CN109190605B - Human body continuous action counting method based on CSI - Google Patents

Human body continuous action counting method based on CSI Download PDF

Info

Publication number
CN109190605B
CN109190605B CN201811277055.1A CN201811277055A CN109190605B CN 109190605 B CN109190605 B CN 109190605B CN 201811277055 A CN201811277055 A CN 201811277055A CN 109190605 B CN109190605 B CN 109190605B
Authority
CN
China
Prior art keywords
action
human body
csi
file
receiver
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.)
Active
Application number
CN201811277055.1A
Other languages
Chinese (zh)
Other versions
CN109190605A (en
Inventor
刘希文
陈海明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo University
Original Assignee
Ningbo University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ningbo University filed Critical Ningbo University
Priority to CN201811277055.1A priority Critical patent/CN109190605B/en
Publication of CN109190605A publication Critical patent/CN109190605A/en
Application granted granted Critical
Publication of CN109190605B publication Critical patent/CN109190605B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

Abstract

The invention relates to a human body continuous action counting method based on CSI, which is used for a human body continuous action counting and identifying system comprising a transmitter and a receiver provided with a wireless network card, wherein the transmitter is a roadN is arranged on the router and the emittertThe strip transmitting terminal antenna, the receiver is internally loaded with a CSI tool kit, the receiver is provided with a CSI information reading interface, and the receiver is provided with NrStrip receiving end antenna, its characterized in that: the method comprises the steps of acquiring a signal; step two, detecting actions; step three, pretreatment; and step four, counting actions, namely detecting the number of wave crests of the waveform file to be calculated by using a peak-to-peak algorithm, taking the detected number of wave crests as the action times contained in the human action file within the T time, and outputting the action times. Compared with the prior art, the method has the advantages that special equipment such as a sensor and a camera is not needed, the method can be realized only by visible wireless signals everywhere, and the method is low in cost, easy to deploy and high in expansibility.

