CN116066977A - Method and device for training air conditioner operation parameter prediction model, electronic equipment and storage medium - Google Patents
Method and device for training air conditioner operation parameter prediction model, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of air conditioners, and discloses a method for training an air conditioner operation parameter prediction model, which comprises the following steps: inputting the first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model; acquiring a plurality of second air conditioner operation data samples with second labels according to a reference air conditioner operation parameter prediction model; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner; and performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model, so that the accuracy of the air conditioner operation parameter prediction model for predicting the air conditioner operation parameters is improved. The application also discloses a device for training the air conditioner operation parameter prediction model, electronic equipment and a storage medium.
Description
Technical Field
The present application relates to the field of air conditioning technologies, for example, to a method and apparatus for training an air conditioning operation parameter prediction model, an electronic device, and a storage medium.
Background
At present, along with the wide application of air conditioners in daily life and work, people pay more attention to the running state of the air conditioner, and in order to avoid the damage of machines caused by the fact that the air conditioner is in a fault running state for a long time, the running parameters of the air conditioner need to be known in real time. Compared with the time and effort consumed by using measuring tools such as a sensor to directly measure the operation parameters of the air conditioner, the air conditioner operation parameter prediction model is more convenient and faster to use.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
in the prior art, when the air conditioner operation parameter prediction model is obtained, all sample data are input into a preset neural network model for training at one time, so that the air conditioner operation parameter prediction model is obtained. However, the time of the air conditioner in the normal running state is far longer than the time of the air conditioner in the abnormal running state in the running process of the air conditioner, so that the number of data samples in the fault running process of the air conditioner in the actual training process of the model is far smaller than that in the normal running process of the air conditioner, and the trained model is difficult to accurately predict the parameters in the abnormal running process of the air conditioner.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
The embodiment of the disclosure provides a method and a device for training an air conditioner operation parameter prediction model, electronic equipment and a storage medium, so that the accuracy of the air conditioner operation parameter prediction model in predicting air conditioner operation parameters can be improved.
In some embodiments, the method for training an air conditioner operating parameter prediction model includes: acquiring a first air conditioner operation data sample with a first tag, wherein the first tag is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner; inputting the first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model; acquiring a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, wherein the second labels are parameters used for representing abnormal operation of the air conditioner or parameters used for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner; and performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
In some embodiments, the apparatus for training an air conditioner operating parameter prediction model comprises: the first acquisition module is configured to acquire a first air conditioner operation data sample with a first label, wherein the first label is a parameter for representing abnormal operation of the air conditioner or a parameter for representing normal operation of the air conditioner; the training module is configured to input the first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model; the second acquisition module is configured to acquire a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, wherein the second labels are parameters used for representing abnormal operation of the air conditioner or parameters used for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner; and the increment learning module is configured to perform increment learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
In some embodiments, the apparatus for training an air conditioner operating parameter prediction model comprises: a processor and a memory storing program instructions, the processor being configured to perform the method for training an air conditioner operating parameter prediction model as described above when the program instructions are executed.
In some embodiments, the electronic device includes means for training an air conditioner operating parameter prediction model as described above.
In some embodiments, the storage medium stores program instructions that, when executed, perform a method for training an air conditioner operating parameter prediction model as described above.
The method and device for training the air conditioner operation parameter prediction model, the electronic equipment and the storage medium provided by the embodiment of the disclosure can realize the following technical effects: the method comprises the steps of inputting a first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model, and obtaining a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, wherein the second labels are parameters for representing abnormal operation of an air conditioner or parameters for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner; and performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain the air conditioner operation parameter prediction model. In this way, the first air conditioner operation data sample is input into the preset neural network model for training to obtain the reference air conditioner operation parameter prediction model, the second air conditioner operation data sample with the parameter representing the abnormal operation of the air conditioner more than the parameter representing the normal operation of the air conditioner is utilized for carrying out incremental learning on the reference air conditioner operation parameter prediction model, and the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameter of the air conditioner more accurately, so that the accuracy of the air conditioner operation parameter prediction by the air conditioner operation parameter prediction model can be improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:
FIG. 1 is a schematic illustration of a method for training an air conditioner operating parameter prediction model provided by an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of another method for training an air conditioner operating parameter prediction model provided by an embodiment of the present disclosure;
FIG. 3 is a schematic illustration of another method for training an air conditioner operating parameter prediction model provided by an embodiment of the present disclosure;
FIG. 4 is a schematic illustration of another method for training an air conditioner operating parameter prediction model provided by an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of an apparatus for training an air conditioner operating parameter prediction model provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of another apparatus for training an air conditioner operation parameter prediction model according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the parameters so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
The term "corresponding" may refer to an association or binding relationship, and the correspondence between a and B refers to an association or binding relationship between a and B.
