CN109739848A - A kind of data extraction method - Google Patents
A kind of data extraction method Download PDFInfo
- Publication number
- CN109739848A CN109739848A CN201811620487.8A CN201811620487A CN109739848A CN 109739848 A CN109739848 A CN 109739848A CN 201811620487 A CN201811620487 A CN 201811620487A CN 109739848 A CN109739848 A CN 109739848A
- Authority
- CN
- China
- Prior art keywords
- data
- degree
- vertex
- correlation
- extracted
- 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.)
- Granted
Links
Abstract
The present invention provides a kind of data extraction method, the data include a kind of data and two class data, and one kind data are the data directly issued, and the two classes data are the comment data for a kind of data, it include: acquisition data acquisition system, the data acquisition system includes a kind of data and two class data；The data acquisition system is pre-processed to obtain data network set { di, the data network element in the data network set is with diThe form of={ V, E } records, and wherein V is user identifier, and the comment relationship for a kind of data that the two class data that E represents the publication of a user identifier issue another user identifier, each vertex includes user identifier, title and content three parts data；Theme vector collection is obtained according to the title on the vertex in the data network set；The mark for obtaining each vertex in data network set concentrates the degree of correlation of each vector with the theme vector, obtains degree of correlation set；Data extraction is carried out according to the degree of correlation set.The present invention can effectively extract target user and significant data relevant to theme.
Description
Technical field
The present invention relates to the communications field, especially a kind of data extraction method.
Background technique
In data analysis field, it is often necessary to be cleaned and be extracted to data.In common interactive website, for example know
, there are a large amount of users mutually to comment class data for Baidu's discussion bar, and this kind of data can react the personal preference of user, also can be used in
Current events hot spot and social phenomenon are studied, there are more social informations, it can be widely used in advertising objective user study,
Hot issue research, the every field such as public sentiment supervision.But lack in the prior art for effective cleaning of this kind of data and section
Analysis method is learned, so as to obtained Limited information.
Summary of the invention
In order to solve the above technical problem, the present invention provides a kind of data extraction methods.
The present invention is realized with following technical solution:
A kind of data extraction method, the data include a kind of data and two class data, and one kind data are directly to send out
The data of cloth, the two classes data are the comment data for a kind of data, comprising:
Data acquisition system is obtained, the data acquisition system includes a kind of data and two class data；
The data acquisition system is handled to obtain data network set { di, the data in the data network set
Network element is with diThe form of={ V, E } records, and wherein V is user identifier, and E represents two class data of user identifier publication
To the comment relationship of a kind of data of another user identifier publication, each vertex includes user identifier, title and content three
Partial data；
Theme vector collection is obtained according to the title on the vertex in the data network set；
The mark for obtaining each vertex in data network set concentrates the degree of correlation of each vector with the theme vector, obtains
To degree of correlation set；
Data extraction is carried out according to the degree of correlation set.
Further, described to include: according to degree of correlation set progress data extraction
Data capsule is generated for each theme that the theme vector is concentrated, each theme there are unique corresponding data to hold
Device, the data capsule is for collecting data corresponding with the theme；
Obtain the degree of correlation set on vertex to be extracted；
Judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is that value is greater than the first preset threshold
The degree of correlation, and if it exists, then the target degree of correlation is extracted；
The corresponding theme of the target degree of correlation is obtained, by the corresponding data of the addition theme on the vertex to be extracted
Among container；
Next vertex to be extracted is obtained, if next vertex to be extracted is not sky, step is repeated: obtaining
The degree of correlation set on vertex to be extracted, otherwise, process terminates.
Further, described to include: according to degree of correlation set progress data extraction
Obtain the degree of correlation set on vertex to be extracted；
Judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is that value is greater than the second preset threshold
The degree of correlation, and if it exists, then determine that the vertex to be extracted is representative points, and the representative points extracted；
Next vertex to be extracted is obtained, if next vertex to be extracted is not sky, step is repeated: obtaining
Otherwise the user identifier of the target complete vertex correspondence extracted is extracted, is obtained by the degree of correlation set on vertex to be extracted
User's set.
Further, include: before the title according to the vertex in the data network set obtains theme vector collection
Data cleansing is carried out to obtain data cleansing as a result, and to the data weight after cleaning according to the data network set
Newly-generated data network set { di}。
Further, the data cleansing includes:
Out-degree statistics is carried out to each vertex of each data network element；
Obtain representative points of the out-degree less than 2 in each data network element；
Delete the representative points.
