CN105302885B - full-text data extraction method and device - Google Patents
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Abstract
The invention discloses a method and a device for extracting full-text data, wherein the method comprises the following steps: analyzing the network package data into session data; judging whether the entity part of the session data conforms to a preset data format, and if so, performing data format marking on the session data; performing multi-mode matching on the session data conforming to the preset data format, judging whether the preset feature string is hit or not, and obtaining the hit position of the preset feature string when the preset feature string is hit; according to the data format label of the session data and the hit position of the preset feature string, the corresponding extraction function of the session data is determined, and the session data is extracted according to the extraction function.
Description
Technical Field
The embodiment of the invention relates to the technical field of mobile and big data processing, in particular to a method and a device for extracting full-text data.
Background
The rapid development of the internet has made data penetrate into every industry and business function field, becoming an important production factor, accompanied by massive data that human beings can analyze and process. In cities of more than medium size, such as beijing, shanghai, various types of data generated in network behavior have exceeded PB level every day. For example, a mobile phone Application program (APP) generates several TB submitted data every day, the data contains longitude and latitude, a mobile phone serial number, a user identification card number, a mobile phone unique identification code and other information, and the information is very useful in the security supervision industry, so that mass extraction of the information becomes an important and complex task.
The traditional full-text extraction method mainly comprises two methods: one is a template-based extraction method, which is suitable for information extraction of a specific website, but is useless for variable mobile phone APP and data generated by different websites; the other method is to extract full-text content based on a regular expression, the method is suitable for offline full-text extraction with small data volume, and once data are submitted in the face of massive APP, the efficiency is low. Therefore, the two methods consume a lot of manpower and are inefficient under the condition of large data volume, and the requirements cannot be met under the condition of large data volume.
Disclosure of Invention
the invention provides a method and a device for extracting full-text data, which aim to improve the full-text data extraction efficiency of mass data.
In a first aspect, an embodiment of the present invention provides a full-text data extraction method, including:
Analyzing the network package data into session data;
Judging whether the entity part of the session data conforms to a preset data format, and if so, performing data format marking on the session data;
Performing multi-mode matching on the session data conforming to the preset data format, judging whether the preset feature string is hit or not, and obtaining the hit position of the preset feature string when the preset feature string is hit;
and determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting the data of the session data according to the extraction function.
in a second aspect, an embodiment of the present invention further provides an apparatus for extracting full-text data, including:
the analysis module is used for analyzing the network package data into session data;
the marking module is used for judging whether the entity part of the session data conforms to a preset data format or not, and if so, marking the data format of the session data;
The multimode matching module is used for carrying out multimode matching on the session data conforming to the preset data format, judging whether the preset feature string is hit or not, and obtaining the hit position of the preset feature string when the preset feature string is hit;
And the data extraction module is used for determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting the data of the session data according to the extraction function.
According to the invention, the analyzed session data is judged in the preset data format, so that invalid data which do not conform to the preset data format can be filtered, and thus the data extraction time is shortened.
Drawings
fig. 1 is a schematic flow chart of a full-text data extraction method according to an embodiment of the present invention;
Fig. 2 is a schematic flow chart of a full-text data extraction method according to a second embodiment of the present invention;
Fig. 3 is a full-text data extraction device according to a third embodiment of the present invention;
Fig. 4 is a schematic diagram of a topology structure of full-text data extraction according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
fig. 1 is a schematic flowchart of a full-text data extraction method according to an embodiment of the present invention, where the full-text data extraction method may be executed by a full-text data extraction device, and the full-text data extraction device may be implemented by hardware and/or software, and the full-text data extraction method specifically includes the following operations:
S110, analyzing the network packet data into session data.
The method provided by this embodiment is applicable to data extraction of multiple Protocol communications, and details are described below by taking HyperText Transfer Protocol (HTTP) data as an example. Firstly, the network package data acquired from the data source is parsed into the session data in the text format. And for the HTTP protocol data, analyzing the HTTP protocol data into HTTPPOST session data by adopting an HTTP protocol stack. The parsed session data includes an HTTP header and an HTTP entity portion. The HTTP post session data is parsed and restored according to the HTTP Protocol stack, and a Transmission Control Protocol (TCP) stream needs to be established first, and then the HTTP post session data is parsed, for example, by using a snort.
S120, judging whether the entity part of the session data conforms to a preset data format, and if so, performing data format marking on the session data.
After the network package data is analyzed into session data, the entity part of the session data is identified, and whether the entity part of the session data conforms to a preset data format or not is judged. The preset data format can be set individually according to needs. Generally, the preset data format is selected according to the requirement, the data to be extracted is generally only contained in the preset data format, and the operation can filter out the session data which do not conform to the preset data format. Therefore, invalid data can be prevented from being extracted, and the data extraction time is saved. And when the entity part of the session data conforms to a preset data format, performing data format marking on the session data to identify which preset data format the session data belongs to.
S130, conducting multi-mode matching on the session data conforming to the preset data format, judging whether the preset feature string is hit or not, and obtaining the hit position of the preset feature string when the preset feature string is hit.
The preset feature strings can be managed through a configuration file, and the number of the preset feature strings is greater than or equal to 1. The preset feature string may be, for example, "phone number", "MAC" (hardware address), and "MAC", etc. Optionally, before performing multi-mode matching on session data conforming to a preset data format, determining whether the preset feature string is hit, and obtaining a hit position of the preset feature string when the preset feature string is hit, the method further includes: and managing the preset characteristic string through a configuration file. The preset feature strings are managed through the configuration files, flexible addition and deletion can be achieved, and the multimode matching algorithm used in the method can be an AC algorithm or other open source algorithms.
And reading the configuration file to obtain a preset characteristic string, and generating a multimode matching algorithm state tree, thereby finally realizing the subsequent multimode matching operation.
S140, determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting the session data according to the extraction function.
The invention refines one-to-one corresponding extraction functions according to different data formats. And determining an extraction function corresponding to the session data according to the data format label of the session data and the hit position of the preset feature string, and then extracting data by using the extraction function corresponding to the session data.
according to the embodiment of the invention, invalid data which does not conform to the preset data format can be filtered out by judging the preset data format of the analyzed session data, so that the data extraction time is shortened. The data extraction method provided by the embodiment of the invention is not only suitable for extracting data of a specific website in an off-line manner, but also suitable for extracting data of unspecified websites with large flow, and a single processing thread can process 10000 effective HTTP sessions per second, thereby achieving the purpose of extracting full-text data in a large amount.
Example two
Fig. 2 is a schematic flow chart of a full-text data extraction method according to a second embodiment of the present invention, as shown in fig. 2, the method includes:
S210, analyzing the network packet data into session data.
S220, judging whether the entity part of the session data conforms to a preset data format.
If so, the operations S230 and S250 are sequentially performed, otherwise, the operations S240 and S220 are sequentially performed.
And S230, carrying out data format marking on the session data.
And S240, analyzing the subsequent network packet data into session data.
And S250, performing multi-mode matching on the session data conforming to the preset data format, and judging whether the preset feature string is hit or not.
When the preset feature string is hit, operations S260, S270, S280, and S290 are sequentially performed, otherwise, operations S240 and S220 are sequentially performed.
S260, obtaining the hit position of the preset feature string and the field tag corresponding to the hit preset feature string.
the preset feature strings can be managed through the configuration files, and specifically, the format of the configuration files can be set as follows:
characteristic string field label
Imsi IMSI
Phone_imsi IMSI
the feature strings "Imsi" and "Phone _ Imsi" both have the same field label "Imsi". Where IMSI represents an international mobile subscriber identity.
s270, determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting the session data according to the extraction function.
And S280, performing normalization processing on the extracted data according to the field label corresponding to the hit preset feature string.
when different feature strings correspond to the same field label, the extracted data is of the same class. For example, the field labels of the feature strings "MAC" and "MAC" are both MAC. That is, the data extracted by the feature strings "MAC" and "MAC" both characterize the hardware address. While the data format extracted by the feature strings "MAC" and "MAC" is different. The normalization refers to converting different formats of the same data into the same format, such as: the mac address has in the data: aa-bb-cc-dd-ee-ff, aa: bb: cc: dd: ee: ff. After normalization, the formats are unified as follows: aa-bb-cc-dd-ee-ff.
and S290, performing structuring processing on the extracted data and outputting the data.
