Method, device, apparatus and storage medium for controlling heating tube in clothes dryer

Abstract

The application provides an improved method, device, equipment and storage medium for controlling a heating pipe in a clothes dryer. The method for controlling the heating pipe in the clothes dryer comprises: obtaining a set temperature Ts and a detected temperature Tc of the heating pipe; obtaining a temperature deviation parameter based on the set temperature Ts and the detected temperature Tc; inputting the temperature deviation parameter into a first BP network and a second BP network which are trained in advance to obtain a first control parameter and a second control parameter of the heating pipe, wherein the second control parameter comprises a control threshold; obtaining a control amount of the heating pipe based on the first control parameter; comparing the control amount with the control threshold, and controlling the opening and closing of the heating pipe based on the comparison result. The application combines the BP network and the PID control theory to control the heating pipe in the clothes dryer, can take into account the stability and accuracy of the control effect, can realize the accurate and constant temperature control of the heating pipe temperature in the drying process of the clothes dryer, and has strong adaptability.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, and in particular to a method, apparatus, device, and storage medium for controlling the heating element in a clothes dryer. Background Technology

[0002] Currently, the control strategy for the heating element in existing dryers on the market is basically the conventional PID control (proportional-integral-derivative control).

[0003] However, PID control is actually a compromise between the proportional, integral, and derivative control actions of the deviation, and it cannot resolve the contradiction between stability and accuracy, nor can it achieve the optimal control effect. Summary of the Invention

[0004] The purpose of this invention is to provide an improved method, apparatus, device, and storage medium for controlling the heating element in a clothes dryer.

[0005] The method for controlling the heating element in a clothes dryer provided in this embodiment of the invention includes: acquiring a set temperature Ts and a detected temperature Tc of the heating element; obtaining a temperature deviation parameter based on the set temperature Ts and the detected temperature Tc; inputting the temperature deviation parameter into a pre-trained first BP network and a second BP network to obtain a first control parameter and a second control parameter of the heating element, wherein the second control parameter includes a control threshold; obtaining a control quantity of the heating element based on the first control parameter; comparing the control quantity with the control threshold, and controlling the heating element to turn on and off based on the comparison result.

[0006] Optionally, the second control parameter includes a first duration n, a second duration ε, a third duration ∈, and a fourth duration q, and the control threshold includes a maximum threshold Judgemax and a minimum threshold Judgemin. Controlling the heating element to turn on and off based on the comparison result includes: when the control quantity is greater than or equal to the maximum threshold Judgemax, controlling the heating element to turn off for a duration of the first duration n; when the control quantity is less than the maximum threshold Judgemax but greater than the minimum threshold Judgemin, controlling the heating element to turn on and off periodically, with the on duration being the second duration ε and the off duration being the third duration ∈ within one cycle; and when the control quantity is less than or equal to the minimum threshold Judgemin, controlling the heating element to turn on for a duration of the fourth duration q.

[0007] Optionally, when training the first BP network and the second BP network, the deviation e(k) between the set temperature Ts and the detection temperature Tc in the training sample data is used as the error. Let the loss function be denoted as , and training will stop when the loss function is minimized to obtain the first and second BP networks after training.

[0008] Optionally, the temperature deviation parameters include: a first temperature deviation parameter x1 = e(k), a second temperature deviation parameter x2 = e(k-1), and a third temperature deviation parameter x3 = e(k) - e(k-1), where e(k) represents the deviation between the set temperature Ts and the detected temperature Tc of the heating tube at time t, and e(k-1) represents the deviation between the set temperature Ts and the detected temperature Tc of the heating tube at time (t-1).

[0009] Optionally, the first control parameter is a PID control parameter, and the control quantity is represented by the PID control transfer function G(s).

[0010] Optionally, the PID control parameters include the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the deviation e(k) of the heating element at time t; the control quantity is:

[0011]

[0012] Optionally, the PID control parameters include the proportional coefficient Kp, integral coefficient Ki, derivative coefficient Kd, arbitrary real number α, and arbitrary real number β of the deviation e(k) of the heating element at time t; the control quantity is:

[0013]

[0014] The device for controlling the heating element in a clothes dryer provided in this embodiment of the invention includes: an acquisition module for acquiring a set temperature Ts and a detection temperature Tc of the heating element; a first calculation module for obtaining a temperature deviation parameter based on the set temperature Ts and the detection temperature Tc; a BP network module for inputting the temperature deviation parameter into a pre-trained first BP network and a second BP network to obtain a first control parameter and a second control parameter of the heating element, wherein the second control parameter includes a control threshold; a second calculation module for obtaining a control quantity of the heating element based on the first control parameter; and a control module for comparing the control quantity with the control threshold and controlling the heating element to turn on and off based on the comparison result.