Description

Human body continuous action counting method based on CSI
Technical Field
The invention relates to a human body continuous action counting method based on CSI.
Background
The human behavior and action recognition means that the human behavior and action are classified and recognized according to a certain algorithm by measuring certain signal data generated when the human body performs various actions.
The existing traditional motion recognition method comprises the following steps: (1) based on computer vision: the human behavior and action image sequence is collected through the camera, and image features are extracted by utilizing a computer vision calculation method to classify actions. The method has accurate identification, but has large calculation amount, is easily influenced by factors such as light, sight distance propagation and the like, and can only sense the specific range which can be illuminated by the camera. (2) Based on the sensor: the human body action recognition is realized by installing special sensors and other devices, such as acceleration sensors, on the human body and collecting relevant human body action information. However, this method requires a portable sensor, and is expensive and difficult to be widely used.
With the increasing deployment quantity of WiFi hotspots and the wide application of WiFi in the sensing field, human behavior and action identification based on CSI signals in the WiFi environment draws a great deal of attention. The CSI signal is extracted from a PHY layer of WiFi communication as an estimate of a channel state in an Orthogonal Frequency Division Multiplexing (OFDM) technique, and describes changes in amplitude and phase of a WiFi signal due to path loss and multipath effects (such as reflection and diffraction) generated by human body behavioral actions. The traditional motion recognition method only simply classifies and recognizes the motion, and does not provide the calculation of the motion occurrence time point and the counting of the human body continuous motion.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for counting consecutive actions of a human body based on CSI in view of the above prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a CSI-based human body continuous action counting method is used for a human body continuous action counting and identifying system comprising a transmitter and a receiver equipped with a wireless network card, wherein the transmitter is a router and N is arranged on the transmittertThe strip transmitting terminal antenna, the receiver is internally loaded with a CSI tool kit, the receiver is provided with a CSI information reading interface, and the receiver is provided with NrStrip receiving end antenna, its characterized in that: comprises the following steps
Step one, signal acquisition: when the human body is positioned in the human body continuous action counting and identifying system, the CSI information file generated by the human body action file in the T time is read through a CSI information reading interface of the receiver, and the CSI information file generated by the human body action file in the T time comprises Nt×Nr×SCxN CSI values, where Nt×NrIs the number of channel links between the transmitter and the receiver, SCThe number of OFDM subcarriers of a transmission packet in a CSI information file loaded on each channel link is N, the number of transmission packets into which the human body action file is divided within T time is N, and the N transmission packets are sequentially transmitted to a receiver through a transmitter;
step two, action detection: judging whether the CSI information file collected in the first step has action or not, and specifically adopting the following mode:
by Ht，r(i) Denotes S between the antenna pair generated by the i-th transmission packet at the transmitting end antenna t and the receiving end antenna rCAmplitude, H, of CSI value of subcarriert，r(i) Is SCX 1-dimensional vector, i 1, 2, … N, t1，2，…Nt，r＝1，2，…Nr；
By Ht，rRepresenting S of N transmission packets between pairs of antennas generated by a transmitting-end antenna t and a receiving-end antenna rCAmplitude, H, of CSI value of subcarriert，rIs NxSCA dimension vector;
Ht，r＝[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]T；
[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]Tis [ H ]t，r(1)|Ht，r(2)|…|Ht，r(N)]The transposed matrix of (2);
h is to bet，rPrincipal component analysis was performed using HPt，r(m) represents Ht，rThe mth principal component, HP, obtained by principal component analysist，r(m) is an N × 1 dimensional vector;
with HPt，r(1 → p) represents Ht，rAfter principal component analysis, the first p principal components, HP, are obtainedt，r(1 → p) is a vector of dimension Nxp;
HPt，r(1→p)＝[HPt，r(1)|HPt，r(2)|…|HPt，r(p)]
taking HPt，r(m) determining whether there is an action occurring, if HPt，r(m) if the standard deviation is greater than or equal to a preset threshold value, judging that the CSI information file collected in the first step has action; otherwise, judging that the CSI information file acquired in the first step does not act, wherein the value of m is 1 or 2 or 3 or … … or p;
step three, pretreatment: and when detecting that the action of the CSI information file occurs in the second step, performing data preprocessing on the CSI information file:
firstly to Ht，rThe signal is Butterworth filtered, and the Butterworth filtered signal is HBt，rRepresents;
then to HBt，rPerforming principal component analysis using HBPt，r(n) represents HBt，rObtaining the nth principal component after principal component analysis;
selection of HBPt，r(1) Or HBPt，r(2) Input data as the count of the actions in step four;
step four, counting actions:
HBP by Hilbert conversion methodt，r(1) Or HBPt，r(2) Envelope analysis is carried out to obtain Zt，rHere, the value of t is 1, 2, … NtAny one of the values, r is 1, 2, … NrAny one of the values;
will Zt，rSmoothing to obtain a waveform file to be calculated;
and detecting the number of wave crests of the waveform file to be calculated by utilizing a peak algorithm, taking the detected number of wave crests as the action times contained in the human body action file within the T time, and outputting the action times.
As an improvement, the method also comprises the step five of extracting the action characteristics:
will Zt，rRecording a time interval between two adjacent wave crests in a waveform file to be calculated after smoothing, obtaining a time sequence of multiple actions in a human body action file within T time according to the recorded time interval, and intercepting HP according to the time sequencet，r(2) And performing wavelet transformation on the output waveforms corresponding to the corresponding time sequences to obtain action waveforms of each action generated by the human body within T time, wherein the action waveforms are action characteristics generated by the human body.
In a further improvement, the method also comprises the following six steps: and (4) action classification:
and matching a plurality of action waveforms of the human body generating actions within the T time obtained in the step five with preset action type waveforms, and obtaining action classification of the human body action files within the T time by adopting a few majority-obeying criteria.
