CN115169245A - Method and device for predicting drying and cooling time, computer equipment and storage medium - Google Patents
Method and device for predicting drying and cooling time, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for predicting drying and cooling time, computer equipment and a storage medium. The method comprises the following steps: detecting whether a drying program of the target drying equipment is finished or not; if the drying process is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the target drying equipment, the motor current of a cooler in the target drying equipment and the motor power; and then, carrying out temperature reduction duration prediction on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and a temperature reduction duration prediction model after the motor power input training to obtain a target temperature reduction duration. The scheme combines the environmental temperature of the target drying equipment, the weight of the dried object, the atmospheric pressure, the motor current of the cooler and the motor power to predict the cooling time, the prediction result is less influenced by factors such as the environment and the working condition of the drying equipment, and the accuracy of predicting the cooling time is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for predicting drying and cooling duration, computer equipment and a storage medium.
Background
Washing machines and clothes dryers (hereinafter referred to as drying devices) having a function of drying clothes are becoming more and more popular, and since drying needs a high temperature, after drying is completed, the whole machine needs a cooling process to ensure safe use of users.
In the drying procedure of the drying equipment at present, the door is opened only when the temperature in the barrel is reduced to meet the door opening condition, and the condition of multiple time jumps or time invariance of part of the drying equipment due to inaccurate judgment of the cooling time can occur; for example, when the drying device temperature reduction stage runs from the residual time t1 (t 1> 1) to the residual time of 1 minute, if the temperature in the barrel is too high and the door is not opened, the residual time jumps to t2 (t 2> 1) and counts down again or stays for 1 minute until the drying temperature reduction stage is finished and the door opening condition is satisfied, the user can wait for a long time without any effect due to the method of uncertain time, poor user experience is brought, and a method for predicting the temperature reduction time of the drying device more accurately is urgently needed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting drying and cooling time, computer equipment and a storage medium, which can improve the accuracy of predicting the drying and cooling time.
In a first aspect, an embodiment of the present invention provides a method for predicting a drying and cooling time period, where the method includes:
detecting whether a drying program of the target drying equipment is finished or not;
if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power;
and predicting the cooling time length of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the cooling time length prediction model after the motor power input training to obtain the target cooling time length.
In a second aspect, an embodiment of the present invention further provides a device for predicting a drying and cooling time period, where the device includes: an acquisition unit and a processing unit, wherein:
the processing unit is used for detecting whether the drying program of the target drying equipment is finished or not;
the acquiring unit is used for acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power when the drying program is finished;
the processing unit is further configured to predict the cooling duration of the temperature reduction duration prediction model after the environmental temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training, so as to obtain a target cooling duration; and displaying the target cooling time length.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the method when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, where a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by a processor, can implement the method described above.
The embodiment of the invention provides a method and a device for predicting drying and cooling time, computer equipment and a storage medium. Wherein the method comprises the following steps: detecting whether a drying program of the target drying equipment is finished or not; if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power; and then, carrying out temperature reduction duration prediction on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and a temperature reduction duration prediction model after the motor power input training to obtain a target temperature reduction duration. The scheme combines the environmental temperature of the target drying equipment, the weight of the dried object, the atmospheric pressure, the motor current of the cooler and the motor power to predict the cooling time, the prediction result is less influenced by factors such as the environment and the working condition of the drying equipment, and the accuracy of predicting the cooling time is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a method for predicting drying and cooling duration according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for predicting drying and cooling duration according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a BP neural network model according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a temperature curve of a drying apparatus according to an embodiment of the present invention during operation;
fig. 5 is a schematic flow chart illustrating a method for predicting drying and cooling duration according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of a training process of a BP neural network model according to an embodiment of the present invention;
fig. 7 is a schematic block diagram of a device for predicting drying and cooling time according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
The embodiment of the invention provides a method and a device for predicting drying and cooling time, computer equipment and a storage medium.
An execution main body of the method for predicting the drying and cooling time duration may be the device for predicting the drying and cooling time duration provided by the embodiment of the present invention, or a computer device integrated with the device for predicting the drying and cooling time duration, where the device for predicting the drying and cooling time duration may be implemented in a hardware or software manner, and the computer device may be a drying device (such as a clothes dryer or a washing machine with a clothes drying function), or a central controller in the drying device, or a terminal or a server in communication connection with the drying device, and at this time, one terminal or server may provide a service for predicting the cooling time duration for multiple drying devices.
