Intelligent control method and system for operation of sliding groove type household appliance control panel

Abstract

The invention discloses an intelligent control method and system for operation of a sliding chute type household appliance control panel, the intelligent control method and system are applied to the sliding chute type household appliance control panel, driving modules of at least two household appliances are integrated on the control panel, and the method comprises the following steps: acquiring environment data, user behavior data and energy consumption data when the driving modules operate; predicting an energy consumption change trend in combination with an LSTM model, and identifying an energy consumption peak time period and an energy consumption valley time period; driving priorities of different driving modules are determined according to user preferences, and energy consumption target values are matched for energy consumption peak time periods and low ebb time periods of the different driving modules so as to control operation of a PID (Proportion Integration Differentiation) controller; and the difference between the real-time energy consumption data and the energy consumption change trend is calculated, the energy consumption target value is dynamically adjusted according to the difference, and operation of the PID controller is updated. According to the invention, through accurate prediction and dynamic adjustment of the energy consumption trend, the driving priority is set in combination with user preference, so that more reasonable energy consumption distribution is realized, and the user experience is improved while the energy utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of control panels, and in particular to an intelligent control method and system for the operation of a chute-type household appliance control panel. Background Art

[0002] The home appliance control panel is the core component for controlling the use of home appliances. It controls the operation of various home appliances through user instructions. In order to realize the detachable function of the home appliance control panel, a slide-type home appliance control panel is proposed. It improves convenience by slidingly connecting with the home appliances to be controlled, and can be quickly disassembled to troubleshoot the cause of the fault when the control panel fails. However, the current operation control of the slide-type home appliance control panel can usually only control a single device or function, and cannot effectively coordinate the energy consumption distribution between multiple home appliance drive modules, and the accuracy of energy consumption supervision prediction is poor. In addition, when it is necessary to simultaneously meet the user preferences of different users when using different home appliances, it is impossible to flexibly adjust the control strategy according to personalized needs, which leads to frequent energy waste or poor user experience. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides an intelligent control method and system for the operation of a slide-type household appliance control panel.

[0004] In a first aspect, the present invention provides an intelligent control method for the operation of a chute-type home appliance control panel, which is applied to the chute-type home appliance control panel, wherein at least two home appliance drive modules are integrated on the chute-type home appliance control panel, and the intelligent control method comprises:

[0005] Collect environmental data, user behavior data, and energy consumption data of different drive modules during operation;

[0006] Input environmental data, user behavior data and energy consumption data into the preset LSTM model to predict the energy consumption trend of different drive modules; identify the peak and valley energy consumption periods of different drive modules from the energy consumption trend;

[0007] Determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​for energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values;

[0008] Collect real-time energy consumption data of different drive modules, calculate the difference between real-time energy consumption data and energy consumption change trend, dynamically adjust the energy consumption target value according to the difference, and update the operation process of the PID controller.

[0009] Preferably, the calculating the difference between the real-time energy consumption data and the energy consumption change trend, and dynamically adjusting the energy consumption target value according to the difference, includes:

[0010] Determine the updated energy consumption target value based on the difference:

[0011] E n =E o +kΔE;

[0012] ΔE=E r -E p ;

[0013] In the formula, E n 、E o They represent the energy consumption target values ​​before and after the update, and ΔE is the actual energy consumption E r and predicted energy consumption E p The difference between the two, k represents the proportional factor of energy consumption adjustment, 0 <k<1。

[0014] Preferably, the operation process of updating the PID controller includes:

[0015] Update the proportional coefficient of the PID controller:

[0016]

[0017] Where P n , P i They represent the proportional coefficients of the PID controller before and after the update, μ is the adjustment factor of the proportional coefficient, 0<μ<0.01.

[0018] Preferably, the operation of the PID controller in the driving module is controlled according to the energy consumption target value, including:

[0019] Based on the drive module, the angular velocity, temperature, output current and output voltage of the motor are collected;

[0020] The parameters of the PID controller are tuned based on the particle swarm optimization algorithm and angular velocity to generate control signals;

[0021] A gain signal is determined according to the temperature value, the output current and the output voltage; and a drive signal is generated according to the energy consumption target value, the control signal and the gain signal.

