CN111578444A - Air conditioner fault prediction method and device, storage medium and air conditioner - Google Patents
Air conditioner fault prediction method and device, storage medium and air conditioner Download PDFInfo
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- CN111578444A CN111578444A CN201910122140.9A CN201910122140A CN111578444A CN 111578444 A CN111578444 A CN 111578444A CN 201910122140 A CN201910122140 A CN 201910122140A CN 111578444 A CN111578444 A CN 111578444A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/52—Indication arrangements, e.g. displays
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/52—Indication arrangements, e.g. displays
- F24F11/526—Indication arrangements, e.g. displays giving audible indications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
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Abstract
The invention provides an air conditioner fault prediction method, an air conditioner fault prediction device, a storage medium and an air conditioner, wherein the method comprises the following steps: training a fault prediction model of the air conditioner through a neural network algorithm; when the air conditioner runs, obtaining current operation data of the air conditioner, wherein the operation data comprises: external data and internal data; inputting the current operation data of the air conditioner into the fault prediction model for fault prediction; and if the air conditioner is predicted to have a fault, sending corresponding prompt information to a user of the air conditioner. The scheme provided by the invention can predict the faults of the air conditioner and discover various faults of the air conditioner in time, particularly soft faults, thereby prolonging the service life of air conditioning equipment.
Description
Technical Field
The invention relates to the field of control, in particular to an air conditioner fault prediction method and device, a storage medium and an air conditioner.
Background
In the wave of home appliance intellectualization, the intelligent voice air conditioner becomes the key point of development. And through a voice interaction system, multifunctional voice operation and intelligent operation are realized. When various faults exist in the intelligent air conditioner, great influence is generated on an air conditioning system and user experience, energy waste is caused, the service life of equipment is shortened, and low-efficiency environmental comfort is brought. For a complex intelligent air conditioning system, various faults occurring in the system are difficult to find in time only by manpower.
The faults can be classified into hard faults and soft faults according to severity. The hard fault is mainly a fault of complete failure of a device, such as sudden faults of incapability of rotating a fan, complete blockage of a valve, complete failure of a sensor and the like. The soft faults are mainly various faults of device performance reduction or partial failure, such as fan coil scaling, valve leakage, sensor deviation and drifting and the like. Hard faults can be directly discovered and repaired in time, but soft faults are gradual, develop slowly and are often difficult to detect in the early stage, so that the damage is larger than that of hard faults. In case of a fault of the intelligent voice air conditioner with a complex system, the user experience effect can be greatly reduced, resource waste is caused, and the maintenance cost is high. The current air conditioner fault processing mode in the market is mainly to check and diagnose an air conditioner system and maintain the air conditioner system when a hard fault occurs. However, for the problem of soft faults which are slow in development, large in damage and difficult to detect in the initial stage, a reliable detection and diagnosis method is not provided temporarily, and how to predict the soft faults of the air conditioner in the early stage and repair the soft faults in time so as to avoid the soft faults from evolving into hard faults is a technical difficulty.
Disclosure of Invention
The main objective of the present invention is to overcome the above-mentioned drawbacks of the prior art, and to provide a method and an apparatus for predicting a failure of an air conditioner, a storage medium, and an air conditioner, so as to solve the problem in the prior art that a soft failure is gradual and is slow to develop and is difficult to be detected in the early stage.
The invention provides an air conditioner fault prediction method on one hand, which comprises the following steps: training a fault prediction model of the air conditioner through a neural network algorithm; when the air conditioner runs, obtaining current operation data of the air conditioner, wherein the operation data comprises: external data and internal data; inputting the current operation data of the air conditioner into the fault prediction model for fault prediction; and if the air conditioner is predicted to have a fault, sending corresponding prompt information to a user of the air conditioner.
Optionally, the pre-training of the fault prediction model includes: collecting historical operation data and fault type data of the air conditioner, wherein the historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner fails; and training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
Optionally, the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
Optionally, sending a corresponding prompt message to a user of the air conditioner includes: and broadcasting corresponding voice prompt information to the user.