In some embodiments, please refer to fig. 1, where fig. 1 is a schematic view of an application scenario of a method for predicting a drying and cooling time period according to an embodiment of the present invention. Taking the implementation subject of the prediction method of the drying and cooling time period as an example of a drying device, the prediction method of the drying and cooling time period is applied to the drying device 10 in fig. 1, and the drying device 10 detects whether the drying program of the drying device 10 is finished; if the drying procedure is finished, obtaining the ambient temperature of the drying device, the weight of the dried object in the drying device, the atmospheric pressure of the environment where the drying device is located, the motor current of a cooler in the drying device, and the motor power (as shown in fig. 1, these parameters are collectively called influence factors); then, the influence factors are input into the trained cooling duration prediction model to predict the cooling duration, so as to obtain a target cooling duration, and finally, the obtained target cooling duration is displayed on a display panel of the drying device 10.
The following describes the method for predicting the drying and cooling time duration provided by the present invention, taking the implementation subject of the method for predicting the drying and cooling time duration as the drying device (embodiment one) or the server (embodiment two) as an example.
The first embodiment is as follows:
the main execution body of the method for predicting the drying and cooling time length in this embodiment is the drying device, and at this time, a trained cooling time length prediction model is preset in the drying device to predict the cooling time length. Referring to fig. 2, fig. 2 is a schematic flow chart of a method for predicting a drying and cooling time period according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110-S140.
And S110, detecting whether the drying program of the target drying equipment is finished.
In this embodiment, the temperature reduction procedure is performed after the drying procedure of the target drying device is finished, whether the drying procedure of the target drying device is finished needs to be detected in real time, if not, monitoring whether the drying procedure is finished is continued, and if so, the process goes to step S120.
The target drying apparatus in this embodiment is a drying apparatus having a cooling time period prediction function, and the drying apparatus is executing a drying program.
It should be noted that the drying program described in this embodiment does not include a temperature reduction program after the drying is finished.
And S120, if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power.
In this embodiment, if the drying procedure of the target drying apparatus is finished, it indicates that the target drying apparatus is ready to enter the cooling stage, and at this time, 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 apparatus, and the motor power of the cooler in the target drying apparatus need to be obtained.
Specifically, the current environment temperature of the target drying equipment is obtained through a temperature sensor arranged outside the target drying equipment, the weight of the object to be dried is obtained through a weight sensor arranged at the bottom of a drying cylinder of the target drying equipment, and the atmospheric pressure of the environment where the target drying equipment is located is obtained through an atmospheric pressure sensor arranged outside the target drying equipment.
The motor current and the motor power of the cooler in the target drying equipment can be motor rated current and motor rated power preset by the cooler, and also can be motor working current and motor working power measured when the cooler works stably.
In this embodiment, the cooler in the target drying device may be a blower.
S130, conducting cooling duration prediction on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the cooling duration prediction model after the motor power input training to obtain target cooling duration.
In some embodiments, the cooling duration prediction model in this embodiment is a Back Propagation (BP) neural network model, and may also be other neural networks with prediction functions, which is not limited herein.
The target cooling time in this embodiment is the required cooling time after the drying procedure of the target drying device is finished, that is, the time required between the drying procedure is finished and the drying device can be opened.
As shown in fig. 3, the BP neural network model in this embodiment includes one input layer, two hidden layers, and one output layer.
In this embodiment, the number of nodes of the input layer is 5, the input parameters respectively correspond to the ambient temperature (C), the weight (W) of the object to be dried, the atmospheric pressure (K), the motor current (I) of the temperature reducer in the drying device and the motor power (P), the number of nodes of the output layer is 1, and the output is the predicted cooling time.
Because the influence of the number of hidden nodes in each layer in the hidden layer on the network performance is large, when the number of the hidden nodes is too large, the network learning time is too long, and even convergence cannot be realized; and when the number of hidden nodes is too small, the fault tolerance of the network is poor.
Therefore, in order to obtain a proper number of hidden layer nodes and avoid the above problem, the number of nodes of each layer of the hidden layer in the BP neural network model in this embodiment is determined according to the following formula:
wherein N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
In this example, N x Is 5,N y And 1, calculating to obtain the number of nodes of each hidden layer as 3.
It should be noted that, in some embodiments, the drying apparatus provided in this embodiment provides a door opening temperature threshold adjusting function for a user, and the user may adjust the door opening temperature threshold within a preset door opening temperature threshold adjusting range, and if the user does not adjust the door opening temperature threshold, use a default door opening temperature threshold.
In some embodiments, the drying apparatus in this embodiment allows the user to adjust the door opening temperature threshold in a range of [25 °,50 ° ], and the user may adjust the door opening temperature threshold of the drying apparatus in the range (specifically, may adjust the door opening temperature threshold in a first step at intervals of 5 °).
For example, if the default door opening temperature threshold is 35 °, but the user feels the door opening wait time is too long and the user can accept a slightly higher door opening temperature, at which point the user can adjust the door opening temperature threshold upward within the door opening temperature threshold adjustment range, and if the user feels the door opening temperature is still too high, at which point the user can adjust the door opening temperature threshold downward within the door opening temperature threshold adjustment range.
When the door opening temperature threshold value is adjusted by a user, at the moment, the drying equipment acquires a door opening temperature threshold value adjusting instruction of the user, wherein the instruction carries the door opening temperature threshold value which is adjusted later, and then the door opening temperature threshold value of the target drying equipment is adjusted according to the door opening temperature threshold value adjusting instruction to obtain the adjusted door opening temperature threshold value.
When the door opening temperature threshold value can be adjusted, in some embodiments, the temperature reduction duration prediction model in this embodiment is trained for each door opening temperature threshold value in the adjustment range of the door opening temperature threshold value, so that after the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the temperature reduction duration prediction model after the motor power input training is used for temperature reduction duration prediction, the trained temperature reduction duration prediction model can obtain predicted temperature reduction durations respectively corresponding to the door opening temperature threshold values of each door in the adjustment range of the door opening temperature threshold value, and then the predicted temperature reduction duration corresponding to the adjusted door opening temperature threshold value is determined as the target temperature reduction duration.
In other embodiments, if the drying device does not have the function of adjusting the door opening temperature threshold, the corresponding temperature reduction duration prediction model only needs to be trained for the default door opening temperature threshold, the output temperature reduction duration is also the duration required by the temperature in the drying drum from the end of drying to the default door opening temperature threshold, and the door opening temperature thresholds of all gears in the door opening temperature threshold adjusting range do not need to be trained, so that the parameters of the model are reduced, and the training speed of the model is increased.
When the drying program is just finished, the temperature in the drying drum of the drying device is relatively constant and is the air outlet temperature of the hot air blower, so the temperature in the drying drum does not need to be considered when the temperature reduction duration is predicted.
As shown in fig. 4, fig. 4 is a temperature curve diagram of the drying device in the present embodiment during operation, where an abscissa is time (unit: second) and an ordinate is temperature (unit: celsius), where point a is a point corresponding to the start of the drying procedure, points C to B are drying and cooling curves, and elapsed time from point C to point B is drying and cooling duration, that is, the target cooling duration required to be predicted in the present embodiment.
And S140, displaying the target cooling time length.
Specifically, the target cooling time length is displayed on a display panel of the drying device, and more specifically, a countdown corresponding to the drying time length is displayed on the display panel, so that a user can more intuitively view the time when the user can open the door.
It should be noted that, in some embodiments, when the countdown of the target cooling time period is 0, the door opening instruction of the target drying device is triggered; and then opening the door lock of the target drying equipment according to the door opening instruction.
In some embodiments, to remind the user that the door of the drying apparatus can be opened, the drying apparatus may generate a door opening reminding alarm, and the drying apparatus may generate a preset sound, such as a "drip" sound, according to the door opening reminding alarm.
In conclusion, the method and the device combine the environmental temperature, the weight of the dried object, the atmospheric pressure, the motor current of the cooler and the motor power of the target drying equipment to predict the cooling time, the prediction result is less influenced by factors such as the environment and the working condition of the drying equipment, and the accuracy of the prediction of the cooling time is improved.
The second embodiment:
in this embodiment, an execution main body of the method for predicting drying and cooling time duration is a server, at this time, the server may provide a function of predicting cooling time duration for a plurality of drying devices, and each drying device may be in communication connection with the server.
Referring to fig. 5, fig. 5 is a flowchart illustrating a method for predicting a drying and cooling time period according to an embodiment of the present invention. As shown in fig. 5, the method includes the following steps S210-S240.
S210, detecting whether the drying program of the target drying equipment is finished.
In some embodiments, the target drying apparatus may send a drying program ending instruction to the server when the drying program ends, where S210 includes that the server continuously detects whether the drying program ending instruction of the target drying apparatus is received, and when the drying program ending instruction is received, determines that the drying program of the target drying apparatus ends, otherwise, continuously detects whether the target drying apparatus ends.
S220, if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power.
Specifically, in some embodiments, when the server receives a drying program end instruction of the target drying device, a time length influence factor acquisition instruction is sent to the target drying device, and after the drying device receives the time length influence factor acquisition instruction, time length influence factors, namely an ambient temperature of the target drying device, a weight of an object to be dried in the target drying device, an atmospheric pressure of an environment where the target drying device is located, a motor current of a cooler in the target drying device, and a motor power of the cooler in the drying device, are sent to the server, so that the server acquires the time length influence factors.
S230, conducting cooling duration prediction on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the cooling duration prediction model after the motor power input training to obtain target cooling duration.
And after receiving the duration influence factors, the service inputs the duration influence factors into a trained cooling duration prediction model to predict the cooling duration, so as to obtain the target cooling duration.
In some embodiments, if the user can adjust the door opening temperature threshold, at this time, the current door opening temperature threshold of the target drying device needs to be sent to the server, then the cooling duration prediction model in the server obtains the cooling durations respectively corresponding to the multiple door opening temperature thresholds according to the duration influence factor, and 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.
S240, sending the target cooling time length to the target drying equipment, and enabling the target drying equipment to display the target cooling time length.
After the server calculates and obtains a target cooling time length corresponding to the target drying equipment, the target cooling time length is sent to the target drying equipment, and then the target drying equipment dynamically displays a countdown corresponding to the target cooling time length; when the countdown of the target cooling time length is 0, the server or the target drying equipment triggers a door opening instruction of the target drying equipment; and then opening the door lock of the target drying equipment according to the door opening instruction.
In conclusion, the server in the scheme 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 equipment to predict the cooling time, the prediction result is less influenced by factors such as the environment and the working condition of the drying equipment, the accuracy of predicting the cooling time is improved, the server in the embodiment can provide a door opening time prediction server for a plurality of drying equipment, the drying equipment only needs to send collected data such as time influence factors to the server, the server can provide accurate door opening time prediction service for the drying equipment, the operational capacity of the drying equipment does not need to be too high, and the operational cost of the drying equipment is saved.
It should be noted that, as can be clearly understood by those skilled in the art, some specific implementation processes of the method for predicting drying and cooling time in the second embodiment may refer to the corresponding descriptions in the first embodiment, and for convenience and brevity of description, no further description is provided herein.
Example three:
the following describes in detail the training procedure of the cooling duration prediction model provided in the embodiment of the present invention, and the training procedure of the cooling duration prediction model in the embodiment of the present invention may be executed in the computer device provided in the present invention. In this embodiment, a cooling duration prediction model is taken as a BP neural network model for explanation, please refer to fig. 6, and fig. 6 is a schematic flow chart of a training method of the cooling duration prediction model according to the embodiment of the present invention. As shown in fig. 6, the method includes the following steps S310-S340.
And S310, acquiring a training sample set.
In some embodiments, since it is not clear which factors have an influence on the cooling duration in the early stage of the training, it is first necessary to determine which factors have an influence on the cooling duration before performing step S310.
Specifically, before step S310, a plurality of types of history sample data are acquired; and then screening the historical sample data by using a multiple linear regression model to obtain an influence factor, wherein the obtained influence factor is an input parameter of the BP neural network model in the embodiment, which needs to predict the cooling time duration, and the influence factor comprises the ambient temperature of the drying equipment, the weight of the dried object, the atmospheric pressure, the current of a cooler in the drying equipment and the power.
Besides the above influence factors, the various historical sample data also include parameters such as environment humidity and environment brightness, and the parameters are judged by the multiple linear regression model to have no relation to the cooling duration, so that the write parameters are removed.
Therefore, the historical sample data can be screened, influence factors influencing the cooling time can be obtained through screening, and the speed and the accuracy of model training can be improved.
And S320, performing forward propagation on a preset BP neural network model according to the training samples in the training sample set to obtain the predicted time length.
