CN105140972B - The frequency method for fast searching of high-transmission efficiency radio energy emission system - Google Patents
The frequency method for fast searching of high-transmission efficiency radio energy emission system Download PDFInfo
- Publication number
- CN105140972B CN105140972B CN201510559086.6A CN201510559086A CN105140972B CN 105140972 B CN105140972 B CN 105140972B CN 201510559086 A CN201510559086 A CN 201510559086A CN 105140972 B CN105140972 B CN 105140972B
- Authority
- CN
- China
- Prior art keywords
- particle
- algorithm
- scale
- population
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Feedback Control In General (AREA)
- Near-Field Transmission Systems (AREA)
Abstract
The invention discloses a kind of high-transmission efficiency radio energy emission system frequency method for fast searching, the population scale in general algorithm is separately set, respectively maximum population scaleNmax=30 and smallest particles group's scaleNmin=2, population scale is gradually reduced as iterations increases, and wherein the overall reduction mode of population scale is similar to the curve of exponential type change.This algorithm is being searched for early stage, and the change of population scale is slower, is advantageous to global search, and in the search later stage, population scale is changed into minimum, has subtracted redundancy particle, has simplified algorithm, has accelerated algorithm late convergence.It is evidence-based that inventive algorithm not only causes particle scale to choose, and algorithm has larger ability of self-teaching and social learning's ability early stage in search, in the search later stage, accelerates convergence rate, the algorithm search time reduces.
Description
Technical field
The invention belongs to be in magnetic coupling wireless power transmission technical field, particularly magnetic coupling radio energy transmission system
The finding method field for efficiency of transmission of uniting, specially a kind of frequency fast search side of high-transmission efficiency radio energy emission system
Method.
Background technology
Wireless power technology is to realize contactless energy of the energy by transmitting terminal to receiving terminal using electromagnetic field or electromagnetic wave
Amount supply.At present, it is to use electromagnetic induction principle to study more, more ripe non-contact energy transmission technology both at home and abroad, though have
Certain advantage, but its efficiency of transmission is low, and transmission range is near.These shortcomings cause the development of the technology to have larger limitation
Property, 2007, MIT successfully adjusted transmitting-receiving line end coil resonance frequency, reached the electromagentic resonance between transmission coil, successfully
The breakthrough of electric energy transmission means is realized, this technology uses electromagnetic coupled resonance principle.The technology excites industry greatly
Interest, turn into the focus studied both at home and abroad.
For a system, the maximum of efficiency of transmission how is determined, and finds the system in system maximum transmitted efficiency
Driving frequency the problem of being current in the urgent need to address, particle cluster algorithm compares when solving general function optimization problem
It is advantageous, but be directed to magnetic coupling radio energy and go out for defeated system, single and two occurs with frequency function curve in efficiency
Individual extreme point, when the situation of system one extreme point of appearance, of short duration stagnation occurs in the search later stage in general particle cluster algorithm
Phenomenon；And for algorithm in itself for, particle scale set conference to cause algorithm to carry out unnecessary calculating, and less scale
Then causing particle directly to miss global optimum, or even can not find extreme point, general population scale is located between 20-40, but
The accurate selection of its particle scale is but all that when solving problem, ceaselessly hit and miss experiment comes out according to individual all the time, non-
Often blindly.For case above, it is badly in need of finding a kind of optimizing algorithm for feature of magnetic coupling radio energy transmission system itself,
Solve system effectiveness and find problem.Therefore, how to be directed to a kind of algorithm of magnetic coupling wireless power supply system design makes algorithm rapid
It is necessary to find system maximal efficiency and corresponding Frequency point.The present invention is intended to provide one kind quickly can accurately be found
The algorithm of system efficiency of transmission optimal value and its corresponding frequency.
The content of the invention
Present invention solves the technical problem that it is quick to there is provided a kind of frequency of high-transmission efficiency radio energy emission system
Searching method, this method mainly solves particle cluster algorithm in magnetic coupling radio energy transmission system can be of short duration in searching process
The problem of phenomenon of stagnation and algorithm particle number selection itself, algorithm can be quickly found out system effectiveness optimal value.
The present invention is to solve above-mentioned technical problem to adopt the following technical scheme that, high-transmission efficiency radio energy emission system
Frequency method for fast searching, it is characterised in that：Population scale in general particle cluster algorithm is separately set, is respectively maximum
Population scale Nmax=30 and smallest particles group scale Nmin=2, population scale with iterations increase and along finger
The mode of number curve is gradually reduced, and its specific implementation step is：
(1), initialization algorithm, including setting particle populations dimension D, maximum iteration MaxNum, while limit particle
Maximal rate vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion
The speed v of particle and the position of particle, primary group scale is set as maximum-norm Nmax=30, initialization iterations t
=1；
(3), using fitness functionCalculate the fitness function of each particle of current population
Value fi, fiThe fitness function value of i-th of particle is represented, wherein The π f of ω=2r, frFor current excitations frequency, ω is the angular frequency of excitation power supply, and M is hair
The mutual inductance penetrated between receiving coil, L1, L2For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor the internal resistance of source,
RLFor load resistance, R1, R2For resistance in loop；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, is used
fi-gbestRepresent when the t times iteration, the adaptive optimal control degree functional value that all particles search, start in particle cluster algorithm
Before iteration, f is seti-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value
fi-bestAnd global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi, piRepresent fitness function
It is worth for fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then
fi-gbest=fi, pG=xi, pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5), by formulaPopulation scale is updated, wherein Npresent is particle
The current scale of group, Nmax are maximum population scale, and Nmin is smallest particles group's scale, and MaxNum is greatest iteration time
Number, t are current iteration number, and n is the power exponent of control population scale changing rule, and adjusting population by parameter n advises
The speed degree of moding, by formulaAnd formulaSpeed and the position of each particle are updated, then makes iterations t=t+1, turns to step (6), wherein vi t+1
Represent the speed of i-th of particle of t+1 iteration, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2Represent and learn
The factor is practised, rand represents the random number between [01], piExpression fitness function value is fi-bestParticle position, pgIt is particle kind
Global optimum is f in groupi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tRepresent the t times repeatedly
For i-th of particle current location, w represents inertia weight；
(6), according to formulaCalculate particle fitness function value variance it
With favgFor the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), it is no
Then, a=1, judge variance whether be equal to 0 or algorithm whether reach maximum iteration, if it is not, then turn to step (3), such as
Fruit is then to turn to step (7)；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position
Put, that is, frequency values corresponding to the optimal value searched；
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuate model
Enclose, i2maxFor the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is
The current peak of+1 current cycle of kth of load, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is determined that
As a result it is yes, then turns to step (1), algorithm is restarted；If it is judged that it is no, algorithm branches step (7).
The present invention is according to current excitations frequency frThe anti-mutual inductance M released between transmitting coil and receiving coil, is first determined
Mutual inductance between transmitting coil and receiving coil so that fitness function is changed into a function relevant with driving frequency.The present invention
Particle scale is set to reduce with iterations increase in the way of similar to exponential type curve, in algorithm search early stage, population rule
Mould is larger and reduction speed is slower, and algorithm can be made fully to carry out global search to function, and in the later stage, population scale reduces journey
Degree is accelerated, and particle convergence rate can be made to accelerate, and reduces particle cluster algorithm search time.This algorithm mainly solve magnetic coupling without
In line electric energy transmission system particle cluster algorithm in searching process can of short duration stagnation phenomenon and algorithm particle number itself
The problem of selection, algorithm can be quickly found out system effectiveness optimal value.What this algorithm was set, which restarts condition, can make dispatch coil
In the case where changing distance, the moment keeps the output of system maximal efficiency.
Brief description of the drawings
Fig. 1 is particle swarm optimization algorithm flow chart of the present invention；
Fig. 2 is general particle cluster algorithm optimizing result analogous diagram；
Fig. 3 is particle swarm optimization algorithm optimizing result analogous diagram of the present invention；
Fig. 4 is that population scale increases reduction figure with iterations.
