Thermal system energy consumption optimization method based on multi-sensor fusion

Abstract

The present application relates to the field of data control, and specifically discloses a heat system energy consumption optimization method based on multi-sensor fusion, comprising: S1, multi-sensor information collection: collecting the weight change data of the heated body and the content, the temperature data of the bottom of the heated body, the atmospheric pressure data of the environment and the heat system energy consumption related data; S2, boiling point dynamic judgment: determining the boiling state based on the temperature and the weight change rate; S3, thermal characteristic modeling: estimating the system heat capacity and the boiling decay time constant through short-time heat stopping test; S4, intelligent power regulation: adaptively adjusting the heating off duration and the reheating power based on the thermal inertia model using the predictive control algorithm; S5, energy efficiency analysis and self-learning: calculating the actual total energy consumption, the theoretical minimum energy consumption and the energy consumption utilization rate, and evaluating, storing and self-learning calling the control parameters based on the heated body and the water quantity information.

Description

Technical Field

[0001] This invention relates to the field of data control, and more specifically, to a method for optimizing energy consumption in thermal systems based on multi-sensor fusion. Background Technology

[0002] Existing energy-saving methods for heating systems primarily involve improving burner structure, adding heat-concentrating coils, and using high-efficiency thermal conductive materials to enhance thermal efficiency and reduce heat loss. Simultaneously, they are equipped with a simple timer function, allowing users to preset heating times, after which the heat source automatically shuts off. The advantages lie in the fact that improved hardware structure leads to a general increase in basic thermal efficiency, and the timer function can, to some extent, prevent energy waste caused by users forgetting to use the system.

[0003] However, in practical use, it still has some shortcomings. First, the accuracy of state determination is insufficient: existing technologies mostly use a single temperature sensor to determine water boiling, which is easily affected by local dry burning at the bottom of the heated object, oil stains, or fluctuations in ambient air pressure, resulting in frequent false alarms and missed alarms, and cannot reliably identify the boiling state. Second, the energy-saving effect is significantly limited: it generally adopts continuous heating or intermittent heating for a fixed duration, and fails to dynamically adjust power and time according to the thermal inertia of the heated object and the water volume. The heat supply does not match the actual demand, and there is still significant energy waste. Third, it lacks parameter adaptation and learning capabilities: the control parameters are usually preset fixed values, and cannot be adaptively adjusted for different materials, capacities, and water volumes of the heated object. It also does not have historical data learning functions, resulting in poor universality and inability to continuously optimize energy efficiency.

[0004] Fourth, it has a low degree of automation and relies on user intervention: users need to manually set the heating time or rely on the water boiling reminder function to make adjustments, which cannot achieve true unattended full-process automatic control, making it difficult to guarantee the cooking effect and increasing the burden on users. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, this invention provides a method for optimizing the energy consumption of a thermal system based on multi-sensor fusion, which addresses the problems mentioned in the background art through the following scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the energy consumption of a thermal system based on multi-sensor fusion, comprising:

[0007] S1. Multi-sensor information acquisition: The weight data of the heated body and its contents, the bottom temperature data of the heated body, the ambient atmospheric pressure data, and the energy consumption data of the thermal system are collected through multiple sensors.

[0008] S2. Dynamic Boiling Point Judgment: Based on the weight data and the bottom temperature data of the heated body, a combined judgment strategy of temperature threshold and weight change rate is adopted. When the temperature reaches or exceeds the boiling point temperature and the weight change rate is continuously lower than a set negative threshold, the boiling state is confirmed.

[0009] S3. Thermal property modeling: After confirming the boiling state, perform a short-term complete stop heating test, monitor the temperature drop rate and weight change rate decay, estimate the heat capacity of the heated body-water system and the boiling decay time constant, and establish a thermal inertia model.

[0010] S4. Intelligent power regulation: Based on the thermal inertia model, a predictive control algorithm is used to dynamically generate and execute an intermittent heating strategy. By monitoring the rate of temperature and weight change, the boiling state decay trend is predicted, and the heating off-time and reheating power are adaptively adjusted to maintain stable boiling with low-power pulses.

