Control optimization methods, devices, equipment and readable storage media for gas water heaters

Abstract

This application discloses a control optimization method, apparatus, device, and readable storage medium for a gas water heater, relating to the field of water heater technology. The control optimization method for a gas water heater includes: collecting operating condition data of the gas water heater; inputting the operating condition data into a digital twin model corresponding to the gas water heater to determine the corresponding simulated operating results; scoring the simulated operating results according to preset scoring rules to obtain a score result for the gas water heater; if the score result is lower than a preset scoring threshold, iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score result of the gas water heater is greater than or equal to the preset scoring threshold, thereby obtaining target controller parameters; and sending the target controller parameters to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters. This application solves the technical problem that the control performance of gas water heaters gradually decreases over time.

Description

Technical Field

[0001] This application relates to the field of water heater technology, and in particular to a control optimization method, a control optimization device, a control optimization equipment, and a computer-readable storage medium for a gas water heater. Background Technology

[0002] Gas water heaters are relatively complex household appliances. Due to limitations in manufacturing processes and processing technology, as well as the complex installation and usage environments in homes, the temperature control performance and other machine performance of gas water heaters can change over time. Even gas water heaters calibrated by the manufacturer and meeting preset performance requirements in testing can easily experience poor temperature control, unsatisfactory operation, and frequent malfunctions during use in a user's home environment. Therefore, gas water heaters suffer from unstable operating performance in real-world applications, negatively impacting the user experience.

[0003] The information disclosed in this background section is only for understanding the background technology of the present application concept, and therefore may contain information that does not constitute prior art. Summary of the Invention

[0004] The main objective of this application is to provide a control optimization method, a control optimization device, a control optimization equipment, and a computer-readable storage medium for a gas water heater, aiming to solve the technical problem that the control performance of a gas water heater gradually decreases over time.

[0005] To achieve the above objectives, this application provides a control optimization method for a gas water heater, the control optimization method for the gas water heater comprising:

[0006] Collect operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results;

[0007] The simulated operation results are scored according to preset scoring rules to obtain the scoring result of the gas water heater;

[0008] If the score result is lower than the preset score threshold, the current controller parameters of the gas water heater are iteratively optimized based on the digital twin model until the score result of the gas water heater is greater than or equal to the preset score threshold, and the target controller parameters are obtained.

[0009] The target controller parameters are sent to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters.

[0010] In one embodiment, prior to the step of collecting the operating condition data of the gas water heater, the method further includes:

[0011] Each component in the gas water heater was modeled separately to obtain the corresponding mechanism model for each component.

[0012] By combining the mechanism models of each component, a complete model of the gas water heater is generated.

[0013] The model parameters of the whole machine model are optimized based on the historical operating data of the gas water heater until the model accuracy of the whole machine model reaches a preset accuracy threshold, thereby obtaining the digital twin model corresponding to the gas water heater.

[0014] In one embodiment, the scoring rules include at least the correspondence between heating time and score and the correspondence between overshoot temperature difference and score; the simulation results include at least the temperature change curve; and the scoring results include at least the temperature control score.

[0015] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0016] The heating score is determined based on the correspondence between the heating time in the temperature change curve and the preset heating time and the score. The closer the heating time is to the preset reference time, the higher the corresponding heating score.

[0017] Based on the overshoot temperature difference in the temperature change curve and the correspondence between the preset overshoot temperature difference and the score, the temperature overshoot score is determined, wherein the smaller the overshoot temperature difference, the higher the corresponding temperature overshoot score.

[0018] The temperature control score is calculated based on the temperature rise score, the temperature overshoot score, and the weights corresponding to the temperature rise score and the temperature overshoot score.

[0019] In one embodiment, the scoring rules include at least the correspondence between acceleration time and score and the correspondence between overshoot wind speed difference and score; the simulation results include at least the wind speed change curve; and the scoring results include at least the wind speed control score.

[0020] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0021] The acceleration score is determined based on the acceleration time in the wind speed change curve and the correspondence between the preset acceleration time and the score. The shorter the acceleration time, the higher the corresponding acceleration score.

[0022] Based on the correspondence between the overshoot wind speed difference in the wind speed change curve and the preset overshoot wind speed difference and the score, the wind speed overshoot score is determined. The smaller the overshoot wind speed difference, the higher the corresponding wind speed overshoot score.

[0023] The wind speed control score is calculated based on the acceleration score, the wind speed overshoot score, and the weights corresponding to the acceleration score and the wind speed overshoot score.

[0024] In one embodiment, the scoring result further includes a wind speed offset score;

[0025] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0026] Obtain the gas proportional valve opening data of the gas water heater, and determine the wind speed reference curve corresponding to the gas proportional valve opening data based on the preset correspondence between the gas proportional valve opening and the wind speed.

[0027] Based on the wind speed variation curve and the wind speed reference curve, determine the wind speed offset value at each time point;

[0028] Based on each wind speed offset value, a corresponding wind speed offset score is determined, wherein the larger the wind speed offset value, the lower the corresponding wind speed offset score.

[0029] In one embodiment, the step of iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score of the gas water heater is higher than the preset score threshold, and obtaining the target controller parameters, includes:

[0030] Update the current controller parameters of the gas water heater in the digital twin model, and based on the updated controller parameters and the operating condition data, simulate operation through the digital twin model to obtain the corresponding updated simulation operation results;

[0031] The updated simulation results are scored according to the scoring rules to obtain the updated score results;

[0032] If the updated score result is lower than the preset score threshold, then return to the execution step: update the current controller parameters of the gas water heater in the digital twin model;

[0033] If the updated score result is greater than or equal to the preset score threshold, then the current control parameter is determined as the target controller parameter.

