CN109784574A - The prediction technique and device of equipment state, storage medium, electronic device - Google Patents
The prediction technique and device of equipment state, storage medium, electronic device Download PDFInfo
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Abstract
The present invention provides a kind of prediction technique of equipment state and device, storage medium, electronic devices, wherein this method comprises: acquisition target device history running parameter during the sampling period and history environment parameter；Physical prediction model is established using the history running parameter, and establishes to return using the history environment parameter and establishes model；The state value of the target device is predicted according to the physical prediction model and the regressive prediction model, wherein the state value is used to characterize the probability that the target device breaks down.Through the invention, it solves in the prior art using the technical problem of the failure rate inaccuracy of single model prediction equipment.
Description
Technical field
The present invention relates to energy technology field, prediction techniques and device in particular to a kind of equipment state, storage
Medium, electronic device.
Background technique
In the prior art, predictive maintenance (Predictive Maintenance, abbreviation PdM) is that " industry 4.0 " proposes
One of key innovations.Based on continuous measurement and analysis, predictive maintenance can predict such as machine parts residue use
Organs' index such as service life.Crucial operational parameter data with aid decision, can judge the operating status of machine, optimize the dimension of machine
Shield opportunity.
In integrated energy system, since there are a large amount of equipment, and there is the relationship of coupling in equipment each other, any
A part goes wrong, and can all whole system be caused to go wrong.And when equipment goes wrong, then go to place under repair, and often
Industrial production can be delayed.So the predictive maintenance of equipment is just particularly important.
In traditional predictive maintenance, for equipment modeling, if only fitting dynamic using inputoutput data
Curve, and it is inaccurate for obtaining prediction result because model is during foundation, it is main it is emphasised that the data collected it
Between relationship, do not reflect the physical characteristic of equipment.And modeled only for the physical characteristic of equipment, in different areas,
In season, under the influence of the factors such as facility switching strategy, obtained prediction result is also inaccuracy, can ignore the true work of equipment
Make state, while the model in physical modeling needs a large amount of expertise.In the prior art simultaneously using single prediction mode
It is unable to the fault condition that Accurate Prediction goes out equipment.
For the above-mentioned problems in the prior art, at present it is not yet found that the solution of effect.
Summary of the invention
The embodiment of the invention provides a kind of prediction technique of equipment state and device, storage medium, electronic devices.
According to one embodiment of present invention, a kind of prediction technique of equipment state is provided, comprising:
Acquire target device history running parameter during the sampling period and history environment parameter；
Physical prediction model is established using the history running parameter, and is established and is returned using the history environment parameter
Establish model；
The state value of the target device is predicted according to the physical prediction model and the regressive prediction model, wherein
The state value is used to characterize the probability that the target device breaks down.
Optionally, the state value of the target device is predicted according to the physical prediction model and the regressive prediction model
Include:
Obtain current operating parameters and current environment parameter of the target device in predetermined period；
The current operating parameters are inputted into the physical prediction model and obtain the first prediction result, by the current environment
Parameter inputs the regressive prediction model and obtains the second prediction result；
First prediction result and second prediction result are input to default forecasting effective measure model, obtained described
State value of the target device in described predetermined period.
Optionally, establishing physical prediction model using the history running parameter includes:
Determine the fault point of the target device；
For each fault point select an original physical model, wherein the original physical model include it is following it
One: pipe crack equation, equation of heat transfer, ACOUSTIC WAVE EQUATION, air force equation；
Using the history running parameter training original physical model, the physical prediction model is obtained.
Optionally, establishing regressive prediction model using the history environment parameter includes:
The history environment parameter is analyzed, the recurrence characteristic of the target device is obtained；
Select corresponding with the recurrence characteristic original regression model, wherein the original regression model include it is following it
One: integrating rolling average autoregression Arima model, seasonalARIMA model；
Using original regression model described in the history environment parameter training, the regressive prediction model is obtained.