[0011] S5. Energy Efficiency Analysis and Self-Learning: Calculate the actual total energy consumption based on the energy consumption data of the thermal system, calculate the theoretical minimum energy consumption based on the weight data, the bottom temperature data of the heated body, and the ambient atmospheric pressure data, calculate the energy utilization rate based on the actual total energy consumption and the theoretical minimum energy consumption, and perform energy efficiency evaluation, optimization storage, and self-learning recall of the dynamic control parameters based on the type of heated body and water volume information identified or recorded in this heating process.

[0012] Preferably, the multi-sensor includes a weighing sensor, a temperature sensor, a barometric pressure sensor, and an energy metering device.

[0013] Preferably, the boiling point temperature is dynamically corrected based on the local atmospheric pressure collected by the pressure sensor, and the negative threshold is set based on the rate characteristics of water evaporation under typical boiling conditions.

[0014] Preferably, the heat capacity is estimated based on the temperature drop curve recorded during the short-term complete cessation of heating test; the boiling decay time constant is estimated based on the decay of the weight change rate during the test.

[0015] Preferably, the predictive control algorithm employs a method based on PID control or fuzzy logic control.

[0016] Preferably, the reheating process uses a short-time pulse power higher than the maintenance power for heating, and the duration of the pulse is dynamically adjusted according to the heat compensation requirements predicted by the thermal inertia model.

[0017] Preferably, the actual total energy consumption The theoretical minimum energy consumption is the total energy consumed accumulated by the energy metering device from the start of heating to the end of the program. The calculation formula is:

[0018] ,

[0019] in, The theoretical heating energy required to heat water from its initial temperature to its boiling point. The theoretical evaporation energy required to maintain boiling and evaporate water; the formula for calculating the energy utilization rate η is:

[0020] .

[0021] Preferably, the theoretical heating energy The calculation formula is:

[0022] ,

[0023] in, The initial mass of water when heating begins. The specific heat capacity of water, This refers to the boiling point temperature of water under the current atmospheric pressure. This is the initial temperature of the water when heating begins;

[0024] The theoretical evaporation energy The calculation formula is:

[0025] ,

[0026] in, This refers to the total mass of water lost through evaporation during the entire boiling process. It is the enthalpy of vaporization of water.

[0027] Preferably, the self-learning call specifically involves: during subsequent heating processes, automatically identifying the current type of heated object and water volume, and calling the pre-stored historical optimal control parameters that match the type of heated object and water volume.

[0028] Preferably, the dynamic control parameters include one or more of the following: heating off duration, reheating power, and reheating pulse duration.

[0029] The technical effects and advantages of this invention are as follows:

[0030] 1. Accurate judgment: By fusing weight and temperature sensors for judgment, the defects of misjudgment by a single sensor are overcome, resulting in extremely high reliability;

[0031] 2. Deep energy saving: The innovative dynamic intermittent heating strategy maximizes the use of waste heat of the heated body and thermal inertia of the system, avoiding energy waste from continuous heating, and can save an additional 15%-25% of energy compared with traditional methods;

[0032] 3. Intelligent Adaptive: It has a self-learning function and can adaptively adjust the control parameters according to different heated objects and water volumes. It has strong universality and continuously improves the optimization effect.

[0033] 4. Excellent user experience: Fully automated, requiring no user supervision, achieving extreme energy savings while ensuring cooking needs (continuous boiling water). Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention.

[0035] Figure 2 This is a schematic diagram of the complete embodiment of the present invention.

[0036] Figure 3 This is a table of 15 sets of experimental data for the global cost function of this invention.

[0037] Figure 4 This is a distribution chart of energy utilization rate and energy consumption value for each experimental group in this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] refer to Figures 1-4 The energy consumption optimization method for thermal systems based on multi-sensor fusion shown includes:

[0040] S1. Multi-sensor information acquisition: The weight data of the heated body and its contents, the bottom temperature data of the heated body, the ambient atmospheric pressure data, and the energy consumption data of the thermal system are collected through multiple sensors.