[0034] In one embodiment, the step of sending the target controller parameters to the controller of the gas water heater includes:

[0035] The target controller parameters are sent to the wireless communication module of the gas water heater, so that the wireless communication module can transmit the target controller parameters to the controller of the gas water heater and update the current control parameters stored in the controller to the target controller parameters. The controller then controls the operation of the gas water heater according to the target control parameters.

[0036] In addition, this application also provides a control optimization device for a gas water heater, the control optimization device for the gas water heater comprising:

[0037] The simulation operation module is used to collect the operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results;

[0038] The scoring module is used to score the simulated operation results according to preset scoring rules, and obtain the scoring result of the gas water heater;

[0039] The parameter optimization module is used to iteratively optimize the current controller parameters of the gas water heater based on the digital twin model if the scoring result is lower than the preset scoring threshold, until the scoring result of the gas water heater is greater than or equal to the preset scoring threshold, and obtain the target controller parameters.

[0040] The parameter sending module is used to send the target controller parameters to the controller of the gas water heater, so as to control the operation of the gas water heater based on the target controller parameters.

[0041] In addition, this application also provides a control optimization device for a gas water heater, the control optimization device for a gas water heater including at least: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control optimization method for a gas water heater as described above.

[0042] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the control optimization method for a gas water heater as described above.

[0043] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the control optimization method for a gas water heater as described above.

[0044] This application provides a control optimization method for a gas water heater. The control optimization method includes: firstly, collecting operating condition data of the gas water heater; inputting the operating condition data into the digital twin model corresponding to the gas water heater to determine the corresponding simulated operating results; then, scoring the simulated operating results according to preset scoring rules to obtain the scoring result of the gas water heater; if the scoring result is lower than a preset scoring threshold, then iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the scoring result of the gas water heater is greater than or equal to the preset scoring threshold to obtain target controller parameters; finally, sending the target controller parameters to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters. The technical solution of this application uses a pre-established digital twin model to simulate the operation of a gas water heater and scores the simulation results to determine whether the operation of the gas water heater meets the requirements. When the score of the gas water heater is lower than the threshold, the digital twin model is used to optimize the controller parameters, and the corresponding score is used as a criterion to stop parameter optimization, thus obtaining better target controller parameters. The technical solution of this application achieves the effect of optimizing the overall performance through data interaction between the twin water heater and the gas water heater. It can simulate the operation of the gas water heater and perform real-time parameter optimization based on actual operating data in various application scenarios, stabilize the water heater performance, and improve the user experience. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an embodiment of the control optimization method for a gas water heater in this application.

[0048] Figure 2 This is a schematic diagram of the digital twin model corresponding to the schematic diagram of each component after disassembly of the whole machine in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the complete model of the gas water heater after combining the digital twin models of each component in the embodiments of this application;

[0050] Figure 4 This is a window plot of the simulated temperature change curve output by the digital twin model in the embodiments of this application;

[0051] Figure 5 This is a schematic diagram of the temperature change curve in the operating condition data of the gas water heater collected in the embodiments of this application;

[0052] Figure 6 This is a schematic diagram of the simulated temperature change curves corresponding to the multiple sets of optimized parameters after optimizing the controller parameters in the embodiments of this application;

[0053] Figure 7 This is a schematic diagram illustrating the data flow of a feasible cloud-based local collaborative control embodiment in this application.

[0054] Figure 8 This is a schematic diagram of the overall process of a feasible control optimization method for a gas water heater in the embodiments of this application;

[0055] Figure 9 This is a schematic diagram of the composition of the control optimization device for a gas water heater in an embodiment of this application;

[0056] Figure 10 This is a schematic diagram of the hardware operating environment of the control optimization device for the gas water heater involved in the control optimization method of the gas water heater in this application embodiment.

[0057] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0060] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0061] Gas water heaters are popular due to their ability to provide hot water quickly and on demand, their energy efficiency, and their compact size. However, limitations in manufacturing processes and processing techniques, as well as the complex installation and usage environments in homes, can cause variations in their temperature control performance or the inherent capabilities of the machine itself. Gas water heaters calibrated by the manufacturer and meeting excellent performance tests may exhibit poor temperature control and frequent malfunctions in users' homes. Therefore, a method to optimize gas water heaters for home use is needed to overcome these shortcomings. Currently, the development of digital twin technology has promoted progress in the gas water heater industry. The technical solution of this application introduces digital twin technology to establish a mechanistic model of the gas water heater and achieves a digital twin water heater through optimization methods. By interacting with the data of the twin water heater and the physical water heater, the overall performance of the machine is optimized, improving the user experience.

[0062] This application provides a control optimization method for a gas water heater, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the control optimization method for a gas water heater according to this application. The control optimization method for the gas water heater includes:

[0063] Step S10: Collect the operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results;

[0064] First, it should be noted that the control optimization method for gas water heaters provided in this application can be applied to cloud devices or local devices that have established a communication connection with the gas water heater, or it can be applied to the gas water heater itself that is pre-loaded with a digital twin model.

[0065] Specifically, during the operation of a gas water heater, its operating condition data is first collected. This operating condition data can include various types of operating parameters, such as water flow rate, inlet water temperature, segment information, proportional valve opening, fan speed, flue gas temperature, oxygen content, outlet water temperature, and a series of other parameters that can reflect the working status of the gas water heater.

[0066] Furthermore, the digital twin model corresponding to the gas water heater is a pre-built model capable of comprehensively simulating the operating conditions of the gas water heater. After the operating condition data is input into the digital twin model, the model can simulate the operation of the gas water heater under those conditions, including the working status of each internal component and output corresponding simulation results. These results can include changes in the output water temperature (represented by a temperature change curve) and changes in the output fan speed (represented by a speed change curve). These simulation results reflect the operating status of the gas water heater, indicating its performance, stability, and whether it meets preset requirements. This information is then used for subsequent evaluation of the gas water heater to determine if controller parameter optimization is necessary.