[0022] Preferably, after calculating the difference between the real-time energy consumption data and the energy consumption change trend, the method further includes:

[0023] According to the difference between the real-time energy consumption data and the energy consumption change trend, it is judged whether the motor operation is faulty. If a fault occurs, the fault type and fault level of the motor operation are determined according to the difference.

[0024] The corresponding processing strategy is matched according to the fault type and fault level, and the driving priority of different driving modules is updated.

[0025] In a second aspect, the present invention further provides an intelligent control system for the operation of a chute-type home appliance control panel, which is applied to the chute-type home appliance control panel, wherein at least two home appliance drive modules are integrated on the chute-type home appliance control panel, and the intelligent control system comprises:

[0026] Data collection module, used to collect environmental data, user behavior data and energy consumption data of different drive modules during operation;

[0027] The trend prediction module is used to input environmental data, user behavior data and energy consumption data into the preset LSTM model to predict the energy consumption change trend of different drive modules; identify the energy consumption peak period and energy consumption valley period of different drive modules from the energy consumption change trend;

[0028] An energy consumption target determination module is used to determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​to the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values;

[0029] The energy consumption target update module is used to collect real-time energy consumption data of different drive modules, calculate the difference between real-time energy consumption data and energy consumption change trend to dynamically adjust the energy consumption target value, and update the operation process of the PID controller.

[0030] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the intelligent control method for the operation of a slide-type household appliance control panel as described in any one of the embodiments of the first aspect above.

[0031] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the intelligent control method for the operation of a slide-type household appliance control panel as described in any one of the embodiments of the first aspect above.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1) In the intelligent control method for the operation of the slide-type home appliance control panel provided by the present invention, at least two home appliance drive modules are integrated on the slide-type home appliance control panel, so that different home appliances can be controlled in a linked manner, thereby improving the intelligence level of the operation of the home appliance control panel and making the control process more convenient.

[0034] 2) The present invention collects environmental data, user behavior data and energy consumption data of different driving modules during operation; then inputs the environmental data, user behavior data and energy consumption data into a preset LSTM model to predict the energy consumption change trend of different driving modules; identifies the energy consumption peak periods and energy consumption trough periods of different driving modules from the energy consumption change trend; through relatively comprehensive data and a pre-trained LSTM model, it can quickly and accurately predict the energy consumption change trend, and effectively screen out the energy consumption peak periods and energy consumption trough periods, providing a reliable basis for subsequent control processes.

[0035] 3) The present invention determines the driving priorities of different driving modules according to user preferences, and matches the corresponding energy consumption target values ​​for the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities to control the operation of the PID controller in the driving module. Since different users have different preferences for the use of different home appliances during the linkage control of different home appliances, considering the driving priorities can reasonably allocate the energy consumption of different home appliances, thereby meeting user needs and improving user experience and energy utilization.

[0036] 4) When the driving module controls the motor operation of the household appliance, the present invention will collect real-time energy consumption data of different driving modules, calculate the difference between the real-time energy consumption data and the energy consumption change trend to dynamically adjust the energy consumption target value, and update the operation process of the PID controller. By considering the deviation between the predicted value and the actual value, the energy consumption target value is dynamically adjusted to ensure that the overall operating energy consumption of the household appliance system is in the optimal state, which greatly improves the energy utilization rate and meets the energy-saving requirements of energy conservation, environmental protection and low-carbon electricity consumption.

[0037] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0039] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0040] Figure 1A schematic flow chart of an intelligent control method for operation of a chute-type household appliance control panel provided by an embodiment of the present invention;

[0041] Figure 2 A schematic structural diagram of an intelligent control system for the operation of a slide-type household appliance control panel provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0044] At present, the operation control of a slide-type home appliance control panel can usually only control a single device or function, and cannot effectively coordinate the energy consumption distribution between multiple home appliance drive modules, and the accuracy of energy consumption supervision prediction is poor. When it is necessary to simultaneously meet the user preferences of different users when using different home appliances, it is impossible to flexibly adjust the control strategy according to personalized needs, which leads to frequent energy waste or poor user experience. To this end, the present invention aims to provide an intelligent control method for the operation of a slide-type home appliance control panel, which can accurately predict the trend of energy consumption changes, reasonably divide energy consumption targets based on user preferences, and thus control the operation of home appliances, thereby improving energy utilization and user experience.

