CN105913010A - Electric appliance type determination device - Google Patents

Abstract

The invention discloses an electric appliance type determination device, comprising an information collection module, an information processing module, and a communication module. The electric appliance type determination device employs electric appliance starting current characteristics including starting process time, a starting current maximum value and starting current maximum value time and load current frequency spectrum characteristics of the electric appliance as identification characteristics, and the characteristic information is rich; the electric appliance type determination device adopts a combined classifier comprising a decision-making tree classifier and a Bayes classifier to perform identification classification and performs comprehensive identification in consideration of characteristics of the decision-making tree classifier and a Bayes classifier; and the identification accuracy is high. Provided methods for obtaining starting current characteristics and load current frequency spectrum characteristics are simple and reliable. The electric appliance type determination device can be used some collective public places like a students' dormitory, a large-scale pedlars market, etc, where need to perform electric appliance management and can also used in some other places where need to perform electric appliance type identification and statistics and to perform electric appliance management.

Description

Electrical appliance type detector

Technical field

The present invention relates to a kind of equipment identification and sorter, especially relate to a kind of electrical appliance type detector.

Background technology

At present, the electric appliance load property identification method of main flow includes electric appliance load identification side based on bearing power coefficient of colligation algorithm Method, electric appliance load recognition methods based on electromagnetic induction, electric appliance load recognition methods based on neural network algorithm, based on the cycle The electric appliance load recognition methods etc. of property discrete transform algorithm.Various methods all can to a certain degree realize electric appliance load character Identifying, but owing to characteristic properties is single, means of identification is single, generally there is generalization ability not and can not entirely accurate identification Problem.

Summary of the invention

It is an object of the invention to, for the defect of present prior art, it is provided that a kind of electrical appliance class being capable of efficient identification Type diagnosis apparatus.Described diagnosis apparatus includes information acquisition module, message processing module, communication module.

Described information acquisition module is for gathering the load current of electrical equipment and being converted into current digital signal；Described current digital signal It is sent to message processing module；Described message processing module, according to the current digital signal of input, uses assembled classifier to carry out electricity Device type identification；Described communication module is for sending the appliance type recognition result of message processing module to host computer.

The input feature vector of described assembled classifier includes the starting current feature of electrical equipment and the load current spectrum signature of electrical equipment；Described Assembled classifier includes decision tree classifier and Bayes classifier；Described starting current feature includes start-up course time, startup Current maxima, starting current maximum time.

Described information acquisition module includes current sensor, preamplifier, wave filter, A/D converter；Described information processing The core of module is DSP, or is ARM, or is single-chip microcomputer, or is FPGA.

Described A/D converter can use the A/D converter that the core of message processing module includes.

Described information acquisition module, message processing module, all or part of function of communication module are integrated in a piece of SoC On.

Described communication module also receives the related work instruction of host computer；Communication mode bag between described communication module and host computer Include communication and wire communication mode；Described communication includes ZigBee, bluetooth, WiFi, 433MHz number Biography mode；Described wire communication mode includes 485 buses, CAN, the Internet, power carrier mode.

Described load current spectrum signature is prepared by the following:

Step one, the steady state current signals of acquisition electric appliance load, and it is converted into the steady-state current digital signal of correspondence；

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic；

Step 3, using odd harmonic signal relative magnitude that overtone order in load current spectral characteristic is n time as load current Spectrum signature, n=1,3 ..., M；Described M represents that harmonic wave high reps and M are more than or equal to 3.

In described assembled classifier, decision tree classifier is Main classification device, and Bayes classifier is subsidiary classification device.

Described assembled classifier carries out appliance type knowledge method for distinguishing: when Main classification device successfully realizes appliance type identification, main The recognition result that appliance type recognition result is assembled classifier of grader；When Main classification device fails to realize appliance type identification, And the recognition result of Main classification device is 2 kinds or two or more appliance type, by Main classification device export 2 kinds or two or more In appliance type recognition result, the appliance type that in the output of subsidiary classification device, probability is the highest is known as the appliance type of assembled classifier Other result；When Main classification device fails to realize failing to provide in appliance type identification, and the recognition result of Main classification device the electrical equipment of identification During type, the appliance type that in being exported by subsidiary classification device, probability is the highest is as the appliance type recognition result of assembled classifier.

Described starting current feature is prepared by the following by message processing module:

Before step 1, appliance starting, start the load current continuous sampling to electrical equipment and load current size is judged；When When load current virtual value is more than ε, it is determined that electrical equipment starts start and turn to step 2；Described ε is the numerical value more than 0；

Step 2, load current to electrical equipment carry out continuous sampling, and protect with power frequency period for unit computational load current effective value Deposit；Calculate the meansigma methods of the load current virtual value of nearest N number of power frequency period；Each within nearest N number of power frequency period The load current virtual value of power frequency period is compared with the meansigma methods of the load current virtual value of this N number of power frequency period, and fluctuate width When degree is respectively less than the relative error range E set, it is determined that electric appliance load enters steady statue, turns to step 3；Described N takes Value scope is 50-500；The span of described E is 2%-20%；

Step 3, the meansigma methods of the load current virtual value within nearest N number of power frequency period is had as electric appliance load steady-state current Valid value；Electrical equipment is started Startup time to the time between nearest N number of power frequency period initial time as the start-up course time； Electrical equipment is started Startup time to the time between the maximum power frequency period of load current virtual value within the start-up course time as opening The streaming current maximum time；By the load current virtual value of starting current maximum time place power frequency period and electric appliance load stable state Ratio between current effective value is as starting current maximum.

The input feature vector of described assembled classifier also includes electric appliance load steady-state current virtual value.

The invention has the beneficial effects as follows: use the starting current feature of electrical equipment, the load current spectrum signature of electrical equipment and electricity simultaneously Device load steady state current effective value enriches as the identification feature of described electrical appliance type detector, characteristic information；Employing includes certainly The assembled classifier of plan Tree Classifier and Bayes classifier is identified classification, takes into account decision tree classifier and Bayes classifier Feature comprehensively identify, generalization ability and recognition accuracy are high；There is provided includes that start-up course time, starting current are maximum Value, starting current maximum time are at interior starting current characteristic-acquisition method, and the letter of load current spectrum signature acquisition methods Single, reliable.

Accompanying drawing explanation

Fig. 1 is the structural representation of electrical appliance type detector embodiment of the present invention；

Fig. 2 is the start-up course current waveform of electric filament lamp desk lamp；

Fig. 3 is the start-up course current waveform of the resistive loads such as resistance furnace；

Fig. 4 is the start-up course current waveform of monophase machine class load；

Fig. 5 is computer and the start-up course current waveform of Switching Power Supply class load；

Fig. 6 is the flow chart that electrical appliance type detector carries out appliance type identification.

Detailed description of the invention

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

Fig. 1 is the structural representation of electrical appliance type detector embodiment of the present invention, at information acquisition module 101, information Reason module 102, communication module 103.

Information acquisition module 102 is for gathering the load current of electrical equipment and load current being converted into current digital signal, electric current number Word signal is sent to message processing module 102.Information acquisition module include current sensor, preamplifier, wave filter, The ingredients such as A/D converter, are respectively completed the sensing of load current signal, amplify, filter and analog-digital conversion function.When negative When load current range is bigger, the preamplifier with programmable function can be selected, or be further added by before A/D converter One independent programmable amplifier, carries out Discrete control to the load current that scope is bigger and amplifies, make input to A/D converter Voltage signal range be maintained at rational interval, it is ensured that conversion accuracy.Wave filter is used for filtering high fdrequency component, it is to avoid frequency spectrum mixes Folded.

Message processing module 102, according to the current digital signal of input, uses and includes decision tree classifier and Bayes classifier Assembled classifier realizes appliance type identification.The input feature vector of assembled classifier includes the starting current feature of electrical equipment and the negative of electrical equipment Carry current spectrum feature.The core of message processing module 102 is DSP, ARM, single-chip microcomputer, or is FPGA.Work as letter The core of breath processing module includes A/D converter and this A/D converter and meets when requiring, in information acquisition module 101 A/D converter can use the A/D converter that the core of message processing module 102 includes.

Recognition result, for realizing the communication between host computer, is sent to host computer by communication module 103.Communication module 102 And the communication mode between host computer includes communication and wire communication mode, and the communication that can use includes The modes such as ZigBee, bluetooth, WiFi, 433MHz number biography, can include 485 buses, CAN in the wire communication mode used The modes such as bus, the Internet, power carrier.Communication module 103 can also receive the related work instruction of host computer, completes to refer to Fixed task.Host computer can be the server of administration section, it is also possible to be various work stations, or various mobile whole End.

Information acquisition module 101, message processing module 102, all or part of function of communication module 103 can be integrated in On a piece of SoC, reduce diagnosis apparatus volume, convenient installation.

Different electric equipments has different starting current features.It is illustrated in figure 2 the start-up course current wave of electric filament lamp desk lamp Shape.Electric filament lamp is by filament electrified regulation to incandescent state, utilizes heat radiation to send the electric light source of visible ray.The filament of electric filament lamp Generally with resistant to elevated temperatures tungsten manufacture, but the resistance of tungsten varies with temperature greatly, with R_tRepresent the tungsten filament electricity when t DEG C Resistance, with R₀Represent the tungsten filament resistance when 0 DEG C, then both have following relation

R_t=R₀(1+0.0045t)

Such as, if the temperature that the filament of electric filament lamp (tungsten filament) is when normal work is 2000 DEG C, one " 220V 100W " Resistance when 2000 DEG C of normal work of the filament of electric filament lamp be

$R_t=\frac{U^2}{P}=\frac{220\×220}{100}=484\mathrm{\Ω}$

Its resistance of 0 DEG C when no power is

$R_0=\frac{R_t}{1+0.0045t}=\frac{484}{1+0.0045\×2000}=48.4\mathrm{\Ω}$

Its resistance of 20 DEG C when no power is

R₂₀=R₀(1+0.0045t)=52.8 Ω

I.e. electric filament lamp exceedes 9 times of its rated current at the immediate current starting energising, and maximum starting current occurs on startup Carve.Along with the rising of electric filament lamp tungsten filament temperature, the load current of electric filament lamp exponentially reduces, subsequently into stable shape State.

If electric appliance load steady-state current virtual value is I_W, and definition electric appliance load current effective value entrance electric appliance load steady-state current Within the relative error range of one setting of virtual value and stably within this relative error range, then electric appliance load enters steady Determine state.Relative error range can be set as 10%, it is also possible to be set as the 2%-20% such as 2%, 5%, 15%, 20% it Between value.In Fig. 2, the relative error range set is as 10%, when the load current of electric filament lamp is exponentially reduced to it I_W10% range of error time, such as the moment T in Fig. 2_S, start-up course terminates.The start-up course time of electric filament lamp is T_S。I_WFor virtual value.

Select start-up course time, starting current maximum I*, starting current maximum time special as the starting current of electrical equipment Levy；Starting current maximum is per unit value, i.e. starting current maximum I* is maximum virtual value I of starting current_MBear with electrical equipment Carry steady-state current virtual value I_WRatio.

In Fig. 2, the start-up course time of electric filament lamp is T_S；Starting current maximum I* is I_M/I_W, its value about 9-10 it Between；The starting current maximum time is T_M, T_M=0.