In the third step, the HBPt，r(1) And HBPt，r(2) The selection method comprises the following steps:
respectively to HBPt，r(1) And HBPt，r(2) Performing discrete wavelet transform on the wavelet-transformedAnd performing windowing processing on the two main components, respectively solving the average information entropy of the two windows, and selecting the main component with the smaller average information entropy as the input data of the action counting in the fourth step.
Compared with the prior art, the invention has the advantages that: (1) the method can be realized only by visible wireless signals without special equipment such as a sensor, a camera and the like, and has low cost, easy deployment and strong expansibility; (2) according to the characteristic that the collected CSI signals change along with the action, the number of peak values is calculated by using envelope analysis so as to determine the action times, so that the counting of continuous actions can be realized; (3) not only is low-pass filtering simply applied, but also a Principal Component Analysis (PCA) method is applied, and a cleaner CSI signal is extracted.
Drawings
Fig. 1 is a schematic diagram of an overall system framework of CSI-based human body continuous motion counting according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of an overall system framework for counting and identifying continuous actions of a human body based on CSI according to a second embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
Example one
As shown in FIG. 1, the present invention provides a CSI-based human body continuous action counting method for a human body continuous action counting and identification system comprising a transmitter and a receiver equipped with a wireless network card, wherein the transmitter is a router, and N is set on the transmittertThe device comprises a strip transmitting terminal antenna, a computer with a wireless network card and a CSI tool kit loaded in a receiver, wherein a CSI information reading interface is arranged on the receiver, and N wireless network cards are arranged on the receiverrThe strip receiving end antenna and the human body continuous action counting method comprise the following steps
Step one, signal acquisition: when the human body is positioned in the human body continuous action counting and identifying system, the CSI information file generated by the human body action file in the T time is read through the CSI information reading interface of the receiver,the CSI information file generated by the human body motion file in the T time comprises Nt×Nr×SCxN CSI values, where Nt×NrIs the number of channel links between the transmitter and the receiver, SCThe number of OFDM subcarriers of a transmission packet in a CSI information file loaded on each channel link is N, the number of transmission packets into which the human body action file is divided within T time is N, and the N transmission packets are sequentially transmitted to a receiver through a transmitter;
step two, action detection: judging whether the CSI information file collected in the first step has action or not, and specifically adopting the following mode:
by Ht，r(i) Denotes S between the antenna pair generated by the i-th transmission packet at the transmitting end antenna t and the receiving end antenna rCAmplitude, H, of CSI value of subcarriert，r(i) Is SCX 1-dimensional vector, i 1, 2, … N, t 1, 2, … Nt，r＝1，2，…Nr；
By Ht，rRepresenting S of N transmission packets between pairs of antennas generated by a transmitting-end antenna t and a receiving-end antenna rCAmplitude, H, of CSI value of subcarriert，rIs NxSCA dimension vector;
Ht，r＝[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]T；
[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]Tis [ H ]t，r(1)|Ht，r(2)|…|Ht，r(N)]The transposed matrix of (2);
h is to bet，rPrincipal component analysis was performed using HPt，r(m) represents Ht，rThe mth principal component, HP, obtained by principal component analysist，r(m) is an N × 1 dimensional vector;
with HPt，r(1 → p) represents Ht，rAfter principal component analysis, the first p principal components, HP, are obtainedt，r(1 → p) is a vector of dimension Nxp;
HPt，r(1→p)＝[HPt，r(1)|HPt，r(2)|…|HPt，r(p)]
taking HPt，r(2) To determine if there is any motion, if HP occurst，r(2) If the standard deviation is larger than or equal to a preset threshold value, judging that the CSI information file collected in the first step has action; otherwise, judging that the CSI information file collected in the first step does not have any action;
step three, pretreatment: and when detecting that the action of the CSI information file occurs in the second step, performing data preprocessing on the CSI information file:
firstly to Ht，rThe signal is Butterworth filtered, and the Butterworth filtered signal is HBt，rRepresents;
then to HBt，rPerforming principal component analysis using HBPt，r(n) represents HBt，rObtaining the nth principal component after principal component analysis;
selection of HBPt，r(1) Or HBPt，r(2) As input data for the counting of actions in step four, HBPt，r(1) Or HBPt，r(2) The method can be selected arbitrarily or in the following way: respectively to HBPt，r(1) And HBPt，r(2) Performing discrete wavelet transform, performing windowing on the two main components after wavelet transform, respectively solving the average information entropy of the two windows, and selecting the main component with the smaller average information entropy as the input data of the action counting in the fourth step;
step four, counting actions:
HBP by Hilbert conversion methodt，r(1) Or HBPt，r(2) Envelope analysis is carried out to obtain Zt，rHere, the value of t is 1, 2, … NtAny one of the values, r is 1, 2, … NrAny one of the values;
will Zt，rSmoothing to obtain a waveform file to be calculated;
and detecting the number of wave crests of the waveform file to be calculated by utilizing a peak algorithm, taking the detected number of wave crests as the action times contained in the human body action file within the T time, and outputting the action times.
Example two
In contrast to the first embodiment, as shown in figure 2,
further comprises the following steps of:
will Zt，rRecording a time interval between two adjacent wave crests in a waveform file to be calculated after smoothing, obtaining a time sequence of multiple actions in a human body action file within T time according to the recorded time interval, and intercepting HP according to the time sequencet，r(2) Performing wavelet transformation on the output waveforms corresponding to the corresponding time sequences to obtain action waveforms of each action generated by the human body within T time, wherein the action waveforms are action characteristics generated by the human body;
step six: and (4) action classification:
and matching a plurality of action waveforms of the human body generating actions within the T time obtained in the step five with preset action type waveforms, and obtaining action classification of the human body action files within the T time by adopting a few majority-obeying criteria.