[0067] Step S20: The simulation results are scored according to the preset scoring rules to obtain the scoring results of the gas water heater;

[0068] The preset scoring rules refer to the pre-set rules for evaluating the operational stability and performance of gas water heaters. For example, during the process of heating the inlet water, the outlet water temperature needs to rise rapidly to the set temperature while minimizing overshoot. The more the preset optimal simulated operating results are met (the water temperature rise time is shorter than the preset duration and there is no overshoot), the higher the score of the gas water heater. In addition, the performance requirements for gas water heaters can also include other dimensions of indicators, such as water temperature stability, noise, fan speed, and other indicators of concern to users. This allows for a preliminary assessment of the current operational performance of the gas water heater and determines whether control optimization is needed.

[0069] Step S30: If the scoring result is lower than the preset scoring threshold, the current controller parameters of the gas water heater are iteratively optimized based on the digital twin model until the scoring result of the gas water heater is greater than or equal to the preset scoring threshold, and the target controller parameters are obtained.

[0070] After obtaining the rating result of the gas water heater, it is compared with the preset rating threshold (e.g., 90 points). If the rating result is lower than the preset rating threshold, it means that the current performance and operating conditions of the gas water heater do not meet the preset requirements. Further control and optimization are needed to improve the performance of the gas water heater, improve its rating result, make it meet the preset requirements, and improve the user's water experience.

[0071] Specifically, the control optimization process primarily focuses on optimizing the controller parameters. These parameters refer to the control parameters used in the feedforward and / or feedback control algorithm modules employed in the gas water heater's control process. Optimizing and updating these controller parameters will cause the simulated operation results to trend towards higher scores. During the iterative optimization of controller parameters, commonly used intelligent algorithms (such as genetic algorithms) can be incorporated. The updated controller parameters are then substituted into a digital twin model, and simulations are performed to output the simulation results corresponding to these controller parameters and determine the corresponding score. The optimization objective is to achieve a higher score until a set of controller parameters is obtained that results in a gas water heater score exceeding a preset score threshold; this set is then identified as the target controller parameters.

[0072] In another feasible embodiment, if the scoring result is greater than or equal to the preset scoring threshold, it indicates that the control performance of the gas water heater is relatively reliable and stable, and the current controller parameters do not need to be optimized.

[0073] Step S40: Send the target controller parameters to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters.

[0074] Finally, the optimized target controller parameters are sent to the gas water heater's controller, allowing the gas water heater to operate based on these parameters in subsequent operations. Since the target controller parameters have been determined in the digital twin model simulation to ensure the score exceeds the preset threshold, the changes in output water temperature and fan speed when the gas water heater operates based on these parameters will meet the user's preset requirements. This results in a significant performance improvement compared to the gas water heater before optimization, enhancing the user's water usage experience.

[0075] Furthermore, in one feasible embodiment, the control optimization method for the gas water heater can be applied to a cloud device, which is communicatively connected to the gas water heater;

[0076] The step of sending the target controller parameters to the controller of the gas water heater includes:

[0077] Step S41: Send the target controller parameters to the wireless communication module of the gas water heater, so that the wireless communication module can transmit the target controller parameters to the controller of the gas water heater and update the current control parameters stored in the controller to the target controller parameters. The controller then controls the gas water heater to operate according to the target control parameters.

[0078] The digital twin model in this embodiment can be pre-deployed in the cloud. When the gas water heater needs to be optimized for control, it can actively upload its own operating condition data to the cloud device, and complete the simulation operation and scoring process in the cloud. If the controller parameters need to be optimized, iterative optimization is performed in the cloud until the target controller parameters are obtained. Finally, the target controller parameters are sent to the gas water heater's wireless communication module (such as the WiFi module) via WiFi (Wireless Fidelity) or other wireless communication methods, so that the gas water heater updates its local controller parameters to the target controller parameters.

[0079] The technical solution of this application embodiment utilizes the computing power resources of cloud devices, reduces the computing power requirements of the gas water heater itself, realizes the cloud-edge collaborative control optimization process, and can maintain the stability of the control performance of the gas water heater in various application scenarios and under high service life.

[0080] This application provides a control optimization method for a gas water heater. The control optimization method includes: firstly, collecting operating condition data of the gas water heater; inputting the operating condition data into the digital twin model corresponding to the gas water heater to determine the corresponding simulated operation result; then, scoring the simulated operation result according to a preset scoring rule to obtain the score result of the gas water heater; if the score result is lower than a preset scoring threshold, iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score result of the gas water heater is greater than or equal to the preset scoring threshold to obtain target controller parameters; finally, sending the target controller parameters to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters. In the technical solution of this application embodiment, a pre-established digital twin model is used to simulate the operation of a gas water heater, and the simulation results are scored to determine whether the operation results of the gas water heater meet the requirements. When the score results of the gas water heater do not meet the requirements, the digital twin model is used to optimize the controller parameters, and the corresponding score results are used as the condition for stopping parameter optimization to obtain better target controller parameters. The technical solution of this application embodiment achieves the optimization of the overall performance effect through data interaction between the twin water heater and the physical water heater. It can simulate the operation of the gas water heater and perform real-time parameter optimization based on actual operating condition data in various application scenarios, stabilize the water heater performance, and improve the user experience.

[0081] Furthermore, in one feasible embodiment, prior to the step of collecting the operating condition data of the gas water heater, the method further includes:

[0082] Step A10: Model each component in the gas water heater separately to obtain the mechanism model corresponding to each component;

[0083] To simulate the working conditions of the components in a gas water heater, each component was first disassembled and modeled individually to obtain corresponding mechanism models.

[0084] For example, refer to Figure 2 The components in a gas water heater may include hot water pipes, cold water pipes, water valves, heat exchangers, combustion chambers, flue pipes, smoke hoods, gas proportional valves, segmented valves, and a gas model (composed of multiple burners). Based on digital twin technology, mechanism models of these components are established respectively, and the operating conditions of these components can be simulated in a multi-disciplinary and multi-physical quantity process.

[0085] Step A20: Combine the mechanism models of each component to generate the complete model of the gas water heater;

[0086] like Figure 3 As shown (red is the water inlet pipe, blue is the water outlet pipe, and yellow is the air inlet and outlet pipes), will Figure 2 The mechanism models of each component are combined according to the actual structure of the gas water heater to obtain the corresponding whole machine model. The whole machine model can simulate the working conditions of each component of the gas water heater under working conditions, including various parameters such as temperature, speed, power, status, and gear.

[0087] Step A30: Optimize the model parameters of the whole machine model based on the historical operating data of the gas water heater until the model accuracy of the whole machine model reaches the preset accuracy threshold, and obtain the digital twin model corresponding to the gas water heater.

[0088] After the complete model is built, data mechanism fusion and optimization can be further carried out so that the operating parameters of the model during the simulation operation are as close as possible to the operating parameters of the real gas water heater when it receives the same input data as the real gas water heater.

[0089] In actual testing, after establishing the complete model of the gas water heater in this embodiment, continuous optimization was performed to achieve a relative accuracy of approximately 1.5% of the preset accuracy threshold. Specifically, the simulated outlet water temperature output by the complete model and the actual outlet water temperature and other parameter values ​​in historical operating data are shown in the table below:

[0090]

[0091] The absolute and relative error values ​​calculated in the table above are based on the comparison between the actual effluent temperature and the simulated effluent temperature in historical operating data. In practical applications, the whole machine model can also output data in other dimensions such as flue gas temperature, oxygen content, and air intake. The accuracy of the model can also be measured by comparing the simulated output data with the actual data.

[0092] For example, after the optimized digital twin model simulates the operation based on the operating data of the gas water heater, the output temperature change curve y(t) is as follows: Figure 4 As shown, the horizontal axis represents time, and the vertical axis represents temperature (unit: degC, ℃). It can be seen that at a certain point, when the time is 13.68s, the temperature of the HotPipe is 38.6424 degC, the slope is 0.000260137, the local minimum is 20.0231at0.005, and the local maximum is 38.6427at36.05.

[0093] In one feasible embodiment, the scoring rules include at least the correspondence between heating time and score and the correspondence between overshoot temperature difference and score, the simulation results include at least the temperature change curve, and the scoring results include at least the temperature control score.

[0094] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0095] Step S21: Determine the heating score based on the correspondence between the heating time in the temperature change curve and the preset heating time and the score. The closer the heating time is to the preset reference time, the higher the corresponding heating score.

[0096] Step S22: Determine the temperature overshoot score based on the overshoot temperature difference in the temperature change curve and the correspondence between the preset overshoot temperature difference and the score. The smaller the overshoot temperature difference, the higher the corresponding temperature overshoot score.

[0097] Step S23: Calculate the temperature control score based on the temperature rise score, temperature overshoot score, and the weights corresponding to the temperature rise score and temperature overshoot score.

[0098] Understandably, when evaluating the operation of a gas water heater, the score can be based on the temperature change curve of its output water temperature. A digital twin model corresponds to the gas water heater and can simulate the temperature change curve output by the gas water heater under the current controller parameters. Furthermore, in scenarios where the temperature change curve is used as the scoring criterion, the gas water heater also includes the actual temperature change curve when uploading its operating condition data. In this case, the outlet water temperature is sampled every 0.2 seconds, forming a... Figure 5As shown in the temperature change curve, after the water temperature rise phase ends, there is a brief period when the outlet water temperature is higher than the set temperature. At this point, it can be determined that the gas water heater is overshooting, which will affect the user's water experience and requires further optimization of the controller parameters.

[0099] The scoring of the outlet water temperature change curve is mainly based on two dimensions: heating time and overshoot temperature difference. Heating time refers to the period from when the gas water heater starts heating until the outlet water temperature reaches the user's set temperature. Generally, a shorter heating time indicates a higher heating power of the gas water heater. However, excessively high heating power can easily lead to excessive overshoot temperature difference, affecting the user experience. Therefore, a reasonable heating time range is usually set as a reference (e.g., 15s-20s). The closer the heating time is to the reference time, the higher the corresponding heating score (up to 100 points). The correspondence between heating time and score can be represented by a pre-set two-dimensional mapping table, reflecting the characteristic that the closer the heating time is to the preset reference time, the higher the corresponding heating score.

[0100] Additionally, overshoot temperature difference refers to the portion of the outlet water temperature that exceeds the set temperature after the heating period ends. For example, in Figure 5 In the graph (horizontal axis represents the number of samplings, vertical axis represents the temperature value), after the outlet water temperature initially reaches the set temperature of 45℃, it continues to rise until it reaches a maximum of approximately 47℃. The temperature difference of 2℃ between 47℃ and the set temperature of 45℃ is the overshoot temperature difference. The larger this overshoot temperature difference, the worse the thermostatic control performance of the gas water heater, and the lower the corresponding temperature overshoot score. The preset correspondence between the overshoot temperature difference and the score is used to reflect the characteristic that the smaller the overshoot temperature difference, the higher the corresponding temperature overshoot score.

