Electric kettle heating control method and system based on intelligent temperature control

Abstract

The application provides a heating control method and system of an electric kettle based on intelligent temperature control, and relates to the technical field of household appliances.The application introduces a multi-dimensional temperature measurement module, a plurality of high-precision temperature sensors are distributed at different positions in the electric kettle, the temperature distribution information of different positions in the kettle can be obtained in real time, and local temperature misjudgment caused by a single sensor can be effectively avoided.Meanwhile, a water level detection module can accurately measure real-time water quantity.Based on this, the system can dynamically allocate the contribution degree index of each temperature measurement point according to the real-time water quantity, and adjust the heating power in real time in combination with a PID algorithm, so that the temperature control precision and stability are significantly improved, temperature misjudgment is reduced, accurate control over the heating process is realized, and it is ensured that the water temperature can accurately reach the target temperature set by the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household appliances, in particular to a heating control method and system for an electric kettle based on intelligent temperature control. BACKGROUND

[0002] In daily life, electric kettles have become a common appliance for people to obtain hot water, and are used frequently in homes, offices and other places. People often use electric kettles to boil water for tea, coffee, instant noodles or to prepare milk powder for babies, and the temperature control accuracy directly affects the user experience.

[0003] Traditional electric kettles usually use a single temperature sensor to detect the water temperature, and the position is usually at the bottom of the kettle. This results in significant temperature measurement errors under different water quantities. For example, when the water quantity is small, the temperature at the bottom of the kettle rises rapidly, and the heating stop instruction is easily triggered, but at this time the water temperature at the upper part of the kettle may not have reached the target value, thereby causing temperature control lag, local overheating or insufficient heating, etc. In addition, traditional electric kettles usually cannot accurately sense the real-time water quantity in the kettle, and it is difficult to dynamically adjust the heating strategy according to the change in water quantity.

[0004] Therefore, how to combine the real-time water quantity in the electric kettle to achieve dynamic adjustment control of the heating process has become a problem that needs to be solved today. SUMMARY

[0005] The present application provides a heating control method and system for an electric kettle based on intelligent temperature control, which can combine the real-time water quantity in the electric kettle to achieve dynamic adjustment control of the heating process.

[0006] In a first aspect, the present application provides a heating control method for an electric kettle based on intelligent temperature control, comprising:

[0007] Starting a multi-dimensional temperature measurement module and a water level detection module to obtain the initial temperature of each temperature measurement point corresponding to the real-time water quantity;

[0008] The calculation module initializes the heating power corresponding to the real-time water quantity according to the target temperature and the set time set by the user;

[0009] The control module allocates the contribution index of each temperature measurement point according to the real-time water quantity, and after starting heating, calculates the real-time temperature of each temperature measurement point according to the contribution index, and dynamically adjusts the heating power after comparing with the target temperature;

[0010] The element loss module compares the current temperature measurement data and the historical temperature measurement data of each temperature measurement point in real time, and according to the comparison result, disables the temperature measurement element corresponding to the abnormal temperature measurement point and issues a warning.

[0011] Optionally, in a possible implementation manner of the first aspect, the multi-dimensional temperature measurement module and the water level detection module are started to obtain the real-time water volume and initial temperatures of the temperature measurement points, including:

[0012] The real-time water volume is obtained according to the water level detection module.

[0013] The multi-dimensional temperature measurement module includes temperature measurement elements corresponding to fixed points and dynamic points, and the initial temperatures of the temperature measurement points are obtained according to the temperature measurement elements, the temperature measurement points including the fixed points and the dynamic points, wherein the temperature measurement elements of the dynamic points are automatically adjusted in position based on the real-time water volume.

[0014] Optionally, in a possible implementation manner of the first aspect, the control module allocates the contribution degree indexes of the temperature measurement points according to the real-time water volume, including:

[0015] A water volume interval corresponding to the real-time water volume is determined, the water volume interval including a low water volume interval, a medium water volume interval and a high water volume interval.

[0016] A standard heating time corresponding to the water volume interval is compared with the set time, when the standard heating time is greater than the set time, it is determined as short-time heating, and when the standard heating time is less than the set time, it is determined as long-time heating.

[0017] Initial indexes of the temperature measurement points are determined according to the water volume interval, and the initial indexes are updated based on adjustment amplitudes corresponding to the short-time heating or the long-time heating to obtain the contribution degree indexes of the temperature measurement points.

[0018] Optionally, in a possible implementation manner of the first aspect, initial indexes of the temperature measurement points are determined according to the water volume interval, and the initial indexes are updated based on adjustment amplitudes corresponding to the short-time heating or the long-time heating to obtain the contribution degree indexes of the temperature measurement points, including:

[0019] The preset indexes of each water volume interval at each position are determined as the initial indexes of the temperature measurement points, the positions of the temperature measurement points including a bottom, a middle and a water surface.

[0020] When the short-time heating, a first adjustment amplitude at each position is determined according to a first difference value of the standard heating time and the set time, and the initial indexes of the temperature measurement points are updated based on the first adjustment amplitude to obtain the contribution degree indexes; or,

[0021] When the long-time heating, a second adjustment amplitude at each position is determined according to a second difference value of the set time and the standard heating time, and the initial indexes of the temperature measurement points are updated based on the second adjustment amplitude to obtain the contribution degree indexes.

[0022] Optionally, in a possible implementation manner of the first aspect, during short-time heating, a first adjustment amplitude at each position is determined according to a first difference between the standard heating time and the set time, and a contribution degree index of each temperature measurement point is obtained by updating the initial index of the temperature measurement point based on the first adjustment amplitude, including:

[0023] During short-time heating, in the low water volume interval, the initial index of the temperature measurement point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measurement point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measurement point; or,

[0024] In the middle water volume interval, the initial index of the temperature measurement point at the middle is added by the first adjustment amplitude, the initial index of the temperature measurement point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measurement point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measurement point; or,

[0025] In the high water volume interval, the initial index of the temperature measurement point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measurement point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measurement point at the middle is not adjusted, to obtain the contribution degree index of each temperature measurement point.

[0026] Optionally, in a possible implementation manner of the first aspect, during long-time heating, a second adjustment amplitude at each position is determined according to a second difference between the set time and the standard heating time, and a contribution degree index of each temperature measurement point is obtained by updating the initial index of the temperature measurement point based on the second adjustment amplitude, including:

[0027] During long-time heating, in the low water volume interval, the initial index of the temperature measurement point at the bottom is subtracted by the second adjustment amplitude, and the initial index of the temperature measurement point at the middle and the water surface is added by the second adjustment amplitude, to obtain the contribution degree index of each temperature measurement point; or,

[0028] In the middle water volume interval and the high water volume interval, the initial index of the temperature measurement point at the middle and the water surface is added by the second adjustment amplitude, and the initial index of the temperature measurement point at the bottom is subtracted by the second adjustment amplitude, to obtain the contribution degree index of each temperature measurement point.

