Prediction Method, Device, Computer Equipment and Storage Medium for Drying and Cooling Duration

Abstract

An embodiment of the present invention discloses a method, device, computer device, and storage medium for predicting the drying and cooling duration. The method includes: detecting whether the drying program of the target drying device ends; if it ends, obtaining the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the target drying device, the motor current and motor power of the cooler in the target drying device; then inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model for predicting the cooling duration to obtain the target cooling duration. This solution combines the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current of the cooler, and the motor power of the target drying device to predict the cooling duration. The prediction result is less affected by factors such as the environment and the working conditions of the drying device, improving the accuracy of the cooling duration prediction.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular, to a method, device, computer device, and storage medium for predicting the drying and cooling duration. Background Art

[0002] Washing machines and dryers (collectively referred to as drying devices hereinafter) with the function of drying clothes are becoming more and more popular. Since drying requires high temperature, after the drying is completed, the whole machine needs a cooling process to ensure the safety of user use.

[0003] Currently, in the drying program of drying devices, the door will only be opened when the temperature inside the drum drops to meet the door opening condition. For some drying devices, due to inaccurate judgment of the cooling time, there will be situations of multiple time jumps or no change in time; for example, when the drying device is in the cooling stage and runs from the remaining time t1 (t1>1) to the remaining 1 minute, if the temperature inside the drum is too high to meet the door opening condition at this time, the remaining time will be jumped to t2 (t2>1) and re-counted down, or it will stay at the display of 1 minute until the drying and cooling stage ends and meets the door opening condition to open the door. This uncertain time approach will make users wait for a long time without result, bringing a very poor user experience. There is an urgent need for a method to more accurately predict the cooling duration of drying devices. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, computer device, and storage medium for predicting the drying and cooling duration, which can improve the accuracy of predicting the cooling duration of drying devices.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting the drying and cooling duration, which includes:

[0006] Detect whether the drying program of the target drying device has ended;

[0007] If the drying program has ended, obtain the ambient temperature of the target drying device, the weight of the object being dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device;

[0008] Input the ambient temperature, the weight of the object being dried, the atmospheric pressure, the motor current, and the motor power into a trained cooling duration prediction model for predicting the cooling duration to obtain the target cooling duration.

[0009] In a second aspect, an embodiment of the present invention further provides a device for predicting the drying and cooling duration, which includes: an acquisition unit and a processing unit, where:

[0010] The processing unit is used to detect whether the drying program of the target drying device has ended;

[0011] The obtaining unit is configured to obtain the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and the motor power of the cooler in the target drying device when the drying program ends;

[0012] The processing unit is further configured to input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into a trained cooling duration prediction model to predict the cooling duration, and obtain a target cooling duration; display the target cooling duration.

[0013] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program, the above method is implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium. The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the above method can be implemented.

[0015] An embodiment of the present invention provides a method, a device, a computer device, and a storage medium for predicting the drying and cooling duration. Among them, the method includes: detecting whether the drying program of the target drying device ends; if the drying program ends, obtaining the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and the motor power of the cooler in the target drying device; and then inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into a trained cooling duration prediction model to predict the cooling duration, and obtaining a target cooling duration. This solution combines the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current of the cooler, and the motor power of the target drying device to predict the cooling duration. The prediction result is less affected by factors such as the environment and the working conditions of the drying device, and the accuracy of the cooling duration prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of the application scenario of the method for predicting the drying and cooling duration provided by the embodiment of the present invention;

[0018] Figure 2 It is a schematic flowchart of a prediction method for drying and cooling duration provided by an embodiment of the present invention;

[0019] Figure 3 It is a schematic structural diagram of a BP neural network model provided by an embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of a temperature curve during the operation of a drying device provided by an embodiment of the present invention;

[0021] Figure 5 It is a schematic flowchart of a prediction method for drying and cooling duration provided by another embodiment of the present invention;

[0022] Figure 6 It is a schematic training flowchart of a BP neural network model provided by an embodiment of the present invention;

[0023] Figure 7 It is a schematic block diagram of a prediction device for drying and cooling duration provided by an embodiment of the present invention;

[0024] Figure 8 It is a schematic block diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0027] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0028] It should be further understood that the term " / and" as used in this specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] An embodiment of the present invention provides a method, apparatus, computer device, and storage medium for predicting the drying and cooling duration.