The BP neural network model in the present embodiment uses Sigmoid function
And calculating layer by layer as an activation function to obtain the predicted duration.
The structure of the BP neural network model in this embodiment is shown in fig. 3, and includes one input layer, two hidden layers, and one output layer.
The number of the nodes of the input layer is 5, the input parameters respectively correspond to the ambient temperature, the weight of the dried object, the atmospheric pressure, the current and the power of a cooler in the drying equipment, the number of the nodes of the output layer is 1, and the output is the predicted cooling time.
Because the influence of the number of hidden nodes in each layer in the hidden layer on the network performance is large, when the number of the hidden nodes is too large, the network learning time is too long, and even convergence cannot be realized; and when the number of hidden nodes is too small, the fault tolerance of the network is poor.
Therefore, in order to obtain a proper number of hidden layer nodes and avoid the above problem, the number of nodes of each layer of the hidden layer in the BP neural network in this embodiment is determined according to the following formula:
wherein, N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
In this example, N x Is 5,N y And 1, calculating to obtain the number of nodes of each hidden layer as 3.
S330, calculating a time length difference value according to the predicted time length and the actual time length corresponding to the training sample.
In this embodiment, each training sample carries a label corresponding to an actual time length, and at this time, when a predicted time length of a certain training sample is obtained, a time length difference between the predicted time length and the actual time length is calculated.
S340, performing back propagation on the preset BP neural network model according to the time length difference value to obtain the trained BP neural network model.
After the time length difference is obtained, negative feedback is performed on the input end in reverse according to the time length difference, so that the weight is adjusted by the neural network, and training is performed again according to the adjusted weight.
It should be noted that, in the present embodiment, steps S320 to S340 need to be sequentially performed for each training sample in the training sample set, so that the trained BP neural network model is obtained through repeated training of a large amount of historical data.
When the door opening temperature threshold value can be adjusted, in some embodiments, the BP neural network model in this embodiment is trained for each door opening temperature threshold value in the door opening temperature threshold value adjustment range, so that after the temperature reduction duration prediction is performed on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current, and the temperature reduction duration prediction model after the motor power input training, the trained BP neural network model obtains the predicted temperature reduction duration corresponding to each door opening temperature threshold value in each door opening temperature threshold value adjustment range, and then determines the predicted temperature reduction duration corresponding to the adjusted door opening temperature threshold value as the target temperature reduction duration.
In other embodiments, if the drying device does not have the function of adjusting the door opening temperature threshold, the BP neural network model only needs to be trained for the default door opening temperature threshold, the output cooling time period is also the time period required by the temperature in the drying drum reaching the default door opening temperature threshold from the end of drying, and the training for a plurality of door opening temperature thresholds is not needed, so that the parameters of the model are reduced, and the training speed of the model is increased.
Example four:
fig. 7 is a schematic block diagram of a device for predicting drying and cooling time according to an embodiment of the present invention. As shown in fig. 7, the present invention also provides a device for predicting a drying and cooling time period, corresponding to the above method for predicting a drying and cooling time period. The device for predicting the drying and cooling time period includes a unit for performing the above method for predicting the drying and cooling time period, and the device may be configured in a computer device such as a dryer or a server for providing a cooling time period prediction function for a dryer. Specifically, referring to fig. 7, the apparatus 700 for predicting drying and cooling time period includes an obtaining unit 701 and a processing unit 702, wherein:
the processing unit 702 is configured to detect whether a drying procedure of a target drying apparatus is finished;
the obtaining unit 701 is configured to obtain, when the drying process ends, an ambient temperature of the target drying apparatus, a weight of an object to be dried in the target drying apparatus, an atmospheric pressure of an environment in which the target drying apparatus is located, a motor current of a cooler in the target drying apparatus, and a motor power;
the processing unit 702 is further configured to predict the cooling duration of the temperature-reduction duration prediction model after the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input training, so as to obtain a target cooling duration; and displaying the target cooling time length.
In some embodiments, before the step of predicting the cooling duration by using the cooling duration prediction model trained on the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input to obtain a target cooling duration is executed, the processing unit 702 is further configured to:
acquiring a training sample set;
carrying out forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain prediction duration;
calculating a time length difference value according to the predicted time length and the actual time length corresponding to the training sample;
and performing back propagation on the preset cooling duration prediction model according to the duration difference value to obtain the trained cooling duration prediction model.
