CN111103475A - Air conditioner detection method and device - Google Patents
Air conditioner detection method and device Download PDFInfo
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- CN111103475A CN111103475A CN201811270165.5A CN201811270165A CN111103475A CN 111103475 A CN111103475 A CN 111103475A CN 201811270165 A CN201811270165 A CN 201811270165A CN 111103475 A CN111103475 A CN 111103475A
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G06F18/24323—Tree-organised classifiers
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- G06N3/02—Neural networks
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Abstract
The invention discloses a detection method and device for an air conditioner. Wherein, the method comprises the following steps: acquiring a first operation parameter of the air conditioner under a detection condition; utilize the detection model to carry out the analysis to first operating parameter, obtain the detection accuracy of air conditioner, wherein, the detection model uses multiunit data to train out through machine learning, and every group data in the multiunit data all includes: the second operation parameter of the air conditioner under the detection condition, the third operation parameter of the air conditioner under the operation condition and a corresponding label matrix of the detection accuracy; and obtaining a detection result of the air conditioner based on the detection accuracy of the air conditioner. The invention solves the technical problem of low accuracy of the detection method of the air conditioner in the prior art.
Description
Technical Field
The invention relates to the field of air conditioner detection, in particular to a method and a device for detecting an air conditioner.
Background
When the finished air conditioner is delivered from a factory, the finished air conditioner needs to be preliminarily detected, and the quality condition of the finished air conditioner is judged. But the standard detection voltage, the standard detection current and the standard operation environment provided by the detection chamber make the detection data unable to accurately feed back the operation performance of the finished air conditioner. After the air conditioner finished product leaves a factory, the air conditioner finished product is often applied to various environments, and various factors can interfere the running condition of the air conditioner to a certain extent.
Obviously, the data detected by the finished air conditioner in the detection chamber is different from the data of the finished air conditioner in practical application. The air conditioner finished product is detected only through the detection chamber, and the accuracy of the detection result is low.
Aiming at the problem of low accuracy of the detection method of the air conditioner in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a detection method and a detection device of an air conditioner, which at least solve the technical problem that the detection method of the air conditioner in the prior art is low in accuracy.
According to an aspect of an embodiment of the present invention, there is provided a detection method of an air conditioner, including: acquiring a first operation parameter of the air conditioner under a detection condition; utilize the detection model to carry out the analysis to first operating parameter, obtain the detection accuracy of air conditioner, wherein, the detection model uses multiunit data to train out through machine learning, and every group data in the multiunit data all includes: the second operation parameter of the air conditioner under the detection condition, the third operation parameter of the air conditioner under the operation condition and a corresponding label matrix of the detection accuracy; and obtaining a detection result of the air conditioner based on the detection accuracy of the air conditioner.
Further, the first operating parameter includes at least one of: the power consumption of the air conditioner and the working capacity of the air conditioner.
Further, the analyzing the first operation parameter by using the detection model to obtain the detection accuracy of the air conditioner includes: analyzing the first operation parameter by using the first sub-model to obtain a first detection error corresponding to the first operation parameter; analyzing the first operation parameter by using a second submodel to obtain a second detection error corresponding to the first operation parameter; and analyzing the first detection error and the second detection error corresponding to the first operation parameter by using the third sub-model to obtain the detection accuracy of the air conditioner.
Further, the method further comprises: acquiring a plurality of groups of data; analyzing the multiple groups of data by using a first sub-model to obtain a first detection error corresponding to each group of data; analyzing the multiple groups of data by using a second submodel to obtain a second detection error corresponding to each group of data; and constructing a third sub-model based on the first detection error and the second detection error corresponding to each group of data.
Further, a third submodel is constructed based on the first detection error and the second detection error corresponding to each group of data, and the method comprises the following steps: constructing an initial model; and taking the first detection error and the second detection error corresponding to each group of data as limiting conditions of the initial model, and constructing a third sub-model by using a preset rule.
Further, the first sub-model is a decision tree model, the second sub-model is a recurrent neural network model, and the third sub-model is an ensemble learning model.
Further, based on the detection accuracy of the air conditioner, obtaining a detection result of the air conditioner, including: comparing the detection accuracy of the air conditioner with a preset accuracy; and obtaining a detection result of the air conditioner based on the first operation parameter under the condition that the detection accuracy is greater than or equal to the preset accuracy.
