CN106602952B  A kind of PMSM permanent magnet flux linkage full rank discrimination method  Google Patents
A kind of PMSM permanent magnet flux linkage full rank discrimination method Download PDFInfo
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 CN106602952B CN106602952B CN201610512256.XA CN201610512256A CN106602952B CN 106602952 B CN106602952 B CN 106602952B CN 201610512256 A CN201610512256 A CN 201610512256A CN 106602952 B CN106602952 B CN 106602952B
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 pmsm
 permanent magnet
 flux linkage
 magnet flux
 identification
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Classifications

 H—ELECTRICITY
 H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
 H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMOELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
 H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
 H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
Abstract
Description
Technical field
The present invention relates to the technical fields of permanent magnet synchronous motor (PMSM), and in particular to a kind of PMSM permanent magnet flux linkage full rank Discrimination method.
Background technique
PMSM has that structure is simple, failure rate is low and the technical advantages such as operational efficiency is high, gradually in industrial servodrive, new The fields such as energy automobile are applied widely.However, for application fields many for electric car etc., PMSM power density Height, radiating condition is poor, and operating condition is complicated, and many places are under torque capacity electric current ratio and weak magnetic jointly control, and there are stronger electricity Pivot reaction, in addition factors such as natural aging are easy to appear permanent magnet and uniformly demagnetizes or local demagnetization failure, lead to motor output turn Square reduces and torque pulsation, directly affects the direct torque precision and operational reliability of power drive system.
Meanwhile in the control algolithms such as the MTPA of PMSM control, weak magnetic control, it is both needed to accurate permanent magnet magnetic chain information. Therefore, in order to realize the monitoring of PMSM drive system permanent magnet health status and the design of high performance controller, it is necessary to accurate to obtain Permanent magnet flux linkage.
In order to accurately obtain PMSM permanent magnet flux linkage, dragon is respectively adopted in French scholar Henwood N and Shandong University Wang Song Burger observer and least square method realize the observation of PMSM permanent magnet flux linkage, however since observed result is to the quick of measurement noise Perception limits application of the program in actual industrial system.In order to solve the problems, such as noise jamming, Tsinghua University Xiao Xi professor It is assumed that being estimated online using expanded Kalman filtration algorithm to permanent magnet flux linkage under the premise of other PMSM parameter constants Meter, however being saturated and run temperature rise by magnetic circuit is influenced, stator resistance R_{s}And dq axis stator inductance L_{d}And L_{q}It can be driven with PMSM There is different degrees of variation in the change of dynamic system conditions, influences the estimated accuracy of permanent magnet flux linkage.
In order to guarantee PMSM permanent magnet flux linkage identification precision, it need to be state variable by the PMSM parameter processing of variation, realize Permanent magnet flux linkage identification under Parameters variation constraint.For this purpose, peace group's great waves establish while recognizing PMSM stator resistance R_{s}, dq axis Stator inductance L_{d}And L_{q}And permanent magnet flux linkage ψ_{f}Adaptive model；The electricity then based on PMSM q axis such as CortesRomero J A Equation is pressed, all electromagnetic parameters of PMSM are realized when participating in without parameter of electric machine initial value using algebra identification algorithm Online identification.However, realizing that the order of the PMSM state equation of permanent magnet flux linkage identification is 2, while can only realizing two parameters Full rank identification, stator resistance R_{s}, stator inductance L_{d}And L_{q}And permanent magnet flux linkage ψ_{f}While identification exist identification equation owe order ask The uniqueness of topic, identification result lacks theory support, easily causes identification result to fall into local optimum and even dissipates.For this purpose, northern Capital aerospace university professor Wang Lina proposes a kind of based on model reference adaptive calculation for face mounted permanent magnet synchronous motor The step identification method of method, this method is first with d shaft voltage equation estimation PMSM armature inductance L_{s}, recycle the L of acquisition_{s}It is real Existing ψ_{f}With R_{s}Synchronization full rank identification.Since face mounted permanent magnet synchronous motor mostly uses i_{d}=0 control, in order to realize ψ_{f}、R_{s}It is same Full rank identification is walked, amplitude and the reasonable d axis current perturbation of frequency need to be injected, and do not account in PMSM drive system operational process L_{s}Variation is to ψ_{f}The influence of identification precision.For this purpose, France scholar Underwood S J is based on IPM synchronous motor R_{s}、 L_{d}、L_{q}、ψ_{f}The different time scales of four parameters are divided into fast varying parameter and slow varying parameter, and use two different time rulers The leastsquares algorithm of degree realizes the online identification of aforementioned four parameter, and although deficient order that this method can solve identification model is asked Topic, but in order to guarantee algorithmic statement, it still needs to inject amplitude and the reasonable current perturbation of frequency in d axis to ensure slow time scale most The identification precision of small two multiplication algorithm, and identification result is vulnerable to measurement influence of noise.Using evolution algorithm as the artificial intelligence of representative by In with stronger Nonlinear Processing ability, there is certain application in the identification of PMSM permanent magnet flux linkage, but how to reduce it Full rank recognizes calculation amount, is but still key technical problem urgently to be resolved.