Referring to fig. 1, an embodiment of the present disclosure provides a method for training an air conditioner operation parameter prediction model, including:
step S101, a first air conditioner operation data sample with a first label is obtained, wherein the first label is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner;
step S102, inputting a first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
step S103, a plurality of second air conditioner operation data samples with second labels are obtained according to a reference air conditioner operation parameter prediction model, wherein the second labels are parameters used for representing abnormal operation of the air conditioner or parameters used for representing normal operation of the air conditioner;
And step S104, performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain the air conditioner operation parameter prediction model.
According to the method for training the air conditioner operation parameter prediction model, the first air conditioner operation data sample is input into the preset neural network model for training, the reference air conditioner operation parameter prediction model is obtained, the second air conditioner operation data sample with the parameters for representing abnormal operation of the air conditioner and the parameters for representing normal operation of the air conditioner are used for carrying out incremental learning on the reference air conditioner operation parameter prediction model, the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameters of the air conditioner more accurately, and accuracy of the air conditioner operation parameter prediction model for predicting the operation parameters of the air conditioner can be improved.
Optionally, the neural network model includes: convolutional neural networks, and the like.
In some embodiments, the first air conditioner operation data sample is a condenser temperature and a condenser pressure of the air conditioner at a first preset time. The first label carried by the first air conditioner operation data sample is the pressure of the compressor exhaust port, the compressor exhaust temperature, the pressure of the compressor air suction port and the chilled water temperature corresponding to a first preset time, and if the air conditioner is in an abnormal operation state at the first preset time, the pressure of the compressor exhaust port, the compressor exhaust temperature, the pressure of the compressor air suction port and the chilled water temperature at the first preset time are parameters representing the abnormal operation of the air conditioner; and at a first preset moment, if the air conditioner is in a normal running state, the pressure of the air outlet of the compressor, the air outlet temperature of the compressor, the pressure of the air suction port of the compressor and the temperature of chilled water at the first preset moment are parameters representing the normal running of the air conditioner. For example, the first preset time is 8:00, 8:01, 8:02, etc., the condenser temperature and the condenser pressure of the air conditioner are obtained at each first preset time, and the pressure of the air outlet of the compressor, the air outlet temperature of the compressor, the pressure of the air suction port of the compressor and the chilled water temperature are obtained, so that a plurality of first air conditioner operation data samples with first labels are obtained; and inputting a plurality of first air conditioner operation data samples with first labels into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model.
Optionally, obtaining a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model includes: acquiring the accuracy of a reference air conditioner operation parameter prediction model; and under the condition that the accuracy rate is larger than a preset threshold value, acquiring a plurality of second air conditioner operation data samples with second labels. In this way, under the condition that the accuracy is larger than a preset threshold, training of the reference air conditioner operation parameter prediction model is completed, a plurality of second air conditioner operation data samples with second labels are obtained, the reference air conditioner operation parameter prediction model is subjected to incremental learning by utilizing the second air conditioner operation data samples, the air conditioner operation parameter prediction model is obtained, and the accuracy of the air conditioner operation parameter prediction model for predicting the air conditioner operation parameters is improved.
Optionally, obtaining the accuracy of the reference air conditioner operation parameter prediction model includes: acquiring a plurality of air conditioner operation data; obtaining a measurement result corresponding to the operation data of each air conditioner; inputting the operation data of each air conditioner into a reference air conditioner operation parameter prediction model to obtain a prediction result corresponding to the operation data of each air conditioner; respectively comparing the prediction result of each air conditioner operation data with the measurement result of each air conditioner operation data to obtain a plurality of comparison results; the comparison result is used for representing whether the difference value between the prediction result and the measurement result is within a preset range; and obtaining the accuracy of the reference air conditioner operation parameter prediction model according to each comparison result. In this way, the accuracy of the reference air conditioner operation parameter prediction model can be obtained more accurately according to the comparison result between the prediction result of each air conditioner operation data and the measurement result of each air conditioner operation data.
Optionally, the air conditioner operation data is a condenser temperature and a condenser pressure of the air conditioner at a second preset time. And respectively acquiring the condenser temperature and the condenser pressure of the air conditioner at different second preset moments, namely acquiring a plurality of air conditioner operation data.
Optionally, the measurement results corresponding to the air conditioner operation data are: and the actual measured values of the pressure of the compressor discharge outlet, the compressor discharge temperature, the pressure of the compressor suction inlet and the chilled water temperature are obtained at corresponding second preset moments.
Optionally, the prediction result corresponding to the air conditioner operation data is: and inputting a plurality of air conditioner operation data into output values of the pressure of the exhaust port of the compressor, the exhaust temperature of the compressor, the pressure of the air suction port of the compressor and the temperature of chilled water, which are obtained by referencing to an air conditioner operation parameter prediction model.
Optionally, obtaining the accuracy of the reference air conditioner operation parameter prediction model according to each comparison result includes: under the condition that the comparison result is that the difference value between the prediction result of the air conditioner operation data and the measurement result of the corresponding air conditioner operation data is within a preset range, determining that the prediction of the reference air conditioner operation parameter prediction model is accurate; and dividing the number of times of accurate prediction of the reference air conditioner operation parameter prediction model by the total number of times of prediction of the reference air conditioner operation parameter prediction model to obtain the accuracy of the reference air conditioner operation parameter prediction model.
As shown in connection with fig. 2, another method for training an air conditioner operation parameter prediction model is provided according to an embodiment of the present disclosure, including:
step S201, a first air conditioner operation data sample with a first label is obtained, wherein the first label is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner;
step S202, inputting a first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
step S203, a plurality of air conditioner operation data are acquired;
step S204, obtaining measurement results corresponding to the operation data of each air conditioner;
step S205, inputting each air conditioner operation data into a reference air conditioner operation parameter prediction model to obtain a prediction result corresponding to each air conditioner operation data;
step S206, respectively comparing the predicted result of each air conditioner operation data with the measured result of each air conditioner operation data to obtain a plurality of comparison results; the comparison result is used for representing whether the difference value between the prediction result and the measurement result is within a preset range;
step S207, obtaining the accuracy of a reference air conditioner operation parameter prediction model according to each comparison result;
step S208, acquiring a plurality of second air conditioner operation data samples with second labels under the condition that the accuracy rate is larger than a preset threshold value; the second label is a parameter for representing abnormal operation of the air conditioner or a parameter for representing normal operation of the air conditioner;
Step S209, performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
In this way, a reference air conditioner operation parameter prediction model is obtained according to the first air conditioner operation data sample, prediction results of a plurality of air conditioner operation data are obtained according to the reference air conditioner operation parameter prediction model, and accordingly, the prediction results of all the air conditioner operation data and the comparison results of measurement results of all the air conditioner operation data are obtained, a plurality of second air conditioner operation data samples with second labels are obtained according to all the comparison results, the reference air conditioner operation parameter prediction model is subjected to incremental learning by using the second air conditioner operation data sample with parameters representing abnormal operation data of the air conditioner more than parameters representing normal operation of the air conditioner, and the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameters of the air conditioner more accurately, so that the accuracy of predicting the operation parameters of the air conditioner by using the air conditioner operation parameter prediction model can be improved.
Optionally, obtaining a plurality of second air conditioner operation data samples with second tags includes: determining the total sample number of second air conditioner operation data samples with second labels according to the accuracy of the reference air conditioner operation parameter prediction model, and determining the second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner; and obtaining a plurality of second air conditioner operation data samples with second labels according to the second air conditioner operation data sample number with parameters representing the abnormal operation of the air conditioner and the total sample number. Optionally, determining the total sample number of the second air conditioner operation data samples with the second tag according to the accuracy of the reference air conditioner operation parameter prediction model includes: obtaining an increment proportion according to the accuracy of the reference air conditioner operation parameter prediction model; and calculating by using the increment proportion through a first preset algorithm to obtain the total sample number of the second air conditioner operation data samples with the second label. In this way, the increment proportion is obtained according to the accuracy of the reference air conditioner operation parameter prediction model, the total sample number of the second air conditioner operation data samples with the second labels is obtained according to the increment proportion, the reference air conditioner operation parameter prediction model is subjected to increment learning by using the second air conditioner operation data samples with the total sample number, and the accuracy of the air conditioner operation parameter prediction model for predicting the air conditioner operation parameters can be improved.
Optionally, the accuracy of the reference air conditioner operating parameter prediction model is proportional to the increment ratio.
Optionally, obtaining a total sample number of second air conditioner operation data samples with a second label by calculating f=e (1-N); wherein F is the total sample number of the second air-conditioning operation data samples with the second label, E is the total sample number of the first air-conditioning operation data samples with the first label, and N is the increment proportion.
Optionally, the accuracy includes a fault accuracy and a normal accuracy, and determining the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner includes: obtaining the fault accuracy of a reference air conditioner operation parameter prediction model; acquiring the failure rate of the reference air conditioner operation parameter prediction model according to the failure accuracy rate of the reference air conditioner operation parameter prediction model; and calculating by using a failure rate of the reference air conditioner operation parameter prediction model through a second preset algorithm to obtain a second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner. In this way, the failure rate of the reference air conditioner operation parameter prediction model is obtained according to the failure accuracy rate of the reference air conditioner operation parameter prediction model, the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner is obtained according to the failure rate, the second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner are utilized to conduct incremental learning on the reference air conditioner operation parameter prediction model, the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameters of the air conditioner more accurately, and accordingly the accuracy rate of predicting the operation parameters of the air conditioner by the air conditioner operation parameter prediction model can be improved.
Optionally, the difference between the measurement result corresponding to the air conditioner operation data and the prediction result corresponding to the air conditioner operation data is within a preset range, and the measurement result corresponding to the air conditioner operation data is measured when the air conditioner is in normal operation, that is, the air conditioner does not report a fault, and then the air conditioner normal operation parameters are predicted correctly by referring to the air conditioner operation data prediction model.
Optionally, the difference between the measurement result corresponding to the air conditioner operation data and the prediction result corresponding to the air conditioner operation data is within a preset range, and the measurement result corresponding to the air conditioner operation data is measured when the air conditioner is in abnormal operation, namely, the air conditioner fails, and the air conditioner abnormal operation parameters are predicted to be correct by referring to the air conditioner operation data prediction model.
Optionally, marking a predicted result within a preset parameter fault range to predict an air conditioner fault; the method for marking and measuring the air conditioner faults according to the measurement result when the air conditioner faults are detected, and obtaining the fault accuracy of the reference air conditioner operation parameter prediction model comprises the following steps: acquiring a first number of prediction results marked with predicted air conditioner faults; obtaining a second number of prediction results for predicting air conditioner faults, wherein the difference value between the prediction results and the measurement results is in a preset range; acquiring a third number marked with measurement results for measuring the air conditioner faults; and obtaining the fault accuracy of the reference air conditioner operation parameter prediction model according to the first quantity, the second quantity and the third quantity.
Alternatively, by calculating m=n b ÷(N a +N c ) Obtaining the fault accuracy of a reference air conditioner operation parameter prediction model; wherein M is the failure accuracy of a reference air conditioner operation parameter prediction model, and N a To mark a first number of predicted results of predicting air conditioning faults, N b In order that the difference between the predicted result and the measured result is within the preset range and marked with the second number of the predicted results for predicting the air conditioner failure, N c A third number of measurement results for measuring air conditioning faults is marked.
Optionally, obtaining the failure rate of the reference air conditioner operation parameter prediction model by calculating p=1 to M; wherein P is failure rate of the reference air conditioner operation parameter prediction model, and M is failure accuracy rate of the reference air conditioner operation parameter prediction model.
Alternatively, by calculating D 2 ＝D 1 * P, obtaining the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner; wherein D is 2 For the second air-conditioning operation data sample number with the parameter representing the abnormal operation of the air conditioner, D 1 And P is the failure rate of the reference air conditioner operation parameter prediction model for the number of first air conditioner operation data samples with parameters representing the abnormal operation of the air conditioner.
Alternatively, by calculating g=f-D 2 Obtaining a second air conditioner operation data sample number with parameters representing normal operation of the air conditioner, wherein G is the second air conditioner operation data sample number with parameters representing normal operation of the air conditioner, and D 2 For the second air-conditioning operation data sample number with the parameter representing the abnormal operation of the air conditioner, F is the total sample number of the second air-conditioning operation data sample with the second label.
Optionally, performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model, including: and carrying out data fusion on the second air conditioner operation data sample and the first air conditioner operation data sample to generate an incremental learning sample, inputting the incremental learning sample into a reference air conditioner operation parameter prediction model to carry out incremental learning, and obtaining the air conditioner operation parameter prediction model. Therefore, the air conditioner operation data sample for training the air conditioner operation parameter prediction model is increased, and the accuracy of the air conditioner operation parameter prediction model for predicting the air conditioner operation parameter is improved.
Referring to fig. 3, another method for training an air conditioner operation parameter prediction model is provided according to an embodiment of the present disclosure, including:
Step S301, a first air conditioner operation data sample with a first label is obtained, wherein the first label is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner;
step S302, inputting a first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
step S303, obtaining the accuracy of a reference air conditioner operation parameter prediction model;
step S304, determining the total sample number of the second air conditioner operation data samples with the second labels according to the accuracy rate under the condition that the accuracy rate is larger than a preset threshold value;
step S305, determining the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner;
step S306, a plurality of second air conditioner operation data samples with second labels are obtained according to the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner and the total number of samples, wherein the second labels are parameters representing abnormal operation of the air conditioner or parameters representing normal operation of the air conditioner;
step S307, performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
In this way, a reference air conditioner operation parameter prediction model is obtained according to the first air conditioner operation data sample, prediction results of a plurality of air conditioner operation data are obtained according to the reference air conditioner operation parameter prediction model, so that accuracy of the reference air conditioner operation parameter prediction model is obtained, under the condition that the accuracy is larger than a preset threshold, training of the reference air conditioner operation parameter prediction model is completed, a plurality of second air conditioner operation data samples with second labels are obtained, the second air conditioner operation data sample is utilized to conduct incremental learning on the reference air conditioner operation parameter prediction model, the air conditioner operation parameter prediction model is obtained, and accuracy of the air conditioner operation parameter prediction model for predicting air conditioner operation parameters is improved.
Optionally, after obtaining the plurality of comparison results, the method further includes: correcting the predicted result corresponding to the air conditioner operation data under the condition that the difference value between the predicted result corresponding to the air conditioner operation data and the measured result corresponding to the air conditioner operation data is not in a preset range; and inputting the corrected prediction result and the air conditioner operation data corresponding to the corrected prediction result into a reference air conditioner operation parameter prediction model for training.
Optionally, correcting the prediction result corresponding to the air conditioner operation data includes: and replacing the predicted result corresponding to the air conditioner operation data with the measured result corresponding to the air conditioner operation data. In this way, under the condition that the difference value between the predicted result corresponding to the air-conditioning operation data and the measured result corresponding to the air-conditioning operation data is not in the preset range, the predicted result corresponding to the air-conditioning operation data is replaced by the measured result corresponding to the air-conditioning operation data, the replaced predicted result and the air-conditioning operation data corresponding to the replaced predicted result are input into the reference air-conditioning operation parameter prediction model to be trained again, and the accuracy of the reference air-conditioning prediction model is improved.
As shown in connection with fig. 4, another method for training an air conditioner operating parameter prediction model is provided according to an embodiment of the present disclosure, including:
step S401, a first air conditioner operation data sample with a first label is obtained, wherein the first label is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner;
step S402, inputting a first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
step S403, acquiring a plurality of air conditioner operation data;
step S404, obtaining a measurement result corresponding to the air conditioner operation data;
step S405, inputting each air conditioner operation data into a reference air conditioner operation parameter prediction model to obtain a prediction result corresponding to each air conditioner operation data;
step S406, judging whether the difference value between the predicted result corresponding to the air conditioner operation data and the measured result corresponding to the air conditioner operation data is within a preset range; executing step S407 when the difference between the predicted result corresponding to the air-conditioning operation data and the measured result corresponding to the air-conditioning operation data is not within the preset range; executing step S409 when the difference between the predicted result corresponding to the air-conditioning operation data and the measured result corresponding to the air-conditioning operation data is within the preset range;
Step S407, replacing the predicted result corresponding to the air conditioner operation data with the measured result corresponding to the air conditioner operation data; step 408 is then performed;
step S408, inputting the replaced prediction result and the air conditioner operation data corresponding to the replaced prediction result into a reference air conditioner operation parameter prediction model for training; then returns to execute step S405;
step S409, obtaining the accuracy of the reference air conditioner operation parameter prediction model according to each comparison result; then step S410 is performed;
step S410, acquiring a plurality of second air conditioner operation data samples with second labels, wherein the second labels are parameters for representing abnormal operation of the air conditioner or parameters for representing normal operation of the air conditioner under the condition that the accuracy rate is larger than a preset threshold value;
step S411, performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
In this way, a reference air conditioner operation parameter prediction model is obtained according to the first air conditioner operation data sample, prediction results of a plurality of air conditioner operation data are obtained according to the reference air conditioner operation parameter prediction model, and under the condition that the difference value between the prediction result corresponding to the air conditioner operation data and the measurement result corresponding to the air conditioner operation data is not in a preset range, the reference air conditioner operation parameter prediction model is trained again according to the measurement result corresponding to the air conditioner operation data; under the condition that the difference value of the prediction result corresponding to the air conditioner operation data and the measurement result corresponding to the air conditioner operation data is in a preset range, obtaining the accuracy of a reference air conditioner operation parameter prediction model according to each comparison result, obtaining a plurality of second air conditioner operation data samples with second labels according to the accuracy, and performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data samples with parameters representing abnormal operation data of the air conditioner more than parameters representing normal operation of the air conditioner, wherein the obtained air conditioner operation parameter prediction model can predict the abnormal operation parameters of the air conditioner more accurately, so that the accuracy of predicting the operation parameters of the air conditioner by using the air conditioner operation parameter prediction model can be improved.
In some embodiments, in a time period T, acquiring a plurality of air conditioners for a plurality of times of air conditioner operation data, storing the acquired air conditioner operation data in a database S, screening a first preset number of air conditioner operation data samples a in an abnormal operation state of the air conditioners and a second preset number of air conditioner operation data samples B in a normal operation state of the air conditioners in the database, synthesizing the a and the B into a first air conditioner operation data sample C with a first tag, inputting the first air conditioner operation data sample C into a preset neural network model for training, and obtaining a reference air conditioner operation parameter prediction model. Acquiring the accuracy of a reference air conditioner operation parameter prediction model; if the accuracy is greater than a preset threshold, for example 90%, acquiring an increment proportion according to the accuracy, acquiring the total sample number of second air conditioner operation data samples with second labels according to the increment proportion, and determining the second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner; and obtaining a plurality of second air conditioner operation data samples D with second labels according to the number of second air conditioner operation data samples with parameters representing abnormal operation of the air conditioner and the total number of samples, and performing incremental learning on a reference air conditioner operation parameter prediction model by utilizing the second air conditioner operation data samples D and the first air conditioner operation data samples C to obtain an air conditioner operation parameter prediction model. In this way, the increment proportion is obtained by referring to the accuracy of the air conditioner operation parameter prediction model, the total sample number of the second air conditioner operation data samples with the second labels is obtained according to the increment proportion, so that the second air conditioner operation data samples with the second labels are obtained in the total sample number, the second air conditioner operation parameter prediction model is subjected to increment learning by utilizing the second air conditioner operation data samples and the first air conditioner operation data samples, and the obtained air conditioner operation parameter prediction model improves the accuracy of the air conditioner operation parameter prediction model for predicting the air conditioner operation parameters.
Referring to fig. 5, an embodiment of the disclosure provides an apparatus for training an air conditioner operation parameter prediction model, which includes a first obtaining module 501, a training module 502, a second obtaining module 503, and an incremental learning module 504. The first obtaining module 501 is configured to obtain a first air conditioner operation data sample with a first tag, where the first tag is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner; the training module 502 is configured to input a first air conditioner operation data sample into a preset neural network model for training, and obtain a reference air conditioner operation parameter prediction model; the second obtaining module 503 is configured to obtain a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, where the second labels are parameters for representing abnormal operation of the air conditioner or parameters for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner; the incremental learning module 504 is configured to perform incremental learning on the reference air conditioner operating parameter prediction model using the second air conditioner operating data sample and the first air conditioner operating data sample to obtain an air conditioner operating parameter prediction model.
According to the device for training the air conditioner operation parameter prediction model, the first air conditioner operation data sample is input into the preset neural network model for training, the reference air conditioner operation parameter prediction model is obtained, the second air conditioner operation data sample with the parameters for representing abnormal operation of the air conditioner and the parameters for representing normal operation of the air conditioner are used for carrying out incremental learning on the reference air conditioner operation parameter prediction model, the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameters of the air conditioner more accurately, and accordingly accuracy of predicting the operation parameters of the air conditioner by the air conditioner operation parameter prediction model can be improved.
Optionally, the third obtaining model is configured to obtain a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model by: acquiring the accuracy of a reference air conditioner operation parameter prediction model; and under the condition that the accuracy rate is larger than a preset threshold value, acquiring a plurality of second air conditioner operation data samples with second labels.
Optionally, the third acquisition model is configured to acquire the accuracy of the reference air conditioner operation parameter prediction model by: acquiring a plurality of air conditioner operation data; obtaining a measurement result corresponding to the operation data of each air conditioner; inputting the operation data of each air conditioner into a reference air conditioner operation parameter prediction model to obtain a prediction result corresponding to the operation data of each air conditioner; respectively comparing the prediction result of each air conditioner operation data with the measurement result of each air conditioner operation data to obtain a plurality of comparison results; the comparison result is used for representing whether the difference value between the prediction result and the measurement result is within a preset range; and obtaining the accuracy of the reference air conditioner operation parameter prediction model according to each comparison result.
Optionally, the third acquisition model is configured to acquire a plurality of second air conditioner operation data samples with second tags by: determining the total sample number of second air conditioner operation data samples with second labels according to the accuracy of the reference air conditioner operation parameter prediction model, and determining the second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner; and obtaining a plurality of second air conditioner operation data samples with second labels according to the second air conditioner operation data sample number with parameters representing the abnormal operation of the air conditioner and the total sample number.
Optionally, the apparatus for training an air conditioner operation parameter prediction model further comprises a correction module. After the correction model is configured to obtain a plurality of comparison results, correcting the prediction result corresponding to the air conditioner operation data under the condition that the difference value between the prediction result corresponding to the air conditioner operation data and the measurement result corresponding to the air conditioner operation data is not in a preset range; and inputting the corrected prediction result and the air conditioner operation data corresponding to the corrected prediction result into a reference air conditioner operation parameter prediction model for training.
Optionally, the correction module is configured to correct the prediction result corresponding to the air conditioner operation data by: and replacing the predicted result corresponding to the air conditioner operation data with the measured result corresponding to the air conditioner operation data.
Referring to fig. 6, an embodiment of the present disclosure provides an apparatus for training an air conditioner operation parameter prediction model, including a processor (processor) 600 and a memory (memory) 601. Optionally, the apparatus may further comprise a communication interface (Communication Interface) 602 and a bus 603. The processor 600, the communication interface 602, and the memory 601 may communicate with each other via the bus 603. The communication interface 602 may be used for information transfer. The processor 600 may invoke logic instructions in the memory 601 to perform the method for training the air conditioner operating parameter prediction model of the above-described embodiments.
Further, the logic instructions in the memory 601 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product.
The memory 601 serves as a computer readable storage medium, and may be used to store a software program, a computer executable program, and program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes the function application and the parameter processing by executing the program instructions/modules stored in the memory 601, that is, implements the method for training the air conditioner operation parameter prediction model in the above-described embodiment.
The memory 601 may include a storage program area and a storage parameter area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage parameter area may store parameters and the like created according to the use of the terminal device. In addition, the memory 601 may include a high-speed random access memory, and may also include a nonvolatile memory.
By adopting the device for training the air conditioner operation parameter prediction model, which is provided by the embodiment of the disclosure, the first air conditioner operation data sample is input into the preset neural network model for training to obtain the reference air conditioner operation parameter prediction model, and the second air conditioner operation data sample with the parameters for representing the abnormal operation of the air conditioner and the parameters for representing the normal operation of the air conditioner are utilized for carrying out incremental learning on the reference air conditioner operation parameter prediction model, so that the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameters of the air conditioner more accurately, and the accuracy of predicting the operation parameters of the air conditioner by using the air conditioner operation parameter prediction model can be improved.
The embodiment of the disclosure provides an electronic device, which comprises the device for training an air conditioner operation parameter prediction model. According to the electronic equipment, the first air conditioner operation data sample is input into the preset neural network model for training, the reference air conditioner operation parameter prediction model is obtained, the second air conditioner operation data sample with the parameter representing the abnormal operation of the air conditioner more than the parameter representing the normal operation of the air conditioner is utilized for performing incremental learning on the reference air conditioner operation parameter prediction model, and the obtained air conditioner operation parameter prediction model can be used for predicting the abnormal operation parameter of the air conditioner more accurately, so that the accuracy of predicting the operation parameter of the air conditioner by utilizing the air conditioner operation parameter prediction model can be improved.
Optionally, the electronic device includes: a computer or server, etc.
The embodiment of the disclosure provides a storage medium storing program instructions which, when running, execute the method for training an air conditioner operation parameter prediction model.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for training an air conditioner operating parameter prediction model.