[0015] Optionally, the control thresholds include a maximum threshold Judgemax and a minimum threshold Judgemin, and the second control parameters include a first duration n, a second duration ε, a third duration ∈, and a fourth duration q. The control module is used to: control the heating element to turn off when the control quantity is greater than or equal to the maximum threshold Judgemax, and make the off duration the first duration n; control the heating element to periodically turn on and off when the control quantity is less than the maximum threshold Judgemax and greater than the minimum threshold Judgemin, with the on duration being the second duration ε and the off duration being the third duration ∈ within one cycle; and control the heating element to turn on when the control quantity is less than or equal to the minimum threshold Judgemin, and make the on duration the fourth duration q.

[0016] Optionally, a BP network module is used to train the first BP network and the second BP network respectively; when training the first BP network and the second BP network, the deviation e(k) between the set temperature Ts and the detection temperature Tc in the training sample data is used as the error. Let the loss function be denoted as , and training will stop when the loss function is minimized to obtain the first and second BP networks after training.

[0017] The device for controlling the heating element in a clothes dryer provided in this embodiment of the invention includes: a processor; a memory storing a computer program that can run on the processor; wherein, when the computer program is executed by the processor, it implements the method for controlling the heating element in a clothes dryer provided in this embodiment of the invention.

[0018] This invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed, implements the method for controlling the heating element in a clothes dryer provided in this invention.

[0019] Compared with the prior art, the technical solutions of the embodiments of the present invention have beneficial effects.

[0020] For example, combining BP networks and PID control theory to control the heating element in a clothes dryer can balance the stability and accuracy of the control action. Increasing the control action to improve accuracy and reduce error will not reduce its stability. Conversely, limiting the control action to ensure stability will not reduce its accuracy.

[0021] For example, by combining BP network and PID control theory to control the heating element in a clothes dryer, precise and constant temperature control of the heating element can be achieved during the drying process. This not only improves the operating efficiency of the clothes dryer but also effectively protects the dried items.

[0022] For example, combining BP network and PID control theory to control the heating element in a dryer has strong adaptability. It can be applied to different models of dryers and can automatically adjust the control parameters based on different models of dryers.

[0023] Further features of the invention will be presented from the claims, the drawings, and the description of the drawings. The features and combinations thereof described in the foregoing description and those illustrated in the following description of the drawings and / or simply shown in the drawings may be presented not only in the described combinations, but also in other combinations or individually, without departing from the scope of the invention. Embodiments of the invention not described or specifically shown in the drawings but conceivable from the detailed embodiments and from combinations of features should therefore be considered included and disclosed. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of the clothes dryer in an embodiment of the present invention;

[0025] Figure 2 This is a flowchart illustrating the method for controlling the heating element in a clothes dryer according to an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of a first BP network in an embodiment of the present invention;

[0027] Figure 4 This is another schematic diagram of the structure of the first BP network in an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of a second BP network in an embodiment of the present invention;

[0029] Figure 6 This is a schematic diagram illustrating the principle of the method for controlling the heating element in a clothes dryer in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of the device for controlling the heating element in the dryer in an embodiment of the present invention. Detailed Implementation

[0031] In the existing technology, the heating element of the dryer uses PID control, which cannot resolve the contradiction between stability and accuracy, nor can it achieve the optimal control effect.

[0032] Unlike existing technologies, this invention provides an improved method, apparatus, device, and storage medium for controlling the heating element in a clothes dryer. The method for controlling the heating element in a clothes dryer provided by this invention includes: acquiring a set temperature Ts and a detected temperature Tc of the heating element; obtaining a temperature deviation parameter based on the set temperature Ts and the detected temperature Tc; inputting the temperature deviation parameter into a pre-trained first BP network and a second BP network to obtain a first control parameter and a second control parameter for the heating element, the second control parameter including a control threshold; obtaining a control quantity for the heating element based on the first control parameter; comparing the control quantity with the control threshold, and controlling the heating element to turn on and off based on the comparison result.

