Optimization method and system for data statistical query in big data scene

Abstract

The invention provides an optimization method and system for data statistical query in a big data scene, and belongs to the technical field of data query optimization. The method comprises the following steps: acquiring historical keyword query data of each associated keyword in an associated keyword group in a preset time period; determining basic importance data of each associated keyword according to the historical keyword query data; obtaining historical associated keyword query times in a plurality of historical time periods, and analyzing a query trend influence coefficient of each associated keyword according to the historical associated keyword query times; calculating query importance data of each associated keyword according to the basic importance data of each associated keyword and the query trend influence coefficient; according to the query importance data of each associated keyword and a preset importance search display result adjustment rule, adjusting query result display data corresponding to the target keyword; by adopting the scheme, the accuracy of data query can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data query optimization, and in particular to an optimization method and system for data statistical query in a big data scenario. Background Art

[0002] Generally speaking, users perform data queries through various search engines. The search engines determine the key content contained therein, such as keywords, key words and even key sentences, based on the information input by the users, and retrieve relevant results based on the above key content to present to the users for viewing, querying and use; these are all part of regular data queries; for some search engine platforms, users use the search engine platform to retrieve keyword-related content, and will enter the target keyword in the input box, and may also enter related keywords related to the target keyword to make the search results more in line with user expectations. The search engine platform obtains the corresponding search results based on the above keywords and displays them to the users; when there are many search results, the users may not be able to view all the search results on the first page of search results. In order to improve the accuracy of data queries and make the query results more in line with the user's query needs, it is very necessary to optimize the data queries. Summary of the invention

[0003] The present application provides an optimization method and system for data statistical query in a big data scenario, which can improve the accuracy of data query.

[0004] In a first aspect, the present application provides an optimization method for data statistical query in a big data scenario. The method comprises: Acquire historical keyword query data within a preset time period for each associated keyword in the associated keyword group; the associated keyword group includes a plurality of associated keywords associated with the target keyword; the historical keyword query data includes the number of historical queries for the associated keyword within the preset time period; Determine basic importance data of each of the associated keywords according to the historical keyword query data; the basic importance data is associated with the historical query times; Obtain the number of historical associated keyword queries in multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the multiple historical time periods are multiple time periods that are continuous in time sequence, and the preset time period is the time period closest to the current time; Calculating query importance data of each of the associated keywords according to the basic importance data of each of the associated keywords and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient; The query result display data corresponding to the target keyword is adjusted according to the query importance data of each associated keyword and a preset importance search display result adjustment rule; the query result display data includes a display order of the query results corresponding to the target keyword.

[0005] Further, the acquisition of the number of historical associated keyword queries within multiple historical time periods, and the analysis of the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the multiple historical time periods are multiple time periods that are sequentially continuous, wherein the preset time period is the time period closest to the current moment and includes: Suppose there are n historical time periods, and the number of historical related keyword queries in the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. The number of historical related keyword queries for the historical time period farthest from the current time; Calculate n-1 query times change values ​​according to n historical time periods; the query times change value is obtained by subtracting the historical associated keyword query times in the later historical time period from the historical associated keyword query times in the previous historical time period in each two adjacent historical time periods; then the query times change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; If yes, then the query trend influence coefficient is calculated as follows: ; If not, the query trend influence coefficient is calculated as follows: ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

[0006] Further, the query importance data of each associated keyword is calculated according to the basic importance data of each associated keyword and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient, including: Obtain target query users of target keywords; the target query users are users who have entered the target keywords in the search engine platform within a preset time period; Acquire historical target user query data of the target query user, and determine historical target user query trend influence data according to the historical target user query data; The query importance data of each of the associated keywords is calculated according to the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords, and the historical target user query trend influence data.

[0007] Further, the acquiring of historical target user query data of the target query user and determining historical target user query trend influence data according to the historical target user query data includes: Determining first target query data according to the historical target user query data; the first target query data is query data involving the associated keyword in the historical target user query data; Determine the target query times in each of the historical time periods according to the first target query data; the target query times are the times that the target query user in each of the historical time periods queries the associated keywords; then the target query times in the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. The target query count for the historical time period farthest from the current time; Calculate n-1 target number change values ​​based on n historical time periods; the target number change value is obtained by subtracting the target query number in the previous historical time period from the target query number in the later historical time period in each two adjacent historical time periods; then the target number change value is ;in, ; Determine whether all target times change values ​​are greater than 0 or not greater than 0; If so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change value greater than 0, The number of target times change value is not greater than 0; Suppose there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

[0008] Furthermore, the calculation method of the query importance data includes: ; In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

[0009] In a second aspect, the present application provides an optimization system for data statistical query in a big data scenario, the system comprising: An acquisition module, used to acquire historical keyword query data within a preset time period for each associated keyword in an associated keyword group; the associated keyword group includes a plurality of associated keywords associated with a target keyword; the historical keyword query data includes the number of historical queries for the associated keyword within the preset time period; A determination module, configured to determine basic importance data of each of the associated keywords according to the historical keyword query data; the basic importance data is associated with the historical query times; An analysis module, used to obtain the number of historical associated keyword queries in multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and multiple historical time periods are multiple time periods that are continuous in time sequence, and the preset time period is the time period closest to the current moment; A calculation module, used for calculating the query importance data of each of the associated keywords according to the basic importance data of each of the associated keywords and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient; An adjustment module is used to adjust the query result display data corresponding to the target keyword according to the query importance data of each associated keyword and a preset importance search display result adjustment rule; the query result display data includes the display order of the query results corresponding to the target keyword.

[0010] Furthermore, the analysis module is further configured to obtain the number of historical associated keyword queries within multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the multiple historical time periods are multiple time periods that are sequentially continuous, and the preset time period is the time period closest to the current time, including: Suppose there are n historical time periods, and the number of historical related keyword queries in the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. The number of historical related keyword queries for the historical time period farthest from the current time; Calculate n-1 query times change values ​​according to n historical time periods; the query times change value is obtained by subtracting the historical associated keyword query times in the later historical time period from the historical associated keyword query times in the previous historical time period in each two adjacent historical time periods; then the query times change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; If yes, then the query trend influence coefficient is calculated as follows: ; If not, the query trend influence coefficient is calculated as follows: ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

[0011] Furthermore, the calculation module is further configured to calculate the query importance data of each associated keyword according to the basic importance data of each associated keyword and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient including: Obtain target query users of target keywords; the target query users are users who have entered the target keywords in the search engine platform within a preset time period; Acquire historical target user query data of the target query user, and determine historical target user query trend influence data according to the historical target user query data; The query importance data of each of the associated keywords is calculated according to the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords, and the historical target user query trend influence data.

[0012] Furthermore, the calculation module is further configured to obtain the historical target user query data of the target query user, and determine the historical target user query trend influence data according to the historical target user query data, including: Determining first target query data according to the historical target user query data; the first target query data is query data involving the associated keyword in the historical target user query data; Determine the target query times in each of the historical time periods according to the first target query data; the target query times are the times that the target query user in each of the historical time periods queries the associated keywords; then the target query times in the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. The target query count for the historical time period farthest from the current time; Calculate n-1 target number change values ​​based on n historical time periods; the target number change value is obtained by subtracting the target query number in the previous historical time period from the target query number in the later historical time period in each two adjacent historical time periods; then the target number change value is ;in, ; Determine whether all target times change values ​​are greater than 0 or not greater than 0; If so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change value greater than 0, The number of target times change value is not greater than 0; Suppose there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

[0013] Furthermore, the calculation module is further configured such that the calculation method of the query importance data includes: ; In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

[0014] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A flowchart of an optimization method for data statistical query in a big data scenario in an embodiment of the present application is shown; Figure 2 A block diagram of an optimization system for data statistical query in a big data scenario in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0018] The present application provides an optimization method and system for data statistical query in a big data scenario, which can improve the accuracy of data query.

[0019] In the first aspect, the present application provides an optimization method for data statistical query in a big data scenario. Figure 1 The specific steps included in the method are as follows.

[0020] Step S110: obtaining historical keyword query data within a preset time period for each associated keyword in an associated keyword group; the associated keyword group includes multiple associated keywords associated with the target keyword; the historical keyword query data includes the number of historical queries of the associated keyword within the preset time period.

[0021] In an embodiment of the present application, a user enters a keyword through a search engine platform to retrieve relevant content; the search engine platform here supports the user to enter more keyword combinations in the form of a keyword plus a space plus a keyword; in this way, historical query data of keyword combinations within a preset time period can be obtained; for the above-mentioned associated keywords, in this solution, the keywords that meet the number of appearances of not less than a preset number threshold within a preset time period are associated keywords; that is, within the preset time period, the user enters the target keyword and the associated keyword in the input box of the search engine platform, and the number of times the associated keyword appears together with the target keyword is not less than the preset number threshold; therefore, there will be multiple associated keywords. In order to optimize the user's search results, the priority of the associated keywords is analyzed and calculated in this solution, thereby improving the search accuracy and making the search results more in line with the user's expectations.

[0022] Step S120: determining basic importance data of each of the associated keywords according to the historical keyword query data; the basic importance data is associated with the historical query times.

[0023] In an embodiment of the present application, the basic importance data of each associated keyword is determined according to a preset basic importance table; the preset basic importance table contains the historical query times of the associated keywords, the basic importance data, and the corresponding relationship between the historical query times and the basic importance data; therefore, when two of the above three are obtained, the third one can be obtained.

[0024] Step S130: Obtain the number of historical associated keyword queries within multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword based on the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and multiple historical time periods are multiple time periods that are continuous in time sequence, wherein the preset time period is the time period closest to the current moment.

[0025] In the embodiment of the present application, the query trend influence coefficient of each associated keyword is analyzed according to the historical associated keyword query times, specifically including: there are n historical time periods, and the historical associated keyword query times of the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. is the number of historical associated keyword queries in the historical time period farthest from the current moment; n-1 query number change values ​​are calculated based on n historical time periods; the query number change value is obtained by subtracting the number of historical associated keyword queries in the previous historical time period from the number of historical associated keyword queries in the later historical time period in each two adjacent historical time periods; then the query number change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; If yes, then the query trend influence coefficient is calculated as follows: ; If not, the query trend influence coefficient is calculated as follows: ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

[0026] It can be understood that in the embodiment of the present application, multiple historical time periods with the same length as the preset time period are selected, and these multiple historical time periods are chronologically continuous and progressive with the preset time period. For example, the preset time period is from last Monday to Sunday, and the historical time periods are the week before last, the week before last, and so on.

[0027] Step S140: Calculating query importance data of each of the associated keywords according to the basic importance data of each of the associated keywords and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient.

[0028] In an embodiment of the present application, calculating the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords and the query trend influence coefficient specifically includes: obtaining a target query user of the target keyword; the target query user is a user who has entered the target keyword in a search engine platform within a preset time period; obtaining historical target user query data of the target query user, and determining historical target user query trend influence data based on the historical target user query data; calculating the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords, and the historical target user query trend influence data.

[0029] It can be understood that in the embodiments of the present application, taking the target keyword as an example, the content of the target keyword is not limited in this solution. It is only for one target keyword and is explained by taking one target keyword as an example. Similarly, other keywords can be analyzed and calculated by analogy; in the case of one target keyword, the target query user refers to the user who enters the target keyword no less than a preset input number threshold within a preset time period; in this case, there are multiple target query users, and the query trends of user queries for associated keywords are analyzed based on the historical query data of these target query users, and then the degree of association between these associated keywords and the target keyword, that is, the importance of the associated keywords themselves, is determined.

[0030] Among them, obtaining the historical target user query data of the target query user, and determining the historical target user query trend influence data based on the historical target user query data specifically includes: determining the first target query data based on the historical target user query data; the first target query data is the query data involving the associated keywords in the historical target user query data; determining the number of target queries in each of the historical time periods based on the first target query data; the target query number is the number of times the target query user queries the associated keywords in each of the historical time periods; then the target query number for the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. is the target query count for the historical time period farthest from the current moment; n-1 target query count changes are calculated based on n historical time periods; the target query count change value is obtained by subtracting the target query count for the previous historical time period from the target query count for the later historical time period in each of two adjacent historical time periods; then the target query count change value is ;in, ; Determine whether all target times change values ​​are greater than 0 or not greater than 0; If so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change value greater than 0, The number of target times change value is not greater than 0; Suppose there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

[0031] Furthermore, the calculation method of query importance data specifically includes: ; In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

[0032] Step S150: adjusting the query result display data corresponding to the target keyword according to the query importance data of each associated keyword and a preset importance search display result adjustment rule; the query result display data includes a display order of the query results corresponding to the target keyword.

[0033] It can be understood that after obtaining the query importance data of each associated keyword, the associated keywords are prioritized according to the query importance data, and these associated keywords are bound to the target keyword in order of priority. When it is detected that the user enters the target keyword for search, the associated keywords with the highest priority order are retrieved together as hidden search auxiliary conditions, and the number of target keywords and the number of associated keywords are used as synchronization conditions to determine the display order of the query results; the associated keywords are the first associated keyword, the second associated keyword, etc. in order of priority from high to low; specifically, display priority data = number of target keywords * preset first weight + number of first associated keywords * preset second weight + number of second associated keywords * preset third weight; it should be noted that the display priority data here is used to characterize the display order of the query results, and the query results are displayed from high to low; the number of associated keywords used in the calculation method of the display priority data here can also be adjusted based on actual analysis; and the preset first weight here is generally 80%, the preset second weight is 15%, and the preset third weight is 5%; the specific weight values ​​can also be adjusted and changed according to specific needs.

[0034] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0035] In the second aspect, the present application provides an optimization system for data statistical query in big data scenarios. Figure 2 As shown, the system includes, an acquisition module 210, used to acquire historical keyword query data within a preset time period of each associated keyword in an associated keyword group; the associated keyword group includes multiple associated keywords associated with a target keyword; the historical keyword query data includes the number of historical queries of the associated keyword within a preset time period; a determination module 220, used to determine basic importance data of each associated keyword according to the historical keyword query data; the basic importance data is associated with the number of historical queries; an analysis module 230, used to acquire the number of historical associated keyword queries within multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period A time period that is the same length as the preset time period; the preset time period and multiple historical time periods are multiple time periods that are time-series continuous, wherein the preset time period is the time period closest to the current moment; a calculation module 240, used to calculate the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient; an adjustment module 250, used to adjust the query result display data corresponding to the target keyword based on the query importance data of each of the associated keywords and a preset importance search display result adjustment rule; the query result display data includes the display order of the query results corresponding to the target keyword.

[0036] Furthermore, the analysis module 230 is further configured to obtain the number of historical associated keyword queries in multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and multiple historical time periods are multiple time periods that are sequentially continuous, wherein the preset time period is the time period closest to the current moment, including: there are n historical time periods, and the number of historical associated keyword queries in the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. is the number of historical associated keyword queries in the historical time period farthest from the current moment; n-1 query number change values ​​are calculated based on n historical time periods; the query number change value is obtained by subtracting the number of historical associated keyword queries in the previous historical time period from the number of historical associated keyword queries in the later historical time period in each two adjacent historical time periods; then the query number change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; if so, the calculation method of the query trend influence coefficient is, ; If not, the calculation method of the query trend influence coefficient is, ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

[0037] Furthermore, the calculation module 240 is further configured to calculate the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords and the query trend influence coefficient; the query importance data is associated with the basic importance data and the query trend influence coefficient including: obtaining the target query user of the target keyword; the target query user is a user who has entered the target keyword in the search engine platform within a preset time period; obtaining the historical target user query data of the target query user, and determining the historical target user query trend influence data based on the historical target user query data; calculating the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords and the historical target user query trend influence data. Wherein, the obtaining of the historical target user query data of the target query user and determining the historical target user query trend influence data according to the historical target user query data comprises: determining the first target query data according to the historical target user query data; the first target query data is the query data involving the associated keywords in the historical target user query data; determining the number of target queries in each of the historical time periods according to the first target query data; the number of target queries is the number of times the target query user queries the associated keywords in each of the historical time periods; then the number of target queries in the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. is the target query count for the historical time period farthest from the current moment; n-1 target query count changes are calculated based on n historical time periods; the target query count change value is obtained by subtracting the target query count for the previous historical time period from the target query count for the later historical time period in each of two adjacent historical time periods; then the target query count change value is ;in, ; Determine whether all target frequency change values ​​are greater than 0 or not greater than 0; if so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change value greater than 0, is the number of target times change value not greater than 0; if there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

[0038] Furthermore, the calculation module 240 is further configured to calculate the query importance data in the following manner: ; In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0040] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. An optimization method for data statistical query in a big data scenario, characterized in that: include: Obtain historical keyword query data within a preset time period for each associated keyword in the associated keyword group; The associated keyword group includes a plurality of associated keywords associated with the target keyword; the historical keyword query data includes the historical query times of the associated keywords within a preset time period; Determine basic importance data of each of the associated keywords according to the historical keyword query data; the basic importance data is associated with the historical query times; Obtain the number of historical associated keyword queries in multiple historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the multiple historical time periods are multiple time periods that are continuous in time sequence, and the preset time period is the time period closest to the current time; Calculating the query importance data of each of the associated keywords according to the basic importance data of each of the associated keywords and the query trend influence coefficient; The query importance data is associated with the basic importance data and the query trend influence coefficient; Adjusting the query result display data corresponding to the target keyword according to the query importance data of each of the associated keywords and a preset importance search display result adjustment rule; The query result display data includes the display order of the query results corresponding to the target keyword.

2. The optimization method for data statistical query in a big data scenario according to claim 1 is characterized in that: The obtaining of the number of historical associated keyword queries within a plurality of historical time periods, and analyzing the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the plurality of historical time periods are a plurality of time periods that are sequentially continuous, wherein the preset time period is the time period closest to the current moment and includes: Suppose there are n historical time periods, and the number of historical related keyword queries in the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. The number of historical related keyword queries for the historical time period farthest from the current time; Calculate n-1 query times change values ​​according to n historical time periods; the query times change value is obtained by subtracting the historical associated keyword query times in the later historical time period from the historical associated keyword query times in the previous historical time period in each two adjacent historical time periods; then the query times change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; If yes, then the query trend influence coefficient is calculated as follows: ; If not, the query trend influence coefficient is calculated as follows: ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

3. The optimization method for data statistical query in big data scenario according to claim 2 is characterized in that: The step of calculating the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords and the query trend influence coefficient; The query importance data associated with the basic importance data and the query trend influence coefficient includes: Obtain target query users of target keywords; the target query users are users who have entered the target keywords in the search engine platform within a preset time period; Acquire historical target user query data of the target query user, and determine historical target user query trend influence data based on the historical target user query data; The query importance data of each of the associated keywords is calculated according to the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords, and the historical target user query trend influence data.

4. The optimization method for data statistical query in a big data scenario according to claim 3 is characterized in that: The acquiring of historical target user query data of the target query user, and determining historical target user query trend influence data according to the historical target user query data comprises: Determining first target query data according to the historical target user query data; the first target query data is query data involving the associated keyword in the historical target user query data; Determine the target query times in each of the historical time periods according to the first target query data; the target query times are the times that the target query user queries the associated keywords in each of the historical time periods; then the target query times in the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. The target query count for the historical time period farthest from the current time; Calculate n-1 target number change values ​​based on n historical time periods; the target number change value is obtained by subtracting the target query number in the previous historical time period from the target query number in the later historical time period in each two adjacent historical time periods; then the target number change value is ;in, ; Determine whether all target times change values ​​are greater than 0 or not greater than 0; If so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change value greater than 0, The number of target times change value is not greater than 0; Suppose there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

5. The optimization method for data statistical query in big data scenario according to claim 4 is characterized in that: The calculation method of the query importance data includes: ； In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

6. An optimization system for data statistical query in big data scenarios, characterized in that: include: An acquisition module (210) is used to acquire historical keyword query data within a preset time period for each associated keyword in the associated keyword group; The associated keyword group includes a plurality of associated keywords associated with the target keyword; the historical keyword query data includes the historical query times of the associated keywords within a preset time period; A determination module (220) is used to determine basic importance data of each of the associated keywords according to the historical keyword query data; the basic importance data is associated with the historical query times; An analysis module (230) is used to obtain the number of historical related keyword queries in multiple historical time periods, and analyze the query trend influence coefficient of each of the related keywords according to the number of historical related keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the multiple historical time periods are multiple time periods that are sequentially continuous, and the preset time period is the time period closest to the current moment; A calculation module (240), configured to calculate the query importance data of each of the associated keywords according to the basic importance data of each of the associated keywords and the query trend influence coefficient; The query importance data is associated with the basic importance data and the query trend influence coefficient; An adjustment module (250) is used to adjust the query result display data corresponding to the target keyword according to the query importance data of each associated keyword and a preset importance search display result adjustment rule; The query result display data includes the display order of the query results corresponding to the target keyword.

7. The optimization system for data statistical query in big data scenarios according to claim 6 is characterized in that: The analysis module (230) is further configured to obtain the number of historical associated keyword queries within a plurality of historical time periods, and analyze the query trend influence coefficient of each associated keyword according to the number of historical associated keyword queries; the historical time period is a time period of the same length as the preset time period; the preset time period and the plurality of historical time periods are a plurality of time periods that are sequentially continuous, wherein the preset time period is the time period closest to the current moment and includes: Suppose there are n historical time periods, and the number of historical related keyword queries in the i-th historical time period is ;but is the number of historical related keyword queries in the historical time period closest to the current time. The number of historical related keyword queries for the historical time period farthest from the current time; Calculate n-1 query times change values ​​according to n historical time periods; the query times change value is obtained by subtracting the historical associated keyword query times in the later historical time period from the historical associated keyword query times in the previous historical time period in each two adjacent historical time periods; then the query times change value is ;in, ; Determine whether all query times change values ​​are greater than 0 or not greater than 0; If yes, then the query trend influence coefficient is calculated as follows: ; If not, the query trend influence coefficient is calculated as follows: ; In the formula, is the number of query times whose change value is greater than 0. The number of query times whose change value is not greater than 0.

8. The optimization system for data statistical query in big data scenarios according to claim 7 is characterized in that: The calculation module (240) is further configured to calculate the query importance data of each of the associated keywords based on the basic importance data of each of the associated keywords and the query trend influence coefficient; The query importance data associated with the basic importance data and the query trend influence coefficient includes: Obtain target query users of target keywords; the target query users are users who have entered the target keywords in the search engine platform within a preset time period; Acquire historical target user query data of the target query user, and determine historical target user query trend influence data based on the historical target user query data; The query importance data of each of the associated keywords is calculated according to the basic importance data of each of the associated keywords, the query trend influence coefficient of each of the associated keywords, and the historical target user query trend influence data.

9. The optimization system for data statistical query in big data scenarios according to claim 8, characterized in that: The calculation module (240) is further configured to obtain the historical target user query data of the target query user, and determine the historical target user query trend influence data according to the historical target user query data, including: Determining first target query data according to the historical target user query data; the first target query data is query data involving the associated keyword in the historical target user query data; Determine the target query times in each of the historical time periods according to the first target query data; the target query times are the times that the target query user queries the associated keywords in each of the historical time periods; then the target query times in the i-th historical time period is ;but is the target query count for the historical time period closest to the current time. The target query count for the historical time period farthest from the current time; Calculate n-1 target number change values ​​based on n historical time periods; the target number change value is obtained by subtracting the target query number in the previous historical time period from the target query number in the later historical time period in each two adjacent historical time periods; then the target number change value is ;in, ; Determine whether all target times change values ​​are greater than 0 or not greater than 0; If so, the trend impact data of any target query user is calculated as follows: ; If not, the trend impact data of any target query user is calculated as follows: ; In the formula, is the number of target times change values ​​greater than 0, is the number of target times change values ​​not greater than 0; Suppose there are m target query users, then the trend impact data of the jth target query user is ; Then the calculation method of the historical target user query trend impact data is, ; In the formula, Query the weight coefficient for the preset target user, and .

10. The optimization system for data statistical query in big data scenarios according to claim 9, characterized in that: The calculation module (240) is further configured such that the calculation method of the query importance data includes: ； In the formula, To query importance data; As basic importance data; To query the trend influence coefficient; Query trend impact data for historical target users.

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