CN111581614B - Intensive website-based big data analysis platform - Google Patents
Intensive website-based big data analysis platform Download PDFInfo
- Publication number
- CN111581614B CN111581614B CN202010379105.8A CN202010379105A CN111581614B CN 111581614 B CN111581614 B CN 111581614B CN 202010379105 A CN202010379105 A CN 202010379105A CN 111581614 B CN111581614 B CN 111581614B
- Authority
- CN
- China
- Prior art keywords
- information
- access
- unit
- identity
- user
- 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
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/44—Program or device authentication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
- H04L63/083—Network architectures or network communication protocols for network security for authentication of entities using passwords
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
- H04L63/102—Entity profiles
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/2141—Access rights, e.g. capability lists, access control lists, access tables, access matrices
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Bioethics (AREA)
- General Health & Medical Sciences (AREA)
- Storage Device Security (AREA)
Abstract
The invention discloses a big data analysis platform based on an intensive website, which comprises a user login unit, an identity library, a display unit, a processor, a data capture unit, a personal inertia library, a deviation data analysis unit, a data collection unit and a data temporary storage unit, wherein the user login unit is connected with the identity library through a network; after the user login unit is combined with the processor and the identity library to identify the identity of the user, the legality of the user equipment is verified through the related protection algorithm disclosed by the invention, and the user equipment is analyzed and known through the key factor of the characteristic information; only when both the two pass, the normal data access of the user can be ensured; then, the deviation data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the corresponding identity information thereof to obtain the sequential access information of the user; and the processor is used for carrying out information recommendation on the identity information by combining the sequential access information in the personal inertia library and the data capture unit.
Description
Technical Field
The invention belongs to the field of big data analysis, and particularly relates to a big data analysis platform based on an intensive website.
Background
A patent with publication number CN109392196A discloses a big data analysis method and system based on a mobile terminal, wherein the big data analysis method comprises the following steps: collecting user information of a specified type by a mobile terminal; the mobile terminal sends a transmission channel establishment request message to a big data analysis center; if the mobile terminal does not receive the transmission channel establishment response message before the first timer is overtime, and does not receive the transmission channel establishment negative message, the mobile terminal immediately resends the transmission channel establishment request message to the big data analysis center; if the mobile terminal receives the transmission channel establishment negative message before the first timer is overtime, the mobile terminal sends a transmission channel establishment request message to the big data analysis center again after waiting for the first time; and if the mobile terminal receives the transmission channel establishment response message before the first timer is overtime, the mobile terminal sends the identity identifier of the mobile terminal to the big data analysis center.
But aiming at the website big data, an effective data mining system is not provided in combination with the user identity, hot spot information liked by the user can be recommended according to the user habit, and meanwhile, the hot spot information is recommended in combination with the real-time heat degree; in order to solve this technical drawback, a solution is now provided.
Disclosure of Invention
The invention aims to provide a big data analysis platform based on an intensive website.
The purpose of the invention can be realized by the following technical scheme:
a big data analysis platform based on an intensive website comprises a user login unit, an identity library, a display unit, a processor, a data capture unit, a personal inertia library, a deviation data analysis unit, a data collection unit and a data temporary storage unit;
the system comprises a user login unit, an identity library and a user authentication unit, wherein the user login unit is used for a user to input identity information and corresponding secret key information, and standard identity information of an authorized user and corresponding authorized secret key information are stored in an identity library; the user login unit is used for transmitting the identity information and the corresponding secret key information to the processor, and the processor is used for carrying out equipment verification processing on the identity information and the secret key information by combining an identity library to generate a passing signal or an equipment error signal;
when the processor generates a device error signal, the processor drives the display unit to display 'used devices are not trusted, please verify'; when the processor generates an error initial signal, the processor drives the display unit to display 'identity key error, please verify';
the processor is used for capturing the identity information by using the personal inertia library when the passing signal is generated;
the data collection unit is used for collecting an access information group formed by a plurality of access information of a user, wherein the access information is specifically the access content of the user during website access; the data collection unit is used for combining the access information group with the corresponding identity information and transmitting the access information group to the data temporary storage unit for storage; the biased data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the identity information corresponding to the access information group to obtain sequence access information corresponding to all the identity information;
the data capturing unit is communicated with the Internet and is used for acquiring information of the Internet in real time; the deviation data analysis unit is used for transmitting the sequence access information to the personal inertia library, the processor is used for recommending the identity information by combining the sequence access information in the personal inertia library and the data capture unit, and the specific recommending process comprises the following steps:
s100: acquiring sequence access information of a user;
s200: acquiring the sequential access information of the first three rows, and acquiring keywords corresponding to the three sequential access information;
s300: calling out real-time search data of the keyword on the network by means of a data capturing unit; the specific calling mode is as follows:
s301: firstly, taking the current time as an end value, acquiring the access times of the keyword within one hour before the current time, and marking the access times as real-time access times;
s302: acquiring the access times of the keyword in the last hour, and marking the access times as the comparison times;
s303: when the real-time visit-contrast times are more than or equal to X1, X1 is a preset value; marking the sequential access information as hit access information;
s400: repeating the third step on the key words of the next sequence access information;
s500: continuing to perform step S400 until three hit access information are obtained, and obtaining an information recommendation group;
the processor is used for transmitting the information recommendation group to the display unit, and the display unit receives the information recommendation group transmitted by the processor and displays the information recommendation group in real time.
Further, the specific process of the verification process is as follows:
the method comprises the following steps: firstly, comparing the identity information with standard identity information, finding out standard identity information consistent with the identity information, and then finding out corresponding standard key information;
step two: comparing the standard key information with the key information, and if the standard key information is consistent with the key information, generating an initial communication signal; otherwise, generating an error initial signal;
step three: after generating the initial communication signal, combining the identity information and the secret key information to form a login character set;
step four: after obtaining the login character set, carrying out value selection confirmation by combining the total typing time length, wherein the specific confirmation mode is that numerical values on all digits of the total typing time length are added to obtain a sum value, then the digit numerical value is taken, and the numerical value is marked as an interval value;
step five: acquiring a login character set, counting from a first number to a second number, and marking the number as a first special character;
step six: removing the first special character, repeating the fifth step, and obtaining a second special character of the rest login character set;
step seven: repeating the sixth step to obtain a third special character; the first special character, the second special character and the third special character form characteristic information;
step eight: after the user login unit approved by the administrator keys in the identity information and the corresponding key information, the characteristic information is obtained according to the modes of the third step to the seventh step and is marked as potential characteristic information;
step nine: generating a pass signal only when the potential property information and the property information coincide; otherwise, a device error signal is generated.
Further, the specific process of data analysis is as follows:
s01: selecting identity information, and acquiring an access information group of a user within a time close to T1, wherein T1 is a preset value;
s02: marking access information within the access information group as Fi, i =1.. N;
s03: acquiring the access times of corresponding access information, wherein once access refers to that a user does not continue to access within T2 time after access to any information is interrupted, and T2 is a preset value; marking the access times of the corresponding access information as Ci, i =1.. N;
s04: acquiring the current time from the last time that the corresponding user accesses the access information Fi, and marking the time as a time distance Gi, i =1.. N;
s05: calculating the value of Qi, qi =0.582 ci +0.418 gi by using a formula;
s06: sequencing and sorting access information Fi corresponding to the sequence from Qi to obtain sequence access information;
s07: the next identity information is selected and steps S01-S07 are repeated until all identity information processing is complete.
Further, the specific combination mode of combining the identity information and the key information to form the login character set is as follows:
s1: acquiring the total typing time length which is measured in seconds from the beginning of typing login information to the end of the time counting after the key information is typed;
s2: acquiring a bit value of the total typing time length, and marking the bit value as a shadow value;
s3: when the shadow value is an odd number, a login character set is formed in a mode of login information + secret key information;
otherwise, the login character set is formed according to the key information and the login information.
The invention has the beneficial effects that:
after the identity of the user is identified through the user login unit in combination with the processor and the identity library, the legality of the user equipment is verified through the related protection algorithm disclosed by the invention, and the legality is analyzed and known through a key factor of characteristic information; only when both the two pass, the normal data access of the user can be ensured; then, the deviation data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the corresponding identity information thereof to obtain the sequential access information of the user; the processor is used for carrying out information recommendation on the identity information by combining the sequence access information in the personal inertia library and the data capturing unit, and judging which information is specifically pushed to the user according to the real-time heat of the corresponding user tendency information, so that the user can conveniently and quickly access the identity information and timely acquire the information required to be known by the user; the invention is simple, effective and easy to use.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, a big data analysis platform based on an intensive website includes a user login unit, an identity library, a display unit, a processor, a data capture unit, a personal inertia library, a deviation data analysis unit, a data collection unit and a data temporary storage unit;
the system comprises a user login unit, an identity library and a user authentication unit, wherein the user login unit is used for a user to input identity information and corresponding secret key information, and standard identity information of an authorized user and corresponding authorized secret key information are stored in an identity library; the user login unit is used for transmitting the identity information and the corresponding secret key information to the processor, the processor is used for carrying out equipment verification processing on the identity information and the secret key information by combining the identity library, and the specific processing process is as follows:
the method comprises the following steps: firstly, comparing the identity information with standard identity information, finding out standard identity information consistent with the identity information, and then finding out corresponding standard key information;
step two: comparing the standard key information with the key information, and generating an initial communication signal if the standard key information is consistent with the key information; otherwise, generating an error initial signal;
step three: after the initial communication signal is generated, combining the identity information and the secret key information to form a login character set, wherein the specific combination mode is as follows:
s1: acquiring total typing time length which is measured in seconds from the beginning of typing login information to the end of the key information typing completion timing;
s2: acquiring a bit value of the total typing time length, and marking the bit value as a shadow value;
s3: when the shadow value is an odd number, a login character set is formed in a mode of login information + secret key information;
otherwise, forming a login character set according to the key information and the login information;
step four: after obtaining the login character set, carrying out value selection confirmation by combining the total typing time length, wherein the specific confirmation mode is that numerical values on all digits of the total typing time length are added to obtain a sum value, then the digit numerical value is taken, and the numerical value is marked as an interval value;
step five: acquiring a login character group, counting from a first digit to a second digit, and marking the digit as a first special character;
step six: removing the first special character, repeating the step five, and obtaining second special characters of the rest login character groups;
step seven: repeating the step six to obtain a third special character; the first special character, the second special character and the third special character form characteristic information;
step eight: after the user login unit approved by the administrator keys in the identity information and the corresponding key information, the characteristic information is obtained according to the modes of the third step to the seventh step and is marked as potential characteristic information;
step nine: generating a pass signal only when the potential property information and the property information coincide; otherwise, generating a device error signal;
when the processor generates a device error signal, the processor drives the display unit to display 'the used device is not trusted, please verify'; when the processor generates an error signal, the processor drives the display unit to display 'identity key error, please verify';
the processor is used for capturing the identity information by utilizing the personal inertia library when the passing signal is generated;
the data collection unit is used for collecting an access information group formed by a plurality of access information of a user, wherein the access information is specifically the access content of the user when the user accesses a website; the data collection unit is used for combining the access information group with the corresponding identity information and transmitting the access information group to the data temporary storage unit for storage; the biased data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the corresponding identity information thereof, and the specific process is as follows:
s01: selecting identity information, and acquiring an access information group of a user within a time close to T1, wherein T1 is a preset value;
s02: marking access information within the access information group as Fi, i =1.. N;
s03: acquiring the access times of corresponding access information, wherein one access refers to that a user does not continue to access within T2 time after any information access interruption, and T2 is a preset value; marking the access times of the corresponding access information as Ci, i =1.. N;
s04: acquiring the current time from the last time that the corresponding user accesses the access information Fi, and marking the time as a time distance Gi, i =1.. N;
s05: calculating the value of Qi, qi =0.582 ci +0.418 gi by using a formula;
s06: sequencing and sorting access information Fi corresponding to the sequence from Qi to obtain sequence access information;
s07: selecting next identity information, and repeating the steps S01-S07 until all the identity information is processed;
the data capturing unit is communicated with the Internet and is used for acquiring information of the Internet in real time; the deviation data analysis unit is used for transmitting the sequence access information to the personal inertia library, the processor is used for carrying out information recommendation on the identity information by combining the sequence access information in the personal inertia library and the data capture unit, and the specific recommendation process comprises the following steps:
s100: acquiring sequence access information of a user;
s200: acquiring the sequential access information of the first three rows, and acquiring keywords corresponding to the three sequential access information;
s300: calling out real-time search data of the keyword on the network by means of a data capturing unit; the specific calling mode is as follows:
s301: firstly, taking the current time as an end value, acquiring the access times of the keyword within one hour ahead of the current time, and marking the access times as real-time access times;
s302: acquiring the access times of the keyword in the last hour, and marking the access times as the comparison times;
s303: when the real-time visit-contrast times are more than or equal to X1, X1 is a preset value; marking the sequential access information as hit access information;
s400: repeating the third step on the key words of the next sequence access information;
s500: continuing to perform step S400 until three hit access information are obtained, and obtaining an information recommendation group;
the processor is used for transmitting the information recommendation group to the display unit, and the display unit receives the information recommendation group transmitted by the processor and displays the information recommendation group in real time.
A big data analysis platform based on an intensive website is characterized in that firstly, after the identity of a user is identified through a user login unit combined with a processor and an identity library, the legality of user equipment is verified through a related protection algorithm disclosed by the invention, and specifically, the user equipment is analyzed and known through a key factor of characteristic information; only when both the two pass, the normal data access of the user can be ensured; then, the deviation data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the corresponding identity information thereof to obtain the sequential access information of the user; the processor is used for carrying out information recommendation on the identity information by combining the sequence access information in the personal inertia library and the data capturing unit, and judging which information is specifically pushed to the user according to the real-time heat of the corresponding user tendency information, so that the user can conveniently and quickly access the identity information and timely acquire the information required to be known by the user; the invention is simple, effective and easy to use.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. A big data analysis platform based on an intensive website is characterized by comprising a user login unit, an identity library, a display unit, a processor, a data capture unit, a personal inertia library, a deviation data analysis unit, a data collection unit and a data temporary storage unit;
the system comprises a user login unit, an identity library and a user authentication unit, wherein the user login unit is used for a user to input identity information and corresponding secret key information, and standard identity information of an authorized user and corresponding authorized secret key information are stored in an identity library; the user login unit is used for transmitting the identity information and the corresponding secret key information to the processor, and the processor is used for carrying out equipment verification processing on the identity information and the secret key information by combining an identity library to generate a passing signal or an equipment error signal;
when the processor generates a device error signal, the processor drives the display unit to display 'used devices are not trusted, please verify'; when the processor generates an error signal, the processor drives the display unit to display 'identity key error, please verify';
the processor is used for capturing the identity information by using the personal inertia library when the passing signal is generated;
the data collection unit is used for collecting an access information group formed by a plurality of access information of a user, wherein the access information is specifically the access content of the user when the user accesses a website; the data collection unit is used for combining the access information group with the corresponding identity information and transmitting the access information group to the data temporary storage unit for storage; the biased data analysis unit is used for carrying out data analysis on the access information group stored in the data temporary storage unit and the identity information corresponding to the access information group to obtain sequence access information corresponding to all the identity information;
the data capturing unit is communicated with the Internet and is used for acquiring information of the Internet in real time; the deviation data analysis unit is used for transmitting the sequence access information to the personal inertia library, the processor is used for carrying out information recommendation on the identity information by combining the sequence access information in the personal inertia library and the data capture unit, and the specific recommendation process comprises the following steps:
s100: acquiring sequence access information of a user;
s200: acquiring the sequential access information of the first three rows, and acquiring keywords corresponding to the three sequential access information;
s300: calling out real-time search data of the keyword on the network by means of a data capturing unit; the specific calling mode is as follows:
s301: firstly, taking the current time as an end value, acquiring the access times of the keyword within one hour ahead of the current time, and marking the access times as real-time access times;
s302: acquiring the access times of the keyword in the last hour, and marking the access times as the comparison times;
s303: when the real-time visit-contrast times are more than or equal to X1, X1 is a preset value; marking the sequential access information as hit access information;
s400: repeating the third step on the key words of the next sequence access information;
s500: continuing to perform step S400 until three hit access information are obtained, and obtaining an information recommendation group;
the processor is used for transmitting the information recommendation group to the display unit, and the display unit receives the information recommendation group transmitted by the processor and displays the information recommendation group in real time.
2. The intensive website-based big data analysis platform of claim 1, wherein the verification process comprises the following specific processes:
the method comprises the following steps: firstly, comparing the identity information with standard identity information, finding out standard identity information consistent with the identity information, and then finding out corresponding standard key information;
step two: comparing the standard key information with the key information, and generating an initial communication signal if the standard key information is consistent with the key information; otherwise, generating an error initial signal;
step three: after generating the initial communication signal, combining the identity information and the secret key information to form a login character set;
step four: after obtaining the login character set, carrying out value selection confirmation by combining the total typing time length, wherein the specific confirmation mode is that numerical values on all digits of the total typing time length are added to obtain a sum value, then the digit numerical value is taken, and the numerical value is marked as an interval value;
step five: acquiring a login character group, counting from a first digit to a second digit, and marking the digit as a first special character;
step six: removing the first special character, repeating the step five, and obtaining second special characters of the rest login character groups;
step seven: repeating the step six to obtain a third special character; the first special character, the second special character and the third special character form characteristic information;
step eight: after the user login unit approved by the administrator keys in the identity information and the corresponding key information, the characteristic information is obtained according to the modes of the third step to the seventh step and is marked as potential characteristic information;
step nine: generating a pass signal only when the potential property information and the property information coincide; otherwise, a device error signal is generated.
3. The intensive website-based big data analysis platform as claimed in claim 1, wherein the specific process of data analysis is as follows:
s01: selecting identity information, and acquiring an access information group of a user within a time close to T1, wherein T1 is a preset value;
s02: marking access information within the access information group as Fi, i =1.. N;
s03: acquiring the access times of corresponding access information, wherein one access refers to that a user does not continue to access within T2 time after any information access interruption, and T2 is a preset value; marking the access times of the corresponding access information as Ci, i =1.. N;
s04: acquiring the current time from the last time that the corresponding user accesses the access information Fi, and marking the time as a time distance Gi, i =1.. N;
s05: calculating the value of Qi, qi =0.582 ci +0.418 gi by using a formula;
s06: sequencing and sorting access information Fi corresponding to the sequence from Qi to obtain sequence access information;
s07: and selecting next identity information, and repeating the steps S01-S07 until all the identity information is processed.
4. The intensive website-based big data analysis platform as claimed in claim 2, wherein the specific combination manner of combining the identity information and the key information to form the login character set is as follows:
s1: acquiring the total typing time length which is measured in seconds from the beginning of typing login information to the end of the time counting after the key information is typed;
s2: acquiring a bit value of the total typing time length, and marking the bit value as a shadow value;
s3: when the shadow value is an odd number, a login character set is formed in a mode of login information plus secret key information;
otherwise, the login character set is formed according to the key information and the login information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010379105.8A CN111581614B (en) | 2020-05-07 | 2020-05-07 | Intensive website-based big data analysis platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010379105.8A CN111581614B (en) | 2020-05-07 | 2020-05-07 | Intensive website-based big data analysis platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111581614A CN111581614A (en) | 2020-08-25 |
| CN111581614B true CN111581614B (en) | 2023-03-14 |
Family
ID=72114503
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010379105.8A Active CN111581614B (en) | 2020-05-07 | 2020-05-07 | Intensive website-based big data analysis platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111581614B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113780798B (en) * | 2021-09-07 | 2024-05-28 | 杭州天宽科技有限公司 | Key index display system based on cloud computing |
| CN117421335A (en) * | 2023-12-19 | 2024-01-19 | 山东山科世鑫科技有限公司 | Equipment data management system and method based on artificial intelligence |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010134711A (en) * | 2008-12-04 | 2010-06-17 | Aqua Web Co Ltd | Access history acquisition system |
| WO2010104280A2 (en) * | 2009-03-12 | 2010-09-16 | 주식회사 로그 | System and method for integratedly managing multiple connection statistics servers |
| CN110659019A (en) * | 2019-09-26 | 2020-01-07 | 北京金山云网络技术有限公司 | Parameter checking method and device and server |
| CN110912932A (en) * | 2019-12-16 | 2020-03-24 | 珠海市鸿瑞信息技术股份有限公司 | Power secondary system information safety operation and maintenance supervision platform |
-
2020
- 2020-05-07 CN CN202010379105.8A patent/CN111581614B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010134711A (en) * | 2008-12-04 | 2010-06-17 | Aqua Web Co Ltd | Access history acquisition system |
| WO2010104280A2 (en) * | 2009-03-12 | 2010-09-16 | 주식회사 로그 | System and method for integratedly managing multiple connection statistics servers |
| CN110659019A (en) * | 2019-09-26 | 2020-01-07 | 北京金山云网络技术有限公司 | Parameter checking method and device and server |
| CN110912932A (en) * | 2019-12-16 | 2020-03-24 | 珠海市鸿瑞信息技术股份有限公司 | Power secondary system information safety operation and maintenance supervision platform |
Non-Patent Citations (1)
| Title |
|---|
| 数字校园中统一身份认证系统的设计与实现;钱银中;《常州工学院学报》;20051230(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111581614A (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109639714B (en) | Internet of things identity registration and verification method based on block chain | |
| CN102624705B (en) | A kind of intelligent image verification method and system | |
| CN104184713B (en) | Terminal identification method, machine identifier register method and corresponding system, equipment | |
| CN111581614B (en) | Intensive website-based big data analysis platform | |
| RU2007130340A (en) | METHOD FOR CREATING A SAFE CODE, METHODS FOR ITS USE AND PROGRAMMABLE DEVICE FOR IMPLEMENTING THE METHOD | |
| Zhang et al. | Efficient and privacy-preserving min and $ k $ th min computations in mobile sensing systems | |
| CN106330448B (en) | User validity verification method, device and system | |
| CN111698088A (en) | Key alternation method, key alternation device, electronic equipment and medium | |
| CN111010381B (en) | Cross-chain-based identity authentication method and system | |
| CN113094747A (en) | Image searching method, block chain, cloud server and image searching device | |
| CN112235260B (en) | Anonymous data storage method, device, equipment and storage medium | |
| CN112187702A (en) | Method and device for verifying client | |
| CN107666470A (en) | A kind of processing method and processing device of checking information | |
| CN104253818A (en) | Server and terminal authentication method, server, terminal | |
| CN113569210A (en) | Distributed identity authentication method, equipment access method and device | |
| KR20130085492A (en) | Authentication system and method by use of non-fixed user id | |
| JP2004518309A (en) | Method and system for generating sequence numbers used for authentication | |
| Douglas et al. | Synthetic aperture active sonar imaging | |
| KR101118605B1 (en) | Log-in system and method with strengthened security | |
| CN113612758B (en) | Block chain-based Internet of things data security management system and method | |
| CN113726763A (en) | Challenge response identity authentication technology based on mobile phone number | |
| US20180197362A1 (en) | Method for generating winning numbers and apparatus | |
| CN113536353A (en) | Private data processing method | |
| CN115643067B (en) | Lightweight Internet of things identity authentication and key negotiation method and device based on blockchain and electronic equipment | |
| Afroz et al. | A Secure Mutual Authentication Protocol for IoT using ID Verifier Based on ECC |
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 |