Water heater control parameter optimization method, water heater, cloud equipment and storage medium

Abstract

The invention discloses a water heater control parameter optimization method, a water heater, electronic equipment and a storage medium, and relates to the technical field of water heaters, and the water heater control parameter optimization method applied to the water heater comprises the steps that local operation data are obtained in the operation process, and the control performance of the water heater is evaluated according to the local operation data; obtaining a control performance evaluation result; and if the control performance evaluation result does not meet the preset requirement, sending the local operation data to the cloud equipment, so that the cloud equipment constructs a corresponding target virtual model according to the local operation data, and performs iterative optimization on the current control parameter based on the target virtual model to obtain a target control parameter, the local operation data at least comprises current control parameters; and target control parameters sent by the cloud equipment are received, and the water heater is controlled to operate based on the target control parameters. The technical problem that the temperature control performance and the constant temperature performance of the water heater are reduced along with the change of the service life is solved.

Description

Technical Field

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

[0002] Currently, gas water heaters typically employ one or more of feedforward or feedback control algorithms for temperature control. The control parameters in these algorithms are usually fixed and pre-set at the factory. However, as gas water heaters age, structural deviations (component inconsistencies, aging issues) and changes in the operating environment (gas source deviations, altitude variations) cause these fixed temperature control parameters to become incompatible with current operating conditions. This leads to decreased temperature control performance, excessively low or high outlet water temperatures, and deviations in temperature stability compared to pre-factory laboratory test results, ultimately impacting the user's water 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 purpose of this application is to provide a method for optimizing the control parameters of a water heater, a water heater, a cloud device, and a computer-readable storage medium, aiming to solve the technical problem that the temperature control and constant temperature performance of a water heater declines with the change of its service life.

[0005] To achieve the above objectives, this application provides a method for optimizing water heater control parameters, applied to a water heater that is communicatively connected to a cloud device. The method for optimizing water heater control parameters includes:

[0006] During operation, local operating data is acquired, and the control performance of the water heater is evaluated based on the local operating data to obtain the control performance evaluation result;

[0007] If the control performance evaluation result does not meet the preset requirements, the local operating data is sent to the cloud device so that the cloud device can construct a corresponding target virtual model based on the local operating data, and iteratively optimize the current control parameters in the local operating data based on the target virtual model to obtain the target control parameters.

[0008] The system receives target control parameters sent by the cloud device and controls the operation of the water heater based on the target control parameters.

[0009] In one embodiment, the local operating data includes at least the temperature rise time, overshoot, outlet water temperature, and set temperature, and the control performance evaluation result is a control performance score.

[0010] The steps of acquiring local operating data during operation, evaluating the control performance of the water heater based on the local operating data, and obtaining the control performance evaluation result include:

[0011] During operation, the temperature rise time, overshoot, and root mean square error between the outlet water temperature and the set temperature are obtained.

[0012] The control performance score is calculated based on the temperature rise time, the overshoot, the root mean square error, and the weighting coefficients corresponding to the temperature rise time, the overshoot, and the root mean square error.

[0013] In one embodiment, the target control parameters include at least feedforward control parameters and feedback control parameters;

[0014] The steps of controlling the water heater operation based on the target control parameters include:

[0015] The opening degree of the feedforward gas proportional valve is determined based on the current set temperature, inlet water temperature, inlet water flow rate, and the feedforward control parameters.

[0016] The opening degree of the feedback gas proportional valve is determined based on the set temperature, the current outlet water temperature, the inlet water temperature, the inlet water flow rate, and the feedback control parameters.

[0017] The target gas proportional valve opening is determined based on the feedforward gas proportional valve opening, the feedback gas proportional valve opening, the feedforward weighting coefficient, and the feedback weighting coefficient.

[0018] Adjust the opening of the gas proportional valve of the water heater to the target gas proportional valve opening.

[0019] Furthermore, this application also provides a method for optimizing water heater control parameters, applied to a cloud device, wherein the cloud device is communicatively connected to the water heater, and the method for optimizing water heater control parameters includes:

[0020] Receive local operating data sent by the water heater, wherein the local operating data is the operating data of the water heater when the control performance evaluation result does not meet the preset requirements;

[0021] Construct a target virtual model of the water heater based on the local operating data;

[0022] Based on the target virtual model, the current control parameters in the local operating data are iteratively optimized to obtain the target control parameters;

[0023] The target control parameters are sent to the water heater.

[0024] In one embodiment, the step of constructing a virtual model of the water heater based on the local operating data includes:

[0025] Based on the local operating data, an initial virtual model corresponding to the water heater is established;

[0026] Control the operation of the initial virtual model and acquire the simulation operation data of the initial virtual model during the operation;

[0027] Calculate the model error of the initial virtual model based on the simulated running data and the local running data;

[0028] When the model error is lower than a preset error threshold, the initial virtual model is determined as the target virtual model;

[0029] When the model error is not lower than the preset error threshold, the initial virtual model is corrected, and the execution steps are returned: control the initial virtual model to run, and acquire the simulation running data of the initial virtual model during the running process.

