CN110471380A - A kind of air conditioning failure monitoring and method for early warning for smart home system - Google Patents

A kind of air conditioning failure monitoring and method for early warning for smart home system Download PDF

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Publication number
CN110471380A
CN110471380A CN201910755029.3A CN201910755029A CN110471380A CN 110471380 A CN110471380 A CN 110471380A CN 201910755029 A CN201910755029 A CN 201910755029A CN 110471380 A CN110471380 A CN 110471380A
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smart home
error
home system
early warning
neural network
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王瑞祥
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Sichuan Changhong Electric Co Ltd
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Abstract

The invention discloses a kind of air conditioning failure monitorings and method for early warning for smart home system, belong to Smart Home technical field, the method includes the steps 1: obtaining the history running parameter of each device inside air-conditioning；Step 2: neural network being trained using the history running parameter of each device accessed by step 1 as input, the neural network model of each device parameters is obtained, and the input parameter given under each device current environmental condition obtains the discreet value of each device；Step 3: obtaining the running parameter of each device in real time, and calculate operating error in conjunction with the discreet value of step 2 gained；Step 4: assigned error allowed band, if the operating error being calculated in step 3 exceeds allowable range of error, warning information and warning information are then sent by intelligentized Furniture system, when solving air-conditioning internal components in the prior art has damage sign, operation maintenance personnel, which has no way of learning, leads to problem high for the maintenance cost of air-conditioning and that difficulty is big.

Description

A kind of air conditioning failure monitoring and method for early warning for smart home system
Technical field
The present invention relates to Smart Home technical field, is a kind of air-conditioning failure prison for smart home system specifically Control and method for early warning.
Background technique
Smart home was constantly introduced in the family life of people in recent years, and wherein air-conditioning is as smart home system In important component, quantity also constantly increases therewith, this make enterprise and operation maintenance personnel for air-conditioning O&M cost and Difficulty is gradually increased.When air-conditioning internal components have damage sign, operation maintenance personnel has no way of learning, causes enterprise for air-conditioning Maintenance cost is higher to be reduced.
Summary of the invention
The purpose of the present invention is to provide a kind of air conditioning failure monitorings and method for early warning for smart home system, are used for When solving air-conditioning internal components in the prior art has damage sign, operation maintenance personnel has no way of learning the maintenance caused for air-conditioning Problem at high cost and big difficulty.
The present invention is solved the above problems by following technical proposals:
A kind of air conditioning failure monitoring and method for early warning for smart home system, described method includes following steps:
Step 1: obtaining the history running parameter of each device inside air-conditioning；
Step 2: neural network being instructed using the history running parameter of each device accessed by step 1 as input Practice, obtains the neural network model of each device parameters, and the input parameter given under each device current environmental condition obtains respectively The discreet value of a device；
Step 3: obtaining the running parameter of each device in real time, and calculate operating error in conjunction with the discreet value of step 2 gained；
Step 4: assigned error allowed band, if the operating error being calculated in step 3 exceeds allowable range of error, Warning information and warning information are sent by intelligentized Furniture system.
Further, the neural network is BP neural network.
Further, the BP neural network training process the following steps are included:
Step 2.1: netinit, then hidden layer does output and calculates；
Step 2.2: doing error calculation, then update weight and update threshold value again；
Step 2.3: judging whether iteration terminates, return step 2.1 carries out hidden layer output meter again if being not finished It calculates；The output estimation value if being over.
Further, the discreet value includes atmospheric pressure value and temperature value.
Further, it is sent a warning message in the step 4 by the communication server of smart home system and is believed with early warning Breath.
Further, the allowable range of error is no more than the 20% of specified normal range of operation numerical value.
Compared with prior art, the present invention have the following advantages that and the utility model has the advantages that
The present invention is monitored by the running parameter to each device in air-conditioning inside, the nerve net with history parameters training The discreet value that network model obtains, which is compared, obtains operating error, and certain operating error allowed band is arranged, when calculating Operating error sent a warning message by the communication server of Internet of things system and early warning when exceed allowable range of error Information, operation maintenance personnel can timely overhaul air-conditioning after receiving message, prevent from damaging, and reduce O&M cost.
Detailed description of the invention
Fig. 1 is air conditioning failure monitoring of the invention and method for early warning data transmission stream journey schematic diagram；
Fig. 2 is the discreet value schematic diagram of calculation flow of BP neural network model of the invention.
Specific embodiment
The present invention is described in further detail below with reference to embodiment, embodiments of the present invention are not limited thereto.
Embodiment 1:
In conjunction with shown in attached drawing 1, a kind of air conditioning failure monitoring and method for early warning for smart home system, the method packet Include following steps:
Step 1: obtaining the history running parameter of each device inside air-conditioning；
Step 2: BP neural network being carried out using the history running parameter of each device accessed by step 1 as input Training, obtains the BP neural network model of each device parameters, and the input parameter given under each device current environmental condition obtains To the discreet value of each device, wherein the training process of BP neural network includes the following steps, as shown in Figure 2:
Step 2.1: netinit, then hidden layer does output and calculates；
Step 2.2: doing error calculation, then update weight and update threshold value again；
Step 2.3: judging whether iteration terminates, return step 2.1 carries out hidden layer output meter again if being not finished It calculates；The output estimation value if being over.
Step 3: obtaining the running parameter of each device in real time, and calculate operating error in conjunction with the discreet value of step 2 gained；
Step 4: assigned error allowed band is no more than the 20% of specified normal range of operation numerical value, if step 3 is fallen into a trap Obtained operating error exceeds allowable range of error, then sends warning information and warning information by intelligentized Furniture system.
This method is monitored by the running parameter to each device in air-conditioning inside, the nerve net with history parameters training The discreet value that network model obtains, which is compared, obtains operating error, and certain operating error allowed band is arranged, when calculating Operating error sent a warning message by the communication server of Internet of things system and early warning when exceed allowable range of error Information, operation maintenance personnel can timely overhaul air-conditioning after receiving message, prevent from damaging, and reduce O&M cost and fortune Tie up difficulty.
Specifically, carrying out the BP neural network model foundation of flabellum working condition by taking air-conditioning fan heater flabellum as an example.It collects Warm-air drier flabellum last year work state information: flabellum temperature, running speed, operating ambient temperature, working time etc..Using above-mentioned BP neural network model of the parameter training about air-conditioning fan heater flabellum working condition.If you need to the work temperature for predicting of that month flabellum Degree.Environment temperature at this time is input parameter, and by the successful BP neural network model of training, the flabellum of output estimation works warm Degree.When error of the operating temperature compared with predicted temperature of practical flabellum, 1.2 times of normal working temperature are arranged to be allowed for error Range, 1.2 times allowed in error when operating temperature, that is, when being no more than the 20% of normal temperature, it is believed that flabellum works just Often.If error is beyond the waving interval allowed, it is believed that flabellum working condition is abnormal, needs to carry out the row in advance of failure It looks into.Related operation maintenance personnel is given to carry out the operation irregularity early warning of air-conditioning flabellum by smart home system.
Although reference be made herein to invention has been described for explanatory embodiment of the invention, and above-described embodiment is only this hair Bright preferable embodiment, embodiment of the present invention are not limited by the above embodiments, it should be appreciated that those skilled in the art Member can be designed that a lot of other modification and implementations, these modifications and implementations will fall in principle disclosed in the present application Within scope and spirit.

