CN109656208A - A kind of construction method of building electric power early warning Internet of Things - Google Patents
A kind of construction method of building electric power early warning Internet of Things Download PDFInfo
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- CN109656208A CN109656208A CN201811540484.3A CN201811540484A CN109656208A CN 109656208 A CN109656208 A CN 109656208A CN 201811540484 A CN201811540484 A CN 201811540484A CN 109656208 A CN109656208 A CN 109656208A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by the network communication
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
Abstract
The present invention relates to a kind of construction methods of building electric power early warning Internet of Things, the Internet of Things includes user platform, cloud service platform, non-intrusion type electric energy Internet of Things meter, power load sensor, and the construction method includes: the arrival end that the power load sensor is configured at the consumer unit of power load by S1.；S2. the non-intrusion type electric energy Internet of Things meter acquires the load electric signal of the power load sensor acquisition and generates the log of the power load；S3. the cloud service platform obtains the log of the power load and carries out multiplexing electric abnormality trend or fault trend analysis, if the power load has multiplexing electric abnormality trend or fault trend, is transmitted to the user platform and carries out visualization display.The building of its Internet of Things is simple, can both realize that the identification of the exception and failure of local building power load is alarmed by the non-intrusion type electric energy Internet of Things meter, the exception and fault trend discriminance analysis of big data can also be set by cloud service platform.
Description
Technical field
The present invention relates to a kind of Internet of Things construction method more particularly to a kind of building sides of building electric power early warning Internet of Things
Method.
Background technique
Building power load can be related to a variety of important electrical equipments, comprising: Central Air Conditioner Used in Buildings, plumbing electric pump system
System, ventilating system, resident's elevator and freight lift system always enter and leave electrically operated gate, large-scale dust catcher, central kitchen electrical equipment and
Its meat grinder, large-scale baking oven, mixing plant, etc., risk brought by building power load failure is related to daily life
It produces, life.Such as: elevator faults are just security-related with life；It is total to enter and leave the general event such as electrically operated gate failure, cooking apparatus failure
Barrier, also will affect life, work be normally carried out.With the development of society, to improve production, life, the energy conservation of building electricity consumption
Etc. situations, there is an urgent need to a kind of systematic methods that load faulty is effectively predicted by people.In the prior art, although also proposed one
System and method for about prediction building power load failure a bit；But the relevant technologies difficult point is also presented therewith, i.e., with
The continuous improvement of quality of life, the quasi- class of electrical equipment increase.Still lack one kind in the prior art not only to have had verified that but also be suitble to building
The big data mathematical modeling processing method of space power load fault pre-alarming, and there is this big data mathematical modeling processing method
Internet of Things prediction, monitoring system
Summary of the invention
The purpose of the present invention is to provide a kind of construction methods of building electric power early warning Internet of Things, can be used in building using
The failure predication of electric loading.
For achieving the above object, the present invention provides a kind of construction method of building electric power early warning Internet of Things, the object
Networking includes user platform, cloud service platform, non-intrusion type electric energy Internet of Things meter, power load sensor, the building side
Method includes:
S1., the power load sensor is configured to the arrival end of the consumer unit of power load；
S2. the non-intrusion type electric energy Internet of Things meter acquires the load of power load sensor acquisition with electric signal simultaneously
Generate the log of the power load；
S3. the cloud service platform obtains the log of the power load and carries out multiplexing electric abnormality trend or failure
Trend analysis, if the power load has multiplexing electric abnormality trend or fault trend, being transmitted to the user platform progress can
It is shown depending on changing.
According to an aspect of the present invention, the non-intrusion type electric energy Internet of Things meter according to setting communication transport protocols pair
The log of the power load is configured, and transmits the operation note to the cloud service platform according to imposing a condition
Record, the log include operating normally record P, misoperation record Pa and failure operation to record Pb.
According to an aspect of the present invention, the non-intrusion type electric energy Internet of Things meter according to setting communication transport protocols pair
Include: in the step of log of the power load is configured
S211. the power load condition monitoring, wherein the electricity of fundamental wave and harmonic wave including the power load
Stream, voltage, frequency spectrum, three-phase symmetrical operating parameter；
S212. it is identified according to the load that the power load is arranged in the communication transport protocols, wherein described to be identified as use
Electric loading characteristic and serial number；
S213. the power load switches on log, closing journal and stable operation record.
According to an aspect of the present invention, the log is transmitted according to imposing a condition to the cloud service platform
Include: in step
S214. it when normal operation record P reaches local rated storage capacity, is then passed to the cloud service platform
The defeated currently stored normal operation records P；
S215. when generating the misoperation record Pa, then presently described exception is transmitted to the cloud service platform
Log Pa；
S216. when generating the failure operation record Pb, then presently described failure is transmitted to the cloud service platform
Log Pb.
According to an aspect of the present invention, in step S211, comprising: the normal operating condition inspection of the power load is set
It surveys, abnormal operating condition detection and failure operation state-detection；
In step S213, the normally-open log Pk of the power load switch is set, the power load is set
The abnormal start-up log Pak of switch, the failure that the power load switch is arranged open log Pbk, are expressed as Pk/
Pak/Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
The normal switching-off log Pg of the power load switch is set, the abnormal of power load switch is set and is closed
Close log Pag, the failure closing journal Pbg of power load switch be set, be expressed as Pg/Pag/Pbg=n,
M, t1, i1, u1, f1 ..., tn, in, un, fn ...,
The normal table log Pz of the power load switch is set, the abnormal steady of the power load switch is set
Determine log Paz, the failure stable operation record Pbz of power load switch be set, be expressed as Pz/Paz/Pbz=n,
M, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length,
T indicates each log data of composition corresponding moment, and i, u, f respectively indicate the electric current, voltage, electricity consumption factor of moment t.
According to an aspect of the present invention, operation of the cloud service platform based on prediction model to the power load
Record carried out in the step of multiplexing electric abnormality trend or fault trend analysis, and the setting of the prediction model includes:
S311. the setting of big data model prediction regression modeling is carried out to the cloud service platform；
S312. the setting of big data load faulty prediction algorithm is carried out to the cloud service platform；
S313. to the constant setting of reception data of the cloud service platform；
S314. calculating and setting is judged to the load faulty of the remote service platform.
According to an aspect of the present invention, in step S311, comprising:
S3111. the first mathematic(al) representation for predicting regression model is set, and sets first mathematic(al) representation
Each composition item, first mathematic(al) representation are as follows: Pm=∑i nPn/n, wherein i and n is respectively natural number, and Pn indicates the use
The large data sets that electric loading operates normally record P are combined into element, ∑i nPn/n indicates each large data sets from i=1 to n
The summation for being combined into element adds up and is averaged；
S3112. the son composition item that first mathematic(al) representation respectively forms item, i.e. Pn={ Pk, Pg, Pz } are set, wherein
Pk is the data record that power load switchs normally-open operation, and Pg is the subdata note of power load switch normal switching-off operation
Record, Pz are the subdata records of power load normal table operation；
S3113. the Sun Zucheng item of each sub- composition item of first mathematic(al) representation is set, it may be assumed that
Pk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length,
T indicates each log data of composition corresponding moment, and i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
According to an aspect of the present invention, in step S312, comprising:
S3121. setting is used for the second mathematic(al) representation of load faulty prediction algorithm, and sets second mathematical expression
Each composition item of formula, second mathematic(al) representation are as follows: Δ P=Pa/Pb-Pm, wherein Pa indicates that the power load is transported extremely
Row record, Pb indicate the power load failure operation record,；
S3122. set second mathematic(al) representation respectively form item son composition item, i.e. Pa={ Pak, Pag, Paz } or
Pb={ Pbk, Pbg, Pbz }, wherein Pak is the data record of power load switch abnormal start-up operation, and Pag is power load
The abnormal data record for closing operation of switch, Paz is the data record of power load exceptional stability operation, and Pbk is power load
Switch fault opens the data record of operation, and Pbg is the data record that power load switch fault closes operation, and Pbz is electricity consumption
The data record of load faulty stable operation；
S3123. the Sun Zucheng item of each sub- composition item of second mathematic(al) representation is set, it may be assumed that
Pak/Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pag/Pbg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Paz/Pbz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n be record element number or record length, m be record element tn, in, un, fn ... length, t table
Show each log data of composition corresponding moment, i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
According to an aspect of the present invention, in step 313, comprising:
S3131. load mark identification setting is carried out to the reception data, wherein according to feature in load mark
Categorical data from characteristic library lookup this feature classify, and by knows state load mark in serial number determine record new and old journey
Degree；
S3132. it carries out the constant processing of data length to the data to be arranged, wherein be by prediction regression model Pm length
Standard determines the length interpolation and pressure for operating normally record P, misoperation record Pa, failure operation record Pb
The quantity of contracting makes length and the institute for operating normally record P, misoperation record Pa, the failure operation and recording Pb
State prediction regression model Pm equal length；
S3133. the data are carried out dividing table addition record setting, wherein being obtained respectively according to load mark
Corresponding classification chart, and recorded according to the received normal operation of serial number sequence addition list item institute in the load mark
P, the misoperation record Pa and the failure operation record Pb.
According to an aspect of the present invention, in step 314, comprising:
S3141. it is calculated by the judgement for setting single operating status and single factor test, wherein by the received operation note of setting institute
Record selects the exceptional stability operation subdata in misoperation record Pa to record Paz, and selects in Paz only with the list
One component i, brings Δ P=Pa/Pb-Pm into, that is, is reduced to Δ P aw=Paz-Pmz, be further deformed into Δ i (t, aw)=
I (t, aw)-i (t, m) then judges that the electricity consumption is negative if Δ i (t, aw) has the threshold value for continuing to exceed the setting time
Carrier has the tendency that breaking down；
S3142. it is calculated by setting operation slice with multifactor association, wherein by the received log of setting institute, use
Misoperation records the abnormal start-up operation subdata record Pak in Pa and records Pag with abnormal operation subdata of closing, and exception is steady
Determine operation data record Paz, and select using all constituent elements in Pak, Pag, Paz respectively, brings Δ P=into
Pa/Pb-Pm, as Δ Pak=Pak-Pmk, Δ Pag=Pag-Pmg, Δ Paw=Paz-Pmz；
S3143. operation slice association judgement is calculated separately by setting.Wherein, Δ Pak=Pak-Pmk=Δ i (t,
Ak) ...；Δ u (t, ak) ...；Δ f (t, ak) ...；, that is, { Δ i (t, ak) ..., Δ i (tn, ak) }={ i (t1, ak)-i
(t, m) ..., i (tn, ak)-i (tn, m) },
{ Δ u (t, ak) ..., Δ u (tn, ak) }={ u (t1, ak)-u (t, m) ..., u (tn, ak)-u (tn, m) },
{ Δ f (t, ak) ..., Δ f (t, ak) }={ f (t1, ak)-f (t, m) ..., f (tn, ak)-f (tn, m) }；
Δ Pag=Pag-Pmg={ Δ i (t, ag) ...；Δ u (t, ag) ...；Δ f (t, ag) ...；, that is, Δ i (t,
Ag) ..., Δ i (t, ag) }={ i (t1, ag)-i (t, m) ..., i (tn, ag)-i (tn, m) },
{ Δ u (t, ag) ..., Δ u (t, ag) }={ u (t1, ag)-u (t, m) ..., u (tn, ag)-u (tn, m) },
{ Δ f (t, ag) ..., Δ f (tn, ag) }={ f (t1, ag)-f (t, m) ..., f (tn, ag)-f (tn, m) }；
Δ Paw=Paz-Pmz={ Δ i (t, aw) ...；Δ u (t, aw) ...；Δ f (t, aw) ...；That is, Δ i (t,
Aw) ..., Δ i (tn, aw) }={ i (t1, aw)-i (t, m) ..., i (tn, aw)-i (tn, m) },
{ Δ u (t, aw) ..., Δ u (tn, aw) }={ u (t1, aw)-u (t, m) ..., u (tn, aw)-u (tn, m) },
{ Δ f (t, aw) ..., Δ f (tn, aw) }={ f (t1, aw)-f (t, m) ..., f (tn, aw)-f (tn, m) }；
Wherein, the setting operation slice, i.e. the threshold value ratio of the Δ i of setting moment t, Δ u, Δ f and setting moment t
Compared with thus combination association is exactly to set the fault signature prediction of power load setting components, and n is natural integer.
According to an aspect of the present invention, operation of the cloud service platform based on prediction model to the power load
Record carries out based on the prediction model and using industrial equipment failure in the step of multiplexing electric abnormality trend or fault trend analysis
Prediction technique carries out multiplexing electric abnormality trend to the log of the power load or fault trend is analyzed.
A kind of scheme according to the present invention, Internet of Things building is simple, can both pass through the non-intrusion type electric energy Internet of Things meter
It realizes that the identification of the exception and failure of local building power load is alarmed, the different of big data can also be set by cloud service platform
Often with fault trend discriminance analysis, the components of the power load that gives warning in advance or even power load are impaired, high efficiency, low damage
The information that building owner needs maintenance and management is delivered on consumption, fining ground, is reduced and is even avoided associated loss.
Detailed description of the invention
Fig. 1 schematically shows a kind of structure chart of the Internet of Things of embodiment according to the present invention；
Fig. 2 is the shape constancy schematic diagram for indicating a kind of setting feature of embodiment according to the present invention；
Fig. 3 is the cloud service platform simple analysis schematic diagram for indicating a kind of embodiment according to the present invention；
Fig. 4 is the cloud service platform slice association analysis schematic diagram for indicating a kind of embodiment according to the present invention.
Specific embodiment
It, below will be to embodiment in order to illustrate more clearly of embodiment of the present invention or technical solution in the prior art
Needed in attached drawing be briefly described.It should be evident that the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skills without creative efforts, can also be according to these
Attached drawing obtains other attached drawings.
When being described for embodiments of the present invention, term " longitudinal direction ", " transverse direction ", "upper", "lower", " preceding ",
" rear ", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", orientation or positional relationship expressed by "outside" are based on phase
Orientation or positional relationship shown in the drawings is closed, is merely for convenience of description of the present invention and simplification of the description, rather than instruction or dark
Show that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore above-mentioned term cannot
It is interpreted as limitation of the present invention.
The present invention is described in detail with reference to the accompanying drawings and detailed description, embodiment cannot go to live in the household of one's in-laws on getting married one by one herein
It states, but therefore embodiments of the present invention are not defined in following implementation.
As shown in Figure 1, a kind of embodiment according to the present invention, a kind of structure of building electric power early warning Internet of Things of the invention
Construction method, Internet of Things include user platform 1, cloud service platform 2, non-intrusion type electric energy Internet of Things meter 3, power load sensor
4.In the present embodiment, construction method includes:
S1., power load sensor is configured to the arrival end of the consumer unit of power load；
S2. non-intrusion type electric energy Internet of Things meter acquires the load electric signal of power load sensor acquisition and generates electricity consumption
The log of load；
S3. cloud service platform obtains the log of power load and carries out multiplexing electric abnormality trend or fault trend point
Analysis is transmitted to the user platform and carries out visualization display if power load has multiplexing electric abnormality trend or fault trend.
A kind of embodiment according to the present invention, in construction method of the invention, power load sensor 4 is configured and uses
The arrival end of the consumer unit of electric loading, and the power load sensor 4. is nested with by non-intrusion type electric energy Internet of Things with cable
Meter 3 is electrically connected to each other with power load sensor 4, and non-intrusion type electric energy Internet of Things meter 3 is connected with cloud service platform 2,
Cloud service platform 2 is connected with user platform.In the present embodiment, non-intrusion type electric energy Internet of Things meter 3 and electricity consumption are negative
Set sensor 4 preferentially selects wired mode to be attached.Non-intrusion type electric energy Internet of Things meter 3 and cloud service platform 2 have line selection
It is connected with the Internet broadband.Cloud service platform 2 is connect with the internet user platform wired selection 4G.
A kind of embodiment according to the present invention, power load sensor 4 are voltage transformer (fire-zero) and Current Mutual Inductance
Device (fire).For example, using three electricity if the consumer unit of the power load of installation power load sensor 4 is three-phase system
Press mutual inductor (ABC-N) and three current transformers (ABC).In the present embodiment, preferably power load sensor 4 is electric current
With the two-in-one mutual inductor of voltage, and by the power load sensor 4 be set as building power supply tandem total input-wire output transducer.
A kind of embodiment according to the present invention, non-intrusion type electric energy Internet of Things meter 3, which is selected, has power load identification, electricity
Device constitutes the Intelligent internet of things energy meter of the functions such as identification, switch events capture, abnormality detection, malfunction monitoring.In this embodiment party
In formula, non-intrusion type electric energy Internet of Things meter is configured the log of power load 5 according to the communication transport protocols of setting,
And log is transmitted to cloud service platform 2 according to imposing a condition, log includes operating normally record P, misoperation
It records Pa and failure operation records Pb.
In the present embodiment, non-intrusion type electric energy Internet of Things meter is according to the communication transport protocols of setting to power load
Include: in the step of log is configured
S211. power load condition monitoring, wherein electric current, voltage, the frequency of fundamental wave and harmonic wave including power load
Spectrum, three-phase symmetrical operating parameter.In the present embodiment, comprising: the normal operating condition detection of power load is set, it is abnormal to transport
Row state-detection and failure operation state-detection；
S212. it is identified according to the load of communication transport protocols setting power load, wherein be identified as power load characteristic
According to and serial number；
S213. power load switches on log, closing journal and stable operation record.In this embodiment party
In formula, the normally-open log Pk that setting power load switchs, the abnormal start-up log that setting power load switchs
Pak, setting power load switch failure open log Pbk, be expressed as Pk/Pak/Pbk=n, m, t1, i1, u1,
F1 ..., tn, in, un, fn ... (Pk/Pak/Pbk is to represent Pk or Pak or Pbk, it is described below in it is other unless otherwise specified
Xiang Jun is identical in this meaning),
The normal switching-off log Pg of power load switch, the abnormal closing operation note that setting power load switchs are set
Record Pag, setting power load switch failure closing journal Pbg, be expressed as Pg/Pag/Pbg=n, m, t1, i1, u1,
F1 ..., tn, in, un, fn ...,
The normal table log Pz of power load switch is set, and the exceptional stability that setting power load switchs runs note
Record Paz, setting power load switch failure stable operation record Pbz, be expressed as Pz/Paz/Pbz=n, m, t1, i1, u1,
F1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length,
T indicates each log data of composition corresponding moment, and i, u, f respectively indicate the electric current, voltage, electricity consumption factor of moment t.
The operation of power load is remembered according to the communication transport protocols of setting based on above-mentioned non-intrusion type electric energy Internet of Things
The step of record is configured is respectively to the set-up mode for operating normally record P, misoperation record Pa and failure operation record Pb
It is described in detail.
A kind of embodiment according to the present invention, setting operate normally record P, and it is normal that the power load 5 is arranged in step 1
Condition monitoring, step 2 identify (ID) by the load of setting communication transport protocols setting power load；Step 3 setting should
The normal switching-off log Pg of the power load 5 switch is arranged in the normally-open log Pk that power load 5 switchs, if
Set the stable operation record Pz of power load work.
In the present embodiment, 5 normal operating condition of power load detects in step 1, including the power load 5
Normal fundamental wave and the electric current of harmonic wave, voltage, frequency spectrum, three-phase symmetrical operating parameter.
In the present embodiment, it is identified in step 2 by the load of setting communication transport protocols setting power load 5,
In, the present invention preferably load mark is that the power load characteristic adds serial number.
In the present embodiment, the normally-open log Pk of the power load 5 switch is set in step 3, which can
Be expressed as Pk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record
Length, m indicate record element tn, in, un, fn ... length, t indicates to form each log data corresponding moment, i,
U, f respectively indicates the electric current, voltage, electricity consumption factor of moment t；
The normal switching-off log Pg of the power load 5 switch is set, the Pg be represented by Pg=n, m, t1, i1,
U1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element tn,
In, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t's
Electric current, voltage, electricity consumption factor；
The normal table log Pz of the power load 5 switch is set, the Pz be represented by Pz=n, m, t1, i1,
U1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element tn,
In, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t's
Electric current, voltage, electricity consumption factor.
A kind of embodiment according to the present invention, setting misoperation record Pa, and it is abnormal that the power load 5 is arranged in step 1
Condition monitoring, step 2 identify (ID) by the load of setting communication transport protocols setting power load；Step 3 setting should
The abnormal closing journal Pag of the power load 5 switch is arranged in the abnormal start-up log Pak that power load 5 switchs,
The exceptional stability log Paz of power load work is set.
In the present embodiment, 5 normal operating condition of power load detects in step 1, including the power load 5
The electric current of abnormal fundamental wave and harmonic wave, voltage, frequency spectrum, three-phase symmetrical operating parameter.
In the present embodiment, it is identified in step 2 by the load of setting communication transport protocols setting power load 5,
In, the present invention preferably load mark is that the power load characteristic adds serial number.
In the present embodiment, abnormal start-up the log Pak, the Pak of the power load 5 switch are set in step 3
Be represented by Pak=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or note
Record length, m indicate record element tn, in, un, fn ... length, t indicates to form each log data corresponding moment,
I, u, f respectively indicate the electric current, voltage, electricity consumption factor of moment t；
Be arranged the power load 5 switch abnormal closing journal Pag, the Pag be represented by Pag=n, m, t1,
I1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element
Tn, in, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t
Electric current, voltage, electricity consumption factor；
The exceptional stability log Paz of the power load 5 switch is set, the Paz be represented by Paz=n, m, t1,
I1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element
Tn, in, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t
Electric current, voltage, electricity consumption factor.
A kind of embodiment according to the present invention, setting failure operation record Pb, and it is abnormal that the power load 5 is arranged in step 1
Condition monitoring, step 2 identify (ID) by the load of setting communication transport protocols setting power load；Step 3 setting should
The failure that power load 5 switchs opens log Pbk, and the failure closing journal Pbg of the power load 5 switch is arranged,
The failure stable operation record Pbz of power load work is set.
In the present embodiment, 5 failure operation state-detection of power load in step 1, including the power load 5
The electric current of failure fundamental wave and harmonic wave, voltage, frequency spectrum, three-phase symmetrical operating parameter.
In the present embodiment, it is identified in step 2 by the load of setting communication transport protocols setting power load 5,
In, the present invention preferably load mark is that the power load characteristic adds serial number.
In the present embodiment, the failure that the power load 5 switch is arranged in step 3 opens log Pbk, the Pbk
Be represented by Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or note
Record length, m indicate record element tn, in, un, fn ... length, t indicates to form each log data corresponding moment,
I, u, f respectively indicate the electric current, voltage, electricity consumption factor of moment t；
The failure closing journal Pbg of the power load 5 switch is set, the Pbg be represented by Pbg=n, m, t1,
I1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element
Tn, in, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t
Electric current, voltage, electricity consumption factor；
Be arranged the power load 5 switch failure stable operation record Pbz, the Pbz be represented by Pbz=n, m, t1,
I1, u1, f1 ..., tn, in, un, fn ..., wherein n indicate record element number or record length, m indicate record element
Tn, in, un, fn ... length, t indicates to form each log data corresponding moment, and i, u, f respectively indicate moment t
Electric current, voltage, electricity consumption factor.
A kind of embodiment according to the present invention, according to the step of the transmitting log to cloud service platform that impose a condition
In include:
S214. it when normal operation record P reaches local rated storage capacity, is then transmitted currently to cloud service platform 2
The normal operation of storage records P；
S215. when generating misoperation record Pa, then current misoperation record Pa is transmitted to cloud service platform 2；
In the present embodiment, to 2 transmission abnormality log Pa of cloud service platform while, it is also necessary to local building management person
It alarms the misoperation feature.
S216. when generating failure operation record Pb, then current failure log Pb is transmitted to cloud service platform.?
In present embodiment, while to 2 transmission fault log Pb of cloud service platform, it is also necessary to be reported to local building management person
The alert failure operation feature.
A kind of embodiment according to the present invention, in the present embodiment, Ali's cloud service can be selected in cloud service platform 2
Device or Azure Cloud Server.In the present embodiment, cloud service platform is based on prediction model to the log of power load
It carries out multiplexing electric abnormality trend or fault trend analysis, the setting of prediction model includes:
S311. the setting of big data model prediction regression modeling is carried out to cloud service platform.In the present embodiment, it wraps
It includes:
S3111. the first mathematic(al) representation for predicting regression model is set, and sets each group of the first mathematic(al) representation
Cheng Xiang, the first mathematic(al) representation are as follows: Pm=∑i nPn/n, wherein i and n is respectively natural number, and Pn indicates that power load is normally transported
The large data sets of row record P are combined into element, ∑i nPn/n indicates that each large data sets from i=1 to n are combined into element
Summation is cumulative and is averaged；
S3112. the son composition item that the first mathematic(al) representation respectively forms item, i.e. Pn={ Pk, Pg, Pz }, wherein Pk is are set
Power load switchs the data record of normally-open operation, and Pg is the data record of power load switch normal switching-off operation, Pz
It is the data record of power load normal table operation；
S3113. the Sun Zucheng item of each sub- composition item of the first mathematic(al) representation is set, it may be assumed that
Pk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length,
T indicates each log data of composition corresponding moment, and i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
S312. the setting of big data load faulty prediction algorithm is carried out to cloud service platform.In the present embodiment, it wraps
It includes:
S3121. setting is used for the second mathematic(al) representation of load faulty prediction algorithm, and sets the second mathematic(al) representation
Each composition item, the second mathematic(al) representation are as follows: Δ P=Pa/Pb-Pm, wherein Pa indicates power load misoperation record, Pb table
Show that power load failure operation records,；
S3122. the son composition item that the second mathematic(al) representation respectively forms item, i.e. Pa={ Pak, Pag, Paz } or Pb=are set
{ Pbk, Pbg, Pbz }, wherein Pak is the data record of power load switch abnormal start-up operation, and Pag is power load switch
The abnormal data record for closing operation, Paz are the data records of power load exceptional stability operation, and Pbk is power load switch
Failure opens the data record of operation, and Pbg is the data record that power load switch fault closes operation, and Pbz is power load
The data record of failure stable operation；
S3123. the Sun Zucheng item of each sub- composition item of the second mathematic(al) representation is set, it may be assumed that
Pak/Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pag/Pbg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Paz/Pbz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n be record element number or record length, m be record element tn, in, un, fn ... length, t table
Show each log data of composition corresponding moment, i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
S313. to the constant setting of reception data of cloud service platform.In the present embodiment, comprising:
S3131. to receive data carry out load mark identification setting, wherein according to load mark in characteristic type data
Classify from characteristic library lookup this feature, and states the determining record newness degree of serial number in load mark by knowing；
S3132. it carries out the constant processing of data length to data to be arranged, wherein pressing prediction regression model Pm length is standard
Determine that operating normally record P, misoperation record Pa, failure operation records the length interpolation of Pb and the quantity of compression, makes normal
Log P, misoperation record Pa, the length of failure operation record Pb and prediction regression model Pm equal length；
S3133. data are carried out dividing table addition record setting, wherein obtaining corresponding point respectively according to load mark
Class table, and Pa is recorded according to the received normal operation record P of serial number sequence addition list item institute in load mark, misoperation
Pb is recorded with failure operation.
S314. calculating and setting is judged to the load faulty of remote service platform.In the present embodiment, comprising:
S3141. it is calculated by the judgement for setting single operating status and single factor test, wherein by the received operation note of setting institute
Record selects the exceptional stability operation subdata in misoperation record Pa to record Paz, and selects in Paz only with the list
One component i, brings Δ P=Pa/Pb-Pm into, that is, is reduced to Δ P aw=Paz-Pmz, be further deformed into Δ i (t, aw)=
I (t, aw)-i (t, m) then judges that the power load has if Δ i (t, aw) has the threshold value for continuing to exceed the setting time
Have the tendency that breaking down；
S3142. it is calculated by setting operation slice with multifactor association, wherein by the received log of setting institute, use
Misoperation records the abnormal start-up operation data record Pak in Pa and records Pag, exceptional stability fortune with abnormal operation data of closing
Row data record Paz, and select using all constituent elements in Pak, Pag, Paz respectively, bring Δ P=Pa/Pb- into
Pm, as Δ Pak=Pak-Pmk, Δ Pag=Pag-Pmg, Δ Paw=Paz-Pmz；
S3143. operation slice association judgement is calculated separately by setting.Wherein, Δ Pak=Pak-Pmk=Δ i (t,
Ak) ...；Δ u (t, ak) ...；Δ f (t, ak) ...；, that is, { Δ i (t, ak) ..., Δ i (tn, ak) }={ i (t1, ak)-i
(t, m) ..., i (tn, ak)-i (tn, m) },
{ Δ u (t, ak) ..., Δ u (tn, ak) }={ u (t1, ak)-u (t, m) ..., u (tn, ak)-u (tn, m) },
{ Δ f (t, ak) ..., Δ f (t, ak) }={ f (t1, ak)-f (t, m) ..., f (tn, ak)-f (tn, m) }；
Δ Pag=Pag-Pmg={ Δ i (t, ag) ...；Δ u (t, ag) ...；Δ f (t, ag) ...；, that is, Δ i (t,
Ag) ..., Δ i (t, ag) }={ i (t1, ag)-i (t, m) ..., i (tn, ag)-i (tn, m) },
{ Δ u (t, ag) ..., Δ u (t, ag) }={ u (t1, ag)-u (t, m) ..., u (tn, ag)-u (tn, m) },
{ Δ f (t, ag) ..., Δ f (tn, ag) }={ f (t1, ag)-f (t, m) ..., f (tn, ag)-f (tn, m) }；
Δ Paw=Paz-Pmz={ Δ i (t, aw) ...；Δ u (t, aw) ...；Δ f (t, aw) ...；That is, Δ i (t,
Aw) ..., Δ i (tn, aw) }={ i (t1, aw)-i (t, m) ..., i (tn, aw)-i (tn, m) },
{ Δ u (t, aw) ..., Δ u (tn, aw) }={ u (t1, aw)-u (t, m) ..., u (tn, aw)-u (tn, m) },
{ Δ f (t, aw) ..., Δ f (tn, aw) }={ f (t1, aw)-f (t, m) ..., f (tn, aw)-f (tn, m) }；
Wherein, setting operation slice, the i.e. threshold value comparison of the Δ i of setting moment t, Δ u, Δ f and setting moment t, by
This combination association is exactly to set the fault signature prediction of power load setting components, and n is natural integer.
A kind of embodiment according to the present invention, cloud service platform 2 remember the operation of power load based on prediction model
Record carries out based on the prediction model being arranged in above-mentioned steps and using work in the step of multiplexing electric abnormality trend or fault trend analysis
Industry equipment fault prediction technique carries out multiplexing electric abnormality trend to the log of power load or fault trend is analyzed.In this implementation
In mode, prediction model is the data record that power load switch opens operation, and power load switch closes the data note of operation
The set of the data record of record and power load stable operation.By the prediction model of setting, and is opened and transported according to above-mentioned switch
Capable data record, switch are closed the data record of operation and the data record of stable operation and then are handled into crossing, and building are obtained
In common power load steady operational status, and then industrial equipment failure prediction method can be made commonly to use suitable for building
The multiplexing electric abnormality trend or fault trend of electric appliance are analyzed.In the present embodiment, industrial equipment failure prediction method can be selected and apply
The PRISM of resistance to moral failure prediction method.By using the above method, by the closing, unlatching and the stabilization that obtain electrical appliance in building
Log and the prediction that handle and then the failure prediction method of industrial equipment can be used for common electrical appliance, meanwhile,
Since the precision of the failure prediction method of industrial equipment is high, stability is good, therefore by means of the present invention, is arranged by setting
Prediction model simultaneously combines the failure prediction method of industrial equipment and then makes the present invention to the multiplexing electric abnormality trend of building power load
Or the result of fault trend analysis is more accurate.Mobile device can be selected in a kind of embodiment according to the present invention, user platform,
Such as, mobile phone, removable computer, tablet computer etc..In the present embodiment, the present invention intends preferred cell phone service offering, if
Visualization Warning Service is delivered using cell phone end, then cell phone end can be Web mobile monitoring mobile phone terminal service offering,
It can also can also be App mobile monitoring mobile phone terminal service offering with small routine mobile monitoring mobile phone terminal service offering, the present invention is excellent
First delivered using Web mobile monitoring mobile phone terminal.
According to the present invention, according to its connection features, pass through the main entrance end line of the consumer unit in the building power load 5
Cable is nested with the power load sensor 4, which is directly connected to the non-intrusion type electric energy Internet of Things meter 3, non-intrusion type electricity
The cloud service platform 2 in 3 broadband connection internet of energy Internet of Things meter, the cloud service platform 2 connect the user by 4G and put down
Platform 1 is achieved in the building of Internet of Things；Meanwhile according to its composition characteristic, power load sensor 4 of the present invention can load use
All AC signals of electricity, including the fundamental wave and harmonic wave, power factor etc., non-intrusion type electric energy Internet of Things meter 3 of the present invention is intelligence
Energy Internet of Things energy meter, it can calculate the AC signal of 5 electricity consumption of power load, identify that loadtype and its electric appliance constitute identification,
It can recognize monitoring load abnormal and malfunction and local Realtime Alerts, it can capture 5 electricity consumption parallel-connection network cloud of power load
Service platform 2 includes the big data modeling method of load multiplexing electric abnormality and failure, to have the analysis of cloud anomaly trend and event
Hinder pre-alerting ability, user platform 1 of the present invention has the abnormal ability with failure of cloud real-time informing；According to it, feature is set, it should
Non-intrusion type electric energy Internet of Things meter 3 can calculate non-intrusion type efficiency and monitor the logs such as normal, exception, failure, on
It is transmitted to the Internet of Things cloud management server, which is calculated by the single factor test of setting operating status, can be sentenced roughly
Break and the simple exception and fault progression trend of building power load, calculates separately and run based on slice and multifactor association by setting
It calculates to be associated with and judges the impaired abnormal and fault progression trend of the components of electricity consumption delivery, which can then deliver the early warning in real time
Service.Therefore, simple based on the internet of things structure of the invention constructed, it is at low cost, it can both pass through non-intrusion type electric energy Internet of Things meter
It realizes that the identification of the exception and failure of local building power load is alarmed, the different of big data can also be set by cloud service platform
Often with fault trend discriminance analysis, the components of the power load that gives warning in advance or even power load are impaired, high efficiency, low damage
The information that building owner needs maintenance and management is delivered on consumption, fining ground, is reduced and is even avoided associated loss.
Below in conjunction with attached drawing, construction method of the invention is illustrated.
A kind of embodiment according to the present invention, according to foregoing teachings by user platform 1, cloud service platform 2, non-intruding
Formula electric energy Internet of Things meter 3, power load sensor 4 are configured.According to abovementioned steps to non-intrusion type electric energy Internet of Things meter 3
Configuration, then all operation datas of its power load 5 acquired by power load sensor 4, can all be remembered by record type
Record is got off.It alarms except real time fail identifies；Meanwhile trend analysis, the number of every kind of record type are done to upload cloud service platform 2
According to the detail data arranged as three subrecords is refined again, they are that power load 5 switches on log, electricity consumption is born
Carry the closing journal of 5 switches, the stable operation record of power load 5.
They can be described with unified array, it may be assumed that
P [type] [state] [ID]={ n, m, t (1), i (1), u (1), f (1) ...；T (n), i (n), u (n), f
(n)…}。
Wherein, P [type] is power load operation characteristic record, they are up record P, misoperation note respectively
Record Pa, failure operation Pb.P [state] is power load state log, they are to switch on log respectively
Pk, the closing journal pg of load switch, load stable operation record Pz.P [ID]={ n, m:t, i, u, f ... } is one
The data acquisition system of details is recorded, ID is power load electric appliance feature record, it is device type+serial number, and the serial number is thus
Number as setting record；The element type m of set { n, m:t, i, u, f ... } is the number of dimensions of element, and n is set length,
It depends on sampling time interval number t=1 ..., n；I, u, f ... are the letters such as electric current, voltage, power factor (PF) for setting sample frequency
Number numerical value.
A kind of embodiment according to the present invention, cloud service platform is based on prediction model to the log of power load
Multiplexing electric abnormality trend or fault trend analysis are carried out, therefore prediction model is configured, preferably, the above-mentioned present invention is excellent
The setting steps for selecting the Internet of Things cloud management server, details are not described herein for narration in detail in foregoing teachings.
Because cloud service platform 2, could be to the building firstly the need of the setting of big data model prediction regression modeling is carried out
Power load does anomaly trend and fault pre-alarming analysis；For this purpose, the regression model mathematic(al) representation of prediction modeling, and realize
The regression model mathematic(al) representation, which respectively forms item, becomes key.
Preferably, the present invention preferably sets the most simple expression formula of the regression mathematical model, it may be assumed that Pm=∑i nPn/n, wherein
I and n is natural number, and Pn is that large data sets of its normal operation record of the load mark (ID) of setting power load P are combined into member
One of element, ∑i nPn/n is the set { ID, P1, P2, P3 ..., Pn }, the summation of each component P from i=1 to n is tired
Add, be then averaged.
It, could be to the building again because cloud service platform 2 needs to carry out in turn the setting of big data load faulty prediction algorithm
Space power load does anomaly trend and fault pre-alarming analysis；For this purpose, the mathematic(al) representation of the big data load faulty prediction algorithm,
And realize that the mathematic(al) representation respectively forms item and becomes key.
As shown in Fig. 2, curve a, curve e are that Internet of Things Cloud Server is received from the non-intrusion type electric energy Internet of Things respectively
Data record Pa, Pb that meter 3 uploads, the Pm=∑ that they may all be modeled than the curve c big datai nThe visualization that Pn/n is obtained
Curve model is long or short.It is handled using interpolation and the constant mathematical algorithm of compression, curve a or curve e switch to that Δ P=can be achieved
(Pa or Pb)-∑i nPn/n, i.e., (curve b or curve d)-curve c isometric can ask poor.
Finally, the load faulty of cloud service platform 2 judges that calculating and setting, feature include again, by the single operation of setting
The judgement of state and single factor test calculates；It is calculated by setting operation slice with multifactor association；Operation slice is calculated separately by setting
Association judgement.
As shown in figure 3, a kind of embodiment according to the present invention, the judgement of single operating status and single factor test is calculated, greatly
Data regression mathematical model alarm setting T (t) is curve h or curve i alarming line, when (curve k) is inclined by the element i of record Pa or Pb
From Pm element i, (curve j) has surmounted curve i and has alarmed, thus given warning in advance, and realizes than conventional threshold values T (threshold value f) or T (threshold
Value g) alarming line shifts to an earlier date delta time amount.
As shown in figure 4, the Pm=∑ of big data modelingi nTri- element i of Pn/n (curve m), u (curve n), f (curve o)
Regression mathematical model, l is t moment record Pa or records the slice analysis of correspondence associated element of Pb and compare place.Three as a result,
The slice analysis method of relation factor can be developed and the trend such as fault pre-alarming with various dimensions multiple features explication de texte load abnormal.
For example, the object that the cloud big data is just monitoring is the plumbing electric pump an of building；According to Δ P=Pa-∑i nPn/n, if
The slice relation factor analytic approach of moment t, if: element i is excessive, element u is too low, element f is excessively poor, they have all surmounted respectively
Kind threshold value T (t)；So, the omen that the electric pump is likely to be at tracking or bearing seizure can be analyzed, it is necessary to pass through at once
Mobile Internet mobile phone terminal notifies owner to detect, in order to avoid cause unnecessary loss.
According to the present invention, Internet of Things building is simple, both can realize local building by the non-intrusion type electric energy Internet of Things meter 3
The identification of the exception and failure of space power load 5 is alarmed, and the exception and failure of big data can also be set by cloud service platform 2
The components of tendency discriminance analysis, the power load that gives warning in advance 5 or even power load 5 are impaired, high efficiency, low-loss, fine
Change ground and deliver the information that building owner needs maintenance and management, reduces and even avoid associated loss.Hereby it is achieved that the invention
Purpose
Above content is only the example of concrete scheme of the invention, for the equipment and structure of wherein not detailed description, is answered
When being interpreted as that the existing common apparatus in this field and universal method is taken to be practiced.
The foregoing is merely a schemes of the invention, are not intended to restrict the invention, for the technology of this field
For personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (11)
1. a kind of construction method of building electric power early warning Internet of Things, which is characterized in that the Internet of Things includes user platform, cloud
Service platform, non-intrusion type electric energy Internet of Things meter, power load sensor, the construction method include:
S1., the power load sensor is configured to the arrival end of the consumer unit of power load；
S2. the non-intrusion type electric energy Internet of Things meter acquires the load electric signal of the power load sensor acquisition and generates
The log of the power load；
S3. the cloud service platform obtains the log of the power load and carries out multiplexing electric abnormality trend or fault trend
Analysis is transmitted to the user platform and is visualized if the power load has multiplexing electric abnormality trend or fault trend
Display.
2. construction method according to claim 1, which is characterized in that the non-intrusion type electric energy Internet of Things meter is according to setting
Communication transport protocols the log of the power load is configured, and it is flat to the cloud service according to imposing a condition
Platform transmits the log, and the log includes operating normally record P, misoperation record Pa and failure operation record
Pb。
3. construction method according to claim 2, which is characterized in that the non-intrusion type electric energy Internet of Things meter is according to setting
The communication transport protocols the step of log of the power load is configured in include:
S211. the power load condition monitoring, wherein electric current, the electricity of fundamental wave and harmonic wave including the power load
Pressure, frequency spectrum, three-phase symmetrical operating parameter；
S212. it is identified according to the load that the power load is arranged in the communication transport protocols, wherein described to be identified as electricity consumption negative
Carry characteristic and serial number；
S213. the power load switches on log, closing journal and stable operation record.
4. construction method according to claim 3, which is characterized in that passed according to imposing a condition to the cloud service platform
Include: in the step of defeated log
S214. when normal operation record P reaches local rated storage capacity, then work as to cloud service platform transmission
The normal operation of preceding storage records P；
S215. when generating the misoperation record Pa, then presently described misoperation is transmitted to the cloud service platform
Record Pa；
S216. when generating the failure operation record Pb, then presently described failure operation is transmitted to the cloud service platform
Record Pb.
5. construction method according to claim 3 or 4, which is characterized in that in step S211, comprising: the electricity consumption is arranged
The normal operating condition of load detects, abnormal operating condition detection and failure operation state-detection；
In step S213, the normally-open log Pk of the power load switch is set, the power load switch is set
Abnormal start-up log Pak, the failure that power load switch is arranged opens log Pbk, is expressed as Pk/Pak/
Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
The normal switching-off log Pg of the power load switch is set, the abnormal of power load switch is set and closes fortune
Row record Pag, is arranged the failure closing journal Pbg of power load switch, be expressed as Pg/Pag/Pbg=n, m,
T1, i1, u1, f1 ..., tn, in, un, fn ...,
The normal table log Pz of the power load switch is set, the exceptional stability fortune of the power load switch is set
The failure stable operation record Pbz of power load switch is arranged in row record Paz, be expressed as Pz/Paz/Pbz=n, m,
T1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length, t table
Show each log data of composition corresponding moment, i, u, f respectively indicate the electric current, voltage, electricity consumption factor of moment t.
6. according to claim 1 to construction method described in 4, which is characterized in that the cloud service platform is based on prediction model
In the step of carrying out multiplexing electric abnormality trend or fault trend analysis to the log of the power load, the prediction model
Setting includes:
S311. the setting of big data model prediction regression modeling is carried out to the cloud service platform；
S312. the setting of big data load faulty prediction algorithm is carried out to the cloud service platform；
S313. to the constant setting of reception data of the cloud service platform；
S314. calculating and setting is judged to the load faulty of the remote service platform.
7. construction method according to claim 6, which is characterized in that in step S311, comprising:
S3111. the first mathematic(al) representation for predicting regression model is set, and sets each group of first mathematic(al) representation
Cheng Xiang, first mathematic(al) representation are as follows: Pm=∑i nPn/n, wherein i and n is respectively natural number, and Pn indicates that the electricity consumption is negative
The large data sets for carrying normal operation record P are combined into element, ∑i nPn/n indicates that each large data sets from i=1 to n are combined
It adds up and is averaged at the summation of element；
S3112. the son composition item that first mathematic(al) representation respectively forms item, i.e. Pn={ Pk, Pg, Pz }, wherein Pk is are set
Power load switchs the data record of normally-open operation, and Pg is the subdata record of power load switch normal switching-off operation,
Pz is the subdata record of power load normal table operation；
S3113. the Sun Zucheng item of each sub- composition item of first mathematic(al) representation is set, it may be assumed that
Pk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n indicate record element number or record length, m indicate record element tn, in, un, fn ... length, t table
Show each log data of composition corresponding moment, i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
8. construction method according to claim 7, which is characterized in that in step S312, comprising:
S3121. setting is used for the second mathematic(al) representation of load faulty prediction algorithm, and sets second mathematic(al) representation
Each composition item, second mathematic(al) representation are as follows: Δ P=Pa/Pb-Pm, wherein Pa indicates the power load misoperation note
Record, Pb indicate the power load failure operation record,；
S3122. the son composition item that second mathematic(al) representation respectively forms item, i.e. Pa={ Pak, Pag, Paz } or Pb=are set
{ Pbk, Pbg, Pbz }, wherein Pak is the data record of power load switch abnormal start-up operation, and Pag is power load switch
The abnormal data record for closing operation, Paz are the data records of power load exceptional stability operation, and Pbk is power load switch
Failure opens the data record of operation, and Pbg is the data record that power load switch fault closes operation, and Pbz is power load
The data record of failure stable operation；
S3123. the Sun Zucheng item of each sub- composition item of second mathematic(al) representation is set, it may be assumed that
Pak/Pbk=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Pag/Pbg=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Paz/Pbz=n, m, t1, i1, u1, f1 ..., tn, in, un, fn ...,
Wherein, n be record element number or record length, m be record element tn, in, un, fn ... length, t expression group
At each log data corresponding moment, i, u, f are the electric current, voltage, electricity consumption factor of moment t respectively.
9. construction method according to claim 8, which is characterized in that in step 313, comprising:
S3131. load mark identification setting is carried out to the reception data, wherein according to characteristic type in load mark
Data are classified from characteristic library lookup this feature, and state the determining record newness degree of serial number in load mark by knowing；
S3132. it carries out the constant processing of data length to the data to be arranged, wherein pressing prediction regression model Pm length is standard
Determine the length interpolation and compression for operating normally record P, misoperation record Pa, failure operation record Pb
Quantity, make it is described operate normally record P, the misoperation record Pa, the failure operation record Pb length with it is described pre-
Survey regression model Pm equal length；
S3133. the data are carried out dividing table addition record setting, wherein being obtained respectively according to load mark corresponding
Classification chart, and according to serial number sequence addition list item received the normals operation record P, institute of institute in the load mark
State misoperation record Pa and failure operation record Pb.
10. construction method according to claim 9, which is characterized in that in step 314, comprising:
S3141. it is calculated by the judgement for setting single operating status and single factor test, wherein by the received log of setting institute, choosing
The exceptional stability operation subdata record Paz in misoperation record Pa is selected, and is selected in Paz only with the single composition
Element i, brings Δ P=Pa/Pb-Pm into, that is, is reduced to Δ Paw=Paz-Pmz, be further deformed into Δ i (t, aw)=i (t,
Aw)-i (t, m) then judges the power load tool if Δ i (t, aw) has the threshold value for continuing to exceed the setting time
Have the tendency that breaking down；
S3142. it is calculated by setting operation slice with multifactor association, wherein by the received log of setting institute, using exception
Abnormal start-up operation subdata record Pak and abnormal operation subdata of closing in log Pa record Pag, exceptional stability fortune
Row data record Paz, and select using all constituent elements in Pak, Pag, Paz respectively, bring Δ P=Pa/Pb- into
Pm, as Δ Pak=Pak-Pmk, Δ Pag=Pag-Pmg, Δ Paw=Paz-Pmz；
S3143. operation slice association judgement is calculated separately by setting.Wherein, Δ Pak=Pak-Pmk={ Δ i (t, ak) ...；
Δ u (t, ak) ...；Δ f (t, ak) ...；, that is, { Δ i (t, ak) ..., Δ i (tn, ak) }=i (t1, ak)-i (t,
M) ..., i (tn, ak)-i (tn, m) },
{ Δ u (t, ak) ..., Δ u (tn, ak) }={ u (t1, ak)-u (t, m) ..., u (tn, ak)-u (tn, m) },
{ Δ f (t, ak) ..., Δ f (t, ak) }={ f (t1, ak)-f (t, m) ..., f (tn, ak)-f (tn, m) }；
Δ Pag=Pag-Pmg={ Δ i (t, ag) ...；Δ u (t, ag) ...；Δ f (t, ag) ...；, that is, Δ i (t,
Ag) ..., Δ i (t, ag) }={ i (t1, ag)-i (t, m) ..., i (tn, ag)-i (tn, m) },
{ Δ u (t, ag) ..., Δ u (t, ag) }={ u (t1, ag)-u (t, m) ..., u (tn, ag)-u (tn, m) },
{ Δ f (t, ag) ..., Δ f (tn, ag) }={ f (t1, ag)-f (t, m) ..., f (tn, ag)-f (tn, m) }；
Δ Paw=Paz-Pmz={ Δ i (t, aw) ...；Δ u (t, aw) ...；Δ f (t, aw) ...；That is, Δ i (t,
Aw) ..., Δ i (tn, aw) }={ i (t1, aw)-i (t, m) ..., i (tn, aw)-i (tn, m) },
{ Δ u (t, aw) ..., Δ u (tn, aw) }={ u (t1, aw)-u (t, m) ..., u (tn, aw)-u (tn, m) },
{ Δ f (t, aw) ..., Δ f (tn, aw) }={ f (t1, aw)-f (t, m) ..., f (tn, aw)-f (tn, m) }；
Wherein, the setting operation slice, the i.e. threshold value comparison of the Δ i of setting moment t, Δ u, Δ f and setting moment t, by
This combination association is exactly to set the fault signature prediction of power load setting components, and n is natural integer.
11. construction method according to claim 6, which is characterized in that the cloud service platform is based on prediction model pair
The log of the power load carries out being based on the prediction model in the step of multiplexing electric abnormality trend or fault trend analysis
And multiplexing electric abnormality trend or fault trend are carried out using log of the industrial equipment failure prediction method to the power load
Analysis.
Priority Applications (1)
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