Further, the data cleansing includes:
To the carry out in-degree statistics on each vertex in each data network element；
The user of each vertex correspondence in each data network element is carried out in the range of the data network element
Attention degree assessment；
Judge whether the vertex is representative points according to the result of attention degree assessment and the in-degree；
If so, deleting the representative points.
In the description of the invention, it is to be understood that term " center ", " longitudinal direction ", " transverse direction ", "upper", "lower",
The orientation or positional relationship of the instructions such as "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outside" is
It is based on the orientation or positional relationship shown in the drawings, is merely for convenience of description the invention and simplifies description, rather than indicate
Or imply that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore cannot understand
For the limitation to the invention.In addition, term " first ", " second " etc. are used for description purposes only, and should not be understood as indicating
Or it implies relative importance or implicitly indicates the quantity of indicated technical characteristic." first ", " second " etc. are defined as a result,
Feature can explicitly or implicitly include one or more of the features.In the description of the invention, unless separately
It is described, the meaning of " plurality " is two or more.
In the description of the invention, it should be noted that unless otherwise clearly defined and limited, term " peace
Dress ", " connected ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integrally
Connection；It can be mechanical connection, be also possible to be electrically connected；Can be directly connected, can also indirectly connected through an intermediary,
It can be the connection inside two elements.For the ordinary skill in the art, on being understood by concrete condition
State concrete meaning of the term in the invention.
The beneficial effects of the present invention are:
A kind of data extraction method is provided in the present invention, can effectively extract target user and relevant to theme important
Data.
Detailed description of the invention
Fig. 1 is a kind of data cleaning method flow chart provided in this embodiment；
Fig. 2 is the first data cleaning method flow chart provided in this embodiment；
Fig. 3 is second of data cleaning method flow chart provided in this embodiment；
Fig. 4 is the attention degree appraisal procedure flow chart of the user of some vertex correspondence provided in this embodiment；
Fig. 5 is the method flow diagram that the data provided in this embodiment to after cleaning extract.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention will be made below further detailed
Description.
The embodiment of the present invention provides a kind of data cleaning method, and the data include a kind of data and two class data, described
A kind of data are the data directly issued, and the two classes data are the comment data for a kind of data.The method such as Fig. 1 institute
Show, comprising:
S101. data acquisition system is obtained, the data acquisition system includes a kind of data and two class data.
S102. the data acquisition system is pre-processed to obtain data network set { di, the data network set
In data network element with diThe form of={ V, E } records, and wherein V is user identifier, and E represents a user identifier publication
The comment relationship for a kind of data that two class data issue another user identifier, each vertex include user identifier, title
With content three parts data.
For example, if user spark has issued an a kind of data, user tony, samby and dazzi carry out it
Comment has then obtained including four vertex, the data network elements of three directed edges, and directed edge is to be directed toward spark from tony,
Samby is directed toward three sides that spark and dazzi is directed toward spark.The direction of directed edge institute is directed toward by the user for issuing two class data
State the user of the corresponding a kind of data of two class data.
It specifically, may include multiple two class data of user and multiple publications for issuing a kind of data in data network element
User, and the user for issuing a kind of data can also be simultaneously as the user for issuing two class data, and the embodiment of the present invention is not
Limit the specific generation method of data network element.Such as can be according to the time, the corresponding number of the data that publication in one day comes out
According to network element.
S103. data cleansing is carried out to obtain data cleansing result according to the data network set.
Specifically, the data cleansing is using each data network element as cleaning object, as shown in Figure 2, comprising:
S1031. out-degree statistics is carried out to each vertex of each data network element.
Specifically, data network element is that the form of digraph counts it and go out for each vertex in the digraph
Degree.
S1033. representative points of the out-degree less than 2 in each data network element are obtained.
S1035. the representative points are deleted.
This cleaning step is intended to clean a kind of data and two class data of liveness lower user publication, liveness compared with
The statistical significance for the data that low user is issued is smaller, therefore, it is necessary to be cleared up.
Further, the data cleansing is as shown in Figure 3, further includes:
S1032. to the carry out in-degree statistics on each vertex in each data network element.
S1034. to the user of each vertex correspondence in each data network element the data network element range
Interior progress attention degree assessment.
Specifically, the attention degree assessment of the user of some vertex correspondence is as shown in Figure 4, comprising:
S10341. other users are obtained to the negative degree of commenting of the user, the negative degree of commenting is according to formulaCalculate and
It obtains, wherein θj tIndicate some other user to the negative weight for commenting word of some in the comment of the user, sumjDescribed in expression
Occurs the negative sum for commenting word in comment.
Specifically, bearing comments word and its weight that can preset, and is recorded in negative comment in word weight table.For example it bears and comments word
" idiot " respective weights 0.9, it is negative to comment word " small fool " respective weights 0.4.Weight is heavier, then bears and comment the negative comment gas of word more sharp
It is strong.