[0041] S2. Dynamic Boiling Point Judgment: Based on the weight data and the bottom temperature data of the heated body, a combined judgment strategy of temperature threshold and weight change rate is adopted. When the temperature reaches or exceeds the boiling point temperature and the weight change rate is continuously lower than a set negative threshold, the boiling state is confirmed.

[0042] S3. Thermal property modeling: After confirming the boiling state, perform a short-term complete stop heating test, monitor the temperature drop rate and weight change rate decay, estimate the heat capacity of the heated body-water system and the boiling decay time constant, and establish a thermal inertia model.

[0043] S4. Intelligent power regulation: Based on the thermal inertia model, a predictive control algorithm is used to dynamically generate and execute an intermittent heating strategy. By monitoring the rate of temperature and weight change, the boiling state decay trend is predicted, and the heating off-time and reheating power are adaptively adjusted to maintain stable boiling with low-power pulses.

[0044] S5. Energy Efficiency Analysis and Self-Learning: Calculate the actual total energy consumption based on the energy consumption data of the thermal system, calculate the theoretical minimum energy consumption based on the weight data, the bottom temperature data of the heated body, and the ambient atmospheric pressure data, calculate the energy utilization rate based on the actual total energy consumption and the theoretical minimum energy consumption, and perform energy efficiency evaluation, optimization storage, and self-learning recall of the dynamic control parameters based on the type of heated body and water volume information identified or recorded in this heating process.

[0045] The hardware structure of the thermal system includes:

[0046] High-precision load cell: This load cell typically employs resistance strain gauge or capacitive pressure sensing technology, and is installed at the bottom of the thermal system support structure or embedded under the heated object, directly coupled to the heated object's support mechanism. The measuring range is designed for 0-5kg, covering the total mass range of common cooking utensils and their contents; the resolution is ≤1g, enabling it to sensitively capture minute changes in mass caused by water evaporation during boiling; the sampling frequency is ≥10Hz, ensuring real-time weight signal accuracy.

[0047] Non-contact infrared temperature sensor: This temperature sensor points to the center area of ​​the bottom of the heated object and inverts its temperature by detecting the infrared radiation energy emitted by the bottom surface of the heated object. The measurement range covers 0-300℃ and is compatible with the range from room temperature to boiling point. The accuracy reaches ±1℃, ensuring the accuracy of boiling point determination.

[0048] Barometric pressure sensor: Used to collect local atmospheric pressure data, providing a data basis for dynamic correction of boiling point temperature. The measurement range is usually 70-110 kPa, and the accuracy is not less than ±0.1 kPa.

[0049] Energy metering device: This device varies depending on the type of heating system. For gas stoves, a flow meter is used to monitor the gas flow in real time; for induction cookers, current and voltage are collected through current and voltage sensors.

[0050] Main control unit: As the core of the system, it is responsible for controlling sensor sampling, performing data preprocessing, running boiling determination algorithm, realizing thermal inertia model modeling and intelligent power regulation strategy, and outputting control commands to the actuator. The main control unit acquires sensor analog signals through ADC channel and synchronously acquires weight data, temperature data, air pressure data and energy consumption related data at a frequency of not less than 10Hz to ensure the timing consistency of multi-source data.

[0051] Actuators: For gas stoves, a proportional regulating valve is used to linearly adjust the gas flow based on the PWM signal output by the main control unit, achieving precise power control. For induction cookers, a power regulator is used to adjust the heating power by changing the switching duty cycle.

[0052] S1. Multi-sensor Information Acquisition: This involves acquiring weight data of the heated object and its contents, bottom temperature data of the heated object, ambient atmospheric pressure data, and energy consumption data of the thermal system using multiple sensors. The specific process is as follows:

[0053] S101, Weight Data Acquisition and Processing

[0054] A high-precision weighing sensor acquires the raw mass signal of the heated object and its contents in real time. After analog-to-digital conversion, the signal is transmitted to the main control unit. The main control unit performs a moving average filtering process on the mass sequence to obtain stable weight data. Simultaneously, the rate of change of weight per unit time is calculated. .in: Used to calculate the initial mass of water when heating begins. And calculate the total mass of water lost through evaporation during the entire boiling process. , Used for boiling state determination, thermal characteristic modeling, and intelligent power control.