[0045] First, the intelligent control method provided by the present invention is applied to a slide-type home appliance control panel, on which at least two home appliance drive modules are integrated. Common home appliances include air conditioners, refrigerators, washing machines, dishwashers, electric water heaters, and the like. At least two home appliance drive modules are integrated, such as the drive modules of the air conditioner and water heater can be integrated on a slide-type home appliance control panel, or the drive modules of the refrigerator and washing machine can be integrated on a slide-type home appliance control panel. For more complex home appliance control panels, more home appliance drive modules can be integrated to achieve linkage control of different home appliances.

[0046] See also Figure 1 , Figure 1The present invention provides a flow chart of an intelligent control method for the operation of a sliding slot type home appliance control panel. Figure 1 As shown, the intelligent control method includes:

[0047] S10, collecting environmental data, user behavior data, and energy consumption data of different drive modules during operation.

[0048] Environmental data is mainly collected through sensors, including indoor and outdoor temperature, humidity, light intensity, etc. User behavior data refers to data related to the user's use of home appliances, including information such as user activity patterns and preference settings at different times. For example, the start-up time, frequency of use, and usage mode preferences (energy-saving mode, fast mode) of various home appliances. User behavior data can be obtained through sensor monitoring, such as using infrared sensors to detect whether there are people in the room. Whether there are people in the room, the number of activities when there are people, and the specific behavior patterns of people will affect the final home appliance drive control results. Energy consumption data includes energy consumption data when different drive modules are running, such as the motor operation energy consumption data of the drive module that drives the air conditioner, or the motor operation energy consumption data of the drive module that drives the refrigerator. The drive modules of the home appliances integrated on the slide-type home appliance control panel correspond to the energy consumption data of the drive motors collected.

[0049] S20, inputting environmental data, user behavior data and energy consumption data into a preset LSTM model to predict the energy consumption change trend of different driving modules; identifying the energy consumption peak period and energy consumption valley period of different driving modules from the energy consumption change trend.

[0050] This embodiment aims to quickly and accurately predict the energy consumption trend of different driving modules. Before the prediction, the LSTM model will be trained, that is, the preset LSTM model in step S20 is obtained. When the model is applied after training, it only needs to input the environmental data, user behavior data and energy consumption data into the model to quickly obtain the prediction result of the energy consumption trend.

[0051] In one embodiment, training the LSTM model includes:

[0052] Collect a large amount of historical data, i.e., the environmental data, user behavior data, and energy consumption data of different drive modules during operation described in step S10, and then perform preprocessing operations on these data, including cleaning data, removing outliers, and filling missing values. Then extract useful features, such as the time period of the day, the day of the week, etc. Finally, standardize or normalize the data to ensure that all features are within the same scale range. After obtaining the data set, it will be divided into a training set and a test set according to the proportion. The training set is mainly used to train the model, and the test set is mainly used to test the prediction accuracy of the trained model.

[0053] Due to the massive amount of data involved, when extracting features, the SAE algorithm can be used to reduce the dimension of the features first, and then the random forest algorithm can be used to fit the reduced-dimensional features. According to the comprehensive indicators after fitting, the feature quantity with the greatest correlation is selected as the target feature.

[0054] It should be noted that the LSTM model is used in this embodiment because the LSTM network is good at processing time series data and can capture long-term dependencies. The energy consumption change trend is closely related to the time change, so the use of this network model can better learn the relationship between energy consumption and time.

[0055] Preferably, the LSTM model used in this embodiment is a bidirectional LSTM model, which considers both past and future context information when processing sequence data to improve the accuracy of prediction. In the bidirectional LSTM model, the input layer is used to receive the data just preprocessed, the forward LSTM layer is used to process the input sequence from front to back, learn the forward context information in the sequence, and the reverse LSTM layer is used to process the input sequence from back to front, learn the reverse context information in the sequence, and finally merge the outputs of the forward LSTM layer and the reverse LSTM layer through the merging layer. The information in the two directions can be merged by simple splicing or weighted summation, and then the predicted energy consumption change trend is output by the output layer. The energy consumption change trend is usually a set of energy consumption data sequences, that is, a function of energy consumption over time.

[0056] After obtaining the energy consumption change trend, the energy consumption peak period and energy consumption valley period of different drive modules are further identified from the energy consumption change trend. The energy consumption peak period usually corresponds to the time period when the user uses it more frequently, and the energy consumption valley period usually corresponds to the time period when the user uses it less frequently or is not at home.

[0057] For example, when the drive modules of air conditioners and water heaters are integrated into the home appliance control panel, if it is a weekday, it can be predicted that the peak usage period of the air conditioner is from 20:00 in the evening to 8:00 in the morning of the next day, and the other time is the low usage period, while the peak usage period of the water heater is from 19:00 to 23:00 in the evening. In this way, the usage rate of each home appliance can be clearly understood.

[0058] S30. Determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​to the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values.

[0059] User preferences are crucial to the use of the driver module and the allocation of energy consumption. For example, when it is predicted that the peak period of air conditioning use is from 20:00 p.m. to 8:00 a.m. the next day, the peak period of water heater use is from 19:00 p.m. to 11:00 p.m., so the overlapping time period for the peak use of the two is from 20:00 p.m. to 11:00 p.m. During these three hours, if the energy consumption of home appliances is not reasonably allocated, it will lead to energy waste, or excessive power load will cause safety accidents and other problems. At this time, user preferences can be combined. For example, in the time period from 20:00 p.m. to 23:00 p.m., the user's preference for using water heaters will be greater than the preference for using air conditioners. Therefore, it can be determined that the driving priority of the water heater driver module in this time period is greater than that of the air conditioner, which is equivalent to considering the driving control process of the water heater first when considering the energy consumption target. While using the water heater, the user generally does not use the air conditioner. At this time, the air conditioner can be temporarily turned off by turning off the air conditioner's driver module, or the air conditioner can be controlled to enter a low power consumption mode to avoid unnecessary energy waste.

[0060] In a specific embodiment, user preferences are mainly learned based on the user's historical usage data, and a neural network model can also be used to predict user preferences by retrieving the user's historical data on the use of home appliances. The neural network model includes but is not limited to support vector machines, random forests, convolutional neural networks, recurrent neural networks, and the like.

[0061] After obtaining the user's preferences, the weights of different drive modules can be assigned according to the user's preferences. For example, users who prioritize energy saving may pay more attention to the energy consumption control of motor drive, while users who prioritize comfort may be more concerned about the stability of temperature control. When determining the drive priority of different home appliance drive modules, you can first identify the key drive modules in the home appliance, such as motor drive, temperature control, etc. Assign a priority to each module based on the importance of the module and the user's preferences. For example, for users who prioritize energy saving, the motor drive module may have a higher priority. For example, during a time period when the demand for water heaters is greater than the demand for air conditioners, the water heater motor drive module may have a higher priority than the air conditioner motor drive module.

[0062] Furthermore, the corresponding energy consumption target values ​​are matched for the energy consumption peak periods and energy consumption valley periods of different drive modules based on the drive priority. For example, for the energy consumption peak period, a higher energy consumption target value is set to ensure that user needs are met. For the energy consumption valley period, a lower energy consumption target value is set to save energy. The corresponding energy consumption target values ​​are matched for different drive modules according to the drive priority. For example, for users who prioritize energy saving, the target value of the motor drive module can be lower during the energy consumption valley period. For different household appliances that are in the same energy consumption demand period at the same time, the energy consumption target value of the one with a higher drive priority is controlled to be larger to meet the user's usage needs.

[0063] When the energy consumption target value is used to control the operation of the PID controller in the drive module, the proportional, integral and differential parameters of the PID controller are adjusted according to different energy consumption target values. When the energy consumption target value is high, a larger proportional coefficient can be set to quickly respond to energy consumption changes. When the energy consumption target value is low, a larger integral coefficient can be set to ensure stable energy consumption. Use the PID controller to automatically adjust the operating state of the drive module according to the set energy consumption target value. Monitor the actual energy consumption through the feedback mechanism, and adjust the PID parameters when necessary to ensure that the drive module operates in accordance with the target value.

[0064] In one embodiment, controlling the operation of a PID controller in a driving module according to an energy consumption target value includes:

[0065] Based on the drive module, the angular velocity, temperature, output current and output voltage of the motor are collected;

[0066] The parameters of the PID controller are tuned based on the particle swarm optimization algorithm and angular velocity to generate control signals;

[0067] A gain signal is determined according to the temperature value, the output current and the output voltage; and a drive signal is generated according to the energy consumption target value, the control signal and the gain signal.

[0068] In this embodiment, the angular velocity is usually acquired by a Hall sensor, the temperature value is measured by a temperature sensor, the output current is measured by a current sensor, and the output voltage is measured by a voltage sensor.

[0069] When performing parameter adjustment, include:

[0070] Initialize the particle swarm: define the position and velocity vectors of the particles, each particle represents a set of PID parameters.

[0071] Fitness function: Define a fitness function to evaluate the quality of the particle, usually based on the performance indicators of angular velocity error, such as minimizing angular velocity tracking error.

[0072] Update particle position and velocity: Update the position and velocity of each particle according to the rules of the particle swarm optimization algorithm to find the optimal solution.

[0073] Iterative optimization: Repeat the update process until the maximum number of iterations is reached or the convergence condition is met.

[0074] Optimal parameter selection: Select the PID parameters with the best fitness value from the particle swarm as the final tuning parameters.

[0075] Furthermore, the PID control output is calculated according to the adjusted PID parameters and the angular velocity error, and the PID control output is converted into a control signal for adjusting the speed or torque of the motor.

[0076] When determining the gain signal, a gain factor is determined based on the changes in temperature value, output current, and output voltage to adjust the control signal. If the temperature is too high, the output power may need to be reduced to avoid overheating. Excessive current may cause overload and requires appropriate adjustment to protect the motor. Changes in output voltage will affect the performance of the motor and need to be adjusted according to actual conditions. Therefore, when determining the gain factor, for example, if the temperature is too high, the gain factor can be less than 1 to reduce the strength of the control signal. Finally, the control signal and the gain signal are combined to generate the final drive signal, which is used to directly control the operation of the motor.

[0077] S40, collecting real-time energy consumption data of different drive modules, calculating the difference between the real-time energy consumption data and the energy consumption change trend, dynamically adjusting the energy consumption target value according to the difference, and updating the operation process of the PID controller.

[0078] In one embodiment, calculating the difference between the real-time energy consumption data and the energy consumption change trend, and dynamically adjusting the energy consumption target value according to the difference, includes:

[0079] Determine the updated energy consumption target value based on the difference:

[0080] E n =E o +kΔE;

[0081] ΔE=E r -E p ;

[0082] In the formula, E n 、E o They represent the energy consumption target values ​​before and after the update, and ΔE is the actual energy consumption E r and predicted energy consumption E p The difference between the two, k represents the proportional factor of energy consumption regulation, 0 <k<1。

[0083] The operation process of updating the PID controller includes:

[0084] Update the proportional coefficient of the PID controller:

[0085]

[0086] Where P n , P o They represent the proportional coefficients of the PID controller before and after the update, respectively. μ is the adjustment factor of the proportional coefficient, 0<μ<0.01.

[0087] This embodiment can timely discover energy consumption deviations by monitoring the energy consumption data of different drive modules in real time and comparing them with the predicted energy consumption change trend, and take measures to adjust them, dynamically adjust the energy consumption target value, and ensure that the drive modules can reach the optimal energy consumption state at different time points, so that the system can quickly adapt to environmental changes and changes in user behavior. By updating the proportional coefficient of the PID controller, the controller's adaptability to the dynamic characteristics of the system is enhanced and the control accuracy is improved. When updating the proportional coefficient of the PID controller, a natural logarithm function is used, which makes the adjustment of the proportional coefficient smoother and reduces the risk of system shock. In this way, energy consumption can be reasonably allocated and energy waste can be reduced, while user needs can be met and user experience can be improved.

[0088] In summary, the method provided in this embodiment can at least achieve the following beneficial effects:

[0089] 1) In the intelligent control method for the operation of the slide-type home appliance control panel provided by the present invention, at least two home appliance drive modules are integrated on the slide-type home appliance control panel, so that different home appliances can be controlled in a linked manner, thereby improving the intelligence level of the operation of the home appliance control panel and making the control process more convenient.

[0090] 2) The present invention collects environmental data, user behavior data and energy consumption data of different driving modules during operation; then inputs the environmental data, user behavior data and energy consumption data into a preset LSTM model to predict the energy consumption change trend of different driving modules; identifies the energy consumption peak periods and energy consumption trough periods of different driving modules from the energy consumption change trend; through relatively comprehensive data and a pre-trained LSTM model, it can quickly and accurately predict the energy consumption change trend, and effectively screen out the energy consumption peak periods and energy consumption trough periods, providing a reliable basis for subsequent control processes.

[0091] 3) The present invention determines the driving priorities of different driving modules according to user preferences, and matches the corresponding energy consumption target values ​​for the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities to control the operation of the PID controller in the driving module. Since different users have different preferences for the use of different home appliances during the linkage control of different home appliances, considering the driving priorities can reasonably allocate the energy consumption of different home appliances, thereby meeting user needs and improving user experience and energy utilization.

[0092] 4) When the driving module controls the motor operation of the household appliance, the present invention will collect real-time energy consumption data of different driving modules, calculate the difference between the real-time energy consumption data and the energy consumption change trend to dynamically adjust the energy consumption target value, and update the operation process of the PID controller. By considering the deviation between the predicted value and the actual value, the energy consumption target value is dynamically adjusted to ensure that the overall operating energy consumption of the household appliance system is in the optimal state, which greatly improves the energy utilization rate and meets the energy-saving requirements of energy conservation, environmental protection and low-carbon electricity consumption.

[0093] In one embodiment, after calculating the difference between the real-time energy consumption data and the energy consumption change trend, the method further includes:

[0094] According to the difference between the real-time energy consumption data and the energy consumption change trend, it is judged whether the motor operation is faulty. If a fault occurs, the fault type and fault level of the motor operation are determined according to the difference.

[0095] The corresponding processing strategy is matched according to the fault type and fault level, and the driving priority of different driving modules is updated.

[0096] In this embodiment, a reasonable threshold range can be defined first to distinguish between normal operating conditions and potential fault conditions. If the difference between the real-time energy consumption data and the predicted trend exceeds the preset threshold, it is considered that the motor may be faulty. The fault type is identified based on the specific characteristics of the difference, such as overload, short circuit, insulation damage, etc. Then, the fault is divided into different levels according to the size of the difference, such as minor faults, medium faults, severe faults, etc. The cause of the fault is further analyzed in combination with other operating parameters of the motor. When matching the processing strategy, the processing strategies for different fault types and levels can be pre-defined, and then the strategies can be automatically matched.

[0097] Furthermore, since the fault condition will change the operation of the drive module, the working mode of each drive module is adjusted based on the requirements of the processing strategy, such as energy saving priority, safety priority, etc. In other scenarios, for situations where energy saving priority is higher, the power output of some non-critical drive modules may be reduced. As the motor operating state changes, the priority of the drive module is dynamically updated to adapt to the new operating conditions.

[0098] Therefore, this embodiment can detect potential faults in time by real-time monitoring of the difference between energy consumption data and predicted trends, thereby preventing more serious faults from occurring. Accurately identifying the type and level of faults helps to take targeted maintenance measures, reduce unplanned downtime, and improve the overall reliability of the system. Matching appropriate processing strategies according to the type and level of faults can effectively reduce energy consumption and improve energy utilization. By adjusting the drive priority of the drive module, the performance of the system can be maximized while ensuring safety, improving operational efficiency and reducing maintenance costs.

[0099] See also Figure 2 In one embodiment, the present invention further provides an intelligent control system for the operation of a chute-type home appliance control panel, which is applied to a chute-type home appliance control panel, wherein at least two home appliance drive modules are integrated on the chute-type home appliance control panel, and the intelligent control system comprises:

[0100] The data collection module 100 is used to collect environmental data, user behavior data, and energy consumption data of different drive modules during operation;

[0101] The trend prediction module 200 is used to input the environmental data, user behavior data and energy consumption data into the preset LSTM model to predict the energy consumption change trend of different driving modules; identify the energy consumption peak period and energy consumption valley period of different driving modules from the energy consumption change trend;

[0102] The energy consumption target determination module 300 is used to determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​to the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values;

[0103] The energy consumption target update module 400 is used to collect real-time energy consumption data of different drive modules, calculate the difference between the real-time energy consumption data and the energy consumption change trend to dynamically adjust the energy consumption target value, and update the operation process of the PID controller.