[0030] The execution subject of the method for predicting the drying and cooling duration may be the apparatus for predicting the drying and cooling duration provided by the embodiment of the present invention, or a computer device integrated with the apparatus for predicting the drying and cooling duration. Among them, the apparatus for predicting the drying and cooling duration may be implemented in a hardware or software manner. The computer device may be a drying device (such as a dryer or a washing machine with a drying function), or a central controller in the drying device, or a terminal or server communicatively connected to the drying device. At this time, one terminal or server may provide the service of predicting the cooling duration for multiple drying devices.

[0031] In some embodiments, please refer to Figure 1 , Figure 1 which is a schematic diagram of the application scenario of the method for predicting the drying and cooling duration provided by the embodiment of the present invention. Taking the execution subject of the method for predicting the drying and cooling duration as a drying device as an example, the method for predicting the drying and cooling duration is applied to Figure 1 the drying device 10 therein. The drying device 10 detects whether its drying program has ended; if the drying program has ended, it obtains the ambient temperature of the drying device, the weight of the object to be dried in the drying device, the atmospheric pressure of the environment where the drying device is located, the motor current and motor power of the cooler in the drying device (as Figure 1 shown, these parameters are collectively referred to as influencing factors); then inputs these influencing factors into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration, and finally displays the obtained target cooling duration on the display panel of the drying device 10.

[0032] The following takes the execution subject of the method for predicting the drying and cooling duration as a drying device (Embodiment 1) or a server (Embodiment 2) as an example to respectively illustrate the method for predicting the drying and cooling duration provided by the present invention.

[0033] Embodiment 1:

[0034] In this embodiment, the execution subject of the method for predicting the drying and cooling duration is a drying device. At this time, a trained cooling duration prediction model is preset in the drying device for cooling duration prediction. Please refer to Figure 2 , Figure 2 which is a flowchart of the method for predicting the drying and cooling duration provided by the embodiment of the present invention. As Figure 2 shown, the method includes the following steps S110 - S140.

[0035] S110. Detect whether the drying program of the target drying device has ended.

[0036] Among them, after the drying program of the target drying device in this embodiment ends, it will enter the cooling program. In this embodiment, it is necessary to detect in real time whether the drying program of the target drying device has ended. If it has not ended, continue to monitor whether the drying program has ended. If it has ended, enter step S120.

[0037] The target drying device in this embodiment is a drying device with a function of predicting the cooling duration, and the drying device is executing the drying program.

[0038] It should be noted that the drying program described in this embodiment does not include the cooling program after drying ends.

[0039] S120. If the drying program ends, obtain the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device.

[0040] In this embodiment, if the drying program of the target drying device ends, it means that the target drying device is ready to enter the cooling stage. At this time, it is necessary to obtain the current ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current of the cooler in the target drying device, and the motor power.

[0041] Specifically, obtain the current ambient temperature of the target drying device through a temperature sensor set outside the target drying device, obtain the weight of the object to be dried through a weight sensor set at the bottom of the drying cylinder of the target drying device, and obtain the atmospheric pressure of the environment where the target drying device is currently located through a barometric pressure sensor set outside the target drying device.

[0042] The motor current and motor power of the cooler in the target drying device in this embodiment can be the preset rated motor current and rated motor power of the cooler, or the measured motor working current and motor working power when the cooler is working stably. If it is the motor working current and motor working power, then at this time, it is necessary to wait until the cooler enters the stable working state (generally, it can enter the stable working state a few seconds after the drying program ends and enters the cooling program), and then obtain the motor working current and motor working power, and then predict the cooling duration.

[0043] Among them, the cooler in the target drying device in this embodiment can be a hair dryer.

[0044] S130. Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the target cooling duration.

[0045] In some embodiments, the cooling duration prediction model in this embodiment is a BackPropagation (BP) neural network model, or it can also be other neural networks with prediction functions. Specifically, it is not limited here.

[0046] The target cooling duration in this embodiment is the time required for cooling after the drying program of the target drying device ends, that is, the time required from the end of the drying program to when the drying device can be opened.

[0047] Among them, as Figure 3 shown, the BP neural network model in this embodiment includes an input layer, two hidden layers, and an output layer.