[0033] Compared with existing technologies, the technical solutions of the embodiments of the present invention have beneficial effects. For example, by combining BP networks and PID control theory to control the heating element in a dryer, both the stability and accuracy of the control action can be considered. When the control action is increased to improve accuracy and reduce errors, the stability of the control action will not be reduced. Conversely, when the control action is limited to ensure stability, the accuracy of the control action will not be reduced. Furthermore, by combining BP networks and PID control theory to control the heating element in a dryer, precise and constant temperature control of the heating element can be achieved during the drying process, which not only improves the operating efficiency of the dryer but also effectively protects the dried items. Moreover, by combining BP networks and PID control theory to control the heating element in a dryer, strong adaptability is achieved; it can be applied to different models of dryers and can automatically adjust control parameters based on different dryer models.

[0034] To make the objectives, features, and beneficial effects of the embodiments of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0035] Figure 1 This is a schematic diagram of the structure of the clothes dryer in an embodiment of the present invention.

[0036] Reference Figure 1 The dryer 100 may include an outer drum 110, a roller 120 rotatably mounted within the outer drum 110, and an airflow passage 130 located between the outer drum 110 and the roller 120. The roller 120 is adapted to receive items (not shown) for drying. Both ends of the airflow passage 130 communicate with the internal space 121 of the roller 120 to form a drying circuit 140 between the airflow passage 130 and the internal space 121 of the roller 120.

[0037] Specifically, the airflow channel 130 may include a heat exchange section 131 and a heating section 132 that are interconnected. The heat exchange section 131 is equipped with a condenser 151 and a fan 152, while the heating section 132 is equipped with a heating element 153. The condenser 151 is used to cool the air entering the heat exchange section 131, the fan 152 is used to generate airflow in the drying circuit 140, and the heating element 153 is used to heat the air entering the heating section 132.

[0038] When the dryer 100 executes the drying program, the condenser 151, fan 152, and heating element 153 are turned on. The humid, hot air inside the drum 120's internal space 121 enters the heat exchange section 131 of the airflow channel 130 under the action of the fan 152. After being cooled by the condenser 151 in the heat exchange section 131, it becomes cold air. This cold air further enters the heating section 132 under the action of the fan 152 and is heated into hot air by the heating element 153. The hot air then enters the internal space 121 of the drum 120 under the action of the fan 152 and exchanges heat with the items inside the internal space 121, forming humid, hot air. This cycle repeats continuously to dry the items.

[0039] In practice, the dryer 100 also includes a temperature sensor suitable for collecting the drying temperature when the drying program is executed.

[0040] Specifically, the temperature sensor is adapted to collect the heating temperature of the heating tube 153 or the drying temperature of the hot air entering the internal space 121 of the drum 120 during the drying process.

[0041] In practice, the dryer 100 may include a washer-dryer combo and a separate dryer.

[0042] In practice, the dryer 100 can be equipped with one heating element 153 or more heating elements 153.

[0043] Figure 2 This is a flowchart illustrating the method for controlling the heating element in a clothes dryer according to an embodiment of the present invention.

[0044] Reference Figure 2 The method for controlling the heating element in a clothes dryer provided in this embodiment of the invention may include:

[0045] S1, obtain the set temperature and detection temperature of the heating element;

[0046] S2, the temperature deviation parameter is obtained based on the set temperature and the detected temperature;

[0047] S3, input the temperature deviation parameters into the pre-trained first BP network and second BP network respectively to obtain the first control parameters and the second control parameters of the heating tube, the second control parameters including the control threshold;

[0048] S4, the control quantity of the heating element is obtained based on the first control parameter;

[0049] S5 compares the control quantity with the control threshold and controls the heating element to turn on and off based on the comparison result.

[0050] Regarding step S1, the set temperature and detection temperature of the heating element 153 can be obtained during the drying program. The detection temperature is either the heating temperature of the heating element 153 collected by the temperature sensor in the dryer 100, or the drying temperature of the hot air entering the internal space 121 of the drum 120. The set temperature is the temperature preset in the drying program.

[0051] In practice, the dryer 100 can be either water-cooled or air-cooled. For a water-cooled dryer 100, the set temperature of its heating element 153 can be set to a range greater than or equal to 90℃ and less than or equal to 110℃. For an air-cooled dryer 100, the set temperature of its heating element 153 can be adjusted according to the season. In summer, the set temperature of the heating element 153 can be set to around 70℃, and in winter, the set temperature of the heating element 153 can be set to around 60℃.