Further, in the case that the detection accuracy is less than the preset accuracy, the method further includes: adjusting the detection condition to obtain the adjusted detection condition; and detecting the air conditioner again under the adjusted detection condition until the detection accuracy is greater than or equal to the preset accuracy.
According to another aspect of the embodiments of the present invention, there is also provided a detection apparatus of an air conditioner, including: the acquisition module is used for acquiring a first operating parameter of the air conditioner under a detection condition; the first processing module is used for analyzing the first operating parameter by using the detection model to obtain the detection accuracy of the air conditioner, wherein the detection model is trained by machine learning by using multiple groups of data, and each group of data in the multiple groups of data comprises: the second operation parameter of the air conditioner under the detection condition, the third operation parameter of the air conditioner under the operation condition and a corresponding label matrix of the detection accuracy; and the second processing module is used for obtaining the detection result of the air conditioner based on the detection accuracy of the air conditioner.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein the apparatus on which the storage medium is located is controlled to execute the above-mentioned detection method of the air conditioner when the program runs.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the detection method of the air conditioner.
In the embodiment of the invention, after the first operation parameter of the air conditioner under the detection condition is obtained, the first operation parameter can be analyzed by using the detection model to obtain the detection accuracy of the air conditioner, and further, the detection result of the air conditioner can be obtained based on the detection accuracy of the air conditioner. Compared with the prior art, the detection result of the air conditioner is not directly obtained according to the first operation parameter of the detection chamber under the detection condition, so that the purpose of evaluating the detection quality of the detection chamber for detecting the finished air conditioner is achieved, the technical effect of improving the detection accuracy of the finished air conditioner is achieved, and the technical problem that the accuracy of the detection method of the air conditioner in the prior art is low is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a detection method of an air conditioner according to an embodiment of the present invention; and
fig. 2 is a schematic diagram of a detection device of an air conditioner according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a detection method of an air conditioner, it should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.
Fig. 1 is a flowchart of a detection method of an air conditioner according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring a first operation parameter of the air conditioner under the detection condition.
Optionally, the first operating parameter comprises at least one of: the power consumption of the air conditioner and the working capacity of the air conditioner.
Specifically, the detection condition may refer to a standard detection condition of a detection chamber, and the detection chamber provides a standard detection voltage, a standard detection current and a standard operation environment for the finished air conditioner. The working capacity can be the condition capacity of the air conditioner to the indoor environment, and comprises cooling capacity, heating capacity, dehumidifying capacity, air purifying capacity and the like.
In an optional scheme, the finished air conditioner product can be controlled to operate under the standard detection condition of the detection room, and parameters such as the power consumption of the air conditioner, the indoor environment adjusting capacity (cooling capacity, heating capacity, dehumidifying capacity and air purifying capacity …) and the like can be obtained in real time.
Step S104, analyzing the first operation parameter by using a detection model to obtain the detection accuracy of the air conditioner, wherein the detection model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises: a second operating parameter of the air conditioner under the detection condition, a third operating parameter of the air conditioner under the operating condition, and error data between the second operating parameter and the third operating parameter.
Specifically, the operating condition may be an actual operating environment after the finished air conditioner is shipped. The detection accuracy can be used for evaluating whether the standard detection conditions provided by the detection chamber can accurately detect the finished air conditioner product, the higher the detection accuracy is, the higher the similarity between the detection data of the finished air conditioner product detected by the detection chamber and the detection data of the finished air conditioner product in various environments is, and the more the detection result can reflect the actual quality of the finished air conditioner product.
In order to evaluate the detection quality of the detection room for detecting the finished air conditioner product, a plurality of groups of data can be constructed in advance according to the standard detection condition of the detection room, the operation condition of the finished air conditioner product in various actual operation environments, and the corresponding detection accuracy label matrix, so that a detection error evaluation model (namely the detection model) of the operation condition of the air conditioner can be trained.
And S106, obtaining a detection result of the air conditioner based on the detection accuracy of the air conditioner.
Specifically, the detection result is used for representing the quality condition of the finished air conditioner product, namely whether the finished air conditioner product meets the primary detection.