In some embodiments, the processing unit 702, before performing the step of obtaining a training sample set, is further configured to:
acquiring various historical sample data;
screening the historical sample data by using a multiple linear regression model to obtain influence factors, wherein the influence factors comprise the ambient temperature of drying equipment, the weight of the dried object, the atmospheric pressure, the current and the power of a cooler in the drying equipment;
the obtaining of the training sample set comprises:
and acquiring the training sample set according to the influence factors.
In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer, and the number of nodes of each hidden layer is determined according to the following formula:
wherein, N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
In some embodiments, before the step of predicting the cooling duration by using the cooling duration prediction model trained on the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input to obtain a target cooling duration is executed, the processing unit 702 is further configured to:
acquiring a door opening temperature threshold value adjusting instruction of a user;
adjusting the door opening temperature threshold of the target drying equipment according to the door opening temperature threshold adjusting instruction to obtain an adjusted door opening temperature threshold;
the step of predicting the cooling time length of the cooling time length prediction model after the input training of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power to obtain the target cooling time length comprises the following steps:
predicting the cooling time length of a cooling time length prediction model after the environmental temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training to obtain predicted cooling time lengths corresponding to a plurality of door opening temperature thresholds respectively;
and determining the predicted cooling time corresponding to the adjusted door opening temperature threshold as the target cooling time.
In some embodiments, after the step of predicting the cooling duration by the processing unit 702 after executing the step of training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power input to obtain a target cooling duration, the processing unit is further configured to:
dynamically displaying countdown corresponding to the target cooling duration;
when the countdown of the target cooling time length is 0, triggering a door opening instruction of the target drying equipment;
and opening the door lock of the target drying equipment according to the door opening instruction.
In some embodiments, after the step of opening the door lock of the target drying apparatus according to the door opening instruction is executed, the processing unit 702 is further configured to:
and generating a door opening reminding alarm.
It should be noted that, as can be clearly understood by those skilled in the art, the concrete implementation processes of the aforementioned device for predicting drying and cooling time and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.
Example five:
the device for predicting the drying and cooling time period may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in fig. 8.
Referring to fig. 8, fig. 8 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 800 may be a control device in the drying device, or may be a server that provides a function of predicting a cooling time period for the drying device, where the server may be an independent server, or a server cluster formed by multiple servers.
Referring to fig. 8, the computer device 800 includes a processor 802, memory and network interface 805 connected by a system bus 801, wherein the memory may include non-volatile storage media 803 and internal memory 804.
The non-volatile storage medium 803 may store an operating system 8031 and computer programs 8032. The computer program 8032 includes program instructions that, when executed, cause the processor 802 to perform a method for predicting a drying and cooling time period.
The processor 802 is used to provide computing and control capabilities to support the operation of the overall computer device 800.
The internal memory 804 provides an environment for the operation of the computer program 8032 in the non-volatile storage medium 803, and when the computer program 8032 is executed by the processor 802, the processor 802 may be enabled to execute a method for predicting a drying and cooling time period.
The network interface 805 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 8 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 800 to which aspects of the present invention may be applied, and that a particular computing device 800 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 802 is configured to run a computer program 8032 stored in the memory to implement the steps of:
detecting whether a drying program of the target drying equipment is finished or not;
if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power;
and predicting the cooling time length of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the cooling time length prediction model after the motor power input training to obtain the target cooling time length.
In some embodiments, before the step of predicting the cooling duration by the processor 802 after training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input, to obtain the target cooling duration, the following steps are further implemented:
acquiring a training sample set;
carrying out forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain prediction duration;
calculating a time length difference value according to the predicted time length and the actual time length corresponding to the training sample;
and performing back propagation on the preset cooling duration prediction model according to the duration difference value to obtain the trained cooling duration prediction model.
In some embodiments, the processor 802 further performs the following steps before performing the step of obtaining the training sample set:
obtaining various historical sample data;
screening the historical sample data by using a multiple linear regression model to obtain influence factors, wherein the influence factors comprise the ambient temperature of drying equipment, the weight of the dried object, the atmospheric pressure, and the current and the power of a cooler in the drying equipment;
the obtaining of the training sample set comprises:
and acquiring the training sample set according to the influence factors.