[0030] In one embodiment, the step of iteratively optimizing the current control parameters in the local running data based on the target virtual model to obtain the target control parameters includes:

[0031] The target virtual model is controlled to run using the current control parameters to determine the outlet water temperature curve;

[0032] The current control parameters and the corresponding outlet water temperature curve are input into a preset genetic algorithm optimization model to iteratively optimize the current control parameters and obtain the target control parameters. The target control parameters include at least feedforward control parameters and feedback control parameters.

[0033] In one embodiment, the step of inputting the current control parameters and the corresponding outlet water temperature curve into a preset genetic algorithm optimization model, and iteratively optimizing the current control parameters to obtain the target control parameters includes:

[0034] The current control parameters are updated by optimizing the model using the genetic algorithm to obtain the updated control parameters;

[0035] Determine the outlet water temperature curve corresponding to the updated control parameters;

[0036] The set temperature is obtained, and the corresponding temperature rise time, overshoot, and root mean square error between the outlet temperature and the set temperature are determined based on the outlet temperature curve.

[0037] Based on the temperature rise time, overshoot, and root mean square error corresponding to the outlet water temperature curve, and the weighting coefficients corresponding to the temperature rise time, overshoot, and root mean square error, the control performance score corresponding to the updated control parameters is determined.

[0038] Return to execution steps: Update the current control parameters using the genetic algorithm optimization model until the control performance score corresponding to the updated control parameters decreases to convergence, and determine the updated control parameters corresponding to the converged control performance score as the target control parameters.

[0039] In addition, this application also provides a water heater that is communicatively connected to a cloud device. The 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 is configured to implement the steps of the water heater control parameter optimization method applied to the water heater as described above.

[0040] In addition, this application also provides a cloud device connected to a water heater. The cloud device includes at least a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the water heater control parameter optimization method applied to the cloud device as described above.

[0041] 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 water heater control parameter optimization method described above.

[0042] 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 water heater control parameter optimization method described above.

[0043] This application provides a method for optimizing the control parameters of a water heater, applied to a water heater that is communicatively connected to a cloud device. The method includes: acquiring local operating data during operation; evaluating the control performance of the water heater based on the local operating data to obtain a control performance evaluation result; if the control performance evaluation result does not meet preset requirements, sending the local operating data to the cloud device so that the cloud device can construct a corresponding target virtual model based on the local operating data; iteratively optimizing the current control parameters in the local operating data based on the target virtual model to obtain target control parameters, wherein the local operating data includes at least the current control parameters; and finally receiving the target control parameters sent by the cloud device and controlling the operation of the water heater based on the target control parameters. When the control performance of a water heater is poor, the technical solution of this application constructs a corresponding virtual model through a cloud connected to the water heater to simulate the water heater's operating conditions. It then utilizes the computing resources of the cloud device to optimize the current control parameters of the water heater. The optimized target control parameters are more in line with the current operating conditions of the water heater, achieving better temperature control. The technical solution of this application uses the computing resources of the cloud device to update and optimize the control parameters of the local controller of the water heater, which improves the temperature control and constant temperature performance of the water heater and enhances the user's water experience. Attached Figure Description

[0044] 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.

[0045] 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.

[0046] Figure 1 This is a flowchart illustrating an embodiment of the water heater control parameter optimization method applied to water heaters in this application.

[0047] Figure 2 This is a flowchart illustrating an embodiment of the water heater control parameter optimization method applied to cloud devices in this application.

[0048] Figure 3 This is a schematic diagram illustrating the overall process of optimizing the parameters of the local controller of the water heater through the collaborative control of the water heater and the cloud device in this embodiment of the application.

[0049] Figure 4This is a schematic diagram of the hardware operating environment of the water heater involved in the water heater control parameter optimization method in this application embodiment;

[0050] Figure 5 This is a schematic diagram of the hardware operating environment of the cloud device involved in the water heater control parameter optimization method in this application embodiment.

[0051] 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

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Currently, gas water heaters exhibit deviations in their temperature control performance compared to laboratory test results due to structural variations (component inconsistencies, aging issues) and changes in the operating environment (gas source deviations, altitude deviations), impacting the user's water experience. With the increasing network connectivity of gas water heaters, the deployment of cloud platforms, and the iteration of intelligent optimization algorithms, it has become possible to optimize temperature control performance using a cloud-local collaborative control algorithm. In this embodiment, the local end of the gas water heater employs a feedforward + feedback controller to ensure the basic temperature control performance. Since the parameters of the local control algorithm directly affect the temperature control performance of the gas water heater, proper tuning of these parameters can improve its performance. Therefore, this embodiment proposes a cloud-local collaborative control method. A corresponding virtual model of the gas water heater is established on the cloud platform, and the controller parameters are tuned using an intelligent optimization algorithm and ultimately sent to the local controller of the water heater to improve its temperature control performance and enhance the user's water experience.

