Method for personalized customization of screening system data

Abstract

The invention discloses a method for screening system data in a personalized customization mode, and relates to the technical field of system data screening, and the method comprises the first step of customization optimization analysis, the second step of screening information obtaining, the third step of customization data sorting and the fourth step of optimization effect evaluation. According to bytes, loading duration and the like of the system data, the compression value of the system data is analyzed, it is guaranteed that system optimization is more comprehensive, the loading duration of user screening is shortened, and the screening experience of a user is improved. The importance index of the system data is analyzed, the screening result of the user is more ideal, and the screening effect of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of system data screening, and in particular to a method for personalized customization of screening system data. Background Art

[0002] Personalized customized screening of system data usually refers to the personalized design and implementation of the system according to the needs or standards of specific users, so as to screen, analyze and manage data. Most of the existing data screening methods have failed to unify data screening into a universal method, and are often limited to the current business scenarios. They are not universal and have problems such as inconsistent interactions and being messy. They cannot provide users with a good user experience. Personalized customized screening can provide accurate data screening results according to the specific needs of users, so that the data queried by users is more in line with the needs, saving users' time and energy. Therefore, personalized customized screening is extremely important for users to screen system data.

[0003] There are still some problems with current personalized screening: 1. In the existing technology, before the target user screens the system data, the compression value of the system data is ignored based on the bytes, loading time, etc. of the system data. The compression value of the system data affects the optimization processing of the system to a certain extent, making the system optimization not comprehensive, increasing the loading time of the user screening, and affecting the user's screening experience.

[0004] 2. In the existing technology, when the target user screens the system data, the importance index of the system data is ignored based on the number of times the system data participates in the screening, the screening preferences of the target user, etc. The importance index of the system data reflects the user's screening needs to a certain extent, resulting in the user's screening results being less than ideal and reducing the system's screening effect. Summary of the Invention

[0005] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method for personalizing screening system data to solve the above-mentioned problems.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a method for personalized customization of screening system data, comprising: Step 1, customized optimization analysis: obtaining the average loading time, usage times, and format of each system data from the data management center, analyzing the compression benefit evaluation index of each system data, screening each system data with high compression benefit, and compressing each system data with high compression benefit; Step 2: Obtaining screening information: When the target user is screening, obtain the target user's current screening input text, and obtain the number of screenings, the last screening time, and the name text of each system data from the data management center; Step 3: Customize data sorting: Filter the data of each matching system belonging to the target user's current screening, analyze the importance evaluation index of each matching system data belonging to the target user's current screening, obtain the target system data corresponding to each historical screening of the target user from the data management center, perform personalized sorting for each matching system data belonging to the target user's current screening, and display the personalized sorted data of each matching system belonging to the target user's current screening on the target user's terminal; Step 4. Optimization effect evaluation: When the data of each matching system belonging to the current screening of the target user after personalized sorting is displayed on the target user end, obtain the loading time of the target user's current screening, obtain the system's loading time and input text in each historical screening from the data management center, analyze the optimization effect evaluation index of the target user's current screening, and determine whether the system optimization meets the standards. If the system optimization does not meet the standards, re-execute step 1.

[0007] Preferably, the specific analysis method for obtaining the average loading time of each system data is as follows: obtaining the loading time corresponding to each system data from the data management center, comparing the loading time corresponding to each system data with a preset timeout period, and if the loading time corresponding to a certain system data is greater than or equal to the preset timeout period, marking the loading of the system data as invalid loading, otherwise, marking it as valid loading, and screening the valid loading of each system data; From the duration corresponding to each loading of each system data, the duration corresponding to each effective loading of each system data is extracted, and the average is processed to obtain the average duration of effective loading of each system data, which is marked as the average loading duration of each system data.

[0008] Preferably, the specific analysis method of screening the high compression efficiency system data is as follows: obtaining the format impact coefficient corresponding to the format of each system data from the data management center, and obtaining the format impact coefficient of each system data according to the format of each system data. ,in Indicates the number of each system data, , is any integer greater than 2; According to the number of times each system data is used , average loading time , analyze the compression benefit evaluation index of each system data ,in Indicates the amount of system data, The weight factor representing the format influence coefficient, The weight factor representing the proportion of system data usage, The weight factor representing the proportion of system data loading time; The compression benefit evaluation index of each system data is compared with the preset compression benefit index threshold. If the compression benefit evaluation index of a system data is greater than or equal to the preset compression benefit index threshold, the system data is marked as high compression benefit system data, and the high compression benefit system data is screened.

[0009] Preferably, the target user is currently screening the matching system data, and the specific screening method is: comparing the input text of the target user's current screening with the name text of each system data, and obtaining the number of identical bytes between the input text of the target user's current screening and the name text of each system data; Obtain the total number of bytes of the target user's currently screened input text, divide the number of identical bytes between the target user's currently screened input text and the name text of each system data by the total number of bytes of the corresponding user's currently screened input text, and obtain the content overlap between the target user's currently screened input text and the name text of each system data; Compare the content overlap between the input text currently screened by the target user and the name text of each system data with the preset lower limit of text content overlap. If the content overlap between the input text currently screened by the target user and the name text of a certain system data is greater than or equal to the preset lower limit of text content overlap, then mark the system data corresponding to the current screening of the target user as matching system data, and filter the matching system data belonging to the current screening of the target user.

[0010] Preferably, the importance evaluation index of each matching system data to which the target user is currently screening is analyzed, and the specific analysis method is: from the number of participation screenings of each system data and the last participation screening time point, the number of participation screenings of each matching system data to which the target user is currently screening belongs is obtained. , the last time of participating in the screening, including Indicates the number of each matching system data, , is any integer greater than 2; Get the current time point, subtract the current time point from the last screening time point of each matching system data, and get the interval length of the last screening time point of each matching system data ; From the content overlap between the input text currently screened by the target user and the name text of each system data, obtain the text content overlap of each matching system data currently screened by the target user ; Analyze the importance evaluation index of each matching system data of the target user's current screening ,in Indicates the number of matching system data, The adjustment factor representing the proportion of selection times, represents the adjustment factor for the effect of the selection interval duration, Indicates the adjustment factor of the text content overlap, 、 、 The value range of is 0 to 1.

[0011] Preferably, the personalized sorting of each matching system data belonging to the current screening of the target user is performed, and the specific analysis method is: obtaining the name text of the target system data corresponding to each historical screening of the target user from the system data management center, and obtaining the name text of each matching system data belonging to the current screening of the target user, comparing the name text of each matching system data belonging to the current screening of the target user with the name text of the target system data corresponding to each historical screening, if the name text of a certain matching system data currently screened by the target user is the same as the name text of the target system data corresponding to a certain historical screening of the corresponding user, then marking the matching system data currently screened by the target user as key matching system data, otherwise, marking the matching system data currently screened by the target user as ordinary matching system data, screening the key matching system data and ordinary matching system data belonging to the current screening of the target user; From the importance evaluation index of each matching system data currently screened by the target user, extract the importance evaluation index of each key matching system data and each ordinary matching system data currently screened by the target user respectively, sort the key matching system data currently screened by the target user from large to small according to the importance evaluation index, sort the ordinary matching system data currently screened by the target user from large to small according to the importance evaluation index, and sort the ordinary matching system data currently screened by the target user after each key matching system data.

[0012] Preferably, the optimization effect evaluation index of the target user's current screening is analyzed by comparing the input text of each historical screening of the system with the input text of the target user's current screening to obtain the overlap between the input text of each historical screening of the system and the input text of the target user; Compare the degree of overlap between each historical screening of the system and the input text of the target user with the set similar text overlap threshold. If the degree of overlap between a historical screening of the system and the input text of the target user is greater than or equal to the set text overlap threshold, mark the historical screening of the system as a similar screening, and filter all similar screenings of the system; Extract the loading time of each similar screening from the system's loading time in each historical screening, and average them to obtain the average loading time of the system in similar screening; Divide the loading time of the target user's current screening by the system's average loading time for similar screenings to obtain the optimization effect evaluation index of the target user's current screening.

[0013] Preferably, the specific judgment method for judging whether the system optimization meets the standards is: comparing the optimization effect evaluation index currently screened by the target user with the set lower limit value of the optimization effect evaluation index. If the optimization effect evaluation index currently screened by the target user is less than or equal to the set lower limit value of the optimization effect evaluation index, then it is judged that the system optimization does not meet the standards; otherwise, it is judged that the system optimization meets the standards.

[0014] The beneficial effects of the present invention are: 1. Before the target user screens the system data, the present invention analyzes the compression value of the system data based on the bytes, loading time, etc. of the system data, ensuring that the system optimization is more comprehensive, reducing the loading time of user screening, and improving the user's screening experience.

[0015] 2. When the target user screens the system data, the present invention analyzes the importance index of the system data based on the number of times the system data has participated in screening, the screening preferences of the target user, etc., to ensure that the user's screening results are more ideal and improve the screening effect of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 The figure is a schematic flow chart of the steps for implementing the method of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0019] Reference Figure 1 As shown, the present invention provides a method for personalized customization of screening system data, including: step 1, customized optimization analysis: obtaining the average loading time, usage times, and format of each system data from the data management center, analyzing the compression benefit evaluation index of each system data, screening each system data with high compression benefit, and compressing each system data with high compression benefit; In a specific embodiment of the present invention, the specific analysis method for obtaining the average loading time of each system data is as follows: obtaining the loading time corresponding to each system data from the data management center, comparing the loading time corresponding to each system data with a preset timeout period, and if the loading time corresponding to a certain system data is greater than or equal to the preset timeout period, marking the loading of the system data as invalid loading; otherwise, marking it as valid loading, and screening the valid loading of each system data; From the duration corresponding to each loading of each system data, the duration corresponding to each effective loading of each system data is extracted, and the average is processed to obtain the average duration of effective loading of each system data, which is marked as the average loading duration of each system data.

[0020] In another specific embodiment of the present invention, the specific analysis method for screening the high compression efficiency system data is as follows: obtaining the format impact coefficient corresponding to the format of each system data from the data management center, and obtaining the format impact coefficient of each system data according to the format of each system data. ,in Indicates the number of each system data, , is any integer greater than 2; According to the number of times each system data is used , average loading time , analyze the compression benefit evaluation index of each system data ,in Indicates the amount of system data, The weight factor representing the format influence coefficient, The weight factor representing the proportion of system data usage, The weight factor representing the proportion of system data loading time; The compression benefit evaluation index of each system data is compared with the preset compression benefit index threshold. If the compression benefit evaluation index of a system data is greater than or equal to the preset compression benefit index threshold, the system data is marked as high compression benefit system data, and the high compression benefit system data is screened.

[0021] Before the target user screens the system data, the present invention analyzes the compression value of the system data according to the bytes, loading time, etc. of the system data, thereby ensuring more comprehensive system optimization, reducing the loading time of user screening, and improving the user's screening experience.

[0022] Step 2: Obtaining screening information: When the target user is screening, obtain the target user's current screening input text, and obtain the number of screenings, the last screening time, and the name text of each system data from the data management center; Step 3: Customize data sorting: Filter the data of each matching system belonging to the target user's current screening, analyze the importance evaluation index of each matching system data belonging to the target user's current screening, obtain the target system data corresponding to each historical screening of the target user from the data management center, perform personalized sorting for each matching system data belonging to the target user's current screening, and display the personalized sorted data of each matching system belonging to the target user's current screening on the target user's terminal; In a specific embodiment of the present invention, the method for filtering the matching system data currently being screened by the target user is as follows: comparing the input text currently being screened by the target user with the name text of each system data, and obtaining the number of identical bytes between the input text currently being screened by the target user and the name text of each system data; Obtain the total number of bytes of the target user's currently screened input text, divide the number of identical bytes between the target user's currently screened input text and the name text of each system data by the total number of bytes of the corresponding user's currently screened input text, and obtain the content overlap between the target user's currently screened input text and the name text of each system data; Compare the content overlap between the input text currently screened by the target user and the name text of each system data with the preset lower limit of text content overlap. If the content overlap between the input text currently screened by the target user and the name text of a certain system data is greater than or equal to the preset lower limit of text content overlap, then mark the system data corresponding to the current screening of the target user as matching system data, and filter the matching system data belonging to the current screening of the target user.

[0023] In a specific embodiment of the present invention, the importance evaluation index of each matching system data to which the target user is currently screening is analyzed, and the specific analysis method is as follows: the number of times the target user is currently screening each matching system data and the last time the target user is currently screening each matching system data are obtained from the number of times the target user is currently screening each matching system data and the last time the target user is currently screening each matching system data. , the last time of participating in the screening, including Indicates the number of each matching system data, , is any integer greater than 2; Get the current time point, subtract the current time point from the last screening time point of each matching system data, and get the interval length of the last screening time point of each matching system data ; From the content overlap between the input text currently screened by the target user and the name text of each system data, obtain the text content overlap of each matching system data currently screened by the target user ; Analyze the importance evaluation index of each matching system data of the target user's current screening ,in Indicates the number of matching system data, The adjustment factor representing the proportion of selection times, represents the adjustment factor for the effect of the selection interval duration, Indicates the adjustment factor of the text content overlap, 、 、 The value range of is 0 to 1.

[0024] In another specific embodiment of the present invention, the personalized sorting of each matching system data belonging to the current screening of the target user is performed, and the specific analysis method is: obtaining the name text of the target system data corresponding to each historical screening of the target user from the system data management center, and obtaining the name text of each matching system data belonging to the current screening of the target user, comparing the name text of each matching system data belonging to the current screening of the target user with the name text of the target system data corresponding to each historical screening, if the name text of a certain matching system data currently screened by the target user is the same as the name text of the target system data corresponding to a certain historical screening of the corresponding user, then the matching system data currently screened by the target user is marked as key matching system data, otherwise, then the matching system data currently screened by the target user is marked as ordinary matching system data, and the key matching system data and ordinary matching system data belonging to the current screening of the target user are screened; From the importance evaluation index of each matching system data currently screened by the target user, extract the importance evaluation index of each key matching system data and each ordinary matching system data currently screened by the target user respectively, sort the key matching system data currently screened by the target user from large to small according to the importance evaluation index, sort the ordinary matching system data currently screened by the target user from large to small according to the importance evaluation index, and sort the ordinary matching system data currently screened by the target user after each key matching system data.

[0025] Step 4. Optimization effect evaluation: When the data of each matching system belonging to the current screening of the target user after personalized sorting is displayed on the target user end, obtain the loading time of the target user's current screening, obtain the system's loading time and input text in each historical screening from the data management center, analyze the optimization effect evaluation index of the target user's current screening, and determine whether the system optimization meets the standards. If the system optimization does not meet the standards, re-execute step 1.

[0026] In a specific embodiment of the present invention, the optimization effect evaluation index of the target user's current screening is analyzed by comparing the input text of each historical screening of the system with the input text of the target user's current screening to obtain the overlap between the input text of each historical screening of the system and the input text of the target user; It should be noted that the specific acquisition method for obtaining the overlap between the historical screenings of the system and the input text of the target user is the same as analyzing the content overlap between the input text of the current screening of the target user and the name text of each system data.

[0027] Compare the degree of overlap between each historical screening of the system and the input text of the target user with the set similar text overlap threshold. If the degree of overlap between a historical screening of the system and the input text of the target user is greater than or equal to the set text overlap threshold, mark the historical screening of the system as a similar screening, and filter all similar screenings of the system; Extract the loading time of each similar screening from the system's loading time in each historical screening, and average them to obtain the average loading time of the system in similar screening; Divide the loading time of the target user's current screening by the system's average loading time for similar screenings to obtain the optimization effect evaluation index of the target user's current screening.

[0028] In another specific embodiment of the present invention, the specific judgment method for judging whether the system optimization meets the standards is: comparing the optimization effect evaluation index currently screened by the target user with the set lower limit value of the optimization effect evaluation index. If the optimization effect evaluation index currently screened by the target user is less than or equal to the set lower limit value of the optimization effect evaluation index, then it is judged that the system optimization does not meet the standards; otherwise, it is judged that the system optimization meets the standards.

[0029] When the target user screens the system data, the present invention analyzes the importance index of the system data according to the number of times the system data has participated in screening, the screening preferences of the target user, etc., to ensure that the user's screening results are more ideal and improve the screening effect of the system.

[0030] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A method for personalizing screening system data, characterized in that: include: Step 1: Customized optimization analysis: Obtain the average loading time, usage count, and format of each system data from the data management center, analyze the compression efficiency evaluation index of each system data, select the system data with high compression efficiency, and compress the system data with high compression efficiency; Step 2: Obtaining screening information: When the target user is screening, obtain the target user's current screening input text, and obtain the number of screenings, the last screening time, and the name text of each system data from the data management center; Step 3: Customize data sorting: Filter the data of each matching system belonging to the target user's current screening, analyze the importance evaluation index of each matching system data belonging to the target user's current screening, obtain the target system data corresponding to each historical screening of the target user from the data management center, perform personalized sorting for each matching system data belonging to the target user's current screening, and display the personalized sorted data of each matching system belonging to the target user's current screening on the target user's terminal; Step 4. Optimization effect evaluation: When the data of each matching system belonging to the current screening of the target user after personalized sorting is displayed on the target user end, obtain the loading time of the target user's current screening, obtain the system's loading time and input text in each historical screening from the data management center, analyze the optimization effect evaluation index of the target user's current screening, and determine whether the system optimization meets the standards. If the system optimization does not meet the standards, re-execute step 1.

2. A method for personalizing screening system data according to claim 1, characterized in that: The specific analysis method for obtaining the average loading time of each system data is as follows: Obtain the duration corresponding to each loading of each system data from the data management center, compare the duration corresponding to each loading of each system data with the preset timeout duration, and if the duration corresponding to a certain system data loading is greater than or equal to the preset timeout duration, mark the loading of the system data as invalid loading; otherwise, mark it as valid loading, and screen the valid loading of each system data; From the duration corresponding to each loading of each system data, the duration corresponding to each effective loading of each system data is extracted, and the average is processed to obtain the average duration of effective loading of each system data, which is marked as the average loading duration of each system data.

3. The method for personalizing screening system data according to claim 1, characterized in that: The specific analysis method for screening the data of each high compression benefit system is as follows: Obtain the format impact coefficient corresponding to each system data format from the data management center, and obtain the format impact coefficient of each system data according to the format of each system data ,in Indicates the number of each system data, , is any integer greater than 2; According to the number of times each system data is used , average loading time , analyze the compression benefit evaluation index of each system data ,in Indicates the amount of system data, The weight factor representing the format influence coefficient, The weight factor representing the proportion of system data usage, The weight factor representing the proportion of system data loading time; The compression benefit evaluation index of each system data is compared with the preset compression benefit index threshold. If the compression benefit evaluation index of a system data is greater than or equal to the preset compression benefit index threshold, the system data is marked as high compression benefit system data, and the high compression benefit system data is screened.

4. The method for personalizing screening system data according to claim 1, characterized in that: The specific screening method for the target user's current screening of the matching system data is as follows: Compare the input text currently screened by the target user with the name text of each system data to obtain the number of identical bytes between the input text currently screened by the target user and the name text of each system data; Obtain the total number of bytes of the target user's currently screened input text, divide the number of identical bytes between the target user's currently screened input text and the name text of each system data by the total number of bytes of the corresponding user's currently screened input text, and obtain the content overlap between the target user's currently screened input text and the name text of each system data; Compare the content overlap between the input text currently screened by the target user and the name text of each system data with the preset lower limit of text content overlap. If the content overlap between the input text currently screened by the target user and the name text of a certain system data is greater than or equal to the preset lower limit of text content overlap, then mark the system data corresponding to the current screening of the target user as matching system data, and filter the matching system data belonging to the current screening of the target user.

5. A method for personalizing screening system data according to claim 4, characterized in that: The specific analysis method for analyzing the importance evaluation index of the target user's current screening data of each matching system is as follows: From the number of times each system data participated in screening and the last time of participating in screening, obtain the number of times the target user participated in screening for each matching system data in the current screening. , the last time of participating in the screening, including Indicates the number of each matching system data, , is any integer greater than 2; Get the current time point, subtract the current time point from the last screening time point of each matching system data, and get the interval length of the last screening time point of each matching system data ; From the content overlap between the input text currently screened by the target user and the name text of each system data, obtain the text content overlap of each matching system data currently screened by the target user ; Analyze the importance evaluation index of each matching system data of the target user's current screening ,in Indicates the number of matching system data, The adjustment factor representing the proportion of selection times, represents the adjustment factor for the effect of the selection interval duration, Indicates the adjustment factor of the text content overlap, 、 、 The value range of is 0 to 1.

6. The method for personalizing screening system data according to claim 1, characterized in that: The personalized sorting of the target user's current screening matching system data is performed, and the specific analysis method is as follows: Obtain the name text of the target system data corresponding to each historical screening of the target user from the system data management center, and obtain the name text of each matching system data belonging to the current screening of the target user, compare the name text of each matching system data belonging to the current screening of the target user with the name text of the target system data corresponding to each historical screening, if the name text of a matching system data currently screened by the target user is the same as the name text of the target system data corresponding to a historical screening of the corresponding user, then mark the matching system data currently screened by the target user as key matching system data, otherwise, mark the matching system data currently screened by the target user as ordinary matching system data, and filter the key matching system data and ordinary matching system data belonging to the current screening of the target user; From the importance evaluation index of each matching system data currently screened by the target user, extract the importance evaluation index of each key matching system data and each ordinary matching system data currently screened by the target user respectively, sort the key matching system data currently screened by the target user from large to small according to the importance evaluation index, sort the ordinary matching system data currently screened by the target user from large to small according to the importance evaluation index, and sort the ordinary matching system data currently screened by the target user after each key matching system data.

7. The method for personalizing screening system data according to claim 1, characterized in that: The specific analysis method for analyzing the optimization effect evaluation index of the target user's current screening is as follows: Compare the input text of each historical screening of the system with the input text of the current screening of the target user to obtain the overlap degree between the input text of each historical screening of the system and the input text of the target user; Compare the degree of overlap between each historical screening of the system and the input text of the target user with the set similar text overlap threshold. If the degree of overlap between a historical screening of the system and the input text of the target user is greater than or equal to the set text overlap threshold, mark the historical screening of the system as a similar screening, and filter all similar screenings of the system; Extract the loading time of each similar screening from the system's loading time in each historical screening, and average them to obtain the average loading time of the system in similar screening; Divide the loading time of the target user's current screening by the system's average loading time for similar screenings to obtain the optimization effect evaluation index of the target user's current screening.

8. The method for personalizing screening system data according to claim 1, characterized in that: The specific method for judging whether the system optimization meets the standards is as follows: Compare the optimization effect evaluation index currently screened by the target user with the set lower limit of the optimization effect evaluation index. If the optimization effect evaluation index currently screened by the target user is less than or equal to the set lower limit of the optimization effect evaluation index, the system optimization is judged to be substandard; otherwise, the system optimization is judged to be substandard.

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