In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer, and the number of nodes of each hidden layer is determined according to the following formula:
wherein N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
In some embodiments, before the step of predicting the cooling duration by the processor 802 after training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input, to obtain the target cooling duration, the following steps are further implemented:
acquiring a door opening temperature threshold value adjusting instruction of a user;
adjusting the door opening temperature threshold of the target drying equipment according to the door opening temperature threshold adjusting instruction to obtain an adjusted door opening temperature threshold;
the step of predicting the cooling time length of the cooling time length prediction model after the input training of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power to obtain the target cooling time length comprises the following steps:
predicting the cooling time length of a cooling time length prediction model after the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training to obtain predicted cooling time lengths corresponding to a plurality of door opening temperature thresholds respectively;
and determining the predicted cooling time corresponding to the adjusted door opening temperature threshold as the target cooling time.
In some embodiments, after the step of predicting the cooling duration by the processor 802 after training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the motor power input, to obtain a target cooling duration, the following steps are further implemented:
dynamically displaying countdown corresponding to the target cooling duration;
when the countdown of the target cooling time length is 0, triggering a door opening instruction of the target drying equipment;
and opening the door lock of the target drying equipment according to the door opening instruction.
In some embodiments, after the step of unlocking the door of the target drying apparatus according to the door opening instruction is implemented, the processor 802 further implements the following steps:
and generating a door opening reminding alarm.
It should be understood that in 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. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Example six:
the invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program comprises program instructions. The program instructions, when executed by the processor, cause the processor to perform the steps of:
detecting whether a drying program of the target drying equipment is finished or not;
if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, and the motor current and the motor power of a cooler in the target drying equipment;
and predicting the cooling time length of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the cooling time length prediction model after the motor power input training to obtain the target cooling time length.
In some embodiments, before the step of predicting the cooling duration by the cooling duration prediction model after training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power input to obtain the target cooling duration by the processor executing the program instructions, the following steps are further implemented:
acquiring a training sample set;
carrying out forward propagation on a preset cooling duration prediction model according to the training samples in the training sample set to obtain prediction duration;
calculating a time length difference value according to the predicted time length and the actual time length corresponding to the training sample;
and performing back propagation on the preset cooling duration prediction model according to the duration difference value to obtain the trained cooling duration prediction model.
In some embodiments, the processor, prior to executing the program instructions to implement the step of obtaining a training sample set, further implements the steps of:
acquiring various historical sample data;
screening the historical sample data by using a multiple linear regression model to obtain influence factors, wherein the influence factors comprise the ambient temperature of drying equipment, the weight of the dried object, the atmospheric pressure, and the current and the power of a cooler in the drying equipment;
the obtaining of the training sample set comprises:
and acquiring the training sample set according to the influence factors.
In some embodiments, the cooling duration prediction model includes an input layer, a hidden layer, and an output layer, and the number of nodes of each hidden layer is determined according to the following formula:
wherein, N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
In some embodiments, before the step of predicting the cooling duration by the cooling duration prediction model after training the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power input to obtain the target cooling duration by the processor executing the program instructions, the following steps are further implemented:
acquiring a door opening temperature threshold value adjusting instruction of a user;
adjusting the door opening temperature threshold of the target drying equipment according to the door opening temperature threshold adjusting instruction to obtain an adjusted door opening temperature threshold;
the predicting the cooling time length of the temperature reduction time length prediction model after the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training is carried out to obtain the target cooling time length, and the predicting method comprises the following steps:
predicting the cooling time length of a cooling time length prediction model after the environmental temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training to obtain predicted cooling time lengths corresponding to a plurality of door opening temperature thresholds respectively;
and determining the predicted cooling time corresponding to the adjusted door opening temperature threshold as the target cooling time.
In some embodiments, after the step of predicting the cooling duration by the cooling duration prediction model trained by the processor according to the environmental temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the motor power input to obtain the target cooling duration is implemented by executing the program instructions, the following steps are further implemented:
dynamically displaying countdown corresponding to the target cooling duration;
when the countdown of the target cooling time length is 0, triggering a door opening instruction of the target drying equipment;
and opening the door lock of the target drying equipment according to the door opening instruction.
In some embodiments, after the processor executes the program instructions to implement the step of opening the door lock of the target drying apparatus according to the door opening instruction, the processor further implements the steps of:
and generating a door opening reminding alarm.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A prediction method of drying and cooling time duration is characterized by comprising the following steps:
detecting whether a drying program of the target drying equipment is finished or not;
if the drying program is finished, acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, and the motor current and the motor power of a cooler in the target drying equipment;
and predicting the cooling time length of the temperature-reducing time length prediction model after the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training to obtain the target cooling time length.