[0101] After obtaining the heating score and temperature overshoot score, the temperature control score is calculated by combining them. Specifically, the temperature control score is obtained by weighted summation based on the pre-set weights of the heating score and temperature overshoot score. The weights are used to reflect the importance of the two indicators of heating time and overshoot temperature difference in the temperature control score, and can be set by the user according to their actual needs.

[0102] Furthermore, when optimizing the current controller parameters of the gas water heater based on the outlet water temperature change curve and temperature control score, multiple sets of parameters are continuously iterated, and the simulated operating results (i.e., temperature change curve) under each set of parameters are simulated and calculated using a digital twin model. For example... Figure 6As shown, the overshoot temperature difference of the outlet water temperature corresponding to the original parameters (i.e., the current controller parameters before optimization) is large. The overshoot temperature difference of the outlet water temperature corresponding to the optimized parameter 1 has decreased. From the perspective of temperature control, the outlet water temperature corresponding to the optimized parameter 2 (i.e., the target controller parameters) is generally no longer overshooting and the heating time is less than t (20s), so the optimization can be considered complete.

[0103] In one feasible embodiment, the scoring rules include at least the correspondence between acceleration time and score and the correspondence between overshoot wind speed difference and score, the simulation results include at least the wind speed change curve, and the scoring results include at least the wind speed control score.

[0104] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0105] Step S24: Determine the acceleration score based on the acceleration time in the wind speed change curve and the correspondence between the preset acceleration time and the score. The shorter the acceleration time, the higher the corresponding acceleration score.

[0106] Step S25: Determine the wind speed overshoot score based on the overshoot wind speed difference in the wind speed change curve and the correspondence between the preset overshoot wind speed difference and the score. The smaller the overshoot wind speed difference, the higher the corresponding wind speed overshoot score.

[0107] Step S26: Calculate the wind speed control score based on the acceleration score, wind speed overshoot score, and the weights corresponding to the acceleration score and wind speed overshoot score.

[0108] Besides scoring based on the outlet water temperature change curve, scoring can also be based on the fan speed change curve of the gas water heater. It's understandable that both temperature and fan speed control can be optimized from the perspective of constant temperature and constant speed. The optimization goal is to control the temperature and fan speed more quickly and stably at the set temperature and target fan speed. When controlling changes in fan speed and temperature, feedback control (i.e., closed-loop control) is required. This involves using feedback on the outlet water temperature and fan speed change curves to stabilize the outlet water temperature and fan speed.

[0109] Similarly, the scoring of wind speed change curves is mainly based on two dimensions: acceleration time and overshoot wind speed difference. Acceleration time refers to the period from when the gas water heater starts its fan until the fan speed reaches the target wind speed that matches the opening of the proportional valve. The correspondence between acceleration time and score can be represented by a pre-set two-dimensional mapping table, showing that the shorter the acceleration time, the higher the acceleration score.

[0110] In addition, the overshoot wind speed difference refers to the portion of the fan speed that exceeds the target wind speed after the acceleration period ends. The larger the overshoot wind speed difference, the worse the constant speed control performance of the gas water heater, and the lower the corresponding wind speed overshoot score. The preset correspondence between the overshoot wind speed difference and the score is used to reflect the characteristic that the smaller the overshoot wind speed difference, the higher the corresponding wind speed overshoot score.

[0111] After obtaining the acceleration score and wind speed overshoot score, the wind speed control score is calculated by combining them. Specifically, the wind speed control score is obtained by weighted summation based on the weights set in advance for the acceleration score and wind speed overshoot score. The weights are used to reflect the importance of the two indicators, acceleration time and overshoot wind speed difference, in the wind speed control score, and can be set by the user according to their actual needs.

[0112] In another feasible embodiment, the scoring result also includes a wind speed offset score;

[0113] The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes:

[0114] Step S27: Obtain the gas proportional valve opening data of the gas water heater, and determine the wind speed reference curve corresponding to the gas proportional valve opening data based on the preset correspondence between the gas proportional valve opening and the wind speed.

[0115] Step S28: Determine the wind speed offset value at each time point based on the wind speed change curve and the wind speed reference curve;

[0116] Step S29: Determine the corresponding wind speed offset score based on each wind speed offset value. The larger the wind speed offset value, the lower the corresponding wind speed offset score.

[0117] In this embodiment, the performance of a gas water heater can also be evaluated using the relationship between wind speed and the opening of the gas proportional valve. It should be noted that, typically, the fan speed of a gas water heater is related to the opening of the gas proportional valve; the larger the opening of the gas proportional valve, the greater the combustion heat, and the higher the required fan speed. Before the gas water heater leaves the factory, the relationship between the gas proportional valve opening and the fan speed is pre-determined, thus establishing a fixed correspondence between the gas proportional valve opening and the fan speed. After obtaining the gas proportional valve opening data of the gas water heater, a corresponding fan speed reference curve is generated based on the correspondence between the gas proportional valve opening data and the fan speed. The gas proportional valve opening data represents the change in the proportional valve opening over time, and the fan speed reference curve represents the standard change in fan speed over time under that proportional valve opening data.

[0118] The wind speed variation curve is used to represent the simulated wind speed changes in the simulation results. The deviation between the wind speed variation curve and the wind speed reference curve at each time point is determined to obtain the wind speed offset value. It can be understood that the larger the wind speed offset value, the greater the control error of the wind turbine and the lower the corresponding wind speed offset score.

[0119] For example, there may be an overall deviation (too large or too small) or an abnormal deviation (too large at some time points and too small at others) between the wind speed change curve and the wind speed reference curve. When calculating the wind speed deviation score, it can be determined according to the maximum wind speed deviation value or according to the average wind speed deviation at each time point.

