CN108874941A - Big data URL De-weight method based on convolution feature and multiple Hash mapping - Google Patents
Big data URL De-weight method based on convolution feature and multiple Hash mapping Download PDFInfo
- Publication number
- CN108874941A CN108874941A CN201810562678.7A CN201810562678A CN108874941A CN 108874941 A CN108874941 A CN 108874941A CN 201810562678 A CN201810562678 A CN 201810562678A CN 108874941 A CN108874941 A CN 108874941A
- Authority
- CN
- China
- Prior art keywords
- convolution
- url
- mapping
- hash
- big data
- 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
Landscapes
- Computer And Data Communications (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The big data URL De-weight method based on convolution feature and multiple Hash mapping that the invention discloses a kind of, by using the swift nature mapping algorithm based on convolution feature and the mapping of multiple hash function, establish a BitSet, each URL is passed through into a convolution algorithm and multiple hash functions are mapped to multiple positions, collision probability is greatly reduced, to identify the URL in independent web log.The method of the present invention more saves resource than traditional Duplicate Removal Algorithm, while greatly reducing the probability of hash-collision again, and recognition speed is very fast.
Description
Technical field
The present invention relates to URL duplicate removal technical field, especially a kind of big number based on convolution feature and multiple Hash mapping
According to URL De-weight method.
Background technique
Existing big data URL processing technique first is that the URL accessed is saved with HashSet, only need to be close to O
(1) cost can find whether a URL is accessed.The method deposits following deficiency：Memory is consumed very much, with URL
Increase, the memory of occupancy can be more and more, even if only 100,000,000 URL, each URL calculates 50 characters, it is necessary to 5GB memory,
Quantity is so great that more when the processing of practical big data.
Existing big data URL processing technique second is that URL is saved in again after the one-way hash functions such as MD5 or SHA-1
HashSet or database, since character string is by MD5 treated informative abstract length only has 128Bit, after SHA-1 processing
Only 160Bit saves several-fold memory.The method has the following disadvantages：Hash mapping has been used, has saved several times
Memory, but many Hash table conflicts are still had in the data volume treatment process of tens ranks, wrong report quantity reaches
Very important quantity causes duplicate removal effect bad.
Relevant technical terms
Convolution：In functional analysis, convolution (Convolution) is to generate third function by two functions f and g
A kind of mathematical algorithm, the mapping of the lap of characterization function f and g by overturning and translation.
URL：Uniform resource locator is one kind of the position and access method to the resource that can be obtained from internet
Succinct expression is the address of standard resource on internet.
Hash function：Keyword in data element is mapped to Hash table by certain functional relation.
Summary of the invention
The big data based on convolution feature and multiple Hash mapping that technical problem to be solved by the invention is to provide a kind of
URL De-weight method can be quick for solving the problems, such as that big data WEB access log URL duplicate removal processing speed is slow, effect is poor
The independent URL that high value is filtered out in super amount data, is convenient for post-processing.
In order to solve the above technical problems, the technical solution adopted by the present invention is that：
A kind of big data URL De-weight method based on convolution feature and multiple Hash mapping, includes the following steps：
Step 1：WEB access log is extracted from WEB server or in tradition WAF equipment, then filters out the domain of requirement
Name HOST and URL；
Step 2：Sequence convolution is carried out to url field and Function Mapping is wished in Doha；
Customized one convolution kernel comprising a numeric string, the Serial No. of convolution kernel arbitrarily determine one, will be each
Need the character string of duplicate removal to be determined as a Serial No. according to mapping table, convolution operation the result is that corresponding number phase
Multiply；A step-length is defined, successively convolution can generate many convolution values to convolution kernel on Serial No.；
Be arranged hash function number k, bit array size m, addition character string quantity n, occur Hash under this condition
Table report by mistake a possibility that be：
The value that k is arranged is k=ln2*m/n, reaches minimum rate of false alarm at this time：
Hash function number to be used is determined according to expected rate of false alarm according to above-mentioned formula；
Step 3：Convolution function is f, is mapped using k hash function, hash function h1, h2, h3, h4...hk,
Then the binary digit assignment principle of convolution characteristic value and hash function mapping value in BitSet is：Initialize one m bit
Array (every is initially set to 0), convolution output and the output of each hash function are a numbers between one (0, m-1)
(corresponding bit array index)；
X is inputted, for each hash function, calculates j=hi (x), m_bit [j] is set as 1；Similarly, for convolution letter
M_bit [c] is set as 1 to number by convolution operation, calculating c=f (x) each time；
Step 4：According to the BitSet after assignment, as the keyword or label of URL, and then independent URL is identified.