In an optional scheme, after the detection accuracy of the air conditioner is determined, if the detection accuracy meets the detection requirement, the detection data of the air conditioner finished product detected by the detection chamber can be determined to be the same as the detection data of the air conditioner finished product in various environments, so that the detection result of the air conditioner finished product can be directly obtained; if the detection accuracy does not meet the detection requirement, the detection data of the air conditioner finished product detected by the detection chamber is different from the detection data of the air conditioner finished product in various environments, and the detection result of the air conditioner finished product cannot be directly obtained.
By the embodiment of the invention, after the first operation parameter of the air conditioner under the detection condition is obtained, the first operation parameter can be analyzed by using the detection model to obtain the detection accuracy of the air conditioner, and the detection result of the air conditioner can be further obtained based on the detection accuracy of the air conditioner. Compared with the prior art, the detection result of the air conditioner is not directly obtained according to the first operation parameter of the detection chamber under the detection condition, so that the purpose of evaluating the detection quality of the detection chamber for detecting the finished air conditioner is achieved, the technical effect of improving the detection accuracy of the finished air conditioner is achieved, and the technical problem that the accuracy of the detection method of the air conditioner in the prior art is low is solved.
Optionally, in the foregoing embodiment of the present invention, in step S104, analyzing the first operation parameter by using the detection model to obtain the detection accuracy of the air conditioner, where the step includes: analyzing the first operation parameter by using the first sub-model to obtain a first detection error corresponding to the first operation parameter; analyzing the first operation parameter by using a second submodel to obtain a second detection error corresponding to the first operation parameter; and analyzing the first detection error and the second detection error corresponding to the first operation parameter by using the third sub-model to obtain the detection accuracy of the air conditioner.
Optionally, the first sub-model is a decision tree model, the second sub-model is a recurrent neural network model, and the third sub-model is an ensemble learning model.
Specifically, the first sub-model may be a multivariate decision tree-based model, the second sub-model may be a recurrent neural network-based model, and the third sub-model may be an adaptive ensemble learning model based on the AdaBoost algorithm.
In an optional scheme, a detection model can be constructed based on a deep learning algorithm and a recurrent neural network, and the purpose of evaluating the detection quality of the detection room for detecting the finished air conditioner is achieved. After the first operation parameter of the air conditioner is obtained, the first operation parameter can be respectively input to a model based on a multivariable decision tree and a model based on a recurrent neural network, and the model based on the multivariable decision tree is used for carrying out weighting training on input data to obtain a detection error (namely the first detection error) of a first final air conditioner operation condition under the model; and performing weighted training on the input data by using a model based on the recurrent neural network to obtain a detection error of the second final air conditioner operation condition under the model (namely the second detection error). And finally, analyzing the detection error of the first final air conditioner operation condition and the detection error of the second final air conditioner operation condition by using an adaptive integrated learning model based on an AdaBoost algorithm, and realizing the evaluation of the detection accuracy of the preliminary detection process of the air conditioner finished product.
Optionally, in the above embodiment of the present invention, the method further includes: acquiring a plurality of groups of data; analyzing the multiple groups of data by using a first sub-model to obtain a first detection error corresponding to each group of data; analyzing the multiple groups of data by using a second submodel to obtain a second detection error corresponding to each group of data; and constructing a third sub-model based on the first detection error and the second detection error corresponding to each group of data.
In an alternative scheme, data of factors influencing the operation of finished air conditioners can be collected and a database is established for storage based on the data, and the data of the factors influencing the operation of the finished air conditioners comprise: the operation condition of the finished air conditioner under the standard detection condition of the detection chamber, the operation condition of the finished air conditioner and various actual operation environments. And determining the error between the operation conditions of the finished air conditioner product under the standard detection condition of the detection chamber and various actual operation passes according to the data of the factors influencing the operation of the finished air conditioner product to obtain error data, and storing the error data in a database.