[0029] Optionally, in a possible implementation manner of the first aspect, after starting heating, a real-time temperature of each temperature measurement point is calculated according to the contribution degree index, and the heating power is dynamically adjusted after comparison with the target temperature, including:

[0030] After starting heating, a current temperature is obtained based on a temperature measurement element corresponding to each temperature measurement point, and a real-time temperature of each temperature measurement point is obtained according to a product of the contribution degree index corresponding to the temperature measurement point and the current temperature;

[0031] A temperature difference value of the target temperature and an average value of each real-time temperature is obtained, and a power adjustment amount obtained based on a PID algorithm is used to adjust the heating power in real time.

[0032] Optionally, in a possible implementation manner of the first aspect, the element loss module compares current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, and according to a comparison result, disables a temperature measurement element corresponding to an abnormal temperature measurement point and issues a warning, including:

[0033] The current temperature measurement data is obtained according to the water level parameter, the time parameter and the temperature parameter, the historical temperature measurement data is compared based on the current temperature measurement data, and historical temperature measurement data with a data similarity greater than a similarity threshold value is determined as reference temperature measurement data;

[0034] A plurality of time nodes corresponding to the current temperature measurement data are generated based on a time interval, and a reference temperature of each temperature measurement point at each time node is obtained according to an average value of each reference temperature measurement data;

[0035] A comparison difference value of a current temperature and a reference temperature of each temperature measurement point at each time node is obtained, and a temperature measurement point with a comparison difference value greater than a temperature measurement comparison threshold value is determined as an abnormal temperature measurement point, and a temperature measurement element corresponding to the abnormal temperature measurement point is disabled and a warning is issued.

[0036] Optionally, in a possible implementation manner of the first aspect, the current temperature measurement data is obtained according to the water level parameter, the time parameter and the temperature parameter, the historical temperature measurement data is compared based on the current temperature measurement data, and historical temperature measurement data with a data similarity greater than a similarity threshold value is determined as reference temperature measurement data, including:

[0037] A water level difference value, a time difference value and a temperature difference value of the current temperature measurement data and each historical temperature measurement data are obtained;

[0038] The historical temperature measurement data with a water level difference value less than a maximum water level difference threshold value is selected as first-level screening data, the first-level screening data with a temperature difference value less than a maximum temperature difference threshold value is selected as second-level screening data, and the second-level screening data with a time difference value less than a maximum time difference threshold value is selected as third-level screening data;

[0039] The weight proportions corresponding to the water level difference value, the time difference value and the temperature difference value of each third-level screening data are added, and a data similarity of each third-level screening data and the current temperature measurement data is obtained.

[0040] In a second aspect of the present application, a heating control system of an electric kettle based on intelligent temperature control is provided, including:

[0041] A multi-dimensional temperature measurement module and a water level detection module are configured to obtain real-time water quantity and initial temperatures of each temperature measurement point.

[0042] A calculation module is configured to initialize a heating power corresponding to the real-time water volume according to a target temperature and a set time set by a user;

[0043] A control module is configured to distribute a contribution index of each temperature measurement point according to the real-time water volume, to calculate a real-time temperature of each temperature measurement point according to the contribution index after starting heating, and to dynamically adjust the heating power after comparing the real-time temperature with the target temperature.

[0044] An element loss module is configured to compare current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, to disable a temperature measurement element corresponding to an abnormal temperature measurement point according to a comparison result, and to issue a warning.

[0045] The present application has the following advantages: The present application introduces a multi-dimensional temperature measurement module, in which a plurality of high-precision temperature sensors are distributed at different positions in an electric kettle, so that temperature distribution information at different positions in the kettle can be obtained in real time, and local temperature misjudgment caused by a single sensor can be effectively avoided. Meanwhile, a water level detection module can accurately measure a real-time water volume. Based on this, the system can dynamically distribute a contribution index of each temperature measurement point according to the real-time water volume, and can adjust a heating power in real time in combination with a PID algorithm, so that temperature control accuracy and stability are significantly improved, temperature misjudgment is reduced, accurate control over a heating process is achieved, and it is ensured that a water temperature can accurately reach a target temperature set by a user.

[0046] The calculation module is built-in with a preset heating power calculation algorithm, which can quickly calculate an optimal initial heating power in combination with a target temperature, a set time and a real-time water volume set by a user, so that accurate matching of a personalized heating scheme is achieved.

[0047] The element loss module compares current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, compares the data at multiple levels in combination with a water level parameter, a time parameter and a temperature parameter, selects historical temperature measurement data with a data similarity greater than a similarity threshold value as reference temperature measurement data, further determines a reference temperature of each temperature measurement point at different time nodes based on this, compares a current temperature with the reference temperature, and determines an abnormal temperature measurement point as soon as a comparison difference of the temperature measurement point is greater than a temperature measurement comparison threshold value, so that the sensor abnormality can be found in advance, measures can be taken in time, and the reliability of the entire temperature control system is ensured, so that the electric kettle can be continuously and stably operated. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of an electric kettle heating control method based on intelligent temperature control provided by an embodiment of the present application;

[0049] Figure 2 is a schematic diagram of a user input set time provided by an embodiment of the present application;

[0050] Figure 3 is a schematic diagram of fault detection of an electric kettle provided by an embodiment of the present application;

[0051] Figure 4 is a structural schematic diagram of a heating control system of an electric kettle based on intelligent temperature control provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort fall within the protection scope of the present application.

[0053] Referring to Figure 1 is a flow schematic diagram of a heating control method of an electric kettle based on intelligent temperature control provided by an embodiment of the present application, Figure 1 The execution subject of the method shown in the figure can be a software and / or hardware device. The execution subject of the present application can include but is not limited to at least one of the following: a user device, a network device, and the like. The user device can include but is not limited to a computer, a smart phone, a personal digital assistant (PDA), and the above-mentioned electronic devices, and the like. The network device can include but is not limited to a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing, wherein cloud computing is a kind of distributed computing, and a super virtual computer composed of a group of loosely coupled computers. The present embodiment does not make any limitation. It includes steps S1 to S4, and the details are as follows:

[0054] S1, start the multi-dimensional temperature measurement module and the water level detection module to obtain real-time water quantity and initial temperature of each temperature measurement point.