[0052] In practice, the set temperature of different models of dryers 100 is not the same.

[0053] In practice, the set temperature of the heating tube 153 can be represented as Ts, and the detected temperature of the heating tube 153 can be represented as Tc.

[0054] Regarding step S2, the temperature deviation parameter can be obtained based on the set temperature Ts and the detection temperature Tc of the heating tube 153.

[0055] In practice, the temperature deviation parameter may include a first temperature deviation parameter, a second temperature deviation parameter, and a third temperature deviation parameter.

[0056] Specifically, the first temperature deviation parameter can be x1 = e(k), the second temperature deviation parameter can be x2 = e(k-1), and the third temperature deviation parameter can be x3 = e(k) - e(k-1), where e(k) represents the deviation between the set temperature Ts and the detected temperature Tc of the heating tube 153 at time t, and e(k-1) represents the deviation between the set temperature Ts and the detected temperature Tc of the heating tube 153 at time (t-1).

[0057] Regarding step S3, the temperature deviation parameters can be input into the pre-trained first BP network (i.e., Back Propagation Neural Network) and the second BP network respectively to obtain the first control parameters and the second control parameters of the heating tube 153. The first control parameters are obtained through the first BP network, and the second control parameters are obtained through the second BP network.

[0058] In practice, the first control parameter may include PID control parameters.

[0059] In some embodiments, the PID control parameters may include the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the deviation e(k) between the set temperature Ts and the detected temperature Tc of the heating element at time t.

[0060] In practice, the first BP network can be constructed based on its input (i.e., temperature deviation parameter) and output (i.e., first control parameter).

[0061] Figure 3 This is a schematic diagram of a first BP network in an embodiment of the present invention.

[0062] Reference Figure 3 The first BP network 200 may include an input layer 210, a hidden layer 220, and an output layer 230 connected in sequence. The input layer 210 includes three neurons, corresponding to the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1), respectively. The output layer 230 includes three neurons, corresponding to the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the deviation e(k), respectively.

[0063] In specific implementation, the number of neurons in hidden layer 220 can be obtained based on Kolmogorov's theorem (i.e., Kolmogorov's theorem) or any known common knowledge or existing technical means in this field.

[0064] exist Figure 3 In the example shown, the number of neurons in the hidden layer 220 of the first BP network 200 is obtained by Kolmogorov's theorem, specifically seven.

[0065] In specific implementation, the hidden layer 220 can use the Sigmoid function as the activation function, and the output layer 230 can use the non-negative Sigmoid function as the activation function.

[0066] In other embodiments, the PID control parameters may also be fractional-order PI. α D βControl parameters (fractional PI) α D β The theory was proposed by Professor Podlubny based on the combination of fractional calculus theory and PID tuning theory, and adopts PI α D β The control parameters can achieve better control performance than traditional PID control parameters (i.e., the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the deviation e(k)).

[0067] Specifically, the PID control parameters can include the proportional coefficient Kp, integral coefficient Ki, derivative coefficient Kd, arbitrary real number α, and arbitrary real number β of the deviation e(k) of the heating element at time t. Therefore, a different PID control mechanism can be constructed. Figure 3 The first BP network shown in the example.

[0068] Figure 4 This is another schematic diagram of the structure of the first BP network in an embodiment of the present invention.

[0069] Reference Figure 4 The first BP network 300 may include an input layer 310, a hidden layer 320, and an output layer 330 connected in sequence. The input layer 310 includes three neurons, corresponding to the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1), respectively. The output layer 330 includes five neurons, corresponding to the proportional coefficient Kp, integral coefficient Ki, differential coefficient Kd, arbitrary real number α, and arbitrary real number β of the deviation e(k), respectively.

[0070] In specific implementation, the number of neurons in the hidden layer 320 of the first BP network 300 can also be obtained based on Kolmogorov's theorem or any known common knowledge or existing technical means in this field.

[0071] exist Figure 4 In the example shown, the number of neurons in the hidden layer 320 of the first BP network 300 is obtained by Kolmogorov's theorem, specifically seven.

[0072] In specific implementations, the hidden layer 320 of the first BP network 300 can also use the Sigmoid function as the activation function, and its output layer 330 can also use the non-negative Sigmoid function as the activation function.

[0073] After the first backpropagation (BP) network is constructed, it can be trained using training sample data to obtain the trained BP network. The training of the first BP network will be described in a later section of this paper.