Further, the step 1 is specially：
1) the extra field in WAF log is filtered out；
2) tag match is carried out using script, filters the URL of static file and undesirable status code；
3) domain name HOST and URL character string is spliced, is exported.
Further, " filtering out the extra field in WAF log " uses the filter in scala language in step 1
Method is filtered, i.e., is first split every log by space character using the split method in scala, then makes by oneself
An adopted filter method, wherein including the rule for wanting filtering.
Further, when domain name HOST and URL character string is spliced, the character string combinations side built in scala is used
Method "+" is spliced.
Compared with prior art, the beneficial effects of the invention are as follows：This method utilizes the convolution feature for representing URL independent characteristic
Function Mapping is wished in value and Doha, represents multiword by the BitSet of generation and accords with URL, more saves resource than traditional Duplicate Removal Algorithm,
Greatly reduce the probability of hash-collision again simultaneously, recognition speed is very fast.
Detailed description of the invention
Fig. 1 is the flow diagram of the big data URL De-weight method the present invention is based on convolution feature and multiple Hash mapping.
Fig. 2 is convolution value and hash function value mapping principle.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
By the present invention in that establishing one with the swift nature mapping algorithm based on convolution feature and the mapping of multiple hash function
Each URL is passed through a convolution algorithm and multiple hash functions is mapped to multiple positions, greatly reduces conflict by a BitSet
Probability, to identify the URL in independent web log.Details are as follows：
One, WEB access log is extracted from WEB server or in tradition WAF equipment, then filters out the HOST of requirement
(domain name) and URL (uniform resource locator).Specific screening technique：
1, the extra field in WAF log is filtered out
The filter method that can be used in scala language is filtered.It first will using the split method in scala
Every log is split by space character：
Val fields=line.split (" ")
A subsequent customized filter method, wherein including the rule for wanting filtering.Rule settings method can be used
The matched mode of scala canonical carries out：
def myfilter()
Val ho=" gov.cn " .r//example：Only filter out government website
ho.findFirstMatchIn(host)！=None
2, tag match is carried out using script, filters the URL of static file and undesirable status code
Wherein static file refers to that URL is accessed is the static page or file, such as .html .xml .js .css
Etc., these URL are not often needed in the application of practical duplicate removal, are equally carried out using filter function customized in scala：
3, by HOST and URL combination, export
The host and URL that filter out are the forms of character string, behind to carry out deduplication operation, need one domain name of duplicate removal
Under all URL the character string combinations method built in scala can be used so host and URL character string is spliced
"+" is spliced：
Val fields=line.split (" ")
Val host=fields (8)
Val url=fields (9)
Val uRL=host+url
Two, sequence convolution is carried out to url field and Function Mapping is wished in Doha
Convolution operation explanation：Customized one convolution kernel comprising a numeric string, wherein convolution kernel length is not preferably low
In 6 numbers.The Serial No. of convolution kernel is arbitrary, and such as (" 453752 "), but once it is determined that cannot be modified, Yi Housuo
Some convolution operations all use this convolution kernel.Each character string for needing duplicate removal is determined as a digital sequence according to mapping table
Column, (being mapped as " 12345 " such as " abcde "), convolution operation the result is that corresponding number is multiplied, such as 123 convolution 234, knot
Fruit is exactly 1*2+2*3+3*4=20.A step-length is defined, successively convolution can produce many convolution to convolution kernel on Serial No.
Value, as convolution kernel " 123 " on " 23456 " convolution, one is generated after 123 and 234 convolution and is worth, 123 generate with 345 convolution again again
One value (step-length 1).