Specific implementation method
The particular content of the present invention is described in detail with reference to accompanying drawing.The present invention is primarily directed to magnetic coupling wireless power transmission system
System, with improved Particle Swarm Algorithm, reduce particle scale, algorithm can be quickly found out efficiency maximum point and its respective tones
Rate.Illustrate below by way of specific instantiation and emulated with Matlab.Particle swarm optimization algorithm flow is see Fig. 1, high-transmission
The frequency method for fast searching of efficiency radio energy emission system, it comprises the following steps：
(1), initialization algorithm, including setting particle populations dimension D=1, maximum iteration MaxNum=200, simultaneously
Limit particle maximal rate vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion
The speed v of particle and the position of particle.Primary group scale is set as maximum-norm Nmax=30, initialization iterations t
=1, at present, population scale is configured without unified rule, is set generally according to optimizing object and personal experience.This
Algorithm only needs directly to set population maximum-norm as Nmax=30, can solve resonant mode electric energy dispensing device improving efficiency
Various situations.Smallest size is set in algorithm, population scale is gradually subtracted with the increase of iterations by maximum-norm Nmax
It is small to arrive smallest size Nmin, Nmin=2 in this algorithm；
(3), using fitness functionCalculate the adaptation of each particle of current population
Spend functional value fi, fiThe fitness function value of i-th of particle is represented, wherein The π f of ω=2r, frCurrent excitations frequency, ω are the angular frequency of excitation power supply, and M is transmitting
Mutual inductance between receiving coil, L1, L2For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor in power supply
Resistance, RLFor load resistance, R1, R2For resistance in loop.This algorithm is first by current excitations frequency frAnd equation groupThe mutual inductance M between transmitting and receiving coil is derived,Then fitness function is derived again, and this algorithm uses suitable
Response function is the function of efficiency and mutual inductance M.Shown equation group can carry out analysis to whole system according to basic circuit theorem and push away
Export comes.WhereinRepresent coil L1Voltage,For input current,Load current, the fitness function that this algorithm uses
It is the function of efficiency and mutual inductance M, so when the change of the distance between two coils, when causing M also to change, fitness function also can
Change, M at this moment can be obtained according to current voltage driving frequency and equation group, further determine that under system current distance
Fitness function；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, is used
fi-gbestRepresent when the t times iteration, the adaptive optimal control degree functional value that all particles search.Algorithm start iteration it
Before, set fi-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value fi-bestAnd
Global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi；piRepresent that fitness function value is
fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then fi-gbest=
fi, pG=xi；pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5) formula is pressedPopulation scale is updated, wherein Npresent is grain
The current scale in subgroup, Nmax are maximum population scale, and MaxNum is maximum iteration, and t is current iteration number, and n is control
The power exponent of granulation subgroup scale changing rule, pass through parameter n and can adjust population scale change speed degree.By formulaAnd formulaUpdate each particle
Speed and position, then make iterations t=t+1, turn to step (6), wherein vi t+1Represent i-th of particle of t+1 iteration
Speed, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2Studying factors are represented, this sets c1=2, c2=
2, rand represent the random number between [01].piExpression fitness function value is fi-bestParticle position, pgIt is in particle populations
Global optimum is fi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tRepresent the t times iteration
I particle current location, w represent inertia weight.It is 30 to initially set up population scale, can solve the problem that population in the prior art
Scale chooses deficiency and causes algorithm to miss the situation of global optimum, and with the operation of algorithm, the setting of particle scale is to calculating
The convergent influence of method is increasing, by the way of particle scale is gradually reduced as iterations increases, population scale
Simplified, subtract redundancy particle so that particle cluster algorithm accelerates convergence in the search later stage, improves the operation speed of algorithm
Degree；
(6), according to formulaCalculate particle fitness function value variance it
With favgFor the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), it is no
Then, a=1.Judge variance whether be equal to 0 or algorithm whether reach maximum iteration, if it is not, then turn to step (3), such as
Fruit is then to turn to step (7)；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position
Put, that is, frequency values corresponding to the optimal value searched.
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuate model
Enclose, i2maxFor the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is
The current peak of+1 current cycle of kth of load, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is determined that
As a result it is yes, then turns to step (1), algorithm is restarted；If it is judged that it is no, algorithm branches step (7).
In order to the advantage for understanding this algorithm that will be apparent that, general particle cluster algorithm is given in figs. 2 and 3 respectively
With the analogous diagram of this algorithm optimizing result, Fig. 4 is that population scale increases reduction figure with iterations.
The general particle cluster algorithm optimizing result figures of Fig. 2, curve is magnetic coupling radio energy transmission system efficiency and frequency in figure
The functional image of rate, five-pointed star is the optimal value that is searched in figure.The frequency finally searched is 13544961.6816Hz.
Fig. 3 is inventive algorithm optimizing result figure, and in the case of low optimization accuracy identical, this algorithm spent time is than general
Algorithm reduces 56%.
Fig. 4 represents the process that this algorithm population scale is gradually reduced with iterations increase.
Embodiment above describes the general principle of the present invention, main features and advantages, the technical staff of the industry should
Understand, the present invention is not limited to the above embodiments, the original for simply illustrating the present invention described in above-described embodiment and specification
Reason, under the scope for not departing from the principle of the invention, various changes and modifications of the present invention are possible, and these changes and improvements are each fallen within
In the scope of protection of the invention.
Claims (1)
1. the frequency method for fast searching of high-transmission efficiency radio energy emission system, it is characterised in that：General population is calculated
Population scale in method is separately set, respectively maximum population scale Nmax=30 and smallest particles group scale Nmin=2,
Population scale is gradually reduced as iterations increases along the mode of exponential curve, and its specific implementation step is：
(1), initialization algorithm, including setting particle populations dimension D, maximum iteration MaxNum, while limit particle maximum
Speed vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion particle
Speed v and particle position, set primary group scale as maximum-norm Nmax=30, initialization iterations t=1；
(3), using fitness functionCalculate the fitness of each particle of current population
Functional value fi, fiThe fitness function value of i-th of particle is represented, wherein The π f of ω=2r, frFor current excitations frequency, ω is the angular frequency of excitation power supply, and M is hair
The mutual inductance penetrated between receiving coil, L1, L2For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor the internal resistance of source,
RLFor load resistance, R1, R2For resistance in loop；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, uses fi-gbest
Represent when the t times iteration, adaptive optimal control degree functional value that all particles search, particle cluster algorithm start iteration it
Before, set fi-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value fi-bestAnd
Global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi, piRepresent that fitness function value is
fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then fi-gbest=
fi, pg=xi, pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5), by formulaPopulation scale is updated, wherein Npresent works as population
Preceding scale, Nmax are maximum population scale, and Nmin is smallest particles group's scale, and MaxNum is maximum iteration, and t is current
Iterations, n are the power exponent of control population scale changing rule, and the speed of population scale change is adjusted by parameter n
Degree, by formulaAnd formula
Speed and the position of each particle are updated, then makes iterations t=t+1, turns to step (6), wherein vi t+1Represent t+1 times repeatedly
The speed of i-th of particle of generation, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2Represent Studying factors, rand
Represent the random number between [0,1], piExpression fitness function value is fi-bestParticle position, pgBe in particle populations it is global most
The figure of merit is fi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tRepresent the t times iteration i-th
Sub- current location, w represent inertia weight；
(6), according to formulaCalculate the variance sum of particle fitness function value, favg
For the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), otherwise, a=
1, judge variance whether be equal to 0 or algorithm whether reach maximum iteration, if it is not, then turn to step (3), if it is
Turn to step (7)；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position, i.e.,
Frequency values corresponding to the optimal value searched；
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuation range, i2max
For the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is load
The current peak of+1 current cycle of kth, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is judged that being
It is then to turn to step (1), algorithm is restarted；If it is judged that it is no, algorithm branches step (7).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510559086.6A CN105140972B (en) | 2015-09-06 | 2015-09-06 | The frequency method for fast searching of high-transmission efficiency radio energy emission system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510559086.6A CN105140972B (en) | 2015-09-06 | 2015-09-06 | The frequency method for fast searching of high-transmission efficiency radio energy emission system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105140972A CN105140972A (en) | 2015-12-09 |
| CN105140972B true CN105140972B (en) | 2018-01-30 |
Family
ID=54726229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510559086.6A Expired - Fee Related CN105140972B (en) | 2015-09-06 | 2015-09-06 | The frequency method for fast searching of high-transmission efficiency radio energy emission system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105140972B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3635760B1 (en) * | 2017-06-06 | 2023-09-13 | Reach Power, Inc. | Method for wireless power delivery |
| CN109347214B (en) * | 2018-07-16 | 2021-12-28 | 福州大学 | Method for searching multi-peak extreme value in frequency modulation control of wireless power transmission system |
| JP7067376B2 (en) * | 2018-08-31 | 2022-05-16 | トヨタ自動車株式会社 | Power transmission device |
| CN110257835B (en) * | 2019-07-31 | 2020-04-28 | 承德前潮慧创科技有限公司 | Cathodic protection feed experiment box |
| CN113526413B (en) * | 2021-07-20 | 2022-10-25 | 宁波如意股份有限公司 | Forklift lifting device and power generation efficiency control method thereof |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8301406B2 (en) * | 2008-07-24 | 2012-10-30 | University Of Cincinnati | Methods for prognosing mechanical systems |
| CN104200264B (en) * | 2014-09-25 | 2017-10-31 | 国家电网公司 | A kind of two benches particle swarm optimization algorithm including independent global search |
-
2015
- 2015-09-06 CN CN201510559086.6A patent/CN105140972B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN105140972A (en) | 2015-12-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105140972B (en) | The frequency method for fast searching of high-transmission efficiency radio energy emission system | |
| CN105160395B (en) | The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device | |
| CN105184361B (en) | The maximal efficiency tracking of electric automobile magnetic coupling wireless charging system | |
| CN107609298B (en) | Method and device for identifying parameters of Jiles-Atherton model | |
| CN104283393B (en) | Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine | |
| CN106655951A (en) | Curve fitting-based maximum torque current control method | |
| CN102819652B (en) | Mechanical parameter optimization design method based on adaptive reverse differential evolution | |
| EP3129839B1 (en) | Controlling a target system | |
| CN106685299A (en) | Current control method of built-in PMSM (Permanent Magnet Synchronous Motor) | |
| CN104362927A (en) | Asynchronous motor key state information tracking method based on improved particle swarm optimization | |
| CN109787531A (en) | A kind of switching magnetic-resistance hub motor forecast Control Algorithm | |
| CN103872798A (en) | Magnetic resonance wireless energy transmission system and optimization method of positions of coils thereof | |
| CN106787695A (en) | A kind of Switching Power Supply control method of dynamic response optimization | |
| Shang et al. | Production scheduling optimization method based on hybrid particle swarm optimization algorithm | |
| CN108615069A (en) | A kind of optimized calculation method based on improved adaptable quanta particle swarm optimization | |
| Liu et al. | Dual-coupled robust wireless power transfer based on parity-time-symmetric model | |
| CN103513574A (en) | Method for building artificial fish swarm algorithm fractional order PID controller of axial mixing magnetic bearing | |
| CN107871024B (en) | Electromagnetic optimization method and device for high-temperature superconductive annular energy storage magnet | |
| CN108375902A (en) | A kind of Two-tank System control algolithm that artificial bee colony algorithm is combined with fuzzy-adaptation PID control | |
| CN105141016B (en) | Electric automobile wireless charging stake efficiency extreme point tracking during frequency bifurcated | |
| CN105184077B (en) | Cross short distance low-resonance electric energy transmission system improving efficiency population index method | |
| Wang et al. | Analysis on the efficiency of magnetic resonance coupling wireless charging for electric vehicles | |
| CN110348106A (en) | A kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process | |
| CN104517141B (en) | Radio frequency identification network topology method based on load balance Yu particle cluster algorithm | |
| CN108804799A (en) | Optimization method, computer readable storage medium, the electronic equipment of nose cone type resonant cavity geometry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Shi Yanyan Inventor after: Chang Qin Inventor after: Wang Meng Inventor after: Jing Jianwei Inventor after: Fan Yue Inventor before: Wang Meng Inventor before: Sun Changxing Inventor before: Shi Yanyan Inventor before: Liang Jie |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180130 Termination date: 20180906 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |