CN117370417B - Business big data analysis system based on artificial intelligence - Google Patents
Business big data analysis system based on artificial intelligence Download PDFInfo
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- 238000007405 data analysis Methods 0.000 title claims abstract description 27
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 12
- 238000013480 data collection Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 14
- 238000012795 verification Methods 0.000 claims abstract description 8
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Abstract
The application discloses a business big data analysis system based on artificial intelligence, which relates to the technical field of business big data analysis, wherein object data to be analyzed is collected through a data collection end and is temporarily stored after being acquired according to a set capacity, meanwhile, the collected object data with the set capacity is subjected to encryption processing by means of the data collection end, after the encryption processing, subsequent data are used for big data analysis, verification of encryption content is performed during analysis, and the fact that the data corresponding to analysis are not subjected to secondary processing or addition after being collected is ensured, so that interference of partial data in the big data is avoided to a certain extent, and an analysis result is more referential.
Description
Technical Field
The invention belongs to the technical field of business big data analysis, and particularly relates to a business big data analysis system based on artificial intelligence.
Background
The patent with publication number CN115796910A discloses a big data analysis method and a system applied to electronic commerce, wherein the first optimized big data of the electronic commerce and the second optimized big data of the electronic commerce with the characteristics of individual users to be processed are output in a visual analysis window, so that the user image vectors which are to be analyzed for the big data of the electronic commerce and the user activity events in the big data of the reference electronic commerce can be provided as rich and various as possible, the decision basis for carrying out joint analysis on the big data of the first optimized big data of the electronic commerce and the big data of the second optimized big data of the electronic commerce is perfected, and the intelligent degree of the big data mining can be improved based on accurate and reliable user demand mining or user interest refinement of the big data of the first optimized big data of the electronic commerce and the second optimized big data of the electronic commerce.
However, when analyzing large data of business, many invalid data are generated passively or actively for some reasons, even if some irrelevant or wrong data are added to the collected data, the large data analysis result is affected after the data are added, and a solution is provided.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art;
To achieve the above object, an embodiment according to a first aspect of the present invention proposes an artificial intelligence based business big data analysis system, comprising:
the data collection end is used for collecting object data to be analyzed and temporarily storing the object data after the object data are acquired according to the set capacity; the data collection end is also used for carrying out encryption processing on the collected object data with set capacity, and the specific processing mode is as follows:
Step one: acquiring the first time corresponding to all object data, firstly automatically sequencing all object data according to a sequence rule, and determining to sequence by adopting one of three preset sequencing modes according to the time when the last object data is acquired and then acquiring an object data sequence by the sequence rule;
step two: then, obtaining the unique identification codes of the storage units corresponding to the object data stored in the data collection end, sequentially marking each character as an encryption identifier, and arranging all the encryption identifiers according to the original unique identification codes to form an encryption identification group;
step three: then, an object data sequence is acquired, and encryption processing is carried out, specifically:
S1: acquiring first object data according to the sequence of the object data, and placing a first encryption identifier in the encryption identification group after a first character in the corresponding object data name;
S2: placing a second encrypted identifier after a second character in the name of the second object data;
S3: according to the principle, selecting a third encryption identifier and corresponding third object data, and processing according to the same principle;
S4: repeating the step S3 until all the names of the object data are processed, and if the X-th character in the names of the object data is performed and only X-1 characters exist in the names of the object data in the process, repeating the process again, and placing the encryption identifier behind the first character in the names of the object data;
s5: completing the insertion of the encryption identifiers of all object data;
S6: obtaining encrypted object data;
The data collection end is connected with the data analysis end through the data transfer unit, and the data analysis end is used for analyzing the data in the data collection end by means of the data transfer unit; when the communication enabling signal transmitted by the sub-occupation unit is not received, the data transfer unit is in a non-conducting state, and data exchange cannot be carried out;
The sub-occupation unit is used for encrypting and verifying the object data in the data collection end, and generating an operation signal according to whether the object data has an encrypted identifier, so that the data transfer unit is conducted.
Compared with the prior art, the invention has the beneficial effects that:
According to the application, the data of the object to be analyzed is collected through the data collection end and is temporarily stored after being acquired according to the set capacity, meanwhile, the data collection end is also used for carrying out encryption processing on the collected object data with the set capacity, after the encryption processing is carried out, the subsequent data can be used for big data analysis, verification of encryption content can be carried out during analysis, the data corresponding to analysis is ensured not to be processed or added for the second time after being collected, so that interference of partial data in the big data is avoided to a certain extent, and therefore, the analysis result is more referential.
Drawings
FIG. 1 is a system block diagram of the present invention;
fig. 2 is a schematic diagram of a data collection end structure of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the application provides a business big data analysis system based on artificial intelligence;
As a first embodiment of the present application, it specifically includes:
the system comprises a data collection end, a data transfer unit, a sub-occupation unit and a data analysis end;
The data collection end is used for collecting object data to be analyzed, and temporarily storing the object data after the object data are acquired according to the set capacity; the set capacity refers to the storage size occupied by the whole acquired data, and the specific value of the set capacity is obtained by presetting by an administrator;
the data collection end is used for carrying out encryption processing on the collected object data with set capacity, and the specific processing mode is as follows:
Step one: the first time corresponding to all the object data is acquired, all the object data are automatically ordered according to a sequence rule, and the sequence rule is as follows:
The method comprises the steps of setting a sorting mode, namely sorting according to a temporary stored time sequence, sorting according to a sequence from small to large of object data, sorting according to first letters of names of the object data, wherein the first letter sorting is a first letter sorting rule in the prior art, and sequentially extending to the next letter if the first letters are identical;
The time when the last object data is imported is acquired, all the values of the last object data are sequentially combined in a time-of-year-month-time-of-day-time-of-time mode to obtain a time array, all the values in the time array are added to obtain the values on the bits of the time array, when the time array is odd, the time array is ordered in a first mode, when the time array is even and not zero, the time array is ordered in a second mode, and when the time array is zero, the time array is ordered in a third mode;
Sequencing all the object data to obtain an object data sequence;
step two: then, obtaining the unique identification codes of the storage units corresponding to the object data stored in the data collection end, sequentially marking each character as an encryption identifier, and arranging all the encryption identifiers according to the original unique identification codes to form an encryption identification group;
step three: then, an object data sequence is acquired, and encryption processing is carried out, specifically:
S1: acquiring first object data according to the sequence of the object data, and placing a first encryption identifier in the encryption identification group after a first character in the corresponding object data name;
S2: placing a second encrypted identifier after a second character in the name of the second object data;
S3: according to the principle, selecting a third encryption identifier and corresponding third object data, and processing according to the same principle;
S4: repeating the step S3 until all the names of the object data are processed, and if the X-th character in the names of the object data is performed and only X-1 characters exist in the names of the object data in the process, repeating the process again, and placing the encryption identifier behind the first character in the names of the object data;
s5: completing the insertion of the encryption identifiers of all object data;
S6: obtaining encrypted object data;
The data collection end is connected with the data analysis end through the data transfer unit, and the data analysis end is used for analyzing the data in the data collection end by means of the data transfer unit; when the communication enabling signal transmitted by the sub-occupation unit is not received, the data transfer unit is in a non-conducting state, and data exchange cannot be carried out;
the sub-occupation unit is used for encrypting and verifying the object data in the data collection end, and the specific mode of encrypting and verifying is as follows:
SS1: verifying all object data in a mode of reverse encryption processing, and automatically marking the object data which does not meet the requirement as reject data when the processed result is inconsistent with the unique identification code of the storage unit;
SS2: all the reject data are obtained, deleted and then a pass signal is generated; if no reject data exists, a pass signal is automatically generated;
The sharing unit transmits the passing signal to the data transfer unit after generating the passing signal;
The data analysis end can generate an analysis completion signal after analyzing the batch of data, at the moment, the analysis completion signal can be returned by the data transfer unit, and the data collection end can automatically delete all stored object data after receiving the analysis completion signal and continuously collect the next batch of data.
The data collection end comprises a data collection unit and a temporary database, wherein the data collection unit is used for obtaining object data, and the data collection unit is used for transmitting the object data to the temporary database for storage.
As another embodiment of the present application, the difference between the present embodiment and the first embodiment is that the specific manner in which the data collecting end in the present embodiment is used to collect the object data to be analyzed is different, and the specific manner in which the object data is collected in the present embodiment is:
SS1: random data acquisition is carried out in different channels, after each batch of data is acquired, the acquisition of the data is automatically stopped after the set capacity is reached, and after the object data with the set capacity is automatically stored, the modification of the object data is forbidden;
The subsequent processing mode is consistent with the embodiment, and the subsequent processing is to avoid the intrusion of the storage unit, and the violent or non-violent mode can be avoided;
As a third embodiment of the present application, the present embodiment may be implemented on the basis of the first embodiment or the second embodiment, and the encryption verification specific implementation manner of the object data in the data collecting end by the sharing unit in the present embodiment is:
Firstly, acquiring the number of object data, automatically generating a verification signal when the number of object data changes, and automatically carrying out the encryption verification specific steps in the first embodiment when the verification signal is generated;
of course, it is also possible to use a method of inquiring about the fluctuation of the target data and automatically generating a verification signal when any of the target data fluctuates;
As a fourth embodiment of the present application, this embodiment is a fusion implementation of the above three embodiments.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The business big data analysis system based on artificial intelligence is characterized by comprising:
the data collection end is used for collecting object data to be analyzed and temporarily storing the object data after the object data are acquired according to the set capacity; the data collection end is also used for carrying out encryption processing on the collected object data with set capacity, and the specific processing mode is as follows:
Step one: acquiring the first time corresponding to all object data, firstly automatically sequencing all object data according to a sequence rule, and determining to sequence by adopting one of three preset sequencing modes according to the time when the last object data is acquired and then acquiring an object data sequence by the sequence rule;
step two: then, obtaining the unique identification codes of the storage units corresponding to the object data stored in the data collection end, sequentially marking each character as an encryption identifier, and arranging all the encryption identifiers according to the original unique identification codes to form an encryption identification group;
step three: then, an object data sequence is acquired, and encryption processing is carried out, specifically:
S1: acquiring first object data according to the sequence of the object data, and placing a first encryption identifier in the encryption identification group after a first character in the corresponding object data name;
S2: placing a second encrypted identifier after a second character in the name of the second object data;
S3: according to the principle, selecting a third encryption identifier and corresponding third object data, and processing according to the same principle;
S4: repeating the step S3 until all the names of the object data are processed, and if the X-th character in the names of the object data is performed and only X-1 characters exist in the names of the object data in the process, repeating the process again, and placing the encryption identifier behind the first character in the names of the object data;
s5: completing the insertion of the encryption identifiers of all object data;
S6: obtaining encrypted object data;
The data collection end is connected with the data analysis end through the data transfer unit, and the data analysis end is used for analyzing the data in the data collection end by means of the data transfer unit; when the communication enabling signal transmitted by the sub-occupation unit is not received, the data transfer unit is in a non-conducting state, and data exchange cannot be carried out;
The sub-occupation unit is used for encrypting and verifying the object data in the data collection end, and generating an operation signal according to whether the object data has an encrypted identifier, so that the data transfer unit is conducted.
2. The artificial intelligence based business big data analysis system according to claim 1, wherein the set capacity refers to a storage size occupied by the acquired data as a whole, and a specific value thereof is preset by an administrator.
3. The business big data analysis system based on artificial intelligence according to claim 1, wherein the data collection end comprises a data collection unit and a temporary database, the data collection unit is used for obtaining the object data, and the data collection unit is used for transmitting the object data to the temporary database for storage.
4. The artificial intelligence based business big data analysis system of claim 1, wherein the sequence rules are:
The method comprises the steps of setting a sorting mode, wherein the first sorting mode is used for sorting according to a temporary stored time sequence, the second sorting mode is used for sorting according to a sequence from small to large of object data, the third sorting mode is used for sorting according to first letters of names of the object data, and if the first sorting mode is the same, the sorting mode is used for sequentially extending to the next letter;
The time when the last object data is imported is acquired, all the values of the last object data are sequentially combined in a time-of-year-month-time-of-day-time-of-time mode to obtain a time array, all the values in the time array are added to obtain the values on the bits of the time array, when the time array is odd, the time array is ordered in a first mode, when the time array is even and not zero, the time array is ordered in a second mode, and when the time array is zero, the time array is ordered in a third mode;
and sequencing all the object data to obtain an object data sequence.
5. The business big data analysis system based on artificial intelligence according to claim 1, wherein the encryption verification is as follows:
SS1: verifying all object data in a mode of reverse encryption processing, and automatically marking the object data which does not meet the requirement as reject data when the processed result is inconsistent with the unique identification code of the storage unit;
SS2: all the reject data are obtained, deleted and then a pass signal is generated; here, if no reject data exists, a pass signal is automatically generated.
6. The artificial intelligence based business big data analysis system according to claim 5, wherein the occupancy dividing unit transmits the permission signal to the data transfer unit after generating the permission signal;
The data analysis end can generate an analysis completion signal after analyzing the batch of data, at the moment, the analysis completion signal can be returned by the data transfer unit, and the data collection end can automatically delete all stored object data after receiving the analysis completion signal and continuously collect the next batch of data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311352716.3A CN117370417B (en) | 2023-10-19 | Business big data analysis system based on artificial intelligence |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311352716.3A CN117370417B (en) | 2023-10-19 | Business big data analysis system based on artificial intelligence |
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| CN117370417A CN117370417A (en) | 2024-01-09 |
| CN117370417B true CN117370417B (en) | 2024-05-17 |
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| CN110162556A (en) * | 2018-02-11 | 2019-08-23 | 陕西爱尚物联科技有限公司 | A kind of effective method for playing data value |
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| CN115796910A (en) * | 2022-10-21 | 2023-03-14 | 徐州鼎知汇科技发展有限公司 | Big data analysis method and system applied to electronic commerce |
| CN116244748A (en) * | 2023-03-16 | 2023-06-09 | 广西壮族自治区数字证书认证中心有限公司 | Traffic big data privacy protection system based on federal learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110162556A (en) * | 2018-02-11 | 2019-08-23 | 陕西爱尚物联科技有限公司 | A kind of effective method for playing data value |
| CN114500035A (en) * | 2022-01-24 | 2022-05-13 | 杭州天宽科技有限公司 | Data encryption system based on service data sharing cloud platform |
| CN115033614A (en) * | 2022-03-24 | 2022-09-09 | 武汉科技大学 | Big data based along-line national data analysis method |
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