It is illustrated in figure 3 the start-up course current waveform of the resistive loads such as resistance furnace.The resistive loads such as resistance furnace generally use The lectrothermal alloy wire such as nickel chromium triangle, ferrum-chromium-aluminum, its common feature is that resistance temperature correction factor is little, and resistance value is stable.With the trade mark it is As a example by the nichrome wire of Cr20Ni80, its resistance correction factor when 1000 DEG C is 1.014, when i.e. 1000 DEG C relative to When 20 DEG C, the trade mark is that the nichrome wire resistance of Cr20Ni80 only increases by 1.4%.Therefore, the resistive load such as resistance furnace is logical Steady statue is entered, the start-up course time T of the resistive load such as resistance furnace when electrically activating_S=0；Starting current maximum I*=1；Starting current maximum time T_M=0.

It is illustrated in figure 4 the start-up course current waveform of monophase machine class load.The load of monophase machine class had both had inductive load Characteristic, has again counter electromotive force load characteristic.Startup time, due to the effect of inductance, the starting current of Startup time is 0； Rise rapidly with after current, before counter electromotive force of motor does not sets up, reach current peak I_M；Hereafter, motor speed increases Adding, motor load electric current progressively reduces, until entering steady statue.In Fig. 4, the start-up course time of monophase machine class load For T_S；Starting current maximum I* is I_M/I_W；The starting current maximum time is T_M。

It is illustrated in figure 5 computer and the start-up course current waveform of Switching Power Supply class load.Computer and the load of Switching Power Supply class Because the impact on electric capacity charging, can produce a surge current the biggest in startup moment, its peak value can reach steady-state current to be had Valid value I_WSeveral times to tens times, the time is 1 to 2 power frequency period.In Fig. 5, computer and Switching Power Supply class load The start-up course time is T_S, about 1 to 2 power frequency period；Starting current maximum I* is I_M/I_W；Starting current maximum Time is T_M=0.

The method of the starting current feature obtaining electrical equipment is:

Before appliance starting, when load current value is 0 (being not keyed up) or the least (being in holding state), message processing module 102 i.e. start load current is carried out continuous sampling；The load current value virtual value obtained when sampling starts more than 0 or opens When beginning more than the standby current of electrical equipment, i.e. judging that electrical equipment has been started up, recording this moment is T₀.With a less non-negative threshold Value ε distinguishes the load current value before and after appliance starting, when special hour of ε value, such as, during ε value 1mA, described in sentence Disconnected device does not consider ideal case, and i.e. thinking standby is also the starting state of electrical equipment；When ε value less but more than electrical equipment treat electromechanics During stream, such as, during ε value 20mA, the holding state of electrical equipment can be considered inactive state by described diagnosis apparatus, but simultaneously Also can the least electrical equipment of Partial Power cause and Lou identify.

Message processing module 102 carries out continuous sampling to load current, and with power frequency period for unit computational load current effective value And preserve；When electrical equipment has been started up, and after continuous sampling reaches N number of power frequency period, the nearest N of Continuous plus while sampling Meansigma methods I of the load current virtual value of individual power frequency period_V；Message processing module 102 is to every within nearest N number of power frequency period The load current virtual value of individual power frequency period compares with the meansigma methods of the load current virtual value of this N number of power frequency period, by mistake When difference (or fluctuation) amplitude is respectively less than the relative error range E set, it is determined that electric appliance load enters steady statue, this nearest N The initial time of individual power frequency period is the finish time of start-up course, and recording this moment is T₁。

Using the meansigma methods of the load current virtual value within nearest N number of power frequency period as electric appliance load steady-state current virtual value I_W；Electrical equipment is started Startup time T₀To nearest N number of power frequency period initial time T₁Between time as the start-up course time T_S；By T₀To T₁Within load current virtual value maximum power frequency period place moment be recorded as T₂, by T₀To T₂Between Time is as starting current maximum time T_M；By T₂The load current virtual value of place power frequency period and electric appliance load stable state electricity Stream virtual value I_WBetween ratio as starting current maximum I*.

Owing to not knowing electric appliance load steady-state current virtual value I in advance_W, therefore, by N number of power frequency period, i.e. one section continue time Between T_PWithin fluctuation range less than the meansigma methods of load current virtual value during the relative error range E set as electric appliance load Steady-state current virtual value I_W.Owing to the start-up course of ordinary appliances load is very fast, so, T_PSpan be 1-10s, allusion quotation Type value is 2s, and the typical value that span is 50-500, N of corresponding power frequency period quantity N is 100.Described phase The typical value that span is 2%-20%, E to range of error E is 10%.

The input feature vector of assembled classifier also includes the load current spectrum signature of electrical equipment.The load current spectrum signature of electrical equipment is by believing Breath processing module 102 controls information acquisition module 101, is obtained by following steps:

Step one, enter after steady statue until electric appliance load, obtain the steady state current signals of electric appliance load, and be converted into right The steady-state current digital signal answered.

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic.For ensureing Fourier Being smoothed out of conversion, at the steady state current signals of aforementioned acquisition electric appliance load, and is converted into the steady-state current numeral of correspondence During signal, the accuracy and speed of A/D converter needs to meet the requirement of Fourier transform, and sample frequency can be set as 10kHz, or other numerical value；Message processing module 102 carries out FFT computing to the steady-state current digital signal collected, Calculate its frequency spectrum.

Step 3, using the nth harmonic signal relative magnitude in load current spectral characteristic as load current spectrum signature, its In, n=1,2 ..., M；When forming the input feature value of assembled classifier, nth harmonic signal relative magnitude is in input According to 1 in characteristic vector, 2 ..., the order of M is arranged in order.Due to load current spectral characteristic mainly by odd harmonic group Becoming, in addition to minority electric equipment, even-order harmonic component is almost 0, accordingly it is also possible to by harmonic wave in load current spectral characteristic Number of times be the odd harmonic signal relative magnitude of n time sequentially as load current spectrum signature, wherein, n=1,3 ..., M.1 subharmonic during n=1 is fundamental frequency.Described harmonic signal relative magnitude is harmonic signal amplitude and electric appliance load stable state Current effective value I_WRatio.Described M represents harmonic wave high reps, and generally, M is more than or equal to 3.

In assembled classifier, decision tree classifier is Main classification device, and Bayes classifier is subsidiary classification device.Assembled classifier Input feature vector include aforesaid starting current feature and load current spectrum signature, the input feature vector of assembled classifier simultaneously as The input feature vector of decision tree classifier and the input feature vector of Bayes classifier.

Being illustrated in figure 6 electrical appliance type detector and carry out the flow chart of appliance type identification, electrical appliance type detector carries out electricity The method of device type identification is:

Step A, wait appliance starting；

Step B, collection appliance starting current data also preserve, until appliance starting process terminates；

The appliance starting current data that step C, analysis gather, obtains the starting current feature of electrical equipment；

Step D, gather electrical equipment steady operation time data and preserve；

Data during the electrical equipment steady operation that step E, analysis gather, obtain the load current spectrum signature of electrical equipment；

Step F, using starting current feature and load current spectrum signature as the input feature vector of assembled classifier；Assembled classifier Carry out appliance type identification；

Step G, output appliance type recognition result.

Described assembled classifier carries out appliance type knowledge method for distinguishing: when Main classification device successfully realizes appliance type identification, the most main When the recognition result of grader output is that in unique appliance type, i.e. recognition result, unique appliance type is for being, by Main classification The appliance type of device identification is as the appliance type recognition result of assembled classifier；Know when Main classification device fails to realize appliance type Not, and the recognition result of Main classification device be 2 kinds or two or more appliance type, i.e. recognition result have 2 kinds or 2 kinds with When upper appliance type is for being, by Main classification device export 2 kinds or two or more appliance type recognition result in, subsidiary classification device The appliance type that in output, probability is the highest is as the appliance type recognition result of assembled classifier；When Main classification device fails to realize electrical equipment Type identification, and the recognition result of Main classification device fail to be given in the appliance type of identification, i.e. recognition result and there is no appliance type During for being, the appliance type that in being exported by subsidiary classification device, probability is the highest is as the appliance type recognition result of assembled classifier.

As a example by a simple embodiment 1, illustrate that assembled classifier carries out appliance type and knows method for distinguishing.It is provided with a group Closing grader, its input feature vector is x={T_S, I*, T_M, A₁, A₂, A₃, A₄, A₅, wherein, T_SIt it is start-up course Time, unit is ms；I* is starting current maximum；T_MBeing the starting current maximum time, unit is ms；A₁、A₂、 A₃、A₄、A₅For the 1-5 rd harmonic signal relative magnitude in load current spectral characteristic.The output of assembled classifier is { B₁, B₂, B₃, B₄, B₁、B₂、B₃、B₄Represent assembled classifier respectively to electric filament lamp, resistance furnace, hair-dryer, computer Recognition result exports, recognition result B₁、B₂、B₃、B₄Value be two-value key words sorting.The input feature vector of Main classification device Also it is x={T_S, I*, T_M, A₁, A₂, A₃, A₄, A₅, its output is { F₁, F₂, F₃, F₄, F₁、F₂、 F₃、F₄Represent Main classification device respectively the recognition result of electric filament lamp, resistance furnace, hair-dryer, computer is exported, recognition result F₁、F₂、F₃、F₄Value be also two-value key words sorting.The input feature vector of subsidiary classification device is similarly x={T_S, I*, T_M, A₁, A₂, A₃, A₄, A₅, its output is { P (y₁| x), P (y₂| x), P (y₃| x), P (y₄| x) }, P(y₁|x)、P(y₂|x)、P(y₃|x)、P(y₄| x) be subsidiary classification device output posterior probability, P (y₁|x)、P(y₂|x)、 P(y₃|x)、P(y₄| the mutual size between x) shows that the current input feature of subsidiary classification device represents that identified electrical equipment belongs to white Vehement lamp, resistance furnace, hair-dryer, the probability size of computer.

In embodiment 1, B₁、B₂、B₃、B₄Key words sorting and F₁、F₂、F₃、F₄Key words sorting all take 1,0. When key words sorting is 1, corresponding appliance type mates with current input feature, for confirm recognition result, the most accordingly Appliance type recognition result is yes；When key words sorting is 0, corresponding appliance type does not mates with input feature vector, fails to become true The recognition result recognized, corresponding appliance type recognition result is no in other words.

In embodiment 1, if the recognition result key words sorting of certain Main classification device is F₁F₂F₃F₄=0100, then it is assumed that main point Class device successfully realizes appliance type identification, therefore, does not consider the recognition result of subsidiary classification device, directly makes B₁B₂B₃B₄= 0100, i.e. the recognition result of assembled classifier is: identified electrical equipment is resistance furnace.

In embodiment 1, if the recognition result key words sorting of certain Main classification device is F₁F₂F₃F₄=1010, then it is assumed that main point Class device fails to realize appliance type identification, and the recognition result of Main classification device is 2 kinds or two or more appliance type；Set this again Time subsidiary classification device recognition result meet P (y₁|x)<P(y₃| x), then make B₁B₂B₃B₄=0010, i.e. the knowledge of assembled classifier Other result is: identified electrical equipment is hair-dryer.