[0056] To achieve the above objectives, this application provides a method for optimizing the control parameters of a water heater, wherein the water heater has a wireless communication module that can communicate and exchange data with cloud devices.

[0057] Reference Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the water heater control parameter optimization method of this application. The water heater control parameter optimization method includes:

[0058] Step S10: Acquire local operating data during operation, evaluate the control performance of the water heater based on the local operating data, and obtain the control performance evaluation result;

[0059] During normal operation of the gas water heater, local operating data that reflects the current operating conditions can be collected in real time, such as the current gas proportional valve opening, outlet water temperature, inlet water temperature, set temperature, inlet water flow rate, outlet water flow rate, and the control parameters currently used to control the operation of the gas water heater.

[0060] The local controller of the gas water heater can include two parts: a feedforward controller and a feedback controller. The feedforward control and feedback control are used to accurately control the opening of the gas proportional valve of the gas water heater, ensuring that the outlet water temperature is consistent with the set temperature set by the user.

[0061] During the operation of a gas water heater, performance evaluation is used to determine whether control parameter optimization is necessary. Better control performance indicates a better match between the current control parameters and the current operating conditions, requiring no optimization. Conversely, poor control performance (poor temperature control and temperature maintenance) necessitates parameter optimization. It's understandable that the control performance evaluation results can be based on the accuracy of temperature control over a period of time.

[0062] Step S20: If the control performance evaluation result does not meet the preset requirements, the local operating data is sent to the cloud device so that the cloud device can build the corresponding target virtual model based on the local operating data, and iteratively optimize the current control parameters in the local operating data based on the target virtual model to obtain the target control parameters.

[0063] When the current control performance evaluation result of the gas water heater is determined to be inconsistent with the preset requirements (which may be lower than the preset control performance scoring threshold), further optimization of the control parameters in the local controller of the gas water heater is required. However, due to the limited computing resources of the gas water heater itself, it cannot run intelligent algorithms with large data volumes, and therefore cannot optimize the control parameters efficiently and accurately. Therefore, a wireless communication module installed on the gas water heater can be used to establish a connection with a cloud device, uploading the currently collected local operating data to the cloud device. This allows the cloud device to build a virtual model corresponding to the gas water heater, and then use intelligent algorithms with large data volumes to iteratively optimize the current control parameters in the local controller. The optimization goal is to make the control performance evaluation result as optimal as possible.

[0064] On the other hand, if the current control performance evaluation results of the gas water heater meet the preset requirements, there is no need to update the control parameters.

[0065] It should be noted that after the cloud device optimizes and updates the current control parameters and determines that the updated target control parameters meet the preset requirements, it will send the target control parameters to the gas water heater.

[0066] Step S30: Receive the target control parameters sent by the cloud device, and control the operation of the water heater based on the target control parameters.

[0067] After the wireless communication module of the gas water heater receives the target control parameters sent by the cloud device, it loads these parameters into the local controller and updates the current control parameters in the local controller. This allows the gas water heater to control its subsequent operation based on the updated target control parameters, thereby improving its temperature control performance and enhancing the user's water experience. Therefore, the water heater control parameter optimization method of this application essentially provides a method for improving the temperature control performance of a gas water heater through cloud collaboration, emphasizing a joint collaborative control strategy between the local end (gas water heater) and the cloud (server, water heater chip, and other components).

[0068] In another feasible embodiment, the local operating data includes at least the temperature rise time, overshoot, outlet water temperature, and set temperature, and the control performance evaluation result is a control performance score.

[0069] The step of acquiring local operating data during operation, evaluating the control performance of the water heater based on the local operating data, and obtaining the control performance evaluation result may include:

[0070] Step S11: During operation, acquire the temperature rise time, overshoot, and root mean square error between the outlet water temperature and the set temperature.

[0071] Step S12: Calculate the control performance score based on the temperature rise time, overshoot, root mean square error, and the weighting coefficients corresponding to the temperature rise time, overshoot, and root mean square error.

[0072] This application also discloses a method for evaluating the control performance of a gas water heater by calculating a control performance score. Specifically, the calculation expression for the control performance score of a gas water heater can be:

[0073] minJ=a1×t r +a2×σ%+a3×RMSE;

[0074] Where minJ is the control performance score, tr σ% represents the temperature rise time, σ% represents the temperature overshoot, RMSE represents the root mean square error between the outlet water temperature and the set temperature, and a1, a2, and a3 are weighting coefficients (all greater than 0).