[0055] S102, Temperature Data Acquisition and Correction

[0056] The non-contact infrared temperature sensor acquires the temperature of the center region at the bottom of the heated object at a sampling frequency of 10Hz. The main control unit performs the following processing: median filtering is used to eliminate transient interference to obtain the effective bottom temperature of the heated object. Used for boiling determination; reading data from the pressure sensor. Dynamically corrected boiling point temperature: Output the initial temperature of the water when heating begins. And the boiling point temperature of water under the current atmospheric pressure. Calculate the theoretical minimum energy consumption .

[0057] S103. Energy Consumption Related Data Acquisition and Conversion

[0058] For gas stoves, the flow meter collects the raw gas flow signal. (Unit: m³ / s), after AD conversion and calibration, the standard volumetric flow rate value is obtained; for induction cookers, the raw input current signal is acquired through current and voltage sensors. (Unit: A) and the original voltage signal (Unit: V), after AD conversion and calibration, the effective values ​​of current and voltage are obtained.

[0059] S104, Data Synchronization and Quality Assurance

[0060] The MCU synchronously triggers three-channel data acquisition at a frequency of 10Hz via a hardware timer to establish a time-aligned dataset: {t, m(t), T(t), T...} boil Kalman filtering is used to fuse multi-sensor data to make optimal estimates of the measured values, thereby reducing random errors and ensuring that the data accuracy meets the requirements of subsequent algorithms.

[0061] Weight measurement error ≤ ±2g: Steady-state measurement accuracy achieved after filtering, based on the weighing sensor's resolution of ≤1g.

[0062] Temperature measurement error ≤ ±1.5℃: The system accuracy takes into account both the basic accuracy of the infrared sensor (±1℃) and environmental interference factors.

[0063] S2. Dynamic Boiling Point Judgment: Based on the weight data and the bottom temperature data of the heated body, a combined judgment strategy of temperature threshold and weight change rate is adopted. When the temperature reaches or exceeds the boiling point temperature and the weight change rate is continuously lower than a set negative threshold, the boiling state is confirmed.

[0064] After completing the synchronous acquisition and preprocessing of multi-source data, the system enters the dynamic boiling point determination stage. Specifically, the thermal system monitors the bottom temperature of the heated body in real time. and real-time weight change rate The dynamic boiling point determination process is as follows:

[0065] S201, Temperature Threshold Prediction

[0066] The thermal system will monitor the bottom temperature of the heated body in real time. The theoretical boiling point temperature of water under current ambient atmospheric pressure Comparison. When When the temperature reaches the target, it indicates that the bottom of the heated body has sufficient conditions for boiling, and the thermal system enters the boiling candidate state.

[0067] S202, Evaporation Rate Verification

[0068] Simply achieving the target temperature may stem from localized overheating at the bottom of the heated object or thermal inertia, and is not a sufficient condition for boiling. Therefore, the thermodynamic system simultaneously analyzes the rate of weight change provided by a high-precision weighing sensor. The negative evaporation rate threshold K and duration set by the thermal system. Determined in the following ways:

[0069] (1) Setting the evaporation rate threshold K: This threshold is mainly determined based on the theoretical evaporation rate of water under boiling conditions. Through a large number of experiments, weight change data during boiling were collected under different power, water volume, and heated body conditions. Statistical methods (such as taking the mean and subtracting three times the standard deviation) were used to determine a reliable negative threshold that can significantly distinguish boiling evaporation from background noise. The typical value range is -0.05 g / s to -0.2 g / s, with a typical value of -0.1 g / s. This setting ensures that verification is triggered only when a significant evaporation rate is reached.

[0070] (2) Duration Setting: This time is used to ensure the continuity and stability of the evaporation signal to resist transient interference. This time is determined by analyzing the duration of weight signal disturbances caused by common user operations (such as stirring and feeding) and mechanical vibrations. It is usually set to 2 to 5 seconds, with a typical value of 3 seconds. This duration is sufficient to cover the cycle of most short-lived disturbances while still allowing for a rapid response to the actual boiling state.