Optionally, the method further comprises: if the air conditioner is predicted to have a fault, the current operation data of the air conditioner is used as fault data and sent to a server; and receiving a corresponding maintenance scheme which is returned by the server and is determined according to the fault data and used for the air conditioner.
Another aspect of the present invention provides an air conditioner fault prediction apparatus, including: the model training unit is used for training a fault prediction model of the air conditioner through a neural network algorithm; a data obtaining unit, configured to obtain current operation data of the air conditioner when the air conditioner is running, where the operation data includes: external data and internal data; the fault prediction unit is used for inputting the current operation data of the air conditioner into the fault prediction model to carry out fault prediction; and the information prompting unit is used for sending corresponding prompting information to a user of the air conditioner if the fault of the air conditioner is predicted.
Optionally, the model training unit trains the fault prediction model of the air conditioner through a neural network algorithm, including: collecting historical operation data and fault type data of the air conditioner, wherein the historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner fails; and training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
Optionally, the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
Optionally, the sending, by the information prompting unit, the corresponding prompting information to the user of the air conditioner includes: and broadcasting corresponding voice prompt information to the user.
Optionally, the method further comprises: the sending unit is used for sending the current operation data of the air conditioner to a server as fault data if the fault of the air conditioner is predicted; and the receiving unit is used for receiving the corresponding maintenance scheme of the air conditioner, which is determined according to the fault data and returned by the server.
A further aspect of the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the methods described above.
Yet another aspect of the present invention provides an air conditioner comprising a processor, a memory, and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of any of the methods described above when executing the program.
In another aspect, the present invention provides an air conditioner including any one of the air conditioner failure prediction apparatuses.
According to the technical scheme of the invention, the fault prediction model is trained through the neural network algorithm and the relevant historical operating data and fault category data of the air conditioner, the fault prediction is carried out on the air conditioner, various faults, particularly soft faults, of the air conditioner are found and processed in time, so that the service life of the air conditioner is prolonged, the equipment maintenance cost is reduced, energy is saved, the user experience effect is improved, when the fault is predicted possibly, the fault prediction and early warning can be accurately given in time, the fault data can be prompted to a user through voice, the fault data can be uploaded to a server, and a corresponding maintenance scheme can be formulated by the server side according to the fault data.
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 specification, illustrate embodiments 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 schematic diagram of an embodiment of an air conditioner fault prediction method provided by the present invention;
FIG. 2 is a schematic flow chart diagram illustrating one embodiment of training a fault prediction model of the air conditioner via a neural network algorithm, according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an embodiment of a method for predicting air conditioner faults according to the present invention;
fig. 4 is a schematic structural diagram of an embodiment of an air conditioner fault prediction apparatus provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
Fig. 1 is a schematic method diagram of an embodiment of an air conditioner fault prediction method provided by the present invention.
As shown in fig. 1, according to an embodiment of the present invention, the failure prediction method includes at least step S110, step S120, step S130, and step S140.
Step S110, training a fault prediction model of the air conditioner through a neural network algorithm;
specifically, the fault prediction model of the air conditioner may be trained in advance through an LSTM neural network algorithm. The LSTM (Long Short-Term Memory) is a time recursive neural network system and can be used for processing and predicting important events with relatively Long time intervals and relatively Long delays in a time sequence, so that a fault prediction model of the air conditioner can be trained through the LSTM neural network and the operation data of the air conditioner, and the fault prediction model with high accuracy and strong real-time performance can be trained.
Fig. 2 is a flowchart illustrating an embodiment of training a fault prediction model of the air conditioner through a neural network algorithm according to an embodiment of the present invention. As shown in fig. 2, in an embodiment, the step of training the fault prediction model of the air conditioner through the neural network algorithm may specifically include step S111 and step S112.
And step S111, collecting historical operation data and fault type data of the air conditioner.
The historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner breaks down. Because the soft fault is gradual, normal operation data when the air conditioner operates normally in history, corresponding fault operation data when the air conditioner has a soft fault and fault type data of the air conditioner are collected respectively, and a fault prediction model for predicting the fault of the air conditioner is trained through an LSTM neural network on the basis of the collected normal operation data, the collected fault operation data and the collected fault type data. The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power consumption and/or voice data, wherein the normal operation data comprises external data and internal data when the air conditioner operates normally, and the fault operation data comprises fault operation data when the air conditioner breaks down.
And step S112, training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
In particular, collecting different faults TiThe following historical operating data. The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data. Performing data preprocessing on the collected data, for example, preprocessing the data such as cleaning, standardizing, and denoising; extracting extrinsic data features F1(a, e, c) and internal data feature F2(t, w, m, p, s) and imported into the input layer of the LSTM model, where the extrinsic data features F1A in (a, e, c) is an indoor area parameterAnd e is an indoor temperature parameter, and c is an outdoor weather parameter. Internal data feature F2And t of (t, w, m, p, s) is an air conditioner set temperature parameter, w is an air conditioner wind speed parameter, m is an air conditioner mode parameter, p is an air conditioner electric quantity parameter, and s is an air conditioner voice parameter. Processing the layer in the middle of the LSTM model; at time t, the input gate, the forgetting gate and the output gate are according to the current input xtLast moment state ct-1And input h at the last momentt-1Respectively determining the retention, removal and output of information; finally outputting the fault type T corresponding to the external data and the internal dataiAnd finishing model training. For a certain neuron of the LSTM intermediate processing layer, xtInput to the neuron for time t (extrinsic data features and intrinsic data features), ct-1The state of the neuron at the previous time (t-1) (a value between 0 and 1, where 1 represents "complete retention", 0 represents "complete rejection"), and ht-1The input to the neuron by the neuron before the time t-1.
And step S120, acquiring the current operation data of the air conditioner when the air conditioner is operated.
The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
And step S130, inputting the current operation data of the air conditioner into the fault prediction model for fault prediction.
Specifically, after the current operation data of the air conditioner is subjected to preset processing, the current operation data is input into the fault prediction model trained in the previous step for fault prediction. The preset processing may specifically include at least one of missing value processing, normalization processing, and noise reduction processing. After the current operation data of the air conditioner is input into the fault prediction model, the possibility of fault occurrence is judged through a threshold value according to the prediction result of the model, wherein the threshold value can be an activation function in the model and is responsible for mapping from an input end to an output end. For example, mapping the input data and different fault types to [0,1], respectively, when all values are less than 0.5, taking the fault corresponding to the maximum value as the predicted fault type, and when there is a value greater than or equal to 0.5, taking all the faults corresponding to the values greater than 0.5 as the predicted fault type.
Step S140, if the air conditioner is predicted to be out of order, corresponding prompt information is sent to a user of the air conditioner.
In a specific embodiment, the corresponding voice prompt message may be broadcasted to the user. For example, if fouling of the air conditioning fan coil is predicted, a voice is broadcast to the user "please note that the air conditioner may have a failure with fouling of the fan coil".
Optionally, the method may further include sending current operation data of the air conditioner as fault data to a server if the air conditioner is predicted to have a fault; and receiving a corresponding maintenance scheme which is returned by the server and determined according to the fault data. The server can be specifically a cloud server, and can analyze the current fault data of the air conditioner and determine a corresponding maintenance scheme.
In order to clearly illustrate the technical solution of the present invention, an execution flow of the air conditioner fault prediction method provided by the present invention is described below with a specific embodiment.
Fig. 3 is a schematic method diagram of an embodiment of an air conditioner fault prediction method according to the present invention. The embodiment shown in FIG. 3 includes steps S1 through S7.
S1: training the LSTM model: the step S1 includes steps S11 to S15.
S11: historical operating data of the air conditioner, including internal and external historical data, is collected.