Specifically, the data extracted by the above operations can be combined into structured data according to a specified format by setting the specified format, and output for use.
the embodiment of the invention analyzes the network package data into the session data, then judges the format of the session data entity, marks the data at the same time, if the data is not in the preset data format, carries out the next extraction flow of the network package data, if the data is not in the preset data format, then carries out multi-mode matching on the session data, returns the position of the feature string, calls corresponding extraction functions according to the position of the feature string and the format of the session data, extracts the data one by one, reduces the matching time during the data extraction process by the multi-mode matching, and avoids the extraction of invalid data by the judgment of the preset data format, so the method is suitable for extracting mass data, and has high data extraction efficiency.
on the basis of the foregoing embodiment, optionally, the preset format includes: at least one of Key-Value format, Mutipart format, json format, and xml format.
The examples of Key-Value format, Mutipart format, json format and xml format are as follows:
Key-Value formula: name-test 1& password-test 2& mac-aa-bb-cc-dd-ee-ff
Mutipart format:
------------7dc120151b0954
Content-Disposition:form-data；name＝"mac"
aa-bb-cc-dd-ee-ff
json format:
{"name":"test1","passwd":"test2","mac":"aa:bb:cc:dd:ee:ff"}
xml format: <? 1.0 encoding UTF-8? < root >
<name>test1</name><mac>aa-bb-cc-dd-ee-ff</mac></root>
EXAMPLE III
Fig. 3 is a full-text data extracting apparatus according to a third embodiment of the present invention, as shown in fig. 3, the apparatus includes:
The analysis module 31 is configured to analyze the network packet data into session data;
a labeling module 32, configured to determine whether an entity portion of the session data conforms to a preset data format, and if so, perform data format labeling on the session data;
The multimode matching module 33 is configured to perform multimode matching on the session data conforming to the preset data format, determine whether the preset feature string is hit, and obtain a hit position of the preset feature string when the preset feature string is hit;
and the data extraction module 34 is configured to determine a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and perform data extraction on the session data according to the extraction function.
According to the embodiment of the invention, invalid data which does not conform to the preset data format can be filtered out by judging the preset data format of the analyzed session data, so that the data extraction time is shortened. The data extraction method provided by the embodiment of the invention is not only suitable for extracting data of a specific website in an off-line manner, but also suitable for extracting data of unspecified websites with large flow, and achieves the purpose of extracting full-text data in a large quantity.
On the basis of the foregoing embodiment, optionally, the multi-pattern matching module is further configured to obtain a field tag corresponding to the hit preset feature string when the hit preset feature string is hit.
The device further comprises: and the normalization processing module is used for performing normalization processing on the extracted data according to the field label corresponding to the hit preset feature string.
on the basis of the foregoing embodiment, optionally, the apparatus further includes: and the structuring processing module is used for performing structuring processing on the extracted data and outputting the data.
On the basis of the foregoing embodiment, optionally, the preset format includes: at least one of Key-Value format, Mutipart format, json format, and xml format.
on the basis of the foregoing embodiment, optionally, the apparatus further includes: and the preset feature string management module is used for managing the preset feature string through a configuration file.
The product can execute the method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
fig. 4 is a schematic diagram of a topology structure for full-text data extraction according to an embodiment of the present invention. As shown in fig. 4, the whole system requires a plurality of interactive routing devices for the full text extraction server, the aggregation and offloading device, at least one database server, and the backbone network. Wherein a plurality of interacting routing devices in the backbone network provide raw data for the data sources. Data generated by the data source is mirrored through the convergence and distribution device, and then the convergence and distribution device outputs the mirrored data to the full-text extraction server. The full-text extraction server includes the full-text data extraction device described in the above embodiments, and performs data extraction by the full-text data extraction method described in each of the above embodiments.
The implementation process needs the following steps:
(1) splitting of raw data
Data distribution is performed on a router link by using a convergence distribution device, and uplink and downlink flows need to be all mirrored to ensure the integrity of one session data.
(2) full text extraction server building
The full text extraction server preferably selects a multi-core and large memory server, utilizes multi-core parallel processing to improve the processing performance, and installs a full text extraction program and a data carrying program on the full text extraction server, and can also carry the full text extraction program by using the conventional FTP service.
(3) database server construction
building a database server according to the data scale, and if the data scale is small, mysql, oracle can be used; if the data volume is large, a distributed storage system, such as Hadoop, needs to be built.
(4) and starting the full text extraction program
and starting a full-text extraction program, wherein the full-text extraction program performs data extraction according to the full-text data extraction method described in each embodiment, and outputs the extracted data to a corresponding database server in a data center for use by different subsequent service systems.
in addition, if the data extraction flow is increased, the number of full-text extraction servers can be increased appropriately.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A full-text data extraction method is characterized by comprising the following steps:
Analyzing the network package data into session data;
judging whether the entity part of the session data conforms to a preset data format, and if so, performing data format marking on the session data;
Obtaining a preset feature string by reading the configuration file, performing multi-mode matching on session data conforming to a preset data format, judging whether the preset feature string is hit or not, and obtaining a hit position of the preset feature string when the preset feature string is hit;
Determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting data of the session data according to the extraction function;
If so, performing data format labeling on the session data, including: and when the entity part of the session data conforms to a preset data format, performing data format marking on the session data to identify which preset data format the session data belongs to.
2. The method of claim 1, wherein when performing multi-mode matching on session data conforming to a preset data format, determining whether a preset feature string is hit, and obtaining a hit position of the preset feature string when the preset feature string is hit, the method further comprises:
When the preset feature string is hit, a field tag corresponding to the hit preset feature string is obtained;
After determining the corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and performing data extraction on the session data according to the extraction function, the method further includes:
and according to the field label corresponding to the hit preset feature string, performing normalization processing on the extracted data.
3. the method according to claim 1, after determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and performing data extraction on the session data according to the extraction function, further comprising:
And carrying out structuring processing on the extracted data and outputting the data.
4. The method of claim 1, wherein the preset format comprises: at least one of Key-Value format, Mutipart format, json format, and xml format.
5. The method of claim 1, wherein before performing multi-mode matching on session data conforming to a preset data format, determining whether a preset feature string is hit, and obtaining a hit position of the preset feature string when the preset feature string is hit, the method further comprises:
and managing the preset characteristic string through a configuration file.
6. An apparatus for extracting full-text data, comprising:
The analysis module is used for analyzing the network package data into session data;
The marking module is used for judging whether the entity part of the session data conforms to a preset data format or not, and if so, marking the data format of the session data;
the multimode matching module is used for obtaining a preset feature string by reading the configuration file, performing multimode matching on the session data conforming to the preset data format, judging whether the preset feature string is hit or not, and obtaining the hit position of the preset feature string when the preset feature string is hit;
The data extraction module is used for determining a corresponding extraction function of the session data according to the data format label of the session data and the hit position of the preset feature string, and extracting the data of the session data according to the extraction function;
If so, performing data format labeling on the session data, including: and when the entity part of the session data conforms to a preset data format, performing data format marking on the session data to identify which preset data format the session data belongs to.
7. The apparatus of claim 6, wherein the multimodal matching module is further configured to, when a preset feature string is hit, obtain a field tag corresponding to the hit preset feature string;
The device further comprises: and the normalization processing module is used for performing normalization processing on the extracted data according to the field label corresponding to the hit preset feature string.
8. the apparatus of claim 6, further comprising:
and the structuring processing module is used for performing structuring processing on the extracted data and outputting the data.
9. The apparatus of claim 6, wherein the preset format comprises: at least one of Key-Value format, Mutipart format, json format, and xml format.
10. The apparatus of claim 6, further comprising:
And the preset feature string management module is used for managing the preset feature string through a configuration file.
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| CN105868311A (en) * | 2016-03-25 | 2016-08-17 | 浪潮通信信息系统有限公司 | Data analyzing method and device |
| CN106033298A (en) * | 2016-05-03 | 2016-10-19 | 海致网络技术（北京）有限公司 | Data extracting method and system |
| CN106776794B (en) * | 2016-11-23 | 2020-10-27 | 北京锐安科技有限公司 | Mass data processing method and system |
| CN106547915B (en) * | 2016-11-29 | 2019-10-29 | 上海轻维软件有限公司 | Intelligent data extracting method based on model library |
| CN109981563A (en) * | 2019-01-23 | 2019-07-05 | 国家新闻出版广电总局广播电视规划院 | A kind of automatic intelligent method for digging of radio and television key message infrastructure network security breaches |
| CN109905372A (en) * | 2019-01-24 | 2019-06-18 | 李惠英 | Transmission system Data Transport Protocol identifying and analyzing method |
| CN110311914A (en) * | 2019-07-02 | 2019-10-08 | 北京微步在线科技有限公司 | Pass through the method and device of image network flow extraction document |
| CN112560038A (en) * | 2020-12-24 | 2021-03-26 | 深信服科技股份有限公司 | Data analysis method, device and equipment and computer readable storage medium |
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