Method and system for improving database query efficiency

Abstract

The invention discloses a method and a system for improving database query efficiency, which relate to the technical field of database management, and are used for performing category division on data, constructing a plurality of category data storage spaces based on data categories, analyzing relevancy among the data, constructing a plurality of data storage subspaces, and storing the data into a database. The method comprises the steps of determining a to-be-called category data storage space based on a plurality of first data demand deviations of a data query demand, screening to-be-called data for caching based on overall performance characteristics and a high-probability queried judgment rule of the first data demand deviations, and performing relevancy deviation analysis on the first data demand deviations to obtain a to-be-called category data storage space; according to the technical scheme, the data storage subspaces are subjected to sequence screening, caching is carried out according to the sequence, through the technical scheme, the data of the screened data storage subspaces and the data to be called are cached, optimization of the data query sequence is achieved, and then the query efficiency of the database is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to a method and system for improving database query efficiency. Background Art

[0002] In today's digital age, databases play a vital role and are used in many fields such as business management, data analysis, and decision support. However, with the continuous growth of data volume and the emergence of complex query requirements, maintaining efficient database queries has become a key challenge.

[0003] In the prior art, if you want to query specific data in a database, you need to find the storage space where the data exists according to the data storage location table, and traverse, scan and analyze the data in the storage space. This method can meet the needs of conventional data queries, but as the requirements for query efficiency increase, changes in the query logic of the database have also become crucial. Summary of the invention

[0004] The purpose of the present invention is to provide a method capable of improving database query efficiency.

[0005] The present application discloses a method for improving database query efficiency, comprising: Classify the data, and construct several category data storage spaces based on the classified data categories, and construct several data storage subspaces in the category data storage space according to the correlation between different data under the same data category; Obtaining data query requirements, analyzing the data query requirements, determining a number of first data requirement biases, and determining the called category data storage space based on the first data requirement biases; Based on the overall performance characteristics of the plurality of first data demand preferences, a plurality of category data storage spaces to be prepared for invocation are determined, and based on a high probability query judgment rule, a plurality of data in the category data storage spaces to be prepared for invocation are selected for caching; Performing a correlation bias analysis on the first data demand bias, and determining a screening order for screening different data storage subspaces based on the analysis result, and caching the data in the data storage subspace according to the screening order; Scan and analyze the data in the cache to determine the data to be queried.

[0006] In some embodiments of the present application, a method for constructing a plurality of categories of data storage space based on divided data categories includes: Determine the volume of monomer data and the monomer storage space of each data category, and determine the basic space volume and the reserve space volume of the corresponding category data storage space based on the monomer data volume and the monomer storage space; Wherein, the volume of the preparation space is also determined according to the update frequency of the monomer data; The expression for calculating the storage space volume of category data is: ; in, The spatial volume of the category data storage space of the nth data category, is the volume of monomer data of the nth data category, It is the single storage space of single data of the nth data category. The update frequency of the individual data of the nth data category, is the conversion coefficient of the reserve space volume of the nth data category, The nth reserve space volume adjustment constant.

[0007] In some embodiments of the present application, according to the correlation between different data under the same data category, a method for constructing a plurality of data storage subspaces in a category data storage space includes: Determining a first correlation between the data based on the time when the different data are stored; Determining a second correlation between the data based on a relative logical relationship between different data belonging to the same storage information; Based on the first correlation and the second correlation, correlation between different data in the same data category is determined.

[0008] In some embodiments of the present application, based on the first data demand bias, a method for determining a called category data storage space includes: Several sub-level classifications are performed for different data categories. Each level of classification is gradually reduced in scale according to the generalization requirements of the preset specific attributes. Different level classifications are set with attribute precision mapping values ​​for the preset specific attributes. Based on the first data demand bias, determining a plurality of first bias characteristics, and based on the first bias characteristics, determining a plurality of data categories to which the data demand belongs; Based on the first bias feature, determine the accuracy requirement for the data category, and compare and analyze the accuracy requirement and the attribute accuracy mapping value to determine the level of the data category corresponding to the data demand bias; Methods for obtaining the first data demand bias include: A data requirement table is established, which includes data category, data source, data storage time, data application scenario, data volume scale and data storage location.