S12: data preprocessing: and preprocessing the historical operating data of the air conditioner such as missing value, standardization, noise reduction and the like.
S13: extracting extrinsic data features F1(a, e, c) and internal data feature F2(t, w, m, p, s) and imported into the input layer of the LSTM model. Wherein the external data characteristic F1A of (a, e, c) is an indoor area parameter, e is an indoor temperature parameter, and c is an outdoor dayAnd (4) gas parameters. Internal data feature F2And t of (t, w, m, p, s) is an air conditioner set temperature parameter, w is an air conditioner wind speed parameter, m is an air conditioner mode parameter, p is an air conditioner electric quantity parameter, and s is an air conditioner voice parameter.
S14: intermediate processing layers of the LSTM model: at time t, the input gate, the forgetting gate and the output gate are according to the current input xtLast moment state ct-1And input h at the last momentt-1Respectively determining the retention, removal and output of information; for a certain neuron of the LSTM intermediate processing layer, xtInput to the neuron for time t (extrinsic data features and intrinsic data features), ct-1The state of the neuron at the previous time (t-1) (a value between 0 and 1, where 1 represents "completely retained", 0 represents "completely discarded"), ht-1The input to the neuron by the neuron before the time t-1.
S15: and a fault output layer: finally outputting the fault type T corresponding to the external data and the internal dataiAnd finishing model training.
S2: real-time monitoring: and monitoring the state of the air conditioner in real time.
S3: current operational data, including internal data and external data, is collected.
S4: data preprocessing: and preprocessing the current operation data of the air conditioner, such as missing value processing, standardization, noise reduction and the like.
S5: a fault prediction model: and (4) utilizing the trained LSTM model to perform fault prediction.
S6: and (3) judging the possibility of failure: judging the possibility of the fault occurrence through a threshold value according to the prediction result of the model, if the fault does not occur, returning to the step S2, otherwise, executing the step S7.
S7: the types of faults are as follows: predicting the kind of fault that may occur.
Wherein the threshold in S6 is an activation function in the model, and is responsible for mapping the input end to the output end. And mapping the input data and different fault types to [0,1], when all values are less than 0.5, taking the fault corresponding to the maximum value as a predicted fault type, and when the values are more than or equal to 0.5, taking all the faults corresponding to the values more than 0.5 as predicted fault types.
Fig. 4 is a schematic structural diagram of an embodiment of an air conditioner fault prediction apparatus provided in the present invention. As shown in fig. 4, the air conditioner fault prediction apparatus 100 includes a model training unit 110, a data acquisition unit 120, and a fault prediction unit 130 information presentation unit 140.
The model training unit 110 is configured to train a fault prediction model of the air conditioner through a neural network algorithm; the data obtaining unit 120 is configured to obtain current operation data of the air conditioner when the air conditioner is running, where the operation data includes: external data and internal data; the fault prediction unit 130 is configured to input current operation data of the air conditioner into the fault prediction model for fault prediction; the information prompt unit 140 is configured to send corresponding prompt information to a user of the air conditioner if the failure of the air conditioner is predicted.
The model training unit 110 trains a fault prediction model of the air conditioner through a neural network algorithm. Specifically, the fault prediction model of the air conditioner may be trained in advance through an LSTM neural network algorithm. The LSTM (Long Short-term memory network) is a time recursive neural network system and can be used for processing and predicting important events with relatively Long time intervals and relatively Long delays in a time sequence, so that a fault prediction model of the air conditioner can be trained through the LSTM neural network and the operation data of the air conditioner, and the fault prediction model with high accuracy and strong real-time performance can be trained.