[0074] In this embodiment of the invention, the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1) can be input into the pre-trained first BP network to obtain the first control parameters of the heating tube 153.

[0075] In some embodiments, the first control parameter includes the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the deviation e(k) of the heating tube at time t.

[0076] In other embodiments, the first control parameters include the proportional coefficient Kp, integral coefficient Ki, differential coefficient Kd, arbitrary real number α, and arbitrary real number β of the deviation e(k) of the heating tube at time t.

[0077] Regarding step S4, the control quantity of heating tube 153 can be obtained based on the first control parameter.

[0078] In practical implementation, the control quantity of heating tube 153 can be represented by the transfer function G(s) of PID control.

[0079] When the first control parameters include the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the deviation e(k) of the heating element at time t, the control quantity of the heating element 153 can be expressed as:

[0080]

[0081] When the first control parameters include the proportional coefficient Kp, integral coefficient Ki, derivative coefficient Kd, arbitrary real number α, and arbitrary real number β of the deviation e(k) of the heating tube at time t, the control quantity of the heating tube 153 can be expressed as:

[0082]

[0083] In some embodiments, the second control parameter may include a control threshold.

[0084] In practice, the second BP network can be constructed based on its input (i.e., temperature deviation parameter) and output (i.e., second control parameter).

[0085] In a specific implementation, the second BP network may also include an input layer, a hidden layer, and an output layer connected in sequence. The input layer includes three neurons, corresponding to the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1), respectively; the number of neurons in the output layer can be the number of control thresholds.

[0086] In practice, the number of neurons in the hidden layer of the second BP network can also be obtained based on Kolmogorov's theorem or any known common knowledge or existing technical means in this field.

[0087] In practical implementation, the hidden layers of the second BP network can also use the Sigmoid function as the activation function, and its output layer can also use the non-negative Sigmoid function as the activation function.

[0088] After the second BP network is constructed, it can be trained using training sample data to obtain the trained second BP network.

[0089] In this embodiment of the invention, the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1) can be input into the pre-trained second BP network to obtain the second control parameters of the heating tube 153, including the control threshold.

[0090] Regarding step S5, the control quantity G(s) of the heating tube 153 can be compared with the control threshold, and the heating tube 153 can be turned on and off based on the comparison result.

[0091] In some embodiments, the control threshold can be a single parameter. When the control quantity G(s) of the heating tube 153 is greater than or equal to the control threshold, an on control signal can be generated to control the heating tube 153 to turn on; when the control quantity G(s) of the heating tube 153 is less than the control threshold, an off control signal can be generated to control the heating tube 153 to turn off.

[0092] In other embodiments, the control threshold may include two parameters: a maximum threshold and a minimum threshold. When the control quantity G(s) of the heating element 153 is greater than or equal to the maximum threshold, a first control signal can be generated to control the heating element 153 to turn on; when the control quantity G(s) of the heating element 153 is less than the maximum threshold but greater than the minimum threshold, a second control signal can be generated to control the heating element 153 to periodically turn on and off; when the control quantity G(s) of the heating element 153 is less than or equal to the minimum threshold, a third control signal can be generated to control the heating element 153 to turn off.

[0093] In specific implementations, the second control parameter may further include a first duration, a second duration, a third duration, and a fourth duration. The step S5, which involves controlling the opening and closing of the heating element 153 based on the comparison result, may include:

[0094] When the control quantity G(s) of the heating element 153 is greater than or equal to the maximum threshold, the heating element 153 is controlled to be turned off and the duration of its shutdown is the first duration.

[0095] When the control quantity G(s) of the heating element 153 is less than the maximum threshold and greater than the minimum threshold, the heating element 153 is controlled to periodically turn on and off. Within one cycle, the duration of being on is the second duration, and the duration of being off is the third duration.

[0096] When the control quantity G(s) of the heating tube 153 is less than or equal to the minimum threshold, the heating tube 153 is controlled to turn on and its on time is the fourth duration.

[0097] In practical implementation, the maximum threshold can be represented as Judgemax, the minimum threshold as Judgemin, the first duration as n, the second duration as ε, the third duration as ∈, and the fourth duration as q. Furthermore, all six parameters (i.e., the second control parameters) are used as the output of the second BP network.

[0098] In practice, the second BP network can be constructed based on its input (i.e., temperature deviation parameter) and output (i.e., second control parameter).

[0099] Figure 5 This is a schematic diagram of a second BP network in an embodiment of the present invention.

[0100] Reference Figure 5 The second BP network 400 may include an input layer 410, a hidden layer 420, and an output layer 430 connected in sequence. The input layer 410 includes three neurons, corresponding to the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1), respectively. The output layer 430 includes six neurons, corresponding to the maximum threshold Judgemax, the minimum threshold Judgemin, the first duration n, the second duration ε, the third duration ε, and the fourth duration q, respectively.

[0101] In specific implementation, the number of neurons in the hidden layer 420 of the second BP network 400 can also be obtained based on Kolmogorov's theorem or any known common knowledge or existing technical means in this field.

[0102] exist Figure 5 In the example shown, the number of neurons in the hidden layer 420 of the second BP network 400 is obtained by Kolmogorov's theorem, specifically seven.

[0103] In specific implementations, the hidden layer 420 of the second BP network 400 can also use the Sigmoid function as the activation function, and its output layer 430 can also use the non-negative Sigmoid function as the activation function.

[0104] After the second BP network 400 is constructed, it can be trained using training sample data to optimize the weights and biases of the second BP network 400 and obtain the trained second BP network 400.

[0105] In this embodiment of the invention, the first temperature deviation parameter x1 = e(k), the second temperature deviation parameter x2 = e(k-1), and the third temperature deviation parameter x3 = e(k) - e(k-1) can be input into the pre-trained second BP network 400 to obtain the second control parameters of the heating tube 153, including the maximum threshold Judgemax, the minimum threshold Judgemin, the first duration n, the second duration ε, the third duration ε, and the fourth duration q.

[0106] In the specific implementation of step S5, the control quantity G(s) of the heating tube 153 can be compared with the maximum threshold Judgemax and the minimum threshold Judgemin, and the heating tube 153 can be turned on or off based on the comparison result and the first duration n, the second duration ε, the third duration ∈ and the fourth duration q.

[0107] Figure 6 This is a schematic diagram illustrating the principle of the method for controlling the heating element in a clothes dryer according to an embodiment of the present invention. The following is in conjunction with... Figure 6 The training of the first BP network and the second BP network is explained, wherein the first BP network is based on... Figure 4 Taking the first BP network 300 as an example, the second BP network 400 is... Figure 5 The second BP network shown is an example.

[0108] In specific implementation, the training sample data suitable for training the first BP network 300 and the second BP network 400 may include the set temperature Ts, the detection temperature Tc and the switching control signal of the heating tube 153.

[0109] In practice, training sample data can be obtained based on different models of dryers 100, so that the first BP network 300 and the second BP network 400 can be trained separately for different models of dryers 100. In this way, the heating element 153 in each model of dryer 100 can be precisely controlled.

[0110] In practice, the training sample data can first be denoised and filtered, and then the processed training sample data can be labeled based on the model of the dryer 100 and the set temperature Ts to obtain the labeled final training sample data.

[0111] The denoising, filtering, and labeling of training sample data can be achieved using common knowledge or existing technologies in this field, and will not be elaborated here.

[0112] In practice, the first BP network 300 and the second BP network 400 can be trained based on the final training sample data.

[0113] During training, well-known common knowledge or existing algorithms in the field can be used to optimize parameters such as weight w, learning rate ρ, and inertial rate α of the first BP network 300 and the second BP network 400. This embodiment of the invention does not limit the choice of specific algorithms, as long as they can optimize parameters such as weight w, learning rate ρ, and inertial rate α of the first BP network 300 and the second BP network 400.

[0114] For example, a genetic algorithm can be used to optimize parameters such as weight w, learning rate ρ, and inertial rate α of the first BP network 300 and the second BP network 400.

[0115] In some embodiments, when the first BP network 300 is optimized using a genetic algorithm, the learning rate ρ = 0.26 and the inertial rate α = 0.05 of the first BP network 300 can be obtained.

[0116] When training the first BP network 300, the error is defined as the deviation e(k) between the set temperature Ts and the detected temperature Tc in the final training sample data. Let the loss function be denoted as , and training will stop when the loss function is minimized to obtain the first BP network 300 that has been trained.

[0117] When training the second BP network 400, the error is also taken as the deviation e(k) between the set temperature Ts and the detected temperature Tc in the final training sample data. Let the loss function be denoted as , and training will stop when the loss function is minimized to obtain the second BP network 400 after training is completed.