[0075] It is understandable that the shorter the temperature rise time, the better the temperature control and constant temperature effect of the gas water heater; the lower the overshoot, the higher the temperature control accuracy of the gas water heater; and the smaller the root mean square error between the outlet water temperature and the set temperature, the stronger the temperature control performance of the gas water heater. Therefore, the temperature rise time, overshoot, and root mean square error between the outlet water temperature and the set temperature are all inversely proportional to the temperature control performance of the gas water heater. The lower the control performance score, the stronger the temperature control performance of the gas water heater.

[0076] In one feasible embodiment, the target control parameters include at least feedforward control parameters and feedback control parameters;

[0077] The steps of controlling the operation of the water heater based on the target control parameters may include:

[0078] Step S31: Determine the opening degree of the feedforward gas proportional valve based on the current set temperature, inlet water temperature, inlet water flow rate and feedforward control parameters.

[0079] Step S32: Determine the opening degree of the feedback gas proportional valve based on the set temperature, the current outlet water temperature, the inlet water temperature, the inlet water flow rate, and the feedback control parameters.

[0080] Step S33: Determine the target gas proportional valve opening based on the feedforward gas proportional valve opening, the feedback gas proportional valve opening, the feedforward weighting coefficient, and the feedback weighting coefficient.

[0081] Step S34: Adjust the opening of the gas proportional valve of the water heater to the target gas proportional valve opening.

[0082] For example, a gas water heater typically includes the following sensors: an inlet water temperature sensor, an outlet water temperature sensor, and a water flow sensor. When combustion is occurring in the gas water heater, the demand load is calculated according to the water flow heat transfer formula. The feedforward controller design is completed by mapping the demand load to the opening degree of the gas proportional valve, the actuator of the gas water heater. The corresponding mathematical expression is:

[0083] u=γ(T set -T in )*F;

[0084] Where u is the opening degree of the gas proportional valve, and T set To set the temperature, T inLet F be the inlet water temperature, F be the inlet water flow rate, and γ be the power coefficient. In the process of designing the feedforward controller, the power coefficient γ is fitted in advance based on the actual data of the proportional valve output opening under different loads.

[0085] For the design of the feedback controller, considering the first-order inertia plus hysteresis model of the instantaneous gas water heater, the following section will take a first-order system as an example to design LADRC (Linear Active Disturbance Rejection Control). The general first-order object model is as follows:

[0086]

[0087] Where d(t) represents the external disturbance, f(x) represents the positional function of variable x, and includes internal and system uncertainties. The value of b is estimated using b0, and the above equation is transformed into the following equation:

[0088]

[0089] Among them, δ(x,t,u)=f(x)+d(t)+(b-b0)u;

[0090] According to the LADRC design methodology, the following formula can be determined from the above formula:

[0091]

[0092] In this case, the control law can be designed as follows:

[0093]

[0094] Where r is the temperature setpoint.

[0095] Furthermore, the characteristic equation is:

[0096] s 2 +β1s+β2=(s+w0) 2 ;

[0097] Therefore, it can be determined that:

[0098] The current parameter configuration can be obtained by calculating the corresponding coefficients. Finally, the three parameters that the feedback controller needs to be tuned are b0, w... c And w0.

[0099] Based on the foregoing, the formulas for feedforward controllers and feedback control can be expressed as follows:

[0100]

[0101] u1 and u2 represent the opening degrees of the feedforward and feedback gas proportional valves, respectively. In practical control scenarios, the final gas proportional valve opening is typically determined by combining u1 and u2, expressed as u = k1u1 and k2u2, where k1 and k2 are the weights corresponding to u1 and u2, respectively. The values ​​of k1 and k2 vary between 0 and 1, and their sum is 1. In specific applications, when the gas water heater is first turned on, k1 is 1 and k2 is 0. As the temperature rises, the value of k1 gradually decreases, and the value of k2 gradually increases until it stabilizes at a constant value. For example, in this application embodiment, k1 = 0.5 and k2 = 0.5 are uniformly used for testing.

[0102] The technical solution of this application embodiment, when the control performance of the water heater needs to be optimized, constructs a corresponding virtual model through the cloud connected to the water heater to simulate the working conditions of the water heater, and uses the computing resources of the cloud device to optimize the current control parameters of the water heater, so that the optimized target control parameters are more in line with the current operating conditions of the water heater. This realizes the updating and optimization of the feedforward control parameters and feedback control parameters of the local controller of the water heater, thereby improving the temperature control and constant temperature performance of the water heater and enhancing the user's water use experience.

[0103] Furthermore, this application also provides a method for optimizing water heater control parameters in a cloud-based device, wherein the cloud-based device is communicatively connected to the water heater, and refers to... Figure 2 The method for optimizing the control parameters of the water heater includes:

[0104] Step A10: Receive local operating data sent by the water heater, wherein the local operating data is the operating data of the water heater when the control performance evaluation result does not meet the preset requirements;

[0105] Step A20: Construct the target virtual model corresponding to the water heater based on local operating data;

[0106] Step A30: Iteratively optimize the current control parameters in the local running data based on the target virtual model to obtain the target control parameters;

[0107] Step A40: Send the target control parameters to the water heater.