S10342. the attention degree of the user is calculated.
Specifically, calculation formula is as followsWherein κ1,κ2It is pre-
If parameter, value is related to the degree of cleaning, can carry out any setting by programmer.Input, output are the vertex
In-degree and out-degree, sum (neg >=0.45) are the summations that the negative degree of commenting of other users of the degree of commenting greater than 0.45 is born to the user
Value, sum (i) is the quantity of the other users of whole commented on the user.
S1036. judge whether the vertex is representative points according to the result of attention degree assessment and the in-degree.
Specifically, if the in-degree of vertex is 0, alternatively, the in-degree of vertex non-zero and attention degree be less than it is default
Threshold value, then the vertex is judged as representative points.
S1038. if so, deleting the representative points.
This cleaning step is intended to clean junk user, the usually not other users of junk user it is commented on or
Junk user usually has certain negative emotions, to the content of the comment of other users with strong negative emotions color,
A kind of data and two class data for junk user publication carry out the objective degree that cleaning is conducive to maintain data, it is reasonable to be based on
Data after cleaning carry out other statistical research.
On the basis of above-mentioned carry out sufficient data cleansing, the embodiment of the present invention further provides data extraction side
Method, the data can be the data after cleaning, as shown in Figure 5, comprising:
S201. data network set { d is regenerated to the datai, the data network in the data network set
Element is with diThe form of={ V, E } records, and wherein V is user identifier, and E represents two class data of user identifier publication to another
The comment relationship of a kind of data of one user identifier publication, each vertex includes user identifier, title and content three parts
Data.
S202. theme vector collection is obtained according to the title on the vertex in the data network set.
Specifically, the theme vector collection can be identified as { topici, wherein topici={ (ti1,pi1)……
(tin,pin), wherein for tijTheme topiciIn the keyword that is likely to occur, PijThe keyword occurs in the theme
Probability.In fact the title on each vertex in data network set and content can regard a series of probability of keywords as
Therefore distribution carries out analysis by the title for each vertex and combines priori knowledge that theme relevant to vertex can be obtained,
Thus the corresponding theme vector collection of data network set is obtained.And the specific method present invention for obtaining theme vector collection is implemented
Example does not make specific restriction, can refer to the prior art.The embodiment of the present invention is using title analysis without considering content analysis
It is considering for saving operand, in the application scenarios that title has content stronger summary effect, for theme
The acquisition loss of significance of vector set is little.
S203. the mark for obtaining each vertex in data network set concentrates the related of each vector to the theme vector
Degree, obtains degree of correlation set.
Specifically, some vertex and the acquisition methods of the degree of correlation of some theme vector include:
Based on formulaThe degree of correlation on some vertex Yu some theme vector is calculated, wherein ViFor the vertex
Title, key is while being under the jurisdiction of the keyword of the theme vector and the title, and the P (key) is the keyword in institute
State the probability in theme vector.
The degree of correlation of each theme is concentrated it is possible to further obtain some vertex and the theme vector, to obtain
Degree of correlation set.
S204. data extraction is carried out according to the degree of correlation set.
Specifically, data are carried out according to the degree of correlation set and, present invention offer related with specific purpose of extracting is provided
Extracting method under two kinds of application scenarios.
Among an application scenarios, need to study the related data of each theme respectively, therefore, it is necessary to mention
The higher data of significance level in each theme are taken out, under this application scenarios, need to carry out mentioning for data according to theme
It takes, it is described to include: according to degree of correlation set progress data extraction
S2041. data capsule is generated for each theme that the theme vector is concentrated, each theme has unique corresponding number
According to container, the data capsule is for collecting data corresponding with the theme.
S2043. the degree of correlation set on vertex to be extracted is obtained.
S2045. judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is that value is greater than first in advance
If the degree of correlation of threshold value, and if it exists, then extract the target degree of correlation.
S2047. the corresponding theme of the target degree of correlation is obtained, the addition on the vertex to be extracted theme is corresponding
Data capsule among.
The target degree of correlation can have it is multiple, correspondingly, corresponding theme is also multiple.
S2049. next vertex to be extracted is obtained, if next vertex to be extracted is not sky, repeats step
Rapid S2043, otherwise, process terminates.
Among another application scenarios, needs to position the higher user of liveness, i.e., many topics are joined
With, and this certain customers is that object is launched in the iron user of many large-scale portal websites and the advertisement of advertisement putting business, in order to
Position these users, it is also desirable to data extraction is carried out, it is described to include: according to degree of correlation set progress data extraction
S2042. the degree of correlation set on vertex to be extracted is obtained.