[0118] Because the method for controlling the heating element 153 in the dryer 100 provided in this embodiment of the invention requires a first control parameter and a second control parameter to be implemented, and the first control parameter is obtained based on a first BP network 300, and the second control parameter is obtained based on a second BP network 400, and the training of both the first BP network 300 and the second BP network 400 uses the deviation e(k) between the set temperature Ts and the detected temperature Tc in the training sample data as the error, Since the loss function is used, the training of the first BP network 300 and the second BP network 400 must be carried out simultaneously. That is, the training of the first BP network 300 and the second BP network 400 can be carried out as the same training process, and the training of the first BP network 300 and the second BP network 400 cannot be separated or carried out independently.

[0119] Figure 7 This is a schematic diagram of the device for controlling the heating element in the dryer in an embodiment of the present invention.

[0120] Reference Figure 7 The device 500 for controlling the heating element 153 in the dryer 100 includes an acquisition module 510, a first calculation module 520, a BP network module 530, a second calculation module 540, and a control module 550.

[0121] Specifically, the acquisition module 510 is used to acquire the set temperature Ts and the detection temperature Tc of the heating element 153 when the dryer 100 executes the drying program; the first calculation module 520 is used to obtain the temperature deviation parameter based on the set temperature Ts and the detection temperature Tc; the BP network module 530 is used to input the temperature deviation parameter into the pre-trained first BP network and the second BP network respectively to obtain the first control parameter and the second control parameter of the heating element 153, the second control parameter including a control threshold; the second calculation module 540 is used to obtain the control quantity G(s) of the heating element 153 based on the first control parameter; and the control module 550 is used to compare the control quantity G(s) with the control threshold and control the heating element 153 to turn on and off based on the comparison result.

[0122] In some embodiments, the control threshold may include a maximum threshold Judgemax and a minimum threshold Judgemin, and the second control parameter may further include a first duration n, a second duration ε, a third duration ∈, and a fourth duration q. The control module 550 can be used to:

[0123] When G(s)≥Judgemax, control the heating tube 153 to turn off and make its off time n;

[0124] When Judgemax > G(s) > Judgemin, the heating tube 153 is controlled to periodically turn on and off. Within one cycle, the duration of being on is ε, and the duration of being off is ∈.

[0125] When G(s)≤Judgemin, control the heating tube 153 to turn on and make it turn on for a duration of q.

[0126] In some embodiments, the BP network module 530 is further configured to train the first BP network and the second BP network respectively; when training the first BP network and the second BP network, the deviation e(k) between the set temperature Ts and the detection temperature Tc in the training sample data is used as the error. Let the loss function be denoted as , and training will stop when the loss function is minimized to obtain the first and second BP networks after training.

[0127] In specific implementations, the acquisition module 510, the first calculation module 520, the BP network module 530, the second calculation module 540, and the control module 550 can all be implemented based on the technical solution of the method for controlling the heating tube in a clothes dryer disclosed in the embodiments of the present invention.

[0128] This invention also provides a device for controlling a heating element in a clothes dryer. The device includes a processor and a memory, wherein the memory stores a computer program executable on the processor, which, when executed by the processor, implements the method for controlling the heating element in a clothes dryer disclosed in this invention.

[0129] Continue to refer to Figure 1 The clothes dryer 100 provided in this embodiment of the invention may further include a control device 160.

[0130] Specifically, the control device 160 may include the processor and memory described above. The processor may be connected to electrical components such as the heating element 153 and is adapted to control the operation of electrical components such as the heating element 153 when executing the computer program described above.

[0131] This invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed, implements the method for controlling the heating element in a clothes dryer disclosed in this invention.

[0132] Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the invention, even when only a single embodiment is described with respect to a particular feature. The feature examples provided in this disclosure are intended to be illustrative and not limiting, unless otherwise stated. In practice, one or more technical features of the dependent claims may be combined with the technical features of the independent claims as needed and where technically feasible, and the technical features from the respective independent claims may be combined in any suitable manner rather than solely by the specific combinations listed in the claims.

