CN115051941A - Enterprise big data analysis platform - Google Patents
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- CN115051941A CN115051941A CN202210591308.2A CN202210591308A CN115051941A CN 115051941 A CN115051941 A CN 115051941A CN 202210591308 A CN202210591308 A CN 202210591308A CN 115051941 A CN115051941 A CN 115051941A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 16
- 230000002159 abnormal effect Effects 0.000 claims abstract description 14
- 238000012856 packing Methods 0.000 claims abstract description 11
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000005206 flow analysis Methods 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000007639 printing Methods 0.000 claims description 4
- 238000004806 packaging method and process Methods 0.000 claims description 3
- 238000012858 packaging process Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000002407 reforming Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000012517 data analytics Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/04—Processing captured monitoring data, e.g. for logfile generation
- H04L43/045—Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
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- 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
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
- H04L63/0442—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply asymmetric encryption, i.e. different keys for encryption and decryption
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- 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
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3263—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving certificates, e.g. public key certificate [PKC] or attribute certificate [AC]; Public key infrastructure [PKI] arrangements
- H04L9/3268—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving certificates, e.g. public key certificate [PKC] or attribute certificate [AC]; Public key infrastructure [PKI] arrangements using certificate validation, registration, distribution or revocation, e.g. certificate revocation list [CRL]
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Data Mining & Analysis (AREA)
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- General Engineering & Computer Science (AREA)
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- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses an enterprise big data analysis platform, which belongs to the technical field of enterprise big data and comprises the following steps: a front-end viewing module: checking the big data analysis result in real time in various modes; a flow statistic module: carrying out quantitative statistics and classification on data flow according to dimensions of different departments at different times; the flow uploading module: packing the counted data flow layer by layer according to the classification of departments and time, adding departments and time tags to prefixes of data packets in the packing process, then encrypting the packed data, and uploading the data after the encryption is finished; a flow analysis module: and decrypting the obtained data packet, and checking whether abnormal flow exists or not according to the sequence of departments and time. This big data analysis platform of enterprise, the department and the emergence time to taking place the risk that can be convenient fix a position and look over, and the flow information to each department arbitrary time that can be convenient carries out audio-visual looking over.
Description
Technical Field
The invention belongs to the technical field of enterprise big data, and particularly relates to an enterprise big data analysis platform.
Background
In the field of ERP data information application, because the data volume of an enterprise is increasingly huge and complicated, the enterprise can not comprehensively and quickly manage and control own data and can not reasonably analyze daily operation conditions and propose management suggestions through the data, and therefore an enterprise big data analysis platform is needed.
For example, the application number is CN201621345179.5, which is an enterprise hidden danger information collecting and processing system based on big data. The system comprises a hidden danger information acquisition system, a transmission system, a big data processing center and a Smart Safety Map enterprise hidden danger Map; monitoring and uploading hidden danger information of an enterprise by using an automatic monitoring sensor and a manual monitoring reporting device; the data is transmitted to an enterprise big data processing center through a transmission system; carrying out grading early warning on the hidden danger information by using a big data method; the monitoring center sends out hidden danger early warning, and displays hidden danger information of each system on an enterprise intelligent safety map in real time; informing the rectifying and reforming main pipe part of the door to reach the responsibility personnel for rectifying and reforming in real time; and the on-site safety supervision personnel or the responsible person of the supervisor check and confirm the rectification effect and upload rectification information. And closed-loop control of information acquisition, hidden danger classification, task assignment of rectification and effect confirmation is formed. By using the system, the enterprise hidden danger information can be monitored in full time and space, and accidents are prevented.
However, it is worth noting that the existing platform cannot conveniently locate and check the risk department and the occurrence time.
Disclosure of Invention
The invention aims to provide an enterprise big data analysis platform to solve the problems in the background technology.
The technical scheme is as follows: an enterprise big data analytics platform comprising:
a front-end viewing module: checking the big data analysis result in real time in various modes;
a flow statistic module: carrying out quantitative statistics and classification on data flow according to dimensions of different departments at different times;
the flow uploading module: packing the counted data flow layer by layer according to the classification of departments and time, adding departments and time tags to prefixes of data packets in the packing process, then encrypting the packed data, and uploading the data after the encryption is finished;
a flow analysis module: decrypting the obtained data packet, checking whether abnormal flow exists or not according to the sequence of departments and time, if so, positioning to a corresponding time period of the department, then marking abnormal information on the data flow and sending an alarm to a front-end checking module;
a summary analysis module: and drawing a curve graph in real time by taking the data flow as an axis according to the department and the time, and storing the curve graph of each department to a server in a classified manner according to the time period.
In a further embodiment, the front-end viewing module comprises:
the browser webpage module: the user performs login authentication through the browser, and operation and viewing can be performed on the webpage end after the authentication is completed;
the mobile phone application module: the user can log in authentication after downloading the exclusive application through the application market, and can operate and check at the application end after the authentication is finished;
an applet module: the user searches the corresponding applet name in the application supporting the applet, and after the applet is opened, login authentication is carried out, and then operation and viewing can be carried out at the applet end.
In a further embodiment, the browser needs to download the corresponding digital certificate before login authentication to ensure security.
In a further embodiment, the traffic statistics module comprises:
upload the flow statistics module: counting the uploaded flow specially;
a download flow statistic module: the downloaded flow is specially counted.
In a further embodiment, the traffic upload module comprises:
a data packaging module: packing the counted data flow layer by layer according to the classification of departments and time;
a label printing module: adding departments and time labels to prefixes of the data packets in the packaging process;
an encryption module: and encrypting the packed data, and uploading the data after encryption.
In a further embodiment, the encryption module uses the RSA algorithm for encryption.
In a further embodiment, the traffic analysis module comprises:
a decryption module: decrypting the obtained data packet;
the exclusive channel: and sending the abnormal information and the alarm to a front-end checking module through a dedicated network channel.
In a further embodiment, the summary analysis module comprises:
a multithreading drawing module: drawing simultaneously according to the obtained multiple groups of data flow through the multi-thread processor;
an interface module: the manager can access and adjust the drawing priority in real time through the interface module.
The invention has the technical effects and advantages that: according to the enterprise big data analysis platform, the flow analysis module checks and positions abnormal flow information according to department and time sequence, department and time information can be attached when an alarm is sent, dangerous location can be conveniently and timely carried out, the alarm is sent through a special channel, sending delay caused by communication blockage is avoided, and the department with risk and occurrence time can be conveniently and quickly checked in a locating mode;
the summarizing and analyzing module can draw a curve graph in real time according to departments and time as axes and store the curve graph in the server, and a user can call the corresponding curve graph from the server for checking through various modes such as a browser webpage, mobile phone application and the like, so that flow information of each department at any time can be checked visually and conveniently;
this big data analysis platform of enterprise, the department and the emergence time to taking place the risk that can be convenient fix a position and look over, and the flow information to each department arbitrary time that can be convenient carries out audio-visual looking over.
Drawings
FIG. 1 is a flow chart of the steps of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
In order to solve the proposed problem, the present invention provides an enterprise big data analysis platform as shown in fig. 1, including:
a front-end viewing module: the big data analysis result is checked in real time through a plurality of modes, including:
the browser webpage module: the user performs login authentication through the browser, operation and checking can be performed on the webpage end after the authentication is completed, the browser needs to download a corresponding digital certificate before the login authentication is performed, the digital certificate can obviously improve the security of browser access, and the user login information and enterprise data are prevented from being stolen;
the mobile phone application module: the user can log in authentication after downloading the exclusive application through the application market, and can operate and check the application terminal after the authentication is finished, so that the operation is smoother than that of a webpage;
an applet module: the user searches the corresponding applet name in the application supporting the applet, and after the applet is opened, login authentication is carried out, then operation and checking can be carried out at the applet end, so that the method is lighter in weight compared with the mobile phone application, and operation of most mobile phone applications can be realized;
a flow statistics module: carrying out quantitative statistics and classification on data flow according to dimensions of different departments at different times, wherein the quantitative statistics and classification comprise the following steps:
upload the flow statistics module: counting the uploaded flow specially;
a download flow statistic module: the downloaded flow is specially counted, and the statistics of the uploading flow and the downloading flow are separated, so that the statistical results are not easily influenced with each other;
the flow uploading module: the method comprises the following steps:
a data packaging module: packing the counted data flow layer by layer according to the classification of departments and time;
a label printing module: adding departments and time labels to prefixes of the data packets in the packaging process;
an encryption module: the data which are packaged are encrypted subsequently, and then uploaded after the encryption is finished, the encryption module adopts an RSA algorithm for encryption, the security of the RSA algorithm is higher, and the uploaded data can be prevented from being obtained by lawbreakers;
a flow analysis module: decrypting the obtained data packet, checking whether abnormal flow exists or not (the abnormal flow is sudden explosive flow uploading/downloading in a short time period) according to the sequence of departments and time, if so, positioning to the corresponding time period of the department, marking abnormal information on the data flow and sending an alarm to a front-end checking module, wherein the method comprises the following steps:
a decryption module: a built-in private key can decrypt the obtained data packet;
a dedicated channel: abnormal information and alarm are sent to the front-end checking module through a dedicated network channel, so that untimely information sending caused by network congestion is prevented;
a summary analysis module: the data flow is used for drawing a curve graph in real time by taking departments and time as axes, and the curve graph of each department is classified and stored to a server according to time periods, and the method comprises the following steps:
a multithreading drawing module: the multi-thread processor draws simultaneously according to the obtained multiple groups of data flow, so that the drawing efficiency is improved;
an interface module: the manager can access and adjust the drawing priority in real time through the interface module, and can check the curve graph of the designated department and time period more quickly.
The enterprise big data analysis platform comprises the following analysis steps:
s1: and (3) counting and uploading flow: the uploading flow statistic module and the downloading flow statistic module are matched to carry out quantitative statistics and classification on data flow according to different time and different department dimensions, then the data flow which is counted is packed layer by layer according to the department and time classification through the data packing module, meanwhile, a department and a time label are added to a prefix of a data packet in the packing process through the label printing module, then the packed data is encrypted through the encryption module, and the data is uploaded after the encryption is finished;
s2: and (3) analyzing data flow: decrypting the obtained data packet through a decryption module, checking whether abnormal flow exists or not according to the sequence of departments and time, if so, positioning to a corresponding time period of the department, marking abnormal information on the data flow and sending an alarm to a front-end checking module through a dedicated channel;
s3: summary analysis of data: drawing simultaneously through a multithreading drawing module according to the obtained multiple groups of data flow, and then storing the curve graphs of each department to a server in a classified manner according to time periods;
s4: user login and viewing: the user can log in and authenticate through the browser webpage module, the mobile phone application module or the small program module, and then the curve graphs of corresponding departments and time periods can be called from the server to be viewed.
It is noted that, herein, relational terms such as one and two are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation. The use of the phrase "comprising one of the elements does not exclude the presence of other like elements in the process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An enterprise big data analysis platform is characterized in that: the method comprises the following steps:
a front-end viewing module: checking the big data analysis result in real time in various modes;
a flow statistic module: carrying out quantitative statistics and classification on data flow according to dimensions of different departments at different times;
the flow uploading module: packing the counted data flow layer by layer according to the classification of departments and time, adding departments and time tags to prefixes of data packets in the packing process, then encrypting the packed data, and uploading the data after the encryption is finished;
a flow analysis module: decrypting the obtained data packet, checking whether abnormal flow exists or not according to the sequence of departments and time, if so, positioning to a corresponding time period of the department, then marking abnormal information on the data flow and sending an alarm to a front-end checking module;
a summary analysis module: and drawing a curve graph in real time by taking the data flow as an axis according to the department and the time, and storing the curve graph of each department to a server in a classified manner according to the time period.
2. The enterprise big data analysis platform according to claim 1, wherein: the front end viewing module comprises:
the browser webpage module: the user performs login authentication through the browser, and operation and viewing can be performed on the webpage end after the authentication is completed;
the mobile phone application module: the user can log in authentication after downloading the exclusive application through the application market, and can operate and check at the application end after the authentication is finished;
an applet module: the user searches the corresponding applet name in the application supporting the applet, and after the applet is opened, login authentication is carried out, and then operation and viewing can be carried out at the applet end.
3. The enterprise big data analysis platform according to claim 2, wherein: the browser needs to download the corresponding digital certificate before login authentication so as to ensure safety.
4. The enterprise big data analysis platform according to claim 1, wherein: the flow statistic module comprises:
upload the statistical module of flow: counting the uploaded flow specially;
a download flow statistic module: the downloaded flow is specially counted.
5. The enterprise big data analysis platform according to claim 1, wherein: the flow uploading module comprises:
a data packaging module: packing the counted data flow layer by layer according to the classification of departments and time;
a label printing module: adding departments and time labels to prefixes of the data packets in the packaging process;
an encryption module: and encrypting the packed data, and uploading the data after encryption.
6. The enterprise big data analysis platform according to claim 5, wherein: the encryption module adopts RSA algorithm for encryption.
7. The enterprise big data analysis platform according to claim 1, wherein: the flow analysis module includes:
a decryption module: decrypting the obtained data packet;
the exclusive channel: and sending the abnormal information and the alarm to a front-end checking module through a dedicated network channel.
8. The enterprise big data analysis platform according to claim 1, wherein: the summary analysis module includes:
a multithread drawing module: drawing simultaneously according to the obtained multiple groups of data flow through the multi-thread processor;
an interface module: the manager can access and adjust the drawing priority in real time through the interface module.
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| CN202210591308.2A CN115051941A (en) | 2022-05-27 | 2022-05-27 | Enterprise big data analysis platform |
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| CN202210591308.2A CN115051941A (en) | 2022-05-27 | 2022-05-27 | Enterprise big data analysis platform |
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