General MD5 algorithm can be used in Hash mapping algorithm.Hash function number k is set, bit array size m, is added
Character string quantity n, a possibility that occurring Hash table wrong report under this condition is：
The value that k is arranged is k=ln2*m/n, reaches minimum rate of false alarm at this time：
Hash function number to be used is determined according to expected rate of false alarm according to above-mentioned formula.
Three, convolution function f is mapped using k hash function, hash function h1, h2, h3, h4...hk, then
The binary digit assignment principle of convolution characteristic value and hash function mapping value in BitSet is as shown in Figure 2.Initialize one m
Bit array, convolution output and the output of each hash function are a numbers between one (0, m-1)；X is inputted, for
Each hash function calculates j=hi (x), m_bit [j] is set as 1；Similarly, for convolution function convolution operation each time,
It calculates c=f (x), m_bit [c] is set as 1.
Note：Many values (value that big step-length generates is few, and the value that small step-length generates is more) can be generated in convolution process, according to certainly
Oneself needs to define step-length, each convolution value should be mapped in BitSet (the convolution value only generated in Fig. 2 with convolution into
Row image display).
It is as follows that Function Mapping procedure division Java code is wished in Doha：
Four, according to the BitSet after assignment, it is easy to the keyword or label as URL, and then identify independent
URL.For example the BitSet that a URL is mapped to has existed, then it is assumed that this URL is duplicate.
Five, the partial results after identifying are as follows：
www.xxxx.com/piwik.php？Action_name=www.wdzj.com%2F%E7%A4%BC%
E5%BE%B7%E8%B4%A2%E5%AF%8C%E7%BD%91%E8%B4%B7%E6%A1%A3%E6 %
A1%88_%E7%A4%BC%E5%BE%B7%E8%B4%A2%E5%AF%8C%E5%AE%98%E7%B D%
91%E8%B5%84%E6%96%99_p2p%E5%B9%B3%E5%8F%B0%E6%A1%A3%E6%A 1%
88_%E7%BD%91%E8%B4%B7%E4%B9%8B%E5%AE%B6&idsite=1&rec=1&r=
931653&h=23&m=31&s=47&url=https%3A%2F%2Fwww.wdzj.com%2Fd angan%
2Fldcf1%2F&urlref=https%3A%2F%2Fwww.wdzj.com%2Fdangan%2F search%
3Ffilter%3De1-b41-n44%26show%3D1&_id=747107e1f17b5566&_i dts=1521648124&_
Idvc=3&_idn=0&_refts=1521732597&_viewts=1521732597&_ref=https%3A%2F%
2Fwww.***.com%2F&send_image=0&pdf=1&qt=0&realp=0&wma=0&dir=0&fla=
0&java=0&gears=0&ag=0&cookie=1&res=1440x900&cvar=%7B%223 %22%3A%
5B%22www%22%2C%22%22%5D%2C%225%22%3A%5B%22uid%22%2C%220% 22%
5D%7D>_ms=888>-1
www.xxxx.com/m/c.ashx？S=35&u=100000&c=4&P=170663&Fl=https%3A//
www.***.com.hk/>3
www.xxxx.com/user/action？Event_type=load&curt_id=7f8745b8-2d39-
11e8-897e-00163e131d5b&prev_id=&event_info=%7B%22ad_uuid %22%3A%22add_
Trwgzad1tkva%22%7D&event=ad_exposure&target=http%3A%2F%
2Fwww.shixiseng.com%2Ftc%2Frpo&uuid=9f2cd019-7402-8948-9 c0b-501353d6a9e5&
Url=https%253A%2F%2Fwww.shixiseng.com%2F&referrer=https% 3A%2F%
2Fwww.***.com%2F&uri=%2F&source=pc>---1
www.xxxx.com/user/action？Event_type=load&curt_id=54c06304-2d8a-
11e8-97ea-00163e131d5b&prev_id=42b3df23-dfea-4651-a86f-8 8ba92a4e42d&event_
Info=%7B%22ad_uuid%22%3A%22add_77mcl4cyo2uu%22%7D&event=ad_exposure&
Target=%2Fcom%2Fcom_qrf1ioxwhvxk&uuid=6e65a594-9034-97f3-ad00-
B1e7a46d39ca&url=https%253A%2F%2Fwww.shixiseng.com%2F&re ferrer=https%
3A%2F%2Fwww.***.com%2F&uri=%2F&source=pc>---1
www.xxxx.com/user/action？Event_type=load&curt_id=d72ad59e-2dcc-