[0009] In some embodiments of the present application, methods for setting attribute precision mapping values ​​for preset specific attributes in different hierarchical classifications include: A data attribute correspondence table is established, wherein the data attribute correspondence table includes a plurality of preset specific attributes, and a first restriction condition interval is set for each preset specific attribute; Several levels of hierarchical restriction intervals are set for the first restriction interval of each preset specific attribute, and the attribute accuracy mapping value is determined according to the ratio of the interval length of the hierarchical restriction intervals at different levels to the interval length of the first restriction interval.

[0010] In some embodiments of the present application, based on the overall performance characteristics of a plurality of data demand preferences, a method for determining a plurality of category data storage spaces to be prepared for invocation includes: Analyze past data call logs to extract past data query requirements and past category data storage space call records; Analyze the past data query requirements, determine a number of corresponding past first data demand preferences, construct the number of past first data demand preferences into a past demand preference overall performance group, and associate the past demand preference overall performance group with the past category data storage space; Construct the current data demand preferences into a current demand preference overall performance group, compare and analyze the current demand preference overall performance group and different past demand preference overall performance groups, and determine the number of matching elements; If the ratio of the number of matching elements to the total number of elements in the past demand bias overall performance group is greater than a preset value, the past demand bias overall performance group is determined to be consistent with the current demand bias overall performance group, and several past category data storage spaces associated with the past demand bias overall performance group are prepared for calling.

[0011] In some embodiments of the present application, based on the high probability query judgment rule, the method of selecting some data in the category data storage space to be called for caching includes: The number of elements in the overall performance group of past demand bias and the overall performance group of current demand bias that match each other is identified as the first factor of interest, and the total number of elements in the overall performance group of past demand bias is identified as the second factor of interest; The number of past calls of each data in the category data storage space is identified as the third concern factor, and the past call frequency of each data in the category data storage space is identified as the fourth concern factor; Determine a probability reference amount of specific data in the data storage space of the corresponding category based on the first concern factor, the second concern factor, the third concern factor, and the fourth concern factor; If the probability reference quantity of the data is greater than the preset probability reference scalar, the data is cached; Among them, the expression of the probability reference of the calculated data is: ; Among them, g is the probability reference of the data, is the first probability conversion adjustment coefficient, is the corresponding value of the first concern factor, is the corresponding value of the second concern factor, is the second probability conversion adjustment coefficient, is the corresponding value of the third concern factor, is the first probability conversion adjustment constant, is the third probability conversion adjustment coefficient, is the corresponding value of the fourth concern factor, Adjustment constant for the second probability transformation.

[0012] In some embodiments of the present application, a method for performing a correlation bias analysis on the first data demand bias and determining a screening order for screening different data storage subspaces based on the analysis result includes: Each data storage subspace is marked with a preset relevance bias type, and the relevance of the data storage subspace is evaluated to obtain a relevance evaluation value; Performing a relevance bias analysis on the first data demand bias to determine a required relevance bias type; According to the required relevance bias type of the first data demand bias, several data storage subspaces to be called are determined, and based on the size relationship of the relevance evaluation value of each data storage subspace, the screening order of the data storage subspace is determined.

[0013] In some embodiments of the present application, a method for evaluating the relevance of a data storage subspace includes: Establishing a relevance bias type table, wherein the relevance bias type table includes a plurality of relevance bias types; According to each relevance bias type in the relevance bias type table, relevance analysis is performed on different data in the category data storage space and classified to generate a number of data storage subspaces; According to the correlation bias type of the data storage subspace, the data in the data storage subspace is evaluated for correlation to obtain a correlation evaluation value; The expression for calculating the correlation evaluation value of the data storage subspace is: ; in, is the correlation evaluation value of the data storage subspace, Evaluate the conversion factor for relevance, is the relevant characteristic factor of the i-th data, t is the standard related characteristic factor, Adjustment constant for correlation evaluation.