In consideration of it, systematic survey noise, PMSM Parameters variation, identification equation owe the PMSM permanent magnet flux linkage height under order constraint Precision, online, full rank identification have become the pass that PMSM permanent magnet health status monitors and high performance control field is urgently to be resolved Key problem.
Summary of the invention
In order to solve the above technical problem, the present invention provides a kind of PMSM permanent magnet flux linkage full rank discrimination methods, based on double Unscented kalman filtering algorithm carries out subregion joint full rank identification to PMSM permanent magnet flux linkage, realizes measurement noise, PMSM parameter PMSM permanent magnet flux linkage highprecision, online, full rank identification under variation, the deficient order constraint of identification model, to realize that PMSM drives system The online monitoring of the high performance control and permanent magnet health status of system provides foundation.
In order to achieve the above object, the technical scheme is that a kind of PMSM permanent magnet flux linkage full rank discrimination method, is adopted Collect the stator voltage u in the dq shafting of PMSM_{d}、u_{q}, stator current i_{d}、i_{q}And PMSM drive system rotor electrical angular velocity omega_{e}, The PMSM state equation in dq shafting for permanent magnet flux linkage identification is established, is realized based on Unscented kalman filtering algorithm The parameters sensitivity analysis of PMSM permanent magnet flux linkage identification precision determines PMSM drive system different rotating speeds area based on analysis result The permanent magnet flux linkage full rank discrimination method based on double Unscented kalman filtering algorithms, realize measurement noise, permanent magnet synchronous motor Parameters variation and identification model are owed permanent magnet flux linkage highprecision, online, full rank under order constraint and are recognized；Its step are as follows:
Step 1: the stator voltage u in the dq shafting of acquisition PMSM_{d}And u_{q}, stator current i_{d}And i_{q}And PMSM driving system System rotor electrical angular velocity omega_{e}；
Step 2: establishing the PMSM state equation in dq shafting, PMSM permanent magnetism is realized based on Unscented kalman filtering algorithm The identification of body magnetic linkage, is analyzed under PMSM drive system difference operating condition, stator resistance R_{s}, dq axis stator inductance L_{d}And L_{q}Variation pair The parameter of the permanent magnet flux linkage identification precision based on Unscented kalman filtering algorithm is realized in the influence of permanent magnet flux linkage identification precision Sensitivity analysis；
Step 3: according to step 2 as a result, in PMSM drive system low speed Operational Zone, by dq axis stator inductance L_{d}、L_{q}Connection Close identification and stator resistance R_{s}, permanent magnet flux linkage ψ_{f}Joint identification is combined, is updated each other, in the case where recognizing equation full rank state, is disappeared Except stator resistance R_{s}, dq axis stator inductance L_{d}And L_{q}Change the influence to permanent magnet flux linkage estimated accuracy；In PMSM drive system Middle and high speed Operational Zone, using permanent magnet flux linkage ψ_{f}Identification and dq axis stator inductance L_{d}、L_{q}Joint identification combines, in identification side Under journey full rank state, dq axis stator inductance L is eliminated_{d}、L_{q}Change the influence to permanent magnet flux linkage identification precision.
Stator voltage u in the dq shafting of the PMSM_{d}And u_{q}, stator current i_{d}And i_{q}Acquisition methods are as follows: sampling PMSM Stator line voltage u_{ab}、u_{bc}, threephase current i_{a}、i_{b}、i_{c}, and obtained by coordinate transform, transformation matrix of coordinates is respectively as follows:
In formula, θ is flux linkage position of the rotor angle.
Stator voltage u in the dq shafting of the PMSM_{d}And u_{q}, stator current i_{d}And i_{q}Acquisition methods are as follows: directly adopt The dq shaft voltage instruction value that PMSM driving system controller is calculatedWithInstead of dq axis stator voltage u_{d}And u_{q}, dq axis Current instruction valueInstead of dq axis stator current i_{d}And i_{q}。
The acquisition methods of the PMSM drive system rotor electrical angular speed are obtained by incremental opticalelectricity encoder, step Suddenly are as follows:
(1) in t_{1}And t_{2}The umber of pulse N that optical rotary encoder issues on the dq axis of neighbouring sample instance sample PMSM_{1}、 N_{2}, sampling instant t_{1}And t_{2}Difference be sampling period T；
(2) according to rotor angular rate ω_{e}With optical rotary encoder impulse sampling value N_{1}、N_{2}And between sampling period T Relationship calculate rotor angular rate ω_{e}, expression formula are as follows:
In formula, M is one week umber of pulse of optical rotary encoder, and p is permanent magnet synchronous motor number of polepairs.
Method in the dq shafting for realizing the PMSM state equation of permanent magnet flux linkage identification is:
In formula, u_{d}And u_{q}Respectively indicate dq axis stator voltage, i_{d}And i_{q}Respectively indicate dq axis stator current, L_{d}And L_{q}Respectively Indicate dq axis stator inductance；R_{s}Indicate stator resistance, ψ_{f}Indicate permanent magnet flux linkage, ω_{e}Indicate rotor electric angle frequency；
In PMSM state equation, state vector are as follows: x=[i_{d} i_{q} ψ_{f}]^{T}, input quantity are as follows: u=[u_{d}/L_{d} u_{q}/L_{q}]^{T}, output Amount are as follows: y=[i_{d} i_{q}]^{T}。
The method for realizing the identification of PMSM permanent magnet flux linkage based on Unscented kalman filtering algorithm is: will realize permanent magnet The PMSM state equation of magnetic linkage identification is described as the general type of nonlinear system, the measurement equation table of state equation and discretization It is shown as:
In formula, x (t) is system mode vector, y (t_{k}) it is output quantity, f () indicates systematic state transfer equation, h () Indicate that system measuring equation, σ (t) are the process noise for considering model uncertainty and measuring uncertainty, μ (t_{k}) it is to consider mould The measurement noise of type uncertainty and measuring uncertainty, the variance matrix of σ (t) are Q (t), μ (t_{k}) variance matrix be R (t), u (t) is certainty input vector, and B indicates control matrix；
Abovementioned PMSM state equation, state vector, input vector, output vector are substituted into Unscented kalman filtering algorithm In, it realizes the state vector recursion based on Unscented kalman filtering algorithm, the online identification of PMSM permanent magnet flux linkage can be realized, Step are as follows:
(1) state vector initializes
Process noise covariance matrix Q and measurement noise covariance matrix R initial value are set according to priori knowledge, initializes shape State vectorInit state varivance matrix
(2) Sigma point calculates
Pass through in each sampling periodIt calculates Sigma point (k=1,2, ∞) and, obtain a n × (2n+1) Sigma dot matrix；Wherein: χ_{k1}For Sigma point square Battle array,P_{k1}It is expressed as the prediction mean value and covariance of last moment state vector, n is state vector dimension, λ=α^{2} It (n+b) is scale parameter, α is dispersion level of the Sigma point near state variable mean value, and b is scale coefficient；
(3) time updates
The transmitting of Sigma point is realized by the system state equation of discretization: i.e.According to Transmit prediction mean value and covariance that result obtains state vector:
Wherein,Indicate the mean value weight of state vector,Indicate the covariance weight of state vector,WithQuantitative relation are as follows:
(4) measurement updaue
According to system measurement data, pass through formula The onestep prediction of state vector can be realized It updates and is updated with kalman gain, obtain the optimal estimation of state vector and variance matrix；Wherein, H indicates calculation matrix, K_{k}Table Show kalman gain；
K=k+1 is enabled, step (2)(4) are repeated, realizes the iteration output of state vector.