[0108] A wireless communication connection has been pre-established between the cloud device and the water heater's wireless communication module. Upon receiving local operating data sent by the water heater, the cloud device determines that the water heater needs to optimize its current control parameters. That is, before step A10, the water heater has already completed steps S10 and S20 as described in the aforementioned embodiment.

[0109] Cloud-based devices can leverage their computing resources to provide cloud-based control parameter optimization and update services for water heaters. Prior to this, to comprehensively simulate the water heater's operating conditions, a corresponding target virtual model needs to be established. This target virtual model can be a virtual model based on digital twin technology or other forms of virtual model, primarily used to simulate the operating conditions of the gas water heater. This facilitates iterative updates of the current control parameters with optimal control performance as the optimization goal. Local operating data reflects the current operating conditions of the gas water heater and may include the current gas proportional valve opening, outlet water temperature, inlet water temperature, set temperature, inlet water flow rate, outlet water flow rate, and the control parameters currently used to control the gas water heater's operation.

[0110] For example, after a certain number of iterations or after the control performance corresponding to the target virtual model has been determined to be optimal, the latest control parameters can be used as the target control parameters and sent to the gas water heater so that the gas water heater can control its operation based on the latest target control parameters, thereby achieving better temperature control and constant temperature effect and improving the user experience.

[0111] Furthermore, in a feasible embodiment, the step of constructing a virtual model corresponding to the water heater based on the local operating data may include:

[0112] Step A21: Based on local operating data, establish an initial virtual model corresponding to the water heater;

[0113] Step A22: Control the initial virtual model to run, and acquire the simulation data of the initial virtual model during the running process;

[0114] Step A23: Calculate the model error of the initial virtual model based on the simulation data and the local data.

[0115] Step A24: When the model error is lower than the preset error threshold, the initial virtual model is determined as the target virtual model;

[0116] Step A25: When the model error is not lower than the preset error threshold, the initial virtual model is corrected, and the execution steps are returned: control the initial virtual model to run, and obtain the simulation running data of the initial virtual model during the running process.

[0117] In this embodiment, when establishing a virtual model corresponding to the water heater, digital twin technology can be used to generate the virtual model. First, the model is initialized based on parameters of each dimension from the local operating data, ensuring that the control effect of the virtual model matches that of the real gas water heater. Environmental learning is then performed to simulate the normal operation of the gas water heater, outputting simulated operating data. This simulated operating data can include input data and output data. Input data can include set temperature, inlet water flow rate, etc., while output data can include outlet water temperature, outlet water flow rate, etc. The simulated operating data characterizes the operating conditions of the virtual model during the simulated water heater operation. The input data may be consistent with the input data in the local operating data, while the output data may not be consistent with the output data in the local operating data. The error between the two reflects the model error of the initial virtual model.

[0118] For example, model error can include the root mean square error between the outlet water temperature in the output data of the initial virtual model and the outlet water temperature in the local operating data, given the same input data. The larger the root mean square error value, the greater the model error of the initial virtual model.

[0119] The preset error threshold is used to determine whether the model error of the initial virtual model is acceptable. If it is acceptable, it means that the accuracy of the initial virtual model meets the requirements, and the parameters can be optimized and updated based on this initial virtual model. Otherwise, the initial virtual model needs to be corrected, and the corresponding model error is recalculated after correction. This process is repeated until the model error of the initial virtual model is lower than the preset error threshold.

[0120] In another feasible embodiment, after the initial virtual model is corrected, the model fitting accuracy can be evaluated. If the model fitting accuracy meets or exceeds a preset accuracy threshold, the initial virtual model is then updated in the cloud device to improve the correction efficiency of the initial virtual model.

[0121] In one feasible embodiment, the step of iteratively optimizing the current control parameters in the local operating data based on the target virtual model to obtain the target control parameters may include:

[0122] Step A31: Control the operation of the target virtual model using the current control parameters to determine the outlet water temperature curve;

[0123] Step A32: Input the current control parameters and the corresponding outlet water temperature curve into the preset genetic algorithm optimization model, and iteratively optimize the current control parameters to obtain the target control parameters. The target control parameters include at least feedforward control parameters and feedback control parameters.

[0124] In this embodiment of the application, the steps of controlling the operation of the target virtual model in the cloud device through the current control parameters can refer to steps S31 to S34. The opening degree of the gas proportional valve is mainly controlled based on the feedforward control parameters, feedback control parameters, and parameters such as set temperature, outlet water temperature, inlet water temperature, and inlet water flow. When the target virtual model is running based on the opening degree of the gas proportional valve, it will output an outlet water temperature curve. The outlet water temperature curve reflects the heating time, heating overshoot, and constant temperature steady-state effect during the temperature rise process over a period of time, which can be used as the data basis for evaluating the control performance of the target virtual model.