[0071] When the thermal system detects And can be continuously set for a certain time. If the evaporation signal is valid, then the evaporation signal is considered valid. This duration setting effectively filters out brief and severe weight fluctuations caused by user stirring, adding ingredients, or pot vibration, ensuring the stability of dynamic boiling point determination.

[0072] S203, Two-Factor Fusion Decision

[0073] Only when the temperature remains within the specified range ( ")" and "evaporation rate continues to exceed threshold ( Continuous setting time Only when both conditions are met synchronously in time will the main control unit confirm the official start of boiling and accurately record the start time of boiling. This fusion strategy greatly improves the robustness of the system. For example, in the initial stage of heating, before the water temperature reaches the boiling point, there may be slight evaporation, at which point the rate of weight change... The result is negative, but because the temperature conditions are not met, it will not be misjudged; conversely, when the water temperature has reached the boiling point but the user is stirring, the weight change rate will be negative. If the drastic fluctuations cannot remain below the evaporation rate threshold K, the system will not recognize it as boiling, thus effectively avoiding false triggering.

[0074] Once the boiling state is confirmed, the thermal system immediately sends a command to the actuator, triggering the subsequent energy-saving control process based on thermal characteristic modeling. Simultaneously, the thermal system continuously monitors the temperature at the bottom of the heated object. With weight change rate When the bottom temperature of the heated body Falling to the theoretical boiling point temperature Below or weight change rate When the evaporation rate rises above the evaporation rate threshold K, the boiling state is considered to have ended. This decision-making mechanism provides precise state switching points for subsequent establishment of thermal inertia models and implementation of dynamic reheating control.

[0075] S3. Thermal characteristic modeling: After confirming the boiling state, perform a short-term complete stop heating test, monitor the rate of temperature drop and the rate of weight change decay, estimate the heat capacity of the heated body-water system and the boiling decay time constant, and establish a thermal inertia model.

[0076] After accurately determining the onset of boiling, the thermal system establishes a dynamic thermal characteristic model of the current heated element-water combination, providing crucial predictive data for subsequent intelligent power regulation. The specific process is as follows:

[0077] S301. Trigger Test and Data Acquisition

[0078] The main control unit sends a command to the actuator to immediately and completely stop heating. For gas stoves, this means closing the proportional valve; for induction cookers, it means reducing the power to zero and initiating a test cycle of a preset duration. (Typically set to 10 seconds). During this test cycle, the thermal system synchronously and rapidly acquires and records data at a sampling frequency of no less than 10Hz:

[0079] Temperature drop curve: The bottom temperature of the heated object is continuously monitored using a non-contact infrared temperature sensor. Calculate its instantaneous rate of descent. ;

[0080] Weight change decay curve: Continuous monitoring of mass using a high-precision weighing sensor. And calculate the real-time weight change rate. Observe the process of its gradual decay (approaching zero) from a high negative value at boiling point.

[0081] S302, Model Parameter Estimation

[0082] After the test cycle ends, the main control unit uses the collected timing data to estimate two key parameters through a mathematical model:

[0083] Estimation of the system heat capacity C of a thermal system: The core of thermal characteristic modeling lies in accurately estimating the system heat capacity C. Its principle is based on energy conservation: after a short period of heating cessation, the rate of temperature decrease of the heated body-water system under near-adiabatic conditions is a direct reflection of its heat capacity. Specifically, by performing a heating cessation test, simultaneously monitoring the temperature decrease curve at the bottom of the heated body, and using known specific heat capacity parameters of water and the heated body material, algorithms such as system identification or least squares fitting are employed to process and analyze the collected time-series temperature data, thereby estimating the overall heat capacity C of the entire system. This parameter is crucial for predicting the system's thermal dynamics and directly determines the accuracy of subsequent control algorithms. A larger heat capacity C indicates greater system thermal inertia and slower temperature decay. The method described in this invention does not require complex formula calculations; it can be achieved through fitting experimental data, combining accuracy and engineering practicality.

