CN106019087A - Intermittent electricity stealing monitoring system - Google Patents

Abstract

The invention discloses an intermittent electricity stealing monitoring system which comprises a sample screening module, a data acquisition module, a first model constructing module, a data screening module, a second model constructing module, a relative average error calculating module, a final reading prediction module and an electricity stealing determination module. The technical effects of the invention lie in combination of on-line monitoring and off-line analysis, focusing on-line acquisition and off-line analysis of electric quantity data of users, screening intermittent electricity stealing suspects among normal users, and assigning workers to conduct on-site processing in priority for the intermittent electricity stealing suspects. Working tasks can be rapidly and highly effectively completed, thereby saving labor and material resources. The integral process is controllable and highly effectively, and normal power consumption is maintained.

Description

A kind of intermittent power theft monitoring system

Technical field

The present invention relates to power theft monitoring field, particularly relate to a kind of intermittent power theft monitoring system.

Background technology

Line loss is an important Management Technology index of country's examination electric power enterprise, is also business administration One of key link.The high low reaction of the line loss economical operation state of electrical network, the management water of power supply enterprise Gentle production technology level.Line loss contains technical loss and management line loss, and wherein opposing electricity-stealing is to reduce pipe Reason line loss, safing important measures.In the electricity filching behavior investigated and prosecuted at present, resident's stealing mode is main Concentrate on the hands such as " line one ground ", " U-shaped line ", " around more table meter direct connection " and " bridging in table " Method.Electricity consumption and stealing not only make management line loss rise in violation of rules and regulations, and electric power enterprise economic benefit declines, Er Qierong It is easily caused with electrification and personal injury.

In order to safeguard normal electricity consumption order, it is achieved quickly find, accurately judge, strike stealing row relentlessly For, electric company has formulated detailed work of electricity anti-stealing flow process in order to instruct work to carry out.It is broadly divided into and " catches Catch stealing signal ", " judgement electricity filching behavior ", " determining electricity filching behavior " and " process power stealing case ", Wherein: " catching stealing signal " main task is to reduce and lock stealing " suspicion " user；" judge Electricity filching behavior " task is site inspection, confirms whether these " suspicion " users exist electricity filching behavior；" really Determine electricity filching behavior " it is to check, analyze stealing user to be how stealing；" process power stealing case " is root Determine user's stealing quantity according to relevant laws, regulation and investigate its civil liability, administrative responsibility or criminal duty Appoint.

Currently oppose electricity-stealing and implement to need the work of electricity anti-stealing experience of power marketing personnel's accumulating and enriching, can be quickly Catch from various channels, the signal of opposing electricity-stealing that transmits in a variety of manners, currently mainly obtain stealing information Approach has: report method, is found by reports, report generally by phone, written, oral, The modes such as network realize.Direct-vision method, by soon, the means inspection such as mouth is asked, ear is listened, nose is heard, hands is touched User power utilization and metering device ruuning situation, thus find the clues and traces of stealing, typically take by outward to In principle.But, reports are difficult to cover all district's electricity consumption situations, uncertain big；Business GeneraI investigation spends manpower and materials huge, and efficiency of opposing electricity-stealing is low, and cannot normalization carry out.Both the above is counter steals There is blindness with uncertain in the follow-up action that causes opposing electricity-stealing of the defect of electric hand section, overall flow exists bottle Neck, overall work expection is difficult to control to.And, current stealing maneuver mostly is and " makes between table meter dead electricity The stealing of having a rest property ", it may be assumed that the online natural law of electric supply meter is less, and before and after day of checking meter, ammeter is online, the most hidden Covering property.Accordingly, it would be desirable to design intermittent power theft monitoring system on the basis of existing power supply enterprise equipment and technology, Thus improve work of electricity anti-stealing level.

Summary of the invention

It is an object of the invention to provide a kind of intermittent power theft monitoring system, so-called intermittent stealing is to keep away Open a day stealing electricity method of checking meter, mostly use the means such as " line one ground ", " around more table meter direct connection ", keep away Open and day of checking meter be intended to not allow the person of checking meter find its electricity filching behavior.These certain customers are nature in network system Existing, the moon of user is used by a kind of intermittent power theft monitoring system of the present invention according to daily power consumption data Electricity is predicted, thus filters out negative error beyond the user of setting value, adopts filtering out user simultaneously Check with multiple months power consumptions, if there is identical analogue to lock suspicion object, final report is relevant Department carries out flow process and scene is checked.A kind of intermittent power theft monitoring system of the present invention is by user's Power consumption data carry out degree of depth excavation and build forecast analysis model, and effectively monitoring makes the interval of ammeter dead electricity Property electricity filching behavior, for oppose electricity-stealing follow-up work provide hard objectives.Staff only needs to open suspicion object Exhibition judges stealing action, both quickly, efficiently can complete task, and use manpower and material resources sparingly, and reach again whole Body link controllable high-efficiency.

The technical scheme realizing above-mentioned purpose is:

A kind of intermittent power theft monitoring system, including:

Screening sample module: for filtering out the of that month online natural law ammeter less than or equal to online natural law threshold value； Alternately ammeter；

Data acquisition module: for selecting a sample ammeter from alternative ammeter, and collecting sample ammeter Data, including:

The of that month actual online natural law n of sample ammeter, each online date t of of that month sample ammeter_iAnd it is right The reading Q answered_i；Wherein i ∈ [1, n], and i is positive integer, t_nRepresent the final online day of of that month sample ammeter Phase；

First model construction module: for building following matrix:

$A=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_n\end{array}\right),X=\left(\begin{array}{c}{k}_0\\ k_1\end{array}\right),b=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_n\end{array}\right);$

And according to formula X=(A^TA)^-1A^TB, tries to achieve parameter k₀And k₁, set up the first forecast analysis model: Q=k₀+k₁×t；

Wherein, Q is the reading of sample ammeter, and t is the online date of of that month sample ammeter；

Data screening module: for according to the first forecast analysis model, that tries to achieve with sample ammeter in this month is each Online date t_iThe reading predictive value of corresponding sample ammeterFormula is:

Reading Q when the sample ammeter corresponding to the arbitrary online date of sample ammeter_iWith reading predictive value Between deviation value beyond deviation threshold time, then assert the reading of the sample ammeter of this online date and correspondence Constitute an exceptional data point, delete all exceptional data points；Sample data after being filtered: bag Include each normal online date t of the normal online natural law m of of that month sample ammeter, sample ammeter_i′And it is corresponding The reading Q of sample ammeter_i', wherein i ' ∈ [1, m], and i ' is integer；

Second model construction module: the matrix for following according to the sample data structure after filtering:

$A^{\′}=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_m\end{array}\right),X^{\′}=\left(\begin{array}{c}{k_0}^{\′}\\ {k_1}^{\′}\end{array}\right),b^{\′}=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_m\end{array}\right);$

Wherein t_mAnd Q_mFor rejecting after all exceptional data points, of that month final online date of sample ammeter and right The reading of the sample ammeter answered；And according to formula: X '=(A '^T A′)-¹A′^TB ', tries to achieve parameter k₀' and k₁', And set up the second analyses and prediction modelThat tries to achieve with of that month sample ammeter is each normal online Date t_iThe reading predictive value of corresponding sample ammeter

Relative average error asks for module: for when m >=5, according to formula Calculate average relative error σ；And judge that whether average relative error σ is less than or equal to the average relative preset Error threshold；

Final reading prediction module: for being less than or equal to average relative error threshold value in average relative error σ Time, according to of that month actual natural law t_s, use the second forecast analysis model to calculate the final reading of sample ammeter Predictive valueComputing formula is

Stealing judge module: for calculating reading difference amount δ of sample ammeter, computing formula is:

Wherein, Q_sFinal reading for of that month sample ammeter；And at sample When reading difference amount δ of ammeter is more than the difference amount threshold value preset, the user of sample ammeter is set to there is interval The user of property stealing suspicion.

Further, described data acquisition module carries out record to the off-line date of sample ammeter, if finding The Offtime of sample ammeter continues to exceed five days, then trigger described stealing judge module, by sample ammeter User be set to the user that there is intermittent stealing suspicion.

Further, the off-line date of nearest some months sample ammeters is compared by described data acquisition module Relatively, if finding, there is once above coincidence in the off-line date of nearest some months sample ammeters, then trigger described Stealing judge module, is set to the user having intermittent stealing suspicion by the user of sample ammeter.

Further, the described stealing judge module actual power consumption and last month in user's this month to sample ammeter Actual power consumption, and this month last year, actual power consumption compared, if finding its of that month actual power consumption And the deviation between last month actual power consumption, or of that month actual power consumption and this month last year actual power consumption it Between deviation exceed marginal value, the user of sample ammeter is set to the user having intermittent stealing suspicion.

Further, the reading of the sample ammeter that described data acquisition module is gathered is divided into section reading at ordinary times With paddy period reading.

The invention provides the technical scheme of a kind of intermittent power theft monitoring system, including screening sample module, Data acquisition module, the first model construction module, data screening module, the second model construction module, phase Mean error is asked for module, final reading prediction module and stealing judge module.It has the technical effect that Combined with off-line analysis by on-line monitoring, focus on user's electric quantity data online acquisition and off-line analysis, In a large amount of normal users colonies, screening screens the suspicion object of minority intermittence stealing, for there being stealing row For the user of suspicion, preferential personnel assigned in-situ processing, both quickly, efficiently can complete task, joint Human-saving material resources, reach again overall link controllable high-efficiency, maintain normal electricity consumption order.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of a kind of intermittent power theft monitoring system of the present invention.

Detailed description of the invention

Below in conjunction with accompanying drawing, the invention will be further described.

A kind of intermittent power theft monitoring system of the present invention is with national grid user power utilization information collecting platform Static electric quantity data, for relying on, uses on-line monitoring and the means that are combined as of off-line analysis, focuses on user and uses Electric quantity data online acquisition and off-line analysis, and then screening screens minority surreptitiously in a large amount of normal users colonies Electricity suspicion object.A kind of intermittent power theft monitoring system of the present invention primarily focuses on monitoring table meter dead electricity Intermittent electricity filching behavior, such as " line one ", " around more table meter direct connection ", can be follow-up work of opposing electricity-stealing Making to provide hard objectives, staff only needs to carry out suspicion object the stealing action that judges, both can quickly, Efficiently complete task, use manpower and material resources sparingly, reach again overall link controllable high-efficiency.

A kind of intermittent power theft monitoring system of the present invention, by state's network users power information acquisition platform, is used Family ammeter collection data can have platform differentiation batch under its command by certain concentrator and carry out screening, analyzing, a selected platform District.

Refer to Fig. 1, a kind of intermittent power theft monitoring system of the present invention of the present invention, including following modules:

Screening sample module 1: for the power information data total for selected analysis station Qu, filter out this month Online natural law is less than or equal to the ammeter of online natural law threshold value；Alternately ammeter.

The effect of this module is: can effectively reduce analyst coverage, for quickly determining stealing suspicion user Good basis is provided；Wherein, the online natural law of ammeter refers to: can effectively collect the natural law of ammeter reading；Sample This screening module 1 makes the user data simultaneously monitored greatly reduce.

Data acquisition module 2: for a sample ammeter selected from alternative ammeter, and collecting sample ammeter Data, including:

The of that month actual online natural law n of sample ammeter, each online date t of of that month sample ammeter_iAnd it is right The reading Q answered_i；Wherein i ∈ [1, n], and i is positive integer, t_nAnd Q_nRepresent of that month sample ammeter last The reading of the sample ammeter of line date and correspondence.

The sample ammeter data that Current data acquisition module 2 is gathered is ammeter every day Pinggu section reading, because of This, the data of all sample ammeters are divided into section reading and paddy period reading at ordinary times, and it also divides about calculating Do not calculate.

Indivedual collection are plagiarized family and be there is interval and copy and accept failure scenarios, sample ammeter have part-time be in non-online State, it is impossible to copy and accept ammeter reading.

First model construction module 3: for building following matrix:

$A=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_n\end{array}\right),X=\left(\begin{array}{c}{k}_0\\ k_1\end{array}\right),b=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_n\end{array}\right);$

And according to formula X=(A^TA)^-1ATb, tries to achieve parameter k₀And k₁, set up the first forecast analysis model: Q=k₀+k₁×t；

Wherein, Q is the reading of sample ammeter, model independent variable t for when month, parameter k₀For upper lunar sample The reading of this ammeter, parameter k to be asked₁For linear coefficient.

The reason using the first model construction module 3 to set up the first forecast analysis model is: intermittence is stolen Electricity to show as the online natural law of electric supply meter less, but average daily power consumption is more stable, it can be assumed that ammeter Reading linear with the electricity consumption time, therefore, for the data construct to the sample ammeter filtered out One forecast analysis model.

Below to X=(A^TA)^-1A^TB verifies:

Characteristic according to the first forecast analysis model and the characteristic of the gathered data of data acquisition module 2, Will be containing model variable t and parameter k to be asked₀And parameter k to be asked₁The form of composition linear function:

Q(t；k₀,k₁)=k₀+k₁t；

When the data of sample ammeter that Usage data collection module 2 gathers, system of linear equations can be obtained:

$\left\{\begin{array}{c}{k}_0+k_1{t}_1=Q_1\\ k_0+k_1{t}_2=Q_2\\ .\\ .\\ .\\ k_0+k_1{t}_i=Q_i\\ .\\ .\\ .\\ k_0+k_1{t}_n=Q_n\end{array}\right.$

Generally by t_iIt is denoted as data matrix A, parameter k₀And k₁It is denoted as parameter vector X, by Q_iIt is denoted as b, then System of linear equations can be write as again:

$\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_n\end{array}\right)\left(\begin{array}{c}{k}_0\\ k_1\end{array}\right)=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_n\end{array}\right);$

That is: AX=b

The form that above-mentioned equation uses method of least square to export as the calculating of lineal square difference is:

$\underset{x}{min}\left|\right|AX-b|{|}^2,A\∈C^{m\×n},b\∈C^n;$

B is splitted into the codomain with A and orthocomplement, orthogonal complement two parts thereof:

B=b₁+b₂

b₁=AA⁺b∈R(A)

b₂=(I-AA⁺)b∈R(A)^⊥；

So AX-b₁∈ R (A), can obtain:

‖AX-b‖²=‖ AX-b₁+(-b₂)‖²=‖ AX-b₁‖²+‖b₂‖²；

Therefore and if only if, and X is AX=b₁=AA⁺During the solution of b, X is least square solution, i.e. X=A⁺b。

Again because:

N (A)=N (AA⁺)=R (I-AA⁺)={ (I-AA⁺)h:h∈Cⁿ}；

Therefore AX=AA⁺The general solution of b is X=A⁺+(I-AA⁺)h:h∈Cⁿ；

Because:

$\begin{array}{c}\left|\right|A^{+}b|{|}^2<|\left|A^{+}b\right|{|}^2+\left|\right|(I-{\mathrm{AA}}^{+})h|{|}^2\\ =\left|\right|A^{+}b+(I-{\mathrm{AA}}^{+})h|{|}^2,(I-{\mathrm{AA}}^{+})h\&NotEqual;0\end{array};$

Therefore prediction model parameters uses principle of least square method, can pass through X=(A^TA)^-1A^TB tries to achieve.

Data screening module 4: for according to the first forecast analysis model, that tries to achieve with sample ammeter in this month is each The reading predictive value of online sample ammeter corresponding for date tiFormula is:

Reading Q when the sample ammeter corresponding to the arbitrary online date of sample ammeter_iWith reading predictive value Between deviation value beyond deviation threshold time, then assert the reading of the sample ammeter of this online date and correspondence Constitute an exceptional data point, delete all exceptional data points；Sample data after being filtered: bag Include each normal online date t of the normal online natural law m of of that month sample ammeter, sample ammeter_i' and its correspondence The reading Q of sample ammeter_i', wherein i ' ∈ [1, m], and i ' is integer, m≤n.

Wherein, exceptional value gathers rub-out signal by mistake or there is interference signal and produce due to system, when There is exceptional value sample data and build forecast model in use, forecast model output will be introduced bigger error, Therefore need the exceptional value in sample to be distinguished and rejects.

Second model construction module 5: the matrix for following according to the sample data structure after filtering:

$A^{\&prime;}=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_m\end{array}\right),X^{\&prime;}=\left(\begin{array}{c}{k_0}^{\&prime;}\\ {k_1}^{\&prime;}\end{array}\right),b^{\&prime;}=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_m\end{array}\right);$

Wherein t_mAnd Q_mFor rejecting after all exceptional data points, of that month final online date of sample ammeter and right The reading of the sample ammeter answered；And according to formula: X '=(A '^T A′)-¹A′^TB ', tries to achieve parameter k₀' and k₁', And set up the second analyses and prediction modelThat tries to achieve with of that month sample ammeter is each normal online Date t_iThe reading predictive value of corresponding sample ammeter

WhereinAnd t_i' it is variable.

Relative average error asks for module 6: for when m >=5, according to formula Calculate average relative error σ；And judge that whether average relative error σ is less than or equal to the average relative preset Error threshold；

Average relative error σ is in order to verify the ageing of the second forecast analysis model, according to sample ammeter Data characteristics, selected average relative error σ is between 5%-15%, and the sample ammeter of m >=5, if averagely Relative error σ is more than average relative error threshold value or m < 5, the second forecast analysis model prediction accuracy rate fall Low.Therefore, it is necessary to judge whether average relative error is more than the average relative error threshold value preset, and Whether m < 5；

If average relative error σ is more than average relative error threshold value, or m < 5, then want data acquisition module Block 2 Resurvey data.

Final reading prediction module 7: for being less than or equal to average relative error threshold value in average relative error σ, Or m is < when 5, according to of that month actual natural law t_s, use the second forecast analysis model to calculate sample ammeter Final reading predictive valueComputing formula is

Stealing judge module 8: for calculating reading difference amount δ of sample ammeter, computing formula is:

Wherein, Q_sFinal reading for of that month sample ammeter；And at sample When reading difference amount δ of ammeter is more than the difference amount threshold value preset, the user of sample ammeter is set to there is interval The user of property stealing suspicion.

If when reading difference amount δ of sample ammeter is less than the difference amount threshold value preset, stealing judge module 8 Issuing a command to data acquisition module 2, the off-line date of sample ammeter is checked by data acquisition module 2, If finding, the Offtime of sample ammeter continues to exceed five days, then trigger stealing judge module 8, by sample electricity The user of table is set to the user having intermittent stealing suspicion.

The off-line date of nearest some months sample ammeters is compared by data acquisition module 2 simultaneously, if sending out There is once above coincidence in the off-line date of the most nearest some months sample ammeters, then trigger stealing judge module 8, the user of sample ammeter is set to the user having intermittent stealing suspicion.Especially show as continuous several Month closing on day of checking meter, sample ammeter is online, its some other time off-line.

Stealing judge module 8 to sample ammeter user's this month actual power consumption with last month actual power consumption, And actual power consumption compares this month last year, if finding its of that month actual power consumption use actual with last month Deviation between deviation between electricity, or of that month actual power consumption and this month last year actual power consumption exceedes Marginal value, is set to the user having intermittent stealing suspicion by the user of sample ammeter.

For there being the user of stealing suspicion, stealing judge module 8 can be excellent by built-in communicator module First personnel assigned in-situ processing, quickly, efficiently can complete task, use manpower and material resources sparingly.

Above example is used for illustrative purposes only, rather than limitation of the present invention, relevant technical field Technical staff, without departing from the spirit and scope of the present invention, it is also possible to make various conversion Or modification, the technical scheme of the most all equivalents also should belong to scope of the invention, should be wanted by each right Ask and limited.

Claims (5)

1. an intermittent power theft monitoring system, it is characterised in that including:

Screening sample module: for filtering out the of that month online natural law ammeter less than or equal to online natural law threshold value； Alternately ammeter；

Data acquisition module: for selecting a sample ammeter from alternative ammeter, and collecting sample ammeter Data, including:

The of that month actual online natural law n of sample ammeter, each online date t of of that month sample ammeter_iAnd it is right The reading Q answered_i；Wherein i ∈ [1, n], and i is positive integer, t_nRepresent the final online day of of that month sample ammeter Phase；

First model construction module: for building following matrix:

$A=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_n\end{array}\right),X=\left(\begin{array}{c}{k}_0\\ k_1\end{array}\right),b=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_n\end{array}\right);$

And according to formula X=(A^TA)^-1A^TB, tries to achieve parameter k₀And k₁, set up the first forecast analysis model: Q=k₀+k₁×t；

Wherein, Q is the reading of sample ammeter, and t is the online date of of that month sample ammeter；

Data screening module: for according to the first forecast analysis model, that tries to achieve with sample ammeter in this month is each Online date t_iThe reading predictive value of corresponding sample ammeterFormula is:

Reading Q when the sample ammeter corresponding to the arbitrary online date of sample ammeter_iWith reading predictive value Between deviation value beyond deviation threshold time, then assert the reading of the sample ammeter of this online date and correspondence Constitute an exceptional data point, delete all exceptional data points；Sample data after being filtered: bag Include each normal online date t of the normal online natural law m of of that month sample ammeter, sample ammeter_i'And it is corresponding The reading Q of sample ammeter_i', wherein i' ∈ [1, m], and i' is integer；

Second model construction module: the matrix for following according to the sample data structure after filtering:

$A^{\&prime;}=\left(\begin{array}{cc}1& t_1\\ .& .\\ .& .\\ .& .\\ 1& t_m\end{array}\right),X^{\&prime;}=\left(\begin{array}{c}{k_0}^{\&prime;}\\ {k_1}^{\&prime;}\end{array}\right),b^{\&prime;}=\left(\begin{array}{c}{Q}_1\\ .\\ .\\ .\\ Q_m\end{array}\right);$

Wherein t_mAnd Q_mFor rejecting after all exceptional data points, of that month final online date of sample ammeter and right The reading of the sample ammeter answered；And according to formula: X'=(A'^T A')^-1A'^TB', tries to achieve parameter k₀' and k₁', And set up the second analyses and prediction modelThat tries to achieve with of that month sample ammeter is each normal online Date t_iThe reading predictive value of corresponding sample ammeter

Relative average error asks for module: for when m >=5, according to formula Calculate average relative error σ；And judge that whether average relative error σ is less than or equal to the average relative preset Error threshold；

Final reading prediction module: for being less than or equal to average relative error threshold value in average relative error σ Time, according to of that month actual natural law t_s, use the second forecast analysis model to calculate the final reading of sample ammeter Predictive valueComputing formula is

Stealing judge module: for calculating reading difference amount δ of sample ammeter, computing formula is:

Wherein, Q_sFinal reading for of that month sample ammeter；And at sample When reading difference amount δ of ammeter is more than the difference amount threshold value preset, the user of sample ammeter is set to there is interval The user of property stealing suspicion.

A kind of intermittent power theft monitoring system the most according to claim 1, it is characterised in that: described Data acquisition module carries out record to the off-line date of sample ammeter, if finding the Offtime of sample ammeter Continue to exceed five days, then trigger described stealing judge module, the user of sample ammeter is set to there is intermittence The user of stealing suspicion.

Base one intermittence power theft monitoring system the most according to claim 1, it is characterised in that: institute State data acquisition module the off-line date of nearest some months sample ammeters is compared, if if finding recently There is once above coincidence in the off-line date of the sample ammeter dry moon, then trigger described stealing judge module, will The user of sample ammeter is set to the user having intermittent stealing suspicion.

A kind of intermittent power theft monitoring system the most according to claim 1, it is characterised in that: described Stealing judge module to sample ammeter user's this month actual power consumption with actual power consumption last month, and go Year of that month actual power consumption compares, if find its of that month actual power consumption and last month actual power consumption it Between deviation, or the deviation between of that month actual power consumption and this month last year actual power consumption exceedes marginal value, The user of sample ammeter is set to the user having intermittent stealing suspicion.

A kind of intermittent power theft monitoring system the most according to claim 1, it is characterised in that: described The reading of the sample ammeter that data acquisition module is gathered is divided into section reading and paddy period reading at ordinary times.