[0120] In one feasible embodiment, the step of iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score of the gas water heater is higher than the preset score threshold, and obtaining the target controller parameters, includes:

[0121] Step S31: Update the current controller parameters of the gas water heater in the digital twin model. Based on the updated controller parameters and operating condition data, simulate the operation through the digital twin model to obtain the corresponding updated simulation operation results.

[0122] Step S32: Score the updated simulation results according to the scoring rules to obtain the updated score results;

[0123] Step S33: If the updated score result is lower than the preset score threshold, return to the execution step: update the current controller parameters of the gas water heater in the digital twin model;

[0124] Step S34: If the updated score result is greater than or equal to the preset score threshold, then the current control parameter is determined as the target controller parameter.

[0125] This application also provides a process for parameter optimization based on a digital twin model. Specifically, it utilizes a commonly used intelligent algorithm for parameter optimization, such as a random search algorithm, grid algorithm, gradient descent algorithm, or genetic algorithm. After updating the controller parameters once using the intelligent algorithm, the updated controller parameters and the collected operating condition data are input into the digital twin model to obtain the corresponding updated simulation results (temperature change curve or fan change curve), and the corresponding score is calculated. Taking the genetic algorithm as an example, the score corresponding to each set of controller parameters can be used as its fitness to guide the controller parameters to mutate and update in a direction with higher fitness, i.e., higher score. After obtaining updated controller parameters with a score greater than or equal to a preset score threshold (e.g., 90 points), these updated controller parameters can be determined as the target controller parameters that meet the conditions.

[0126] In addition, the number of iterations can be used as a condition for stopping optimization. That is, the controller parameters of the gas water heater are continuously updated iteratively according to the preset algorithm until the preset number of iterations is reached (such as 3000 times). Based on the score result corresponding to the latest controller parameters, it is determined whether the requirements have been met. If they have been met, the update is stopped. Otherwise, the preset number of iterations is performed again for optimization updates until the score result is higher than the preset score threshold or it is determined that the performance of the gas water heater cannot be optimized to a certain level through parameter optimization.

[0127] For example, when the control optimization method for a gas water heater according to the embodiments of this application is applied in the cloud, the data flow between the local end and the cloud is as follows: Figure 7 As shown, the control algorithm modules A and B used by the gas water heater and the cloud device are essentially the same. They are achieved through digital cloning technology, enabling the digital twin model to possess the same control algorithms as the gas water heater, thus simulating the gas water heater's operation in the cloud. First, the gas water heater transmits its operating data to the cloud device. The cloud then uses control algorithm module B and the digital twin model to determine if optimization is needed. If so, iterative parameter optimization is performed, and then the target controller parameters are sent back to the local gas water heater to control subsequent operations.

[0128] To facilitate understanding, a general overview of the control optimization method for the gas water heater in this application embodiment will be provided, such as... Figure 8 As shown, firstly, a digital twin model is established. The model's accuracy is assessed to determine if validation and optimization are necessary. If the accuracy meets the requirements, the digital twin model is downloaded to the cloud and deployed, along with controller A. Then, the gas water heater uploads its operating data to the cloud. The cloud uses an evaluation system to determine if optimization is needed. If so, intelligent algorithm training is performed to find the optimal solution. The evaluation results for the control parameters are checked to see if they meet the distribution conditions. If not, the controller parameters are iteratively optimized until the stopping condition is met, and then the distribution conditions are checked again. Once the distribution conditions are met, the target controller parameters are distributed to the gas water heater's local controller to control its operation, thereby optimizing the gas water heater's constant temperature and / or constant speed performance.

[0129] It should be noted that this example is only for the purpose of assisting in understanding this application and does not constitute a limitation on the control optimization method of the gas water heater in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0130] This application also provides a control optimization device for a gas water heater, see reference. Figure 9 The control optimization device for gas water heaters includes:

[0131] The simulation operation module 10 is used to collect the operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results.

[0132] The scoring module 20 is used to score the simulated operation results according to preset scoring rules, and obtain the scoring result of the gas water heater.

[0133] The parameter optimization module 30 is used to iteratively optimize the current controller parameters of the gas water heater based on the digital twin model if the scoring result is lower than the preset scoring threshold, until the scoring result of the gas water heater is greater than or equal to the preset scoring threshold, and obtain the target controller parameters.

[0134] The parameter sending module 40 is used to send the target controller parameters to the controller of the gas water heater so as to control the operation of the gas water heater based on the target controller parameters.

[0135] In one embodiment, the control optimization device for the gas water heater further includes a model building module, which is used for:

[0136] Each component in the gas water heater was modeled separately to obtain the corresponding mechanism model for each component.

[0137] By combining the mechanism models of each component, a complete model of the gas water heater is generated.

[0138] The model parameters of the whole machine model are optimized based on the historical operating data of the gas water heater until the model accuracy of the whole machine model reaches a preset accuracy threshold, thereby obtaining the digital twin model corresponding to the gas water heater.

[0139] In one embodiment, the scoring rules include at least the correspondence between heating time and score and the correspondence between overshoot temperature difference and score; the simulation results include at least the temperature change curve; and the scoring results include at least the temperature control score.

[0140] The scoring module 20 is also used for:

[0141] The heating score is determined based on the correspondence between the heating time in the temperature change curve and the preset heating time and the score. The closer the heating time is to the preset reference time, the higher the corresponding heating score.

[0142] Based on the overshoot temperature difference in the temperature change curve and the correspondence between the preset overshoot temperature difference and the score, the temperature overshoot score is determined, wherein the smaller the overshoot temperature difference, the higher the corresponding temperature overshoot score.