[0048] In this embodiment, the number of nodes in the input layer is 5, and the input parameters respectively correspond to the ambient temperature (C), the weight of the object to be dried (W), the atmospheric pressure (K), the motor current (I) of the cooler in the drying device, and the motor power (P). The number of nodes in the output layer is 1, and the output is the predicted cooling duration.

[0049] Since the number of hidden nodes in each layer of the hidden layer has a great influence on the network performance, when the number of hidden nodes is too large, it will lead to too long network learning time and even non-convergence; while when the number of hidden nodes is too small, the fault tolerance of the network is poor.

[0050] Therefore, in order to obtain the appropriate number of hidden layer nodes and avoid the above problems, the number of nodes in each hidden layer of the BP neural network model in this embodiment is determined according to the following formula:

[0051]

[0052] Among them, the N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, and N y is the number of nodes in the output layer.

[0053] In this embodiment, N x is 5, and N y is 1. By calculation, the number of nodes in each hidden layer is 3.

[0054] It should be noted that in some embodiments, the drying device provided in this embodiment provides a function for the user to adjust the opening temperature threshold. The user can adjust the opening temperature threshold within the preset adjustment range of the opening temperature threshold. If the user does not make an adjustment, the default opening temperature threshold is used.

[0055] In some embodiments, the drying device in this embodiment allows the user to adjust the opening temperature threshold within the range of [25°, 50°], and the user can adjust the opening temperature threshold of the drying device within this range (specifically, it can be adjusted in increments of 5°).

[0056] For example, if the default opening temperature threshold is 35°, but the user feels that the waiting time for opening the door is too long and the user can accept a slightly higher opening temperature, at this time, the user can adjust the opening temperature threshold upward within the opening temperature threshold adjustment range. If the user feels that the opening temperature is still too high, at this time, the user can adjust the opening temperature threshold downward within the opening temperature threshold adjustment range.

[0057] When the user adjusts the opening temperature threshold, at this time, the drying device will obtain the user's opening temperature threshold adjustment instruction, where the instruction carries the adjusted opening temperature threshold, and then adjusts the opening temperature threshold of the target drying device according to the opening temperature threshold adjustment instruction to obtain the adjusted opening temperature threshold.

[0058] It should be noted that in some embodiments, when the opening temperature threshold can be adjusted, the cooling duration prediction model in this embodiment is trained for each opening temperature threshold within the opening temperature threshold adjustment range. Therefore, after inputting the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model for cooling duration prediction, the trained cooling duration prediction model will obtain the predicted cooling durations corresponding to each opening temperature threshold within the opening temperature threshold adjustment range, and then determine the predicted cooling duration corresponding to the adjusted opening temperature threshold as the target cooling duration.

[0059] In some other embodiments, if the drying device does not have the function of adjusting the opening temperature threshold, then the corresponding cooling duration prediction model only needs to be trained for the default opening temperature threshold, and the output cooling duration is also the duration required for the temperature in the drying cylinder to reach the default opening temperature threshold from the end of drying, without the need to train for each opening temperature threshold within the opening temperature threshold adjustment range, reducing the parameters of the model and improving the training speed of the model.

[0060] Among them, since the temperature inside the drying cylinder of the drying device is relatively constant when the drying program just ends, which is the outlet temperature of the hot air blower, this embodiment does not need to consider the temperature inside the cylinder at the end of the drying program when predicting the cooling duration.

[0061] As Figure 4 shown, Figure 4This is the temperature curve diagram during the operation of the drying equipment in this embodiment. Among them, the abscissa is time (unit: second), and the ordinate is temperature (unit: degree Celsius). Among them, point A is the corresponding point when the drying program starts, and the curve from point C to point B is the drying cooling curve. The time elapsed from point C to point B is the drying cooling duration, that is, the target cooling duration to be predicted in this embodiment.

[0062] S140. Display the target cooling duration.

[0063] Specifically, display the target cooling duration on the display panel of the drying equipment. More specifically, display the countdown corresponding to the drying duration on the display panel, so that the user can more intuitively view the time remaining until the door can be opened.

[0064] It should be noted that in some embodiments, when the countdown of the target cooling duration reaches 0, an opening instruction for the target drying equipment is triggered; then, the door lock of the target drying equipment is opened according to the opening instruction.

