CN112445193A - Method, device and equipment for predicting air conditioner fault and storage medium - Google Patents
Method, device and equipment for predicting air conditioner fault and storage medium Download PDFInfo
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- CN112445193A CN112445193A CN201910750242.5A CN201910750242A CN112445193A CN 112445193 A CN112445193 A CN 112445193A CN 201910750242 A CN201910750242 A CN 201910750242A CN 112445193 A CN112445193 A CN 112445193A
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- air conditioner
- operation data
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/027—Alarm generation, e.g. communication protocol; Forms of alarm
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The application relates to a method, a device, equipment and a storage medium for predicting air conditioner faults, wherein the method comprises the following steps: acquiring operation data when the air conditioner operates; and inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result. According to the embodiment of the application, the air conditioner fault can be predicted, a user can take safety precaution measures in advance, and potential safety hazards are avoided.
Description
Technical Field
The present application relates to the field of failure prediction technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting an air conditioner failure.
Background
With the coming of the era of intelligent household appliances, the increasing requirements of people on the intellectualization of the household appliances are eliminated, the safety performance requirements of the intelligent household appliances are also improved, the air conditioner is used as an indispensable household appliance of a family, the failure of the air conditioner during operation is unavoidable, the failure reason is probably caused by aging of a compressor, improper operation of the air conditioner or external factors of the environment where the air conditioner is located, potential safety hazards are likely to occur after the failure of the air conditioner, and the life and property safety of a user is affected.
Disclosure of Invention
In order to solve the technical problems that potential safety hazards are likely to occur after the air conditioner breaks down and life and property safety of a user is affected, the application provides a method, a device, equipment and a storage medium for predicting air conditioner faults.
In a first aspect, the present application provides a method for predicting an air conditioner fault, including:
acquiring operation data when the air conditioner operates;
and inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
Optionally, the establishing process of the fault model prediction model includes:
collecting historical operating data of a fault air conditioner and historical operating data of a normal air conditioner as training samples;
and training the fault prediction model by using the training samples.
Optionally, the fault prediction model comprises: the step of training the fault prediction model by using the training sample comprises the following steps:
classifying the historical operation data of the normal air conditioner and the historical operation data of the fault air conditioner to obtain a positive sample interval and a negative sample interval, wherein the historical operation data of the normal air conditioner belongs to the positive sample interval, and the historical operation data of the fault air conditioner belongs to the negative sample interval;
and determining a perceptron model according to the positive sample interval and the negative sample interval.
Optionally, the step of analyzing the operation data by using the fault prediction model to obtain a fault prediction result includes:
judging whether the operation data is in a negative sample interval or not;
and if the operation data are in the negative sample interval, predicting that the air conditioner fails.
Optionally, the step of obtaining the operation data when the air conditioner is running comprises:
and controlling a data acquisition device on the air conditioner to acquire the operating data of the air conditioner during operation.
Optionally, the method for predicting the air conditioner fault further comprises:
and if the prediction result is that the air conditioner fails, sending failure early warning information to a client pre-bound by the user.
Optionally, the step of inputting the operation data into a failure prediction model comprises:
performing modeling processing on the operation data to obtain modeled operation data;
inputting the patterned operational data into the fault prediction model.
In a second aspect, the present application provides an apparatus for predicting air conditioner failure, comprising:
the acquisition module is used for acquiring operation data when the air conditioner operates;
and the analysis module is used for inputting the operation data into a fault prediction model and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
In a third aspect, the present application provides a device for predicting air conditioner failure, including a memory and a processor, where the memory stores a computer program executable on the processor, and the processor implements the steps of the method according to any one of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of the first aspects.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: the method comprises the steps of obtaining operation data when the air conditioner operates; and inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result, so that the air conditioner fault can be predicted, and a user can take safety precautionary measures in advance to avoid potential safety hazards.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for predicting an air conditioner fault according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method of step S102 according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of a failure prediction model building process provided by an embodiment of the present application;
fig. 4 is a flowchart of a method of step S202 according to an embodiment of the present application;
fig. 5 is a flowchart of another method of step S102 according to the embodiment of the present application;
fig. 6 is a schematic diagram of an apparatus module for predicting air conditioner failure according to an embodiment of the present disclosure.
Icon:
01-an acquisition module; 02-analysis module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
First, the present invention provides a method for predicting an air conditioner fault, as shown in fig. 1, the method for predicting an air conditioner fault includes:
step S101, acquiring operation data when the air conditioner operates;
in the embodiment of the present invention, acquiring the operation data of the air conditioner during operation is to detect the air conditioner data in real time, where the operation data of the air conditioner includes: the method for acquiring the running data of the motor inside the air conditioner, the temperature inside the air conditioner, the pressure inside the air conditioner and other parameter information comprises the following steps: the air conditioner actively reports the operation data, or the server sends a control instruction to the air conditioner, the air conditioner uploads the operation data to the maintenance center, the operation data of the air conditioner is collected in a network capturing mode such as wifi or a gateway, the time of the operation data is specifically obtained, the mode of obtaining the operation data in real time is preferably adopted, the purpose of monitoring the operation state of the air conditioner is achieved, the operation data stored in a storage medium can be obtained when the air conditioner is turned off, the specific setting can be determined according to the actual situation, and the method is not specifically limited to this.
In the embodiment of the present invention, step S101, before the step of obtaining the operation data when the air conditioner is operating, includes:
and controlling a data acquisition device on the air conditioner to acquire the operating data of the air conditioner during operation.
In the embodiment of the present invention, it is preferable that a data acquisition device is provided on the air conditioner, and after acquiring the operation data of the air conditioner, the data is wirelessly transmitted to the server, and the server analyzes the operation data.
And S102, inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
In the embodiment of the invention, the operation data of the air conditioner during operation is acquired in real time and is input into the fault prediction model, so that the operation state of the air conditioner is judged according to the operation data, and the fault prediction result is obtained. The fault prediction model is a pre-established model, and is obtained by training the model through a machine learning method by using a large number of training samples, and the machine learning algorithm and the training samples which are specifically selected can be determined according to actual conditions.
In the embodiment of the present invention, as shown in fig. 2, step S102, the step of inputting the operation data into a failure prediction model includes:
step S201, performing modeling processing on the operation data to obtain modeled operation data;
step S202, inputting the patterned operation data into the fault prediction model.
In the embodiment of the invention, in order to make the input data adapt to the model, the operating data needs to be subjected to modeling processing before being input into the fault prediction model to obtain the modeled operating data, and the format field of the data is kept consistent with the input data format field of the fault prediction model, so that the data format is unified, and the accuracy of the output of the fault prediction model is ensured.
In the embodiment of the present invention, as shown in fig. 3, the process of establishing the fault model prediction model includes:
step S301, collecting historical operation data of a fault air conditioner and historical operation data of a normal air conditioner as training samples;
in the embodiment of the invention, the operation data of the air conditioner before the fault of the invention has a certain rule, for example, when the air conditioner normally operates, the waveform output by a motor of the air conditioner is a regular waveform which shows periodic change, but when the motor is locked, the waveform can be distorted.
Step S302, the training sample is used for training the fault prediction model.
In the embodiment of the invention, the running data of the air conditioner during running is obtained; and then, the operation data is input into a fault prediction model, and the operation data is analyzed by using the fault prediction model to obtain a fault prediction result, so that the prediction of the air conditioner fault is realized, and a user can take safety precautionary measures in advance to avoid potential safety hazards.
In yet another embodiment of the present invention, the fault prediction model includes: as shown in fig. 4, in step S202, training the failure prediction model by using the training samples includes:
step S401, classifying the historical operation data of the normal air conditioner and the historical operation data of the fault air conditioner to obtain a positive sample interval and a negative sample interval, wherein the historical operation data of the normal air conditioner belongs to the positive sample interval, and the historical operation data of the fault air conditioner belongs to the negative sample interval;
step S402, determining a sensor model according to the positive sample interval and the negative sample interval.
In the embodiment of the invention, the fault prediction model is trained by taking the historical operating data of the normal air conditioner and the historical operating data of the fault air conditioner as training samples, preferably, the fault prediction model can adopt a sensor model, namely, a class plane is found between the historical operating data of the normal air conditioner and the historical operating data of the fault air conditioner by utilizing a classification idea, the two data are divided, a positive sample interval is established by using the historical operating data of the normal air conditioner, a negative sample interval is established by using the historical operating data of the fault air conditioner, and after the sensor model is determined by using the positive sample interval and the negative sample interval, the air conditioner operating data acquired in real time can be judged to belong to which interval, so that the conclusion whether the air conditioner fails or not can be obtained.
In this embodiment of the present invention, as shown in fig. 5, step S102, analyzing the operation data by using the fault prediction model to obtain a fault prediction result, includes:
step S501, judging whether the running data is in a negative sample interval;
in the embodiment of the present invention, a positive sample interval is established by using historical operating data of a normal air conditioner, a negative sample interval is established by using historical operating data of a failed air conditioner, and after determining a sensor model through the positive sample interval and the negative sample interval, real-time operating data of the air conditioner is obtained, and which interval the data is in is determined, where the data type may be two-dimensional data or high-dimensional data, and specifically may be determined according to field settings used by air conditioner transmission data in an actual application process, which is not specifically limited by the present invention.
Step S502, if the operation data is in the negative sample interval, the air conditioner is predicted to be in fault.
In the embodiment of the invention, if the operation data of the air conditioner belongs to the negative sample interval, the air conditioner is judged to possibly fail, the air conditioner failure is predicted, a user can take safety precaution measures in advance, and potential safety hazards are avoided.
In an embodiment of the present invention, the method for predicting an air conditioner fault further includes:
and if the prediction result is that the air conditioner fails, sending failure early warning information to a client pre-bound by the user.
In the embodiment of the invention, when the air conditioner is judged to have a fault, the server sends fault early warning information to the terminals such as the mobile phone or the tablet personal computer which are bound by the user in advance so as to achieve the purpose of warning the user, so that the user has enough time to take safety precautionary measures in advance, and potential safety hazards are avoided.
In another embodiment of the present invention, there is provided an apparatus for predicting an air conditioner failure, as shown in fig. 6, including:
the acquisition module 01 is used for acquiring operation data when the air conditioner operates;
and the analysis module 02 is used for inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
In the embodiment of the present invention, the acquiring module is configured to acquire the operation data of the air conditioner during operation, and detect the air conditioner data in real time, where the operation data of the air conditioner includes: the acquisition module is arranged in the air conditioner and can actively report the operation data, or the server sends a control instruction to the air conditioner, the air conditioner uploads the operation data to a maintenance center, the acquisition module can also be arranged outside the air conditioner, for example, at home of a user, the acquisition module acquires the operation data of the air conditioner in a network capturing mode such as wifi or a gateway and then uploads the operation data to the analysis module, the time of the operation data is specifically acquired, the mode of acquiring the operation data in real time is preferably adopted, the purpose of monitoring the operation state of the air conditioner is achieved, the operation data stored in a storage medium can be acquired when the air conditioner is closed, the specific setting can be determined according to actual conditions, and the invention is not specifically limited to the specific setting.
In the embodiment of the invention, the operation data of the air conditioner during operation is acquired in real time and is input into the fault prediction model, so that the operation state of the air conditioner is judged according to the operation data, and the fault prediction result is obtained. The fault prediction model is a pre-established model, a large number of training samples are used for training the model to obtain the fault prediction model through a machine learning method, and a specifically selected machine learning algorithm and the training samples can be determined according to actual conditions.
In yet another embodiment of the present invention, a device for predicting air conditioner failure is further provided, which includes a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the steps of the method of any one of the above embodiments when executing the computer program.