Claims (6)

1. a kind of air conditioning failure monitoring and method for early warning for smart home system, which is characterized in that the method includes such as Lower step:
Step 1: obtaining the history running parameter of each device inside air-conditioning；
Step 2: neural network is trained using the history running parameter of each device accessed by step 1 as input, The neural network model of each device parameters is obtained, and the input parameter given under each device current environmental condition obtains each device The discreet value of part；
Step 3: obtaining the running parameter of each device in real time, and calculate operating error in conjunction with the discreet value of step 2 gained；
Step 4: assigned error allowed band passes through if the operating error being calculated in step 3 exceeds allowable range of error Intelligentized Furniture system sends warning information and warning information.
2. the air conditioning failure monitoring and method for early warning according to claim 1 for smart home system, which is characterized in that The neural network is BP neural network.
3. the air conditioning failure monitoring and method for early warning according to claim 2 for smart home system, which is characterized in that The training process of the BP neural network the following steps are included:
Step 2.1: netinit, then hidden layer does output and calculates；
Step 2.2: doing error calculation, then update weight and update threshold value again；
Step 2.3: judging whether iteration terminates, return step 2.1 carries out hidden layer output calculating again if being not finished；Such as Fruit is over then output estimation value.
4. the air conditioning failure monitoring and method for early warning according to claim 1 for smart home system, which is characterized in that The discreet value includes atmospheric pressure value and temperature value.
5. the air conditioning failure monitoring and method for early warning according to claim 1 for smart home system, which is characterized in that The communication server in the step 4 by smart home system sends a warning message and warning information.
6. the air conditioning failure monitoring and method for early warning according to claim 1 for smart home system, which is characterized in that The allowable range of error is no more than the 20% of specified normal range of operation numerical value.
CN201910755029.3A 2019-08-15 2019-08-15 A kind of air conditioning failure monitoring and method for early warning for smart home system Pending CN110471380A (en)

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