[0014] In some embodiments of the present application, a system for improving database query efficiency is also disclosed, including: The first module is used to classify the data, and construct several category data storage spaces based on the classified data categories, and construct several data storage subspaces in the category data storage space according to the correlation between different data under the same data category; The second module is used to obtain data query requirements, analyze the data query requirements, determine a number of first data requirement biases, and determine the called category data storage space based on the first data requirement biases; The third module is used to determine a number of category data storage spaces to be called based on the overall performance characteristics of the several data demand biases, and select a number of data in the category data storage spaces to be called for caching based on the high probability query judgment rule, perform a relevance bias analysis on the first data demand bias, and determine a screening order for screening different data storage subspaces based on the analysis result, and cache the data in the data storage subspace according to the screening order; The fourth module is used to scan and analyze the data in the cache to determine the data to be queried.

[0015] The present application discloses a method and system for improving database query efficiency, which relates to the field of database management technology. Data is classified and a plurality of category data storage spaces are constructed based on the data categories. The correlation between the data is analyzed to construct a plurality of data storage subspaces. Based on a plurality of first data demand biases of data query requirements, the category data storage space to be called is determined. Based on the overall performance characteristics of the first data demand biases and the high probability query judgment rules, the data to be called is screened for caching, the first data demand biases are analyzed for correlation bias, the data storage subspaces are screened in order, and cached according to the order. Through the above technical scheme, the present application caches the data of the screened data storage subspaces and the data to be called, optimizes the data query order, and thereby improves the query efficiency of the database.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A method step diagram of a method for improving database query efficiency disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0019] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in this application should be the common meanings understood by the technical personnel of the present invention.

[0020] Example: The purpose of the present invention is to provide a method capable of improving database query efficiency.

[0021] This application discloses a method for improving database query efficiency. Figure 1 ,include: Step S100, classify the data, and construct several class data storage spaces based on the classified data categories, and construct several data storage subspaces in the class data storage space according to the correlation between different data under the same data category.

[0022] It is important to understand that data is classified and divided into different categories. Based on these data categories, corresponding category data storage spaces are established, and data storage subspaces are constructed based on the correlation between different data under the same data category, where data categories can be categories between document data or categories between numerical data such as certain parameters.

[0023] Step S200, obtaining data query requirements, analyzing the data query requirements, determining a number of first data requirement biases, and determining the category data storage space to be called based on the first data requirement biases.

[0024] What needs to be understood is that based on the data query requirements, we can preliminarily infer the data type, characteristics, purpose, storage location, etc. required by the demand side, which is the first data demand bias.

[0025] Step S300, based on the overall performance characteristics of the first data demand preferences, a number of category data storage spaces to be prepared for invocation are determined, and based on a high probability query judgment rule, a number of data in the category data storage spaces to be prepared for invocation are selected for caching.

[0026] It should be understood that a data query demand includes several first data demand preferences. The overall performance characteristics of different data demand preferences can, to a certain extent, determine the purpose of the current data query demand on the demand side. Therefore, based on this analysis, it is possible to find out the data to be called and cache it to reduce the time consumption of traversing data.

[0027] Step S400 , performing correlation bias analysis on the first data demand bias, and determining a screening order for screening different data storage subspaces based on the analysis result, and caching data in the data storage subspaces according to the screening order.

[0028] It should be understood that the significance of conducting relevance bias analysis on the first data demand bias is that the first data demand bias can determine which category of data storage space to select, and can also provide bias guidance for the selection of data storage subspaces in the category data storage space. For example, the category of the category data storage space is generator set operation log data, and the first data demand bias includes the demand bias for generator set operation log data, which is further limited to a specific time period. The corresponding data storage subspace can be determined based on the demand bias (relevance bias) for a specific time period.

[0029] Step S500, scanning and analyzing the data in the cache to determine the data to be queried.

[0030] In some embodiments of the present application, a method for constructing a plurality of categories of data storage space based on divided data categories includes: The first step is to determine the volume of monomer data and the monomer storage space of each data category, and based on the monomer data volume and the monomer storage space, determine the basic space volume and the reserve space volume of the corresponding category data storage space.

[0031] The volume of the reserve space is also determined according to the updating frequency of the monomer data.

[0032] The expression for calculating the storage space volume of category data is: ; in, The spatial volume of the category data storage space of the nth data category, is the volume of monomer data of the nth data category, It is the single storage space of single data of the nth data category. The update frequency of the individual data of the nth data category, is the conversion coefficient of the reserve space volume of the nth data category, The nth reserve space volume adjustment constant.

[0033] In some embodiments of the present application, according to the correlation between different data under the same data category, a method for constructing a plurality of data storage subspaces in a category data storage space includes: The first step is to determine the first correlation between the data based on the storage time of different data.

[0034] The second step is to determine the second correlation between the data based on the relative logical relationship between different data belonging to the same storage information.

[0035] The third step is to determine the correlation between different data in the same data category based on the first correlation and the second correlation.

[0036] In some embodiments of the present application, based on the first data demand bias, a method for determining a called category data storage space includes: In the first step, several sub-level classifications are performed for different data categories. Each level of classification is gradually reduced in scale according to the generalization requirements of preset specific attributes. Different level classifications are set with attribute precision mapping values ​​for preset specific attributes.

[0037] In the second step, based on the first data demand bias, a plurality of first biasing features are determined, and based on the first biasing features, a plurality of data categories to which the data demand belongs are determined.

[0038] The third step is to determine the accuracy requirements for the data category based on the first bias feature, and compare and analyze the accuracy requirements and attribute accuracy mapping values ​​to determine the level of the data category corresponding to the data demand bias.

[0039] The method for obtaining the first data demand bias includes: establishing a data demand table, wherein the data demand table includes data category, data source, data storage time, data application scenario, data volume scale and data storage location.

[0040] In some embodiments of the present application, methods for setting attribute precision mapping values ​​for preset specific attributes in different hierarchical classifications include: In the first step, a data attribute correspondence table is established, wherein the data attribute correspondence table includes a plurality of preset specific attributes, and a first restriction condition interval is set for each preset specific attribute.

[0041] In the second step, for each preset specific attribute, a plurality of hierarchical restriction intervals are set for the first restriction interval, and the attribute accuracy mapping value is determined according to the ratio of the interval length of the hierarchical restriction intervals at different levels to the interval length of the first restriction interval.

[0042] In some embodiments of the present application, based on the overall performance characteristics of a plurality of data demand preferences, a method for determining a plurality of category data storage spaces to be prepared for invocation includes: The first step is to analyze past data call logs to extract past data query requirements and past category data storage space call records.

[0043] The second step is to analyze the past data query needs, determine the corresponding past first data demand tendencies, construct the past first data demand tendencies into a past demand tendency overall performance group, and associate the past demand tendency overall performance group with the past category data storage space.

[0044] The third step is to construct the current data demand tendencies into the current demand tendency overall performance group, compare and analyze the current demand tendency overall performance group and different past demand tendency overall performance groups, and determine the number of matching elements.

[0045] In the fourth step, if the ratio of the number of matching elements to the total number of elements in the overall performance group of past demand bias is greater than a preset value, it is determined that the overall performance group of past demand bias is consistent with the overall performance group of current demand bias, and several past category data storage spaces associated with the overall performance group of past demand bias are prepared for calling.

[0046] In some embodiments of the present application, based on the high probability query judgment rule, the method of selecting some data in the category data storage space to be called for caching includes: In the first step, the number of elements in the overall performance group of past demand bias and the overall performance group of current demand bias that match each other is identified as the first factor of interest, and the total number of elements in the overall performance group of past demand bias is identified as the second factor of interest.

[0047] In the second step, the number of past calls of each data in the category data storage space is identified as the third concern factor, and the past call frequency of each data in the category data storage space is identified as the fourth concern factor.

[0048] In the third step, based on the first concern factor, the second concern factor, the third concern factor and the fourth concern factor, a probability reference amount of specific data in the data storage space of the corresponding category is determined.

[0049] If the probability reference quantity of the data is greater than the preset probability reference scalar, the data is cached; Among them, the expression of the probability reference of the calculated data is: .

[0050] Among them, g is the probability reference of the data, is the first probability conversion adjustment coefficient, is the corresponding value of the first concern factor, is the corresponding value of the second concern factor, is the second probability conversion adjustment coefficient, is the corresponding value of the third concern factor, is the first probability conversion adjustment constant, is the third probability conversion adjustment coefficient, is the corresponding value of the fourth concern factor, Adjustment constant for the second probability transformation.

[0051] In some embodiments of the present application, a method for performing a correlation bias analysis on the first data demand bias and determining a screening order for screening different data storage subspaces based on the analysis result includes: In the first step, each data storage subspace is marked with a preset relevance bias type, and the relevance of the data storage subspace is evaluated to obtain a relevance evaluation value.

[0052] The second step is to perform a relevance bias analysis on the first data demand bias to determine the required relevance bias type.

[0053] The third step is to determine a number of data storage subspaces to be called according to the required relevance bias type of the first data demand bias, and determine the screening order of the data storage subspaces based on the size relationship of the relevance evaluation value of each data storage subspace.

[0054] In some embodiments of the present application, a method for evaluating the relevance of a data storage subspace includes: The first step is to establish a relevance bias type table, wherein the relevance bias type table includes several relevance bias types.

[0055] In the second step, according to each relevance bias type in the relevance bias type table, the different data in the category data storage space are subjected to relevance analysis and classification to generate several data storage subspaces.

[0056] The third step is to evaluate the relevance of the data in the data storage subspace according to the relevance bias type of the data storage subspace to obtain a relevance evaluation value.

[0057] The expression for calculating the correlation evaluation value of the data storage subspace is: .

[0058] in, is the correlation evaluation value of the data storage subspace, Evaluate the conversion factor for relevance, is the relevant characteristic factor of the i-th data, t is the standard related characteristic factor, Adjustment constant for correlation evaluation.

[0059] In some embodiments of the present application, a system for improving database query efficiency is also disclosed, including: a first module, a second module, a third module and a fourth module.

[0060] The first module is used to classify data, and construct a number of category data storage spaces based on the classified data categories, and construct a number of data storage subspaces in the category data storage space according to the correlation between different data under the same data category.

[0061] The second module is used to obtain data query requirements, analyze the data query requirements, determine a number of first data requirement biases, and determine the called category data storage space based on the first data requirement biases.

[0062] The third module is used to determine several category data storage spaces to be called based on the overall performance characteristics of several data demand preferences, and based on the high probability query judgment rules, select several data in the category data storage spaces to be called for caching, perform relevance bias analysis on the first data demand preference, and based on the analysis results, determine the screening order for screening different data storage subspaces, and cache the data in the data storage subspace according to the screening order.

[0063] The fourth module is used to scan and analyze the data in the cache to determine the data to be queried.

[0064] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0065] The present application discloses a method and system for improving database query efficiency, which relates to the field of database management technology. Data is classified and a plurality of category data storage spaces are constructed based on the data categories. The correlation between the data is analyzed to construct a plurality of data storage subspaces. Based on a plurality of first data demand biases of data query requirements, the category data storage space to be called is determined. Based on the overall performance characteristics of the first data demand biases and the high probability query judgment rules, the data to be called is screened for caching, the first data demand biases are analyzed for correlation bias, the data storage subspaces are screened in order, and cached according to the order. Through the above technical scheme, the present application caches the data of the screened data storage subspaces and the data to be called, optimizes the data query order, and thereby improves the query efficiency of the database.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for improving database query efficiency, characterized in that: include: Classify the data, and construct several category data storage spaces based on the classified data categories, and construct several data storage subspaces in the category data storage space according to the correlation between different data under the same data category; Obtaining data query requirements, analyzing the data query requirements, determining a number of first data requirement biases, and determining the called category data storage space based on the first data requirement biases; Based on the overall performance characteristics of the plurality of first data demand preferences, a plurality of category data storage spaces to be prepared for invocation are determined, and based on a high probability query judgment rule, a plurality of data in the category data storage spaces to be prepared for invocation are selected for caching; Performing a correlation bias analysis on the first data demand bias, and determining a screening order for screening different data storage subspaces based on the analysis result, and caching the data in the data storage subspace according to the screening order; Scan and analyze the data in the cache to determine the data to be queried.

2. A method for improving database query efficiency according to claim 1, characterized in that: The method of constructing several categories of data storage space based on the divided data categories includes: Determine the volume of monomer data and the monomer storage space of each data category, and determine the basic space volume and the reserve space volume of the corresponding category data storage space based on the monomer data volume and the monomer storage space; Wherein, the volume of the preparation space is also determined according to the update frequency of the monomer data; The expression for calculating the storage space volume of category data is: ； in, The spatial volume of the category data storage space of the nth data category, is the volume of monomer data of the nth data category, It is the single storage space of single data of the nth data category. The update frequency of the individual data of the nth data category, is the conversion coefficient of the reserve space volume of the nth data category, The nth reserve space volume adjustment constant.

3. A method for improving database query efficiency according to claim 1, characterized in that: According to the correlation between different data under the same data category, the method of constructing several data storage subspaces in the category data storage space includes: Determining a first correlation between the data based on the time when the different data are stored; Determining a second correlation between the data based on a relative logical relationship between different data belonging to the same storage information; Based on the first correlation and the second correlation, correlation between different data in the same data category is determined.

4. A method for improving database query efficiency according to claim 1, characterized in that: The method for determining the called category data storage space based on the first data demand bias includes: Several sub-level classifications are performed for different data categories. Each level of classification is gradually reduced in scale according to the generalization requirements of the preset specific attributes. Different level classifications are set with attribute precision mapping values ​​for the preset specific attributes. Based on the first data demand bias, determining a plurality of first bias characteristics, and based on the first bias characteristics, determining a plurality of data categories to which the data demand belongs; Based on the first bias feature, determine the accuracy requirement for the data category, and compare and analyze the accuracy requirement and the attribute accuracy mapping value to determine the level of the data category corresponding to the data demand bias; Methods for obtaining the first data demand bias include: A data requirement table is established, which includes data category, data source, data storage time, data application scenario, data volume scale and data storage location.

5. A method for improving database query efficiency according to claim 1, characterized in that: Different hierarchical classifications all set attribute precision mapping values ​​for preset specific attributes, including: A data attribute correspondence table is established, wherein the data attribute correspondence table includes a plurality of preset specific attributes, and a first restriction condition interval is set for each preset specific attribute; Several levels of hierarchical restriction intervals are set for the first restriction interval of each preset specific attribute, and the attribute accuracy mapping value is determined according to the ratio of the interval length of the hierarchical restriction intervals at different levels to the interval length of the first restriction interval.

6. A method for improving database query efficiency according to claim 1, characterized in that: Based on the overall performance characteristics of several data demand preferences, the method for determining several category data storage spaces to be called includes: Analyze past data call logs to extract past data query requirements and past category data storage space call records; Analyze the past data query requirements, determine a number of corresponding past first data demand preferences, construct the number of past first data demand preferences into a past demand preference overall performance group, and associate the past demand preference overall performance group with the past category data storage space; Construct the current data demand preferences into a current demand preference overall performance group, compare and analyze the current demand preference overall performance group and different past demand preference overall performance groups, and determine the number of matching elements; If the ratio of the number of matching elements to the total number of elements in the past demand bias overall performance group is greater than a preset value, the past demand bias overall performance group is determined to be consistent with the current demand bias overall performance group, and several past category data storage spaces associated with the past demand bias overall performance group are prepared for calling.

7. A method for improving database query efficiency according to claim 6, characterized in that: The method of selecting some data in the category data storage space to be called for caching based on the high probability query judgment rule includes: The number of elements in the overall performance group of past demand bias and the overall performance group of current demand bias that match each other is identified as the first factor of interest, and the total number of elements in the overall performance group of past demand bias is identified as the second factor of interest; The number of past calls of each data in the category data storage space is identified as the third concern factor, and the past call frequency of each data in the category data storage space is identified as the fourth concern factor; Determine a probability reference amount of specific data in the data storage space of the corresponding category based on the first concern factor, the second concern factor, the third concern factor, and the fourth concern factor; If the probability reference quantity of the data is greater than the preset probability reference scalar, the data is cached; Among them, the expression of the probability reference of the calculated data is: ； Among them, g is the probability reference of the data, is the first probability conversion adjustment coefficient, is the corresponding value of the first concern factor, is the corresponding value of the second concern factor, is the second probability conversion adjustment coefficient, is the corresponding value of the third concern factor, is the first probability conversion adjustment constant, is the third probability conversion adjustment coefficient, is the corresponding value of the fourth concern factor, Adjustment constant for the second probability transformation.

8. A method for improving database query efficiency according to claim 1, characterized in that: The method of performing correlation bias analysis on the first data demand bias and determining the screening order of screening different data storage subspaces based on the analysis result includes: Each data storage subspace is marked with a preset relevance bias type, and the relevance of the data storage subspace is evaluated to obtain a relevance evaluation value; Performing a relevance bias analysis on the first data demand bias to determine a required relevance bias type; According to the required relevance bias type of the first data demand bias, several data storage subspaces to be called are determined, and based on the size relationship of the relevance evaluation value of each data storage subspace, the screening order of the data storage subspace is determined.

9. A method for improving database query efficiency according to claim 8, characterized in that: Methods for evaluating the relevance of data storage subspaces include: Establishing a relevance bias type table, wherein the relevance bias type table includes a plurality of relevance bias types; According to each relevance bias type in the relevance bias type table, relevance analysis is performed on different data in the category data storage space and classified to generate a number of data storage subspaces; According to the correlation bias type of the data storage subspace, the data in the data storage subspace is evaluated for correlation to obtain a correlation evaluation value; The expression for calculating the correlation evaluation value of the data storage subspace is: ； in, is the correlation evaluation value of the data storage subspace, Evaluate the conversion factor for relevance, is the relevant characteristic factor of the i-th data, t is the standard related characteristic factor, Adjustment constant for correlation evaluation.

10. A system for improving database query efficiency, characterized in that: include: The first module is used to classify the data, and construct several category data storage spaces based on the classified data categories, and construct several data storage subspaces in the category data storage space according to the correlation between different data under the same data category; The second module is used to obtain data query requirements, analyze the data query requirements, determine a number of first data requirement biases, and determine the called category data storage space based on the first data requirement biases; The third module is used to determine a number of category data storage spaces to be called based on the overall performance characteristics of the several data demand biases, and select a number of data in the category data storage spaces to be called for caching based on the high probability query judgment rule, perform a relevance bias analysis on the first data demand bias, and determine a screening order for screening different data storage subspaces based on the analysis result, and cache the data in the data storage subspace according to the screening order; The fourth module is used to scan and analyze the data in the cache to determine the data to be queried.

Images