A computer data integration management system and method based on the Internet

Abstract

The present invention relates to the technical field of computer data processing. Specifically, it is a computer data integration management system and method based on the Internet, the computer data integration management system includes a data acquisition module, a data transmission module, a data analysis module and a data integration module; the data acquisition module collects data information on the number of transmission links in the data source front end, the data source log and the data information generated by different identity identification numbers of users; the data transmission module transmits the data information corresponding to the identity identification numbers that need to be integrated; the data analysis module analyzes the identity identification numbers of the same household in the data source and analyzes the importance of the identity identification numbers; the data integration module screens the identity identification numbers that need data integration. The present invention reduces the loss or forgetting of data information by analyzing and integrating the data of different identity identification numbers of users.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to an Internet-based computer data integration management system and method. Background Art

[0002] Computer data refers to the general term for all symbolic media that can be input into a computer and processed by a computer program. It is a general term for numbers, letters, symbols, analog quantities, etc. that have a certain meaning and are used to be input into an electronic computer for processing. Due to the diversity of computer data, a large amount of identical and increasing data source information will appear in computer devices. The same user may register and log in to the same data source with different identity identification numbers, and the user may forget the data information corresponding to different identity identification numbers in the data source. Moreover, when users register and log in to the same data source with different identity identification numbers, the same data will exist in the data source and occupy the computer device memory. In order to save the storage space of the computer device, it is necessary to integrate the data information of different identity identification numbers belonging to users in the data source of the computer device.

[0003] Under the existing technology, when integrating the data information of users with different identity identification numbers in the computer device data source, the data information that needs to be integrated is simply merged, without fully considering the importance of the different identity identification numbers of users and the possibility that the same data information may exist in different identity identification numbers. Summary of the invention

[0004] The object of the present invention is to provide an Internet-based computer data integration management system and method to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a computer data integration management method based on the Internet, the computer data integration management method comprises the following specific steps:

[0006] S100, collecting historical user behavior data from a data source front end through a computer device, analyzing users with different identity identification numbers according to the user behavior data, and obtaining different identity identification numbers of users in the same data source;

[0007] S200, analyzing the user behaviors of different identity identification numbers of the user in the same data source according to the relevant data of the data source log corresponding to different identity identification numbers of the user, and determining the importance of different identity identification numbers of the user;

[0008] S300, filtering data corresponding to different identity identification numbers of users that need to be integrated according to the importance of different identity identification numbers of users;

[0009] S400: Analyze the data integration status of the data sources to be integrated according to the screening results of the data sources, and determine that the user needs to extract data and reorganize the corresponding identity identification number.

[0010] Furthermore, the specific method of obtaining different identity identification numbers belonging to the same user in S100 is as follows:

[0011] S101, defining key behaviors through interface configuration to complete the front-end tracking of the data source of the computer device, collecting user behaviors occurring at the front-end of the data source of the computer device through any front-end tracking of the data source in the computer device, collecting statistics on the user behaviors and sending them to the background data server; obtaining the account and password entered by the user when logging in to the data source of the computer device and the operation behaviors of the user on the data source login page in the computer device through the background data server;

[0012] S102, the operation behaviors Wi and Wv of the identity identification number at two random logins on the data source login page in the computer device are mapped to the n-dimensional space to obtain (X Wi , Y Wi ) and (X Wv , Y Wv ), using the association formula:

[0013] D=X Wi -X Wv 2 +Y Wi -Y Wv 2

[0014] The correlation D between the operation behaviors of the identity identification numbers at the two logins on the data login page of the computer device is calculated. When D is less than the set threshold, it means that the operation behaviors of the identity identification numbers at the two logins on the data login page of the computer device are similar, and it is very likely that the same person logs in on the data source login page. The identity identification numbers at the two logins are marked as the same user. However, since the data information logged in to the data source of the computer device is obtained from the background data service, there must be the same identity identification number logged in multiple times in the data source of the computer device. The identity identification numbers marked as the same user at the two logins are further analyzed.

[0015] S103, map the account number and password marked as the identity identification number when the same user logs in twice to an n-dimensional space, and use the cosine similarity formula to calculate the similarity d of the identity identification number when the same user logs in twice; since the similarity between the account number and the password of the identity identification number when the two logins are selected is 1, it means that the identity identification numbers when the two logins are selected are the same identity identification; only when the similarity is 1 and greater than the set threshold, it means that the account number of the identity identification number when the two logins are different and the password is similar, which can be expressed as different identity identification numbers belonging to the same user; when d∈[C,1), the identity identification numbers when the two logins are selected are different identity identification numbers belonging to the same user; when d=1, the identity identification numbers when the two logins are the same identity identification number belonging to the same user, where C is a constant greater than 0 and less than 1.

[0016] Furthermore, the specific method for determining the importance of different identity identification numbers of the user in S200 is as follows:

[0017] S201, traversing and integrating the identity identification numbers left by the users in the backend data server when logging in to the data source in the computer device to obtain different identity identification numbers belonging to all the same users in the data source of the computer device, and obtaining the relevant data information of different identity identifications belonging to any user through the data source log for analysis; the process of traversing and integrating the identity identification numbers left by the users when logging in to the data source in the computer device is to compare the correlation and similarity of the data information left by the users when logging in to the data source in the computer device, and analyze the different identity identification numbers belonging to all the same users in the data source of the computer device;

[0018] S202, the data information related to the different identities of any one of the users includes the number of times Ta that the different identity identification numbers of the user log in to the data source in the computer device, and the amount of data information generated when the different identity identification numbers of the user log in is ma. According to the formula Ma=Ta*ma, all the data information amounts generated when the different identity identification numbers of the user log in to the data source can be obtained, where a=1, 2, 3, ..., ..., A, where A is a constant;

[0019] S203. Calculate the importance of different identity identification numbers of any user according to the formula Pa=k1*Ma+k2*Ta, where k1 and k2 are the coefficients between the amount of all data information generated by different identity identification numbers of the user when logging in and the number of login times of different identity identification numbers of the user in the data source in the computer device and the importance of different identity identification numbers of the user, and Ta is represented by the number of login times of different identity identification numbers of the user in the data source in the computer device;

[0020] Furthermore, the specific method of screening the data corresponding to different identity identification numbers of users to be integrated in S300 is as follows:

[0021] S301, obtaining data information generated by different identity identification numbers of the user from the computer device data source log, analyzing the data information generated by any one of the different identity identification numbers of the user to obtain the number of transmission links in the data information generated by the identity identification number in the computer device data source, and traversing the different identity identification numbers of the user to obtain the number of transmission links Ra in the data information generated by the different identity identification numbers of the user;

[0022] S302. After sorting Ua from high to low according to the formula Ua=Pa*Ra, the sequence is divided into important identity identification numbers, secondary identity identification numbers and irrelevant identity identification numbers; after eliminating the data information of irrelevant identity identification numbers from the different identity identification numbers of the users to be integrated, the different identity identification numbers of the users are Lb, b=1, 2, 3, ..., B, B is a constant and B is less than A.

[0023] Furthermore, the specific method of analyzing the user's identity identification number corresponding to the data extraction and reorganization in S400 is as follows:

[0024] S401. The integration degree of the data information is calculated according to the data integration analysis model: Yb=k3*Rb+k4*Ub, and the identity identification numbers are sorted from high to low according to the integration degree of the data information, and the identity identification number with the highest integration degree is selected as the data trunk, and the other identity identification numbers except the identity identification number with the highest integration degree are used as branches; all the data in the branches are integrated into the trunk, and the data information in the specific subset of the branches is extracted, and the extracted data information in the specific subset of the branches is sent to the destination location in the trunk for processing; wherein the specific subset in the sub-branch refers to the data information generated in the data source logged in by different identity identification numbers in the computer device, so as to ensure that the relevant data information can be accurately copied from the large amount of data;

[0025] S402. The trunk cleans the data information transmitted by the branches and stores and saves it to complete the integration of data information in data sources corresponding to different identity identification numbers of users in computer devices, wherein the data information cleaning is to compare the similarity between the data information transmitted by the branches and the data information of the trunk, and eliminate the data whose similarity between the data information transmitted by the branches and the data information of the trunk is 1; relying on data cleaning can ensure the standardization of data in the trunk data and reduce the number of repeated data occurrences.

[0026] A computer data integration management system based on the Internet, the computer data integration management system comprises a data acquisition module, a data transmission module, a data analysis module and a data integration module; the data acquisition module collects the data information of the front end of the data source of the computer equipment, the data information in the data source log and the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission module transmits the data information corresponding to the identity identification numbers to be integrated to the identity identification number data information to be integrated; the data analysis module analyzes the identity identification numbers of the same user in the computer equipment data source and analyzes the importance of different identity identification numbers of the users; the data integration module analyzes and selects the identity identification numbers to be integrated among the different identity identification numbers of the users.

[0027] Furthermore, the data collection module includes a data source front-end data collection unit, a data source log information collection unit and a transmission link collection unit; the data source front-end data collection unit collects user behaviors occurring at the data source front-end of the computer device at any data source front-end point in the computer device, wherein the user behaviors include the account and password entered by the user when logging in to the data source of the computer device and the user's operation behaviors on the data source login page in the computer device. The importance of different identity identification numbers of the user can be analyzed based on the account and password entered by the user when logging in to the data source of the computer device and the user's operation behaviors on the data source login page in the computer device; the data source log information collection unit collects data information generated after different identity identification numbers of the user log in to the data source; the transmission link collection unit collects the number of transmission links between data information generated after different identity identification numbers of the user log in to the data source. By collecting the transmission links between the data information, the correlation of data in different identity identification numbers can be analyzed. The more transmission links there are between the data information, the more important the data information is.

[0028] Furthermore, the data transmission module includes a data storage unit and a data transmission unit; the data storage unit stores the collected front-end data information of the computer device data source, the data information in the data source log, and the data information on the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission unit transmits the data to be integrated to a designated location when integrating the data of different identity identification numbers of the users.

[0029] Furthermore, the data analysis module includes a data information volume analysis unit, a user's different identity identification number analysis unit and an identity identification number importance analysis unit; the data information volume analysis unit performs analysis based on the data information volume generated by the user logging in to the computer device data source, and the data information volume generated by the user logging in to the computer device data source is one of the key factors for judging the importance of the identity identification number; the user's different identity identification number analysis unit analyzes all the identity identification numbers in the computer device data source to obtain different identity identification numbers belonging to each user; the identity identification number importance analysis unit analyzes the importance of the user's identity identification number, and after analyzing the importance, the identity identification numbers that are not related to data integration can be eliminated.

[0030] Furthermore, the data integration module includes a data integration analysis unit and an identity number integration screening unit; the data integration analysis unit analyzes the data information of the identity numbers that need to be integrated, determines which identity number is used as the main identity number for data integration, and integrates the data information of the remaining identity numbers into the main identity number; the identity number integration screening unit screens the identity numbers that are not related to the data integration based on the importance of different identity numbers belonging to the user.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention obtains the user behavior at the front end of the computer device data source to analyze the importance of different identity identification numbers of users, eliminates data information of identity identification numbers that are irrelevant to data integration, and reduces the retrieval time when subsequently integrating data; when integrating data information of users with different identity identification numbers, the same data information is eliminated, reducing the occupation of computer device storage space; and integrating data of users with different identity identification numbers reduces the loss of data information and the occurrence of user forgetfulness. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 It is a structural diagram of a computer data integration management system based on the Internet. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 The present invention provides a technical solution: a computer data integration management method based on the Internet, the computer data integration management method comprises the following specific steps:

[0036] S100, collecting historical user behavior data from a data source front end through a computer device, analyzing users with different identity identification numbers according to the user behavior data, and obtaining different identity identification numbers of users in the same data source;

[0037] S200, analyzing the user behaviors of different identity identification numbers of the user in the same data source according to the relevant data of the data source log corresponding to different identity identification numbers of the user, and determining the importance of different identity identification numbers of the user;

[0038] S300, filtering data corresponding to different identity identification numbers of users that need to be integrated according to the importance of different identity identification numbers of users;

[0039] S400: Analyze the data integration status of the data sources to be integrated according to the screening results of the data sources, and determine that the user needs to extract data and reorganize the corresponding identity identification number.

[0040] Furthermore, the specific method of obtaining different identity identification numbers belonging to the same user in S100 is as follows:

[0041] S101, defining key behaviors through interface configuration to complete the front-end tracking of the data source of the computer device, collecting user behaviors occurring at the front-end of the data source of the computer device through any front-end tracking of the data source in the computer device, collecting statistics on the user behaviors and sending them to the background data server; obtaining the account and password entered by the user when logging in to the data source of the computer device and the operation behaviors of the user on the data source login page in the computer device through the background data server;

[0042] S102, the operation behaviors Wi and Wv of the data source login page of the computer device at the time of the identity identification number at two random logins in the data information obtained by the background data server are mapped to the n-dimensional space to obtain (5, 6) and (7, 4) respectively, and the association formula is used:

[0043] D=5-7 2 +6-4 2 ≈2.83

[0044] The correlation D between the operation behaviors of the identity identification numbers at the two logins on the data login page of the computer device is calculated. When D is less than the set threshold value 4, it means that the operation behaviors of the identity identification numbers at the two logins on the data login page of the computer device are similar, and it is very likely that the same person logs in to the data source login page. The identity identification numbers at the two logins are marked as the same user. However, since the data information logged in to the data source of the computer device is obtained from the background data service, there must be the same identity identification number logged in multiple times in the data source of the computer device. The identity identification numbers marked as the same user at the two logins are further analyzed.

[0045] S103, map the account number and password marked as the identity identification number when the same user logs in twice to an n-dimensional space, and use the cosine similarity formula to calculate the similarity d of the identity identification number when the same user logs in twice; since the similarity between the account number and the password of the identity identification number when the two logins are selected is 1, it means that the identity identification numbers when the two logins are selected are the same identity identification; only when the similarity is 1 and greater than the set threshold, it means that the account number of the identity identification number when the two logins are different and the password is similar, which can be expressed as different identity identification numbers belonging to the same user; when d∈[C,1), the identity identification numbers when the two logins are selected are different identity identification numbers belonging to the same user; when d=1, the identity identification numbers when the two logins are the same identity identification number belonging to the same user, where C is a constant greater than 0 and less than 1.

[0046] Furthermore, the specific method for determining the importance of different identity identification numbers of the user in S200 is as follows:

[0047] S201, traversing and integrating the identity identification numbers left by the users in the backend data server when logging in to the data source in the computer device to obtain different identity identification numbers belonging to all the same users in the data source of the computer device, and obtaining the relevant data information of different identity identifications belonging to any user through the data source log for analysis; the process of traversing and integrating the identity identification numbers left by the users when logging in to the data source in the computer device is to compare the correlation and similarity of the data information left by the users when logging in to the data source in the computer device, and analyze the different identity identification numbers belonging to all the same users in the data source of the computer device;

[0048] S202, the data information related to the different identities of any one user includes the number of times the different identity identification numbers of the user log in to the data source in the computer device 6, the amount of data information generated by the different identity identification numbers of the user when logging in is 4, according to the formula Ma=Ta*ma=24, all the data information amounts generated by the different identity identification numbers of the user when logging in to the data source can be obtained;

[0049] S203. According to the formula Pa=k1*Ma+k2*Ta=0.8*24+0.2*6=20.4, the importance of different identity identification numbers belonging to any user is calculated.

[0050] Furthermore, the specific method of screening the data corresponding to different identity identification numbers of users to be integrated in S300 is as follows:

[0051] S301, obtaining data information generated by different identity identification numbers of the user from the computer device data source log, analyzing the data information generated by any one of the different identity identification numbers of the user to obtain the number of transmission links in the data information generated by the identity identification number in the computer device data source, and traversing the different identity identification numbers of the user to obtain the number of transmission links Ra in the data information generated by the different identity identification numbers of the user;

[0052] S302. After sorting Ua from high to low according to the formula Ua=Pa*Ra, the sequence is divided into important identity identification numbers, secondary identity identification numbers and irrelevant identity identification numbers; after eliminating the data information of irrelevant identity identification numbers from the different identity identification numbers of the users to be integrated, the different identity identification numbers of the users are Lb, b=1, 2, 3, ..., B, B is a constant and B is less than A.

[0053] Furthermore, the specific method of analyzing the user's identity identification number corresponding to the data extraction and reorganization in S400 is as follows:

[0054] S401. The integration degree of the data information is calculated according to the data integration analysis model: Yb=0.7*Rb+0.3*Ub, and the identity identification numbers are sorted from high to low according to the integration degree of the data information, and the identity identification number with the highest integration degree is selected as the data trunk, and the other identity identification numbers except the identity identification number with the highest integration degree are used as branches; all the data in the branches are integrated into the trunk, and the data information in the specific subset of the branches is extracted, and the extracted data information in the specific subset of the branches is sent to the destination location in the trunk for processing; wherein the specific subset in the sub-branch refers to the data information generated in the data source logged in by different identity identification numbers in the computer device, so as to ensure that the relevant data information can be accurately copied from the large amount of data;

[0055] S402. The trunk cleans the data information transmitted by the branches and stores and saves it to complete the integration of data information in data sources corresponding to different identity identification numbers of users in computer devices, wherein the data information cleaning is to compare the similarity between the data information transmitted by the branches and the data information of the trunk, and eliminate the data whose similarity between the data information transmitted by the branches and the data information of the trunk is 1; relying on data cleaning can ensure the standardization of data in the trunk data and reduce the number of repeated data occurrences.

[0056] A computer data integration management system based on the Internet, the computer data integration management system comprises a data acquisition module, a data transmission module, a data analysis module and a data integration module; the data acquisition module collects the data information of the front end of the data source of the computer equipment, the data information in the data source log and the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission module transmits the data information corresponding to the identity identification numbers to be integrated to the identity identification number data information to be integrated; the data analysis module analyzes the identity identification numbers of the same user in the computer equipment data source and analyzes the importance of different identity identification numbers of the users; the data integration module analyzes and selects the identity identification numbers to be integrated among the different identity identification numbers of the users.

[0057] Furthermore, the data collection module includes a data source front-end data collection unit, a data source log information collection unit and a transmission link collection unit; the data source front-end data collection unit collects user behaviors occurring at the data source front-end of the computer device at any data source front-end point in the computer device, wherein the user behaviors include the account and password entered by the user when logging in to the data source of the computer device and the user's operation behaviors on the data source login page in the computer device. The importance of different identity identification numbers of the user can be analyzed based on the account and password entered by the user when logging in to the data source of the computer device and the user's operation behaviors on the data source login page in the computer device; the data source log information collection unit collects data information generated after different identity identification numbers of the user log in to the data source; the transmission link collection unit collects the number of transmission links between data information generated after different identity identification numbers of the user log in to the data source. By collecting the transmission links between the data information, the correlation of data in different identity identification numbers can be analyzed. The more transmission links there are between the data information, the more important the data information is.

[0058] Furthermore, the data transmission module includes a data storage unit and a data transmission unit; the data storage unit stores the collected front-end data information of the computer device data source, the data information in the data source log, and the data information on the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission unit transmits the data to be integrated to a designated location when integrating the data of different identity identification numbers of the users.

[0059] Furthermore, the data analysis module includes a data information volume analysis unit, a user's different identity identification number analysis unit and an identity identification number importance analysis unit; the data information volume analysis unit performs analysis based on the data information volume generated by the user logging in to the computer device data source, and the data information volume generated by the user logging in to the computer device data source is one of the key factors for judging the importance of the identity identification number; the user's different identity identification number analysis unit analyzes all the identity identification numbers in the computer device data source to obtain different identity identification numbers belonging to each user; the identity identification number importance analysis unit analyzes the importance of the user's identity identification number, and after analyzing the importance, the identity identification numbers that are not related to data integration can be eliminated.

[0060] Furthermore, the data integration module includes a data integration analysis unit and an identity number integration screening unit; the data integration analysis unit analyzes the data information of the identity numbers that need to be integrated, determines which identity number is used as the main identity number for data integration, and integrates the data information of the remaining identity numbers into the main identity number; the identity number integration screening unit screens the identity numbers that are not related to the data integration based on the importance of different identity numbers belonging to the user.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0062] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A computer data integration management method based on the Internet, characterized in that: The computer data integration management method comprises the following specific steps: S100, collecting historical user behavior data from a data source front end through a computer device, analyzing users with different identity identification numbers according to the user behavior data, and obtaining different identity identification numbers of users in the same data source; S200, analyzing the user behaviors of different identity identification numbers of the user in the same data source according to the relevant data of the data source log corresponding to different identity identification numbers of the user, and determining the importance of different identity identification numbers of the user; S300, filtering data corresponding to different identity identification numbers of users that need to be integrated according to the importance of different identity identification numbers of users; S400, analyzing the data integration status of the data sources to be integrated according to the screening results of the data sources, and determining that the user needs to extract data and reorganize the corresponding identity identification number; The specific method of obtaining different identity identification numbers belonging to the same user in S100 is as follows: S101, collecting user behaviors occurring at the front end of a data source of a computer device by embedding points at the front end of any data source in the computer device, collecting statistics on the user behaviors and sending them to a background data server; The user's account number and password entered when logging in to the data source of the computer device and the user's operation behavior on the data source login page of the computer device are obtained through the backend data server; S102, the operation behaviors Wi and Wv of the identity identification number at two random logins on the data source login page in the computer device are mapped to the n-dimensional space to obtain (X Wi , Y Wi ) and (X Wv , Y Wv ), using the association formula: Calculate the correlation D between the operation behaviors of the identity identification numbers at the two logins on the data login page of the computer device, and when D is less than a set threshold, mark the identity identification numbers at the two logins as the same user, and further analyze the identity identification numbers at the two logins marked as the same user; S103, mapping the account number and password marked as the identity identification number of the same user when logging in twice to an n-dimensional space, and calculating the similarity of the identity identification number marked as the same user when logging in twice using the cosine similarity formula as d; When d∈[C,1), the identity identification number at any two logins is different from the identity identification number of the same user; when d=1, the identity identification number at any two logins is the same identity identification number of the same user, where C is a constant greater than 0 and less than 1; The specific method for determining the importance of different identity identification numbers of the user in S200 is as follows: S201, traversing and integrating the identity identification numbers left by the users in the backend data server when logging into the data source in the computer device to obtain different identity identification numbers of all the same users in the data source of the computer device, and obtaining the relevant data information of different identity identifications of any user through the data source log for analysis; S202, the data information related to the different identity identifications of any one of the users includes the number of times Ta that the different identity identification numbers of the user log in to the data source in the computer device, and the amount of data information generated when the different identity identification numbers of the user log in is ma. According to the formula Ma=Ta*ma, all the data information amounts generated when the different identity identification numbers of the user log in to the data source can be obtained, where a=1, 2, 3, ..., A, and A is a constant; S203, according to the formula Pa = k1*Ma+k2*Ta, calculate the importance of different identity identification numbers belonging to any user, where k1 and k2 are the coefficients between the amount of all data information generated by different identity identification numbers of the user when logging in and the number of login times of different identity identification numbers of the user in the data source of the computer device and the importance of different identity identification numbers of the user; The specific method of screening the data corresponding to different identity identification numbers of users to be integrated in S300 is as follows: S301, obtaining data information generated by different identity identification numbers of the user from the computer device data source log, analyzing the data information generated by any one of the different identity identification numbers of the user to obtain the number of transmission links in the data information generated by the identity identification number in the computer device data source, and traversing the different identity identification numbers of the user to obtain the number of transmission links Ra in the data information generated by the different identity identification numbers of the user; S302, after sorting Ua from high to low by formula Ua=Pa*Ra, the sequence is divided into important identity identification numbers, secondary identity identification numbers and irrelevant identity identification numbers; after removing the data information of irrelevant identity identification numbers from the different identity identification numbers of the users to be integrated, the different identity identification numbers of the users are Lb, b=1, 2, 3, ..., B, B is a constant and B is less than A; The specific method of analyzing the user's identity identification number for data extraction and reorganization in S400 is as follows: S401. The integration degree of data information is calculated according to the data integration analysis model: Yb=k3*Rb+k4*Ub, and the identity identification numbers are sorted from high to low according to the integration degree of data information, and the identity identification number with the highest integration degree is selected as the data trunk, and the other identity identification numbers except the identity identification number with the highest integration degree are used as branches; all the data in the branches are integrated into the trunk, and the data information in a specific subset of the branches is extracted, and the extracted data information in the specific subset of the branches is sent to the destination location in the trunk for processing; wherein K3 and K4 are respectively the number of transmission links in the data information generated by different identity identification numbers of users and the coefficient between the importance of the identity identification number and the integration degree of data information; S402, the trunk cleans the data information transmitted by the branch and stores it to complete the integration of the data information in the data source corresponding to the different identity identification numbers of the users in the computer device, wherein the data information cleaning is to compare the similarity between the data information transmitted by the branch and the data information of the trunk, and eliminate the data whose similarity between the data information transmitted by the branch and the data information of the trunk is 1.

2. A computer data integration management system using the Internet-based computer data integration management method of claim 1, characterized in that: The computer data integration management system includes a data acquisition module, a data transmission module, a data analysis module and a data integration module; the data acquisition module collects the data information of the front end of the data source of the computer equipment, the data information in the data source log and the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission module transmits the data information corresponding to the identity identification number to be integrated to the data information of the identity identification number to be integrated; The data analysis module analyzes the identity identification numbers of the same user in the computer device data source and analyzes the importance of different identity identification numbers of the user; the data integration module analyzes and selects the identity identification numbers that need to be integrated among the different identity identification numbers of the user.

3. The computer data integration management system according to claim 2, characterized in that: The data acquisition module includes a data source front-end data acquisition unit, a data source log information acquisition unit and a transmission link acquisition unit; The data source front-end data collection unit collects user behaviors occurring at the data source front-end of the computer device at any data source front-end embedding point in the computer device; The data source log information collection unit collects data information generated after different identity identification numbers of users log in to the data source; The transmission link collection unit collects the number of transmission links between data information generated after different identity identification numbers of users log in to the data source.

4. The computer data integration management system according to claim 3, characterized in that: The data transmission module includes a data storage unit and a data transmission unit; the data storage unit stores the collected data information of the front-end of the computer device data source, the data information in the data source log, and the data information of the number of transmission links in the data information generated by different identity identification numbers of the users; the data transmission unit transmits the data to be integrated to the designated location when integrating the data of different identity identification numbers of the users.

5. The computer data integration management system according to claim 4, characterized in that: The data analysis module includes a data information volume analysis unit, a user's different identity identification number analysis unit and an identity identification number importance analysis unit; the data information volume analysis unit performs analysis based on the data information volume generated by the user logging into the computer device data source; the user's different identity identification number analysis unit analyzes all the identity identification numbers in the computer device data source to obtain different identity identification numbers for each user; the identity identification number importance analysis unit analyzes the importance of the user's identity identification number.

6. The computer data integration management system according to claim 5, characterized in that: The data integration module includes a data integration analysis unit and an identity number integration screening unit; the data integration analysis unit analyzes the data information that needs to be integrated with the identity number; the identity number integration screening unit screens the identity numbers that are not related to data integration based on the importance of different identity numbers belonging to the user.

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