CN112115176A - Big data-based user database analysis management system - Google Patents
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Abstract
The invention discloses a big data-based user database analysis management system, which comprises a database partitioning module, a data extraction module, a data analysis module, a data processing module, an analysis server, an intelligent pushing module, a user grade evaluation module, a personnel distribution module and a storage database; the method divides the user data in the logistics database, counts the mailing address in each logistics data information of each user, acquires the address with the most mails of each user, sets the address as the default address, counts the mailing weight and the mailing amount in each logistics data information of each user in each set time period, calculates the average value of the mailing weight and the average value of the mailing amount of each user in each set time period, carries out intelligent pushing, calculates the comprehensive rating influence coefficient of each user, screens the service level corresponding to each user, and distributes corresponding managers for butt joint, thereby meeting the requirements of the users and improving the experience feeling and the importance of the users.
Description
Technical Field
The invention relates to the field of database analysis management, in particular to a user database analysis management system based on big data.
Background
With the continuous development of network information technology, databases have been widely applied to many express enterprises. The database can store a large amount of user logistics data, but when the logistics data in the database is queried, the data in the database needs to be queried one by one due to the huge data volume in the database, so how to manage the database becomes the content that people need to pay attention to.
At present, the existing database management technology generally has some defects, the existing database management technology can not divide and store the stored logistics data, so that the query time is high in complexity, unnecessary repeated query links are added, the database query time is greatly wasted, meanwhile, the existing database management technology can not count the logistics data information of each user, which can not ensure the integrity and the availability of the stored data, so that the user can not quickly know the logistics data information in the past, thereby reducing the experience of the users, simultaneously being incapable of screening the service level corresponding to each user according to the logistics data information, thereby failing to meet the requirements of partial users, reducing the superiority and importance of the users, causing the loss of user resources, in order to solve the above problems, a user database analysis management system based on big data is designed.
Disclosure of Invention
The invention aims to provide a user database analysis management system based on big data, which divides user data in a logistics database through a database dividing module, extracts data stored in a plurality of user folders, counts the mailing addresses and the mailing numbers in each logistics data message of each user, acquires the most mailing addresses of each user, sets the mailing addresses as default addresses, classifies the counted mailing numbers according to set time periods, counts the mailing weight and the mailing amount in each logistics data message of each user in each set time period, calculates the average value of the mailing weight and the average value of the mailing amount of each user in each set time period, and carries out intelligent pushing; meanwhile, the comprehensive rating influence coefficient of each user is calculated, the service level corresponding to each user is screened, and corresponding managers are distributed for butt joint, so that the problems in the background technology are solved.
The purpose of the invention can be realized by the following technical scheme:
a big data-based user database analysis management system comprises a database partitioning module, a data extraction module, a data analysis module, a data processing module, an analysis server, an intelligent pushing module, a user grade evaluation module, a personnel distribution module and a storage database;
the analysis server is respectively connected with the data analysis module, the data processing module, the intelligent pushing module, the user grade evaluation module, the personnel allocation module and the storage database, the data extraction module is respectively connected with the database partitioning module and the data analysis module, the data processing module is connected with the data analysis module, the storage database is respectively connected with the intelligent pushing module and the user grade evaluation module, and the personnel allocation module is connected with the user grade evaluation module;
the database dividing module is used for dividing user data stored in the logistics database into a plurality of name file groups according to different dividing modes of stored names, dividing the name file groups into a plurality of user folders according to the dividing modes of different users with the same name, and sending the user folders to the data extraction module;
the data extraction module is used for receiving the user folders sent by the database analysis module, extracting data stored in the received user folders, extracting the stored logistics data information of each user, and sending the extracted and stored logistics data information of each user to the data analysis module;
the data analysis module is used for receiving the stored logistics data information of each user sent by the data extraction module, counting the received logistics data information of each user, counting the mail addresses in the logistics data information of each user, and forming a mail address set Aw in the logistics data information of each userij(a1wij,a2wij,...,alwij,...,avwij)，axwijThe address is expressed as the ith sending address in the logistics data information of the jth user in the ith name file group, and the sending address set in the logistics data information of each user is sent to the analysis server; meanwhile, counting the number of the mails in the logistics data information of each user, classifying the counted number of the mails in the logistics data information of each user according to a set time period, and forming a mail number set Kw of each user in each set time periodij(k1wij,k2wij,...,krwij,...,kfwij)，krwijThe number of mails of the jth user in the ith name file group in the r-th set time period is represented, and the number set of mails of all users in all set time periods is sent to the data processing module;
the data processing module is used for receiving the consignment number set of each user in each set time period sent by the data analysis module, counting the consignment weight and the consignment amount in each logistics data information of each user in each set time period, and respectively forming the consignment weight set in each logistics data information of each user in each set time period
And a mail amount set in each logistics data information of each user in each set time period
Expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
the method comprises the steps of representing the xth sending amount in logistics data information of the jth user in the ith name file group in the r set time period, and sending a sending weight set and a sending amount set in each logistics data information of each user in each set time period to an analysis server;
the analysis server is used for receiving a mail address set in each piece of logistics data information of each user sent by the data analysis module, receiving a mail weight set and a mail amount set in each piece of logistics data information of each user in each set time period sent by the data processing module, screening the received mail address set in each piece of logistics data information of each user, acquiring the address with the most mails in each piece of logistics data information of each user, setting the address with the most mails in each piece of logistics data information of each user as a default address, and sending the default mail address of each user to the personnel allocation module; meanwhile, calculating the average value of the sending weight and the average value of the sending amount of each user in each set time period, and sending the calculated average value of the sending weight and the calculated average value of the sending amount of each user in each set time period to an intelligent pushing module; extracting rating influence weight coefficients corresponding to the number of the mails, the weight of the mails and the money of the mails in the logistics data information stored in the storage database, calculating a comprehensive rating influence coefficient of each user, and sending the calculated comprehensive rating influence coefficient of each user to a user grade evaluation module;
the intelligent pushing module is used for receiving the average value of the sending weight and the average value of the sending amount of each user in each set time period sent by the analysis server, extracting the common contact ways of each user stored in the storage database, and pushing the average value of the sending weight and the average value of the sending amount of each user in each set time period to the corresponding user;
the user grade evaluation module is used for receiving the comprehensive rating influence coefficient of each user sent by the analysis server, extracting the standard comprehensive rating influence coefficient range corresponding to each service grade stored in the storage database, comparing the received comprehensive rating influence coefficient of each user with the stored standard comprehensive rating influence coefficient corresponding to each service grade, screening the service grade corresponding to the comprehensive rating influence coefficient of each user, counting the service grade corresponding to each user, and sending the service grade corresponding to each user to the personnel distribution module;
the personnel allocation module is used for receiving the default mail address of each user sent by the analysis server, receiving the service level corresponding to each user sent by the user level evaluation module, screening corresponding regional management personnel according to the default mail address of each user, and allocating the corresponding management personnel for butt joint according to the service level of each user;
the storage database is used for storing logistics data information of each user, and simultaneously storing rating influence weight coefficients corresponding to the number of the mailpieces, the weight of the mailpieces and the money of the mailpieces in the logistics data information, and the rating influence weight coefficients are respectively expressed as lambda1,λ2,λ3And storing the common contact ways of the users and the standard comprehensive rating influence coefficient range corresponding to each service level.
Further, the user data stored in the logistics database is divided, and the method comprises the following steps:
s1, dividing user data stored in the logistics database into a plurality of name file groups according to different division modes of stored names;
s2, sequencing the names according to the sequence of the pinyin letters of the names, wherein the sequence is 1,2, 1, i, n, i represents the ith name file group, and n represents the number of the name file groups;
s3, dividing a plurality of name file groups into a plurality of user folders according to the dividing modes of different users with the same name;
and S4, numbering the user folder groups in sequence, wherein the user folder groups are respectively 1,2, a.
Further, the logistics data information of the user comprises a user sending address, a user sending number, a user sending weight and a user sending amount.
Further, the average value of the sending object weight of each user in each set time period is calculated according to the formula
Expressed as the average value of the mail weights of the jth user in the ith name file group in the r set time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time periodrwijAnd the number of mails of the jth user in the ith name file group in the r set time period is expressed.
Further, at the time of each settingThe average value of the sending money of each user in the time interval is calculated by the formula
Expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
the sum of the x-th mail in the logistics data information of the j-th user in the ith name file group in the r-th set time period is represented as krwijAnd the number of mails of the jth user in the ith name file group in the r set time period is expressed.
Further, the calculation formula of the comprehensive rating influence coefficient of each user isζwijExpressed as the overall rating impact coefficient, λ, of the jth user in the ith name file group1,λ2,λ3Respectively expressed as rating influence weight coefficients, k, corresponding to the number of the sent items, the weight of the sent items and the money of the sent items in the logistics data informationrwijThe number of mails of the jth user in the ith name file group in the r set time period is shown, v is the total number of mails in each logistics data information of each user,
expressed as the average value of the mail weights of the jth user in the ith name file group in the r set time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
the sum of the x-th mail in the logistics data information of the j-th user in the ith name file group in the r-th set time period is represented, and e is represented as a natural number and is equal to 2.718.
Furthermore, each service level includes a first star level, a second star level, a third star level, a fourth star level and a fifth star level, the manager corresponding to the first star service level is a common employee, the manager corresponding to the second star service level is a middle-level employee, the manager corresponding to the third star service level is a high-level employee, the manager corresponding to the fourth star service level is a supervisor, and the manager corresponding to the fifth star service level is a manager.
Has the advantages that:
(1) the invention provides a user database analysis management system based on big data, which divides the user data in a logistics database through a database dividing module, reduces the complexity of query time, thereby avoiding unnecessary repeated query links, saving a large amount of database query time, laying a foundation for logistics data in a later volume database, simultaneously extracting the stored data in a plurality of user folders, counting the mailing address and the mailing number in each logistics data information of each user, obtaining the most mailing address of each user, setting the address as a default address, providing a reliable reference basis for corresponding managers allocated at a later stage, classifying the counted mailing address number according to a set time period, counting the mailing weight and the mailing amount in each logistics data information of each user in each set time period, calculating the average value of the mailing weight and the average value of the mailing amount of each user in each set time period, and intelligent pushing is carried out, so that the integrity and the usability of stored data are ensured, a user can quickly know the past logistics data information, and the experience of the user is improved.
(2) According to the invention, the comprehensive rating influence coefficient of each user is calculated by the analysis server, meanwhile, the service level corresponding to each user is compared and screened by the user level evaluation module, the corresponding regional manager is screened according to the default mail address of each user, and the corresponding manager is allocated according to the service level of each user for butt joint, so that the requirements of part of users are met, the superiority and importance of the users are improved, and the problem of loss of user resources is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a big data-based user database analysis management system includes a database partitioning module, a data extracting module, a data analyzing module, a data processing module, an analysis server, an intelligent pushing module, a user rating evaluating module, a staff allocation module, and a storage database;
the analysis server is respectively connected with the data analysis module, the data processing module, the intelligent pushing module, the user grade evaluation module, the personnel allocation module and the storage database, the data extraction module is respectively connected with the database partitioning module and the data analysis module, the data processing module is connected with the data analysis module, the storage database is respectively connected with the intelligent pushing module and the user grade evaluation module, and the personnel allocation module is connected with the user grade evaluation module;
the database dividing module is used for dividing user data stored in the logistics database into a plurality of name file groups according to different dividing modes of stored names, dividing the name file groups into a plurality of user folders according to the dividing modes of different users with the same name, and reducing the complexity of query time, thereby avoiding unnecessary repeated query links, saving a large amount of database query time, laying a foundation for logistics data in a later-stage volume database, and sending the user folders to the data extraction module;
the user data stored in the logistics database is divided, and the method comprises the following steps:
s1, dividing user data stored in the logistics database into a plurality of name file groups according to different division modes of stored names;
s2, sequencing the names according to the sequence of the pinyin letters of the names, wherein the sequence is 1,2, 1, i, n, i represents the ith name file group, and n represents the number of the name file groups;
s3, dividing a plurality of name file groups into a plurality of user folders according to the dividing modes of different users with the same name;
and S4, numbering the user folder groups in sequence, wherein the user folder groups are respectively 1,2, a.
The data extraction module is used for receiving the user folders sent by the database analysis module, extracting data stored in the received user folders, extracting and storing logistics data information of each user, wherein the logistics data information of the user comprises a user sending address, a user sending number, a user sending weight and a user sending amount, and sending the extracted and stored logistics data information of each user to the data analysis module;
the data analysis module is used for receiving the stored logistics data information of each user sent by the data extraction module and carrying out the received logistics data information of each userCounting, the mailing address in each logistics data information of each user is counted, and a mailing address set Aw in each logistics data information of each user is formedij(a1wij,a2wij,...,alwij,...,avwij)，axwijThe address is expressed as the ith sending address in the logistics data information of the jth user in the ith name file group, and the sending address set in the logistics data information of each user is sent to the analysis server; meanwhile, counting the number of the mails in the logistics data information of each user, classifying the counted number of the mails in the logistics data information of each user according to a set time period, and forming a mail number set Kw of each user in each set time periodij(k1wij,k2wij,...,krwij,...,kfwij)，krwijThe number of the mails of the jth user in the ith name file group in the r-th set time period is represented, and the number set of the mails of the users in each set time period is sent to the data processing module.
The data processing module is used for receiving the consignment number set of each user in each set time period sent by the data analysis module, counting the consignment weight and the consignment amount in each logistics data information of each user in each set time period, and respectively forming the consignment weight set in each logistics data information of each user in each set time period
And a mail amount set in each logistics data information of each user in each set time period
Expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
and the sending amount is represented as the xth sending amount in the logistics data information of the jth user in the ith name file group in the r-th set time period, and the sending weight set and the sending amount set in the logistics data information of each user in each set time period are sent to the analysis server.
The analysis server is used for receiving a mail address set in each piece of logistics data information of each user sent by the data analysis module, receiving a mail weight set and a mail amount set in each piece of logistics data information of each user in each set time period sent by the data processing module, screening the received mail address set in each piece of logistics data information of each user, acquiring the most mail addresses in each piece of logistics data information of each user, setting the most mail addresses in each piece of logistics data information of each user as default addresses, providing reliable reference basis for corresponding managers in later-period distribution, and sending the default mail addresses of each user to the personnel distribution module; meanwhile, the average value of the sending weight of each user and the average value of the sending money amount in each set time period are calculated, and the calculation formula of the average value of the sending weight of each user in each set time period is
Expressed as the average value of the mail weights of the jth user in the ith name file group in the r set time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time periodrwijThe number of mails of the jth user in the ith name file group in the r set time period is represented; the average value of the mail sending money of each user in each set time period is calculated according to the formula
Expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
the sum of the x-th mail in the logistics data information of the j-th user in the ith name file group in the r-th set time period is represented as krwijThe sending number is expressed as the sending number of the jth user in the ith name file group in the r set time period, and the calculated sending weight average value and sending amount average value of the sending of each user in each set time period are sent to the intelligent pushing module;
meanwhile, the analysis server extracts rating influence weight coefficients corresponding to the number of the mails, the weight of the mails and the money of the mails in the logistics data information stored in the storage database respectively, and calculates a comprehensive rating influence coefficient of each user, wherein the calculation formula of the comprehensive rating influence coefficient of each user is
ζwijExpressed as the overall rating impact coefficient, λ, of the jth user in the ith name file group1,λ2,λ3Respectively expressed as rating influence weight coefficients, k, corresponding to the number of the sent items, the weight of the sent items and the money of the sent items in the logistics data informationrwijThe number of mails of the jth user in the ith name file group in the r set time period is shown, v is the total number of mails in each logistics data information of each user,
expressed as the average value of the mail weights of the jth user in the ith name file group in the r set time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
and e is expressed as a natural number and is equal to 2.718, and the calculated comprehensive rating influence coefficient of each user is sent to the user grade evaluation module.
The intelligent pushing module is used for receiving the average value of the sending weight of the sending object and the average value of the sending amount of the sending object of each user in each set time period sent by the analysis server, extracting the common contact ways of each user stored in the storage database, and pushing the average value of the sending weight of the sending object and the average value of the sending amount of the sending object of each user in each set time period to the corresponding user, so that the integrity and the usability of stored data are guaranteed, the user can quickly know past logistics data information, and the experience of the user is improved.
The user grade evaluation module is used for receiving the comprehensive rating influence coefficient of each user sent by the analysis server, extracting the standard comprehensive rating influence coefficient range corresponding to each service grade stored in the storage database, comparing the received comprehensive rating influence coefficient of each user with the stored standard comprehensive rating influence coefficient corresponding to each service grade, screening the service grade corresponding to the comprehensive rating influence coefficient of each user, counting the service grade corresponding to each user, and sending the service grade corresponding to each user to the personnel distribution module.
The personnel allocation module is used for receiving the default mail address of each user sent by the analysis server, receiving the service level corresponding to each user sent by the user level evaluation module, screening corresponding region managers according to the default mail address of each user, and allocating the corresponding managers to be in butt joint according to the service level of each user, so that the requirements of part of users are met, the superiority and the importance of the users are improved, and the problem of loss of user resources is avoided.
The storage database is used for storing logistics data information of each user, and simultaneously storing rating influence weight coefficients corresponding to the number of the mailpieces, the weight of the mailpieces and the money of the mailpieces in the logistics data information, and the rating influence weight coefficients are respectively expressed as lambda1,λ2,λ3And storing the common contact ways of the users and the standard comprehensive rating influence coefficient range corresponding to each service level, wherein each service level comprises a first-star level, a second-star level, a third-star level, a fourth-star level and a fifth-star level, the manager corresponding to the first-star service level is a common employee, the manager corresponding to the second-star service level is a middle-level employee, the manager corresponding to the third-star service level is a high-level employee, the manager corresponding to the fourth-star service level is a supervisor, and the manager corresponding to the fifth-star service level is a manager.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (7)
1. A big data-based user database analysis management system is characterized in that: the system comprises a database partitioning module, a data extraction module, a data analysis module, a data processing module, an analysis server, an intelligent pushing module, a user grade evaluation module, a personnel allocation module and a storage database;
the analysis server is respectively connected with the data analysis module, the data processing module, the intelligent pushing module, the user grade evaluation module, the personnel allocation module and the storage database, the data extraction module is respectively connected with the database partitioning module and the data analysis module, the data processing module is connected with the data analysis module, the storage database is respectively connected with the intelligent pushing module and the user grade evaluation module, and the personnel allocation module is connected with the user grade evaluation module;
the database dividing module is used for dividing user data stored in the logistics database into a plurality of name file groups according to different dividing modes of stored names, dividing the name file groups into a plurality of user folders according to the dividing modes of different users with the same name, and sending the user folders to the data extraction module;
the data extraction module is used for receiving the user folders sent by the database analysis module, extracting data stored in the received user folders, extracting the stored logistics data information of each user, and sending the extracted and stored logistics data information of each user to the data analysis module;
the data analysis module is used for receiving the stored logistics data information of each user sent by the data extraction module, counting the received logistics data information of each user, counting the mail addresses in the logistics data information of each user, and forming a mail address set Aw in the logistics data information of each userij(a1wij,a2wij,...,alwij,...,avwij)，axwijThe address is expressed as the ith sending address in the logistics data information of the jth user in the ith name file group, and the sending address set in the logistics data information of each user is sent to the analysis server; meanwhile, counting the number of the mails in the logistics data information of each user, classifying the counted number of the mails in the logistics data information of each user according to a set time period, and forming a mail number set Kw of each user in each set time periodij(k1wij,k2wij,...,krwij,...,kfwij)，krwijThe number of mails of the jth user in the ith name file group in the r-th set time period is represented, and the number set of mails of all users in all set time periods is sent to the data processing module;
the data processing module is used for receiving the mail number set of each user in each set time period sent by the data analysis module and counting the logistics data information of each user in each set time periodThe medium sending weight and sending money amount respectively form a sending weight set in each logistics data information of each user in each set time period
And a mail amount set in each logistics data information of each user in each set time period
Expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
the method comprises the steps of representing the xth sending amount in logistics data information of the jth user in the ith name file group in the r set time period, and sending a sending weight set and a sending amount set in each logistics data information of each user in each set time period to an analysis server;
the analysis server is used for receiving a mail address set in each piece of logistics data information of each user sent by the data analysis module, receiving a mail weight set and a mail amount set in each piece of logistics data information of each user in each set time period sent by the data processing module, screening the received mail address set in each piece of logistics data information of each user, acquiring the address with the most mails in each piece of logistics data information of each user, setting the address with the most mails in each piece of logistics data information of each user as a default address, and sending the default mail address of each user to the personnel allocation module; meanwhile, calculating the average value of the sending weight and the average value of the sending amount of each user in each set time period, and sending the calculated average value of the sending weight and the calculated average value of the sending amount of each user in each set time period to an intelligent pushing module; extracting rating influence weight coefficients corresponding to the number of the mails, the weight of the mails and the money of the mails in the logistics data information stored in the storage database, calculating a comprehensive rating influence coefficient of each user, and sending the calculated comprehensive rating influence coefficient of each user to a user grade evaluation module;
the intelligent pushing module is used for receiving the average value of the sending weight and the average value of the sending amount of each user in each set time period sent by the analysis server, extracting the common contact ways of each user stored in the storage database, and pushing the average value of the sending weight and the average value of the sending amount of each user in each set time period to the corresponding user;
the user grade evaluation module is used for receiving the comprehensive rating influence coefficient of each user sent by the analysis server, extracting the standard comprehensive rating influence coefficient range corresponding to each service grade stored in the storage database, comparing the received comprehensive rating influence coefficient of each user with the stored standard comprehensive rating influence coefficient corresponding to each service grade, screening the service grade corresponding to the comprehensive rating influence coefficient of each user, counting the service grade corresponding to each user, and sending the service grade corresponding to each user to the personnel distribution module;
the personnel allocation module is used for receiving the default mail address of each user sent by the analysis server, receiving the service level corresponding to each user sent by the user level evaluation module, screening corresponding regional management personnel according to the default mail address of each user, and allocating the corresponding management personnel for butt joint according to the service level of each user;
the storage database is used for storing logistics data information of each user, and simultaneously storing rating influence weight coefficients corresponding to the number of the mailpieces, the weight of the mailpieces and the money of the mailpieces in the logistics data information, and the rating influence weight coefficients are respectively expressed as lambda1,λ2,λ3And storing the common contact ways of the users and the standard comprehensive rating influence coefficient range corresponding to each service level.
2. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the user data stored in the logistics database is divided, and the method comprises the following steps:
s1, dividing user data stored in the logistics database into a plurality of name file groups according to different division modes of stored names;
s2, sequencing the names according to the sequence of the pinyin letters of the names, wherein the sequence is 1,2, 1, i, n, i represents the ith name file group, and n represents the number of the name file groups;
s3, dividing a plurality of name file groups into a plurality of user folders according to the dividing modes of different users with the same name;
and S4, numbering the user folder groups in sequence, wherein the user folder groups are respectively 1,2, a.
3. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the logistics data information of the user comprises a user sending address, a user sending number, a user sending weight and a user sending amount.
4. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the average value of the sending piece weight of each user in each set time period is calculated according to the formula
Expressed as the average value of the mail weights of the jth user in the ith name file group in the r set time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time periodrwijThe number of mails of the jth user in the ith name file group in the ith set time period is expressedTo achieve the purpose.
5. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the average value calculation formula of the mail sending amount of each user in each set time period is
Expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
the sum of the x-th mail in the logistics data information of the j-th user in the ith name file group in the r-th set time period is represented as krwijAnd the number of mails of the jth user in the ith name file group in the r set time period is expressed.
6. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the calculation formula of the comprehensive rating influence coefficient of each user is
ζwijExpressed as the overall rating impact coefficient, λ, of the jth user in the ith name file group1,λ2,λ3Respectively expressed as rating influence weight coefficients, k, corresponding to the number of the sent items, the weight of the sent items and the money of the sent items in the logistics data informationrwijThe number of mails of the jth user in the ith name file group in the r set time period is shown, v is the total number of mails in each logistics data information of each user,
when expressed as the r-th settingAverage of the mail weights of the jth user in the ith name file group in the time period,
expressed as the weight of the xth mail in the logistics data information of the jth user in the ith name file group in the r set time period,
expressed as the average value of the mail money of the jth user in the ith name file group in the r set time period,
the sum of the x-th mail in the logistics data information of the j-th user in the ith name file group in the r-th set time period is represented, and e is represented as a natural number and is equal to 2.718.
7. The big data based analysis and management system for the user database as claimed in claim 1, wherein: the service levels comprise a first star level, a second star level, a third star level, a fourth star level and a fifth star level, the manager corresponding to the first star service level is a common employee, the manager corresponding to the second star service level is a middle employee, the manager corresponding to the third star service level is a high-level employee, the manager corresponding to the fourth star service level is a supervisor, and the manager corresponding to the fifth star service level is a manager.
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