In a further embodiment of the invention, there is also provided a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of the above embodiments.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for predicting air conditioner faults is characterized by comprising the following steps:
acquiring operation data when the air conditioner operates;
and inputting the operation data into a fault prediction model, and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
2. The method of predicting air conditioner faults as claimed in claim 1, wherein the establishing process of the fault model prediction model comprises:
collecting historical operating data of a fault air conditioner and historical operating data of a normal air conditioner as training samples;
and training the fault prediction model by using the training samples.
3. The method of predicting air conditioner faults as claimed in claim 2, wherein the fault prediction model includes: the step of training the fault prediction model by using the training sample comprises the following steps:
classifying the historical operation data of the normal air conditioner and the historical operation data of the fault air conditioner to obtain a positive sample interval and a negative sample interval, wherein the historical operation data of the normal air conditioner belongs to the positive sample interval, and the historical operation data of the fault air conditioner belongs to the negative sample interval;
and determining a perceptron model according to the positive sample interval and the negative sample interval.
4. The method of claim 3, wherein the step of analyzing the operation data by using the fault prediction model to obtain a fault prediction result comprises:
judging whether the operation data is in a negative sample interval or not;
and if the operation data are in the negative sample interval, predicting that the air conditioner fails.
5. The method of predicting a malfunction of an air conditioner according to claim 1, wherein the step of acquiring operation data when the air conditioner is operated is preceded by:
and controlling a data acquisition device on the air conditioner to acquire the operating data of the air conditioner during operation.
6. The method of predicting an air conditioning fault as claimed in claim 1, further comprising:
and if the prediction result is that the air conditioner fails, sending failure early warning information to a client pre-bound by the user.
7. The method of predicting air conditioner faults as claimed in claim 1, wherein the step of inputting the operation data into a fault prediction model includes:
performing modeling processing on the operation data to obtain modeled operation data;
inputting the patterned operational data into the fault prediction model.
8. An apparatus for predicting a failure of an air conditioner, comprising:
the acquisition module is used for acquiring operation data when the air conditioner operates;
and the analysis module is used for inputting the operation data into a fault prediction model and analyzing the operation data by using the fault prediction model to obtain a fault prediction result.
9. A predictive air conditioning failure device comprising a memory, a processor, a computer program being stored in the memory and being executable on the processor, wherein the processor when executing the computer program performs the steps of the method according to any of the preceding claims 1 to 7.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1 to 7.
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| CN201910750242.5A CN112445193A (en) | 2019-08-14 | 2019-08-14 | Method, device and equipment for predicting air conditioner fault and storage medium |
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| CN201910750242.5A CN112445193A (en) | 2019-08-14 | 2019-08-14 | Method, device and equipment for predicting air conditioner fault and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114236314A (en) * | 2021-12-17 | 2022-03-25 | 瀚云科技有限公司 | Fault detection method, device, equipment and storage medium |
| WO2023020081A1 (en) * | 2021-08-18 | 2023-02-23 | 青岛海尔空调器有限总公司 | Method and apparatus for controlling air conditioner, and server |
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| CN109492667A (en) * | 2018-10-08 | 2019-03-19 | 国网天津市电力公司电力科学研究院 | A kind of feature selecting discrimination method for non-intrusive electrical load monitoring |
| CN109946544A (en) * | 2019-03-29 | 2019-06-28 | 广东美的制冷设备有限公司 | Household electrical appliances fault detection method and device |
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| CN107679649A (en) * | 2017-09-13 | 2018-02-09 | 珠海格力电器股份有限公司 | A kind of failure prediction method of electrical equipment, device, storage medium and electrical equipment |
| CN109492667A (en) * | 2018-10-08 | 2019-03-19 | 国网天津市电力公司电力科学研究院 | A kind of feature selecting discrimination method for non-intrusive electrical load monitoring |
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| WO2023020081A1 (en) * | 2021-08-18 | 2023-02-23 | 青岛海尔空调器有限总公司 | Method and apparatus for controlling air conditioner, and server |
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