[0065] In some embodiments, in order to remind the user that the door of the drying equipment can be opened, at this time, the drying equipment will also generate an opening reminder alarm. At this time, the drying equipment will emit a preset sound according to the opening reminder alarm, such as a "beep" sound.

[0066] In summary, this solution combines the ambient temperature of the target drying equipment, the weight of the object to be dried, the atmospheric pressure, and the motor current and motor power of the cooler to predict the cooling duration. The prediction result is less affected by factors such as the environment and the working conditions of the drying equipment, and the accuracy of the cooling duration prediction is improved.

[0067] Embodiment Two:

[0068] In this embodiment, the execution subject of the method for predicting the drying cooling duration is a server. At this time, the server can provide the function of predicting the cooling duration for multiple drying equipment, and each drying equipment can be communicatively connected to the server.

[0069] Please refer to Figure 5 , Figure 5 is the flowchart of the method for predicting the drying cooling duration provided by the embodiment of the present invention. As Figure 5 shown, the method includes the following steps S210 - S240.

[0070] S210. Detect whether the drying program of the target drying equipment has ended.

[0071] In some embodiments, when the target drying device sends a drying program end instruction to the server at the end of the drying program, S210 includes that the server continuously detects whether it receives the drying program end instruction of the target drying device. When receiving this instruction, it is determined that the drying program of the target drying device ends. Otherwise, it continues to detect whether the target drying device ends.

[0072] S220. If the drying program ends, obtain the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device.

[0073] Specifically, in some embodiments, when the server receives the drying program end instruction of the target drying device, it will send a duration influence factor acquisition instruction to the target drying device. After the drying device receives this duration influence factor acquisition instruction, it will send the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current of the cooler in the target drying device, and the motor power of the cooler in the drying device, these duration influence factors, to the server, so that the server obtains the above-mentioned duration influence factors.

[0074] S230. Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the target cooling duration.

[0075] After the server receives the above-mentioned duration influence factors, it will input these duration influence factors into the trained cooling duration prediction model to predict the cooling duration and obtain the target cooling duration.

[0076] In some embodiments, if the user can adjust the door opening temperature threshold, at this time, it is also necessary to send the current door opening temperature threshold of the target drying device to the server. Then, the cooling duration prediction model in the server will obtain the cooling durations corresponding to multiple door opening temperature thresholds according to the above-mentioned duration influence factors. At this time, the server determines the cooling duration corresponding to the currently set door opening temperature threshold of the target drying device as the target cooling duration.

[0077] S240. Send the target cooling duration to the target drying device so that the target drying device displays the target cooling duration.

[0078] After the server calculates the target cooling duration corresponding to the target drying device, it sends the target cooling duration to the target drying device, and then the target drying device dynamically displays the countdown corresponding to the target cooling duration; when the countdown of the target cooling duration reaches 0, the server or the target drying device itself triggers the door opening instruction of the target drying device; then, according to the door opening instruction, the door lock of the target drying device is opened.

[0079] In summary, the server in this solution combines the ambient temperature of the target drying device, the weight of the object to be dried, the atmospheric pressure, and the motor current and motor power of the cooler to predict the cooling duration. The prediction result is less affected by factors such as the environment and the working conditions of the drying device, improving the accuracy of the cooling duration prediction. Moreover, the server in this embodiment can provide the door opening duration prediction service for multiple drying devices. The drying device only needs to send data such as the duration influencing factors collected to the server, and the server can provide accurate door opening duration prediction services for the drying devices. The computing power of the drying device does not need to be too high, saving the computing cost of the drying device.

[0080] It should be noted that those skilled in the art can clearly understand that some specific implementation processes of the drying and cooling duration prediction method in the second embodiment above can refer to the corresponding descriptions in the first embodiment. For the convenience and conciseness of description, they will not be repeated here.

[0081] Embodiment 3:

[0082] The following details the training steps of the cooling duration prediction model provided in the embodiments of the present invention. The training steps of the cooling duration prediction model in this embodiment can be executed in the computer device provided by the present invention. This embodiment takes the cooling duration prediction model as a BP neural network model as an example for illustration. Please refer to Figure 6 , Figure 6 which is a schematic flowchart of the training method of the cooling duration prediction model provided in the embodiments of the present invention. As Figure 6 shown, the method includes the following steps S310 - S340.

[0083] S310. Obtain a training sample set.

[0084] In some embodiments, since it is not clear which factors affect the cooling duration in the early stage of training, before executing step S310, it is first necessary to determine which factors will affect the cooling duration.

[0085] Specifically, before step S310, a variety of historical sample data is obtained; then, a multiple linear regression model is used to screen the historical sample data to obtain influencing factors. Among them, the obtained influencing factors are the input parameters for predicting the cooling duration in the BP neural network model of this embodiment. The influencing factors include the ambient temperature of the drying equipment, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying equipment.

[0086] Among them, in addition to the above-mentioned influencing factors, the variety of historical sample data also includes parameters such as environmental humidity and environmental brightness. Through the multiple linear regression model, it is judged that these parameters have no correlation with the cooling duration, so these parameters are removed.

[0087] It can be seen that this embodiment screens the historical sample data to obtain influencing factors that affect the cooling duration, which can improve the speed and accuracy of model training.

[0088] S320. Perform forward propagation on the preset BP neural network model according to the training samples in the training sample set to obtain the predicted duration.

[0089] The BP neural network model in this embodiment uses the Sigmoid function as the activation function to calculate the predicted duration layer by layer.

[0090] Among them, the structure of the BP neural network model in this embodiment is as Figure 3 shown, including one input layer, two hidden layers, and one output layer.

[0091] And the number of nodes in the input layer is 5, and the input parameters respectively correspond to the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying equipment. The number of nodes in the output layer is 1, and the output is the predicted cooling duration.

[0092] Since the number of hidden nodes in each layer of the hidden layer has a great influence on the network performance. When the number of hidden nodes is too large, it will lead to too long network learning time and even non-convergence; while when the number of hidden nodes is too small, the fault tolerance of the network is poor.

[0093] Therefore, in order to obtain the appropriate number of hidden layer nodes and avoid the above problems, the number of nodes in each hidden layer of the BP neural network in this embodiment is determined according to the following formula:

[0094]

[0095] Among them, the N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, N y is the number of nodes in the output layer.

[0096] In this embodiment, N x is 5, and N y is 1. The number of nodes in each hidden layer is calculated to be 3.

[0097] S330. Calculate the duration difference according to the predicted duration and the actual duration corresponding to the training sample.

[0098] In this embodiment, each training sample carries a label of the corresponding actual duration. At this time, when the predicted duration of a certain training sample is obtained, the duration difference between the predicted duration and the actual duration will be calculated.

[0099] S340. Perform backpropagation on the preset BP neural network model according to the duration difference to obtain the trained BP neural network model.

[0100] After obtaining the duration difference, a negative feedback will be applied to the input end according to this duration difference, so that the neural network adjusts the weights, and then retrains according to the adjusted weights.

[0101] It should be noted that in this embodiment, steps S320 to S340 need to be sequentially executed for each training sample in the training sample set. In this way, through repeated training with a large amount of historical data, the trained BP neural network model is obtained.

[0102] It should be noted that in some embodiments, when the opening temperature threshold can be adjusted, the BP neural network model in this embodiment is trained for each opening temperature threshold within the adjustable range of the opening temperature threshold. Therefore, after inputting the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model for cooling duration prediction, the trained BP neural network model will obtain the predicted cooling durations corresponding to the respective opening temperature thresholds within the adjustable range of each opening temperature threshold, and then determine the predicted cooling duration corresponding to the adjusted opening temperature threshold as the target cooling duration.

[0103] In other embodiments, if the drying device does not have the function of adjusting the opening temperature threshold, then at this time, the BP neural network model only needs to be trained for the default opening temperature threshold, and the output cooling duration is also the duration required for the temperature in the drying cylinder to reach the default opening temperature threshold from the end of drying, without training for multiple opening temperature thresholds, reducing the parameters of the model and improving the training speed of the model.

[0104] Embodiment 4:

[0105] Figure 7It is a schematic block diagram of a prediction device for the drying and cooling duration provided by an embodiment of the present invention. As Figure 7 shown, corresponding to the above prediction method for the drying and cooling duration, the present invention also provides a prediction device for the drying and cooling duration. The prediction device for the drying and cooling duration includes a unit for executing the above prediction method for the drying and cooling duration, and the device can be configured in a dryer or a computer device such as a server that provides a cooling duration prediction function for a dryer. Specifically, please refer to Figure 7 , the prediction device 700 for the drying and cooling duration includes an acquisition unit 701 and a processing unit 702, where:

[0106] The processing unit 702 is used to detect whether the drying program of the target drying device ends;

[0107] The acquisition unit 701 is used to, when the drying program ends, acquire the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device;

[0108] The processing unit 702 is further used to input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain a target cooling duration; and display the target cooling duration.

[0109] In some embodiments, before the processing unit 702 executes the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain a target cooling duration, it is further used to:

[0110] Acquire a training sample set;

[0111] Perform forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain a predicted duration;

[0112] Calculate a duration difference according to the predicted duration and the actual duration corresponding to the training sample;

[0113] Perform backpropagation on the preset cooling duration prediction model according to the duration difference to obtain the trained cooling duration prediction model.

[0114] In some embodiments, before the processing unit 702 executes the step of acquiring the training sample set, it is further used to:

[0115] Acquire a variety of historical sample data;

[0116] The historical sample data is screened using a multiple linear regression model to obtain influencing factors, which include the ambient temperature of the drying equipment, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying equipment;

[0117] The obtaining of the training sample set includes:

[0118] The training sample set is obtained according to the influencing factors.

[0119] In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer. The number of nodes in each hidden layer is determined according to the following formula:

[0120]

[0121] where N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, and N y is the number of nodes in the output layer.

[0122] In some embodiments, before the processing unit 702 executes the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration, it is further configured to:

[0123] Obtain an opening temperature threshold adjustment instruction from the user;

[0124] Adjust the opening temperature threshold of the target drying equipment according to the opening temperature threshold adjustment instruction to obtain an adjusted opening temperature threshold;

[0125] The step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration includes:

[0126] Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the predicted cooling durations corresponding to multiple opening temperature thresholds;

[0127] Determine the predicted cooling duration corresponding to the adjusted opening temperature threshold as the target cooling duration.

[0128] In some embodiments, after the processing unit 702 performs the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration, it is further configured to:

[0129] Dynamically display a countdown corresponding to the target cooling duration;

[0130] When the countdown of the target cooling duration reaches 0, trigger an opening instruction for the target drying device;

[0131] Open the door lock of the target drying device according to the opening instruction.

[0132] In some embodiments, after the processing unit 702 performs the step of opening the door lock of the target drying device according to the opening instruction, it is further configured to:

[0133] Generate an opening reminder alarm.

[0134] It should be noted that those skilled in the art can clearly understand that the specific implementation processes of the above-mentioned drying and cooling duration prediction device and each unit can refer to the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity of description, they will not be elaborated here.

[0135] Embodiment 5:

[0136] The above-mentioned drying and cooling duration prediction device can be implemented in the form of a computer program, and this computer program can run on a computer device as shown in Figure 8 Figure.

[0137] Please refer to Figure 8 , Figure 8 which is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device 800 can be a control device in a drying device or a server that can provide a cooling duration prediction function for the drying device. Among them, the server can be an independent server or a server cluster composed of multiple servers.

[0138] Refer to Figure 8 , this computer device 800 includes a processor 802, a memory, and a network interface 805 connected through a system bus 801. Among them, the memory can include a non-volatile storage medium 803 and an internal memory 804.

[0139] The non-volatile storage medium 803 can store an operating system 8031 and a computer program 8032. The computer program 8032 includes program instructions, and when these program instructions are executed, the processor 802 can be made to execute a drying and cooling duration prediction method.

[0140] The processor 802 is used to provide computing and control capabilities to support the operation of the entire computer device 800.

[0141] The internal memory 804 provides an environment for the operation of the computer program 8032 in the non-volatile storage medium 803. When the computer program 8032 is executed by the processor 802, the processor 802 can be caused to execute a method for predicting the drying and cooling duration.

[0142] The network interface 805 is used for network communication with other devices. Those skilled in the art can understand that Figure 8 the structure shown in is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device 800 to which the solution of the present invention is applied. The specific computer device 800 may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0143] Wherein, the processor 802 is used to run the computer program 8032 stored in the memory to implement the following steps:

[0144] Detect whether the drying program of the target drying device has ended;

[0145] If the drying program has ended, obtain the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and the motor power of the cooler in the target drying device;

[0146] Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for predicting the cooling duration, and obtain the target cooling duration.