[0125] Cloud-based devices can utilize genetic algorithms to optimize control parameters. The goal of genetic algorithm optimization is to achieve the best control performance of the target virtual model. The control parameters corresponding to the target virtual model in its optimal control performance state are the target control parameters. These target control parameters enable the gas water heater to achieve the best temperature control and constant temperature effect during operation, thereby improving the user's water usage experience.

[0126] Specifically, the step of inputting the current control parameters and the corresponding outlet water temperature curve into a preset genetic algorithm optimization model, and iteratively optimizing the current control parameters to obtain the target control parameters may include:

[0127] Step A321: Optimize the model using a genetic algorithm to update the current control parameters, thus obtaining the updated control parameters;

[0128] Step A322: Determine the outlet water temperature curve corresponding to the updated control parameters;

[0129] Step A323: Obtain the set temperature, and determine the corresponding temperature rise time, overshoot, and root mean square error between the outlet temperature and the set temperature based on the outlet temperature curve.

[0130] Step A324: Based on the temperature rise time, overshoot, and root mean square error corresponding to the outlet water temperature curve, and the weighting coefficients corresponding to the temperature rise time, overshoot, and root mean square error, determine the control performance score corresponding to the updated control parameters.

[0131] Step A325, return to the execution steps: optimize the model using a genetic algorithm to update the current control parameters until the control performance score corresponding to the updated control parameters decreases to convergence, and determine the updated control parameters corresponding to the converged control performance score as the target control parameters.

[0132] During the iterative optimization of current control parameters on cloud-based devices, genetic algorithms can be used for updates. Genetic algorithms employ a population-based search approach, effectively exploring the entire search space and avoiding getting trapped in local optima. Furthermore, genetic algorithms are naturally suited for parallel computing because the evaluation and selection process of each individual is relatively independent, facilitating distributed implementation. In addition, genetic algorithms are highly suitable for complex problems such as simulating the operation of gas water heaters. For optimization problems lacking gradient information or where it is difficult to establish a highly accurate mathematical model, genetic algorithms can often find the optimal parameter combination.

[0133] For example, when updating the current control parameters using a genetic algorithm, the specific steps may include: initialization, first generating an initial population based on the current control parameters, with each individual representing a possible solution; then performing fitness evaluation, using a fitness function to evaluate the fitness of each individual, wherein the fitness can be evaluated based on the control performance of the target virtual model corresponding to the control parameters (which can be determined by the formula for calculating the control performance score of the gas water heater in steps S11 to S12), the lower the control performance score, the stronger the control performance and the higher the fitness; the next step is natural selection, selecting excellent individuals (individuals with high fitness) for reproduction based on fitness; generating new individuals through crossover operations, and increasing the diversity of the population through mutation operations; then iterating, repeating the above steps until the stopping condition is met. It should be noted that, in this embodiment, the stopping condition of the genetic algorithm is that the control performance score converges to the minimum value, when the control performance score is the minimum, the corresponding target control model has the strongest control performance.

[0134] To facilitate understanding, a complete process for tuning and optimizing control parameters by combining a local control system for a gas water heater with a cloud platform is described as follows: Figure 3 As shown, firstly, under the normal operation of the local gas water heater, local control system data (i.e., local operating data) is collected, and then local control performance is evaluated. Based on the evaluation results, it is determined whether the control parameters need to be updated. If so, the control system data and relevant operating condition data are uploaded to the cloud platform, which then constructs a corresponding virtual model and determines whether the model error is acceptable. If not, the cloud platform virtual system model undergoes self-calibration, and the cloud platform virtual system is updated if the model fitting accuracy evaluation meets the requirements. If so, the control parameters (of the gas water heater) are tuned using a genetic algorithm, the controller parameters are updated, and the data is sent to the local device, thus completing the optimization of the local control parameters of the gas water heater.

[0135] The water heater control parameter optimization method for cloud-based devices provided in this application can solve the technical problem of declining temperature control and constant temperature performance of water heaters due to changes in service life. Compared with the prior art, the beneficial effects of the water heater control parameter optimization method for cloud-based devices provided in this application are the same as those of the water heater control parameter optimization method for water heaters provided in the above embodiments. Furthermore, other technical features in the water heater control parameter optimization method for cloud-based devices provided in this application are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0136] 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 water heater control parameter optimization method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0137] This application embodiment also provides a water heater that is communicatively connected to a cloud device. The water heater includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the water heater control parameter optimization method in the above embodiment.

[0138] The following is for reference. Figure 4 It shows a structural schematic diagram of a water heater suitable for implementing the embodiments of this application. Figure 4 The water heater shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0139] like Figure 4As shown, the water heater may include a processing device 101 (e.g., a central processing unit, a graphics processor, etc.) that can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 102 or a program loaded from storage device 103 into random access memory (RAM) 104. RAM 104 also stores various programs and data required for the operation of the water heater. The processing device 101, ROM 102, and RAM 104 are interconnected via bus 105. Input / output (I / O) interface 106 is also connected to the bus. Typically, the following systems can be connected to I / O interface 106: input devices 107 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 108 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 103 including, for example, magnetic tapes, hard disks, etc.; and communication devices 109. Communication device 109 allows the water heater to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows water heaters with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0140] 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.

[0141] The water heater provided in this application embodiment employs the water heater control parameter optimization method described in the above embodiments, which can solve the technical problem of decreased temperature control and constant temperature performance caused by changes in the service life of the water heater. Compared with the prior art, the beneficial effects of the water heater provided in this application embodiment are the same as those of the water heater control parameter optimization method provided in the above embodiments, and other technical features of this water heater are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0142] 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.

[0143] 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.

[0144] This application embodiment also provides a cloud device, which is communicatively connected to a water heater. The cloud device includes at least: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the water heater control parameter optimization method in the above embodiment.

[0145] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a cloud device suitable for implementing the embodiments of this application. Figure 5 The cloud device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0146] like Figure 5As shown, the cloud device may include a processing unit 201 (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 read-only memory (ROM) 202 or a program loaded from storage device 203 into random access memory (RAM) 204. RAM 204 also stores various programs and data required for the operation of the cloud device. The processing unit 201, ROM 202, and RAM 204 are interconnected via bus 205. Input / output (I / O) interface 206 is also connected to the bus. Typically, the following systems can be connected to I / O interface 206: input devices 207 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 208 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 203 including, for example, magnetic tape, hard disks, etc.; and communication devices 209. Communication device 209 allows the cloud device to communicate wirelessly or wiredly with other devices to exchange data. While the diagram shows cloud devices 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.

[0147] 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 203, or installed from ROM 202. When the computer program is executed by processing device 201, it performs the functions defined in the methods of the embodiments of this application.

[0148] The cloud device provided in this application, employing the water heater control parameter optimization method described in the above embodiments, can solve the technical problem of declining temperature control and constant temperature performance of water heaters due to changes in service life. Compared with the prior art, the beneficial effects of the cloud device provided in this application are the same as those of the water heater control parameter optimization method provided in the above embodiments, and other technical features of the cloud device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0149] 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.

[0150] 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.

[0151] 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 water heater control parameter optimization method in the above embodiments.

[0152] 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.

[0153] The aforementioned computer-readable storage medium may be included in a water heater or cloud device; or it may exist independently and not be assembled into a water heater or cloud device.

[0154] The aforementioned computer-readable storage medium carries one or more programs. When the water heater executes one or more of these programs, the water heater: acquires local operating data during operation; evaluates the control performance of the water heater based on the local operating data; obtains a control performance evaluation result; if the control performance evaluation result does not meet preset requirements, it sends the local operating data to a cloud device, so that the cloud device can construct a corresponding target virtual model based on the local operating data, and iteratively optimize the current control parameters in the local operating data based on the target virtual model to obtain target control parameters; receives the target control parameters sent by the cloud device, and controls the operation of the water heater based on the target control parameters.

[0155] Alternatively, the aforementioned computer-readable storage medium carries one or more programs, which, when executed by a cloud device, cause the cloud device to: receive local operating data sent by the water heater, wherein the local operating data is the operating data of the water heater when the control performance evaluation result does not meet the preset requirements; construct a target virtual model corresponding to the water heater based on the local operating data; iteratively optimize the current control parameters in the local operating data based on the target virtual model to obtain target control parameters; and send the target control parameters to the water heater.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the above-described water heater control parameter optimization method, which can solve the technical problem that the temperature control and constant temperature performance of water heaters declines with the change of service life. 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 water heater control parameter optimization method provided in the above embodiments, and will not be repeated here.

[0160] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the water heater control parameter optimization method described above.

[0161] The computer program product provided in this application can solve the technical problem that the temperature control and constant temperature performance of water heaters declines with the change of service life. 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 water heater control parameter optimization method provided in the above embodiments, and will not be repeated here.

[0162] 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 method for optimizing water heater control parameters, characterized in that, Applied to water heaters, wherein the water heaters are communicatively connected to cloud devices, the method for optimizing the control parameters of the water heaters includes: During operation, local operating data is acquired, and the control performance of the water heater is evaluated based on the local operating data to obtain the control performance evaluation result; If the control performance evaluation result does not meet the preset requirements, the local operating data is sent to the cloud device so that the cloud device can construct a corresponding target virtual model based on the local operating data, and iteratively optimize the current control parameters in the local operating data based on the target virtual model to obtain the target control parameters. The system receives target control parameters sent by the cloud device and controls the operation of the water heater based on the target control parameters.