[0084] (2) Estimation of boiling decay time constant τ: weight change rate The decay process of after heating stops approximately follows an exponential decay law, which can be expressed as: ,in, This represents the initial rate of weight change when heating ceases. This is determined by data collected during the test period. By fitting the data to an exponential function, the boiling decay time constant τ can be estimated. This parameter is used to quantify the decay characteristics of the boiling evaporation effect after heating is stopped. The larger the value of τ, the stronger the system's ability to maintain evaporation and the slower the rate of heat loss.

[0085] S303, Model Establishment

[0086] The estimated heat capacity C and boiling decay time constant τ are used as thermal inertia model parameters for this specific heated body-water combination and stored in the main control unit.

[0087] S4. Intelligent power regulation: Based on the aforementioned thermal inertia model, a predictive control algorithm is used to dynamically generate and execute an intermittent heating strategy. By monitoring the rate of temperature and weight change, the boiling state decay trend is predicted, and the heating off-time and reheating power are adaptively adjusted to maintain stable boiling with low-power pulses.

[0088] After successfully establishing the thermal inertia model of the current heated body-water system, the thermal system enters the intelligent power regulation stage. This model will be used for predictive control algorithms: using the heat capacity C, the algorithm can predict the time required for the bottom temperature of the heated body to drop below the theoretical boiling point temperature under the current heat loss; using the boiling decay time constant τ, the algorithm can predict the time required for the evaporation rate to decay to below the evaporation rate threshold K; combining these two prediction results, the thermal system can accurately determine when to trigger reheating to avoid boiling interruption, thereby achieving precise on-demand heating. The specific process is as follows:

[0089] S401, Initial Control Parameter Setting and Boiling Maintenance

[0090] Once boiling is confirmed, the main control unit immediately sends a command to the actuator to suspend the main heating. For gas stoves, the proportional control valve opening is reduced to a very low sustaining power, such as 10%-15% of the rated power; for induction cookers, the operating power is switched to the corresponding low sustaining power level, or even the heating is completely turned off.

[0091] S402, Real-time Monitoring and Model Prediction

[0092] During the heating pause, the thermal system continuously monitors the temperature at the bottom of the heated object. and rate of change of weight Data. The thermal system does not passively wait for the temperature or evaporation rate to drop to a threshold before responding; instead, it actively predicts: using the estimated heat capacity C, combined with the real-time temperature drop rate, it predicts the bottom temperature of the heated body under the current heat dissipation conditions. Decrease to theoretical boiling point temperature Time required Using the estimated boiling decay time constant τ, combined with the real-time evaporation rate, the time required for the evaporation rate to decay to the judgment threshold K is predicted. The thermal system takes the smaller of these two predicted times as the critical time for determining that the boiling state is about to end. .

[0093] S403, Predicted triggering reheat pulse

[0094] When the predicted critical time If the boiling point is less than a preset safety margin time (e.g., 2 seconds), it indicates that the system is about to exit the boiling state, and the main control unit immediately triggers reheating. Reheating is performed in the form of short-duration high-power pulses: reheating power... Set to a value significantly higher than the sustaining power, such as 70%-90% of the rated power, to inject sufficient heat into the system for a short period of time to quickly compensate for heat loss; pulse duration The required compensation heat is dynamically adjusted based on the heat gap predicted by the thermal inertia model. By solving this equation, the pulse duration can be dynamically calculated. The heating time is typically controlled within 2-4 seconds. The purpose is to quickly bring the system back to a stable boiling range, rather than heating at full power for an extended period.

[0095] S404, Closed-loop dynamic optimization

[0096] The system records the actual shutdown duration of each "pause-reheat" cycle. Actual reheat pulse duration And the energy consumption for that cycle. These data, along with the current information on the heated object and water volume, serve as inputs to the optimization algorithm. A parameter-self-tuning PID controller or fuzzy logic controller is employed, with the minimum average energy consumption per unit time as the core optimization objective, and uninterrupted boiling as a constraint. Two key control parameters are dynamically optimized in a closed-loop manner: optimal shutdown duration. The optimal reheating power is the longest time a thermodynamic system can maintain boiling at a given power level. That is, the optimal power value that can compensate for heat loss the fastest and most economically.