The parameters sensitivity analysis for realizing the PMSM permanent magnet flux linkage identification precision based on Unscented kalman algorithm Method is: determining the PMSM stator resistance R influenced by system conditions and operation temperature rise_{s}, d axle inductance L_{d}, q axle inductance L_{q}'s Variation range, and the identification of the permanent magnet flux linkage under different system conditions based on Unscented kalman filtering algorithm is analyzed within this range Error.
In the PMSM drive system low speed operation area, full rank recognizes equation are as follows:
In the middle and high fast operation area of the PMSM drive system, full rank recognizes equation are as follows:
The invention has the advantages that: compared with prior art, the present invention can realize PMSM in the case where identification model owes order constraint The full rank of permanent magnet flux linkage recognizes, and eliminates measurement noise, PMSM Parameters variation and the deficient order of identification model and recognizes to permanent magnet flux linkage The influence of precision；Avoid simultaneously linearized stability of the conventional Extension Kalman filtering algorithm in state vector identification process and The calculating of complicated Jacobian matrix reduces algorithm while improving identification precision and realizes difficulty；In PMSM drive system, Highspeed cruising area can reduce parameter R to be identified under the premise of guaranteeing permanent magnet flux linkage identification precision_{s}, reduce identification and calculate Method calculation amount.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with It obtains other drawings based on these drawings.
Fig. 1 is the parameter sensitivity of the PMSM permanent magnet flux linkage identification precision of the invention based on Unscented kalman filtering algorithm Property analysis result.
Fig. 2 is that the PMSM permanent magnet flux linkage subregion of the invention based on double Unscented kalman filtering algorithms combines full rank identification The structural block diagram of method.
Fig. 3 is the permanent magnet flux linkage identification result of the middle and high fast Operational Zone PMSM of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other under that premise of not paying creative labor Embodiment shall fall within the protection scope of the present invention.
A kind of PMSM permanent magnet flux linkage full rank discrimination method, steps are as follows:
Step 1: obtaining the stator voltage u in the dq shafting of PMSM_{d}And u_{q}, stator current i_{d}And i_{q}And PMSM driving system System rotor electrical angular velocity omega_{e}。
Stator voltage u in the dq shafting of the PMSM_{d}And u_{q}, stator current i_{d}And i_{q}Acquisition methods have following two:
(1) the stator line voltage u of PMSM is sampled_{ab}、u_{bc}, threephase current i_{a}、i_{b}、i_{c}, and obtained by coordinate transform.Coordinate Transformation matrix is respectively as follows:
In formula, θ is flux linkage position of the rotor angle.
(2) the dq shaft voltage instruction value that PMSM driving system controller is calculated is directlyed adoptWithInstead of dq axis Stator voltage u_{d}And u_{q}, dq shaft current instruction valueInstead of dq axis stator current i_{d}And i_{q}。
Since PMSM drive system mostly uses electric current, revolving speed double circle structure, method (1) needs to increase voltage and adopts Sample and isolation circuit increase system hardware expense；And method (2) using instruction value substitute actual value, though without voltage sample with Isolation circuit, but need to consider to substitute offset issue caused by inverter is nonlinear and sample circuit time lag, it has needed when necessary Effect compensation.
The acquisition methods of the PMSM drive system rotor electrical angular speed are obtained by incremental opticalelectricity encoder, step Suddenly are as follows:
(1) in t_{1}And t_{2}The umber of pulse N that optical rotary encoder issues on the dq axis of neighbouring sample instance sample PMSM_{1}、 N_{2}, sampling instant t_{1}And t_{2}Difference be sampling period T.
(2) according to rotor angular rate ω_{e}With optical rotary encoder impulse sampling value N_{1}、N_{2}And between sampling period T Relationship calculate rotor angular rate ω_{e}, expression formula are as follows:
In formula, M is one week umber of pulse of optical rotary encoder, and p is permanent magnet synchronous motor number of polepairs.
Step 2: establishing the PMSM state equation in dq shafting, PMSM permanent magnetism is realized based on Unscented kalman filtering algorithm The identification of body magnetic linkage, is analyzed under PMSM drive system difference operating condition, stator resistance R_{s}, stator inductance L_{d}、L_{q}Variation is to permanent magnet Magnetic linkage ψ_{f}The permanent magnet flux linkage ψ based on Unscented kalman filtering algorithm is realized in the influence of identification precision_{f}The parameter of identification precision is quick Perceptual analysis；
For realizing the PMSM state equation of permanent magnet flux linkage identification in the dq shafting are as follows:
Wherein, u_{d}And u_{q}Respectively indicate dq axis stator voltage, i_{d}And i_{q}Respectively indicate dq axis stator current, L_{d}And L_{q}Respectively Indicate dq axis stator inductance；R_{s}Indicate stator resistance, ψ_{f}Indicate permanent magnet flux linkage, ω_{e}Indicate rotor electric angle frequency.
For the state equation of formula (3) description, state vector x, input quantity u and output quantity y are respectively as follows:
X=[i_{d} i_{q} ψ_{f}]^{T}, u=[u_{d}/L_{d} u_{q}/L_{q}]^{T}, y=[i_{d} i_{q}]^{T} (4)
The method for realizing the identification of PMSM permanent magnet flux linkage based on Unscented kalman filtering algorithm is:
The PMSM state equation for realizing permanent magnet flux linkage identification of formula (3) description is characterized as the one of nonlinear system As form, the measurement equation of state equation and discretization is expressed as
In formula: x (t) is system state variables, y (t_{k}) it is output quantity, f () indicates systematic state transfer equation, h () Indicate system measuring equation, σ (t), μ (t_{k}) it is respectively process noise and the survey for considering model uncertainty and measuring uncertainty Noise is measured, the variance matrix of σ (t) is Q (t), μ (t_{k}) variance matrix be R (t), u (t) be certainty input vector, B be control Matrix processed is constant value matrix.
PMSM state equation, state vector, input vector, output vector are substituted into Unscented kalman filtering algorithm, it is real Now based on the state vector recursion of Unscented kalman filtering algorithm, PMSM permanent magnet flux linkage ψ can be achieved_{f}Online identification, Step are as follows:
(1) state vector initializes
Process noise covariance matrix Q and measurement noise covariance matrix R initial value are set according to priori knowledge, and is initialized State vector x and State error variance matrix P:
(2) Sigma point calculates
In each sampling period (k=1,2, ∞) and according to formula (7) calculating Sigma point, obtain a n × (2n + 1) Sigma dot matrix.
In formula: χ_{k1}For Sigma dot matrix,P_{k1}The prediction mean value and covariance, n for being expressed as last moment be State vector dimension, λ=α^{2}It (n+b) is scale parameter；α is dispersion level of the Sigma point near state variable mean value, is determined Sigma point distribution situation, is usually taken to be section [10^{4}, 1] on small positive number；B is scale coefficient, is usually taken to be 0 or 3n.
(3) time updates
The system state equation for passing through discretization realizes the transmitting of Sigma point, as shown in formula (8), according to transmitting result The prediction mean value and its covariance for obtaining state vector, as shown in formula (9).
Wherein,Indicate the mean value weight of state vector,Indicate the covariance weight of state vector, quantitative relation Are as follows:
(4) measurement updaue
According to measurement data, onestep prediction can be realized by formula (11)(16) and kalman gain updates, obtain state The optimal estimation of vector and its variance matrix.
(5) k=k+1 is enabled, the iteration output of state vector is realized in duplication stages (2)(4).
