CN-121993902-A - Water heater control parameter optimization method, water heater, cloud device and storage medium

CN121993902ACN 121993902 ACN121993902 ACN 121993902ACN-121993902-A

Abstract

The application 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, wherein the water heater control parameter optimization method applied to the water heater comprises the steps of acquiring local operation data in an operation process, and evaluating the control performance of the water heater according to the local operation data to obtain a control performance evaluation result; if the control performance evaluation result does not meet the preset requirement, the local operation data are sent to the cloud device so that the cloud device can construct a corresponding target virtual model according to the local operation data, and the current control parameters are subjected to iterative optimization based on the target virtual model to obtain the target control parameters, wherein the local operation data at least comprise the current control parameters, the target control parameters sent by the cloud device are received, and the operation of the water heater is controlled based on the target control parameters. The application solves the technical problem that the temperature and constant temperature performance of the water heater are reduced along with the change of the service life.

Inventors

HOU XIAOBIN
JIANG TAO
WU HAITAO

Assignees

芜湖美的厨卫电器制造有限公司

Dates

Publication Date: 20260508
Application Date: 20241104

Claims (10)

1. The water heater control parameter optimization method is characterized by being applied to a water heater, wherein the water heater is in communication connection with cloud equipment, and the water heater control parameter optimization method comprises the following steps: Acquiring local operation data in the operation process, and evaluating the control performance of the water heater according to the local operation data to obtain a control performance evaluation result; if the control performance evaluation result does not meet the preset requirement, the local operation data are sent to cloud equipment, so that the cloud equipment builds a corresponding target virtual model according to the local operation data, and the current control parameters in the local operation data are subjected to iterative optimization based on the target virtual model to obtain target control parameters; And receiving target control parameters sent by the cloud equipment, and controlling the operation of the water heater based on the target control parameters.
2. The water heater control parameter optimization method as set forth in claim 1, wherein the local operation data includes at least a temperature rise time, an overshoot, a water outlet temperature, and a set temperature, and the control performance evaluation result is a control performance score; the step of obtaining local operation data in the operation process, evaluating the control performance of the water heater according to the local operation data, and obtaining a control performance evaluation result comprises the following steps: acquiring the rising time length, overshoot and root mean square error between the water outlet temperature and the set temperature in the running process; and calculating a control performance score according to the temperature rising time length, the overshoot and the root mean square error, and weight coefficients respectively corresponding to the temperature rising time length, the overshoot and the root mean square error.
3. The water heater control parameter optimization method of claim 1, wherein the target control parameters include at least a feedforward control parameter and a feedback control parameter; the step of controlling the operation of the water heater based on the target control parameter comprises the following steps: determining the opening of a feedforward fuel gas proportional valve according to the current set temperature, the water inlet flow and the feedforward control parameter; Determining the opening of a feedback fuel gas proportional valve according to the set temperature, the current water outlet temperature, the water inlet flow and the feedback control parameters; Determining a target gas proportional valve opening according to the feedforward gas proportional valve opening, the feedback gas proportional valve opening, a feedforward weight coefficient and a feedback weight coefficient; And adjusting the opening of the gas proportional valve of the water heater to the opening of the target gas proportional valve.
4. The water heater control parameter optimization method is characterized by being applied to cloud equipment, wherein the cloud equipment is in communication connection with a water heater, and the water heater control parameter optimization method comprises the following steps: Receiving local operation data sent by a water heater, wherein the local operation data is the operation data of the water heater under the condition that a control performance evaluation result does not meet a preset requirement; Constructing a target virtual model corresponding to the water heater according to the local operation data; Performing iterative optimization on the current control parameters in the local operation data based on the target virtual model to obtain target control parameters; And sending the target control parameters to the water heater.
5. The water heater control parameter optimization method as set forth in claim 4, wherein said constructing a virtual model corresponding to said water heater based on said local operating data comprises: Based on the local operation data, establishing an initial virtual model corresponding to the water heater; Controlling the operation of the initial virtual model, and acquiring simulation operation data of the initial virtual model in the operation process; calculating a model error of the initial virtual model according to the simulation operation data and the local operation data; When the model error is lower than a preset error threshold, determining the initial virtual model as a target virtual model; and when the model error is not lower than the preset error threshold, correcting the initial virtual model, and returning to the execution step, wherein the operation of the initial virtual model is controlled, and simulation operation data of the initial virtual model are acquired in the operation process.
6. The method of optimizing control parameters of a water heater as recited in claim 4, wherein the step of iteratively optimizing current control parameters in the local operating data based on the target virtual model to obtain target control parameters comprises: controlling the target virtual model to run through the current control parameters, and determining a water outlet temperature curve; inputting the current control parameters and the corresponding water outlet temperature curves into a preset genetic algorithm optimization model, and performing iterative optimization on the current control parameters to obtain target control parameters, wherein the target control parameters at least comprise feedforward control parameters and feedback control parameters.
7. The water heater control parameter optimization method as set forth in claim 6, wherein the step of inputting the current control parameter and the corresponding water outlet temperature curve into a preset genetic algorithm optimization model, and performing iterative optimization on the current control parameter to obtain a target control parameter comprises: Updating the current control parameters through the genetic algorithm optimization model to obtain updated control parameters; Determining a water outlet temperature curve corresponding to the updated control parameter; acquiring a set temperature, and determining corresponding temperature rising time length, overshoot and root mean square error between the water outlet temperature and the set temperature according to the water outlet temperature curve; determining a control performance score corresponding to the updated control parameter according to the temperature rising time length, the overshoot and the root mean square error corresponding to the water outlet temperature curve and weight coefficients respectively corresponding to the temperature rising time length, the overshoot and the root mean square error; and returning to the execution step, namely updating the current control parameters through the genetic algorithm optimization model until the control performance scores corresponding to the updated control parameters are reduced to convergence, and determining the updated control parameters corresponding to the converged control performance scores as target control parameters.
8. A water heater in communication with a cloud device, the water heater comprising at least a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the water heater control parameter optimization method of any one of claims 1 to 3.
9. The cloud device is in communication connection with a water heater and at least comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is configured to implement the steps of the water heater control parameter optimization method according to any one of claims 4 to 7.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a program for realizing a water heater control parameter optimization method is stored, the program for realizing the water heater control parameter optimization method being executed by a processor to realize the steps of the water heater control parameter optimization method according to any one of claims 1 to 3 or claims 4 to 7.

Description

Water heater control parameter optimization method, water heater, cloud device and storage medium Technical Field The application relates to the technical field of water heaters, in particular to a water heater control parameter optimization method, a water heater, cloud equipment and a computer readable storage medium. Background At present, when the temperature of the gas water heater is controlled, one or more of a feedforward control algorithm and a feedback control algorithm are generally adopted for controlling the temperature, and the control parameters in the algorithm are usually fixed parameters preset before delivery. In the use process of the gas water heater, as the service life increases, due to structural deviation (component consistency and ageing problems) and working environment change (air source deviation and altitude deviation), the fixed temperature control parameters are not matched with the current working conditions, so that the temperature control performance of the water heater is reduced, the water outlet temperature is too low or too high, and the constant temperature performance is deviated from the laboratory test result before delivery, so that the water consumption experience of the user is influenced. The above information disclosed in this background section is only for the understanding of the background of the inventive concept and, therefore, it may contain information that does not form the prior art. Disclosure of Invention The application mainly aims to provide a water heater control parameter optimization method, a water heater, cloud equipment and a computer readable storage medium, and aims to solve the technical problem that the temperature and constant temperature performance of the water heater are reduced due to the change of the service life. In order to achieve the above object, the present application provides a method for optimizing control parameters of a water heater, which is applied to a water heater, wherein the water heater is in communication connection with cloud equipment, and the method for optimizing control parameters of the water heater comprises: Acquiring local operation data in the operation process, and evaluating the control performance of the water heater according to the local operation data to obtain a control performance evaluation result; if the control performance evaluation result does not meet the preset requirement, the local operation data are sent to cloud equipment, so that the cloud equipment builds a corresponding target virtual model according to the local operation data, and the current control parameters in the local operation data are subjected to iterative optimization based on the target virtual model to obtain target control parameters; And receiving target control parameters sent by the cloud equipment, and controlling the operation of the water heater based on the target control parameters. In an embodiment, the local operation data at least includes a temperature rising time length, an overshoot, a water outlet temperature and a set temperature, and the control performance evaluation result is a control performance score; the step of obtaining local operation data in the operation process, evaluating the control performance of the water heater according to the local operation data, and obtaining a control performance evaluation result comprises the following steps: acquiring the rising time length, overshoot and root mean square error between the water outlet temperature and the set temperature in the running process; and calculating a control performance score according to the temperature rising time length, the overshoot and the root mean square error, and weight coefficients respectively corresponding to the temperature rising time length, the overshoot and the root mean square error. In an embodiment, the target control parameters include at least a feedforward control parameter and a feedback control parameter; the step of controlling the operation of the water heater based on the target control parameter comprises the following steps: determining the opening of a feedforward fuel gas proportional valve according to the current set temperature, the water inlet flow and the feedforward control parameter; Determining the opening of a feedback fuel gas proportional valve according to the set temperature, the current water outlet temperature, the water inlet flow and the feedback control parameters; Determining a target gas proportional valve opening according to the feedforward gas proportional valve opening, the feedback gas proportional valve opening, a feedforward weight coefficient and a feedback weight coefficient; And adjusting the opening of the gas proportional valve of the water heater to the opening of the target gas proportional valve. In addition, the application also provides a water heater control parameter optimization method which is applied to cloud equipment, wherein the cloud equipment is in co