11e8-97ea-00163e131d5b&prev_id=&event_info=%7B%22ad_uuid %22%3A%22add_
5zj7701ibn7t%22%7D&event=ad_exposure&target=http%3A%2F%
2Fcampus.51job.com%2Funiqlo%2F&uuid=f6878545-7dff-4c90-9 ec4-d3f0b0be2cb7&
Url=https%253A%2F%2Fwww.shixiseng.com%2F&referrer=https% 3A%2F%
2Fwww.***.com%2F&uri=%2F&source=pc>---1
www.xxxx.com/user/action？Event_type=load&curt_id=dc320a40-2dd5-
11e8-86b1-00163e0e0af8&prev_id=&event_info=%7B%22ad_uuid %22%3A%22add_
Q5h0sozpfgsg%22%7D&event=ad_exposure&target=%2Fcom%2Fcom _ ohgsahcs55rv&
Uuid=4d6eab5f-7e0a-da6c-a70a-75cacb5b8e2f&url=https%253A %2F%
2Fwww.shixiseng.com%2F&referrer=https%3A%2F%2Fwww.*** .com.hk%2F&uri
=%2F&source=pc>1
www.xxxx.com/user/action？Event_type=load&curt_id=0ae48218-2db9-
11e8-99e6-00163e040372&prev_id=&event_info=%7B%22ad_uuid %22%3A%22add_
Trwgzad1tkva%22%7D&event=ad_exposure&target=http%3A%2F%
2Fwww.shixiseng.com%2Ftc%2Frpo&uuid=21bf3ae9-d652-1e0a-8 67a-1f9c29660cd5&
Url=https%253A%2F%2Fwww.shixiseng.com%2F&referrer=https% 3A%2F%
2Fwww.***.com.hk%2F&uri=%2F&source=pc>1
Claims (4)
1. a kind of big data URL De-weight method based on convolution feature and multiple Hash mapping, which is characterized in that including following step
Suddenly：
Step 1：WEB access log is extracted from WEB server or in tradition WAF equipment, then filters out the domain name of requirement
HOST and URL；
Step 2：Sequence convolution is carried out to url field and Function Mapping is wished in Doha；
Customized one convolution kernel comprising a numeric string, the Serial No. of convolution kernel arbitrarily determines one, by each needs
The character string of duplicate removal is determined as a Serial No. according to mapping table, convolution operation the result is that corresponding number is multiplied；It is fixed
An adopted step-length, successively convolution can generate many convolution values to convolution kernel on Serial No.；
Be arranged hash function number k, bit array size m, addition character string quantity n, occur Hash table mistake under this condition
A possibility that report is：
The value that k is arranged is k=ln2*m/n, reaches minimum rate of false alarm at this time：
Hash function number to be used is determined according to expected rate of false alarm according to above-mentioned formula；
Step 3：Convolution function is f, is mapped using k hash function, and hash function h1, h2, h3, h4...hk are then rolled up
Accumulating the binary digit assignment principle of characteristic value and hash function mapping value in BitSet is：Initialize one m bit numbers
Group, convolution output and the output of each hash function are a numbers between one (0, m-1)；X is inputted, for each Kazakhstan
Uncommon function, calculates j=hi (x), m_bit [j] is set as 1；Similarly, for convolution function convolution operation each time, c=f is calculated
(x), m_bit [c] is set as 1；
Step 4：According to the BitSet after assignment, as the keyword or label of URL, and then independent URL is identified.
2. the big data URL De-weight method based on convolution feature and multiple Hash mapping, feature exist as described in claim 1
In the step 1 is specially：
1) the extra field in WAF log is filtered out；
2) tag match is carried out using script, filters the URL of static file and undesirable status code；
3) domain name HOST and URL character string is spliced, is exported.