The step of training the fault prediction model of the air conditioner through the neural network algorithm may specifically include: collecting historical operation data and fault category data of the air conditioner; and training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
The historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner breaks down. Because the soft fault is gradual, normal operation data when the air conditioner operates normally in history, corresponding fault operation data when the air conditioner has a soft fault and fault type data of the air conditioner are collected respectively, and a fault prediction model for predicting the soft fault of the air conditioner is trained through an LSTM neural network on the basis of the collected normal operation data, the collected fault operation data and the collected fault type data. The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power consumption and/or voice data, wherein the normal operation data comprises external data and internal data when the air conditioner operates normally, and the fault operation data comprises fault operation data when the air conditioner breaks down.
And training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data. In particular, collecting different faults TiThe following historical operating data. The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data. Performing data preprocessing on the collected data, for example, preprocessing the data such as cleaning, standardizing, and denoising; extracting extrinsic data features F1(a, e, c) and internal data feature F2(t, w, m, p, s) and imported into the input layer of the LSTM model, where the extrinsic data features F1And a of (a, e, c) is an indoor area parameter, e is an indoor temperature parameter, and c is an outdoor weather parameter. Internal data feature F2And t of (t, w, m, p, s) is an air conditioner set temperature parameter, w is an air conditioner wind speed parameter, m is an air conditioner mode parameter, p is an air conditioner electric quantity parameter, and s is an air conditioner voice parameter. Processing the layer in the middle of the LSTM model; at the intermediate processing level of the LSTM model, at time t, an "input gate", a "forgetting gate" and an "output gate" are operated according to the current input xtLast moment state ct-1And input h at the last momentt-1Determining the retention, removal and output of information, respectivelyFor a certain neuron of the LSTM intermediate processing layer, xtInput to the neuron for time t (extrinsic data features and intrinsic data features), ct-1The state of the neuron at the previous time (t-1) (a value between 0 and 1, where 1 represents "completely retained", 0 represents "completely discarded"), ht-1The input to the neuron by the neuron before the time t-1. Finally outputting external data and internal data corresponding to the fault type T at a fault output layeriAnd finishing model training.
The data obtaining unit 120 obtains current operation data of the air conditioner when the air conditioner is operated. The operating data may specifically comprise external data and internal data; the external data includes: indoor area data, indoor environment data, and/or outdoor environment data; the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
The fault prediction unit 130 inputs the current operation data of the air conditioner into the fault prediction model for fault prediction. Specifically, after the current operation data of the air conditioner is subjected to preset processing, the current operation data is input into the fault prediction model trained in the previous step for fault prediction. The preset processing may specifically include at least one of missing value processing, normalization processing, and noise reduction processing. After the current operation data of the air conditioner is input into the fault prediction model, the possibility of fault occurrence is judged through a threshold value according to the prediction result of the model, wherein the threshold value can be an activation function in the model and is responsible for mapping from an input end to an output end. For example, mapping the input data and different fault types to [0,1], respectively, when all values are less than 0.5, taking the fault corresponding to the maximum value as the predicted fault type, and when there is a value greater than or equal to 0.5, taking all the faults corresponding to the values greater than 0.5 as the predicted fault type.
The information prompt unit 140 sends corresponding prompt information to the user of the air conditioner. In one embodiment, the information prompt unit 140 may broadcast a corresponding voice prompt message to the user. For example, if fouling of the air conditioning fan coil is predicted, a voice is broadcast to the user "please note that the air conditioner may have a failure with fouling of the fan coil".
Optionally, the apparatus may further include a transmitting unit and a receiving unit (not shown). The sending unit is used for sending the current operation data of the air conditioner to a server as fault data if the fault of the air conditioner is predicted; and the receiving unit is used for receiving the corresponding maintenance scheme of the air conditioner, which is determined according to the fault data and returned by the server. The server can be specifically a cloud server, and can analyze the current fault data of the air conditioner and determine a corresponding maintenance scheme.
The invention also provides a storage medium corresponding to the air conditioner fault prediction method, and a computer program is stored on the storage medium, and when the program is executed by a processor, the program realizes the steps of any one of the methods.
The invention also provides an air conditioner corresponding to the air conditioner fault prediction method, which comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of any one of the methods when executing the program.
The invention also provides an air conditioner corresponding to the air conditioner fault prediction device, which comprises the air conditioner fault prediction device.
Therefore, according to the scheme provided by the invention, the fault prediction model is trained through the neural network algorithm and the relevant historical operating data and fault category data of the air conditioner, the fault prediction is carried out on the air conditioner, various faults, particularly soft faults, of the air conditioner are found and processed in time, the service life of the air conditioner is prolonged, the equipment maintenance cost is reduced, the energy is saved, the user experience effect is improved, when the fault is predicted possibly, the fault prediction and early warning can be accurately given in time, the fault is prompted to a user through voice, the fault data can be uploaded to the server, and the server side can make a corresponding maintenance scheme according to the fault data.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the invention and the following claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or a combination of any of these. In addition, each functional unit may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
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 the parts serving as the control device 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.
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 above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (13)
1. An air conditioner fault prediction method is characterized by comprising the following steps:
training a fault prediction model of the air conditioner through a neural network algorithm;
when the air conditioner runs, obtaining current operation data of the air conditioner, wherein the operation data comprises: external data and internal data;
inputting the current operation data of the air conditioner into the fault prediction model for fault prediction;
and if the air conditioner is predicted to have a fault, sending corresponding prompt information to a user of the air conditioner.
2. The method of claim 1, wherein pre-training the fault prediction model comprises:
collecting historical operation data and fault type data of the air conditioner, wherein the historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner fails;
and training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
3. The method according to claim 1 or 2,
the external data includes: indoor area data, indoor environment data, and/or outdoor environment data;
the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
4. The method according to any one of claims 1 to 3, wherein sending a corresponding prompt message to a user of the air conditioner comprises:
and broadcasting corresponding voice prompt information to the user.
5. The method according to any one of claims 1-4, further comprising:
if the air conditioner is predicted to have a fault, the current operation data of the air conditioner is used as fault data and sent to a server;
and receiving a corresponding maintenance scheme which is returned by the server and is determined according to the fault data and used for the air conditioner.
6. An air conditioner failure prediction device, comprising:
the model training unit is used for training a fault prediction model of the air conditioner through a neural network algorithm;
a data obtaining unit, configured to obtain current operation data of the air conditioner when the air conditioner is running, where the operation data includes: external data and internal data;
the fault prediction unit is used for inputting the current operation data of the air conditioner into the fault prediction model to carry out fault prediction;
and the information prompting unit is used for sending corresponding prompting information to a user of the air conditioner if the fault of the air conditioner is predicted.
7. The apparatus of claim 6, wherein the model training unit trains the fault prediction model of the air conditioner through a neural network algorithm, comprising:
collecting historical operation data and fault type data of the air conditioner, wherein the historical operation data comprises normal operation data when the air conditioner operates normally and fault operation data when the air conditioner fails;
and training a fault prediction model of the air conditioner through an LSTM neural network according to the normal operation data, the fault operation data and the fault category data.
8. The apparatus according to claim 6 or 7,
the external data includes: indoor area data, indoor environment data, and/or outdoor environment data;
the internal data includes: setting temperature, wind speed, mode, power usage, and/or voice data.
9. The apparatus according to any one of claims 6 to 8, wherein the information prompting unit sends corresponding prompting information to the user of the air conditioner, and comprises:
and broadcasting corresponding voice prompt information to the user.
10. The apparatus of any one of claims 6-9, further comprising:
the sending unit is used for sending the current operation data of the air conditioner to a server as fault data if the fault of the air conditioner is predicted;
and the receiving unit is used for receiving the corresponding maintenance scheme of the air conditioner, which is determined according to the fault data and returned by the server.
11. A storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
12. An air conditioner comprising a processor, a memory, and a computer program stored on the memory and operable on the processor, the processor implementing the steps of the method of any one of claims 1-5 when executing the program.
13. An air conditioner characterized by comprising the air conditioner failure prediction apparatus according to any one of claims 6 to 10.
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