Formula (5) are substituted into Unscented kalman filtering algorithm flow described in formula (6)(16), can be realized based on nothing The state vector recursion of mark Kalman filtering algorithm.
The PMSM permanent magnet flux linkage ψ of the realization based on Unscented kalman algorithm_{f}The parameters sensitivity analysis of identification precision Method be: determine by operating condition and the PMSM stator resistance R that is influenced of operation temperature rise_{s}And dq axis stator inductance L_{d}、L_{q}Change Change range, and analyzes the ψ based on Unscented kalman filtering algorithm under different system conditions within this range_{f}Identification Errors.
Step 3: according to step 2 conclusion, in PMSM drive system low speed Operational Zone, by dq axis stator inductance L_{d}、 L_{q}Carry out joint full rank identification, and with stator resistance R_{s}, permanent magnet flux linkage ψ_{f}The identification of joint full rank is combined, is updated each other, is being distinguished Know under equation full rank state, eliminates stator resistance R_{s}, dq axis stator inductance L_{d}、L_{q}Variation is to permanent magnet flux linkage ψ_{f}Identification precision It influences, full rank recognizes shown in equation such as formula (16).
Further, in the middle and high fast Operational Zone of PMSM drive system, due to stator resistance R_{s}Variation is to permanent magnet flux linkage ψ_{f} The influence of identification precision is smaller, therefore uses permanent magnet flux linkage ψ_{f}Identification and dq axis stator inductance L_{d}、L_{q}The identification of joint full rank is mutually tied The method of conjunction eliminates dq axis stator inductance L_{d}、L_{q}Variation is to permanent magnet flux linkage ψ_{f}The influence of identification precision, full rank recognize equation such as Shown in formula (17).
Consider that permanent magnet flux linkage subregion when PMSM Parameters variation combines full rank discrimination method according to what abovementioned thinking determined Structural block diagram is as shown in Figure 2.It is less than the PMSM drive system Operational Zone of 100rpm in revolving speed, using shown in formula (16), dq axis is fixed Sub inductance L_{d}、L_{q}The identification of joint full rank and stator resistance R_{s}, permanent magnet flux linkage ψ_{f}Joint full rank recognizes the full rank identification side combined Method eliminates R_{s}、L_{d}、L_{q}Variation is to permanent magnet flux linkage ψ_{f}The influence of identification precision realizes permanent magnet under the constraint of PMSM Parameters variation Magnetic linkage ψ_{f}Full rank identification.In revolving speed fast Operational Zone middle and high greater than the PMSM drive system of 100rpm, using shown in formula (17), Dq stator inductance L_{d}、L_{q}The identification of joint full rank and permanent magnet flux linkage ψ_{f}The full rank discrimination method combined is recognized, dq stator is eliminated Inductance L_{d}、L_{q}Variation is to permanent magnet flux linkage ψ_{f}The influence of estimated accuracy realizes permanent magnet flux linkage ψ under the constraint of PMSM Parameters variation_{f} Full rank identification.
Experimental verification carried out to method of the invention, experiment condition is given load torque 3Nm, and take revolving speed from 900 revs/min are down to 450 revs/min of dynamic process.In view of under laboratory environment, the parameter of electric machine changes smaller, this hair in the short time The bright verifying that aforementioned high speed scheme is completed near motor parameters value.Stator resistance R_{s}When being taken as the 150% of design value, D axis stator inductance L_{d}, q axis stator inductance L_{q}And permanent magnet flux linkage ψ_{f}Identification result is respectively such as Fig. 3 (a), 3 (b) and Fig. 3 (c) institute Show.With PMSM design value (L_{d}=L_{q}=0.001283H, ψ_{f}=0.1278Wb) it compares, the identification precision of presently disclosed method Higher, Identification Errors mean value can control within 7%, and in the middle and high fast Operational Zone of PMSM drive system, without considering stator Resistance R_{s}Change the influence to permanent magnet flux linkage identification precision, reduces parameter stator resistance R to be identified_{s}, effectively reduce identification Algorithm calculation amount.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto, In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art, It should be covered by the protection scope of the present invention.
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