3. the big data URL De-weight method based on convolution feature and multiple Hash mapping, feature exist as claimed in claim 2
In " filtering out the extra field in WAF log " in step 1 and be filtered using the filter method in scala language, i.e.,
Every log is split by space character using the split method in scala first, a subsequent customized side filter
Method, wherein including the rule for wanting filtering.
4. the big data URL De-weight method based on convolution feature and multiple Hash mapping, feature exist as claimed in claim 3
In being spliced using the character string combinations method "+" built in scala when domain name HOST and URL character string is spliced.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810562678.7A CN108874941B (en) | 2018-06-04 | 2018-06-04 | Big data URL duplication removing method based on convolution characteristics and multiple Hash mapping |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810562678.7A CN108874941B (en) | 2018-06-04 | 2018-06-04 | Big data URL duplication removing method based on convolution characteristics and multiple Hash mapping |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108874941A true CN108874941A (en) | 2018-11-23 |
| CN108874941B CN108874941B (en) | 2021-09-21 |
Family
ID=64335996
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810562678.7A Active CN108874941B (en) | 2018-06-04 | 2018-06-04 | Big data URL duplication removing method based on convolution characteristics and multiple Hash mapping |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108874941B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334251A (en) * | 2019-07-01 | 2019-10-15 | 南京邮电大学 | A kind of effective element sequence generation method for solving rehash conflict |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101958883A (en) * | 2010-03-26 | 2011-01-26 | 湘潭大学 | Bloom Filter and open-source kernel-based method for defensing SYN Flood attack |
| CN103106219A (en) * | 2011-11-15 | 2013-05-15 | 盛乐信息技术（上海）有限公司 | Method and system of web page grabbing |
| US20140107997A1 (en) * | 2010-04-19 | 2014-04-17 | Altera Corporation | Simulation Tool for High-Speed Communications Links |
| CN104657350A (en) * | 2015-03-04 | 2015-05-27 | 中国科学院自动化研究所 | Hash learning method for short text integrated with implicit semantic features |
| CN104809182A (en) * | 2015-04-17 | 2015-07-29 | 东南大学 | Method for web crawler URL (uniform resource locator) deduplicating based on DSBF (dynamic splitting Bloom Filter) |
| CN105956068A (en) * | 2016-04-27 | 2016-09-21 | 湖南蚁坊软件有限公司 | Webpage URL repetition elimination method based on distributed database |
| CN106295629A (en) * | 2016-07-15 | 2017-01-04 | 北京市商汤科技开发有限公司 | Structured text detection method and system |
| CN106599022A (en) * | 2016-11-01 | 2017-04-26 | 中山大学 | User portrait forming method based on user access data |
| CN107832476A (en) * | 2017-12-01 | 2018-03-23 | 北京百度网讯科技有限公司 | A kind of understanding method of search sequence, device, equipment and storage medium |
| CN107871014A (en) * | 2017-11-23 | 2018-04-03 | 清华大学 | A kind of big data cross-module state search method and system based on depth integration Hash |
-
2018
- 2018-06-04 CN CN201810562678.7A patent/CN108874941B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101958883A (en) * | 2010-03-26 | 2011-01-26 | 湘潭大学 | Bloom Filter and open-source kernel-based method for defensing SYN Flood attack |
| US20140107997A1 (en) * | 2010-04-19 | 2014-04-17 | Altera Corporation | Simulation Tool for High-Speed Communications Links |
| CN103106219A (en) * | 2011-11-15 | 2013-05-15 | 盛乐信息技术（上海）有限公司 | Method and system of web page grabbing |
| CN104657350A (en) * | 2015-03-04 | 2015-05-27 | 中国科学院自动化研究所 | Hash learning method for short text integrated with implicit semantic features |
| CN104809182A (en) * | 2015-04-17 | 2015-07-29 | 东南大学 | Method for web crawler URL (uniform resource locator) deduplicating based on DSBF (dynamic splitting Bloom Filter) |
| CN105956068A (en) * | 2016-04-27 | 2016-09-21 | 湖南蚁坊软件有限公司 | Webpage URL repetition elimination method based on distributed database |