[0097] Through continuous iterative optimization, the thermal system will adaptively converge to the optimal operating point for the current combination of heated body and water volume, achieving ultimate energy saving.

[0098] S405, Model Update and State Switching

[0099] Throughout the process, the thermal system continuously compares the model's predicted data (such as the predicted temperature drop curve) with the actual monitoring data. If the deviation is found to be excessively large, it can be determined that the thermal inertia may have changed (such as a significant reduction in water volume due to evaporation), thereby triggering online fine-tuning or re-estimation of the heat capacity C and the boiling decay time constant τ to ensure the accuracy of predictive control.

[0100] S5. Energy Efficiency Analysis and Self-Learning: Calculate the actual total energy consumption based on the energy consumption data of the thermal system, calculate the theoretical minimum energy consumption based on the weight data, the bottom temperature data of the heated body, and the ambient atmospheric pressure data, calculate the energy utilization rate based on the actual total energy consumption and the theoretical minimum energy consumption, and perform energy efficiency evaluation, optimization storage, and self-learning recall of the dynamic control parameters based on the type of heated body and water volume information identified or recorded in this heating process.

[0101] After the entire heating process is completed, or when the user manually terminates the program, the thermal system automatically enters the energy efficiency analysis and self-learning phase. The specific process is as follows:

[0102] S501, Calculation of Actual Total Energy Consumption

[0103] For gas stoves, the main control unit adjusts the gas flow rate. and gas heating (Default value: 35 MJ / m³) Calculate instantaneous heat power (Unit: W) and actual total energy consumption (Unit: J), the calculation formula is as follows:

[0104] ,

[0105] .

[0106] For induction cookers, the main control unit adjusts the input current. (Unit: A) and voltage (Unit: V), calculate instantaneous electric power (Unit: W) and actual total energy consumption (Unit: J), the calculation formula is as follows:

[0107] ,

[0108] .

[0109] S502, Calculation of Theoretical Minimum Energy Consumption

[0110] Theoretical minimum energy consumption The calculation formula is:

[0111] ,

[0112] in, The theoretical heating energy required to heat water from its initial temperature to its boiling point. Theoretical evaporation energy required to maintain boiling and evaporate water; theoretical heating energy. The calculation formula is:

[0113] ,

[0114] in, The initial mass of water when heating begins. The specific heat capacity of water, This refers to the boiling point temperature of water under the current atmospheric pressure. This is the initial temperature of the water when heating begins;

[0115] The theoretical evaporation energy The calculation formula is:

[0116] ,

[0117] in, This refers to the total mass of water lost through evaporation during the entire boiling process. It is the enthalpy of vaporization of water.

[0118] S503, Energy Consumption Utilization Rate Calculation and Energy Efficiency Assessment

[0119] The energy utilization rate of the thermal system in this process is calculated using the following formula:

[0120] .

[0121] Energy utilization rate is used to objectively evaluate energy efficiency: the closer this value is to 100%, the more efficient the energy utilization and the more optimized the control strategy. The system compares the current energy utilization rate η with the historical energy utilization rate under the same operating conditions to optimize the combination of control parameters used this time, including the optimal shutdown duration. Optimal reheating power and reheat pulse duration The advantages and disadvantages of energy efficiency are evaluated.

[0122] S504, Optimize parameter storage and self-learning call

[0123] The thermal system stores key information and control results from this heating process as a record. Each record includes at least: the characteristic identifier of the heated body, the water volume range, the final energy utilization rate, and a corresponding set of optimal dynamic control parameters, including the optimal shutdown duration. Optimal reheating power and reheat pulse duration .