[0143] The temperature control score is calculated based on the temperature rise score, the temperature overshoot score, and the weights corresponding to the temperature rise score and the temperature overshoot score.

[0144] In one embodiment, the scoring rules include at least the correspondence between acceleration time and score and the correspondence between overshoot wind speed difference and score; the simulation results include at least the wind speed change curve; and the scoring results include at least the wind speed control score.

[0145] The scoring module 20 is also used for:

[0146] The acceleration score is determined based on the acceleration time in the wind speed change curve and the correspondence between the preset acceleration time and the score. The shorter the acceleration time, the higher the corresponding acceleration score.

[0147] Based on the correspondence between the overshoot wind speed difference in the wind speed change curve and the preset overshoot wind speed difference and the score, the wind speed overshoot score is determined. The smaller the overshoot wind speed difference, the higher the corresponding wind speed overshoot score.

[0148] The wind speed control score is calculated based on the acceleration score, the wind speed overshoot score, and the weights corresponding to the acceleration score and the wind speed overshoot score.

[0149] In one embodiment, the scoring result further includes a wind speed offset score;

[0150] The scoring module 20 is also used for:

[0151] Obtain the gas proportional valve opening data of the gas water heater, and determine the wind speed reference curve corresponding to the gas proportional valve opening data based on the preset correspondence between the gas proportional valve opening and the wind speed.

[0152] Based on the wind speed variation curve and the wind speed reference curve, determine the wind speed offset value at each time point;

[0153] Based on each wind speed offset value, a corresponding wind speed offset score is determined, wherein the larger the wind speed offset value, the lower the corresponding wind speed offset score.

[0154] In one embodiment, the parameter optimization module 30 is further configured to:

[0155] Update the current controller parameters of the gas water heater in the digital twin model, and based on the updated controller parameters and the operating condition data, simulate operation through the digital twin model to obtain the corresponding updated simulation operation results;

[0156] The updated simulation results are scored according to the scoring rules to obtain the updated score results;

[0157] If the updated score result is lower than the preset score threshold, then return to the execution step: update the current controller parameters of the gas water heater in the digital twin model;

[0158] If the updated score result is greater than or equal to the preset score threshold, then the current control parameter is determined as the target controller parameter.

[0159] In one embodiment, the parameter sending module 40 is further configured to:

[0160] The target controller parameters are sent to the wireless communication module of the gas water heater, so that the wireless communication module can transmit the target controller parameters to the controller of the gas water heater and update the current control parameters stored in the controller to the target controller parameters. The controller then controls the operation of the gas water heater according to the target control parameters.

[0161] The gas water heater control optimization device provided in this application adopts the gas water heater control optimization method in the above embodiments, which can solve the technical problem that the control performance of gas water heaters gradually decreases over time. Compared with the prior art, the beneficial effects of the gas water heater control optimization device provided in this application are the same as those of the gas water heater control optimization method provided in the above embodiments, and other technical features in the gas water heater control optimization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0162] This application also provides a control optimization device for a gas water heater, which includes at least: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the control optimization method for the gas water heater described in the above embodiments.

[0163] The following is for reference. Figure 10 It shows a structural schematic diagram of a control optimization device suitable for implementing the gas water heater of the embodiments of this application. Figure 10 The control optimization device for the gas water heater shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0164] like Figure 10As shown, the control optimization device for a gas water heater may include a processing unit 101 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 103 into a random access memory (RAM) 104. The RAM 104 also stores various programs and data required for the operation of the control optimization device for the gas water heater. The processing unit 101, ROM 102, and RAM 104 are interconnected via a bus 105. An input / output (I / O) interface 106 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 106: input devices 107 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 108 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 103 including, for example, magnetic tape, hard disk, etc.; and communication devices 109. Communication device 109 allows the control optimization device of the gas water heater to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows control optimization devices for gas water heaters with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0165] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 103, or installed from ROM 102. When the computer program is executed by processing device 101, it performs the functions defined in the methods of the embodiments of this application.

[0166] The control optimization device for gas water heaters provided in this application, employing the control optimization method for gas water heaters described in the above embodiments, can solve the technical problem of the control performance of gas water heaters gradually decreasing over time. Compared with the prior art, the beneficial effects of the control optimization device for gas water heaters provided in this application are the same as those of the control optimization method for gas water heaters provided in the above embodiments, and other technical features in the control optimization device for gas water heaters are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0167] It should be understood that various parts of the embodiments of this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0168] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the above claims.

[0169] This application also provides a computer-readable storage medium storing a computer program that can run on a processor. The computer program is used to execute the control optimization method for a gas water heater in the above embodiments.

[0170] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0171] The aforementioned computer-readable storage medium may be included in the control optimization device of the gas water heater; or it may exist independently and not be assembled into the control optimization device of the gas water heater.

[0172] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the control optimization device of the gas water heater, the control optimization device performs the following actions: collecting operating condition data of the gas water heater; inputting the operating condition data into the digital twin model corresponding to the gas water heater to determine the corresponding simulated operating results; scoring the simulated operating results according to preset scoring rules to obtain a score result for the gas water heater; if the score result is lower than a preset scoring threshold, iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score result of the gas water heater is greater than or equal to the preset scoring threshold to obtain target controller parameters; and sending the target controller parameters to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters.

[0173] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0175] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0176] The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the control optimization method for the gas water heater described above, which can solve the technical problem that the control performance of the gas water heater gradually decreases over time. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the control optimization method for the gas water heater provided in the above embodiments, and will not be repeated here.

[0177] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the control optimization method for a gas water heater as described above.

[0178] The computer program product provided in this application can solve the technical problem that the control performance of gas water heaters gradually decreases over time. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the gas water heater control optimization method provided in the above embodiments, and will not be repeated here.