Claims (4)

1. A CSI-based human body continuous action counting method is used for a human body continuous action counting and identifying system comprising a transmitter and a receiver equipped with a wireless network card, wherein the transmitter is a router and N is arranged on the transmittertThe strip transmitting terminal antenna, the receiver is internally loaded with a CSI tool kit, the receiver is provided with a CSI information reading interface, and the receiver is provided with NrStrip receiving end antenna, its characterized in that: comprises the following steps
Step one, signal acquisition: when the human body is positioned in the human body continuous action counting and identifying system, the CSI information file generated by the human body action file in the T time is read through a CSI information reading interface of the receiver, and the CSI information file generated by the human body action file in the T time comprises Nt×Nr×SCxN CSI values, where Nt×NrIs the number of channel links between the transmitter and the receiver, SCThe number of OFDM subcarriers of a transmission packet in a CSI information file carried on each channel link, wherein N is the human body action within T timeThe number of transmission packets into which the file is divided is N, and the N transmission packets are sequentially transmitted to a receiver through a transmitter;
step two, action detection: judging whether the CSI information file collected in the first step has action or not, and specifically adopting the following mode:
by Ht，r(i) Denotes S between the antenna pair generated by the i-th transmission packet at the transmitting end antenna t and the receiving end antenna rCAmplitude, H, of CSI value of subcarriert，r(i) Is SCX 1-dimensional vector, i 1, 2, … N, t 1, 2, … Nt，r＝1，2，…Nr；
By Ht，rRepresenting S of N transmission packets between pairs of antennas generated by a transmitting-end antenna t and a receiving-end antenna rCAmplitude, H, of CSI value of subcarriert，rIs NxSCA dimension vector;
Ht，r＝[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]T；
[Ht，r(1)|Ht，r(2)|…|Ht，r(N)]Tis [ H ]t，r(1)|Ht，r(2)|…|Ht，r(N)]The transposed matrix of (2);
h is to bet，rPrincipal component analysis was performed using HPt，r(m) represents Ht，rThe mth principal component, HP, obtained by principal component analysist，r(m) is an N × 1 dimensional vector;
with HPt，r(1 → p) represents Ht，rAfter principal component analysis, the first p principal components, HP, are obtainedt，r(1 → p) is a vector of dimension Nxp;
HPt，r(1→p)＝[HPt，r(1)|HPt，r(2)|…|HPt，r(p)]
taking HPt，r(m) determining whether there is an action occurring, if HPt，r(m) if the standard deviation is greater than or equal to a preset threshold value, judging that the CSI information file collected in the first step has action; otherwise, judging that the CSI information file acquired in the first step does not act, wherein the value of m is 1 or 2 or 3 or … … or p;
step three, pretreatment: and when detecting that the action of the CSI information file occurs in the second step, performing data preprocessing on the CSI information file:
firstly to Ht，rThe signal is Butterworth filtered, and the Butterworth filtered signal is HBt，rRepresents;
then to HBt，rPerforming principal component analysis using HBPt，r(n) represents HBt，rObtaining the nth principal component after principal component analysis;
selection of HBPt，r(1) Or HBPt，r(2) Input data as the count of the actions in step four;
step four, counting actions:
HBP by Hilbert conversion methodt，r(1) Or HBPt，r(2) Envelope analysis is carried out to obtain Zt，rHere, the value of t is 1, 2, … NtAny one of the values, r is 1, 2, … NrAny one of the values;
will Zt，rSmoothing to obtain a waveform file to be calculated;
and detecting the number of wave crests of the waveform file to be calculated by utilizing a peak algorithm, taking the detected number of wave crests as the action times contained in the human body action file within the T time, and outputting the action times.
2. The CSI-based human body continuous motion counting method according to claim 1, wherein: further comprises the following steps of:
will Zt，rRecording a time interval between two adjacent wave crests in a waveform file to be calculated after smoothing, obtaining a time sequence of multiple actions in a human body action file within T time according to the recorded time interval, and intercepting HP according to the time sequencet，r(2) And performing wavelet transformation on the output waveforms corresponding to the corresponding time sequences to obtain action waveforms of each action generated by the human body within T time, wherein the action waveforms are action characteristics generated by the human body.
3. The CSI-based human body successive motion counting method according to claim 2, wherein: further comprises the following steps: and (4) action classification:
and matching a plurality of action waveforms of the human body generating actions within the T time obtained in the step five with preset action type waveforms, and obtaining action classification of the human body action files within the T time by adopting a few majority-obeying criteria.
4. The CSI-based human body continuous motion counting method according to claim 1, 2 or 3, wherein: in the third step, for HBPt，r(1) And HBPt，r(2) The selection method comprises the following steps:
respectively to HBPt，r(1) And HBPt，r(2) And performing discrete wavelet transform, performing windowing on the two main components after the wavelet transform, respectively solving the average information entropy of the two windows, and selecting the main component with the smaller average information entropy as the input data of the action counting in the fourth step.
CN201811277055.1A 2018-10-30 2018-10-30 Human body continuous action counting method based on CSI Active CN109190605B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811277055.1A CN109190605B (en) 2018-10-30 2018-10-30 Human body continuous action counting method based on CSI