The computer readable storage medium may be a transitory computer readable storage medium or a non-transitory computer readable storage medium.
Embodiments of the present disclosure may be embodied in a software product stored on a storage medium, including one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of a method according to embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium including: a plurality of media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or a transitory storage medium.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in this application, the terms "comprises," "comprising," and/or "includes," and variations thereof, mean that the stated features, integers, steps, operations, elements, and/or components are present, but that the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for training an air conditioner operating parameter prediction model, comprising:
acquiring a first air conditioner operation data sample with a first tag, wherein the first tag is a parameter for representing abnormal operation of an air conditioner or a parameter for representing normal operation of the air conditioner;
inputting the first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
acquiring a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, wherein the second labels are parameters used for representing abnormal operation of the air conditioner or parameters used for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner;
and performing incremental learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
2. The method of claim 1, wherein obtaining a plurality of second air conditioner operation data samples with second tags according to the reference air conditioner operation parameter prediction model comprises:
Acquiring the accuracy of the reference air conditioner operation parameter prediction model;
and under the condition that the accuracy rate is larger than a preset threshold value, acquiring a plurality of second air conditioner operation data samples with second labels.
3. The method of claim 2, wherein obtaining the accuracy of the reference air conditioner operating parameter prediction model comprises:
acquiring a plurality of air conditioner operation data; obtaining a measurement result corresponding to each air conditioner operation data;
inputting the air conditioner operation data into the reference air conditioner operation parameter prediction model to obtain a prediction result corresponding to the air conditioner operation data;
respectively comparing the predicted result of each air conditioner operation data with the measured result of each air conditioner operation data to obtain a plurality of comparison results; the comparison result is used for representing whether the difference value between the prediction result and the measurement result is within a preset range;
and obtaining the accuracy of the reference air conditioner operation parameter prediction model according to each comparison result.
4. The method of claim 2, wherein obtaining a plurality of second air conditioner operation data samples with second tags comprises:
determining the total sample number of second air conditioner operation data samples with second labels according to the accuracy rate, and determining the second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner;
And obtaining a plurality of second air conditioner operation data samples with second labels according to the second air conditioner operation data sample number with parameters representing abnormal operation of the air conditioner and the total sample number.
5. The method of claim 3, wherein after obtaining the plurality of comparison results, further comprising:
correcting the predicted result corresponding to the air conditioner operation data under the condition that the difference value between the predicted result corresponding to the air conditioner operation data and the measured result corresponding to the air conditioner operation data is not in a preset range;
and inputting the corrected predicted result and the air conditioner operation data corresponding to the corrected predicted result into the reference air conditioner operation parameter prediction model for training.
6. The method of claim 5, wherein correcting the predicted result corresponding to the air conditioner operation data comprises:
and replacing the predicted result corresponding to the air conditioner operation data with the measured result corresponding to the air conditioner operation data.
7. An apparatus for training an air conditioner operating parameter prediction model, comprising:
the first acquisition module is configured to acquire a first air conditioner operation data sample with a first label, wherein the first label is a parameter for representing abnormal operation of the air conditioner or a parameter for representing normal operation of the air conditioner;
The training module is configured to input the first air conditioner operation data sample into a preset neural network model for training to obtain a reference air conditioner operation parameter prediction model;
the second acquisition module is configured to acquire a plurality of second air conditioner operation data samples with second labels according to the reference air conditioner operation parameter prediction model, wherein the second labels are parameters used for representing abnormal operation of the air conditioner or parameters used for representing normal operation of the air conditioner; wherein, the second air conditioner operation data sample with the parameter for representing the abnormal operation of the air conditioner is more than the second air conditioner operation data sample with the parameter for representing the normal operation of the air conditioner;
and the increment learning module is configured to perform increment learning on the reference air conditioner operation parameter prediction model by using the second air conditioner operation data sample and the first air conditioner operation data sample to obtain an air conditioner operation parameter prediction model.
8. An apparatus for training an air conditioner operating parameter prediction model comprising a processor and a memory storing program instructions, wherein the processor is configured, when executing the program instructions, to perform the method for training an air conditioner operating parameter prediction model of any one of claims 1 to 6.
9. An electronic device comprising the apparatus for training an air conditioner operation parameter prediction model as claimed in claim 8.
10. A storage medium storing program instructions which, when executed, perform the method for training an air conditioner operating parameter prediction model of any one of claims 1 to 6.
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