[0055] Under different water quantity and initial temperature conditions, the water body temperature rising characteristics are significantly different, for example, small water quantity rises fast, and large water quantity has complex heat distribution. The present embodiment introduces a multi-dimensional temperature measurement module and a water level detection module, so that the system can obtain the temperature distribution and water quantity information at different positions in the kettle in real time, and provide accurate data basis for subsequent dynamic heating control. The error caused by the fact that the traditional electric kettle usually only installs a single temperature sensor for temperature measurement under different water quantity can be solved, for example, when the temperature sensor is installed at the bottom of the kettle, the temperature at the bottom of the kettle rises quickly when the water quantity is small, which easily triggers the heating stop instruction, but the water temperature at the upper part may not have reached the target value, thereby causing temperature control lag, local overheating, or insufficient heating, and the like.

[0056] The multi-dimensional temperature measurement module in the embodiment is composed of multiple high-precision temperature sensors, which are distributed at different positions in the electric kettle. The water level detection module adopts a capacitive liquid level sensor, which can calculate the water level height by detecting the change of capacitance between electrodes based on the difference in dielectric constant between liquid and air during measurement. The temperature measurement point refers to the specific measurement position of the temperature sensor in the electric kettle. Among them, the water level detection module is used to obtain the real-time water volume added by the user, and the multi-dimensional temperature measurement module is used to obtain the temperature measurement data at different positions in the electric kettle.

[0057] Through multi-point temperature collection, the system can monitor the temperature gradient in the kettle at different water volumes in real time, avoiding local temperature misjudgment caused by a single sensor. For example, when the kettle bottom temperature rises rapidly to 80℃, if only relying on the kettle bottom sensor, it may misjudge that the overall water temperature has approached the target value, but at this time the middle temperature may be only 60℃, and the water surface temperature is even lower. By fusing the temperature data of different points at different water volumes, the system can accurately judge the actual water temperature, avoid premature stop heating, and improve the accuracy of heating control.

[0058] The specific implementation mode of step S101 on the basis of the above embodiment can be:

[0059] According to the water level detection module, the real-time water volume is obtained; the multi-dimensional temperature measurement module includes temperature measurement elements corresponding to fixed points and dynamic points, and the initial temperature of each temperature measurement point is obtained according to the temperature measurement elements, wherein the temperature measurement point includes fixed points and dynamic points, and the temperature measurement element of the dynamic point automatically adjusts the position based on the real-time water volume.

[0060] The fixed point in the embodiment refers to a temperature sensor with fixed position, including a kettle bottom sensor (monitoring temperature near the heating element) and a middle sensor (monitoring middle layer temperature of water body). The dynamic point is a temperature sensor with position automatically adjustable according to water level, i.e. a water surface sensor. In some embodiments, the sensor can ensure contact with the water surface at all times through the driving of a stepper motor.

[0061] The fixed sensor can stably monitor the temperature of the key fixed area, and the dynamic sensor can adjust the position following the change of water volume, so as to obtain dynamic data under different water volumes. The combination of the two can realize three-dimensional and accurate perception of the temperature field, and improve the accuracy of heating control.

[0062] S2, the calculation module initializes the heating power corresponding to the real-time water volume according to the target temperature and the set time set by the user.

[0063] Different users have different hot water requirements, such as 80°C warm water for tea and 100°C boiling water for instant noodles, and different requirements for heating speed. By combining the target temperature, set time and real-time water volume to calculate the initial heating power, the user's requirements can be combined for heating control operation, the personalized heating scheme can be accurately matched, and the heating time can also be accurately controlled.

[0064] The calculation module can have a pre-set heating power calculation algorithm built in, which can quickly calculate the optimal initial heating power according to the real-time water volume, target temperature, set time and pre-set thermal efficiency parameters (such as heating pipe conversion efficiency, water specific heat capacity, etc.). The target temperature is the expected water temperature set by the user through the touch panel or other interactive methods. The set time is the expected heating time specified by the user, that is, the time required from starting heating to reaching the target temperature, as shown in Figure 2 A schematic diagram for the user to set the time according to the embodiment of the application is provided. This parameter allows the user to flexibly adjust according to their own needs, for example, it can be set to 10 minutes as shown in Figure 2

[0065] S3, the control module dynamically adjusts the heating power according to the real-time temperature of each temperature measurement point after starting heating according to the contribution index of each temperature measurement point according to the real-time water volume.

[0066] During heating, the temperature distribution in the kettle under different water conditions varies significantly. For example, when the water volume is low, the heat is mainly concentrated near the bottom of the kettle, and the temperature at the bottom rises much faster than the water surface; when the water volume is high, the thermal convection of the water body is enhanced, and the temperature in the middle and at the water surface rises relatively quickly. By dynamically allocating the contribution index of each temperature measurement point according to the real-time water volume and adjusting the heating power in real time, the temperature control accuracy and stability can be significantly improved. In some embodiments, the heating power can be adjusted in real time by combining the PID algorithm. The PID algorithm can calculate the required heating power adjustment amount according to the difference (error) between the fused temperature and the target temperature.

[0067] The control module is the core unit responsible for executing the heating strategy. This module can receive the initial heating power and contribution index allocation scheme generated by the calculation module, and dynamically adjust the heating power through the PID algorithm according to the real-time temperature feedback data. The contribution index is the weight proportion of the temperature data of each temperature measurement point in the overall temperature calculation, which reflects the representativeness of the temperature of this point to the overall water temperature.

[0068] In the above manner, the accuracy of temperature control can be improved, and temperature misjudgment can be reduced.

[0069] ​The specific implementation of the content "the control module allocates the contribution degree index of each temperature measurement point according to the real-time water volume" in step S103 based on the above embodiments can be:

[0070] S31, determine the water volume interval corresponding to the real-time water volume, the water volume interval includes a low water volume interval, a medium water volume interval and a high water volume interval.

[0071] The thermal characteristics of different water volumes are significantly different, for example, low water volume has fast temperature rise but is easy to locally overheat, and high water volume has slow temperature rise but has more uniform heat distribution. By dividing the water volume into multiple intervals, the system can match different control strategies to optimize heating efficiency and safety.

[0072] For example, for an electric kettle with a capacity of 1500 ml, 50-300 ml can be divided into a low water volume interval, 301-800 ml can be divided into a medium water volume interval, and 801-1500 ml can be divided into a high water volume interval.

[0073] S32, compare the standard heating time corresponding to the water volume interval and the set time, when the standard heating time is greater than the set time, determine short-time heating, and when the standard heating time is less than the set time, determine long-time heating.