Optionally, acquisition target device history running parameter during the sampling period and history environment parameter include:
The input data of target device during the sampling period, output data, and operation number are acquired in first database
According to, and the environment temperature of target device during the sampling period, ambient humidity, light intensity are acquired in the second database.
Optionally, in the state for predicting the target device according to the physical prediction model and the regressive prediction model
After value, the method also includes:
When the state value that prediction obtains is greater than threshold value, generates and be used to indicate the early warning that the target device needs to safeguard
Information.
According to another embodiment of the invention, a kind of prediction meanss of equipment state are provided, comprising:
Acquisition module, for acquiring target device history running parameter during the sampling period and history environment parameter；
Module is established, for establishing physical prediction model, and the use history ring using the history running parameter
Border parameter, which is established to return, establishes model；
Prediction module, for predicting the target device according to the physical prediction model and the regressive prediction model
State value, wherein the state value is used to characterize the probability that the target device breaks down.
Optionally, the prediction module includes:
Acquiring unit, for obtaining current operating parameters and current environment ginseng of the target device in predetermined period
Number；
Processing unit obtains the first prediction result for the current operating parameters to be inputted the physical prediction model,
The current environment parameter is inputted into the regressive prediction model and obtains the second prediction result；
Predicting unit, for first prediction result and second prediction result to be input to default forecasting effective measure
Model obtains state value of the target device in described predetermined period.
Optionally, the module of establishing includes:
Determination unit, for determining the fault point of the target device；
First selecting unit, for selecting an original physical model for each fault point, wherein the original object
Managing model includes following one: pipe crack equation, equation of heat transfer, ACOUSTIC WAVE EQUATION, air force equation；
First training unit, for obtaining the object using the history running parameter training original physical model
Manage prediction model.
Optionally, the module of establishing includes:
Analytical unit obtains the recurrence characteristic of the target device for analyzing the history environment parameter；
Second selecting unit, for selecting original regression model corresponding with the recurrence characteristic, wherein described original time
Returning model includes following one: integrating rolling average autoregression Arima model, seasonalARIMA model；
Second training unit, for obtaining described return using original regression model described in the history environment parameter training
Return prediction model.
Optionally, the acquisition module includes:
Acquisition unit exports number for acquiring the input data of target device during the sampling period in first database
According to and operation data, and target device environment temperature during the sampling period, environmental wet are acquired in the second database
Degree, light intensity.
Optionally, described device further include: generation module is used in the prediction module according to the physical prediction model
After the state value for predicting the target device with the regressive prediction model, it is greater than threshold value in the state value that prediction obtains
When, it generates and is used to indicate the warning information that the target device needs to safeguard.
According to still another embodiment of the invention, a kind of storage medium is additionally provided, meter is stored in the storage medium
Calculation machine program, wherein the computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
According to still another embodiment of the invention, a kind of electronic device, including memory and processor are additionally provided, it is described
Computer program is stored in memory, the processor is arranged to run the computer program to execute any of the above-described
Step in embodiment of the method.
Through the invention, acquisition target device history running parameter during the sampling period and history environment parameter, and make
Physical prediction model is established with the history running parameter, and establishes to return using the history environment parameter and establishes model,
The state value that the target device is finally predicted according to the physical prediction model and the regressive prediction model is more by acquiring
The device parameter of a dimension, and establish physics and return two kinds of models, forecast analysis, solution are carried out to the probability of malfunction of target device
It has determined in the prior art using the technical problem of the failure rate inaccuracy of single model prediction equipment.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of hardware block diagram of the prediction terminal of equipment state of the embodiment of the present invention；
Fig. 2 is the flow chart of the prediction technique of equipment state according to an embodiment of the present invention；
Fig. 3 is the flow diagram that the embodiment of the present invention predicts the state of equipment；
Fig. 4 is the structural block diagram of the prediction meanss of equipment state according to an embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
Embodiment 1
Embodiment of the method provided by the embodiment of the present application one can in server, the network terminal, terminal or
It is executed in similar arithmetic unit.For running on the server, Fig. 1 is a kind of the pre- of equipment state of the embodiment of the present invention
Survey the hardware block diagram of terminal.As shown in Figure 1, server 10 may include at one or more (only showing one in Fig. 1)
It manages device 102 (processing unit that processor 102 can include but is not limited to Micro-processor MCV or programmable logic device FPGA etc.)
Memory 104 for storing data, optionally, above-mentioned server can also include the transmission device for communication function
106 and input-output equipment 108.It will appreciated by the skilled person that structure shown in FIG. 1 is only to illustrate, simultaneously
The structure of above-mentioned server is not caused to limit.For example, server 10 may also include than shown in Fig. 1 more or less groups
Part, or with the configuration different from shown in Fig. 1.
Memory 104 can be used for storing computer program, for example, the software program and module of application software, such as this hair
The corresponding computer program of the prediction technique of equipment state in bright embodiment, processor 102 are stored in memory by operation
Computer program in 104 realizes above-mentioned method thereby executing various function application and data processing.Memory 104
May include high speed random access memory, may also include nonvolatile memory, as one or more magnetic storage device, flash memory,
Or other non-volatile solid state memories.In some instances, memory 104 can further comprise relative to processor 102
Remotely located memory, these remote memories can pass through network connection to server 10.The example of above-mentioned network includes
But be not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmitting device 106 is used to that data to be received or sent via a network.Above-mentioned network specific example may include
The wireless network that the communication providers of server 10 provide.In an example, transmitting device 106 includes a network adapter
(Network Interface Controller, referred to as NIC), can be connected by base station with other network equipments so as to
It is communicated with internet.In an example, transmitting device 106 can be radio frequency (Radio Frequency, referred to as RF)
Module is used to wirelessly be communicated with internet.
A kind of prediction technique of equipment state is provided in the present embodiment, and Fig. 2 is equipment according to an embodiment of the present invention
The flow chart of the prediction technique of state, as shown in Fig. 2, the process includes the following steps:
Step S202, acquisition target device history running parameter during the sampling period and history environment parameter；
Step S204 is established physical prediction model using the history running parameter, and is joined using the history environment
Number, which is established to return, establishes model；
Step S206 predicts the state of the target device according to the physical prediction model and the regressive prediction model
Value, wherein the state value is used to characterize the probability that the target device breaks down.It, can also be into other than failure rate
The fault messages such as one-step prediction time of failure.
Through the above steps, joined by acquisition target device history running parameter during the sampling period and history environment
Number, and physical prediction model is established using the history running parameter, and establish to return using the history environment parameter and build
Formwork erection type finally predicts the state value of the target device according to the physical prediction model and the regressive prediction model, leads to
The device parameter for acquiring multiple dimensions is crossed, and establishes physics and returns two kinds of models, the probability of malfunction of target device is carried out pre-
Analysis is surveyed, is solved in the prior art using the technical problem of the failure rate inaccuracy of single model prediction equipment.
Optionally, the executing subject of above-mentioned steps can be plant maintenance platform, and server, terminal etc. specifically can be
Processor, algoritic module etc., but not limited to this.
The application scenarios of the present embodiment can be applied and be detected etc. in scenes in the node O&M of energy network, equipment fault,
Target device can be gas internal-combustion engine, waste heat boiler, steam boiler, bromine cooling machine, photovoltaic apparatus, earth source heat pump, wind energy plant,
Energy storage device etc..