[0147] In some embodiments, before the processor 802 implements the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for predicting the cooling duration and obtaining the target cooling duration, the following steps are also implemented:

[0148] Obtain a training sample set;

[0149] Perform forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain a predicted duration;

[0150] Calculate the duration difference according to the predicted duration and the actual duration corresponding to the training sample;

[0151] Backpropagate the preset cooling duration prediction model according to the duration difference to obtain the trained cooling duration prediction model.

[0152] In some embodiments, before the processor 802 implements the step of obtaining the training sample set, the following steps are also implemented:

[0153] Obtain various historical sample data;

[0154] Use a multiple linear regression model to screen the historical sample data to obtain influencing factors, where the influencing factors include the ambient temperature of the drying equipment, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying equipment;

[0155] The obtaining of the training sample set includes:

[0156] Obtain the training sample set according to the influencing factors.

[0157] In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer. The number of nodes in each hidden layer is determined according to the following formula:

[0158]

[0159] where N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, and N y is the number of nodes in the output layer.

[0160] In some embodiments, before the processor 802 implements the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the target cooling duration, the following steps are also implemented:

[0161] Obtain the user's door opening temperature threshold adjustment instruction;

[0162] Adjust the door opening temperature threshold of the target drying equipment according to the door opening temperature threshold adjustment instruction to obtain the adjusted door opening temperature threshold;

[0163] The step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the target cooling duration includes:

[0164] Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtain the predicted cooling durations corresponding to multiple door opening temperature thresholds;

[0165] Determine the predicted cooling duration corresponding to the adjusted door opening temperature threshold as the target cooling duration.

[0166] In some embodiments, after the processor 802 implements the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration, the following steps are further implemented:

[0167] Dynamically display the countdown corresponding to the target cooling duration;

[0168] When the countdown of the target cooling duration reaches 0, trigger the door opening instruction of the target drying device;

[0169] Open the door lock of the target drying device according to the door opening instruction.

[0170] In some embodiments, after the processor 802 implements the step of opening the door lock of the target drying device according to the door opening instruction, the following steps are further implemented:

[0171] Generate an opening reminder alarm.

[0172] It should be understood that in the embodiments of the present invention, the processor 802 may be a central processing unit (CPU), and the processor 802 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0173] Those of ordinary skill in the art can understand that all or part of the processes of the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, and the storage medium is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0174] Embodiment Six:

[0175] The present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, where the computer program includes program instructions. When the program instructions are executed by a processor, the processor performs the following steps:

[0176] Detect whether the drying program of the target drying device has ended;

[0177] If the drying program has ended, obtain the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device;

[0178] Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model for predicting the cooling duration, and obtain the target cooling duration.

[0179] In some embodiments, before the processor executes the program instructions to implement the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model for predicting the cooling duration and obtaining the target cooling duration, the following steps are also implemented:

[0180] Obtain a training sample set;

[0181] Perform forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain a predicted duration;

[0182] Calculate a duration difference according to the predicted duration and the actual duration corresponding to the training sample;

[0183] Perform backpropagation on the preset cooling duration prediction model according to the duration difference to obtain the trained cooling duration prediction model.

[0184] In some embodiments, before the processor executes the program instructions to implement the step of obtaining the training sample set, the following steps are also implemented:

[0185] Obtain various historical sample data;

[0186] Use a multiple linear regression model to screen the historical sample data to obtain influencing factors, where the influencing factors include the ambient temperature of the drying device, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying device;

[0187] The obtaining of the training sample set includes:

[0188] Obtain the training sample set according to the influencing factors.

[0189] In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer, and the number of nodes in each hidden layer is determined according to the following formula:

[0190]

[0191] where N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, and N y is the number of nodes in the output layer.

[0192] In some embodiments, before the processor executes the program instructions to implement the step of inputting the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration, the following steps are further implemented:

[0193] Obtain the user's instruction to adjust the opening temperature threshold;

[0194] Adjust the opening temperature threshold of the target drying device according to the instruction to adjust the opening temperature threshold to obtain the adjusted opening temperature threshold;

[0195] The step of inputting the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration includes:

[0196] Input the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration, and obtain the predicted cooling durations corresponding to multiple opening temperature thresholds;

[0197] Determine the predicted cooling duration corresponding to the adjusted opening temperature threshold as the target cooling duration.