[0133] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for controlling the heating element (153) in a clothes dryer (100), characterized in that, include: Obtain the set temperature of the heating element (153) Ts and detection temperature Tc ; Based on the set temperature Ts and the detected temperature Tc Obtain the temperature deviation parameters; The temperature deviation parameters are respectively input into the pre-trained first BP network (200, 300) and second BP network (400) to obtain the first control parameter and the second control parameter of the heating tube (153). The first control parameter is obtained through the first BP network, and the second control parameter is obtained through the second BP network. The first control parameter is a PID control parameter, and the second control parameter includes a control threshold. The control quantity of the heating tube (153) is obtained based on the first control parameter; The control quantity is compared with the control threshold, and the heating tube (153) is turned on and off based on the comparison result; The second control parameter includes a first duration. n Second duration Third duration and the fourth duration q The control threshold includes a maximum threshold. Judgemax and minimum threshold Judgemin The control of turning the heating element (153) on and off based on the comparison result includes: When the control quantity is greater than or equal to the maximum threshold Judgemax At that time, the heating element (153) is controlled to be turned off and the duration of its shutdown is the first duration. n ; When the control quantity is less than the maximum threshold Judgemax And greater than the minimum threshold Judgemin At that time, the heating tube (153) is controlled to periodically turn on and off, and the on duration within one cycle is the second duration. The duration of the shutdown is the third duration. ; When the control quantity is less than or equal to the minimum threshold Judgemin At that time, the heating tube (153) is controlled to turn on and its on time is set to the fourth duration. q .

2. The method according to claim 1, characterized in that, When training the first BP network (200, 300) and the second BP network (400), a set temperature from the training sample data is used. Ts and detection temperature Tc Deviation between e (k) For the error, with The loss function is used to stop training until the loss function is minimized, thus obtaining the first BP network (200, 300) and the second BP network (400) after training is completed.

3. The method according to claim 1, characterized in that, The temperature deviation parameter includes: a first temperature deviation parameter. x 1 =e(k) Second temperature deviation parameter x 2 =e(k-1) and the third temperature deviation parameter x3 = e(k) - e(k-1) ,in, e (k) This indicates that the heating element (153) is in t Setting temperature at any time Ts and detection temperature Tc The deviation between them e(k-1) This indicates that the heating element (153) is in (t-1) Setting temperature at any time Ts and detection temperature Tc The deviation between them.

4. A device (500) for controlling the heating element (153) in a clothes dryer (100), characterized in that, include: The acquisition module (510) is used to acquire the set temperature of the heating element (153). Ts and detection temperature Tc ; The first calculation module (520) is used to calculate based on the set temperature. Ts and the detected temperature Tc Obtain the temperature deviation parameters; The BP network module (530) is used to input the temperature deviation parameters into the pre-trained first BP network (200, 300) and second BP network (400) respectively to obtain the first control parameter and the second control parameter of the heating tube (153). The first control parameter is obtained through the first BP network, and the second control parameter is obtained through the second BP network. The first control parameter is a PID control parameter, and the second control parameter includes a control threshold. The second calculation module (540) is used to obtain the control quantity of the heating tube (153) based on the first control parameter; The control module (550) is used to compare the control quantity with the control threshold and control the heating tube (153) to turn on and off based on the comparison result; The control threshold includes a maximum threshold. Judgemax and minimum threshold Judgemin The second control parameter includes a first duration. n Second duration Third duration and the fourth duration q The control module (550) is used to: When the control quantity is greater than or equal to the maximum threshold Judgemax At that time, the heating element (153) is controlled to be turned off and the duration of its shutdown is the first duration. n ; When the control quantity is less than the maximum threshold Judgemax And greater than the minimum threshold Judgemin At that time, the heating tube (153) is controlled to periodically turn on and off, and the on duration within one cycle is the second duration. The duration of the shutdown is the third duration. ; When the control quantity is less than or equal to the minimum threshold Judgemin At that time, the heating tube (153) is controlled to turn on and its on time is set to the fourth duration. q .

5. The apparatus according to claim 4, characterized in that, The BP network module (530) is used to train the first BP network (200, 300) and the second BP network (400) respectively; When training the first BP network (200, 300) and the second BP network (400), the set temperature in the training sample data is used. Ts and detection temperature Tc Deviation between e(k) For the error, with The loss function is used to stop training until the loss function is minimized, thus obtaining the first BP network (200, 300) and the second BP network (400) after training is completed.

6. A device for controlling the heating element (153) in a clothes dryer (100), characterized in that, include: processor; A memory on which computer programs that can run on the processor are stored; The computer program, when executed by the processor, implements the method as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1 to 3.

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