The error data and data of factors influencing the operation of finished air-conditioning products are used as input data, and a model based on a multivariable decision tree is utilized to perform weighted training on the input data to obtain a detection error of a first final air-conditioning operation condition under the model; taking the error data and data of factors influencing the operation of the finished air conditioner as input data, and performing weighted training on the input data by using a model based on a recurrent neural network to obtain a detection error of a second final air conditioner operation condition under the model; and on the basis of the detection error of the first final air conditioner operation condition and the detection error of the second final air conditioner operation condition, constructing a final detection model of the air conditioner operation condition by using an adaptive integrated learning model based on an AdaBoost algorithm, and evaluating the detection quality (detection accuracy) of the preliminary detection process of the air conditioner finished product.
Optionally, in the above embodiment of the present invention, constructing a third submodel based on the first detection error and the second detection error corresponding to each group of data includes: constructing an initial model; and taking the first detection error and the second detection error corresponding to each group of data as limiting conditions of the initial model, and constructing a third sub-model by using a preset rule.
In an optional scheme, an adaptive ensemble learning model based on an AdaBoost algorithm may be used, the magnitude of the detection error of the final air conditioner operation condition obtained through analysis by the two evaluation models is used as a limiting condition, and a final detection model of the air conditioner operation condition is constructed by using a weighted voting mechanism.
Optionally, in the foregoing embodiment of the present invention, in step S106, obtaining a detection result of the air conditioner based on the detection accuracy of the air conditioner includes: comparing the detection accuracy of the air conditioner with a preset accuracy; and obtaining a detection result of the air conditioner based on the first operation parameter under the condition that the detection accuracy is greater than or equal to the preset accuracy.
Specifically, the preset accuracy may be an accuracy threshold determined according to actual detection requirements, and the higher the threshold is, the higher the monitoring accuracy of the air conditioner is, the more accurate the detection quality is.
In an optional scheme, after the detection accuracy of the air conditioner is obtained by using the detection model for analysis, the detection accuracy may be compared with an accuracy threshold, and if the detection accuracy exceeds the accuracy threshold, it may be determined that the first operating parameter obtained under the standard detection condition of the detection chamber is the same as the operating parameter obtained under the actual operating environment, and no error exists, and the quality condition of the finished air conditioner may be determined directly based on the first operating parameter.
Optionally, in the above embodiment of the present invention, in a case that the detection accuracy is less than the preset accuracy, the method further includes: adjusting the detection condition to obtain the adjusted detection condition; and detecting the air conditioner again under the adjusted detection condition until the detection accuracy is greater than or equal to the preset accuracy.
In an optional scheme, if the detection accuracy of the air conditioner obtained by using the detection model for analysis is smaller than the accuracy threshold, it may be determined that a first operation parameter obtained under a standard detection condition of the detection chamber is different from an operation parameter obtained under an actual operation environment, an error exists, the quality condition of a finished air conditioner product cannot be determined based on the first operation parameter, a standard detection voltage, a standard detection current and a standard operation environment provided by the detection chamber need to be adjusted, the finished air conditioner product is detected again according to the adjusted standard detection condition, and if the detection accuracy obtained this time exceeds the accuracy threshold, the quality condition of the finished air conditioner product may be determined based on the obtained first operation parameter.
Example 2
According to an embodiment of the present invention, there is provided an embodiment of a detection apparatus of an air conditioner.
Fig. 2 is a schematic view of a detection apparatus of an air conditioner according to an embodiment of the present invention, as shown in fig. 2, the apparatus including:
the obtaining module 22 is configured to obtain a first operating parameter of the air conditioner under the detection condition.
Optionally, the first operating parameter comprises at least one of: the power consumption of the air conditioner and the working capacity of the air conditioner.
Specifically, the detection condition may refer to a standard detection condition of a detection chamber, and the detection chamber provides a standard detection voltage, a standard detection current and a standard operation environment for the finished air conditioner. The working capacity can be the condition capacity of the air conditioner to the indoor environment, and comprises cooling capacity, heating capacity, dehumidifying capacity, air purifying capacity and the like.
In an optional scheme, the finished air conditioner product can be controlled to operate under the standard detection condition of the detection room, and parameters such as the power consumption of the air conditioner, the indoor environment adjusting capacity (cooling capacity, heating capacity, dehumidifying capacity and air purifying capacity …) and the like can be obtained in real time.
The first processing module 24 is configured to analyze the first operating parameter by using a detection model to obtain a detection accuracy of the air conditioner, where the detection model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: a second operating parameter of the air conditioner under the detection condition, a third operating parameter of the air conditioner under the operating condition, and error data between the second operating parameter and the third operating parameter.
Specifically, the operating condition may be an actual operating environment after the finished air conditioner is shipped. The detection accuracy can be used for evaluating whether the standard detection conditions provided by the detection chamber can accurately detect the finished air conditioner product, the higher the detection accuracy is, the higher the similarity between the detection data of the finished air conditioner product detected by the detection chamber and the detection data of the finished air conditioner product in various environments is, and the more the detection result can reflect the actual quality of the finished air conditioner product.
In order to evaluate the detection quality of the detection room for detecting the finished air conditioner product, a plurality of groups of data can be constructed in advance according to the standard detection condition of the detection room, the operation condition of the finished air conditioner product in various actual operation environments, and the corresponding detection accuracy label matrix, so that a detection error evaluation model (namely the detection model) of the operation condition of the air conditioner can be trained.
And the second processing module 26 is configured to obtain a detection result of the air conditioner based on the detection accuracy of the air conditioner.
Specifically, the detection result is used for representing the quality condition of the finished air conditioner product, namely whether the finished air conditioner product meets the primary detection.
In an optional scheme, after the detection accuracy of the air conditioner is determined, if the detection accuracy meets the detection requirement, the detection data of the air conditioner finished product detected by the detection chamber can be determined to be the same as the detection data of the air conditioner finished product in various environments, so that the detection result of the air conditioner finished product can be directly obtained; if the detection accuracy does not meet the detection requirement, the detection data of the air conditioner finished product detected by the detection chamber is different from the detection data of the air conditioner finished product in various environments, and the detection result of the air conditioner finished product cannot be directly obtained.
By the embodiment of the invention, after the first operation parameter of the air conditioner under the detection condition is obtained, the first operation parameter can be analyzed by using the detection model to obtain the detection accuracy of the air conditioner, and the detection result of the air conditioner can be further obtained based on the detection accuracy of the air conditioner. Compared with the prior art, the detection result of the air conditioner is not directly obtained according to the first operation parameter of the detection chamber under the detection condition, so that the purpose of evaluating the detection quality of the detection chamber for detecting the finished air conditioner is achieved, the technical effect of improving the detection accuracy of the finished air conditioner is achieved, and the technical problem that the accuracy of the detection method of the air conditioner in the prior art is low is solved.
Optionally, in the foregoing embodiment of the present invention, the first processing module includes: the first processing unit is used for analyzing the first operation parameter by using the first sub-model to obtain a first detection error corresponding to the first operation parameter; the second processing unit is used for analyzing the first operation parameter by using a second submodel to obtain a second detection error corresponding to the first operation parameter; and the third processing unit is used for analyzing the first detection error and the second detection error corresponding to the first operation parameter by using a third sub-model to obtain the detection accuracy of the air conditioner.
Optionally, the first sub-model is a decision tree model, the second sub-model is a recurrent neural network model, and the third sub-model is an ensemble learning model.
Optionally, in the above embodiment of the present invention, the apparatus further includes: the acquisition module is also used for acquiring a plurality of groups of data; the third processing module is used for analyzing the multiple groups of data by using the first sub-model to obtain a first detection error corresponding to each group of data; the fourth processing module is used for analyzing the multiple groups of data by using the second submodel to obtain a second detection error corresponding to each group of data; and the building module is used for building a third sub-model based on the first detection error and the second detection error corresponding to each group of data.
Optionally, in the above embodiment of the present invention, the building module includes: a first construction unit for constructing an initial model; and the second construction unit is used for constructing a third sub-model by using the first detection error and the second detection error corresponding to each group of data as the limiting conditions of the initial model and using a preset rule.
Optionally, in the foregoing embodiment of the present invention, the second processing module includes: the comparison unit is used for comparing the detection accuracy of the air conditioner with the preset accuracy; and the fourth processing unit is used for obtaining the detection result of the air conditioner based on the first operation parameter under the condition that the detection accuracy is greater than or equal to the preset accuracy.
Optionally, in the above embodiment of the present invention, in a case that the detection accuracy is less than the preset accuracy, the apparatus further includes: the adjusting unit is used for adjusting the detection conditions to obtain the adjusted detection conditions; and the detection unit is used for detecting the air conditioner again under the adjusted detection condition until the detection accuracy is greater than or equal to the preset accuracy.
Example 3
According to an embodiment of the present invention, there is provided an embodiment of a storage medium including a stored program, wherein a device in which the storage medium is located is controlled to execute the detection method of the air conditioner in the above-described embodiment 1 when the program is executed.
Example 4
According to an embodiment of the present invention, an embodiment of a processor for running a program is provided, where the program runs to execute the detection method of the air conditioner in the above embodiment 1.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
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 can be realized in a form of hardware, and can also be realized in a form of a software functional 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 computer readable storage medium. Based on such understanding, the technical solution of the present invention may 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 server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (11)
1. A detection method of an air conditioner is characterized by comprising the following steps:
acquiring a first operation parameter of the air conditioner under a detection condition;
analyzing the first operating parameter by using a detection model to obtain the detection accuracy of the air conditioner, wherein the detection model is trained by machine learning by using multiple groups of data, and each group of data in the multiple groups of data comprises: the second operation parameter of the air conditioner under the detection condition, the third operation parameter of the air conditioner under the operation condition and a corresponding label matrix of detection accuracy;
and obtaining the detection result of the air conditioner based on the detection accuracy of the air conditioner.
2. The method of claim 1, wherein the first operating parameter comprises at least one of: the power consumption of the air conditioner and the working capacity of the air conditioner.
3. The method of claim 2, wherein analyzing the first operating parameter using a detection model to obtain a detection accuracy of the air conditioner comprises:
analyzing the first operation parameter by using a first sub-model to obtain a first detection error corresponding to the first operation parameter;
analyzing the first operation parameter by using a second sub-model to obtain a second detection error corresponding to the first operation parameter;
and analyzing the first detection error and the second detection error corresponding to the first operation parameter by using a third sub-model to obtain the detection accuracy of the air conditioner.
4. The method of claim 3, further comprising:
acquiring the multiple groups of data;
analyzing the multiple groups of data by using the first submodel to obtain a first detection error corresponding to each group of data;
analyzing the multiple groups of data by using the second submodel to obtain a second detection error corresponding to each group of data;
and constructing the third submodel based on the first detection error and the second detection error corresponding to each group of data.
5. The method of claim 4, wherein constructing the third submodel based on the first detection error and the second detection error corresponding to each set of data comprises:
constructing an initial model;
and taking the first detection error and the second detection error corresponding to each group of data as the limiting conditions of the initial model, and constructing the third sub-model by using a preset rule.
6. The method of claim 3, wherein the first sub-model is a decision tree model, the second sub-model is a recurrent neural network model, and the third sub-model is an ensemble learning model.
7. The method of claim 1, wherein obtaining the detection result of the air conditioner based on the detection accuracy of the air conditioner comprises:
comparing the detection accuracy of the air conditioner with a preset accuracy;
and obtaining the detection result of the air conditioner based on the first operation parameter under the condition that the detection accuracy is greater than or equal to the preset accuracy.
8. The method of claim 7, wherein in the case that the detection accuracy is less than the preset accuracy, the method further comprises:
adjusting the detection condition to obtain an adjusted detection condition;
and detecting the air conditioner again under the adjusted detection condition until the detection accuracy is greater than or equal to the preset accuracy.
9. A detection device of an air conditioner is characterized by comprising:
the acquisition module is used for acquiring a first operating parameter of the air conditioner under a detection condition;
a first processing module, configured to analyze the first operating parameter by using a detection model to obtain a detection accuracy of the air conditioner, where the detection model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the second operation parameter of the air conditioner under the detection condition, the third operation parameter of the air conditioner under the operation condition and a corresponding label matrix of detection accuracy;
and the second processing module is used for obtaining the detection result of the air conditioner based on the detection accuracy of the air conditioner.
10. A storage medium, characterized in that the storage medium includes a stored program, wherein, when the program runs, an apparatus where the storage medium is located is controlled to execute the detection method of the air conditioner according to any one of claims 1 to 8.
11. A processor, characterized in that the processor is configured to run a program, wherein the program is executed to execute the detection method of the air conditioner according to any one of claims 1 to 8 when running.
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