[0074] In actual application, users have different demands for heating speed (such as fast heating for tea making and slow heating for energy-saving mode). By comparing the standard time and the set time, the system can intelligently adjust the heating strategy. The standard heating time is the theoretical heating time built in the system, which can be the preset reference time for each water volume interval to heat from the current temperature to the target temperature.

[0075] When the user-set heating time is less than the standard heating time preset by the system for the current water volume interval, it is determined as short-time heating. Subsequently, the heating efficiency can be improved to complete the heating target in less than the conventional time.

[0076] When the user-set heating time is greater than the standard heating time preset by the system, it is determined as long-time heating. Subsequently, the heating power can be reduced to complete the heating in a more gentle manner, to preferentially meet the energy-saving or uniform heating demand.

[0077] Setting short-time heating and long-time heating can adapt to the individual needs of users, dynamically adjust the heating strategy, and ensure efficient, safe and accurate heating process.

[0078] S33, determine the initial index of each temperature measurement point according to the water volume interval, update the initial index based on the adjustment amplitude corresponding to the short-time heating or long-time heating, and obtain the contribution degree index of each temperature measurement point.

[0079] The temperature of each temperature measurement point is different in representing the overall temperature under different water amounts and heating modes. For example, the temperature of the bottom of the kettle is easily affected by the heating element when the water amount is low, and the temperature of the water surface changes more gently when the water amount is high. Dynamically adjusting the contribution index can improve the accuracy of temperature fusion calculation. The initial index refers to the basic weight of each temperature measurement point in different water amount intervals, which can be preset.

[0080] In some embodiments, the contribution index of each temperature measurement point can be determined by the following steps:

[0081] S331, determining that the preset index of each water amount interval configured at each position is the initial index of each temperature measurement point, and the positions of the temperature measurement points include the bottom, the middle and the water surface.

[0082] The preset index is a weight value preset for the bottom, middle and water surface points of each water amount interval.

[0083] For example, the system preset index can be as follows: low water amount interval: bottom 70%, middle 20%, water surface 10% (the bottom temperature has the greatest impact on the overall); medium water amount interval: bottom 50%, middle 30%, water surface 20% (the representativeness of the middle temperature is improved); high water amount interval: bottom 30%, middle 40%, water surface 30% (the influence of the water surface temperature is enhanced). When the real-time water amount is 200 ml (low water amount), the initial index directly adopts “bottom 70%, middle 20%, water surface 10%”.

[0084] S332, when short-time heating, determining a first adjustment range at each position according to a first difference value between the standard heating time and the set time, and updating the initial index of each temperature measurement point to obtain the contribution index based on the first adjustment range.

[0085] Short-time heating needs to be completed with higher efficiency, at which time the temperature change rates of each point are different (for example, the bottom temperature rises quickly and the water surface lags behind). By adjusting the weight through the first difference value, the attention to the fast-changing point can be enhanced, and the heating can be avoided due to the temperature measurement lag.

[0086] The first difference value is the difference between the standard heating time and the set time, and the greater the difference, the more urgent the need for acceleration. The first adjustment range is a weight correction value calculated according to the first difference value, and the greater the difference, the greater the adjustment range.

[0087] In some embodiments, the contribution index of each temperature measurement point during short-time heating can be determined by the following steps:

[0088] In the low water volume interval, the initial index of the temperature measuring point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or in the middle water volume interval, the initial index of the temperature measuring point at the middle is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measuring point; or in the high water volume interval, the initial index of the temperature measuring point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the middle is not adjusted, to obtain the contribution degree index of each temperature measuring point.

[0089] In the low water volume interval, the initial index of the temperature measuring point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or in the middle water volume interval, the initial index of the temperature measuring point at the middle is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measuring point; or in the high water volume interval, the initial index of the temperature measuring point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the middle is not adjusted, to obtain the contribution degree index of each temperature measuring point.

[0090] In the low water volume interval, the initial index of the temperature measuring point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or in the middle water volume interval, the initial index of the temperature measuring point at the middle is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measuring point; or in the high water volume interval, the initial index of the temperature measuring point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the middle is not adjusted, to obtain the contribution degree index of each temperature measuring point.

[0091] In the low water volume interval, the initial index of the temperature measuring point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or in the middle water volume interval, the initial index of the temperature measuring point at the middle is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measuring point; or in the high water volume interval, the initial index of the temperature measuring point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the middle is not adjusted, to obtain the contribution degree index of each temperature measuring point.

[0092] The temperature measuring point at the bottom is a temperature sensor point located directly above the heating element, which can reflect the temperature near the heating source. The temperature measuring point at the middle is a sensor point located in the middle of the kettle body, which reflects the temperature of the middle layer of the water body. The temperature measuring point at the water surface is a sensor point adjusted with the water level.

[0093] In some embodiments, a plurality of difference intervals (e.g., 0-2 minutes, 2-5 minutes, 5 minutes or more) can be set in advance, and each difference interval is pre-set with a corresponding amplitude value (e.g., ±3%, ±8%, ±15%), so that the first difference and its corresponding first adjustment amplitude can be determined through the difference interval.

[0094] For example, in the short-time heating scenario, if the system pre-set difference interval and corresponding amplitude are: interval 1 (0 < first difference ≤ 2 minutes) corresponding to first adjustment amplitude ±5%, interval 2 (2 < first difference ≤ 5 minutes) corresponding to first adjustment amplitude ±10%, interval 3 (first difference > 5 minutes) corresponding to first adjustment amplitude ±15%, when it is detected that the current first difference is 3 minutes (belongs to interval 2), the first adjustment amplitude is determined to be ±10%, at this time, if it is in the low water level interval, and the initial indicators of the bottom, middle and water surface are 70%, 20% and 10% respectively, then according to the adjustment rule of the low water level interval in the short-time heating scenario, the initial indicators of the temperature measuring points at the bottom are increased by 10%, and the initial indicators of the temperature measuring points at the middle and water surface are reduced by 5% respectively, and finally the contribution degree indicators of each temperature measuring point are obtained as 80%, 15% and 5% respectively.

[0095] S333, or, in the long-time heating scenario, a second adjustment amplitude of each position is determined according to the second difference between the set time and the standard heating time, and the initial indicators of each temperature measuring point are updated to obtain the contribution degree indicators based on the second adjustment amplitude.

[0096] Long-time heating needs to be heated in a more gentle way to avoid local overheating (e.g., continuous high temperature at the bottom leading to scale deposition). By adjusting the weight through the second difference, the monitoring of the points prone to overheating can be enhanced to ensure uniform temperature rise.