[0179] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A control optimization method for a gas water heater, characterized in that, The control optimization method for the gas water heater includes: Collect operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results; The simulated operation results are scored according to preset scoring rules to obtain the scoring result of the gas water heater; If the score result is lower than the preset score threshold, the current controller parameters of the gas water heater are iteratively optimized based on the digital twin model until the score result of the gas water heater is greater than or equal to the preset score threshold, and the target controller parameters are obtained. The target controller parameters are sent to the controller of the gas water heater to control the operation of the gas water heater based on the target controller parameters.

2. The control optimization method for a gas water heater as described in claim 1, characterized in that, Before the step of collecting the operating condition data of the gas water heater, the method further includes: Each component in the gas water heater was modeled separately to obtain the corresponding mechanism model for each component. By combining the mechanism models of each component, a complete model of the gas water heater is generated. The model parameters of the whole machine model are optimized based on the historical operating data of the gas water heater until the model accuracy of the whole machine model reaches a preset accuracy threshold, thereby obtaining the digital twin model corresponding to the gas water heater.

3. The control optimization method for a gas water heater as described in claim 1, characterized in that, The scoring rules include at least the correspondence between heating time and score and the correspondence between overshoot temperature difference and score; the simulation results include at least the temperature change curve; and the scoring results include at least the temperature control score. The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes: The heating score is determined based on the correspondence between the heating time in the temperature change curve and the preset heating time and the score. The closer the heating time is to the preset reference time, the higher the corresponding heating score. Based on the overshoot temperature difference in the temperature change curve and the correspondence between the preset overshoot temperature difference and the score, the temperature overshoot score is determined, wherein the smaller the overshoot temperature difference, the higher the corresponding temperature overshoot score. The temperature control score is calculated based on the temperature rise score, the temperature overshoot score, and the weights corresponding to the temperature rise score and the temperature overshoot score.

4. The control optimization method for a gas water heater as described in claim 1, characterized in that, The scoring rules include at least the correspondence between acceleration time and score and the correspondence between overshoot wind speed difference and score; the simulation results include at least the wind speed change curve; and the scoring results include at least the wind speed control score. The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes: The acceleration score is determined based on the acceleration time in the wind speed change curve and the correspondence between the preset acceleration time and the score. The shorter the acceleration time, the higher the corresponding acceleration score. Based on the correspondence between the overshoot wind speed difference in the wind speed change curve and the preset overshoot wind speed difference and the score, the wind speed overshoot score is determined. The smaller the overshoot wind speed difference, the higher the corresponding wind speed overshoot score. The wind speed control score is calculated based on the acceleration score, the wind speed overshoot score, and the weights corresponding to the acceleration score and the wind speed overshoot score.

5. The control optimization method for a gas water heater as described in claim 4, characterized in that, The scoring results also include wind speed deviation scores; The step of scoring the simulation results according to preset scoring rules to obtain the scoring result of the gas water heater includes: Obtain the gas proportional valve opening data of the gas water heater, and determine the wind speed reference curve corresponding to the gas proportional valve opening data based on the preset correspondence between the gas proportional valve opening and the wind speed. Based on the wind speed variation curve and the wind speed reference curve, determine the wind speed offset value at each time point; Based on each wind speed offset value, a corresponding wind speed offset score is determined, wherein the larger the wind speed offset value, the lower the corresponding wind speed offset score.

6. The control optimization method for a gas water heater as described in claim 1, characterized in that, The step of iteratively optimizing the current controller parameters of the gas water heater based on the digital twin model until the score of the gas water heater is higher than the preset score threshold, and obtaining the target controller parameters, includes: Update the current controller parameters of the gas water heater in the digital twin model, and based on the updated controller parameters and the operating condition data, simulate operation through the digital twin model to obtain the corresponding updated simulation operation results; The updated simulation results are scored according to the scoring rules to obtain the updated score results; If the updated score result is lower than the preset score threshold, then return to the execution step: update the current controller parameters of the gas water heater in the digital twin model; If the updated score result is greater than or equal to the preset score threshold, then the current control parameter is determined as the target controller parameter.

7. The control optimization method for a gas water heater as described in any one of claims 1 to 6, characterized in that, The step of sending the target controller parameters to the controller of the gas water heater includes: The target controller parameters are sent to the wireless communication module of the gas water heater, so that the wireless communication module can transmit the target controller parameters to the controller of the gas water heater and update the current control parameters stored in the controller to the target controller parameters. The controller then controls the operation of the gas water heater according to the target control parameters.

8. A control optimization device for a gas water heater, characterized in that, The control optimization device for the gas water heater includes: The simulation operation module is used to collect the operating condition data of the gas water heater, input the operating condition data into the digital twin model corresponding to the gas water heater, and determine the corresponding simulation operation results; The scoring module is used to score the simulated operation results according to preset scoring rules, and obtain the scoring result of the gas water heater; The parameter optimization module is used to iteratively optimize the current controller parameters of the gas water heater based on the digital twin model if the scoring result is lower than the preset scoring threshold, until the scoring result of the gas water heater is greater than or equal to the preset scoring threshold, and obtain the target controller parameters. The parameter sending module is used to send the target controller parameters to the controller of the gas water heater, so as to control the operation of the gas water heater based on the target controller parameters.

9. A control optimization device for a gas water heater, characterized in that, The control optimization device for the gas water heater includes at least: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control optimization method for the gas water heater as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium is a computer-readable storage medium, and the computer-readable storage medium stores a program for implementing a control optimization method for a gas water heater. The program for implementing the control optimization method for a gas water heater is executed by a processor to implement the steps of the control optimization method for a gas water heater as described in any one of claims 1 to 7.

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