A consulting data management system and method based on data analysis

Abstract

The present invention discloses a consulting data management system and method based on data analysis, which relates to the technical field of data management, including: using a consulting forgetting analysis method to obtain the number of data repetitions CF and establish a preliminary deletion strategy; obtaining recovery data in the data to be deleted; optimizing the preliminary deletion strategy based on the recovery data, and using the optimized deletion strategy to manage the consulting data; the present invention is used to solve the problem that in the prior art, in terms of deleting consulting data, the consulting data is uniformly deleted only through existing preset conditions, and the consulting data cannot be deleted in combination with the storage situation of the consulting data of the consultant in the consulting data, which will cause the loss of part of the consulting data consulted by the consultant.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a consulting data management system and method based on data analysis. Background Art

[0002] Data is the expression and carrier of information, which can be symbols, text, numbers, voice, images, videos, etc.; information consulting is a business activity based on the collection, processing, transmission, effective use and feedback of various information; consulting data is the data generated by consultants when conducting information consulting, which usually occurs in corporate consulting.

[0003] In the existing methods for consulting data management, the compression effect of consulting data is usually improved, so that the compressed consulting data is easier to store and transmit, thereby improving the storage efficiency and transmission efficiency of consulting data. Although this improved method can reduce the space occupied by consulting data, when the amount of consulting data is large and the storage space is limited, if the consulting data is not deleted, it will still cause problems such as insufficient storage space and reduced transmission efficiency. For example, in the Chinese patent with publication number CN117216022A, a digital engineering consulting data management system is disclosed. This scheme is to improve the storage and transmission efficiency of data in the digital engineering consulting data management system by improving the compression effect of data. At the same time, in the existing improvements in deletion for consulting data management, the consulting data is usually deleted uniformly according to the existing preset conditions, and the deletion cannot be combined with the storage situation of the consulting data of the consultant in the consulting data. This will cause when the same consultant conducts different consultations at different times, if the consultation data is deleted only according to the existence time or the file size of the consultation data, part of the consulting data consulted by the consultant will be missing, resulting in the consultant facing the risk of data missing. In view of this, it is necessary to improve the existing management of consulting data. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. It proposes a consulting data management system and method based on data analysis to solve the problem that in the prior art, the consulting data is only uniformly deleted according to the existing preset conditions, and the consulting data cannot be deleted in combination with the storage situation of the consultant in the consulting data, which will cause part of the consulting data consulted by the consultant to be missing.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a consulting data management system based on data analysis, including a preliminary deletion establishment module, a retained data analysis module, and a deletion management module;

[0006] The preliminary deletion establishment module is used to obtain the data repetition number CF of each consultation type in the consultation database based on the Ebbinghaus forgetting curve and the consultation data using the consultation forgetting analysis method, and establish a preliminary deletion strategy based on the data repetition number CF;

[0007] The retained data analysis module is used to perform data analysis on the consulting data based on the preliminary deletion strategy, and obtain the retained data in the consulting data based on the analysis result;

[0008] The deletion management module is used to optimize the preliminary deletion strategy based on the retained data. When the preliminary deletion strategy is used for data analysis, consulting data other than the retained data is deleted first.

[0009] Furthermore, the consultation forgetting analysis method includes: recording a database for storing consultation data as a consultation database, obtaining multiple time nodes corresponding to the Ebbinghaus forgetting curve based on big data, and recording the multiple time nodes in chronological order as forgetting time YW 1 Until the time of forgetting YW h ; Record the type of consultation data in the consultation database as consultation type ZL. For any consultation data in any consultation type ZL, obtain the consultation record with the forgetting time YW from the current time. 1 Until the time of forgetting YW h The number of times the internal consultation data is consulted by different consultants is recorded as the number of data repetitions CF. 1 To data repetition times CF h , where the consultant is the user who conducts data consultation.

[0010] Furthermore, the consultation forgetting analysis method also includes:

[0011] Get the data repetition count CF of all consulting data of consulting type ZL; for the data repetition count CF 1 To data repetition times CF h The number of repetitions of any data in CF h1 , use the type repetition algorithm to calculate the number of data repetitions CF for all consulting data h1 Calculate and get the number of repetitions CF of the data of consultation type ZL h1 , the type repetition algorithm is: Where M is the number of repetitions of the data of the consultation type ZL CF h1 , m i The number of repetitions CF of the i-th consultation data in the consultation type ZL h1 , n is the number of consultation data in consultation type ZL, m max The number of data repetitions CF for all consulting data h1 The maximum value, m minThe number of data repetitions CF for all consulting data h1 The minimum value of The number of data repetitions CF for all consulting data h1 The average value; based on the type repetition algorithm, obtain the number of repetitions CF of all data corresponding to the consultation type ZL;

[0012] Get the number of data repetitions CF for each consultation type in the consultation database.

[0013] Furthermore, the preliminary deletion strategy is: for any consulting data in the consulting database, it is recorded as data to be deleted, the consulting type of the data to be deleted is recorded as the deletion reference type, and the time difference between the acquisition time of the data to be deleted and the current time is recorded as the deletion reference time; the forgetting time YW that is greater than the deletion reference time and has the smallest difference with the deletion reference time is recorded as the reference forgetting time; the number of data repetitions CF corresponding to the reference forgetting time in the data repetition number CF of the deletion reference type is recorded as the deletion retention number; the latest acquired deletion retention number of data to be deleted in the consulting data corresponding to the deletion reference type is obtained, and recorded as storable data; when the data to be deleted does not exist in the storable data, the data to be deleted is deleted in the consulting database and put into the recycle bin, and the consultant corresponding to the data to be deleted is recorded; when the data to be deleted exists in the storable data, the data to be deleted is retained in the consulting database, and the mark of the retained data to be deleted is restored to the consulting data.

[0014] Furthermore, the reserved data analysis module includes a reserved data acquisition unit, and the reserved data acquisition unit is configured with a reserved data acquisition strategy, and the reserved data acquisition strategy includes:

[0015] Based on the preliminary deletion strategy, the consultation data in the consultation database are processed, and the consultant of the data to be deleted placed in the recycle bin is recorded after each deletion of the consultation data; for any recorded consultant, the number of data to be deleted corresponding to the consultant in the recycle bin is recorded as the number to be deleted, and the number of consultation data corresponding to the consultant in the consultation database is recorded as the number of consultations; a plane rectangular coordinate system is established, recorded as the recovery analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the recovery analysis coordinate system are the forgetting time YW respectively. 1 Until the time of forgetting YW h , the unit of the Y axis is quantity; obtain the reference forgetting time corresponding to all the data to be deleted of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the number of points is equal, and the points are marked in the recovery analysis coordinate system as the points to be deleted. For the h1th point to be deleted from left to right, the horizontal coordinate of the point to be deleted is the forgetting time YW h1The vertical coordinate of the point to be deleted is the reference forgetting time of all the data to be deleted of the consultant, which is the forgetting time YW. h1 , and the sum of the ordinates of all points to be deleted is the number to be deleted.

[0016] Furthermore, the retention data acquisition strategy also includes:

[0017] Obtain the reference forgetting time corresponding to all consulting data of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the ... h1 The vertical coordinate of the consulting point is the reference forgetting time in all consulting data of the consultant, which is the forgetting time YW. h1 The sum of the vertical coordinates of all consultation points is the number of consultations.

[0018] Furthermore, the retention data acquisition strategy also includes:

[0019] The curve obtained by fitting all the points to be deleted is recorded as the curve to be deleted; the curve obtained by fitting all the consulting points is recorded as the consulting curve; the curve to be deleted, the X-axis, the Y-axis and X = forgetting time YW h The enclosed area is recorded as the area to be deleted; the consultation curve, X-axis, Y-axis and X = forgetting time YW h The enclosed area is recorded as the consulting area; when the area of ​​the consulting area is greater than or equal to the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, (SS 1 -SS 2 ) / SS 2 The value of is recorded as the recovery ratio; when the area of ​​the consultation area is smaller than the area of ​​the area to be deleted or the area of ​​the area to be deleted is 0, the recovery ratio is recorded as 0, where SS 1 is the area of ​​the consultation area, SS 2 is the area of ​​the region to be deleted.

[0020] Furthermore, the retention data acquisition strategy also includes:

[0021] The value of multiplying the number of to-be-deleted data by the recovery ratio and rounding up is recorded as the recovery amount, and the restored data of the consultant whose acquisition time of the to-be-deleted data is closest to the current time is put back into the consultation database and the mark of the returned to-be-restored data is restored to the consultation data.

[0022] Furthermore, the retention data acquisition strategy also includes: acquiring the number of consultants in the consultation database, recorded as the total number of consultations; when the number of data put back into the consultation database by any consultant from the recycle bin is greater than the total number of consultations, the consultant is recorded as a retained consultant, and when the acquisition time of the consultation data of the retained consultant in the preliminary deletion strategy is the same as the acquisition time of other consultation data, the consultation data of the retained consultant is preferentially recorded as retained data; when the consultation data of any consultant is put into the recycle bin, the consultant is recorded as a retained consultant, and when the latest consultation data of the retained consultant is acquired, the latest consultation data of the retained consultant is recorded as priority retained data, and when the acquisition time of the priority retained data is the same as the acquisition time of other consultation data, the priority retained data is preferentially recorded as retained data.

[0023] In a second aspect, the present application also provides a consulting data management method based on data analysis, comprising the following sub-steps:

[0024] Based on the Ebbinghaus forgetting curve and consulting data, the consulting forgetting analysis method is used to obtain the data duplication times CF of each consulting type in the consulting database, and a preliminary deletion strategy is established based on the data duplication times CF;

[0025] Performing data analysis on the consulting data based on the preliminary deletion strategy, and obtaining the retained data in the consulting data based on the analysis results;

[0026] The preliminary deletion strategy is optimized based on the retained data. When the preliminary deletion strategy is used for data analysis, consulting data other than the retained data is deleted first.

[0027] Beneficial effects of the present invention: The present invention first uses the consultation forgetting analysis method to obtain the data repetition number CF of each consultation type in the consultation database, and establishes a preliminary deletion strategy based on the data repetition number CF. The advantage of this is that by using the consultation forgetting analysis method to obtain the data repetition number CF, the consultation situation of the same consultation data in each time period can be obtained, which helps to put the consultation data of the same type of consultation data in the same time period into the recycling bin when the consultation data is put into the recycling bin in the preliminary deletion strategy, thereby ensuring that the retained consultation data is the consultation data that is consistent with the consultation situation of the consultation data;

[0028] The present invention also performs data analysis on the consulting data based on the preliminary deletion strategy, and obtains the recovery data in the data to be deleted based on the analysis results; finally, the preliminary deletion strategy is optimized based on the recovery data, and the optimized preliminary deletion strategy is recorded as the optimized deletion strategy, and the consulting data is managed using the optimized deletion strategy. The advantage of this is that the consulting data placed in the recycle bin can be effectively analyzed, and the deleted consulting data can be restored in combination with the storage status of the consultant's consulting data, to prevent the loss of part of the consulting data consulted by the consultant, and further ensure that the deleted consulting data are invalid and will not cause risks to the consultant. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the steps of the method of the present invention;

[0030] Figure 2 It is a schematic diagram of the consulting curve and the curve to be deleted of the present invention;

[0031] Figure 3 It is a schematic diagram of obtaining the recovery ratio of the present invention;

[0032] Figure 4 It is a functional block diagram of the system of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] Example 1, first aspect, please refer to Figure 1 As shown, the present application provides a consulting data management method based on data analysis, comprising the following sub-steps:

[0035] Step S1, using the consultation forgetting analysis method based on the Ebbinghaus forgetting curve and the consultation data to obtain the data repetition number CF of each consultation type in the consultation database, and establishing a preliminary deletion strategy based on the data repetition number CF;

[0036] The consultation forgetting analysis method includes the following sub-steps: Step S1011, record the database used to store consultation data as the consultation database, obtain multiple time nodes corresponding to the Ebbinghaus forgetting curve based on big data, and record the multiple time nodes in chronological order as the forgetting time YW 1 Until the time of forgetting YW h; Record the type of consultation data in the consultation database as consultation type ZL. For any consultation data in any consultation type ZL, obtain the consultation record with the forgetting time YW from the current time. 1 Until the time of forgetting YW h The number of times the internal consultation data is consulted by different consultants is recorded as the number of data repetitions CF. 1 To data repetition times CF h , where the consultant is the user who conducts data consultation;

[0037] In the specific implementation process, by obtaining multiple time periods corresponding to the Ebbinghaus forgetting curve, it is possible to obtain the generation of the consultant's consulting data in different time periods from the perspective of the consultant's memory, so that the analysis method in this embodiment can be consistent with the consultant's memory of the consulting data, so as to ensure that the removed consulting data are data that the consultant will basically not consult again when removing invalid consulting data; at the same time, in this embodiment, the forgetting time YW 1 Until the time of forgetting YW h They are recorded as 20 minutes, 1 hour, 9 hours, 1 day, 2 days, 9 days and 21 days respectively, that is, h is 7;

[0038] Step S1012, obtaining the data repetition times CF of all consulting data in the consulting type ZL; 1 To data repetition times CF h The number of repetitions of any data in CF h1 , use the type repetition algorithm to calculate the number of data repetitions CF for all consulting data h1 Calculate and get the number of repetitions CF of the data of consultation type ZL h1 , the type repetition algorithm is: Where M is the number of repetitions of the data of the consultation type ZL CF h1 , m i The number of repetitions CF of the i-th consultation data in the consultation type ZL h1 , n is the number of consultation data in consultation type ZL, m max The number of data repetitions CF for all consulting data h1 The maximum value, m min The number of data repetitions CF for all consulting data h1 The minimum value of The number of data repetitions CF for all consulting data h1 The average value; based on the type repetition algorithm, obtain the number of repetitions CF of all data corresponding to the consultation type ZL;

[0039] In specific implementation, for example, in an analysis, the consultation type ZL is logistics management, and the consultation data in logistics management includes: price consultation data, weight consultation data, account consultation data, and vehicle consultation data; at the same time, the data repetition times CF corresponding to the consultation data in logistics management can be found in Table 1: The repetition times table is shown as follows:

[0040] Table 1: Repetition times table

[0041]

[0042] Through analysis, it can be obtained that the number of data repetitions CF of all consulting data 2 They are 40, 40, 10 and 10 respectively. By calculation, we can get the number of repetitions of logistics management data CF 2 is 25; this indicates that in the consultation data of logistics management, the number of consultations of the same type of consultation data within 1 hour is usually 25. By obtaining the number of consultations of the same type of consultation data in each time period, it is helpful to delete the data with a longer consultation time in a time period in the subsequent preliminary deletion strategy, thereby reducing the storage space while making the deleted consultation data the consultation data with a longer consultation time among the same type of consultation data and therefore without practical significance.

[0043] Step S1013, obtaining the data repetition count CF of each consultation type in the consultation database;

[0044] The preliminary deletion strategy includes the following sub-steps: Step S1021, for any consulting data in the consulting database, record it as the data to be deleted, record the consulting type of the data to be deleted as the deletion reference type, and record the time difference between the acquisition time of the data to be deleted and the current time as the deletion reference time; record the forgetting time YW that is greater than the deletion reference time and has the smallest difference with the deletion reference time as the reference forgetting time; Step S1022, record the data repetition number CF corresponding to the reference forgetting time in the data repetition number CF of the deletion reference type as the deletion retention number; obtain the latest obtained deletion retention number of consulting data corresponding to the deletion reference type Data is recorded as storable data; step S1023, when there is no data to be deleted in the storable data, the data to be deleted is deleted in the consultation database and put into the recycle bin, and the consultant corresponding to the data to be deleted is recorded; when there is data to be deleted in the storable data, the data to be deleted is retained in the consultation database, and the mark of the retained data to be deleted is restored to the consultation data; for example, in a data processing, the data to be deleted is the data with a time of 0.9h in the vehicle consultation data, then through analysis, it can be obtained that the forgetting time YW greater than 0.9h and with the smallest difference from 0.9h is 1h, and at the same time, the consultation type of the vehicle consultation data is logistics management, physical management If the number of data repetitions CF corresponding to 1h in the data repetition number CF of the management is 25 times, the 25 most recently acquired vehicle consultation data in the consultation data of logistics management are obtained and recorded as storable data. Through analysis, it is found that the data to be deleted is not in the storable data, so the data to be deleted is deleted in the consultation database and put into the recycle bin, and the consultant corresponding to the data to be deleted is recorded; when the data to be deleted is in the storable data, it means that the data to be deleted is the most recently acquired data in the consultation data corresponding to the data to be deleted, which can facilitate the consultant to provide a consultation basis when conducting repeated consultations. Therefore, the data to be deleted has the meaning of being stored, and the data to be deleted can be stored in the consultation data and retained in the database; when the data to be deleted is not in the storable data, it means that the data to be deleted has existed for a long time in the consulting data corresponding to the data to be deleted. For example, in the above analysis, although the data to be deleted is vehicle consulting data, it is outside the 25 latest vehicle consulting data obtained in the consulting data of logistics management. Therefore, the data to be deleted has been consulted for a long time in the same consulting data. Compared with the 25 latest vehicle consulting data obtained, the data to be deleted does not have much significance. Therefore, the data to be deleted should be put into the recycle bin. At the same time, recording the corresponding consultant will help to conduct secondary analysis of the data to be deleted in combination with the consultant's consulting situation in the subsequent analysis process.

[0045] Step S2, analyzing the consulting data based on the preliminary deletion strategy, and obtaining the retained data in the consulting data based on the analysis result; Step S2 includes the following sub-steps:

[0046] Step S201, based on the preliminary deletion strategy, the consultation data in the consultation database are processed, and after each deletion of the consultation data, the consultant of the data to be deleted is recorded; for any recorded consultant, the number of the data to be deleted in the recycle bin corresponding to the consultant is recorded as the number to be deleted, and the number of the consultation data corresponding to the consultant in the consultation database is recorded as the number of consultations; a plane rectangular coordinate system is established, recorded as the recovery analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the recovery analysis coordinate system are the forgetting time YW 1 Until the time of forgetting YW h , the unit of the Y axis is quantity; obtain the reference forgetting time corresponding to all the data to be deleted of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the number of points is equal, and the points are marked in the recovery analysis coordinate system as the points to be deleted. For the h1th point to be deleted from left to right, the horizontal coordinate of the point to be deleted is the forgetting time YW h1 The vertical coordinate of the point to be deleted is the reference forgetting time of all the data to be deleted of the consultant, which is the forgetting time YW. h1 , and the sum of the ordinates of all points to be deleted is the number to be deleted;

[0047] Step S202: Obtain the reference forgetting time corresponding to all consulting data of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the ... h1 The vertical coordinate of the consulting point is the reference forgetting time in all consulting data of the consultant, which is the forgetting time YW. h1 The sum of the vertical coordinates of all consultation points is the number of consultations;

[0048] Step S203: The curve obtained by fitting all the points to be deleted is recorded as the curve to be deleted; the curve obtained by fitting all the consulting points is recorded as the consulting curve; the curve to be deleted, the X-axis, the Y-axis and X = forgetting time YW h The enclosed area is recorded as the area to be deleted; the consultation curve, X-axis, Y-axis and X = forgetting time YW h The enclosed area is recorded as the consulting area; when the area of ​​the consulting area is greater than or equal to the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, (SS 1 -SS 2 ) / SS 2 The value of is recorded as the recovery ratio; when the area of ​​the consultation area is smaller than the area of ​​the area to be deleted or the area of ​​the area to be deleted is 0, the recovery ratio is recorded as 0, where SS 1is the area of ​​the consultation area, SS 2 is the area of ​​the area to be deleted;

[0049] In the specific implementation process, by obtaining the points to be deleted and the consultation points, the time distribution of the data to be deleted and the consultation data of the same consultant can be visualized, which is helpful for analyzing the data to be deleted of the consultant; when the area of ​​the consultant's consultation area is larger than the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, it means that the consultant has some consultation data that is not of reference significance while frequently consulting data, that is, the data to be deleted. At this time, a certain amount of the data to be deleted of the consultant can be restored according to the obtained recovery ratio, so as to provide the consultant with a certain reference of consultation data; for example, in a data processing, the recovery analysis coordinate system established based on the data to be deleted and the consultation data of a consultant is as follows Figure 2 As shown in the figure, the point corresponding to the center of □ is the consulting point, and the point corresponding to the center of ○ is the point to be deleted. Figure 2 The curve QQ1 is the consulting curve, and the curve QQ2 is the curve to be deleted; please refer to Figure 3 As shown, the area formed by DD1, the coordinate origin and YW7 is the consulting area, and the area formed by DD2, the coordinate origin and YW7 is the area to be deleted. Figure 3 It can be obtained that the area of ​​the consultation area is greater than or equal to the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, so the (SS 1 -SS 2 ) / SS 2 The value of is recorded as the recovery ratio of the consultant in this data processing;

[0050] Step S204, multiply the number of to-be-deleted data by the restoration ratio and round up to an integer value as the restoration amount, put the restored data of the consultant's to-be-deleted data whose acquisition time is closest to the current time back into the consultation database, and restore the mark of the returned to-be-restored data to the consultation data;

[0051] Step S205, obtaining the number of consultants in the consultation database, recorded as the total number of consultations; when the number of data put back to the consultation database by any consultant from the recycling bin is greater than the total number of consultations, the consultant is recorded as a retained consultant, and when the acquisition time of the consultation data of the retained consultant in the preliminary deletion strategy is the same as the acquisition time of other consultation data, the consultation data of the retained consultant is preferentially recorded as retained data; when the consultation data of any consultant are all put into the recycling bin, the consultant is recorded as a retained consultant, and when the latest consultation data of the retained consultant is obtained, the latest consultation data of the retained consultant is recorded as priority retained data, and when the acquisition time of the priority retained data is the same as the acquisition time of other consultation data, the priority retained data is preferentially recorded as retained data;

[0052] In the specific implementation process, when a consultant puts a large amount of data back to the consultation database from the Recycle Bin, it means that the consultant is a user who frequently consults data. Therefore, in order to facilitate the consultant's data consultation, the consultant's consultation data should be retained first when deleting the data to be deleted; and when a consultant's consultation data are all placed in the Recycle Bin, in order to provide a reference for the consultant's next consultation, the consultant's latest consultation data should be retained to prevent the consultant from encountering obstacles in data consultation due to the deletion of all consultation data.

[0053] Step S3, optimizing the preliminary deletion strategy based on the retained data. When using the preliminary deletion strategy for data analysis, the consulting data other than the retained data is deleted first.

[0054] Example 2, second aspect, please refer to Figure 4 As shown, the present application also provides a consulting data management system based on data analysis, including a preliminary deletion establishment module, a retention data analysis module and a deletion management module.

[0055] The preliminary deletion establishment module is used to obtain the number of data repetitions CF of each consultation type in the consultation database using the consultation forgetting analysis method based on the Ebbinghaus forgetting curve and the consultation data, and establish a preliminary deletion strategy based on the number of data repetitions CF; the consultation forgetting analysis method includes: recording the database used to store consultation data as the consultation database, obtaining multiple time nodes corresponding to the Ebbinghaus forgetting curve based on big data, and recording the multiple time nodes in chronological order as the forgetting time YW 1 Until the time of forgetting YW h ; Record the type of consultation data in the consultation database as consultation type ZL. For any consultation data in any consultation type ZL, obtain the consultation record with the forgetting time YW from the current time. 1 Until the time of forgetting YW h The number of times the internal consultation data is consulted by different consultants is recorded as the number of data repetitions CF. 1 To data repetition times CF h , where the consultant is the user who conducts data consultation;

[0056] Get the data repetition count CF of all consulting data of consulting type ZL; for the data repetition count CF 1 To data repetition times CF h The number of repetitions of any data in CF h1 , use the type repetition algorithm to calculate the number of data repetitions CF for all consulting data h1 Calculate and get the number of repetitions CF of the data of consultation type ZL h1, the type repetition algorithm is: Where M is the number of repetitions of the data of the consultation type ZL CF h1 , m i The number of repetitions CF of the i-th consultation data in the consultation type ZL h1 , n is the number of consultation data in consultation type ZL, m max The number of data repetitions CF for all consulting data h1 The maximum value, m min The number of data repetitions CF for all consulting data h1 The minimum value of The number of data repetitions CF for all consulting data h1 The average value; based on the type repetition algorithm, obtain the number of repetitions CF of all data corresponding to the consultation type ZL;

[0057] Get the number of data repetitions CF for each consultation type in the consultation database.

[0058] The preliminary deletion strategy is: for any consulting data in the consulting database, record it as data to be deleted, record the consulting type of the data to be deleted as the deletion reference type, and record the time difference between the acquisition time of the data to be deleted and the current time as the deletion reference time; record the forgetting time YW that is greater than the deletion reference time and has the smallest difference with the deletion reference time as the reference forgetting time; record the number of data repetitions CF corresponding to the reference forgetting time in the data repetition number CF of the deletion reference type as the deletion retention number; obtain the latest number of deletion retention times of data to be deleted in the consulting data corresponding to the deletion reference type, and record them as storable data; when there is no data to be deleted in the storable data, delete the data to be deleted in the consulting database and put it in the recycle bin, and record the consultant corresponding to the data to be deleted; when there is data to be deleted in the storable data, retain the data to be deleted in the consulting database, and restore the mark of the retained data to be deleted to the consulting data.

[0059] The retained data analysis module includes a retained data acquisition unit, which is configured with a retained data acquisition strategy. The retained data acquisition strategy is used to analyze the consulting data based on the preliminary deletion strategy and acquire the retained data in the consulting data based on the analysis result. The retained data acquisition strategy includes:

[0060] Based on the preliminary deletion strategy, the consultation data in the consultation database are processed, and the consultant of the data to be deleted placed in the recycle bin is recorded after each deletion of the consultation data; for any recorded consultant, the number of data to be deleted corresponding to the consultant in the recycle bin is recorded as the number to be deleted, and the number of consultation data corresponding to the consultant in the consultation database is recorded as the number of consultations; a plane rectangular coordinate system is established, recorded as the recovery analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the recovery analysis coordinate system are the forgetting time YW respectively. 1 Until the time of forgetting YW h , the unit of the Y axis is quantity; obtain the reference forgetting time corresponding to all the data to be deleted of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the number of points is equal, and the points are marked in the recovery analysis coordinate system as the points to be deleted. For the h1th point to be deleted from left to right, the horizontal coordinate of the point to be deleted is the forgetting time YW h1 The vertical coordinate of the point to be deleted is the reference forgetting time of all the data to be deleted of the consultant, which is the forgetting time YW. h1 , and the sum of the ordinates of all points to be deleted is the number to be deleted;

[0061] Obtain the reference forgetting time corresponding to all consulting data of the consultant and the forgetting time YW 1 Until the time of forgetting YW h The number of times the ... h1 The vertical coordinate of the consulting point is the reference forgetting time in all consulting data of the consultant, which is the forgetting time YW. h1 The sum of the vertical coordinates of all consultation points is the number of consultations;

[0062] The curve obtained by fitting all the points to be deleted is recorded as the curve to be deleted; the curve obtained by fitting all the consulting points is recorded as the consulting curve; the curve to be deleted, the X-axis, the Y-axis and X = forgetting time YW h The enclosed area is recorded as the area to be deleted; the consultation curve, X-axis, Y-axis and X = forgetting time YW h The enclosed area is recorded as the consulting area; when the area of ​​the consulting area is greater than or equal to the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, (SS 1 -SS 2 ) / SS 2 The value of is recorded as the recovery ratio; when the area of ​​the consultation area is smaller than the area of ​​the area to be deleted or the area of ​​the area to be deleted is 0, the recovery ratio is recorded as 0, where SS 1 is the area of ​​the consultation area, SS 2is the area of ​​the area to be deleted;

[0063] The value of multiplying the number of to-be-deleted data by the recovery ratio and rounding up is recorded as the recovery amount, and the to-be-restored data of the consultant whose acquisition time of the to-be-deleted data is closest to the current time is put back into the consultation database, and the mark of the put-back to-be-restored data is restored to the consultation data;

[0064] The number of consultants in the consultation database is obtained and recorded as the total number of consultations. When the number of data put back into the consultation database by any consultant from the Recycle Bin is greater than the total number of consultations, the consultant will be recorded as a retained consultant. When the acquisition time of the consultation data of the retained consultant in the preliminary deletion strategy is the same as the acquisition time of other consultation data, the consultation data of the retained consultant will be recorded as the retained data in priority. When the consultation data of any consultant are all put into the Recycle Bin, the consultant will be recorded as a retained consultant. When the latest consultation data of the retained consultant is obtained, the latest consultation data of the retained consultant will be recorded as the priority retained data. When the acquisition time of the priority retained data is the same as the acquisition time of other consultation data, the priority retained data will be recorded as the retained data in priority.

[0065] The deletion management module is used to optimize the preliminary deletion strategy based on the retained data. When the preliminary deletion strategy is used for data analysis, consulting data other than the retained data is deleted first.

[0066] Working principle: First, the consultation forgetting analysis method is used to obtain the number of data repetitions CF for each consultation type in the consultation database, and a preliminary deletion strategy is established based on the number of data repetitions CF. The consultation data is analyzed based on the preliminary deletion strategy, and the recovery data in the data to be deleted is obtained based on the analysis results; finally, the preliminary deletion strategy is optimized based on the recovery data, and the optimized preliminary deletion strategy is recorded as the optimized deletion strategy, and the consultation data is managed using the optimized deletion strategy.

[0067] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0068] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A consulting data management system based on data analysis, characterized in that: It includes a preliminary deletion establishment module, a retention data analysis module, and a deletion management module; The preliminary deletion establishment module is used to obtain the data repetition number CF of each consultation type in the consultation database based on the Ebbinghaus forgetting curve and the consultation data using the consultation forgetting analysis method, and establish a preliminary deletion strategy based on the data repetition number CF; The retained data analysis module is used to perform data analysis on the consulting data based on the preliminary deletion strategy, and obtain the retained data in the consulting data based on the analysis result; The deletion management module is used to optimize the preliminary deletion strategy based on the retained data. When the preliminary deletion strategy is used for data analysis, the consulting data other than the retained data is deleted first; The consulting forgetting analysis method includes: recording the database used to store consulting data as the consulting database, obtaining multiple time nodes corresponding to the Ebbinghaus forgetting curve based on big data, and recording the multiple time nodes in chronological order as forgetting time YW1 to forgetting time YW h ; Record the type of consultation data in the consultation database as consultation type ZL, and for any consultation data in any consultation type ZL, obtain the consultation records from the current time to the forgotten time YW1 to the forgotten time YW h The number of times the internal consultation data is consulted by different consultants is recorded as data repetition number CF1 to data repetition number CF h , where the consultant is the user who conducts data consultation; Consultation forgetting analysis also includes: Get the data repetition count CF of all consulting data of consulting type ZL; for data repetition counts CF1 to CF h The number of repetitions of any data in CF h1 , use the type repetition algorithm to calculate the number of data repetitions CF for all consulting data h1 Calculate and get the number of repetitions CF of the data of consultation type ZL h1 , the type repetition algorithm is: Where M is the number of repetitions of the data of the consultation type ZL CF h1 , m i The number of repetitions CF of the i-th consultation data in the consultation type ZL h1 , n is the number of consultation data in consultation type ZL, m max The number of data repetitions CF for all consulting data h1 The maximum value, m min The number of data repetitions CF for all consulting data h1 The minimum value of The number of data repetitions CF for all consulting data h1 The average value; based on the type repetition algorithm, obtain the number of repetitions CF of all data corresponding to the consultation type ZL; Get the data repetition count CF of each consultation type in the consultation database; The preliminary deletion strategy is: for any consulting data in the consulting database, record it as data to be deleted, record the consulting type of the data to be deleted as the deletion reference type, record the time difference between the acquisition time of the data to be deleted and the current time as the deletion reference time; record the forgetting time YW that is greater than the deletion reference time and has the smallest difference with the deletion reference time as the reference forgetting time; record the number of data repetitions CF corresponding to the reference forgetting time in the data repetition number CF of the deletion reference type as the deletion retention number; obtain the latest number of deletion retention data to be deleted in the consulting data corresponding to the deletion reference type, and record it as storable data; when there is no data to be deleted in the storable data, delete the data to be deleted in the consulting database and put it in the recycle bin, and record the consultant corresponding to the data to be deleted; when there is data to be deleted in the storable data, retain the data to be deleted in the consulting database, and restore the mark of the retained data to be deleted to the consulting data; The reserved data analysis module includes a reserved data acquisition unit, which is configured with a reserved data acquisition strategy. The reserved data acquisition strategy includes: Based on the preliminary deletion strategy, the consultation data in the consultation database are processed, and the consultant of the data to be deleted placed in the recycle bin is recorded after each deletion of the consultation data; for any consultant recorded, the number of data to be deleted corresponding to the consultant in the recycle bin is recorded as the number to be deleted, and the number of consultation data corresponding to the consultant in the consultation database is recorded as the number of consultations; a plane rectangular coordinate system is established, recorded as the recovery analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the recovery analysis coordinate system are respectively the forgotten time YW1 to the forgotten time YW h , the unit of the Y axis is quantity; obtain the reference forgetting time corresponding to all the data to be deleted of the consultant and the forgetting time YW1 to the forgetting time YW h The number of times the number of points is equal, and the points are marked in the recovery analysis coordinate system as the points to be deleted. For the h1th point to be deleted from left to right, the horizontal coordinate of the point to be deleted is the forgetting time YW h1 The vertical coordinate of the point to be deleted is the reference forgetting time of all the data to be deleted of the consultant, which is the forgetting time YW. h1 , and the sum of the ordinates of all points to be deleted is the number to be deleted; The retention data acquisition strategy also includes: Obtain the reference forgetting time corresponding to all consulting data of the consultant and the forgetting time YW1 to the forgetting time YW h The number of times the ... h1 The vertical coordinate of the consulting point is the reference forgetting time in all consulting data of the consultant, which is the forgetting time YW. h1 The sum of the vertical coordinates of all consultation points is the number of consultations; The retention data acquisition strategy also includes: The curve obtained by fitting all the points to be deleted is recorded as the curve to be deleted; the curve obtained by fitting all the consulting points is recorded as the consulting curve; the curve to be deleted, the X-axis, the Y-axis and X = forgetting time YW h The enclosed area is recorded as the area to be deleted; the consultation curve, X-axis, Y-axis and X = forgetting time YW h The enclosed area is recorded as the consultation area; when the area of ​​the consultation area is greater than or equal to the area of ​​the area to be deleted and the area of ​​the area to be deleted is not 0, the value of (SS1-SS2) / SS2 is recorded as the restoration ratio; when the area of ​​the consultation area is less than the area of ​​the area to be deleted or the area of ​​the area to be deleted is 0, the restoration ratio is recorded as 0, where SS1 is the area of ​​the consultation area and SS2 is the area of ​​the area to be deleted; The retention data acquisition strategy also includes: The value of multiplying the number of to-be-deleted data by the recovery ratio and rounding up is recorded as the recovery amount, and the to-be-restored data of the consultant whose acquisition time of the to-be-deleted data is closest to the current time is put back into the consultation database, and the mark of the put-back to-be-restored data is restored to the consultation data; The retention data acquisition strategy also includes: acquiring the number of consultants in the consultation database, which is recorded as the total number of consultations; when the number of data put back into the consultation database by any consultant from the recycling bin is greater than the total number of consultations, the consultant is recorded as a retained consultant; when the acquisition time of the consultation data of the retained consultant in the preliminary deletion strategy is the same as the acquisition time of other consultation data, the consultation data of the retained consultant is preferentially recorded as retained data; when the consultation data of any consultant is put into the recycling bin, the consultant is recorded as a retained consultant; when the latest consultation data of the retained consultant is acquired, the latest consultation data of the retained consultant is recorded as priority retained data; when the acquisition time of the priority retained data is the same as the acquisition time of other consultation data, the priority retained data is preferentially recorded as retained data.

2. A consulting data management method based on data analysis, applicable to the consulting data management system based on data analysis according to claim 1, characterized in that: The following sub-steps are included: Based on the Ebbinghaus forgetting curve and consulting data, the consulting forgetting analysis method is used to obtain the data duplication times CF of each consulting type in the consulting database, and a preliminary deletion strategy is established based on the data duplication times CF; Performing data analysis on the consulting data based on the preliminary deletion strategy, and obtaining the retained data in the consulting data based on the analysis results; The preliminary deletion strategy is optimized based on the retained data. When the preliminary deletion strategy is used for data analysis, consulting data other than the retained data is deleted first.

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