[0124] Therefore, the heating system constructs a continuously growing knowledge base of optimization parameters. When a user uses the heating system again, the system automatically searches the knowledge base for matching historical records after obtaining the weight of the heated object and the water volume by weighing it. If a record with the same heated object and similar water volume is found, the system directly calls that set of historically optimal parameters as the initial values ​​for the control algorithm, thus skipping the lengthy online trial-and-error process and achieving rapid convergence to the efficient operating point. This is the self-learning call function, which makes the heating system more energy-efficient with continued use and continuously improves the user experience.

[0125] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0126] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing energy consumption in a thermal system based on multi-sensor fusion, characterized in that, include: S1. Multi-sensor information acquisition: The weight data of the heated body and its contents, the bottom temperature data of the heated body, the ambient atmospheric pressure data, and the energy consumption data of the thermal system are collected through multiple sensors. S2. Dynamic Boiling Point Judgment: Based on the weight data and the bottom temperature data of the heated body, a combined judgment strategy of temperature threshold and weight change rate is adopted. When the temperature reaches or exceeds the boiling point temperature and the weight change rate is continuously lower than a set negative threshold, the boiling state is confirmed. S3. Thermal property modeling: After confirming the boiling state, perform a short-term complete stop heating test, monitor the rate of temperature drop and the rate of weight change, estimate the heat capacity of the heated body-water system and the boiling decay time constant, and establish a thermal inertia model. The heat capacity is estimated based on the temperature drop curve recorded during the short-term complete cessation of heating test; the boiling decay time constant is estimated based on the decay of the weight change rate during the test. S4. Intelligent power regulation: Based on the thermal inertia model, a predictive control algorithm is used to dynamically generate and execute an intermittent heating strategy. By monitoring the rate of temperature and weight change, the boiling state decay trend is predicted, and the heating off-time and reheating power are adaptively adjusted to maintain stable boiling with low-power pulses. S5. Energy Efficiency Analysis and Self-Learning: Calculate the actual total energy consumption based on the energy consumption data of the thermal system, calculate the theoretical minimum energy consumption based on the weight data, the bottom temperature data of the heated body, and the ambient atmospheric pressure data, calculate the energy utilization rate based on the actual total energy consumption and the theoretical minimum energy consumption, and perform energy efficiency evaluation, optimization storage, and self-learning recall of the dynamic control parameters based on the type of heated body and water volume information identified or recorded in this heating process. The dynamic control parameters include heating off duration, reheating power, and reheating pulse duration.

2. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 1, characterized in that, The multi-sensor system includes a weighing sensor, a temperature sensor, a pressure sensor, and an energy metering device.

3. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 2, characterized in that, The boiling point temperature is dynamically corrected based on the local atmospheric pressure collected by the pressure sensor, and the negative threshold is set based on the rate characteristics of water evaporation under typical boiling conditions.

4. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 1, characterized in that, The predictive control algorithm employs a method based on PID control or fuzzy logic control.

5. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 1, characterized in that, The heating process employs a short-duration pulse power higher than the sustaining power, and the duration of the pulse is dynamically adjusted based on the heat compensation requirements predicted by the thermal inertia model.

6. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 1, characterized in that, The actual total energy consumption The theoretical minimum energy consumption is the total energy consumed accumulated by the energy metering device from the start of heating to the end of the program. The calculation formula is: ， in, The theoretical heating energy required to heat water from its initial temperature to its boiling point. The theoretical evaporation energy required to maintain boiling and evaporate water; the formula for calculating the energy utilization rate η is: 。 7. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 6, characterized in that, The theoretical heating energy The calculation formula is: ， in, The initial mass of water when heating begins. The specific heat capacity of water, This refers to the boiling point temperature of water under the current atmospheric pressure. This is the initial temperature of the water when heating begins; The theoretical evaporation energy The calculation formula is: ， in, This refers to the total mass of water lost through evaporation during the entire boiling process. It is the enthalpy of vaporization of water.

8. The method for optimizing energy consumption of a thermal system based on multi-sensor fusion according to claim 1, characterized in that, The self-learning call specifically refers to: during the subsequent heating process, automatically identifying the current type of heated object and water volume, and calling the pre-stored historical optimal control parameters that match the type of heated object and water volume.

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