2. The water heater control parameter optimization method as described in claim 1, characterized in that, The local operating data includes at least the temperature rise time, overshoot, outlet water temperature, and set temperature, and the control performance evaluation result is a control performance score. The steps of acquiring local operating data during operation, evaluating the control performance of the water heater based on the local operating data, and obtaining the control performance evaluation result include: During operation, the temperature rise time, overshoot, and root mean square error between the outlet water temperature and the set temperature are obtained. The control performance score is calculated based on the temperature rise time, the overshoot, the root mean square error, and the weighting coefficients corresponding to the temperature rise time, the overshoot, and the root mean square error.

3. The water heater control parameter optimization method as described in claim 1, characterized in that, The target control parameters include at least feedforward control parameters and feedback control parameters; The steps of controlling the water heater operation based on the target control parameters include: The opening degree of the feedforward gas proportional valve is determined based on the current set temperature, inlet water temperature, inlet water flow rate, and the feedforward control parameters. The opening degree of the feedback gas proportional valve is determined based on the set temperature, the current outlet water temperature, the inlet water temperature, the inlet water flow rate, and the feedback control parameters. The target gas proportional valve opening is determined based on the feedforward gas proportional valve opening, the feedback gas proportional valve opening, the feedforward weighting coefficient, and the feedback weighting coefficient. Adjust the opening of the gas proportional valve of the water heater to the target gas proportional valve opening.

4. A method for optimizing water heater control parameters, characterized in that, Applied to a cloud-based device, which is communicatively connected to the water heater, the method for optimizing the water heater control parameters includes: Receive local operating data sent by the water heater, wherein the local operating data is the operating data of the water heater when the control performance evaluation result does not meet the preset requirements; Construct a target virtual model of the water heater based on the local operating data; Based on the target virtual model, the current control parameters in the local operating data are iteratively optimized to obtain the target control parameters; The target control parameters are sent to the water heater.

5. The water heater control parameter optimization method as described in claim 4, characterized in that, The step of constructing the virtual model corresponding to the water heater based on the local operating data includes: Based on the local operating data, an initial virtual model corresponding to the water heater is established; Control the operation of the initial virtual model and acquire the simulation operation data of the initial virtual model during the operation; Calculate the model error of the initial virtual model based on the simulated running data and the local running data; When the model error is lower than a preset error threshold, the initial virtual model is determined as the target virtual model; When the model error is not lower than the preset error threshold, the initial virtual model is corrected, and the execution steps are returned: control the initial virtual model to run, and acquire the simulation running data of the initial virtual model during the running process.

6. The water heater control parameter optimization method as described in claim 4, characterized in that, The step of iteratively optimizing the current control parameters in the local operating data based on the target virtual model to obtain the target control parameters includes: The target virtual model is controlled to run using the current control parameters to determine the outlet water temperature curve; The current control parameters and the corresponding outlet water temperature curve are input into a preset genetic algorithm optimization model to iteratively optimize the current control parameters and obtain the target control parameters. The target control parameters include at least feedforward control parameters and feedback control parameters.

7. The method for optimizing water heater control parameters as described in claim 6, characterized in that, The step of inputting the current control parameters and the corresponding outlet water temperature curve into a preset genetic algorithm optimization model, and iteratively optimizing the current control parameters to obtain the target control parameters includes: The current control parameters are updated by optimizing the model using the genetic algorithm to obtain the updated control parameters; Determine the outlet water temperature curve corresponding to the updated control parameters; The set temperature is obtained, and the corresponding temperature rise time, overshoot, and root mean square error between the outlet temperature and the set temperature are determined based on the outlet temperature curve. Based on the temperature rise time, overshoot, and root mean square error corresponding to the outlet water temperature curve, and the weighting coefficients corresponding to the temperature rise time, overshoot, and root mean square error, the control performance score corresponding to the updated control parameters is determined. Return to execution steps: Update the current control parameters using the genetic algorithm optimization model until the control performance score corresponding to the updated control parameters decreases to convergence, and determine the updated control parameters corresponding to the converged control performance score as the target control parameters.

8. A water heater, characterized in that, The water heater is communicatively connected to a cloud device, and the 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 water heater control parameter optimization method as described in any one of claims 1 to 3.

9. A cloud device, characterized in that, The cloud device is communicatively connected to the water heater, and the cloud device 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 water heater control parameter optimization method as described in any one of claims 4 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the computer-readable storage medium stores a program for implementing a method for optimizing water heater control parameters. The program for implementing the method for optimizing water heater control parameters is executed by a processor to implement the steps of the method for optimizing water heater control parameters as described in any one of claims 1 to 3 or claims 4 to 7.

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