| CN106295629A (en) * | 2016-07-15 | 2017-01-04 | 北京市商汤科技开发有限公司 | Structured text detection method and system |
| CN106599022A (en) * | 2016-11-01 | 2017-04-26 | 中山大学 | User portrait forming method based on user access data |
| CN107871014A (en) * | 2017-11-23 | 2018-04-03 | 清华大学 | A kind of big data cross-module state search method and system based on depth integration Hash |
| CN107832476A (en) * | 2017-12-01 | 2018-03-23 | 北京百度网讯科技有限公司 | A kind of understanding method of search sequence, device, equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| F_URY: "BloomFilter 确定合适的 k(hash函数个数) 值", 《HTTPS://BLOG.CSDN.NET/U012400327/ARTICLE/DETAILS/62222922》 * |
| XIANG ZHANG 等: "Character-level Convolutional Networks for Text Classification", 《ARXIV:1509.01626V3》 * |
| 刘小云: "网络爬虫技术在云平台上的研究与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334251A (en) * | 2019-07-01 | 2019-10-15 | 南京邮电大学 | A kind of effective element sequence generation method for solving rehash conflict |
| CN110334251B (en) * | 2019-07-01 | 2021-10-19 | 南京邮电大学 | Element sequence generation method for effectively solving rehash conflict |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108874941B (en) | 2021-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9405910B2 (en) | Automatic library detection | |
| US10216848B2 (en) | Method and system for recommending cloud websites based on terminal access statistics | |
| US20190065444A1 (en) | Techniques for efficient & high-throughput web content-creation | |
| WO2019100645A1 (en) | Method for realizing multilevel interactive drop-down box, electronic device, and storage medium | |
| US9977818B2 (en) | Local extrema based data sampling system | |
| Lohr et al. | Orbital period changes and the higher-order multiplicity fraction amongst SuperWASP eclipsing binaries | |
| CN102930038A (en) | Combined method of search result similar items and system of the same | |
| CN104778164A (en) | Method and device for detecting repeated URL (Uniform Resource Locator) | |
| US9792355B2 (en) | Searches for similar documents | |
| CN104065736A (en) | URL redirection method, device, and system | |
| CN103793508A (en) | Method, device and system for loading recommend information and detecting websites | |
| CN105138912A (en) | Method and device for generating phishing website detection rules automatically | |
| CN108874941A (en) | Big data URL De-weight method based on convolution feature and multiple Hash mapping | |
| CN110392032B (en) | Method, device and storage medium for detecting abnormal URL | |
| CN103618742A (en) | Method and system for acquiring sub domain names and webmaster permission verification method | |
| CN106557483A (en) | A kind of data processing, data query method and apparatus | |
| CN103778181B (en) | The introduction method and device of icon in browser collection folder | |
| Ravi Kumar et al. | Application of Markov Chain in the PageRank Algorithm. | |
| CN108304545A (en) | A kind of URL log storing methods and device | |
| CN104809146B (en) | System and method for determining index of the object in object sequence | |
| US10915594B2 (en) | Associating documents with application programming interfaces | |
| US10572560B2 (en) | Detecting relevant facets by leveraging diagram identification, social media and statistical analysis software | |
| CN109426358B (en) | Information input method and device | |
| Valera et al. | An efficient web recommender system based on approach of mining frequent sequential pattern from customized web log preprocessing | |
| US10229095B2 (en) | Expanded icon navigation |
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 | ||
| CP02 | Change in the address of a patent holder | ||
| CP02 | Change in the address of a patent holder |
Address after: 9/F, Block C, No. 28 Tianfu Avenue North Section, Chengdu High tech Zone, China (Sichuan) Pilot Free Trade Zone, Chengdu City, Sichuan Province, 610000 Patentee after: CHENGDU KNOWNSEC INFORMATION TECHNOLOGY Co.,Ltd. Address before: 610000, 11th floor, building 2, No. 219, Tianfu Third Street, hi tech Zone, Chengdu, Sichuan Province Patentee before: CHENGDU KNOWNSEC INFORMATION TECHNOLOGY Co.,Ltd. |