S2044. judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is that value is greater than second in advance
If the degree of correlation of threshold value, and if it exists, then determine that the vertex to be extracted is representative points, and the representative points are extracted
Come.
S2046. next vertex to be extracted is obtained, if next vertex to be extracted is not sky, repeats step
Otherwise rapid S2042 executes step S2048.
S2048. the corresponding user identifier of the representative points extracted is extracted, obtains user's set.
The user's set extracted is iron user, can be with by further being studied user set
Analyze the behavioural characteristic of this certain customers, social relationships, to launch or subsequent provide preferably for these clients for advertisement
Service.
Claims (6)
1. a kind of data extraction method, the data include a kind of data and two class data, and one kind data are directly to issue
Data, the two classes data be for a kind of data comment data characterized by comprising
Data acquisition system is obtained, the data acquisition system includes a kind of data and two class data；
The data acquisition system is handled to obtain data network set { di, the data network in the data network set
Element is with diThe form of={ V, E } records, and wherein V is user identifier, and E represents two class data of user identifier publication to another
The comment relationship of a kind of data of one user identifier publication, each vertex includes user identifier, title and content three parts
Data；
Theme vector collection is obtained according to the title on the vertex in the data network set；
The mark for obtaining each vertex in data network set concentrates the degree of correlation of each vector with the theme vector, obtains phase
Guan Du set；
Data extraction is carried out according to the degree of correlation set.
2. the method according to claim 1, wherein described carry out data extraction packet according to the degree of correlation set
It includes:
Data capsule is generated for each theme that the theme vector is concentrated, each theme has unique corresponding data capsule, institute
State data capsule for collect corresponding with theme data；
Obtain the degree of correlation set on vertex to be extracted；
Judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is the phase that value is greater than the first preset threshold
Guan Du, and if it exists, then extract the target degree of correlation；
The corresponding theme of the target degree of correlation is obtained, by the corresponding data capsule of the addition theme on the vertex to be extracted
Among；
Next vertex to be extracted is obtained, if next vertex to be extracted is not sky, step is repeated: obtaining wait mention
The degree of correlation set on vertex is taken, otherwise, process terminates.
3. the method according to claim 1, wherein described carry out data extraction packet according to the degree of correlation set
It includes:
Obtain the degree of correlation set on vertex to be extracted；
Judge with the presence or absence of the target degree of correlation in the degree of correlation set, the degree of correlation is the phase that value is greater than the second preset threshold
Guan Du, and if it exists, then determine that the vertex to be extracted is representative points, and the representative points are extracted；
Next vertex to be extracted is obtained, if next vertex to be extracted is not sky, step is repeated: obtaining wait mention
The degree of correlation set on vertex is taken, otherwise, the user identifier of the target complete vertex correspondence extracted is extracted, user is obtained
Set.
4. method according to claim 3, it is characterised in that:
The title according to the vertex in the data network set obtains theme vector collection
Data cleansing is carried out to obtain data cleansing as a result, and giving birth to again to the data after cleaning according to the data network set
At data network set { di}。
5. method according to claim 4, it is characterised in that:
The data cleansing includes:
Out-degree statistics is carried out to each vertex of each data network element；
Obtain representative points of the out-degree less than 2 in each data network element；
Delete the representative points.