[0104] It can be understood that the functions or modules included in the intelligent control system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0105] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0106] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0113] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. An intelligent control method for the operation of a chute-type home appliance control panel, applied to a chute-type home appliance control panel, wherein at least two home appliance drive modules are integrated on the chute-type home appliance control panel, characterized in that: The intelligent control method comprises: Collect environmental data, user behavior data, and energy consumption data of different drive modules during operation; Input environmental data, user behavior data and energy consumption data into the preset LSTM model to predict the energy consumption trend of different drive modules; identify the peak and valley energy consumption periods of different drive modules from the energy consumption trend; Determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​for energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values; Collect real-time energy consumption data of different drive modules, calculate the difference between real-time energy consumption data and energy consumption change trend, dynamically adjust the energy consumption target value according to the difference, and update the operation process of the PID controller.

2. The intelligent control method for the operation of a chute-type household appliance control panel according to claim 1, characterized in that: The calculating the difference between the real-time energy consumption data and the energy consumption change trend, and dynamically adjusting the energy consumption target value according to the difference, includes: Determine the updated energy consumption target value based on the difference: AND n =And o +kΔE; ΔE=E r -E p ; In the formula, E n 、E o They represent the energy consumption target values ​​before and after the update, and ΔE is the actual energy consumption E r and predicted energy consumption E p The difference between the two, k represents the proportional factor of energy consumption regulation, 0 <k<1。 3. The intelligent control method for the operation of a chute-type household appliance control panel according to claim 2, characterized in that: The operation process of updating the PID controller includes: Update the proportional coefficient of the PID controller: Where P n , P o They represent the proportional coefficients of the PID controller before and after the update, respectively. μ is the adjustment factor of the proportional coefficient, 0<μ<0.

01.

4. The intelligent control method for the operation of a chute-type household appliance control panel according to claim 1, characterized in that: The operation of the PID controller in the driving module is controlled according to the energy consumption target value, comprising: Based on the drive module, the angular velocity, temperature, output current and output voltage of the motor are collected; The parameters of the PID controller are tuned based on the particle swarm optimization algorithm and angular velocity to generate control signals; A gain signal is determined according to the temperature value, the output current and the output voltage; and a drive signal is generated according to the energy consumption target value, the control signal and the gain signal.

5. The intelligent control method for the operation of a chute-type household appliance control panel according to claim 1, characterized in that: After calculating the difference between the real-time energy consumption data and the energy consumption change trend, the method further includes: According to the difference between the real-time energy consumption data and the energy consumption change trend, it is judged whether the motor operation is faulty. If a fault occurs, the fault type and fault level of the motor operation are determined according to the difference. The corresponding processing strategy is matched according to the fault type and fault level, and the driving priority of different driving modules is updated.

6. An intelligent control system for the operation of a chute-type home appliance control panel, applied to a chute-type home appliance control panel, wherein at least two types of home appliance drive modules are integrated on the chute-type home appliance control panel, characterized in that: The intelligent control system comprises: Data collection module, used to collect environmental data, user behavior data and energy consumption data of different drive modules during operation; The trend prediction module is used to input environmental data, user behavior data and energy consumption data into the preset LSTM model to predict the energy consumption change trend of different drive modules; identify the energy consumption peak period and energy consumption valley period of different drive modules from the energy consumption change trend; An energy consumption target determination module is used to determine the driving priorities of different driving modules according to user preferences, match corresponding energy consumption target values ​​to the energy consumption peak periods and energy consumption valley periods of different driving modules based on the driving priorities, and control the operation of the PID controller in the driving module according to the energy consumption target values; The energy consumption target update module is used to collect real-time energy consumption data of different drive modules, calculate the difference between real-time energy consumption data and energy consumption change trend to dynamically adjust the energy consumption target value, and update the operation process of the PID controller.

7. An electronic device, characterized in that: include:A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the intelligent control method for the operation of a slide-type household appliance control panel as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the intelligent control method for the operation of a slide-type household appliance control panel as described in any one of claims 1 to 5.

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