In embodiment 1, if the recognition result key words sorting of certain Main classification device is F₁F₂F₃F₄=0000, then it is assumed that main point Class device fails to realize failing to provide in appliance type identification, and the recognition result of Main classification device the appliance type of identification；Set again now The recognition result of subsidiary classification device meets P (y₁|x)>P(y₂| x) and P (y₁|x)>P(y₃| x) and P (y₁|x)>P(y₄| x), then make B₁B₂B₃B₄=1000, i.e. the recognition result of assembled classifier is: identified electrical equipment is electric filament lamp.

Assembled classifier, the recognition result key words sorting of Main classification device can also use other scheme, such as, respectively with classification Labelling 1 ,-1, or 0,1, or-1,1, and other schemes represent corresponding electric appliance recognition result be yes, No.The key words sorting scheme of assembled classifier and Main classification device can be identical, it is also possible to differs.

In the input feature vector of described assembled classifier, it is also possible to include electric appliance load steady-state current virtual value I_W.Such as, have 2 Planting different electrical equipment, electric cautery and resistance furnace need to identify, electric cautery, resistance furnace are all pure resistor loads, and all have resistance Temperature correction coefficient is little, the common feature that resistance value is stable.Therefore, the aforesaid starting current feature of simple dependence and load current They cannot be made a distinction by spectrum signature.Input feature vector increases electric appliance load steady-state current virtual value I_WAfter, electric cautery merit Rate is little, electric appliance load steady-state current virtual value I_WLittle；Resistance furnace power is big, electric appliance load steady-state current virtual value I_WGreatly, special Levying difference, assembled classifier can carry out and complete identifying.

Subsidiary classification device is Bayes classifier.NBC grader (Naive Bayes Classifier), TAN can be selected to classify Three kinds of Bayes classifiers such as device (crown pruning), BAN grader (Bayes classifier of enhancing) Among one as subsidiary classification device.

Embodiment 2 selects NBC grader as subsidiary classification device.Naive Bayes Classification is defined as follows:

(1) set x={a₁,a₂,…,a_mIt is an item to be sorted, and each a is x characteristic attribute；

(2) there is category set C={y₁,y₂,…,y_n}；

(3) calculate P (y₁|x),P(y₂|x),…,P(y_n|x)；

If (4) P (y_k| x)=max{P (y₁|x),P(y₂|x),…,P(y_n| x) }, then x ∈ y_k。

The concrete grammar calculating the (3) each conditional probability in step is:

1. find the item set to be sorted of a known classification as training sample set；

2. statistics obtains the conditional probability estimation of each characteristic attribute lower of all categories；

P(a₁|y₁),P(a₂|y₁),…,P(a_m|y₁)；

P(a₁|y₂),P(a₂|y₂),…,P(a_m|y₂)；

…；

P(a₁|y_n),P(a₂|y_n),…,P(a_m|y_n)。

3. according to Bayes theorem, have:

$P\left(y_i\right|x)=\frac{P\left(x\right|y_i)P\left(y_i\right)}{P\left(x\right)}---\left(1\right)$

Because denominator is constant for all categories, as long as therefore molecule is maximized by we；Again because at naive Bayesian In each characteristic attribute be conditional sampling, so having:

$P\left(x\right|y_i)P\left(y_i\right)=P\left(a_1\right|y_i)P\left(a_2\right|y_i)...P\left(a_m\right|y_i)P\left(y_i\right)=P\left(y_i\right)\underset{j=1}{\overset{m}{\&Pi;}}P\left(a_j\right|y_i)$

In embodiment 2, the input feature vector of assembled classifier is { T_S, I*, T_M, A₁, A₃, I_W, wherein, T_SIt is to open Dynamic process time, unit is ms；I* is starting current maximum；T_MBeing the starting current maximum time, unit is ms； A₁、A₃For 1 in load current spectral characteristic, 3 odd harmonic signal relative magnitude；I_WHave for electric appliance load steady-state current Valid value, unit is ampere.Require that the electrical equipment classification identified is electric filament lamp, resistance furnace, electric fan, computer, electric cautery.Order The characteristic attribute combination x={a of Naive Bayes Classifier₁,a₂,a₃,a₄,a₅,a₆Element in } is special with the input of assembled classifier Element sequentially { T in collection conjunction_S, I*, T_M, A₁, A₃, I_WOne_to_one corresponding；The output class of Naive Bayes Classifier Do not gather C={y₁,y₂,y₃,y₄,y₅The most respectively with electrical equipment classification electric filament lamp, resistance furnace, electric fan, computer, electric cautery One_to_one corresponding.

The process of training NBC grader includes:

1, characteristic attribute is carried out segmentation division, carry out sliding-model control.In embodiment 2, the characteristic attribute discretization taked Method is:

a₁: { a₁<50,50≤a₁≤1000,a₁>1000}；

a₂: { a₂<7,7≤a₂≤11,a₂>11}；

a₃: { a₃<20,20≤a₃≤300,a₃>300}；

a₄: { a₄<0.7,0.7≤a₄≤0.9,a₄>0.9}；

a₅: { a₅<0.02,0.02≤a₅≤0.05,a₅>0.05}；

a₆: { a₆<0.45,a₆≥0.45}。

2, every electric appliances type is all gathered, and how group sample is as training sample, calculate every electric appliances type sample all simultaneously The ratio occupied in appliance type sample, calculates P (y the most respectively₁)、P(y₂)、P(y₃)、P(y₄)、P(y₅).When every class When electrical equipment all gathers identical sample size, such as, every electric appliances all gathers the sample more than 100 groups, wherein every electric appliances with Machine selects 100 groups of samples as training sample, and other are then as test sample, and total training sample is 500 groups, and has

P(y₁)=P (y₂)=P (y₃)=P (y₄)=P (y₅)=0.2.

3, calculating the frequency (ratio) of each characteristic attribute segmentation under each class condition of training sample, statistics obtains of all categories The conditional probability of each characteristic attribute lower is estimated, statistical computation the most respectively

P(a₁<50|y₁)、P(50≤a₁≤1000|y₁)、P(a₁>1000|y₁)；

P(a₁<50|y₂)、P(50≤a₁≤1000|y₂)、P(a₁>1000|y₂)；

…；

P(a₁<50|y₅)、P(50≤a₁≤1000|y₅)、P(a₁>1000|y₅)；

P(a₂<7|y₁)、P(7≤a₂≤11|y₁)、P(a₂>11|y₁)；

P(a₂<7|y₂)、P(7≤a₂≤11|y₂)、P(a₂>11|y₂)；

…；

P(a₂<7|y₅)、P(7≤a₂≤11|y₅)、P(a₂>11|y₅)；

P(a₃<20|y₁)、P(20≤a₃≤300|y₁)、P(a₃>300|y₁)；

P(a₃<20|y₂)、P(20≤a₃≤300|y₂)、P(a₃>300|y₂)；

…；

P(a₃<20|y₅)、P(20≤a₃≤300|y₅)、P(a₃>300|y₅)；

P(a₄<0.7|y₁)、P(0.7≤a₄≤0.9|y₁)、P(a₄>0.9|y₁)；

P(a₄<0.7|y₂)、P(0.7≤a₄≤0.9|y₂)、P(a₄>0.9|y₂)；

…；

P(a₄<0.7|y₅)、P(0.7≤a₄≤0.9|y₅)、P(a₄>0.9|y₅)；

P(a₅<0.02|y₁)、P(0.02≤a₅≤0.05|y₁)、P(a₅>0.05|y₁)；

P(a₅<0.02|y₂)、P(0.02≤a₅≤0.05|y₂)、P(a₅>0.05|y₂)；

P(a₅<0.02|y₅)、P(0.02≤a₅≤0.05|y₅)、P(a₅>0.05|y₅)；

P(a₆<0.45|y₁)、P(a₆≥0.45|y₁)；

P(a₆<0.45|y₂)、P(a₆≥0.45|y₂)；

…；

P(a₆<0.45|y₅)、P(a₆≥0.45|y₅)。

Through above-mentioned step 1, step 2, step 3, NBC classifier training completes.Wherein, step 1 is to characteristic attribute Carrying out segmentation to divide by manually determining, when each input feature vector is carried out disperse segmentaly, the quantity of segmentation is 2 sections or 2 More than Duan, such as, in embodiment 2, feature a₁-a₅All it is divided into 3 sections, feature a₆It is divided into 2 sections.Each feature is specifically divided For how many sections, the result after test sample can be tested by the selection of fragmentation threshold according to the Bayes classifier after training is adjusted Whole.Step 2, step 3 have been calculated by message processing module 102 or computer.

The method using Bayes classifier to carry out classifying in the present invention is:

1, using the input feature vector of assembled classifier as the input feature vector of Bayes classifier.In example 2, by combination point Input feature vector set { the T of class device_S, I*, T_M, A₁, A₃, I_WAs the input feature vector x of Bayes classifier, and have X={a₁,a₂,a₃,a₄,a₅,a₆}。

2, the conditional probability of each characteristic attribute of all categories lower obtained according to training is estimated, determines each input feature vector attribute respectively Segmentation place and determine its probability P (a to every electric appliances classification₁|y₁)～P (a_m|y_n), wherein, electrical equipment category set is C={y₁,y₂,…,y_n}.In embodiment 2, electrical equipment category set C={y₁,y₂,y₃,y₄,y₅The corresponding electrical equipment classification represented It is electric filament lamp, resistance furnace, electric fan, computer, electric cautery, determines P (a₁|y₁)～P) a₆|y₅) method be use training The conditional probability of each characteristic attribute obtained during NBC grader is estimated.

3, according to formula

$P\left(y_i\right|x)=\frac{P\left(x\right|y_i)P\left(y_i\right)}{P\left(x\right)}$

Calculate every kind of other posterior probability of electric type.Because denominator P (x) is constant for all electrical equipment classifications, P (x)=1 is made to replace P (x) value that generation is actual, the mutual size not affected between every kind of electrical equipment classification posterior probability compares, and now has

$P\left(y_i\right|x)=P\left(x\right|y_i)P\left(y_i\right)=P\left(y_i\right)\underset{j=1}{\overset{m}{\&Pi;}}P\left(a_j\right|y_i)$

In embodiment 2, have

$P\left(y_1\right|x)=P\left(x\right|y_1)P\left(y_1\right)=P\left(y_1\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_1);$

$P\left(y_2\right|x)=P\left(x\right|y_2)P\left(y_2\right)=P\left(y_2\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_2);$

$P\left(y_3\right|x)=P\left(x\right|y_3)P\left(y_3\right)=P\left(y_3\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_3);$

$P\left(y_4\right|x)=P\left(x\right|y_4)P\left(y_4\right)=P\left(y_4\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_4);$

$P\left(y_5\right|x)=P\left(x\right|y_5)P\left(y_5\right)=P\left(y_5\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_5).$

Use test sample that the Bayes classifier trained is tested, decide whether to adjust input spy according to test result The discretization method (i.e. adjusting number of fragments and threshold value) levied, re-training Bayes classifier.

Main classification device is decision tree classifier, and the algorithm of decision tree classifier can select ID3, C4.5, CART etc..Implement Example 2 selects to use ID3 decision tree classifier as Main classification device.The several of ID3 decision tree classifier are defined as follows:

If D is the division carried out training tuple by classification, then the entropy of D is expressed as:

$info\left(D\right)=-\underset{i=1}{\overset{u}{\&Sigma;}}{p}_i{\log }_2\left(p_i\right);$

Wherein p_iRepresent the probability that i-th classification occurs in whole training tuple (i.e. sample), can be with belonging to this classification unit The quantity of element is divided by training tuple elements total quantity as estimation.The practical significance of entropy represent be tuple in D class label needed for The average information wanted.

Assume to divide training tuple D by attribute A, then the expectation information that D is divided by A is:

${\mathrm{info}}_A\left(D\right)=-\underset{j=1}{\overset{v}{\&Sigma;}}\frac{\left|D_j\right|}{\left|D\right|}\inf o\left(D_j\right)---\left(2\right)$

And information gain is both differences:

Gain (A)=info (D)-info_A(D) (3)

ID3 algorithm, when needing division every time, calculates the ratio of profit increase of each attribute, and the attribute then selecting ratio of profit increase maximum is carried out Division.

Training ID3 decision tree classifier can use characteristic attribute discretization method, it would however also be possible to employ characteristic attribute is potential continuously Disintegrating method.Its concrete grammar is: detect all of attribute, and the attribute selecting information gain maximum produces decision tree node, by this The different values of attribute set up branch, subset recursive call the method for more each branch is set up the branch of decision tree node, until Till all subsets only comprise same category of data.Finally obtaining a decision tree, it can be used to carry out new sample point Class.In example 2, every electric appliances type is all gathered and organizes sample more, randomly draw part as training sample, remaining As test sample.

The process of characteristic attribute discretization method training ID3 decision tree classifier includes:

1) each characteristic attribute is realized feature differentiation.In embodiment 2, the feature differentiation method taked is:

a₁: { a₁<50,50≤a₁≤1000,a₁>1000}；

a₂: { a₂<4,a₂≥4}；

a₃: { a₃<30,a₃≥30}；

a₄: { a₄<0.85,a₄≥0.85}；

a₅: { a₅<0.1,a₅≥0.05}；

a₆: { a₆<0.45,a₆≥0.45}。

2) information gain of each attribute is calculated.In example 2, for training sample according to formula (2) and formula (3) difference Calculate the information gain of 6 characteristic attributes.

3) select to have the attribute of maximum information gain as division (decision-making) attribute of this division and decision tree node, take Result must be divided, set up branch；If sample is all at same class, then this node becomes leaves, and uses such labelling.

4) on the basis of having divided result, recurrence uses abovementioned steps to calculate the Split Attribute of child node, sets up branch, Finally give whole decision tree.

Through above-mentioned step, ID3 decision tree classifier has been trained.Wherein, step 1) that characteristic attribute carries out segmentation is special Levy differentiation by manually determining, when each input feature vector is carried out disperse segmentaly, the quantity of segmentation be 2 sections or 2 sections with On, such as, in embodiment 2, feature a₁It is divided into 3 sections, feature a₂-a₆It is divided into 2 sections.Each feature is specifically divided into many Few section, the result after test sample can be tested by the selection of fragmentation threshold according to the decision tree classifier after training is adjusted. Step 2) to step 4) completed by message processing module 102 or computer.

The process of the potential disintegrating method training ID3 decision tree classifier of characteristic attribute includes continuously:

I, the information gain of each attribute is calculated.First element in training sample D is sorted according to characteristic attribute, then each two phase The intermediate point of neighbors can regard potential split point as, and from the beginning of first potential split point, division D also calculates two set Expectation information, the point with minimum expectation information is referred to as the best splitting point of this attribute, and its information is expected as this attribute Information is expected.In example 2, for training sample, find out best splitting point and calculate respectively according to formula (2) and formula (3) The information gain of 6 characteristic attributes.

II, select to have the attribute of maximum information gain as division (decision-making) attribute of this division and decision tree node, take Result must be divided, set up branch；If sample is all at same class, then this node becomes leaves, and uses such labelling.

III, on the basis of having divided result, recurrence uses abovementioned steps to calculate the Split Attribute of child node, sets up branch, Finally give whole decision tree.

During the training of aforementioned decision tree, when all samples of given node belong to same class, terminate recursive procedure, decision-making Tree has built up.All samples of given node belong to same class, it may be possible to single electric type other confirmation result, it is also possible to It it is the negative decision of all appliance type.

During the training of aforementioned decision tree classifier, when not remaining attribute and can be used to Further Division sample, equally Need to terminate recursive procedure, but now some subset is not the most pure collection, i.e. element in set is not belonging to same category；This Time, can use increase characteristic attribute, such as, increase by 5 times in load current spectral characteristic, 7 inferior in example 2 Odd harmonic signal relative magnitude, as new characteristic attribute, carries out re-training to decision tree.When training after or re-training After the final part subset of decision tree classifier be not pure collection, when the element in its set is not belonging to same category, do not use Subset " majority voting " mode using classifications most for occurrence number in subset as this node classification, but directly by subset All categories is as this node classification, and the most described decision tree classifier can export multiple electric type other confirmation result.

Main classification device can also select to be made up of multiple two class output decision tree classifiers, each two class output decision tree classifiers pair A kind of appliance type should be identified, such as, embodiment 1 can use 4 two class output decision tree classifiers identify white heat respectively Lamp, resistance furnace, hair-dryer, computer, can use 5 two class output decision tree classifiers to identify white respectively in embodiment 2 Vehement lamp, resistance furnace, electric fan, computer, electric cautery.Main classification device selects multiple two class common group of decision tree classifiers of output Cheng Shi, the input feature vector of all two class output decision tree classifiers is the input feature vector of Main classification device, and all of training sample is equal Training sample as each two class output decision tree classifiers.Main classification device selects multiple two class output decision tree classifiers common During composition, each two class output decision tree classifiers only need to be performed the identification of a kind of appliance type, and the training of decision tree is the simplest Single.After the training of certain two class output decision tree classifier described terminates, or increase characteristic attribute re-training terminates After, some subset is not the most pure collection, i.e. has subset can't confirm to input whether attribute belongs to this two class output decision tree classification During the appliance type that device is identified, it is yes by the node definition at this subset place, i.e. allows this two class output decision tree classifier at this Judge in the case of Zhong that the characteristic attribute of this time input belongs to identified appliance type.Owing to now Main classification device is defeated by multiple two classes Go out decision tree classifier composition, separate between each two class output decision tree classifiers, therefore, a certain characteristic attribute is carried out During identification, the recognition result that Main classification device likely exports is unique appliance type, or recognition result is 2 kinds or 2 kinds Above appliance type, or fail to provide the appliance type of identification.

Claims (10)

1. an electrical appliance type detector, it is characterised in that include information acquisition module, message processing module, communication module；

Described information acquisition module is for gathering the load current of electrical equipment and being converted into current digital signal；Described current digital signal is sent To message processing module；

Described message processing module, according to the current digital signal of input, uses assembled classifier to carry out appliance type identification；

Described communication module is for sending the appliance type recognition result of message processing module to host computer；

The input feature vector of described assembled classifier includes the starting current feature of electrical equipment and the load current spectrum signature of electrical equipment；

Described assembled classifier includes decision tree classifier and Bayes classifier；

Described starting current feature includes start-up course time, starting current maximum, starting current maximum time.

2. electrical appliance type detector as claimed in claim 1, it is characterised in that described information acquisition module includes current sense Device, preamplifier, wave filter, A/D converter；The core of described message processing module is DSP, or is ARM, Or it is single-chip microcomputer, or is FPGA.

3. electrical appliance type detector as claimed in claim 2, it is characterised in that described A/D converter uses information processing mould The A/D converter that the core of block includes.

4. electrical appliance type detector as claimed in claim 1, it is characterised in that described information acquisition module, information processing mould Block, all or part of function of communication module are integrated on a piece of SoC.

5. electrical appliance type detector as claimed in claim 1, it is characterised in that described communication module also receives the phase of host computer Close work order；Communication mode between described communication module and host computer includes communication and wire communication mode；Institute State communication and include that ZigBee, bluetooth, WiFi, 433MHz number pass mode；Described wire communication mode includes 485 Bus, CAN, the Internet, power carrier mode.

6. the electrical appliance type detector as according to any one of claim 1-5, it is characterised in that in described assembled classifier, Decision tree classifier is Main classification device, and Bayes classifier is subsidiary classification device.

7. electrical appliance type detector as claimed in claim 6, it is characterised in that described assembled classifier carries out appliance type knowledge Method for distinguishing is: when Main classification device successfully realizes appliance type identification, and the appliance type recognition result of Main classification device is combination point The recognition result of class device；When Main classification device fails to realize appliance type identification, and the recognition result of Main classification device is 2 kinds or 2 The above appliance type of kind, in 2 kinds exported by Main classification device or two or more appliance type recognition result, subsidiary classification device is defeated Go out the middle probability the highest appliance type appliance type recognition result as assembled classifier；When Main classification device fails to realize electric type Type identification, and the recognition result of Main classification device fail the appliance type providing identification time, in being exported by subsidiary classification device, probability is High appliance type is as the appliance type recognition result of assembled classifier.

8. electrical appliance type detector as claimed in claim 6, it is characterised in that described load current spectrum signature is by following Method obtains:

Step one, the steady state current signals of acquisition electric appliance load, and it is converted into the steady-state current digital signal of correspondence；

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic；

Step 3, using odd harmonic signal relative magnitude that overtone order in load current spectral characteristic is n time as load current frequency spectrum Feature, wherein, n=1,3 ..., M；Described M represents that harmonic wave high reps and M are more than or equal to 3.

9. electrical appliance type detector as claimed in claim 6, it is characterised in that described starting current feature is by information processing mould Block is prepared by the following:

Before step 1, appliance starting, start the load current continuous sampling to electrical equipment and load current size is judged；Work as load When current effective value is more than ε, it is determined that electrical equipment starts start and turn to step 2；Described ε is the numerical value more than 0；

Step 2, load current to electrical equipment carry out continuous sampling, and preserve with power frequency period for unit computational load current effective value； Calculate the meansigma methods of the load current virtual value of nearest N number of power frequency period；Each power frequency within nearest N number of power frequency period The load current virtual value in cycle is compared with the meansigma methods of the load current virtual value of this N number of power frequency period, and fluctuating margin is equal During less than the relative error range E set, it is determined that electric appliance load enters steady statue, turns to step 3；The value model of described N Enclose for 50-500；The span of described E is 2%-20%；

Step 3, using effective as electric appliance load steady-state current for the meansigma methods of the load current virtual value within nearest N number of power frequency period Value；Electrical equipment is started Startup time to the time between nearest N number of power frequency period initial time as the start-up course time；Will Electrical equipment starts Startup time to the time between the maximum power frequency period of load current virtual value within the start-up course time as startup The current maxima time；By the load current virtual value of starting current maximum time place power frequency period and electric appliance load stable state electricity Ratio between stream virtual value is as starting current maximum.

10. electrical appliance type detector as claimed in claim 9, it is characterised in that the input feature vector of described assembled classifier also wraps Include electric appliance load steady-state current virtual value.