[0097] The second difference is the difference between the set time and the standard heating time, and the greater the difference, the more prominent the need for gentle heating. The second adjustment amplitude is a weight correction value calculated according to the second difference, and the greater the difference, the greater the adjustment amplitude.

[0098] In some embodiments, the contribution degree indicators of each temperature measuring point in the long-time heating scenario can be determined by the following steps:

[0099] In the low water level interval in the long-time heating scenario, the initial indicators of the temperature measuring points at the bottom are reduced by the second adjustment amplitude, and the initial indicators of the temperature measuring points at the middle and water surface are increased by the second adjustment amplitude, to obtain the contribution degree indicators of each temperature measuring point; or, in the middle water level interval and the high water level interval, the initial indicators of the temperature measuring points at the middle and water surface are increased by the second adjustment amplitude, and the initial indicators of the temperature measuring points at the bottom are reduced by the second adjustment amplitude, to obtain the contribution degree indicators of each temperature measuring point.

[0100] In the low water volume interval, the water volume is small, the heat capacity is small, and when long-time heating, the bottom is easy to appear local overheating due to continuous heating, while the middle and water surface temperature rise relatively lag. By reducing the weight of the bottom temperature measurement point, and increasing the weight of the middle and water surface, the balance of the overall temperature can be paid more attention to, the uneven heating caused by the bottom overheating can be avoided, and the water temperature can be ensured to rise stably during long-time heating.

[0101] For example, assuming that the initial indicators of the low water volume interval are 70% for the bottom, 20% for the middle, and 10% for the water surface, and the second adjustment amplitude is 10%. During long-time heating, the bottom indicator is adjusted to 60%, the middle indicator is adjusted to 25%, and the water surface indicator is adjusted to 15%, and the final contribution degree indicator is 60% for the bottom, 25% for the middle, and 15% for the water surface.

[0102] In the medium and high water volume intervals, the water volume is relatively large, and when long-time heating, the bottom is easy to concentrate heat due to continuous heating, while the middle and water surface need a longer time to reach the target temperature. By reducing the weight of the bottom, and increasing the weight of the middle and water surface, the rising of the overall water temperature can be more accurately monitored, the bottom overheating can be avoided, the temperature of each part of the water body can be ensured to be uniform, and the requirement of long-time heating on water temperature stability can be met.

[0103] For example, assuming that the initial indicators of the medium water volume interval are 50% for the bottom, 30% for the middle, and 20% for the water surface, and the second adjustment amplitude is 10%; and the initial indicators of the high water volume interval are 30% for the bottom, 40% for the middle, and 30% for the water surface, and the second adjustment amplitude is 10%. During long-time heating, the bottom indicator of the medium water volume interval is adjusted to 40%, the middle indicator is adjusted to 36%, and the water surface indicator is adjusted to 24%, and the contribution degree indicator is 40% for the bottom, 36% for the middle, and 24% for the water surface; the bottom indicator of the high water volume interval is adjusted to 20%, the middle indicator is adjusted to 45%, and the water surface indicator is adjusted to 35%, and the contribution degree indicator is 20% for the bottom, 45% for the middle, and 35% for the water surface.

[0104] Through the above steps, the key temperature change can be accurately captured, the accuracy of temperature judgment can be improved, and different heating requirements can be adapted.

[0105] On the basis of the above embodiment, the specific implementation mode of the content "after starting heating, the real-time temperature of each temperature measurement point is calculated according to the contribution degree indicator, and after comparing with the target temperature, the heating power is dynamically adjusted" in step S3 can be:

[0106] S34, after starting heating, the current temperature is obtained based on the temperature measurement element corresponding to each temperature measurement point, and the real-time temperature of each temperature measurement point is obtained according to the product of the contribution degree indicator corresponding to the temperature measurement point and the current temperature.

[0107] The temperatures of each temperature measurement point are different in representing the overall water temperature. The real-time temperature is calculated by the product of the contribution index and the current temperature, which can highlight the influence of key points and weaken the interference of secondary points, so that the real-time temperature is more in line with the actual heating demand.

[0108] S35, obtaining a temperature difference value of the target temperature and the average value of each real-time temperature, and adjusting the heating power in real time based on the power adjustment value calculated by the PID algorithm.

[0109] By calculating the difference between the target temperature and the average value of the real-time temperature, the gap between the current temperature and the target temperature can be determined. Based on this difference, the heating power is adjusted in real time by using the PID algorithm, which can achieve more accurate and stable temperature control, avoid large temperature fluctuations, and ensure efficient heating process and accurate target temperature.

[0110] The PID algorithm is a closed-loop control algorithm based on proportion (P), integral (I) and differential (D). By calculating the error (temperature difference), the appropriate control amount (power adjustment amount) is output to achieve accurate control of the controlled object (heating process). The power adjustment amount is a value calculated by the PID algorithm for adjusting the current heating power.

[0111] For example, assuming the target temperature is 100℃, and the real-time temperatures are 32℃, 30℃ and 14℃ respectively, the average value of the real-time temperatures is 25.3℃, and the temperature difference is 74.7℃. The PID algorithm calculates the power adjustment amount based on this temperature difference, and assumes that the power adjustment amount is increased by 500W. If the current heating power is 800W, the adjusted heating power is 1300W. As the temperature rises, the temperature difference decreases. If the subsequent calculation shows that the power adjustment amount is reduced by 300W, and the current power is 1300W, the adjusted power is 1000W. In this way, real-time and accurate power adjustment is achieved, and the temperature gradually approaches the target temperature.

[0112] The above steps can effectively improve the accuracy and stability of heating by weighting the temperatures of each temperature measurement point and adjusting the heating power in real time based on the temperature difference by using the PID algorithm, which can meet the requirements of temperature control in different scenarios.

[0113] S4, the element loss module compares the current temperature measurement data and the historical temperature measurement data of each temperature measurement point in real time, and disables the temperature measurement element corresponding to the abnormal temperature measurement point according to the comparison result and sends a warning.

[0114] Temperature sensors may fail due to aging, drift and other faults during long-term use, resulting in inaccurate measurement data and affecting the performance of the entire temperature control system. Referring to Figure 3A schematic diagram of electric kettle fault detection provided by the embodiment of the present application can find sensor abnormalities in advance by comparing current temperature measurement data with historical data in real time through the element loss module, and timely warn the user when abnormalities occur, for example, the warning information can be sent to the user terminal held by the user.

[0115] The element loss module is a fault diagnosis unit responsible for monitoring the working state of the temperature sensor. The current temperature measurement data is a temperature data sequence collected by the multi-dimensional temperature measurement module in the current heating period, including the temperature values of three point positions of the kettle bottom, the middle part and the water surface and their change curves with time. The historical temperature measurement data is a set of temperature measurement data collected in the past heating period stored by the system. The comparison result includes the evaluation index generated after comparing the current temperature measurement data with the historical data, which is used to determine whether the sensor is abnormal. The abnormal temperature measurement point position is a temperature measurement point position whose temperature data has a significant deviation from the historical data after comparison and analysis. The temperature measurement element is the temperature sensor corresponding to the temperature measurement point position.

[0116] When the system determines that a temperature measurement point position is abnormal, it can automatically exclude it from temperature fusion calculation, reassign the contribution index of other normal point positions, and ensure that the temperature control system continues to operate normally. And convey fault information to the user, for example, the user can be reminded through visual prompts, sound alarms and remote notifications, while recording fault logs for subsequent analysis.

[0117] The specific implementation of step S4 based on the above embodiment can be:

[0118] S41, obtain current temperature measurement data according to water level parameters, time parameters and temperature parameters, perform multi-level comparison on historical temperature measurement data based on the current temperature measurement data, and determine that the historical temperature measurement data with a data similarity greater than a similarity threshold as reference temperature measurement data.

[0119] The water level, the user-set heating time and the target temperature jointly determine the overall characteristics of the heating process. Different combinations of water level, set time and target temperature correspond to different normal temperature change rules. By fusing the current three key parameters into the current temperature measurement data and comparing them with the temperature measurement data under the same or similar parameter combination in history, the normal data template can be found, which provides a reliable basis for subsequent judgment of whether the temperature measurement point position is abnormal, and avoids the deviation caused by single parameter judgment.

[0120] The water level parameter is a real-time water value obtained by the water level detection module; the time parameter is a desired total heating time input by the user through the control system; the temperature parameter includes a target temperature set by the user and real-time temperatures of each temperature measurement point; the current temperature measurement data is a comprehensive data set that fuses the water level parameter, the time parameter, and the temperature parameter; the historical temperature measurement data is temperature measurement data recorded by the system in a normal heating process for different combinations of water level, time parameter, and temperature parameter in the past operation process; the data similarity is a matching degree of the current temperature measurement data and a certain piece of historical temperature measurement data in each parameter dimension; the similarity threshold is a critical value for judging whether the data is similar, and when the data similarity is higher than the threshold, it means that the heating scenes and rules corresponding to the two pieces of data are highly consistent; the reference temperature measurement data is data selected from the historical temperature measurement data and having a similarity greater than the similarity threshold with the current temperature measurement data, and these data can be used as a benchmark for judging whether the current heating process is normal.

[0121] In multi-level comparison, the similarity of the current temperature measurement data and the historical temperature measurement data can be compared in layers. In some embodiments, the reference temperature measurement data can be selected based on multi-level comparison through the following steps:

[0122] The water level difference, the time difference, and the temperature difference of the current temperature measurement data and each historical temperature measurement data are obtained; the historical temperature measurement data with a water level difference less than a maximum water level difference threshold is selected as first-level screening data, the first-level screening data with a temperature difference less than a maximum temperature difference threshold is selected as second-level screening data, and the second-level screening data with a time difference less than a maximum time difference threshold is selected as third-level screening data; the weights corresponding to the water level difference, the time difference, and the temperature difference of each third-level screening data are added to obtain the data similarity of each third-level screening data and the current temperature measurement data, and the third-level screening data with a data similarity greater than the similarity threshold is determined as the reference temperature measurement data.

[0123] Water level, time, and temperature are core parameters reflecting the heating state. By calculating the difference between the current temperature measurement data and each historical temperature measurement data in these three parameters, the difference between the two can be quantified, ensuring that the selected historical data has high comparability with the current data.

[0124] The water level difference is the difference between the water level parameter of the current temperature measurement data and the water level parameter of a certain historical temperature measurement data; the time difference is the difference between the time parameter (user-set time) of the current temperature measurement data and the time parameter of a certain historical temperature measurement data, which can be taken as an absolute value; and the temperature difference is the difference between the temperature parameter (target temperature) of the current temperature measurement data and the temperature parameter of a certain historical temperature measurement data, which can be taken as an absolute value.

[0125] By setting multiple filtering conditions, the range of historical temperature measurement data can be gradually narrowed. Water level is a basic and decisive parameter for heating scenarios, and priority filtering can greatly narrow the range. Temperature parameter (target temperature) determines the final state of heating, and secondary priority filtering can focus on key requirements. On the basis of similar water levels, by filtering through the maximum temperature difference threshold (such as 10℃), historical data with too large a target temperature difference can be excluded. The time parameter (set time) reflects the heating rhythm, but under the same water level and temperature target, the time can allow certain fluctuations. On the premise of similar water level and temperature, by filtering through the maximum time difference threshold, extreme data with too large a time deviation can be excluded, while time fluctuation data within a certain range is retained to avoid excessive restrictions leading to insufficient effective data.

[0126] The maximum water level difference threshold is a pre-set critical value for judging whether the water level parameters are similar (such as 50ml). When the water level difference is less than the threshold, it is considered that the water level parameters of the two are similar. The first-level filtered data is the historical temperature measurement data with a water level difference less than the maximum water level difference threshold selected from all historical temperature measurement data. The maximum temperature difference threshold is a pre-set critical value for judging whether the temperature parameters are similar. The second-level filtered data is the historical temperature measurement data with a temperature difference less than the maximum temperature difference threshold selected from the first-level filtered data. The maximum time difference threshold is a pre-set critical value for judging whether the time parameters are similar. The third-level filtered data is the historical temperature measurement data with a time difference less than the maximum time difference threshold selected from the second-level filtered data.

[0127] Due to the different degrees of influence of water level, time, and temperature parameters on heating scenarios, by assigning different weight proportions, the data similarity can be more reasonably calculated, and the similarity result can be more in line with the actual situation. For example, the influence weight of water level on temperature distribution is the highest, the influence of temperature on heating target is second, and the influence of time is relatively flexible. By weighting, the similarity calculation can be more in line with the actual influence weight, and the high similarity of water level and temperature can be avoided due to the deviation of a single parameter (such as time). Selecting the third-level filtered data with high similarity as the reference temperature measurement data can ensure that it can provide effective data benchmark for the abnormal judgment of the current heating process.

[0128] The weight proportion is a proportion set according to the degree of influence of each parameter on the heating scenario. The similarity threshold is a critical value for judging whether the data is similar enough (such as 80%). When the data similarity is greater than the threshold, it is considered that the third-level filtered data can be used as a reference.

[0129] S42, generate a plurality of time nodes corresponding to the current temperature measurement data based on the time interval, and obtain the reference temperature of each temperature measurement point at each time node according to the average value of each reference temperature measurement data.

[0130] Since the temperature changes dynamically over time during the heating process, multiple time nodes can be divided to accurately determine whether the temperature at each time point is normal. The reference temperature is determined based on the average temperature of the reference temperature measurement data at these nodes, which can eliminate accidental errors of a single historical data and provide a stable and reliable benchmark for temperature judgment at each time node.

[0131] The time interval is the fixed time length for dividing the time nodes (e.g., 1 minute, which can be adjusted according to the length of the time parameter). The time node is a specific time point divided in sequence from the heating start time according to the time interval (e.g., when the time parameter is 5 minutes and the time interval is set to 1 minute, the time nodes are 1 minute, 2 minutes, 3 minutes, 4 minutes, and 5 minutes). The reference temperature is the temperature value obtained by averaging the temperatures of the corresponding temperature measurement points in all reference temperature measurement data at each time node, representing the normal temperature of the point at the time node.

[0132] For example, assuming that the current time parameter is 5 minutes and the time interval is set to 1 minute, the generated time nodes are 1 minute, 2 minutes, 3 minutes, 4 minutes, and 5 minutes. For the 3-minute time node, the temperatures of the bottom in the 3 reference temperature measurement data are 65℃, 67℃, and 66℃, with an average of 66℃, so the reference temperature of the bottom at the 3-minute node is 66℃; the temperatures of the middle are 60℃, 62℃, and 61℃, with an average of 61℃, so the reference temperature of the middle at the 3-minute node is 61℃; and the temperatures of the water surface are 55℃, 57℃, and 56℃, with an average of 56℃, so the reference temperature of the water surface at the 3-minute node is 56℃.

[0133] S43, obtaining the comparison difference between the current temperature and the reference temperature of each temperature measurement point at each time node, determining that the temperature measurement point with a comparison difference greater than the temperature measurement comparison threshold as an abnormal temperature measurement point, disabling the temperature measurement element corresponding to the abnormal temperature measurement point and issuing a warning.

[0134] By comparing the current temperature and the reference temperature of each temperature measurement point at each time node, the temperature measurement point deviating from the normal range can be found in time. For the abnormal point deviating too much, disabling the corresponding temperature measurement element can avoid the influence of false temperature data on the heating control, and issuing a warning can remind the user to repair, ensuring the safe and stable operation of the heating system.

[0135] The comparison difference is the difference between the current temperature and the reference temperature of a certain temperature measurement point at the same time node; the temperature measurement comparison threshold is a pre-set critical value for judging whether the temperature is abnormal. When the absolute value of the comparison difference exceeds the threshold, it is considered that the temperature of the point is abnormal.

[0136] For example, assuming that at the 3-minute time node, the bottom current temperature is 68℃, the reference temperature is 66℃, the comparison difference is 2℃, which is less than the 3℃ temperature measurement comparison threshold, and it is a normal point; the middle current temperature is 65℃, the reference temperature is 61℃, the comparison difference is 4℃, which is greater than the 3℃ threshold, and it is an abnormal temperature measurement point; and the water surface current temperature is 58℃, the reference temperature is 56℃, and the comparison difference is 2℃, which is a normal point. The system will immediately disable the temperature measurement element corresponding to the middle temperature measurement point and issue a warning through the buzzer and red warning light flashing.

[0137] By fusing water level, user-set time, target temperature and other multi-dimensional parameters to generate current temperature measurement data, and comparing with historical data at multiple levels, reference data with high similarity is selected as a benchmark, avoiding the limitations of single parameter or fixed threshold judgment. Based on time interval division of multiple time nodes, and in combination with the average value of reference data to determine the reference temperature of each node, dynamic tracking of the heating process is realized. Once an abnormal temperature measurement point with an out-of-standard comparison difference is detected, the corresponding temperature measurement element is immediately disabled and a warning is issued, which can avoid the interference of false temperature data on heating power adjustment.

[0138] Referring to Figure 4 , it is a structure schematic diagram of a heating control system of an electric kettle based on intelligent temperature control provided by an embodiment of the present application. The heating control system of the electric kettle based on intelligent temperature control comprises:

[0139] A multi-dimensional temperature measurement module and a water level detection module are used to obtain real-time water quantity and initial temperature of each temperature measurement point.

[0140] A calculation module is used to initialize the heating power corresponding to the real-time water quantity according to the target temperature and set time set by the user.

[0141] A control module is used to allocate the contribution degree index of each temperature measurement point according to the real-time water quantity, and after starting heating, the real-time temperature of each temperature measurement point is fused and calculated according to the contribution degree index, and the heating power is dynamically adjusted after comparison with the target temperature.

[0142] An element loss module is used to compare the current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, and according to the comparison result, the temperature measurement element corresponding to the abnormal temperature measurement point is disabled and a warning is issued.

[0143] Figure 2 The device of the embodiment shown can be used to execute the steps in the method embodiment shown, and the implementation principles and technical effects are similar, which will not be described here. Figure 1 The steps in the method embodiment shown can be executed by the device of the embodiment shown, and the implementation principles and technical effects are similar, which will not be described here.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A heating control method for an electric kettle based on intelligent temperature control, characterized in that, The method comprises the steps of: starting a multi-dimensional temperature measurement module and a water level detection module to obtain real-time water volume and initial temperatures of each temperature measurement point; a calculation module initializes a heating power corresponding to the real-time water volume according to a target temperature and a set time set by a user; a control module allocates a contribution degree index of each temperature measurement point according to the real-time water volume, and after starting heating, fuses and calculates real-time temperatures of each temperature measurement point according to the contribution degree index, and dynamically adjusts the heating power after comparing the real-time temperatures with the target temperature; a component wear module compares current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, and according to a comparison result, deactivates a temperature measurement component corresponding to an abnormal temperature measurement point and issues a warning.

2. The method of claim 1, wherein starting a multi-dimensional temperature measurement module and a water level detection module to obtain real-time water volume and initial temperatures of each temperature measurement point comprises: obtaining real-time water volume according to the water level detection module; the multi-dimensional temperature measurement module comprises temperature measurement components corresponding to fixed points and dynamic points, and initial temperatures of each temperature measurement point are obtained according to the temperature measurement components, wherein the temperature measurement components of the dynamic points automatically adjust positions based on the real-time water volume.