6. method according to claim 4, it is characterised in that:
The data cleansing includes:
To the carry out in-degree statistics on each vertex in each data network element；
The user of each vertex correspondence in each data network element is paid attention in the range of the data network element
Degree assessment；
Judge whether the vertex is representative points according to the result of attention degree assessment and the in-degree；
If so, deleting the representative points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811620487.8A CN109739848B (en) | 2018-12-28 | 2018-12-28 | Data extraction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811620487.8A CN109739848B (en) | 2018-12-28 | 2018-12-28 | Data extraction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109739848A true CN109739848A (en) | 2019-05-10 |
| CN109739848B CN109739848B (en) | 2021-11-09 |
Family
ID=66361780
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811620487.8A Active CN109739848B (en) | 2018-12-28 | 2018-12-28 | Data extraction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109739848B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109753597A (en) * | 2018-12-31 | 2019-05-14 | 杭州翼兔网络科技有限公司 | A kind of data processing method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073730A (en) * | 2011-01-14 | 2011-05-25 | 哈尔滨工程大学 | Method for constructing topic web crawler system |
| CN102404411A (en) * | 2011-12-23 | 2012-04-04 | 创新科存储技术有限公司 | Data synchronization method of cloud storage system |
| CN102662956A (en) * | 2012-03-05 | 2012-09-12 | 西北工业大学 | Method for identifying opinion leaders in social network based on topic link behaviors of users |
| CN102929918A (en) * | 2012-09-20 | 2013-02-13 | 西北工业大学 | False online public opinion identification method |
| CN106126705A (en) * | 2016-07-01 | 2016-11-16 | 武汉泰迪智慧科技有限公司 | A kind of large scale network data crawl system in real time |
| CN107018062A (en) * | 2016-06-24 | 2017-08-04 | 卡巴斯基实验室股份公司 | System and method for recognizing rubbish message using subject information |
| US20180349355A1 (en) * | 2017-05-31 | 2018-12-06 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Artificial Intelligence Based Method and Apparatus for Constructing Comment Graph |
-
2018
- 2018-12-28 CN CN201811620487.8A patent/CN109739848B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073730A (en) * | 2011-01-14 | 2011-05-25 | 哈尔滨工程大学 | Method for constructing topic web crawler system |
| CN102404411A (en) * | 2011-12-23 | 2012-04-04 | 创新科存储技术有限公司 | Data synchronization method of cloud storage system |
| CN102662956A (en) * | 2012-03-05 | 2012-09-12 | 西北工业大学 | Method for identifying opinion leaders in social network based on topic link behaviors of users |
| CN102929918A (en) * | 2012-09-20 | 2013-02-13 | 西北工业大学 | False online public opinion identification method |
| CN107018062A (en) * | 2016-06-24 | 2017-08-04 | 卡巴斯基实验室股份公司 | System and method for recognizing rubbish message using subject information |
| CN106126705A (en) * | 2016-07-01 | 2016-11-16 | 武汉泰迪智慧科技有限公司 | A kind of large scale network data crawl system in real time |
| US20180349355A1 (en) * | 2017-05-31 | 2018-12-06 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Artificial Intelligence Based Method and Apparatus for Constructing Comment Graph |
Non-Patent Citations (1)
| Title |
|---|
| 周雪妍: ""在线社会网络关键用户挖掘方法研究"", 《中国博士学位论文全文数据库 信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109753597A (en) * | 2018-12-31 | 2019-05-14 | 杭州翼兔网络科技有限公司 | A kind of data processing method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109739848B (en) | 2021-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106202211B (en) | Integrated microblog rumor identification method based on microblog types | |
| CN106980692A (en) | A kind of influence power computational methods based on microblogging particular event | |
| CN106940732A (en) | A kind of doubtful waterborne troops towards microblogging finds method | |
| CN103116605B (en) | A kind of microblog hot event real-time detection method based on monitoring subnet and system | |
| CN105095211B (en) | The acquisition methods and device of multi-medium data | |
| CN105095419B (en) | A kind of informational influence power maximization approach towards microblogging particular type of user | |
| CN102591475B (en) | A kind of content input method of online editor and system | |
| CN109918656B (en) | Live broadcast hotspot acquisition method and device, server and storage medium | |
| CN105488092A (en) | Time-sensitive self-adaptive on-line subtopic detecting method and system | |
| CN105354305A (en) | Online-rumor identification method and apparatus | |
| CN103970801B (en) | Microblogging advertisement blog article recognition methods and device | |
| CN109240637A (en) | Processing method, device, equipment and the storage medium of volume adjustment | |
| CN106980651B (en) | Crawling seed list updating method and device based on knowledge graph | |
| Zainol et al. | Association analysis of cyberbullying on social media using Apriori algorithm | |
| WO2014029314A1 (en) | Information aggregation, classification and display method and system | |
| Rizzo et al. | What Fresh Media Are You Looking For? Retrieving Media Items from Multiple Social Networks | |
| CN103886020A (en) | Quick search method of real estate information | |
| CN109739848A (en) | A kind of data extraction method | |
| CN103942226B (en) | The method and apparatus for obtaining Hot Contents | |
| CN109726199A (en) | A kind of data cleaning method | |
| CN105468780A (en) | Normalization method and device of product name entity in microblog text | |
| CN112035604A (en) | Influence algorithm of Internet hotspot event | |
| Heravi et al. | Tweet location detection | |
| CN103312584A (en) | Method and apparatus for releasing information in network community | |
| CN104091280A (en) | Intelligent network marketing system |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211011 Address after: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Applicant after: Shenzhen Kelian Huitong Technology Co.,Ltd. Address before: Room 209, floor 2, building 7, No. 1180 Bin'an Road, Binjiang District, Hangzhou City, Zhejiang Province Applicant before: HANGZHOU MINGZHIYUN EDUCATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |