Water dispenser temperature control method and system based on spectrum feedback

Abstract

This application provides a water dispenser temperature control method and system based on spectrum feedback, relating to the field of temperature control technology. This application utilizes near-infrared spectral detection to acquire near-infrared spectral data of drinking water within the water dispenser, and uses temperature spectrum detection to acquire water temperature spectrum data of the drinking water within the water dispenser. Spectral feedback analysis is performed on the near-infrared spectral data to determine the heat absorption coefficient of the drinking water corresponding to its water quality type. Based on the drinking water quality type and water temperature spectrum data, drinking water with a pre-uniform temperature is generated after processing with a specific frequency sound wave. The temperature deviation between the actual temperature of the pre-uniform drinking water and the preset target temperature is calculated to generate heating control parameters. Based on the heating control parameters, temperature control of the water dispenser is achieved under different water qualities, realizing the reduction of local temperature differences during the heating process under different water qualities and dynamic optimization of heating parameters, thus improving the accuracy and adaptability of the water dispenser's temperature control.

Description

Technical Field

[0001] This application relates to the field of temperature control technology, and in particular to a method and system for temperature control of a water dispenser based on spectrum feedback. Background Technology

[0002] In home and office settings, users' demands for water dispensers are increasing. They not only require rapid heating to the set temperature but also prioritize energy efficiency during the heating process and the uniformity and safety of the final water temperature. Because drinking water from different regions and sources varies in mineral content, its heat absorption characteristics differ. Therefore, there is an urgent need for intelligent temperature control technology that can adaptively adjust heating parameters based on the actual incoming water quality and effectively suppress uneven temperature distribution during the heating process.

[0003] Currently, mainstream solutions attempt to analyze the composition of water entering the water dispenser by integrating near-infrared spectral sensors, identifying its approximate mineral content level, and then pre-setting a fixed heating power level accordingly. However, existing solutions still have significant shortcomings. For example, they lack a feedback adjustment mechanism, making them unable to cope with the non-linear characteristics of water temperature changes during heating, and prone to overshooting or response hysteresis. The fixed frequency of the acoustic processing used fails to match and optimize according to specific water quality characteristics, resulting in limited effectiveness in promoting uniform heating, especially in high-hardness water where significant local temperature differences still exist. Summary of the Invention

[0004] The purpose of this application is to provide a water dispenser temperature control method and system based on spectrum feedback, so as to solve the problems in the prior art, such as the lack of feedback adjustment mechanism, easy overshoot or response hysteresis, and the inability to match and optimize the sound wave processing frequency according to the specific water quality characteristics.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a water dispenser temperature control method based on spectrum feedback, comprising:

[0006] Near-infrared spectroscopy is used to obtain near-infrared spectral data of drinking water in the water dispenser, and temperature spectrum detection is used to obtain water temperature spectrum data of drinking water in the water dispenser.

[0007] Spectral feedback analysis is performed on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type;

[0008] Based on the drinking water quality type and the water temperature spectrum data, drinking water with a preliminary uniform temperature is generated after being processed by a specific frequency sound wave, so as to reduce the local temperature difference generated during the heating process of different water qualities. The specific frequency sound wave is matched with the drinking water quality.

[0009] The temperature deviation between the actual temperature of the pre-uniform drinking water and the preset target temperature is calculated. The heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner through a fuzzy logic control algorithm to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters.

[0010] Based on the heating control parameters, the heating power and heating time of the water dispenser are adjusted to obtain drinking water with uniform temperature that meets the preset target temperature, thereby achieving temperature control of the water dispenser under different water qualities.

[0011] Optionally, spectral feedback analysis is performed on the near-infrared spectral data to obtain the drinking water quality type, including:

[0012] The first characteristic absorption signal of calcium ions under a preset first wavelength is extracted from the near-infrared spectral data, and the second characteristic absorption signal of magnesium ions under a preset second wavelength is extracted from the near-infrared spectral data.

[0013] Obtain the first characteristic absorption intensity range of samples of different standard water quality types in the preset water quality type database under the preset first wavelength feature, and the second characteristic absorption intensity range under the preset second wavelength feature;

[0014] Calculate the first characteristic absorption intensity value of the first characteristic absorption signal and the second characteristic absorption intensity value of the second characteristic absorption signal;

[0015] The water quality type of the standard water quality type sample whose first and second characteristic absorption intensity values ​​are both within the corresponding characteristic absorption intensity range is taken as the drinking water quality type. If there is no standard water quality type sample whose first and second characteristic absorption intensity values ​​are both within the corresponding characteristic absorption intensity range, the comprehensive matching degree of each standard water quality type sample is calculated, and the water quality type of the standard water quality type sample with the highest comprehensive matching degree is selected as the drinking water quality type.

[0016] Optionally, based on the drinking water quality type and the water temperature spectrum data, generating drinking water with a preliminarily uniform temperature after treatment with a specific frequency sound wave includes:

[0017] Based on the preset spatial division rules, the area where the drinking water is located in the water dispenser is divided into multiple sub-regions, and the temperature value of each sub-region is extracted from the water temperature spectrum data.

[0018] Based on the drinking water quality type, determine the range of sound wave frequency and the range of sound wave intensity;

[0019] Calculate the temperature difference between each sub-region temperature value and the corresponding average temperature value, and select the target sound wave frequency and target sound wave intensity that are compatible with the temperature difference of each sub-region from the sound wave frequency range and the sound wave intensity range, respectively.

[0020] Based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, the corresponding sub-region is subjected to specific frequency sound wave processing until the temperature difference value of all actual sub-regions is within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

[0021] Optionally, the sound wave frequency range and sound wave intensity range are determined according to the drinking water quality type, including:

[0022] Match a target standard water quality type sample that matches the drinking water quality type from the preset standard water quality acoustic parameter correspondence table, and take the reference acoustic frequency range corresponding to the target standard water quality type sample as the acoustic frequency range and the corresponding reference acoustic intensity range as the acoustic intensity range.

[0023] If there is no target standard water quality type sample that matches the drinking water quality type in the preset standard water quality acoustic parameter correspondence table, then the first reference water quality sample and the second reference water quality sample with the highest similarity to the drinking water quality type are selected from the preset standard water quality acoustic parameter correspondence table.

[0024] The first reference acoustic frequency range and the first reference acoustic intensity range are extracted from the first reference water quality sample, and the second reference acoustic frequency range and the second reference acoustic intensity range are extracted from the second reference water quality sample.

[0025] Calculate the first similarity between the drinking water quality type and the first reference water quality sample, and the second similarity between the drinking water quality type and the second reference water quality sample;

[0026] Based on the first similarity and the second similarity, the first reference sound wave frequency range and the second reference sound wave frequency range are weighted and integrated to obtain the sound wave frequency range. The first reference sound wave intensity range and the second reference sound wave intensity range are weighted and integrated to obtain the sound wave intensity range.

[0027] Optionally, based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, specific frequency sound wave processing is performed on the corresponding sub-region until the temperature difference values ​​of all actual sub-regions are within a preset temperature difference range, thereby obtaining drinking water with initially uniform temperature, including:

[0028] Based on the target acoustic parameters corresponding to each sub-region, a specific frequency acoustic wave is emitted to the corresponding sub-region to obtain the actual sub-region temperature value under the action of the specific acoustic wave, and the actual sub-region temperature difference value between each actual sub-region temperature value and the corresponding actual average temperature value is calculated.

[0029] The target acoustic parameters corresponding to the actual sub-region whose actual temperature difference value is not within the preset temperature difference range are enhanced to obtain the enhanced target acoustic parameters.

[0030] Based on the enhanced target acoustic wave parameters, the operation of transmitting specific frequency acoustic waves to the corresponding sub-regions is repeated until the temperature difference values ​​of all actual sub-regions are within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

[0031] Optionally, the heat absorption coefficient of the drinking water and the water temperature deviation are processed collaboratively using a fuzzy logic control algorithm to generate heating control parameters, including:

[0032] Using a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water is fuzzified according to the physical characteristic range of the heat absorption coefficient of the drinking water to obtain the corresponding fuzzy coefficient. The water temperature deviation is fuzzified according to the degree of deviation between the water temperature deviation and the preset target temperature to obtain the corresponding fuzzy deviation.

[0033] Based on the fuzzy coefficient and the fuzzy deviation, and combined with the heating characteristics of different drinking water quality types, target fuzzy control rules that match both the fuzzy coefficient and the fuzzy deviation are selected from a pre-built fuzzy control rule library.

[0034] Based on the target fuzzy control rule, a fuzzy output quantity is generated through a fuzzy inference algorithm. The fuzzy output quantity is then clarified to calculate the heating control parameters.

[0035] Optionally, the heating power and heating time of the water dispenser are adjusted according to the heating control parameters to obtain drinking water with a uniform temperature that meets the preset target temperature, including:

[0036] Based on the heat absorption coefficient of the drinking water, the first heating stage and the second heating stage of the drinking water with initially uniform temperature are determined.

[0037] Based on the heating control parameters, the first heating power and the first heating duration of the first heating stage are determined, as well as the second heating power and the second heating duration of the second heating stage.

[0038] The average temperature of the drinking water after heating, which is initially uniform in temperature, is collected under the heating effect of the first heating power and the first heating time, and the temperature deviation between the average temperature after heating and the preset target temperature is calculated.

[0039] If the temperature deviation is greater than a preset deviation threshold, the second heating power and the second heating time are adjusted to obtain the adjusted second heating power and the adjusted second heating time. If the temperature deviation is less than or equal to the preset deviation threshold, the second heating power is used as the adjusted second heating power and the second heating time is used as the adjusted second heating time.

[0040] Based on the adjusted second heating power and second heating duration, the heating control is performed on the drinking water whose temperature is initially uniform after being heated by the first heating power and first heating duration, so as to obtain drinking water with uniform temperature that meets the preset target temperature.

[0041] Secondly, this application provides a water dispenser temperature control system based on spectrum feedback, comprising:

[0042] The acquisition module is used to acquire near-infrared spectral data of drinking water in the water dispenser using near-infrared spectroscopy detection and to acquire water temperature spectral data of drinking water in the water dispenser using temperature spectrum detection.

[0043] The analysis module is used to perform spectral feedback analysis on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type.

[0044] The processing module is used to generate drinking water with a preliminarily uniform temperature after being processed by a specific frequency sound wave, based on the drinking water quality type and the water temperature spectrum data, so as to reduce the local temperature difference generated during the heating process of different water qualities, wherein the specific frequency sound wave is matched with the drinking water quality.

[0045] The calculation module is used to calculate the temperature deviation between the actual temperature and the preset target temperature of the drinking water with initially uniform temperature. Through a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters.

[0046] The adjustment module is used to adjust the heating power and heating time of the water dispenser according to the heating control parameters, so as to obtain drinking water with uniform temperature and in line with the preset target temperature, and realize the temperature control of the water dispenser under different water quality.

[0047] Thirdly, this application provides an electronic device, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor is configured to execute the computer program to implement the steps of a water dispenser temperature control method based on spectrum feedback as described in the first aspect above.

[0050] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of a water dispenser temperature control method based on spectrum feedback as described in the first aspect above.

[0051] This application provides a water dispenser temperature control method based on spectrum feedback. It utilizes near-infrared spectral detection to acquire near-infrared spectral data of the drinking water inside the dispenser, and temperature spectrum detection to acquire water temperature spectrum data of the drinking water inside the dispenser. Spectral feedback analysis is performed on the near-infrared spectral data to determine the drinking water quality type and the corresponding heat absorption coefficient of the drinking water. Based on the drinking water quality type and the water temperature spectrum data, drinking water with a pre-uniform temperature is generated after being processed by a specific frequency sound wave to reduce local temperature differences generated during the heating process of different water qualities. The specific frequency sound wave is matched with the drinking water quality. The temperature deviation between the actual temperature of the pre-uniform drinking water and the preset target temperature is calculated. A fuzzy logic control algorithm is used to collaboratively process the heat absorption coefficient of the drinking water and the temperature deviation to generate heating control parameters, including heating power adjustment parameters and heating time adjustment parameters. According to the heating control parameters, the heating power and heating time of the water dispenser are adjusted to obtain drinking water with a uniform temperature that meets the preset target temperature, thus achieving temperature control of the water dispenser under different water qualities. By acquiring near-infrared spectral data and water temperature spectrum data of drinking water, a data foundation is provided for subsequent differentiated temperature control strategies based on water quality differences. This enables the system to understand the energy demand characteristics of different water qualities during the heating process, providing a basis for precise energy input. It alleviates the problem of uneven heating caused by water quality differences, making the water temperature distribution more uniform. It overcomes the limitations of fixed strategies in response speed and control accuracy, improving the system's adaptability to nonlinear heating processes. It achieves closed-loop control from water quality sensing to heating execution, ensuring that hot water with uniform and accurate temperatures is output under different influent conditions, enhancing the intelligence and environmental adaptability of the temperature control system. Furthermore, this application divides the heating chamber into multiple regions, monitors the temperature distribution differences in each region, and determines the adjustable range of acoustic parameters based on the water quality type. This allows for the intelligent matching of the optimal acoustic frequency and intensity for different temperature difference regions, achieving precise control of the local thermal balance of the water body. This solves the problem of local overheating or heating lag that easily occurs in water bodies with high mineral content, and also provides a more stable and consistent initial temperature field for the subsequent main heating stage. This reduces the difficulty of temperature control from the source and supports the improvement of overall temperature control accuracy and response consistency. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A schematic flowchart illustrating a water dispenser temperature control method based on spectrum feedback, provided in an embodiment of this application;

[0054] Figure 2 A schematic diagram illustrating a specific implementation of a water dispenser temperature control method based on spectrum feedback, provided in this application embodiment;

[0055] Figure 3 A schematic diagram of a water dispenser temperature control system based on spectrum feedback provided in an embodiment of this application; Detailed Implementation

[0056] To address the issues of unstable heating performance and uneven temperature distribution caused by differences in drinking water quality in home and office settings, this application simultaneously acquires the characteristics of water composition and the dynamic changes in water temperature, overcoming the limitations of single-parameter perception. Furthermore, by analyzing water quality types to obtain their heat absorption characteristics, and combining this with water temperature spectrum data to drive preprocessing of specific frequency sound waves matched to the water quality, it actively suppresses local temperature differences in the initial heating stage, solving the problem of poor adaptability of fixed sound wave strategies. Based on this, a fuzzy logic control algorithm is introduced to collaboratively process water quality-related heat absorption capacity and temperature deviation, dynamically generating optimal heating power and duration parameters. This replaces traditional open-loop or simple feedback control, addressing the nonlinear response of the heating process and avoiding overshoot and hysteresis. This improves the uniformity, accuracy, and consistency of water dispenser heating under different water quality conditions.

[0057] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] The core of this application is to provide a water dispenser temperature control method based on spectrum feedback, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0059] Step 101: Use near-infrared spectroscopy to obtain near-infrared spectral data of the drinking water in the water dispenser, and use temperature spectrum detection to obtain water temperature spectrum data of the drinking water in the water dispenser.

[0060] In this step, near-infrared spectroscopy detection refers to the method of irradiating drinking water with near-infrared light and receiving reflected or transmitted light to obtain spectral data; water dispenser refers to equipment used to heat and provide drinking water; drinking water refers to water that can be drunk directly; near-infrared spectral data refers to the spectral response signal of drinking water in the near-infrared band; temperature spectrum detection refers to the method of collecting the temperature of drinking water at different locations through a temperature sensor array and forming spectral data; water temperature spectrum data refers to the spectral signal reflecting the temperature distribution of drinking water at different locations.

[0061] In this embodiment, a near-infrared spectroscopy detection device emits near-infrared light into the drinking water in the water dispenser, receives the near-infrared light signal reflected by the drinking water, and converts it into near-infrared spectral data; at the same time, a temperature sensor array collects temperature signals from different areas of the drinking water in the water dispenser and converts them into water temperature spectrum data.

[0062] Step 102: Perform spectral feedback analysis on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type.

[0063] In this step, near-infrared spectral data refers to the spectral response signal of drinking water in the near-infrared band; spectral feedback analysis refers to the process of extracting and comparing spectral data to obtain water quality information; drinking water quality type refers to the water quality category classified according to the ionic composition of the water; drinking water heat absorption coefficient refers to the quantitative parameter of the ability of drinking water to absorb heat, which is related to the water quality type.

[0064] Step 103: Based on the drinking water quality type and the water temperature spectrum data, generate drinking water with a preliminary uniform temperature after being processed by a specific frequency sound wave, so as to reduce the local temperature difference generated during the heating process of different water qualities. The specific frequency sound wave is matched with the drinking water quality.

[0065] In this step, drinking water quality type refers to the category of water quality; water temperature spectrum data refers to the spectrum signal reflecting the temperature distribution of drinking water; specific frequency sound wave processing refers to the process of applying a specific frequency sound wave that matches the water quality to the drinking water; drinking water with initially uniform temperature refers to drinking water whose local temperature difference is within a preset range after sound wave processing; local temperature difference refers to the temperature difference between different sub-regions of drinking water; specific frequency sound wave refers to a specific frequency sound wave that matches the drinking water quality; and drinking water quality refers to the characteristics of drinking water, such as its composition and ion content.

[0066] Step 104: Calculate the temperature deviation between the actual temperature of the preliminarily uniform drinking water and the preset target temperature. Using a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters.

[0067] In this step, the actual temperature refers to the actual measured temperature of the drinking water when the temperature is initially uniform; the preset target temperature refers to the pre-set target heating temperature of the drinking water; the water temperature deviation refers to the difference between the actual temperature and the preset target temperature; the fuzzy logic control algorithm refers to the control algorithm that processes the input parameters through processes such as fuzzification, rule matching, and declarative analysis; the drinking water heat absorption coefficient refers to the quantitative parameter of its heat absorption capacity; the heating control parameter refers to the parameter used to adjust the heating process; the heating power adjustment parameter refers to the parameter used to adjust the heating power; and the heating time adjustment parameter refers to the parameter used to adjust the heating time.

[0068] Step 105: Adjust the heating power and heating time of the water dispenser according to the heating control parameters to obtain drinking water with uniform temperature that meets the preset target temperature, thereby achieving temperature control of the water dispenser under different water qualities.

[0069] In this step, heating control parameters refer to the parameters used to adjust the heating process; heating power refers to the power of the water dispenser when heating; heating time refers to the heating time of the water dispenser; and drinking water with uniform temperature and meeting the preset target temperature refers to drinking water with uniform temperature and reaching the preset target temperature.

[0070] This application embodiment obtains near-infrared spectral data and water temperature spectrum data to determine the water quality type and heat absorption coefficient. After processing with a specific frequency sound wave, it obtains initially uniform drinking water. Then, it generates heating parameters and adjusts the heating process through fuzzy logic control, thereby achieving precise temperature control of the water dispenser under different water qualities and improving the uniformity and accuracy of drinking water temperature control.

[0071] This application provides a specific embodiment. Step 102 involves performing spectral feedback analysis on the near-infrared spectral data to obtain the drinking water quality type, specifically including the following steps:

[0072] Step 201: Extract the first characteristic absorption signal of calcium ions under a preset first wavelength from the near-infrared spectral data, and extract the second characteristic absorption signal of magnesium ions under a preset second wavelength from the near-infrared spectral data.

[0073] In this step, the preset first wavelength characteristic refers to a pre-defined wavelength range related to the absorption characteristics of calcium ions; the first characteristic absorption signal refers to the spectral absorption signal generated by calcium ions under the preset first wavelength characteristic; the preset second wavelength characteristic refers to a pre-defined wavelength range related to the absorption characteristics of magnesium ions; the second characteristic absorption signal refers to the spectral absorption signal generated by magnesium ions under the preset second wavelength characteristic; and the near-infrared spectral data refers to the spectral response signal of drinking water in the near-infrared band.

[0074] In this embodiment, a preset first wavelength feature corresponding to calcium ion absorption and a preset second wavelength feature corresponding to magnesium ion absorption are determined. The spectral bands corresponding to these two wavelength features are located in near-infrared spectral data, and the first characteristic absorption signal generated by calcium ions and the second characteristic absorption signal generated by magnesium ions are separated from them.

[0075] Step 202: Obtain the first characteristic absorption intensity range of different standard water quality type samples in the preset water quality type database under the preset first wavelength feature, and the second characteristic absorption intensity range under the preset second wavelength feature.

[0076] In this step, the preset water quality type database refers to a database that stores multiple known water quality types and their spectral characteristic data; the standard water quality type sample refers to a standard water sample with a clear water quality type in the preset water quality type database; the first characteristic absorption intensity range refers to the characteristic absorption intensity range of the standard water quality type sample under the preset first wavelength characteristic; and the second characteristic absorption intensity range refers to the characteristic absorption intensity range of the standard water quality type sample under the preset second wavelength characteristic.

[0077] In this embodiment, a preset water quality type database is invoked to filter out all standard water quality type samples. The first characteristic absorption intensity range under a preset first wavelength feature and the second characteristic absorption intensity range under a preset second wavelength feature of each standard water quality type sample are extracted and sorted according to the standard water quality type samples.

[0078] Step 203: Calculate the first characteristic absorption intensity value of the first characteristic absorption signal and the second characteristic absorption intensity value of the second characteristic absorption signal.

[0079] In this step, the first characteristic absorption intensity value refers to the intensity quantization value of the first characteristic absorption signal; the second characteristic absorption intensity value refers to the intensity quantization value of the second characteristic absorption signal.

[0080] In this embodiment of the application, the first characteristic absorption signal is subjected to intensity quantization processing, and the calculation formula is: first characteristic absorption intensity value = peak intensity of the first characteristic absorption signal; the second characteristic absorption signal is processed in the same way, and the calculation formula is: second characteristic absorption intensity value = peak intensity of the second characteristic absorption signal.

[0081] Step 204: The water quality type of the standard water quality type sample whose first characteristic absorption intensity value and second characteristic absorption intensity value are both within the corresponding characteristic absorption intensity range is taken as the drinking water quality type. If there is no standard water quality type sample whose first characteristic absorption intensity value and second characteristic absorption intensity value are both within the corresponding characteristic absorption intensity range, the comprehensive matching degree of each standard water quality type sample is calculated, and the water quality type of the standard water quality type sample with the highest comprehensive matching degree is selected as the drinking water quality type.

[0082] In this step, the overall matching degree refers to the combined quantitative value of the degree of matching between the first characteristic absorption intensity value and the first characteristic absorption intensity range of the standard water quality type sample, and the degree of matching between the second characteristic absorption intensity value and the second characteristic absorption intensity range of the standard water quality type sample.

[0083] In this embodiment, the first characteristic absorption intensity value is compared with the first characteristic absorption intensity range of each standard water quality type sample, and the second characteristic absorption intensity value is compared with the second characteristic absorption intensity range of the corresponding standard water quality type sample. If both are within the corresponding range of the same standard water quality type sample, then the water quality type of that standard water quality type sample is taken as the drinking water quality type. If the above situation does not exist, the comprehensive matching degree is calculated for each standard water quality type sample. The calculation formula is: comprehensive matching degree = (α + β) ÷ 2, where α is the matching degree between the first characteristic absorption intensity value and the first characteristic absorption intensity range of the standard water quality type sample. When it is within the range, α = 1; otherwise, α = 1 - |first characteristic absorption intensity value - midpoint of range| ÷ half width of range), and β is the matching degree between the second characteristic absorption intensity value and the second characteristic absorption intensity range of the standard water quality type sample. The calculation method is the same as α. The water quality type of the standard water quality type sample with the highest comprehensive matching degree is selected as the drinking water quality type.

[0084] This application embodiment achieves accurate identification of drinking water quality types by extracting the absorption signals of characteristic ions, comparing the absorption intensity range of standard samples, and calculating the comprehensive matching degree, providing a reliable basis for subsequent temperature control based on water quality.

[0085] This application provides a specific embodiment, such as Figure 2 As shown, step 103, based on the drinking water quality type and the water temperature spectrum data, generates drinking water with a preliminarily uniform temperature after being processed by a specific frequency sound wave, specifically including the following steps:

[0086] Step 301: Based on the preset spatial division rules, the area where the drinking water is located in the water dispenser is divided into multiple sub-regions, and the temperature value of each sub-region is extracted from the water temperature spectrum data.

[0087] In this step, the preset spatial division rule refers to the pre-set rule that divides the drinking water area in the water dispenser into multiple parts; the sub-region refers to the small drinking water area obtained after being divided according to the preset spatial division rule; the sub-region temperature value refers to the temperature value corresponding to each sub-region; and the water temperature spectrum data refers to the spectrum signal that reflects the temperature distribution of drinking water at different locations.

[0088] In this embodiment of the application, the area where the drinking water is located in the water dispenser is divided into multiple sub-regions of the same size according to a preset spatial division rule. The temperature signal corresponding to each sub-region is located from the water temperature spectrum data, and the sub-region temperature value of each sub-region is obtained by conversion.

[0089] Step 302: Determine the range of sound wave frequency and the range of sound wave intensity based on the type of drinking water quality.

[0090] In this step, the drinking water quality type refers to the category of drinking water quality determined by near-infrared spectroscopy analysis; the sound wave frequency range refers to the range of sound wave frequencies suitable for this water quality type; and the sound wave intensity range refers to the range of sound wave intensity suitable for this water quality type.

[0091] In this embodiment, a preset standard water quality acoustic parameter correspondence table is called to find the corresponding acoustic parameter range according to the drinking water quality type. If a completely matching standard water quality type sample exists, the acoustic frequency range and acoustic intensity range corresponding to the sample are directly obtained; if not, the acoustic frequency range and acoustic intensity range are determined by similarity calculation and weighted integration.

[0092] Step 303: Calculate the temperature difference between each sub-region temperature value and the corresponding average temperature value, and select the target sound wave frequency and target sound wave intensity that are compatible with the temperature difference of each sub-region from the sound wave frequency range and the sound wave intensity range.

[0093] In this step, the average temperature value refers to the arithmetic mean of the temperature values ​​of all sub-regions; the sub-region temperature difference value refers to the difference between the temperature value of each sub-region and the average temperature value; the target sound wave frequency refers to a specific frequency selected from the sound wave frequency range that matches the sub-region temperature difference value; and the target sound wave intensity refers to a specific intensity selected from the sound wave intensity range that matches the sub-region temperature difference value.

[0094] In this embodiment, the average temperature value of all sub-regions is calculated using the formula: Average temperature value = (Sub-region temperature value 1 + Sub-region temperature value 2 + ... + Sub-region temperature value n) ÷ n, where n is the number of sub-regions; then, the sub-region temperature difference value of each sub-region is calculated using the formula: Sub-region temperature difference value = Sub-region temperature value - Average temperature value; based on the preset matching relationship between the sub-region temperature difference value and the acoustic parameters, a target acoustic frequency is selected for each sub-region from the acoustic frequency range, and a target acoustic intensity is selected from the acoustic intensity range.

[0095] Step 304: Based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, perform specific frequency sound wave processing on the corresponding sub-region until the temperature difference value of all actual sub-regions is within the preset temperature difference range, and obtain drinking water with initially uniform temperature.

[0096] In this step, the actual sub-region temperature difference value refers to the difference between the actual temperature and the actual average temperature of each sub-region after processing with a specific frequency of sound waves; the preset temperature difference range refers to the pre-set acceptable sub-region temperature difference range; and drinking water with initially uniform temperature refers to drinking water in which the actual sub-region temperature difference values ​​are all within the preset temperature difference range.

[0097] In this embodiment, based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, the sound wave generator is controlled to emit a specific frequency sound wave to the corresponding sub-region. After processing, the actual temperature of each sub-region is re-acquired, and the actual average temperature and actual sub-region temperature difference are calculated. The calculation formula is: actual sub-region temperature difference = actual sub-region temperature - actual average temperature. It is then checked whether the actual sub-region temperature difference is within the preset temperature difference range. If there are sub-regions that do not meet the standard, their target sound wave frequency and intensity are adjusted and the processing is repeated until the actual sub-region temperature difference meets the requirements, thus obtaining drinking water with a preliminary uniform temperature.

[0098] This application embodiment achieves preliminary homogenization of drinking water temperature by spatially dividing the area to obtain sub-region temperatures, matching water quality to determine the range of acoustic parameters, selectively choosing acoustic parameters and repeatedly processing them, thereby reducing local temperature differences when heating different water qualities.

[0099] For example, in a specific embodiment, the drinking water quality type is hard water A. According to a preset spatial division rule, the hard water A area within the A-brand water dispenser is divided into 6 sub-areas. The temperature values ​​for each sub-area are extracted from the water temperature spectrum data as 22℃, 25℃, 28℃, 23℃, 26℃, and 27℃, respectively. Based on the hard water A type, the sound wave frequency range is determined to be 25 to 35 kHz, and the sound wave intensity range is 0.8 to 1.5 W / cm². 2The average temperature was calculated as (22+25+28+23+26+27)÷6 = 25℃. The temperature differences for each sub-region were -3℃, 0℃, 3℃, -2℃, 1℃, and 2℃, respectively. Based on the temperature difference adaptation relationship, a target sound wave frequency of 35kHz and an intensity of 1.5W / cm were selected for the sub-region with a temperature difference of 3℃. 2 For the sub-region with a temperature difference of -3℃, a frequency of 32kHz and a bandwidth of 1.3W / cm² were selected. 2 The parameters for the remaining sub-regions are selected according to the corresponding temperature difference. After the sound waves are emitted and processed according to these parameters, the actual temperature difference values ​​of the sub-regions are re-detected and found to be within the preset temperature difference range of -1℃ to 1℃, thus obtaining hard water A with a preliminary uniform temperature.

[0100] This application provides a specific embodiment. Step 302, determining the sound wave frequency range and sound wave intensity range based on the drinking water quality type, specifically includes the following steps:

[0101] Step 311: Match a target standard water quality type sample that matches the drinking water quality type from the preset standard water quality acoustic parameter correspondence table, and take the reference acoustic frequency range corresponding to the target standard water quality type sample as the acoustic frequency range and the corresponding reference acoustic intensity range as the acoustic intensity range.

[0102] In this step, the preset standard water quality acoustic parameter correspondence table refers to a pre-established table storing standard water quality type samples and their corresponding acoustic parameter ranges; the target standard water quality type sample refers to the standard sample in the preset standard water quality acoustic parameter correspondence table that is consistent with the drinking water quality type; the reference acoustic frequency range refers to the acoustic frequency range corresponding to the target standard water quality type sample; the reference acoustic intensity range refers to the acoustic intensity range corresponding to the target standard water quality type sample; and the drinking water quality type refers to the drinking water quality category determined by near-infrared spectroscopy analysis.

[0103] In this embodiment of the application, a preset standard water quality acoustic parameter correspondence table is called, and the drinking water quality type is compared with all standard water quality type samples in the table one by one. If there is a target standard water quality type sample that is completely consistent with the drinking water quality type, the reference acoustic frequency range and reference acoustic intensity range corresponding to the sample are directly extracted and used as the acoustic frequency range and acoustic intensity range, respectively.

[0104] Step 312: If there is no target standard water quality type sample that matches the drinking water quality type in the preset standard water quality acoustic parameter correspondence table, then select the first reference water quality sample and the second reference water quality sample that have the highest similarity to the drinking water quality type from the preset standard water quality acoustic parameter correspondence table.

[0105] In this step, the first reference water quality sample refers to the first standard sample in the preset standard water quality acoustic parameter correspondence table that has the highest similarity to the drinking water quality type; the second reference water quality sample refers to the second standard sample in the preset standard water quality acoustic parameter correspondence table that has the second highest similarity to the drinking water quality type.

[0106] In this embodiment of the application, if there is no target standard water quality type sample that matches the drinking water quality type in the preset standard water quality acoustic parameter correspondence table, the similarity between the drinking water quality type and each standard water quality type sample in the table is calculated, and the standard water quality type samples ranked first and second are selected as the first reference water quality sample and the second reference water quality sample, respectively.

[0107] Step 313: Extract the first reference sound wave frequency range and the first reference sound wave intensity range from the first reference water quality sample, and extract the second reference sound wave frequency range and the second reference sound wave intensity range from the second reference water quality sample.

[0108] In this step, the first reference acoustic frequency range refers to the acoustic frequency range corresponding to the first reference water quality sample; the first reference acoustic intensity range refers to the acoustic intensity range corresponding to the first reference water quality sample; the second reference acoustic frequency range refers to the acoustic frequency range corresponding to the second reference water quality sample; and the second reference acoustic intensity range refers to the acoustic intensity range corresponding to the second reference water quality sample.

[0109] In this embodiment of the application, the record of the first reference water quality sample is searched from the preset standard water quality acoustic parameter correspondence table, and the corresponding first reference acoustic frequency range and first reference acoustic intensity range are extracted; at the same time, the record of the second reference water quality sample is searched, and the corresponding second reference acoustic frequency range and second reference acoustic intensity range are extracted.

[0110] Step 314: Calculate the first similarity between the drinking water quality type and the first reference water quality sample, and the second similarity between the drinking water quality type and the second reference water quality sample.

[0111] In this step, the first similarity refers to the quantitative value of the similarity between the drinking water quality type and the first reference water quality sample; the second similarity refers to the quantitative value of the similarity between the drinking water quality type and the second reference water quality sample.

[0112] In this embodiment of the application, a first similarity is calculated based on the drinking water quality type and the characteristic absorption intensity values ​​of calcium and magnesium ions in the first reference water quality sample. The calculation formula is: First similarity = 1 - |(Drinking water calcium ion intensity - First reference calcium ion intensity) ÷ (Upper limit of the first reference calcium ion intensity range - Lower limit of the first reference calcium ion intensity range) + (Drinking water magnesium ion intensity - First reference magnesium ion intensity) ÷ (Upper limit of the first reference magnesium ion intensity range - Lower limit of the first reference magnesium ion intensity range)| ÷ 2; The second similarity is calculated in the same way. The calculation formula is: Second similarity = 1 - |(Drinking water calcium ion intensity - Second reference calcium ion intensity) ÷ (Upper limit of the second reference calcium ion intensity range - Lower limit of the second reference calcium ion intensity range) + (Drinking water magnesium ion intensity - Second reference magnesium ion intensity) ÷ (Upper limit of the second reference magnesium ion intensity range - Lower limit of the second reference magnesium ion intensity range)| ÷ 2.

[0113] Step 315: Based on the first similarity and the second similarity, the first reference sound wave frequency range and the second reference sound wave frequency range are weighted and integrated to obtain the sound wave frequency range. The first reference sound wave intensity range and the second reference sound wave intensity range are weighted and integrated to obtain the sound wave intensity range.

[0114] In this step, the sound wave frequency range refers to the range of sound wave frequencies suitable for the drinking water quality type obtained after weighted integration; the sound wave intensity range refers to the range of sound wave intensity suitable for the drinking water quality type obtained after weighted integration.

[0115] In this embodiment, the first reference sound wave frequency range and the second reference sound wave frequency range are weighted and integrated. The calculation formula is as follows: lower limit of the integrated frequency range = lower limit of the first reference frequency × first similarity + lower limit of the second reference frequency × second similarity; upper limit of the integrated frequency range = upper limit of the first reference frequency × first similarity + upper limit of the second reference frequency × second similarity, thus obtaining the sound wave frequency range. The first reference sound wave intensity range and the second reference sound wave intensity range are similarly weighted and integrated. The calculation formula is as follows: lower limit of the integrated intensity range = lower limit of the first reference intensity × first similarity + lower limit of the second reference intensity × second similarity; upper limit of the integrated intensity range = upper limit of the first reference intensity × first similarity + upper limit of the second reference intensity × second similarity, thus obtaining the sound wave intensity range.

[0116] This application embodiment accurately determines the sound wave frequency range and intensity range that are suitable for the drinking water quality type by matching standard samples, selecting similar samples, extracting parameters, calculating similarity and weighted integration, providing a reliable parameter basis for subsequent sound wave processing.

[0117] For example, in a specific embodiment, the drinking water quality type is hard water A. A preset standard water quality acoustic parameter correspondence table is invoked. In the table, the reference acoustic frequency range for the target standard water quality type sample corresponding to hard water A is 25 to 35 kHz, and the reference acoustic intensity range is 0.8 to 1.5 W / cm². 2 Therefore, this range is directly used as the sound wave frequency range and sound wave intensity range. If there is no target sample of hard water A in the table, the first reference water quality sample with the highest similarity, hard water B (similarity 0.8), and the second reference water quality sample, hard water C (similarity 0.7), are selected. The first reference sound wave frequency range of hard water B (28 to 40 kHz) and the first reference intensity range of hard water B (1.0 to 1.8 W / cm²) are extracted. 2 The second reference acoustic wave frequency range for hard water C is 20 to 30 kHz, and the second reference intensity range is 0.6 to 1.2 W / cm². 2 The first similarity was calculated to be 0.8, and the second similarity was 0.7. After weighted integration, the lower limit of the sound wave frequency range was 28×0.8+20×0.7=22.4+14=36.4kHz, and the upper limit was 40×0.8+30×0.7=32+21=53kHz. Similarly, the range of sound wave intensity was obtained, which can be used as the range of sound wave parameters for hard water A.

[0118] This application provides a specific embodiment. Step 302 involves processing the corresponding sub-regions with specific frequency sound waves based on the target sound wave frequency and target sound wave intensity, until the temperature difference values ​​of all actual sub-regions are within a preset temperature difference range, thus obtaining drinking water with initially uniform temperature. The specific steps include:

[0119] Step 321: Based on the target acoustic parameters corresponding to each sub-region, emit acoustic waves of a specific frequency to the corresponding sub-region to obtain the actual sub-region temperature value under the action of the specific acoustic waves, and calculate the actual sub-region temperature difference between each actual sub-region temperature value and the corresponding actual average temperature value.

[0120] In this step, the target acoustic wave parameter refers to the combination of the target acoustic wave frequency and the target acoustic wave intensity corresponding to each sub-region; the specific frequency acoustic wave refers to the acoustic wave of a specific frequency that matches the drinking water quality; under the action of a specific acoustic wave refers to the state of the specific frequency acoustic wave acting on the sub-region; the actual sub-region temperature value refers to the actual temperature value of each sub-region after the action of the specific acoustic wave; the actual average temperature value refers to the arithmetic mean of the temperature values ​​of all actual sub-regions after the action of the specific acoustic wave; and the actual sub-region temperature difference value refers to the difference between the temperature value of each actual sub-region and the actual average temperature value.

[0121] In this embodiment, based on the target acoustic parameters corresponding to each sub-region, the acoustic wave emitting device is controlled to emit acoustic waves of a specific frequency to the corresponding sub-region. After a preset duration, the temperature of each sub-region is collected to obtain the actual sub-region temperature value. The actual average temperature value is calculated using the formula: Actual average temperature value = (Actual sub-region temperature value 1 + Actual sub-region temperature value 2 + ... + Actual sub-region temperature value n) / n, where n is the number of sub-regions. Then, the actual sub-region temperature difference value for each sub-region is calculated using the formula: Actual sub-region temperature difference value = Actual sub-region temperature value - Actual average temperature value.

[0122] Step 322: Enhance the target acoustic parameters corresponding to the actual sub-regions whose actual sub-region temperature difference values ​​are not within the preset temperature difference range to obtain enhanced target acoustic parameters.

[0123] In this step, the enhanced target acoustic parameters refer to the acoustic parameters obtained after enhancing the target acoustic parameters corresponding to the sub-regions where the actual sub-region temperature difference value does not meet the standard.

[0124] In this embodiment, all actual sub-region temperature difference values ​​are compared with preset temperature difference ranges, and sub-regions whose actual sub-region temperature difference values ​​are not within the preset temperature difference range are selected. For these sub-regions, the target sound wave frequency and target sound wave intensity in their corresponding target sound wave parameters are adjusted according to the degree of deviation between the actual sub-region temperature difference value and the preset temperature difference range, so as to obtain the enhanced target sound wave parameters.

[0125] Step 323: Based on the enhanced target acoustic wave parameters, repeat the operation of transmitting specific frequency acoustic waves to the corresponding sub-regions until the temperature difference values ​​of all actual sub-regions are within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

[0126] In this step, the preset temperature difference range refers to the pre-defined acceptable temperature difference range of the actual sub-regions; drinking water with initially uniform temperature refers to drinking water where the temperature difference values ​​of all actual sub-regions are within the preset temperature difference range.

[0127] In this embodiment, based on the enhanced target acoustic wave parameters, the acoustic wave emitting device is controlled to re-emit a specific frequency acoustic wave to the corresponding sub-region. After the same preset duration, the actual sub-region temperature value is collected again, and the actual average temperature value and the actual sub-region temperature difference value are calculated. The above operation is repeated until the actual sub-region temperature difference values ​​are all within the preset temperature difference range. At this time, the drinking water is drinking water with a preliminary uniform temperature.

[0128] This application embodiment ensures that the temperature difference in each sub-region of drinking water meets the preset range by emitting sound waves of a specific frequency, calculating the actual temperature difference, amplifying substandard parameters, and repeatedly processing them, thus achieving initial temperature uniformity and laying the foundation for subsequent precise heating.

[0129] This application provides a specific embodiment. Step 104 involves using a fuzzy logic control algorithm to collaboratively process the heat absorption coefficient of the drinking water and the water temperature deviation to generate heating control parameters. This specifically includes the following steps:

[0130] Step 401: Using a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water is fuzzified according to the physical characteristic range of the heat absorption coefficient of the drinking water to obtain the corresponding fuzzy coefficient. The water temperature deviation is fuzzified according to the degree of deviation between the water temperature deviation and the preset target temperature to obtain the corresponding fuzzy deviation.

[0131] In this step, the physical characteristic range refers to the possible range of values ​​for the heat absorption coefficient of drinking water; the fuzzy coefficient refers to the fuzzy quantity obtained after fuzzification of the heat absorption coefficient of drinking water; the deviation degree refers to the magnitude of the deviation between the water temperature deviation and the preset target temperature; the fuzzy deviation refers to the fuzzy quantity obtained after fuzzification of the water temperature deviation; the heat absorption coefficient of drinking water refers to the quantitative parameter of the heat absorption capacity of drinking water; the water temperature deviation refers to the difference between the actual temperature and the preset target temperature; and the preset target temperature refers to the pre-set target heating temperature of drinking water.

[0132] In this embodiment, the physical characteristic range of the drinking water heat absorption coefficient is determined, and this range is divided into three fuzzy subsets: low, medium, and high. The membership degree of the drinking water heat absorption coefficient to each subset is calculated using a membership function, and the fuzzy subset with the highest membership degree is selected as the corresponding fuzzy coefficient. At the same time, based on the degree of deviation between the water temperature deviation and the preset target temperature, the water temperature deviation is divided into seven fuzzy subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership degree of the water temperature deviation to each subset is calculated, and the fuzzy subset with the highest membership degree is selected as the corresponding fuzzy deviation.

[0133] Step 402: Based on the fuzzy coefficient and the fuzzy deviation, and combined with the heating characteristics of different drinking water quality types, select a target fuzzy control rule from the pre-built fuzzy control rule library that matches both the fuzzy coefficient and the fuzzy deviation.

[0134] In this step, heating characteristics refer to the heat absorption and temperature rise characteristics exhibited by different types of drinking water during the heating process; the pre-built fuzzy control rule base refers to the set of rules that are pre-established and store the correspondence between fuzzy coefficients, fuzzy deviations and control strategies; the target fuzzy control rule refers to the rule selected from the pre-built fuzzy control rule base that matches both the fuzzy coefficients and fuzzy deviations.

[0135] In this embodiment, a pre-built fuzzy control rule library is invoked. Each rule in the library combines the heating characteristics of different drinking water quality types. The fuzzy coefficient and fuzzy deviation are matched with the antecedents of all rules in the rule library. The rule whose antecedent is completely consistent with the fuzzy coefficient and fuzzy deviation is selected as the target fuzzy control rule. If there are multiple matching rules, the final target fuzzy control rule is determined by the rule priority.

[0136] Step 403: Based on the target fuzzy control rule, generate fuzzy output through fuzzy inference algorithm, and perform defuzzification processing on the fuzzy output to calculate the heating control parameters.

[0137] In this step, the fuzzy inference algorithm refers to the process of deriving the fuzzy output quantity from the fuzzy input quantity according to the target fuzzy control rule; the fuzzy output quantity refers to the fuzzy form of the control quantity obtained by the fuzzy inference algorithm; the defuzzification process refers to the process of converting the fuzzy output quantity into a precise value; and the heating control parameters refer to the parameters used to adjust the heating process.

[0138] In this embodiment, based on the target fuzzy control rule, a fuzzy inference algorithm is used to derive the fuzzy output quantity by taking the fuzzy coefficient and fuzzy deviation as inputs. The membership degree of each fuzzy subset is calculated, such as 0.9 for the large subset and 0.1 for the medium subset. The fuzzy output quantity is then processed to make it clear. The calculation formula is: precise output quantity = [Σ(fuzzy subset center value × membership degree)] / Σ membership degree. The heating power adjustment parameter and heating time adjustment parameter are obtained respectively and integrated into the heating control parameter.

[0139] This application embodiment achieves precise control of the heating process of drinking water with different water qualities by fuzzifying the heat absorption coefficient and water temperature deviation, matching fuzzy control rules, reasoning and clarifying to obtain heating control parameters, thereby improving the adaptability and accuracy of temperature control.

[0140] For example, consider hard water A with initially uniform temperature, an actual temperature of 25℃, a preset target temperature of 50℃, a drinking water heat absorption coefficient of 0.8, and a water temperature deviation of 25-50=-25℃. The physical characteristic range is 0.5-1.2, and 0.8 belongs to the medium fuzzy subset, resulting in a medium fuzzy coefficient. The deviation of -25℃ is negatively large, resulting in a negatively large fuzzy deviation. In the pre-built fuzzy control rule library, considering the heating characteristics of hard water (slow heating), a rule exists where, if the fuzzy coefficient is medium and the fuzzy deviation is negatively large, then the heating power adjustment is large and the heating time adjustment is long. This rule is selected as the target fuzzy control rule. Through fuzzy inference algorithm, the fuzzy output is obtained as: heating power adjustment: large, membership degree 0.9; heating time adjustment: long, membership degree 0.9. After declarative processing, the calculated heating power adjustment parameter is 800W and the heating time adjustment parameter is 3 minutes, which are the heating control parameters.

[0141] This application provides a specific embodiment. Step 105 involves adjusting the heating power and heating time of the water dispenser according to the heating control parameters to obtain drinking water with a uniform temperature that meets the preset target temperature. This specifically includes the following steps:

[0142] Step 501: Based on the heat absorption coefficient of the drinking water, determine the first heating stage and the second heating stage of the drinking water with initially uniform temperature.

[0143] In this step, the first heating stage refers to the initial stage of the heating process; the second heating stage refers to the stage after the first heating stage; the drinking water heat absorption coefficient refers to the quantitative parameter of the ability of drinking water to absorb heat; and the drinking water with initially uniform temperature refers to drinking water whose temperature difference between sub-regions is within a preset range after being treated with a specific frequency of sound waves.

[0144] In this embodiment, the heating stage is divided into a first heating stage and a second heating stage according to the magnitude of the heat absorption coefficient of drinking water. If the heat absorption coefficient of drinking water is high, the first heating stage is set as a rapid heating stage and the second heating stage is set as a heat preservation and fine adjustment stage. If the heat absorption coefficient of drinking water is low, the first heating stage is set as a slow heating stage and the second heating stage is set as a heat consolidation stage, so as to adapt to drinking water with different heat absorption characteristics.

[0145] Step 502: Based on the heating control parameters, determine the first heating power and first heating duration of the first heating stage, and the second heating power and second heating duration of the second heating stage.

[0146] In this step, the first heating power refers to the heating power of the first heating stage; the first heating duration refers to the heating time of the first heating stage; the second heating power refers to the heating power of the second heating stage; the second heating duration refers to the heating time of the second heating stage; and the heating control parameters refer to the parameters used to adjust the heating process, including heating power adjustment parameters and heating duration adjustment parameters.

[0147] In this embodiment, heating power adjustment parameters are extracted from heating control parameters and allocated to the first heating stage and the second heating stage according to a preset ratio to obtain the first heating power and the second heating power; at the same time, heating duration adjustment parameters are extracted from heating control parameters and allocated to the two stages according to the same preset ratio to obtain the first heating duration and the second heating duration, ensuring that the power and duration of the two stages are adapted to the overall heating requirements.

[0148] Step 503: Collect the average temperature of the drinking water after heating based on the first heating power and the first heating time, and calculate the temperature deviation between the average temperature after heating and the preset target temperature.

[0149] In this step, the average temperature after heating refers to the average temperature of the drinking water after the first heating stage is completed and the temperature is initially uniform; the temperature deviation refers to the difference between the average temperature after heating and the preset target temperature; the preset target temperature refers to the preset target heating temperature of the drinking water; the first heating power refers to the heating power of the first heating stage; and the first heating duration refers to the heating time of the first heating stage.

[0150] In this embodiment of the application, drinking water with a initially uniform temperature is heated according to a first heating power and a first heating time. After heating, the temperature of the drinking water at multiple locations is collected by a temperature sensor, and the average temperature after heating is calculated. The calculation formula is: average temperature after heating = (sum of temperature values ​​at each location) / number of locations; then the temperature deviation is calculated. The calculation formula is: temperature deviation = average temperature after heating - preset target temperature.

[0151] Step 504: If the temperature deviation is greater than the preset deviation threshold, the second heating power and the second heating time are adjusted to obtain the adjusted second heating power and the adjusted second heating time. If the temperature deviation is less than or equal to the preset deviation threshold, the second heating power is used as the adjusted second heating power and the second heating time is used as the adjusted second heating time.

[0152] In this step, the preset deviation threshold refers to the upper limit of the acceptable temperature deviation set in advance; the adjusted second heating power refers to the power obtained after adjusting the second heating power; the adjusted second heating duration refers to the time obtained after adjusting the second heating duration; the second heating power refers to the initial heating power of the second heating stage; the second heating duration refers to the initial heating time of the second heating stage; and the temperature deviation refers to the difference between the average temperature after heating and the preset target temperature.

[0153] In this embodiment, the temperature deviation is compared with a preset deviation threshold. If the temperature deviation is greater than the preset deviation threshold, the second heating power is increased and the second heating time is extended according to the extent of the deviation, so as to obtain the adjusted second heating power and the adjusted second heating time. If the temperature deviation is less than or equal to the preset deviation threshold, the second heating power and the second heating time are not changed, and are directly used as the adjusted second heating power and the adjusted second heating time.

[0154] Step 505: Based on the adjusted second heating power and the second heating duration, perform heating control on the drinking water whose temperature is initially uniform after being heated by the first heating power and the first heating duration, to obtain drinking water with uniform temperature that meets the preset target temperature.

[0155] In this step, the adjusted second heating power refers to the power of the second heating stage; the second heating duration refers to the time of the second heating stage; the first heating power refers to the heating power of the first heating stage; the first heating duration refers to the heating time of the first heating stage; and drinking water with uniform temperature and meeting the preset target temperature refers to drinking water with uniform temperature and reaching the preset target temperature.

[0156] In this embodiment of the application, drinking water after the first heating stage is taken as the object, and it is heated according to the adjusted second heating power and the second heating time. The temperature is monitored during the heating process until the temperature of the drinking water is uniform and reaches the preset target temperature, and then the heating is stopped. At this time, the drinking water is drinking water with uniform temperature and meets the preset target temperature.

[0157] This application embodiment achieves precise control of drinking water temperature by heating in stages, monitoring temperature deviation, and dynamically adjusting heating parameters, ensuring that the final drinking water is both uniform in temperature and meets the preset target temperature, thus adapting to the heating characteristics of different water qualities.

[0158] For example, in a specific embodiment, the heating control parameters for hard water A are: heating power adjustment parameter 800W, heating time adjustment parameter 3 minutes, drinking water heat absorption coefficient 0.8, preset target temperature 50℃. Based on the heat absorption coefficient of 0.8, the first heating stage is determined to be a rapid heating stage, and the second heating stage is a heat preservation and fine-tuning stage. Based on the heating control parameters, the first heating power is determined to be 480W and the first heating time is 1.8 minutes, and the second heating power is determined to be 320W and the second heating time is 1.2 minutes, according to a 6:4 ratio. After heating according to the first heating power and time, the average temperature of hard water A after heating is collected to be 42℃, and the temperature deviation is calculated as 42-50=-8℃. The preset deviation threshold is 5℃. Since the absolute value of -8℃ is greater than 5℃, the second heating power is adjusted to 384W, and the second heating time is adjusted to 1.44 minutes, resulting in the adjusted second heating time. Heating according to the adjusted parameters finally yields hard water A with a uniform temperature of 50℃.

[0159] Figure 3 This is a schematic diagram illustrating a specific implementation of a water dispenser temperature control system based on spectrum feedback, as provided in this application. Figure 3 The system may include:

[0160] The acquisition module 21 is used to acquire near-infrared spectral data of drinking water in the water dispenser using near-infrared spectral detection and to acquire water temperature spectral data of drinking water in the water dispenser using temperature spectral detection.

[0161] Analysis module 22 is used to perform spectral feedback analysis on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type.

[0162] Processing module 23 is used to generate drinking water with a preliminarily uniform temperature after being processed by a specific frequency sound wave based on the drinking water quality type and the water temperature spectrum data, so as to reduce the local temperature difference generated during the heating process of different water qualities, wherein the specific frequency sound wave is matched with the drinking water quality.

[0163] The calculation module 24 is used to calculate the temperature deviation between the actual temperature of the drinking water with initially uniform temperature and the preset target temperature. Through a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters.

[0164] The adjustment module 25 is used to adjust the heating power and heating time of the water dispenser according to the heating control parameters, so as to obtain drinking water with uniform temperature and in line with the preset target temperature, and realize the temperature control of the water dispenser under different water quality.

[0165] This application provides an embodiment of a water dispenser temperature control system based on spectrum feedback to implement the aforementioned water dispenser temperature control method based on spectrum feedback. Therefore, the specific implementation of the water dispenser temperature control system based on spectrum feedback can be found in the embodiment section of the aforementioned water dispenser temperature control method based on spectrum feedback. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0166] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described methods for temperature control of a water dispenser based on spectrum feedback.

[0167] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for temperature control of a water dispenser based on spectrum feedback.

[0168] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0169] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the water dispenser temperature control method based on spectrum feedback described above.

[0170] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0171] The above provides a detailed description of a water dispenser temperature control method and system based on spectrum feedback provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for temperature control of a water dispenser based on spectral feedback, the method comprising: include: Near-infrared spectroscopy is used to obtain near-infrared spectral data of drinking water in the water dispenser, and temperature spectrum detection is used to obtain water temperature spectrum data of drinking water in the water dispenser. Spectral feedback analysis is performed on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type; Based on the drinking water quality type and the water temperature spectrum data, drinking water with a preliminary uniform temperature is generated after being processed by a specific frequency sound wave, so as to reduce the local temperature difference generated during the heating process of different water qualities. The specific frequency sound wave is matched with the drinking water quality. The temperature deviation between the actual temperature of the pre-uniform drinking water and the preset target temperature is calculated. The heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner through a fuzzy logic control algorithm to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters. According to the heating control parameters, the heating power and heating time of the water dispenser are adjusted to obtain drinking water with uniform temperature that meets the preset target temperature, thereby achieving temperature control of the water dispenser under different water qualities. Based on the drinking water quality type and the water temperature spectrum data, a preliminary uniform temperature drinking water solution is generated after treatment with a specific frequency sound wave, including: Based on the preset spatial division rules, the area where the drinking water is located in the water dispenser is divided into multiple sub-regions, and the temperature value of each sub-region is extracted from the water temperature spectrum data. Based on the drinking water quality type, determine the range of sound wave frequency and the range of sound wave intensity; Calculate the temperature difference between each sub-region temperature value and the corresponding average temperature value, and select the target sound wave frequency and target sound wave intensity that are compatible with the temperature difference of each sub-region from the sound wave frequency range and the sound wave intensity range, respectively. Based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, the corresponding sub-region is subjected to specific frequency sound wave processing until the temperature difference value of all actual sub-regions is within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

2. The method according to claim 1, characterized in that, Spectral feedback analysis was performed on the near-infrared spectral data to obtain the drinking water quality type, including: The first characteristic absorption signal of calcium ions under a preset first wavelength is extracted from the near-infrared spectral data, and the second characteristic absorption signal of magnesium ions under a preset second wavelength is extracted from the near-infrared spectral data. Obtain the first characteristic absorption intensity range of samples of different standard water quality types in the preset water quality type database under the preset first wavelength feature, and the second characteristic absorption intensity range under the preset second wavelength feature; Calculate the first characteristic absorption intensity value of the first characteristic absorption signal and the second characteristic absorption intensity value of the second characteristic absorption signal; The water quality type of the standard water quality type sample whose first and second characteristic absorption intensity values ​​are both within the corresponding characteristic absorption intensity range is taken as the drinking water quality type. If there is no standard water quality type sample whose first and second characteristic absorption intensity values ​​are both within the corresponding characteristic absorption intensity range, the comprehensive matching degree of each standard water quality type sample is calculated, and the water quality type of the standard water quality type sample with the highest comprehensive matching degree is selected as the drinking water quality type.

3. The method according to claim 1, characterized in that, Based on the drinking water quality type, determine the range of sound wave frequency and the range of sound wave intensity, including: Match a target standard water quality type sample that matches the drinking water quality type from the preset standard water quality acoustic parameter correspondence table, and take the reference acoustic frequency range corresponding to the target standard water quality type sample as the acoustic frequency range and the corresponding reference acoustic intensity range as the acoustic intensity range. If there is no target standard water quality type sample that matches the drinking water quality type in the preset standard water quality acoustic parameter correspondence table, then the first reference water quality sample and the second reference water quality sample with the highest similarity to the drinking water quality type are selected from the preset standard water quality acoustic parameter correspondence table. The first reference acoustic frequency range and the first reference acoustic intensity range are extracted from the first reference water quality sample, and the second reference acoustic frequency range and the second reference acoustic intensity range are extracted from the second reference water quality sample. Calculate the first similarity between the drinking water quality type and the first reference water quality sample, and the second similarity between the drinking water quality type and the second reference water quality sample; Based on the first similarity and the second similarity, the first reference sound wave frequency range and the second reference sound wave frequency range are weighted and integrated to obtain the sound wave frequency range. The first reference sound wave intensity range and the second reference sound wave intensity range are weighted and integrated to obtain the sound wave intensity range.

4. The method according to claim 1, characterized in that, Based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, specific frequency sound wave processing is applied to the corresponding sub-region until the temperature difference values ​​of all actual sub-regions are within the preset temperature difference range, resulting in drinking water with initially uniform temperature, including: Based on the target acoustic parameters corresponding to each sub-region, a specific frequency acoustic wave is emitted to the corresponding sub-region to obtain the actual sub-region temperature value under the action of the specific acoustic wave, and the actual sub-region temperature difference between the actual sub-region temperature value and the corresponding actual average temperature value is calculated. The target acoustic parameters corresponding to the actual sub-region whose actual temperature difference value is not within the preset temperature difference range are enhanced to obtain the enhanced target acoustic parameters. Based on the enhanced target acoustic wave parameters, the operation of transmitting specific frequency acoustic waves to the corresponding sub-regions is repeated until the temperature difference values ​​of all actual sub-regions are within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

5. The method according to claim 1, characterized in that, A fuzzy logic control algorithm is used to collaboratively process the heat absorption coefficient of the drinking water and the water temperature deviation to generate heating control parameters, including: Using a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water is fuzzified according to the physical characteristic range of the heat absorption coefficient of the drinking water to obtain the corresponding fuzzy coefficient. The water temperature deviation is fuzzified according to the degree of deviation between the water temperature deviation and the preset target temperature to obtain the corresponding fuzzy deviation. Based on the fuzzy coefficient and the fuzzy deviation, and combined with the heating characteristics of different drinking water quality types, target fuzzy control rules that match both the fuzzy coefficient and the fuzzy deviation are selected from a pre-built fuzzy control rule library. Based on the target fuzzy control rule, a fuzzy output quantity is generated through a fuzzy inference algorithm. The fuzzy output quantity is then clarified to calculate the heating control parameters.

6. The method according to claim 1, characterized in that, According to the heating control parameters, the heating power and heating time of the water dispenser are adjusted to obtain drinking water with a uniform temperature that meets the preset target temperature, including: Based on the heat absorption coefficient of the drinking water, the first heating stage and the second heating stage of the drinking water with initially uniform temperature are determined. Based on the heating control parameters, the first heating power and the first heating duration of the first heating stage are determined, as well as the second heating power and the second heating duration of the second heating stage. The average temperature of the drinking water after heating, which is initially uniform in temperature, is collected under the heating effect of the first heating power and the first heating time, and the temperature deviation between the average temperature after heating and the preset target temperature is calculated. If the temperature deviation is greater than a preset deviation threshold, the second heating power and the second heating time are adjusted to obtain the adjusted second heating power and the adjusted second heating time. If the temperature deviation is less than or equal to the preset deviation threshold, the second heating power is used as the adjusted second heating power and the second heating time is used as the adjusted second heating time. Based on the adjusted second heating power and the second heating duration, the heating control is performed on the drinking water whose temperature is initially uniform after being heated by the first heating power and the first heating duration, so as to obtain drinking water with uniform temperature that meets the preset target temperature.

7. A water dispenser temperature control system based on spectrum feedback, characterized in that, include: The acquisition module is used to acquire near-infrared spectral data of drinking water in the water dispenser using near-infrared spectroscopy detection and to acquire water temperature spectral data of drinking water in the water dispenser using temperature spectrum detection. The analysis module is used to perform spectral feedback analysis on the near-infrared spectral data to obtain the drinking water quality type and determine the drinking water heat absorption coefficient corresponding to the drinking water quality type. The processing module is used to generate drinking water with a preliminarily uniform temperature after being processed by a specific frequency sound wave, based on the drinking water quality type and the water temperature spectrum data, so as to reduce the local temperature difference generated during the heating process of different water qualities, wherein the specific frequency sound wave is matched with the drinking water quality. The calculation module is used to calculate the temperature deviation between the actual temperature of the drinking water with initially uniform temperature and the preset target temperature. Through a fuzzy logic control algorithm, the heat absorption coefficient of the drinking water and the temperature deviation are processed in a coordinated manner to generate heating control parameters, which include heating power adjustment parameters and heating time adjustment parameters. The adjustment module is used to adjust the heating power and heating time of the water dispenser according to the heating control parameters, so as to obtain drinking water with uniform temperature and in line with the preset target temperature, and realize the temperature control of the water dispenser under different water quality. Based on the drinking water quality type and the water temperature spectrum data, a preliminary uniform temperature drinking water solution is generated after treatment with a specific frequency sound wave, including: Based on the preset spatial division rules, the area where the drinking water is located in the water dispenser is divided into multiple sub-regions, and the temperature value of each sub-region is extracted from the water temperature spectrum data. Based on the drinking water quality type, determine the range of sound wave frequency and the range of sound wave intensity; Calculate the temperature difference between each sub-region temperature value and the corresponding average temperature value, and select the target sound wave frequency and target sound wave intensity that are compatible with the temperature difference of each sub-region from the sound wave frequency range and the sound wave intensity range, respectively. Based on the target sound wave frequency and target sound wave intensity corresponding to each sub-region, the corresponding sub-region is subjected to specific frequency sound wave processing until the temperature difference value of all actual sub-regions is within the preset temperature difference range, thus obtaining drinking water with initially uniform temperature.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of a water dispenser temperature control method based on spectral feedback as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables a water dispenser temperature control method based on spectral feedback as described in any one of claims 1 to 6.

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