3. The method of claim 1, wherein the control module allocates a contribution degree index of each temperature measurement point according to the real-time water volume, comprising: determining a water volume interval corresponding to the real-time water volume, wherein the water volume interval comprises a low water volume interval, a medium water volume interval, and a high water volume interval; comparing a standard heating time corresponding to the water volume interval with the set time, and when the standard heating time is greater than the set time, determining short-time heating, and when the standard heating time is less than the set time, determining long-time heating; determining initial indexes of each temperature measurement point according to the water volume interval, and updating the initial indexes based on an adjustment amplitude corresponding to the short-time heating or the long-time heating to obtain the contribution degree index of each temperature measurement point.

4. The method of claim 3, wherein determining initial indexes of each temperature measurement point according to the water volume interval, and updating the initial indexes based on an adjustment amplitude corresponding to the short-time heating or the long-time heating to obtain the contribution degree index of each temperature measurement point, comprising: determining preset indexes configured at each position of each water volume interval as the initial indexes of each temperature measurement point, wherein the positions of the temperature measurement points comprise a bottom, a middle, and a water surface; when short-time heating, determining a first adjustment amplitude at each position according to a first difference between the standard heating time and the set time, and updating the initial indexes of each temperature measurement point based on the first adjustment amplitude to obtain the contribution degree index; or when long-time heating, determining a second adjustment amplitude at each position according to a second difference between the set time and the standard heating time, and updating the initial indexes of each temperature measurement point based on the second adjustment amplitude to obtain the contribution degree index.

5. The method of claim 4, wherein when short-time heating, determining a first adjustment amplitude at each position according to a first difference between the standard heating time and the set time, and updating the initial indexes of each temperature measurement point based on the first adjustment amplitude to obtain the contribution degree index, comprising: In the short-time heating, in the low water volume interval, the initial index of the temperature measuring point at the bottom is added by the first adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is subtracted by the first adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or, In the medium water volume interval, the initial index of the temperature measuring point at the middle is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the water surface is not adjusted, to obtain the contribution degree index of each temperature measuring point; or, In the high water volume interval, the initial index of the temperature measuring point at the water surface is added by the first adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the first adjustment amplitude, and the initial index of the temperature measuring point at the middle is not adjusted, to obtain the contribution degree index of each temperature measuring point.

6. The method of claim 4, wherein, In the long-time heating, the second adjustment amplitude at each position is determined according to the second difference value between the set time and the standard heating time, and the initial index of each temperature measuring point is updated to obtain the contribution degree index based on the second adjustment amplitude, including: In the long-time heating, in the low water volume interval, the initial index of the temperature measuring point at the bottom is subtracted by the second adjustment amplitude, the initial index of the temperature measuring point at the middle and the water surface is added by the second adjustment amplitude, to obtain the contribution degree index of each temperature measuring point; or, In the medium water volume interval and the high water volume interval, the initial index of the temperature measuring point at the middle and the water surface is added by the second adjustment amplitude, the initial index of the temperature measuring point at the bottom is subtracted by the second adjustment amplitude, to obtain the contribution degree index of each temperature measuring point.

7. The method of claim 1, wherein, After starting heating, the real-time temperature of each temperature measuring point is calculated according to the contribution degree index, and after comparing with the target temperature, the heating power is dynamically adjusted, including: After starting heating, the current temperature of each temperature measuring point is obtained based on the corresponding temperature measuring element, and the real-time temperature of each temperature measuring point is obtained according to the product of the contribution degree index corresponding to the temperature measuring point and the current temperature; The temperature difference value of the target temperature and the average value of each real-time temperature is obtained, and the heating power is adjusted in real time based on the power adjustment amount calculated by the PID algorithm.

8. The method of claim 1, wherein, The element loss module compares the current temperature measuring data and the historical temperature measuring data of each temperature measuring point in real time, and according to the comparison result, the temperature measuring element corresponding to the abnormal temperature measuring point is disabled and a warning is issued, including: The current temperature measuring data is obtained according to the water level parameter, the time parameter and the temperature parameter, the historical temperature measuring data is compared based on the current temperature measuring data, and the historical temperature measuring data with a data similarity greater than a similarity threshold is determined as reference temperature measuring data; A plurality of time nodes corresponding to the current temperature measuring data are generated based on the time interval, and the reference temperature of each temperature measuring point at each time node is obtained according to the average value of each reference temperature measuring data; The comparison difference value of the current temperature and the reference temperature of each temperature measuring point at each time node is obtained, and the temperature measuring point with a comparison difference value greater than a temperature measuring comparison threshold is determined as an abnormal temperature measuring point, and the temperature measuring element corresponding to the abnormal temperature measuring point is disabled and a warning is issued.

9. The method of claim 8, wherein, According to the water level parameter, the time parameter and the temperature parameter, current temperature measurement data is obtained, and based on the current temperature measurement data, historical temperature measurement data is compared in multiple stages to determine reference temperature measurement data with a data similarity greater than a similarity threshold, including: Obtaining water level difference values, time difference values and temperature difference values of the current temperature measurement data and each historical temperature measurement data; Selecting historical temperature measurement data with a water level difference value less than a maximum water level difference threshold as first-stage screening data, selecting first-stage screening data with a temperature difference value less than a maximum temperature difference threshold as second-stage screening data, and selecting second-stage screening data with a time difference value less than a maximum time difference threshold as third-stage screening data; Adding the weight proportions corresponding to the water level difference value, the time difference value and the temperature difference value of each third-stage screening data to obtain the data similarity of each third-stage screening data and the current temperature measurement data, and determining third-stage screening data with a data similarity greater than the similarity threshold as the reference temperature measurement data.

10. A heating control system for an electric kettle based on intelligent temperature control, characterized in that, Including: A multi-dimensional temperature measurement module and a water level detection module are used to obtain real-time water volume and initial temperatures of each temperature measurement point; A calculation module is used to initialize a heating power corresponding to the real-time water volume according to a target temperature and a set time set by a user; A control module is used to distribute a contribution degree index of each temperature measurement point according to the real-time water volume, to fuse and calculate real-time temperatures of each temperature measurement point according to the contribution degree index after starting heating, and to dynamically adjust the heating power after comparing the real-time temperatures with the target temperature; An element loss module is used to compare current temperature measurement data and historical temperature measurement data of each temperature measurement point in real time, to disable a temperature measurement element corresponding to an abnormal temperature measurement point according to a comparison result, and to issue a warning.

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