impact power: Impact power = K_old × average temperature difference = 0.05 kJ / (s・℃) × 10℃ = 0.5 kJ / s Calculate the initial estimated time: t_initial=Q / (P-heat dissipation power)=126kJ / (1.5kJ / s-0.5kJ / s)=126 seconds.

[0043] After a 60-second interval: After 60 seconds, the device detected the current water temperature as T_current = 28℃. Recalculate the rate of temperature change: ΔT_actual = T_current - Tinitial = 28℃ - 20℃ = 8℃, time Δt = 60s, rate of temperature change = 8℃ / 60s ≈ 0.133℃ / s Calculate the current actual K value (K_calculated): K_calculated=1.5kJ / s-(4.2×1×8) / 60=0.94kJ / (s・℃) Smoothly adjust the K value: K_new=α×K_old+(1-α)×K_calculated=0.9×0.05+0.1×0.94≈0.139kJ / (s·℃) Recalculate remaining time: The remaining temperature difference ΔT_remaining = Tgoal - T_current = 50℃ - 28℃ = 22℃ The remaining heat Q_remaining = 4.2 × 1 × 22 = 92.4 kJ New heat dissipation power = 0.139 × [(28 + 50) / 2 - 25] = 0.139 × 9 ≈ 1.251 kJ / s Remaining time t_remaining = 92.4 / (1.5 - 1.251) ≈ 371 seconds In a refrigeration environment, assuming the water mass M is 1 kg, the specific heat capacity is 4.2 kJ / (kg·℃), the real-time temperature Tinitial is 25℃, the ambient temperature Tambient is 25℃, the target temperature Tgoal is 10℃, the heating power P is 800 W (0.8 kJ / s), and the preset target thermal conductivity k_old is 0.03 kJ / (s·℃), then: Calculate the water temperature change: ΔT = Tinitial - Tgoal = 25℃ - 10℃ = 15℃ Calculate the total heat that needs to be removed from the water: Q = C × M × ΔT = 4.2 kJ / (kg・℃) × 1 kg × 15℃ = 63 kJ Calculate the average influence power: Influence power = K_old × average temperature difference = 0.03 × 7.5 = 0.225 kJ / s Calculate the initial estimated time: t_initial=Q / (P+influence power)=63kJ / (0.8+0.225)kJ / s≈61.5 seconds.

[0044] After a 60-second interval: Current water temperature T_current = 12℃ Recalculate the rate of temperature change: ΔT_actual = Tinitial - T_current = 25℃ - 12℃ = 13℃, time Δt = 60s, rate of temperature change = 13℃ / 60s ≈ 0.217℃ / s Calculate the current actual K value (K_calculated): K_calculated×6.5=(4.2×1×13) / 60≈0.017kJ / (s・℃) Smoothly adjust the K value: K_new=0.9×0.03+0.1×0.017≈0.0287kJ / (s·℃) Recalculate remaining time: The remaining temperature difference ΔT_remaining = T_current - Tgoal = 12℃ - 10℃ = 2℃ The remaining heat Q_remaining = 4.2 × 1 × 2 = 8.4 kJ New input power = 0.0287 × [25 - (12 + 10) / 2] = 0.0287 × 9 ≈ 0.258 kJ / s The remaining time t_remaining = 8.4 / (0.8 + 0.258) ≈ 7.9 seconds.

[0045] The present invention also provides a temperature control device, wherein the temperature control device is configured with the temperature control time estimation method based on adaptive learning as described in any of the above embodiments.

[0046] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0047] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting temperature adjustment time based on adaptive learning, characterized in that, The method includes: Obtain the basic parameters of the liquid to be heated, and calculate the total heat based on the basic parameters; Obtain the preset thermal conductivity and calculate the initial estimated time based on the preset thermal conductivity and the total heat. After a preset time interval, the current water temperature is re-detected to update the basic parameters, and the remaining heat is calculated based on the updated basic parameters, and the preset thermal conductivity is updated to obtain the target heat dissipation coefficient. The target estimated time is calculated based on the remaining heat and the target heat dissipation coefficient.

2. The method as described in claim 1, characterized in that, The basic parameters include ambient temperature, target temperature, real-time temperature, and real-time power. The step of obtaining the basic parameters and calculating the total heat based on the basic parameters includes: The target temperature and the real-time temperature are obtained, and the temperature change is calculated based on the target temperature and the real-time temperature. The temperature change is substituted into a preset heat calculation formula to calculate the total heat.

3. The method as described in claim 2, characterized in that, The preset heat calculation formula is as follows: Q = C×M×ΔT Where Q is the total heat, C is the specific heat capacity of the liquid to be conditioned, M is the mass of the liquid to be conditioned, and ΔT is the temperature change.

4. The method as described in claim 2, characterized in that, The step of calculating the initial estimated time based on the preset thermal conductivity and the total heat includes: The average temperature difference is calculated based on the ambient temperature and the real-time temperature, and the product of the average temperature difference and the preset thermal conductivity is calculated to determine the impact power. The total heat, the power of influence, and the real-time power are substituted into the first preset calculation formula or the second preset calculation formula to calculate the initial estimated time. The first preset calculation formula is used to calculate the initial estimated time in the cooling mode, and the second preset calculation formula is used to calculate the initial estimated time in the heating mode.

5. The method as described in claim 4, characterized in that, The first preset calculation formula is: t = Q / (P + O) Where t is the initial estimated time, Q is the total heat, P is the real-time power, and O is the influencing power.

6. The method as described in claim 4, characterized in that, The first preset calculation formula is: t = Q / (P - O) Where t is the initial estimated time, Q is the total heat, P is the real-time power, and O is the influencing power.

7. The method as described in claim 2, characterized in that, The step of calculating the remaining heat based on the updated basic parameters includes: The remaining temperature difference is calculated based on the target temperature and the updated real-time temperature. The remaining heat is calculated based on the remaining temperature difference.

8. The method as described in claim 2, characterized in that, The step of updating the preset thermal conductivity based on the updated basic parameters to obtain the target heat dissipation coefficient includes: The current temperature is obtained to update the real-time temperature, and the rate of temperature change is calculated based on the updated real-time temperature and the target temperature. The temperature change rate is substituted into the third preset calculation formula to calculate the target heat dissipation coefficient.

9. The method as described in claim 8, characterized in that, The method further includes: The target heat dissipation coefficient is substituted into a preset smoothing adjustment formula to obtain the adjusted target heat dissipation coefficient.

10. A temperature regulating device, characterized in that, The temperature control device is configured with a temperature control time estimation method based on adaptive learning as described in any one of claims 1-9.

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