In an embodiment of the present embodiment, predicted according to the physical prediction model and the regressive prediction model
The state value of the target device includes:
S11 obtains current operating parameters and current environment parameter of the target device in predetermined period；Prediction week
Phase can be above-mentioned using the period, be also possible to other times section, current operating parameters and current environment parameter can represent mesh
Operating condition in the marking device proximal segment time；
The current operating parameters are inputted the physical prediction model and obtain the first prediction result by S12, will be described current
Environmental parameter inputs the regressive prediction model and obtains the second prediction result；
First prediction result and second prediction result are input to default forecasting effective measure model, obtained by S13
State value of the target device in described predetermined period.
Specifically, the default forecasting effective measure model of the present embodiment can be a Weighted Average Algorithm either physical
Equal algorithm.
Optionally, the event that physical prediction model comprises determining that the target device is established using the history running parameter
Barrier point；An original physical model is selected for each fault point, wherein the original physical model includes following one:
Pipe crack equation, equation of heat transfer, ACOUSTIC WAVE EQUATION, air force equation；Use the history running parameter training original
Beginning physical model obtains the physical prediction model.
Optionally, establishing regressive prediction model using the history environment parameter includes: the analysis history environment parameter,
Obtain the recurrence characteristic of the target device；Select original regression model corresponding with the recurrence characteristic, wherein described original
Regression model includes following one: integrating rolling average autoregression (Autoregressive Integrated Moving
Average model, Arima) model, seasonalARIMA model；Using original time described in the history environment parameter training
Return model, obtains the regressive prediction model.
Optionally, acquisition target device history running parameter during the sampling period and history environment parameter include: the
The input data of target device during the sampling period, output data and operation data are acquired in one database, and second
The environment temperature of target device during the sampling period, ambient humidity, light intensity are acquired in database.
In an embodiment of the present embodiment, pre- according to the physical prediction model and the regressive prediction model
It surveys after the state value of the target device, further includes: when the state value that prediction obtains is greater than threshold value, generation is used to indicate
The target device needs the warning information safeguarded.Further, the difference of threshold value can also be higher than according to predicted value, is generated
Different grades of warning information, for example, being higher than, threshold value is more, and warning level is higher, and the probability that target device breaks down is got over
Height, equipment is more dangerous, needs emergency service.
The complete scheme of the present embodiment is illustrated below, Fig. 3 is that the embodiment of the present invention carries out in advance the state of equipment
The flow diagram of survey, comprising:
S31. a period is selected, from scada (data acquisition and supervisor control) and weather environment database
Data are obtained, the input of equipment is obtained in scada, is exported, operation data, collects environment temperature, humidity, light in weather data library
The data such as strong.Photovoltaic, the presence of the equipment such as underground heat, needs to obtain environmental data.
S32. the point for being likely to occur failure to equipment is classified, and is selected suitable physical model and is established the differential equation.
The device data being collected into is tested simultaneously, sees whether data have the properties such as periodical, the suitable recurrence mould of reselection
Type.
S33. by the data of the environmental data and equipment collected, the relevant parameter of model is solved in physical model,
Obtain complete physical model.So establishing two kinds of models, and applied forecasting availability model reasonably divides two kinds of results
Analysis, so as to obtain more accurately prediction result.
S34. go out the dynamical equation of equipment by Regression Model Simulator.
S35. the equation calculation prediction result obtained by step S33 and step S34.
S36. by prediction result input prediction availability model, final result is obtained.As result display apparatus needs to tie up
Shield, then safeguarded.
For the equipment after maintenance, above-mentioned 1-6 step, more new physics and regression model are repeated at once, and update.For not
The equipment of maintenance, with the increase of operation hours, the physical model and regression model of corresponding equipment can all become, and be
The accuracy for guaranteeing predictive maintenance result, needs to repeat at periodic or other desired above-mentioned steps, new predictive maintenance result is calculated.
In the present embodiment, the dynamic model of equipment constructs the differential equation of equipment fault, passes through from the principle of physics
Evolution equation predicts the severity of failure.Pass through the input of collecting device, output, operation data and facility environment data
(such as photovoltaic apparatus needs temperature, humidity, the data such as sunshine), can determine the parameter of the differential equation, and then can pass through prediction
Failure changes the fault time to determine equipment.The device systems complicated for one, it is assumed that dynamic model meets certain relationship,
Then by the data that observe output and input estimate the parameter of model allow we assume that model be similar to
Real dynamic model.In the technical program, carried out for same equipment, while using failure predication of two kinds of models to equipment
Modeling, one is physical model (the building failure differential equation), the dynamic of two fittings obtained for the input and output by data
Model.Two kinds of modelings bring two kinds of prediction results, in this two kinds of results, using forecasting effective measure model, finally obtain equipment
The prediction result of failure.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described because
According to the present invention, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, and related actions and modules is not necessarily of the invention
It is necessary.
Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation
The method of example can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but it is very much
In the case of the former be more preferably embodiment.Based on this understanding, technical solution of the present invention is substantially in other words to existing
The part that technology contributes can be embodied in the form of software products, which is stored in a storage
In medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, calculate
Machine, server or network equipment etc.) method that executes each embodiment of the present invention.
Embodiment 2
A kind of load prediction device neural network based is additionally provided in the present embodiment, and the device is for realizing above-mentioned
Embodiment and preferred embodiment, the descriptions that have already been made will not be repeated.As used below, term " module " can be real
The combination of the software and/or hardware of existing predetermined function.Although device described in following embodiment is preferably realized with software,
But the realization of the combination of hardware or software and hardware is also that may and be contemplated.
Fig. 4 is the structural block diagram of the prediction meanss of equipment state according to an embodiment of the present invention, as shown in figure 4, the device
Include: acquisition module 40, establish module 42, prediction module 44, wherein
Acquisition module 40, for acquiring target device history running parameter during the sampling period and history environment parameter；
Module 42 is established, for establishing physical prediction model, and the use history using the history running parameter
Environmental parameter, which is established to return, establishes model；
Prediction module 44, for predicting the target device according to the physical prediction model and the regressive prediction model
State value, wherein the state value is used to characterize the probability that the target device breaks down.
Optionally, the prediction module includes: acquiring unit, for obtaining the target device working as in predetermined period
Preceding running parameter and current environment parameter；Processing unit, for the current operating parameters to be inputted the physical prediction model
The first prediction result is obtained, the current environment parameter is inputted into the regressive prediction model and obtains the second prediction result；Prediction
Unit obtains institute for first prediction result and second prediction result to be input to default forecasting effective measure model
State state value of the target device in described predetermined period.
Optionally, the module of establishing comprises determining that unit, for determining the fault point of the target device；First choosing
Unit is selected, for selecting an original physical model for each fault point, wherein the original physical model includes following
One of: pipe crack equation, equation of heat transfer, ACOUSTIC WAVE EQUATION, air force equation；First training unit, for described in use
The history running parameter training original physical model, obtains the physical prediction model.
Optionally, the module of establishing includes: analytical unit, for analyzing the history environment parameter, obtains the mesh
The recurrence characteristic of marking device；Second selecting unit, for selecting original regression model corresponding with the recurrence characteristic, wherein
The original regression model includes following one: integrating rolling average autoregression Arima model, seasonalARIMA model；The
Two training units, for obtaining the regressive prediction model using original regression model described in the history environment parameter training.
Optionally, the acquisition module includes: acquisition unit, is being sampled for acquiring target device in first database
Input data in period, output data and operation data, and target device is acquired in sampling week in the second database
Environment temperature in phase, ambient humidity, light intensity.
Optionally, described device further include: generation module is used in the prediction module according to the physical prediction model
After the state value for predicting the target device with the regressive prediction model, it is greater than threshold value in the state value that prediction obtains
When, it generates and is used to indicate the warning information that the target device needs to safeguard.
It should be noted that above-mentioned modules can be realized by software or hardware, for the latter, Ke Yitong
Following manner realization is crossed, but not limited to this: above-mentioned module is respectively positioned in same processor；Alternatively, above-mentioned modules are with any
Combined form is located in different processors.
Embodiment 3
The embodiments of the present invention also provide a kind of storage medium, computer program is stored in the storage medium, wherein
The computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
Optionally, in the present embodiment, above-mentioned storage medium can be set to store by executing based on following steps
Calculation machine program:
S1, acquisition target device history running parameter during the sampling period and history environment parameter；
S2 is established physical prediction model using the history running parameter, and is established using the history environment parameter
Model is established in recurrence；
S3 predicts the state value of the target device according to the physical prediction model and the regressive prediction model,
In, the state value is used to characterize the probability that the target device breaks down.
Optionally, in the present embodiment, above-mentioned storage medium can include but is not limited to: USB flash disk, read-only memory (Read-
Only Memory, referred to as ROM), it is random access memory (Random Access Memory, referred to as RAM), mobile hard
The various media that can store computer program such as disk, magnetic or disk.
The embodiments of the present invention also provide a kind of electronic device, including memory and processor, stored in the memory
There is computer program, which is arranged to run computer program to execute the step in any of the above-described embodiment of the method
Suddenly.
Optionally, above-mentioned electronic device can also include transmission device and input-output equipment, wherein the transmission device
It is connected with above-mentioned processor, which connects with above-mentioned processor.
Optionally, in the present embodiment, above-mentioned processor can be set to execute following steps by computer program:
S1, acquisition target device history running parameter during the sampling period and history environment parameter；
S2 is established physical prediction model using the history running parameter, and is established using the history environment parameter
Model is established in recurrence；
S3 predicts the state value of the target device according to the physical prediction model and the regressive prediction model,
In, the state value is used to characterize the probability that the target device breaks down.
Optionally, the specific example in the present embodiment can be with reference to described in above-described embodiment and optional embodiment
Example, details are not described herein for the present embodiment.
Obviously, those skilled in the art should be understood that each module of the above invention or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
It is performed by computing device in the storage device, and in some cases, it can be to be different from shown in sequence execution herein
Out or description the step of, perhaps they are fabricated to each integrated circuit modules or by them multiple modules or
Step is fabricated to single integrated circuit module to realize.In this way, the present invention is not limited to any specific hardware and softwares to combine.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.It is all within principle of the invention, it is made it is any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of prediction technique of equipment state characterized by comprising
Acquire target device history running parameter during the sampling period and history environment parameter；
Physical prediction model is established using the history running parameter, and establishes to return using the history environment parameter and establish
Model；
The state value of the target device is predicted according to the physical prediction model and the regressive prediction model, wherein described
State value is used to characterize the probability that the target device breaks down.
2. the method according to claim 1, wherein according to the physical prediction model and the regression forecasting mould
Type predicts that the state value of the target device includes:
Obtain current operating parameters and current environment parameter of the target device in predetermined period；
The current operating parameters are inputted into the physical prediction model and obtain the first prediction result, by the current environment parameter
It inputs the regressive prediction model and obtains the second prediction result；
First prediction result and second prediction result are input to default forecasting effective measure model, obtain the target
State value of the equipment in described predetermined period.
3. the method according to claim 1, wherein establishing physical prediction model using the history running parameter
Include:
Determine the fault point of the target device；
An original physical model is selected for each fault point, wherein the original physical model includes following one: pipe
Road crack equation, equation of heat transfer, ACOUSTIC WAVE EQUATION, air force equation；
Using the history running parameter training original physical model, the physical prediction model is obtained.
4. the method according to claim 1, wherein establishing regressive prediction model using the history environment parameter
Include:
The history environment parameter is analyzed, the recurrence characteristic of the target device is obtained；
Select original regression model corresponding with the recurrence characteristic, wherein the original regression model includes following one: whole
Close rolling average autoregression Arima model, seasonalARIMA model；
Using original regression model described in the history environment parameter training, the regressive prediction model is obtained.
5. the ginseng the method according to claim 1, wherein the history of acquisition target device during the sampling period works
It is several to include: with history environment parameter
The input data of target device during the sampling period, output data and operation data are acquired in first database, with
And the environment temperature of target device during the sampling period, ambient humidity, light intensity are acquired in the second database.
6. the method according to claim 1, wherein according to the physical prediction model and the regression forecasting
After the state value of target device described in model prediction, the method also includes:
When the state value that prediction obtains is greater than threshold value, generates and be used to indicate the early warning letter that the target device needs to safeguard
Breath.
7. a kind of prediction meanss of equipment state characterized by comprising
Acquisition module, for acquiring target device history running parameter during the sampling period and history environment parameter；
Module is established, for establishing physical prediction model using the history running parameter, and is joined using the history environment
Number, which is established to return, establishes model；
Prediction module, for predicting the state of the target device according to the physical prediction model and the regressive prediction model
Value, wherein the state value is used to characterize the probability that the target device breaks down.
8. device according to claim 7, which is characterized in that the prediction module includes:
Acquiring unit, for obtaining current operating parameters and current environment parameter of the target device in predetermined period；
Processing unit obtains the first prediction result for the current operating parameters to be inputted the physical prediction model, by institute
It states the current environment parameter input regressive prediction model and obtains the second prediction result；
Predicting unit, for first prediction result and second prediction result to be input to default forecasting effective measure mould
Type obtains state value of the target device in described predetermined period.
9. a kind of storage medium, which is characterized in that be stored with computer program in the storage medium, wherein the computer
Program is arranged to execute method described in any one of claim 1 to 6 when operation.
10. a kind of electronic device, including memory and processor, which is characterized in that be stored with computer journey in the memory
Sequence, the processor are arranged to run the computer program to execute side described in any one of claim 1 to 6
Method.
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| CN110388773A (en) * | 2019-07-15 | 2019-10-29 | 重庆美的通用制冷设备有限公司 | Fault detection method, system and the water cooler of water cooler |
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| CN113574482B (en) * | 2019-08-30 | 2023-12-08 | Oppo广东移动通信有限公司 | Rule checking method and device and computer equipment |
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| CN113051700B (en) * | 2019-12-27 | 2023-12-29 | 北京国双科技有限公司 | Equipment reliability monitoring method and device |
| CN113051700A (en) * | 2019-12-27 | 2021-06-29 | 北京国双科技有限公司 | Equipment reliability monitoring method and device |
| CN111368890A (en) * | 2020-02-26 | 2020-07-03 | 珠海格力电器股份有限公司 | Fault detection method and device and information physical fusion system |
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| CN111626498B (en) * | 2020-05-25 | 2023-10-24 | 成都京东方显示科技有限公司 | Equipment running state prediction method, device, equipment and storage medium |
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| CN112578725A (en) * | 2020-12-16 | 2021-03-30 | 中国水产科学研究院南海水产研究所 | Fishery water aquaculture facility safety early warning system and method based on Internet of things |
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| CN113505533A (en) * | 2021-07-06 | 2021-10-15 | 新智数字科技有限公司 | Equipment health state prediction method and device |
| CN113849987A (en) * | 2021-11-03 | 2021-12-28 | 中国民用航空飞行学院 | Performance test method and device of piston engine, electronic equipment and medium |
| CN113917911A (en) * | 2021-11-05 | 2022-01-11 | 重庆赛迪奇智人工智能科技有限公司 | Electronic tag fault detection method and device |
| CN115310240A (en) * | 2022-10-11 | 2022-11-08 | 烟台杰瑞石油装备技术有限公司 | Oil-gas fracturing pump equipment service life prediction method and device and nonvolatile storage medium |
| CN116204387A (en) * | 2023-04-26 | 2023-06-02 | 之江实验室 | Chip current prediction method and device, medium and electronic equipment |
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