2. The method according to claim 1, wherein before the temperature-decreasing duration prediction is performed on the environment temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the temperature-decreasing duration prediction model after the motor power input training, and a target temperature-decreasing duration is obtained, the method further comprises:
acquiring a training sample set;
carrying out forward propagation on a preset cooling duration prediction model according to training samples in the training sample set to obtain a prediction duration;
calculating a time length difference value according to the predicted time length and the actual time length corresponding to the training sample;
and performing back propagation on the preset cooling duration prediction model according to the duration difference value to obtain the trained cooling duration prediction model.
3. The method of claim 2, wherein prior to obtaining the training sample set, the method further comprises:
obtaining various historical sample data;
screening the historical sample data by using a multiple linear regression model to obtain influence factors, wherein the influence factors comprise the ambient temperature of drying equipment, the weight of the dried object, the atmospheric pressure, the current and the power of a cooler in the drying equipment;
the acquiring of the training sample set comprises:
and acquiring the training sample set according to the influence factors.
4. The method according to claim 2, wherein the cooling duration prediction model comprises an input layer, a hidden layer and an output layer, and the number of nodes of the hidden layer in each layer is determined according to the following formula:
wherein, N is the node number of each layer of the hidden layer, N x Is the number of nodes of the input layer, N y The number of the nodes of the output layer.
5. The method according to claim 1, wherein before the temperature-decreasing duration prediction is performed on the environment temperature, the weight of the object to be dried, the atmospheric pressure, the motor current and the temperature-decreasing duration prediction model after the motor power input training, and a target temperature-decreasing duration is obtained, the method further comprises:
acquiring a door opening temperature threshold value adjusting instruction of a user;
adjusting the door opening temperature threshold of the target drying equipment according to the door opening temperature threshold adjusting instruction to obtain an adjusted door opening temperature threshold;
the step of predicting the cooling time length of the cooling time length prediction model after the input training of the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power to obtain the target cooling time length comprises the following steps:
predicting the cooling time length of a cooling time length prediction model after the environmental temperature, the weight of the dried object, the atmospheric pressure, the motor current and the motor power input training to obtain predicted cooling time lengths corresponding to a plurality of door opening temperature thresholds respectively;
and determining the predicted cooling time corresponding to the adjusted door opening temperature threshold as the target cooling time.
6. The method according to any one of claims 1 to 5, wherein after the temperature-reducing duration prediction is performed on the environment temperature, the weight of the dried object, the atmospheric pressure, the motor current and the temperature-reducing duration prediction model trained by the motor power input, and a target temperature-reducing duration is obtained, the method further comprises:
dynamically displaying countdown corresponding to the target cooling duration;
when the countdown of the target cooling time length is 0, triggering a door opening instruction of the target drying equipment;
and opening the door lock of the target drying equipment according to the door opening instruction.
7. The method of claim 6, wherein after the door lock of the target drying apparatus is opened according to the door opening instruction, the method further comprises:
and generating a door opening reminding alarm.
8. The utility model provides a prediction unit that stoving cooling is long, its characterized in that includes acquisition unit and processing unit, wherein:
the processing unit is used for detecting whether the drying program of the target drying equipment is finished or not;
the acquiring unit is used for acquiring the ambient temperature of the target drying equipment, the weight of the dried object in the target drying equipment, the atmospheric pressure of the environment where the target drying equipment is located, the motor current of a cooler in the target drying equipment and the motor power when the drying program is finished;
the processing unit is further configured to predict the cooling duration of the ambient temperature, the weight of the object to be dried, the atmospheric pressure, the motor current, and the cooling duration prediction model after the motor power input training, so as to obtain a target cooling duration.
9. A computer arrangement, characterized in that the computer arrangement comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program, carries out the method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed by a processor, implement the method according to any one of claims 1-7.
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| CN115969152A (en) * | 2022-12-31 | 2023-04-18 | 云南钜盛电器科技有限公司 | Air blowing equipment inspection control system based on airflow multiplication mode |
| CN115969152B (en) * | 2022-12-31 | 2023-06-30 | 云南钜盛电器科技有限公司 | Air blowing equipment inspection control system based on airflow multiplication mode |
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