[0198] In some embodiments, after the processor executes the program instructions to implement the step of inputting the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power into the trained cooling duration prediction model to predict the cooling duration and obtaining the target cooling duration, the following steps are further implemented:

[0199] Dynamically display the countdown corresponding to the target cooling duration;

[0200] When the countdown of the target cooling duration reaches 0, trigger the opening instruction of the target drying device;

[0201] Open the door lock of the target drying device according to the door opening instruction.

[0202] In some embodiments, after the processor executes the program instructions to implement the step of opening the door lock of the target drying device according to the door opening instruction, the following steps are further implemented:

[0203] Generate a door opening reminder alarm.

[0204] The storage medium can be various computer-readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disc, etc., which can store program codes.

[0205] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0206] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0207] The steps in the method embodiments of the present invention can be adjusted, combined, and deleted according to actual needs. The units in the device embodiments of the present invention can be combined, divided, and deleted according to actual needs. In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0208] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0209] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for predicting the drying and cooling duration, characterized in that, Including: Detecting whether the drying program of the target drying device has ended; If the drying program has ended, obtaining the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device; Inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration; Wherein, before inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration, the method further includes: Obtaining a user's opening temperature threshold adjustment instruction; Adjusting the opening temperature threshold of the target drying device according to the opening temperature threshold adjustment instruction to obtain the adjusted opening temperature threshold; The step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration includes: Inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the predicted cooling durations corresponding to multiple opening temperature thresholds; Determining the predicted cooling duration corresponding to the adjusted opening temperature threshold as the target cooling duration.

2. The method according to claim 1, wherein Before inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration, the method further includes: Obtaining a training sample set; Performing forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain a predicted duration; Calculating a duration difference according to the predicted duration and the actual duration corresponding to the training sample; Performing backpropagation on the preset cooling duration prediction model according to the duration difference to obtain the trained cooling duration prediction model.

3. The method according to claim 2, wherein Before obtaining the training sample set, the method further includes: Obtaining various historical sample data; Using a multiple linear regression model to screen the historical sample data to obtain influencing factors, where the influencing factors include the ambient temperature of the drying device, the weight of the object to be dried, the atmospheric pressure, the current and power of the cooler in the drying device; The step of obtaining the training sample set includes: Obtaining the training sample set according to the influencing factors.

4. The method according to claim 2, characterized in that, The cooling duration prediction model includes an input layer, a hidden layer and an output layer, and the number of nodes in each hidden layer is determined according to the following formula: where N is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, and N y is the number of nodes in the output layer.

5. The method according to any one of claims 1 to 4, characterized in that, After inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration, the method further includes: Dynamically display the countdown corresponding to the target cooling duration; When the countdown of the target cooling duration is 0, trigger the door opening instruction for the target drying device; Open the door lock of the target drying device according to the door opening instruction.

6. The method according to claim 5, characterized in that, After opening the door lock of the target drying device according to the door opening instruction, the method further includes: Generate a door opening reminder alarm.

7. A prediction device for the drying and cooling duration, characterized in that, Include an acquisition unit and a processing unit, where: The processing unit is used to detect whether the drying program of the target drying device is completed; The acquisition unit is used to, when the drying program is completed, acquire the ambient temperature of the target drying device, the weight of the object to be dried in the target drying device, the atmospheric pressure of the environment where the target drying device is located, the motor current and motor power of the cooler in the target drying device; The processing unit is further used to input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration; Wherein, before the processing unit executes the step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration, it is further used to: Acquire the user's door opening temperature threshold adjustment instruction; Adjust the door opening temperature threshold of the target drying device according to the door opening temperature threshold adjustment instruction to obtain the adjusted door opening temperature threshold; The step of inputting the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the target cooling duration includes: Input the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power into the trained cooling duration prediction model for cooling duration prediction to obtain the predicted cooling durations corresponding to multiple door opening temperature thresholds; Determine the predicted cooling duration corresponding to the adjusted door opening temperature threshold as the target cooling duration.

8. A computer device, characterized in that, The computer device includes a memory and a processor, and a computer program is stored on the memory. When the processor executes the computer program, the method described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the method described in any one of claims 1-6 can be implemented.

Images