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811277055.1A CN109190605B (en) 2018-10-30 2018-10-30 Human body continuous action counting method based on CSI

Publications (2)

Publication Number Publication Date
CN109190605A CN109190605A (en) 2019-01-11
CN109190605B true CN109190605B (en) 2020-05-15

Family

ID=64940668

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811277055.1A Active CN109190605B (en) 2018-10-30 2018-10-30 Human body continuous action counting method based on CSI

Country Status (1)

Country Link
CN (1) CN109190605B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109977890B (en) * 2019-03-30 2021-08-17 绵阳硅基智能科技有限公司 Action recognition method and recognition system thereof
CN111553276B (en) * 2020-04-28 2023-06-20 北京邮电大学 Action counting method based on self-adaptive feature selection and signal trend removal
CN111864348B (en) * 2020-06-24 2021-03-09 宁波大学 Initial satellite finding method of VICTS antenna

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4924106B2 (en) * 2006-04-27 2012-04-25 ソニー株式会社 Wireless communication system, wireless communication apparatus, and wireless communication method
US9226706B2 (en) * 2012-12-19 2016-01-05 Alert Core, Inc. System, apparatus, and method for promoting usage of core muscles and other applications
CN103606248B (en) * 2013-09-30 2016-08-10 广州市香港科大霍英东研究院 A kind of falling over of human body automatic testing method and system
CN106772219A (en) * 2017-03-08 2017-05-31 南京大学 Indoor orientation method based on CSI signals
CN107947874B (en) * 2017-11-21 2020-10-30 太原理工大学 Indoor map semantic identification method based on WiFi channel state information

Also Published As

Publication number Publication date
CN109190605A (en) 2019-01-11

Similar Documents

Publication Publication Date Title
CN107968689B (en) Perception identification method and device based on wireless communication signals
CN109190605B (en) Human body continuous action counting method based on CSI
CN105844216B (en) Detection and matching mechanism for recognizing handwritten letters by WiFi signals
CN105678273B (en) The starting point detection algorithm of radio-frequency fingerprint identification technology transient signal
CN101371569B (en) Detection of the presence of television signals embedded in noise using cyclostationary toolbox
CN108828548A (en) A kind of three Parameter fusion data set construction methods based on fmcw radar
CN112131972B (en) Method for recognizing human body behaviors by using WiFi data based on attention mechanism
CN115294615A (en) Radio frequency fingerprint identification method based on machine learning
CN112381063A (en) Channel state information-based people counting method
JP2009244138A (en) Apparatus and method for measuring/separating/analyzing signal
CN113935373A (en) Human body action recognition method based on phase information and signal intensity
CN112929141B (en) Unmanned aerial vehicle detection and recognition method and system based on graph signaling signal matching
CN111142668B (en) Interaction method based on Wi-Fi fingerprint positioning and activity gesture joint recognition
US8175829B2 (en) Analyzer for signal anomalies
CN105743756A (en) Frame detection method based on Adaboost algorithm in Wi-Fi system
EP3499732B1 (en) Test arrangement, device and method for measuring a directed signal
CN116524537A (en) Human body posture recognition method based on CNN and LSTM combination
CN116343261A (en) Gesture recognition method and system based on multi-modal feature fusion and small sample learning
JP7070243B2 (en) Arrival direction estimation device
CN110233687A (en) A kind of multichannel data combined signal judgement detection method
CN102027680B (en) For detection of and for generating method and relevant device and the communication system of useful signal
CN115866615A (en) Wireless network communication relation discovery method based on electromagnetic spectrum characteristics
CN114639169A (en) Human body action recognition system based on attention mechanism feature fusion and position independence
CN114358162A (en) Falling detection method and device based on continuous wavelet transform and electronic equipment
CN114676739A (en) Method for detecting and identifying time sequence action of wireless signal based on fast-RCNN

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant