Advertisement visual element intelligent matching system and method based on big data

Abstract

The invention discloses an advertisement visual element intelligent matching system and method based on big data, and relates to the technical field of big data advertisement analysis, and the method comprises the steps: obtaining the historical advertisement watching record data of a user, and determining a periodic user characteristic advertisement data set based on the personalized screening analysis of the user; extracting corresponding advertisement texture element features, constructing a texture element feature path of the user characteristic advertisement data of the corresponding period, analyzing the score of the corresponding advertisement data, and determining the optimal user characteristic advertisement data of the period; taking the texture element characteristic path nodes of the periodic optimal user characteristic advertisement data as reference nodes, respectively carrying out clustering processing on the texture element characteristic path nodes corresponding to the periodic user characteristic advertisement data, and analyzing adaptive texture element characteristics of the texture element characteristic path nodes of the optimal user characteristic advertisement data; comprehensively determining an adaptive advertisement data recommendation scoring interval of the corresponding user; according to the invention, accurate user delivery and reasonable element matching of advertisements can be realized.

Description

Technical Field

[0001] The present invention relates to the field of big data advertising analysis, and in particular to a system and method for intelligently matching advertising visual elements based on big data. Background Art

[0002] With the rapid development of the Internet, the amount of data generated every day is exploding. These massive data cover a wealth of information such as consumers' browsing behavior, purchase records, interest preferences, etc. For the advertising industry, traditional advertising and visual design methods are difficult to fully tap the value behind these data and cannot accurately meet the personalized needs of different consumers; at the same time, consumers are increasingly distracted by advertisements in an environment of information overload, which requires the combination of visual elements of advertisements to attract users' attention more accurately and deliver them accurately; and the current user advertising push phenomenon is mainly characterized by multiple delivery quantities and multiple repetitive types, which can easily cause users to suffer from visual fatigue and poor experience in data browsing due to the random delivery of advertisements; there is a lack of accurate delivery of advertisements and reasonable combination of elements. Summary of the invention

[0003] The purpose of the present invention is to provide a system and method for intelligently matching advertising visual elements based on big data to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for intelligently matching advertisement visual elements based on big data, the method comprising the following steps: Obtain the user's historical ad viewing record data, determine the analysis period, build the corresponding period ad viewing data set, and determine the period user characteristic ad data set based on user personalized screening analysis; Based on the periodic user characteristic advertising data set, the corresponding advertising texture element features are extracted, the texture element feature path of the corresponding periodic user characteristic advertising data is constructed, and the corresponding periodic user characteristic advertising data score is analyzed to determine the periodic best user characteristic advertising data; with the texture element feature path nodes of the periodic best user characteristic advertising data as reference nodes, the corresponding texture element feature path nodes of the periodic user characteristic advertising data are clustered, the adapted texture element features of the texture element feature path nodes of each best user characteristic advertising data are analyzed, and the recommended score range of the adapted advertising data of the corresponding user is comprehensively determined.

[0005] Furthermore, by calling the user's historical advertisement viewing record database, determining the analysis period window, retrieving the user's historical advertisement viewing record data in the corresponding period, and constructing a corresponding period advertisement viewing data set; wherein the user's historical advertisement viewing record database includes a website advertisement viewing record database and an application advertisement viewing record database; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; A personalized screening analysis is performed based on the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user, and advertisement data with a viewing time shorter than the average viewing time is screened out, otherwise it is retained, and a periodic user characteristic advertisement data set after personalized screening analysis is determined.

[0006] Further, according to each periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, texture element features of the corresponding periodic user characteristic advertisement data are extracted respectively, corresponding texture element feature nodes are constructed, and the nodes are connected in series to construct texture element feature paths of the corresponding periodic user characteristic advertisement data; wherein the texture element features of the advertisement data include text type texture element features, graphic type texture element features and color type texture element features, etc., wherein the text type, graphic type and color type are further subdivided into specific texts, such as Chinese, English, etc.; the graphic type is further subdivided into rectangle, circle, etc.; the color type is further subdivided into red, blue, etc.; according to the texture element feature paths corresponding to each periodic user characteristic advertisement data, scoring analysis is performed on each periodic user characteristic advertisement data; the texture element feature node feature matrix of the texture element feature nodes of each periodic user characteristic advertisement data is constructed respectively; the texture element feature node feature matrix is ​​composed of the advertisement data proportion weight and type experience score of the corresponding texture element feature; wherein the type experience score of the texture element feature is the reference score of the corresponding advertisement texture element feature given by big data; based on the texture element feature node feature matrix corresponding to each periodic user characteristic advertisement data, texture element feature node scoring analysis is performed, and its calculation formula is:

[0007] Wherein, P[W,Q]n is the score data of the texture element feature node n in the corresponding period user characteristic advertising data; W is the advertising data proportion weight of the texture element feature node n; Q is the advertising data type experience score data of the texture element feature node n; According to the texture element feature node scoring data corresponding to each period user characteristic advertisement data, the node scores on the texture element feature path corresponding to each period user characteristic advertisement data are summed up and calculated to determine the scoring data corresponding to each period user characteristic advertisement data.

[0008] Further, based on the scoring data of each period user characteristic advertising data, the maximum value of the scoring data is output as the period best user characteristic advertising data; each texture element feature node on the texture element feature path corresponding to the period best user characteristic advertising data is used as a reference node, and the texture element feature nodes corresponding to the remaining period user characteristic advertising data in the period user characteristic advertising data set are used for node clustering processing; the remaining period user characteristic advertising data refers to the period user characteristic advertising data remaining after the period best user characteristic advertising data is removed from the period user characteristic advertising data set; according to the node clustering processing result, the clustering adaptation state of the texture element feature node corresponding to the period best user characteristic advertising data of each reference node and the texture element feature node corresponding to the remaining period user characteristic advertising data is determined; Concentric circles are constructed with each reference node as the center, and density analysis is performed on the texture element feature nodes corresponding to the remaining periodic user characteristic advertising data contained in the concentric circles; the node density value of the concentric circles corresponding to each radius is determined by gradually increasing the radius of the concentric circles, and the ratio of the number of nodes to the area in the concentric circles corresponding to each radius is calculated respectively, and the concentric circle corresponding to the maximum node density value is taken as the best adaptation node concentric circle corresponding to the reference node; based on the best adaptation node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertising data, the type and score of each texture element feature node in the concentric circle are comprehensively divided by the best adaptation node. If there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are taken and retained respectively; in the best adaptation node concentric circles where each texture element feature node of the periodic best user characteristic advertising data is located, all texture element feature nodes of different types are traversed with the center of the circle as the starting point, and for the same type of texture element feature nodes, branches are performed respectively. Traverse and output the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node; because after the same type of texture element feature nodes are classified and screened out, only the maximum value scoring node and the minimum value scoring node are retained for the same type of nodes, so through branch traversal, it can be ensured that the types of each texture element feature node on a traversal path are different; calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, determine the upper and lower limits of the adaptation advertising recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period respectively, and construct the adaptation advertising recommendation score interval corresponding to the current texture element feature node; integrate the adaptation advertising recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period, sum and average the upper and lower limits of each interval, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation advertising recommendation score interval, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation advertising recommendation score interval, then construct the comprehensive adaptation advertising recommendation score interval corresponding to the current user period.

[0009] Furthermore, based on the comprehensive adaptive advertising recommendation score range within the user cycle, the push advertising data is scored and calculated, and then screened; if the score of the push advertising data is within the comprehensive adaptive advertising recommendation score range, the user is pushed; otherwise, the advertising data is screened out; Achieve periodic self-update cycle, and update and re-analyze the periodic comprehensive adaptation advertising recommendation score interval by allowing users to set the update time.

[0010] An intelligent matching system for advertising visual elements based on big data, the system comprising an advertising record acquisition module, a texture element feature processing module, an advertising adaptation recommendation analysis module and a feedback update module; The advertising record acquisition module acquires the user's historical advertising viewing record data, determines the analysis period, constructs the corresponding period advertising viewing data set, and determines the period user characteristic advertising data set based on the user personalized screening analysis; the texture element feature processing module extracts the corresponding advertising texture element features based on the period user characteristic advertising data set, constructs the texture element feature path of the corresponding period user characteristic advertising data, analyzes the corresponding period user characteristic advertising data score, and determines the period best user characteristic advertising data; the advertising adaptation recommendation analysis module uses the texture element feature path nodes of the period best user characteristic advertising data as reference nodes, performs clustering processing on the texture element feature path nodes corresponding to the period user characteristic advertising data, analyzes the adaptation texture element features of the texture element feature path nodes of each best user characteristic advertising data, and comprehensively determines the corresponding user's adaptation advertising data recommendation score interval; the feedback update module makes a push judgment on the push advertisement based on the adaptation advertising data recommendation score interval, and performs a cyclic update processing on the period adaptation advertising data recommendation score interval.

[0011] Further, the advertisement record acquisition module includes an advertisement data acquisition unit and an advertisement data personalization processing unit; The advertisement data acquisition unit determines the analysis period window by calling the user's historical advertisement viewing record database, retrieves the user's historical advertisement viewing record data in the corresponding period, and constructs a corresponding period advertisement viewing data set; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; The advertisement data personalization processing unit performs personalized screening analysis according to the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user; and determines the periodic user characteristic advertisement data set after the personalized screening analysis.

[0012] Further, the texture element feature processing module includes an advertisement texture element path construction unit and an advertisement data scoring analysis unit; The advertisement texture element path construction unit extracts texture element features of the corresponding periodic user characteristic advertisement data according to each periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, constructs corresponding texture element feature nodes, and constructs texture element feature paths of the corresponding periodic user characteristic advertisement data by connecting the nodes in series; The advertising data scoring and analysis unit performs scoring and analysis on the advertising data with user characteristics of each period respectively according to the texture element feature path corresponding to the advertising data with user characteristics of each period; it constructs the corresponding texture element feature node feature matrix for the texture element feature nodes of the advertising data with user characteristics of each period respectively; the texture element feature node feature matrix is ​​composed of the advertising data proportion weight and type experience score combination of the corresponding texture element feature; based on the texture element feature node feature matrix corresponding to the advertising data with user characteristics of each period, a texture element feature node scoring analysis is performed; based on the texture element feature node scoring data corresponding to the advertising data with user characteristics of each period, the node scores on the texture element feature path corresponding to the advertising data with user characteristics of each period are summed up and calculated to determine the scoring data corresponding to the advertising data with user characteristics of each period.

[0013] Further, the advertisement adaptation recommendation analysis module includes an advertisement feature node clustering processing unit and an advertisement adaptation recommendation analysis unit; The advertising feature node clustering processing unit outputs the maximum value of the scoring data as the advertising data with the best user characteristics for the period based on the scoring data of the advertising data with the user characteristics of each period; then, each texture element feature node on the texture element feature path corresponding to the advertising data with the best user characteristics for the period is used as a reference node, and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period in the advertising data set with the user characteristics of each period are used for node clustering processing; according to the node clustering processing result, the clustering adaptation state of the texture element feature node corresponding to the advertising data with the best user characteristics for the period corresponding to each reference node and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period is determined; The advertisement adaptation recommendation analysis unit constructs concentric circles with each reference node as the center, and respectively performs density analysis on the texture element feature nodes corresponding to the remaining periodic user characteristic advertisement data contained in the concentric circles; by gradually increasing the radius of the concentric circles, and respectively calculating the ratio of the number of nodes to the area in the concentric circles corresponding to each radius, the node density value of the concentric circles corresponding to each radius is determined, and the concentric circle corresponding to the maximum node density value is taken as the best adaptation node concentric circle corresponding to the reference node; based on the best adaptation node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertisement data, the type and score of each texture element feature node in the concentric circle of the best adaptation node are comprehensively divided; if there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are respectively retained; in the best adaptation node concentric circles where each texture element feature node of the periodic best user characteristic advertisement data is located, A full traversal of different types of texture element feature nodes is performed starting from the center of the circle. For texture element feature nodes of the same type, branch traversals are performed respectively, and the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node are output; the node scores of the maximum value node traversal path and the minimum value node traversal path are calculated and summed respectively, and the upper and lower limits of the adaptation advertisement recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period are determined respectively, and the adaptation advertisement recommendation score interval corresponding to the current texture element feature node is constructed; the adaptation advertisement recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period is integrated, and the upper and lower limits of each interval are summed and averaged, and the upper and lower limits of the upper limits are output as the upper limit of the period comprehensive adaptation advertisement recommendation score interval, and the lower limit is output as the lower limit of the period comprehensive adaptation advertisement recommendation score interval, and then the comprehensive adaptation advertisement recommendation score interval corresponding to the current user period is constructed.

[0014] Further, the feedback update module includes an advertisement push determination unit and a data self-update unit; The advertising push judgment unit calculates and filters the push advertising data based on the comprehensive adaptive advertising recommendation score interval within the user cycle; pushes the advertising data to the user if the score is within the comprehensive adaptive advertising recommendation score area; otherwise, the advertising data is filtered out; The data self-update unit implements a periodic self-update cycle, and updates and re-analyzes the periodic comprehensive adaptation advertisement recommendation score interval through the user setting the update time.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains and screens personalized advertising data for users, extracts advertising texture element features based on the processed advertising data, determines the advertising data texture element feature path, and calculates the advertising data score; determines the user period optimal characteristic advertising data, and performs node clustering analysis on each node of the optimal characteristic advertising data, determines the adapted texture element nodes, analyzes the adapted advertising recommendation score interval, and thereby makes an accurate judgment on the user's advertising delivery and realizes self-circulation updating; the present invention can more accurately attract the user's attention and deliver the advertisement accurately through the combination of visual elements of the advertisement; improves the user's effective browsing of advertising data and improves the full utilization of advertising delivery resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural schematic diagram of an intelligent matching system for advertising visual elements based on big data according to the present invention; Figure 2 The present invention is a flowchart of a method for intelligently matching advertising visual elements based on big data. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1 As shown, the present invention provides a technical solution: An intelligent matching system for advertising visual elements based on big data, the system comprising an advertising record acquisition module, a texture element feature processing module, an advertising adaptation recommendation analysis module and a feedback update module; The advertising record acquisition module acquires the user's historical advertising viewing record data, determines the analysis period, constructs the corresponding period advertising viewing data set, and determines the period user characteristic advertising data set based on the user personalized screening analysis; the texture element feature processing module extracts the corresponding advertising texture element features based on the period user characteristic advertising data set, constructs the texture element feature path of the corresponding period user characteristic advertising data, analyzes the corresponding period user characteristic advertising data score, and determines the period best user characteristic advertising data; the advertising adaptation recommendation analysis module uses the texture element feature path nodes of the period best user characteristic advertising data as reference nodes, performs clustering processing on the texture element feature path nodes corresponding to the period user characteristic advertising data, analyzes the adaptation texture element features of the texture element feature path nodes of each best user characteristic advertising data, and comprehensively determines the corresponding user's adaptation advertising data recommendation score interval; the feedback update module makes a push judgment on the push advertisement based on the adaptation advertising data recommendation score interval, and performs a cyclic update processing on the period adaptation advertising data recommendation score interval.

[0019] Further, the advertisement record acquisition module includes an advertisement data acquisition unit and an advertisement data personalization processing unit; The advertisement data acquisition unit determines the analysis period window by calling the user's historical advertisement viewing record database, retrieves the user's historical advertisement viewing record data in the corresponding period, and constructs a corresponding period advertisement viewing data set; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; The advertisement data personalization processing unit performs personalized screening analysis according to the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user; and determines the periodic user characteristic advertisement data set after the personalized screening analysis.

[0020] Further, the texture element feature processing module includes an advertisement texture element path construction unit and an advertisement data scoring analysis unit; The advertisement texture element path construction unit extracts texture element features of the corresponding periodic user characteristic advertisement data according to each periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, constructs corresponding texture element feature nodes, and constructs texture element feature paths of the corresponding periodic user characteristic advertisement data by connecting the nodes in series; The advertising data scoring and analysis unit performs scoring and analysis on the advertising data with user characteristics of each period respectively according to the texture element feature path corresponding to the advertising data with user characteristics of each period; it constructs the corresponding texture element feature node feature matrix for the texture element feature nodes of the advertising data with user characteristics of each period respectively; the texture element feature node feature matrix is ​​composed of the advertising data proportion weight and type experience score combination of the corresponding texture element feature; based on the texture element feature node feature matrix corresponding to the advertising data with user characteristics of each period, a texture element feature node scoring analysis is performed; based on the texture element feature node scoring data corresponding to the advertising data with user characteristics of each period, the node scores on the texture element feature path corresponding to the advertising data with user characteristics of each period are summed up and calculated to determine the scoring data corresponding to the advertising data with user characteristics of each period.

[0021] Further, the advertisement adaptation recommendation analysis module includes an advertisement feature node clustering processing unit and an advertisement adaptation recommendation analysis unit; The advertising feature node clustering processing unit outputs the maximum value of the scoring data as the advertising data with the best user characteristics for the period based on the scoring data of the advertising data with the user characteristics of each period; then, each texture element feature node on the texture element feature path corresponding to the advertising data with the best user characteristics for the period is used as a reference node, and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period in the advertising data set with the user characteristics of each period are used for node clustering processing; according to the node clustering processing result, the clustering adaptation state of the texture element feature node corresponding to the advertising data with the best user characteristics for the period corresponding to each reference node and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period is determined; The advertisement adaptation recommendation analysis unit constructs concentric circles with each reference node as the center, and respectively performs density analysis on the texture element feature nodes corresponding to the remaining periodic user characteristic advertisement data contained in the concentric circles; by gradually increasing the radius of the concentric circles, and respectively calculating the ratio of the number of nodes to the area in the concentric circles corresponding to each radius, the node density value of the concentric circles corresponding to each radius is determined, and the concentric circle corresponding to the maximum node density value is taken as the best adaptation node concentric circle corresponding to the reference node; based on the best adaptation node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertisement data, the type and score of each texture element feature node in the concentric circle of the best adaptation node are comprehensively divided; if there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are respectively retained; in the best adaptation node concentric circles where each texture element feature node of the periodic best user characteristic advertisement data is located, A full traversal of different types of texture element feature nodes is performed starting from the center of the circle. For texture element feature nodes of the same type, branch traversals are performed respectively, and the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node are output; the node scores of the maximum value node traversal path and the minimum value node traversal path are calculated and summed respectively, and the upper and lower limits of the adaptation advertisement recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period are determined respectively, and the adaptation advertisement recommendation score interval corresponding to the current texture element feature node is constructed; the adaptation advertisement recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period is integrated, and the upper and lower limits of each interval are summed and averaged, and the upper and lower limits of the upper limits are output as the upper limit of the period comprehensive adaptation advertisement recommendation score interval, and the lower limit is output as the lower limit of the period comprehensive adaptation advertisement recommendation score interval, and then the comprehensive adaptation advertisement recommendation score interval corresponding to the current user period is constructed.

[0022] Further, the feedback update module includes an advertisement push determination unit and a data self-update unit; The advertising push judgment unit calculates and filters the push advertising data based on the comprehensive adaptive advertising recommendation score interval within the user cycle; pushes the advertising data to the user if the score is within the comprehensive adaptive advertising recommendation score area; otherwise, the advertising data is filtered out; The data self-update unit implements a periodic self-update cycle, and updates and re-analyzes the periodic comprehensive adaptation advertisement recommendation score interval through the user setting the update time; like Figure 2 As shown, the present invention provides another technical solution: A method for intelligently matching advertising visual elements based on big data, the method comprising the following steps: Obtain the user's historical ad viewing record data, determine the analysis period, build the corresponding period ad viewing data set, and determine the period user characteristic ad data set based on user personalized screening analysis; Based on the periodic user characteristic advertising data set, the corresponding advertising texture element features are extracted, the texture element feature path of the corresponding periodic user characteristic advertising data is constructed, and the corresponding periodic user characteristic advertising data score is analyzed to determine the periodic best user characteristic advertising data; with the texture element feature path nodes of the periodic best user characteristic advertising data as reference nodes, the corresponding texture element feature path nodes of the periodic user characteristic advertising data are clustered, the adapted texture element features of the texture element feature path nodes of each best user characteristic advertising data are analyzed, and the recommended score range of the adapted advertising data of the corresponding user is comprehensively determined.

[0023] Furthermore, by calling the user's historical advertisement viewing record database, determining the analysis period window, retrieving the user's historical advertisement viewing record data in the corresponding period, and constructing a corresponding period advertisement viewing data set; wherein the user's historical advertisement viewing record database includes a website advertisement viewing record database and an application advertisement viewing record database; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; A personalized screening analysis is performed based on the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user, and advertisement data with a viewing time shorter than the average viewing time is screened out, otherwise it is retained, and a periodic user characteristic advertisement data set after personalized screening analysis is determined.

[0024] Further, according to each periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, texture element features of the corresponding periodic user characteristic advertisement data are extracted respectively, corresponding texture element feature nodes are constructed, and the nodes are connected in series to construct texture element feature paths of the corresponding periodic user characteristic advertisement data; wherein the texture element features of the advertisement data include text type texture element features, graphic type texture element features and color type texture element features, etc., wherein the text type, graphic type and color type are further subdivided into specific texts, such as Chinese, English, etc.; the graphic type is further subdivided into rectangle, circle, etc.; the color type is further subdivided into red, blue, etc.; according to the texture element feature paths corresponding to each periodic user characteristic advertisement data, scoring analysis is performed on each periodic user characteristic advertisement data; the texture element feature node feature matrix of the texture element feature nodes of each periodic user characteristic advertisement data is constructed respectively; the texture element feature node feature matrix is ​​composed of the advertisement data proportion weight and type experience score of the corresponding texture element feature; wherein the type experience score of the texture element feature is the reference score of the corresponding advertisement texture element feature given by big data; based on the texture element feature node feature matrix corresponding to each periodic user characteristic advertisement data, texture element feature node scoring analysis is performed, and its calculation formula is:

[0025] Wherein, P[W,Q]n is the score data of the texture element feature node n in the corresponding period user characteristic advertising data; W is the advertising data proportion weight of the texture element feature node n; Q is the advertising data type experience score data of the texture element feature node n; According to the texture element feature node scoring data corresponding to each period user characteristic advertisement data, the node scores on the texture element feature path corresponding to each period user characteristic advertisement data are summed up and calculated to determine the scoring data corresponding to each period user characteristic advertisement data.

[0026] Further, based on the scoring data of each period user characteristic advertising data, the maximum value of the scoring data is output as the period best user characteristic advertising data; each texture element feature node on the texture element feature path corresponding to the period best user characteristic advertising data is used as a reference node, and the texture element feature nodes corresponding to the remaining period user characteristic advertising data in the period user characteristic advertising data set are used for node clustering processing; the remaining period user characteristic advertising data refers to the period user characteristic advertising data remaining after the period best user characteristic advertising data is removed from the period user characteristic advertising data set; according to the node clustering processing result, the clustering adaptation state of the texture element feature node corresponding to the period best user characteristic advertising data of each reference node and the texture element feature node corresponding to the remaining period user characteristic advertising data is determined; Concentric circles are constructed with each reference node as the center, and density analysis is performed on the texture element feature nodes corresponding to the remaining periodic user characteristic advertising data contained in the concentric circles; the node density value of the concentric circles corresponding to each radius is determined by gradually increasing the radius of the concentric circles, and the ratio of the number of nodes to the area in the concentric circles corresponding to each radius is calculated respectively, and the concentric circle corresponding to the maximum node density value is taken as the best adaptation node concentric circle corresponding to the reference node; based on the best adaptation node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertising data, the type and score of each texture element feature node in the concentric circle are comprehensively divided by the best adaptation node. If there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are taken and retained respectively; in the best adaptation node concentric circles where each texture element feature node of the periodic best user characteristic advertising data is located, all texture element feature nodes of different types are traversed with the center of the circle as the starting point, and for the same type of texture element feature nodes, branches are performed respectively. Traverse and output the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node; because after the same type of texture element feature nodes are classified and screened out, only the maximum value scoring node and the minimum value scoring node are retained for the same type of nodes, so through branch traversal, it can be ensured that the types of each texture element feature node on a traversal path are different; calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, determine the upper and lower limits of the adaptation advertising recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period respectively, and construct the adaptation advertising recommendation score interval corresponding to the current texture element feature node; integrate the adaptation advertising recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period, sum and average the upper and lower limits of each interval, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation advertising recommendation score interval, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation advertising recommendation score interval, then construct the comprehensive adaptation advertising recommendation score interval corresponding to the current user period.

[0027] Furthermore, based on the comprehensive adaptive advertising recommendation score range within the user cycle, the push advertising data is scored and calculated, and then screened; if the score of the push advertising data is within the comprehensive adaptive advertising recommendation score range, the user is pushed; otherwise, the advertising data is screened out; Achieve periodic self-update cycle, and update and re-analyze the periodic comprehensive adaptation advertising recommendation score interval by allowing users to set the update time.

[0028] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for intelligently matching advertising visual elements based on big data, characterized by: The method comprises the following steps: Obtain the user's historical ad viewing record data, determine the analysis period, build the corresponding period ad viewing data set, and determine the period user characteristic ad data set based on user personalized screening analysis; Based on the periodic user characteristic advertising data set, the corresponding advertising texture element features are extracted, the texture element feature path of the corresponding periodic user characteristic advertising data is constructed, and the corresponding periodic user characteristic advertising data score is analyzed to determine the periodic best user characteristic advertising data; with the texture element feature path nodes of the periodic best user characteristic advertising data as reference nodes, the corresponding texture element feature path nodes of the periodic user characteristic advertising data are clustered, the adapted texture element features of the texture element feature path nodes of each best user characteristic advertising data are analyzed, and the recommended score range of the adapted advertising data of the corresponding user is comprehensively determined.

2. The method for intelligently matching advertising visual elements based on big data according to claim 1, characterized in that: By calling the user's historical advertisement viewing record database, determining the analysis period window, retrieving the user's historical advertisement viewing record data in the corresponding period, and constructing a corresponding period advertisement viewing data set; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; A personalized screening analysis is performed based on the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user to determine the periodic user characteristic advertisement data set after the personalized screening analysis.

3. The method for intelligently matching advertising visual elements based on big data according to claim 2 is characterized by: According to the periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, texture element features of the corresponding periodic user characteristic advertisement data are extracted respectively, corresponding texture element feature nodes are constructed, and the nodes are connected in series to construct texture element feature paths of the corresponding periodic user characteristic advertisement data; according to the texture element feature paths of the corresponding periodic user characteristic advertisement data, scoring analysis is performed on the respective periodic user characteristic advertisement data; the texture element feature node feature matrix of the texture element feature nodes of the respective periodic user characteristic advertisement data is constructed; the texture element feature node feature matrix is ​​composed of the corresponding texture element feature advertisement data proportion weight and type experience score combination; based on the texture element feature node feature matrix of the corresponding periodic user characteristic advertisement data, texture element feature node scoring analysis is performed; According to the texture element feature node scoring data corresponding to each period user characteristic advertisement data, the node scores on the texture element feature path corresponding to each period user characteristic advertisement data are summed up and calculated to determine the scoring data corresponding to each period user characteristic advertisement data.

4. The method for intelligently matching advertising visual elements based on big data according to claim 3 is characterized by: Based on the scoring data of the user characteristic advertising data of each period, the maximum value of the scoring data is output as the best user characteristic advertising data of the period; each texture element feature node on the texture element feature path corresponding to the best user characteristic advertising data of the period is used as a reference node, and the texture element feature nodes corresponding to the remaining user characteristic advertising data of each period in the periodic user characteristic advertising data set are used for node clustering processing; according to the node clustering processing result, the clustering adaptation state of the texture element feature node of the best user characteristic advertising data of the period corresponding to each reference node and the texture element feature nodes corresponding to the remaining user characteristic advertising data of each period is determined; Concentric circles are constructed with each reference node as the center, and density analysis is performed on the texture element feature nodes corresponding to the remaining periodic user characteristic advertising data contained in the concentric circles; the node density value of the concentric circles corresponding to each radius is determined by gradually increasing the radius of the concentric circles, and the ratio of the number of nodes to the area in the concentric circles corresponding to each radius is calculated respectively, and the concentric circle corresponding to the maximum node density value is taken as the best fitting node concentric circle corresponding to the reference node; based on the best fitting node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertising data, the type and score of each texture element feature node in the concentric circle are comprehensively divided by the best fitting node; if there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are taken and retained respectively; respectively, in the best fitting node concentric circles where each texture element feature node of the periodic best user characteristic advertising data is located, the maximum and minimum scores of the texture element feature nodes of the periodic best user characteristic advertising data are taken as the starting point. Perform a full traversal of the texture element feature nodes of different types, and for the texture element feature nodes of the same type, perform branch traversal respectively, and output the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node; calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, and determine the upper and lower limits of the adaptation advertisement recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period, and construct the adaptation advertisement recommendation score interval corresponding to the current texture element feature node; integrate the adaptation advertisement recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period, sum and average the upper and lower limits of each interval, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation advertisement recommendation score interval, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation advertisement recommendation score interval, and then construct the comprehensive adaptation advertisement recommendation score interval corresponding to the current user period.

5. The method for intelligently matching advertising visual elements based on big data according to claim 4 is characterized by: Based on the comprehensive adaptive advertising recommendation score range within the user cycle, the push advertising data is scored and filtered; if the score of the push advertising data is within the comprehensive adaptive advertising recommendation score range, the user will be pushed; otherwise, the advertising data will be filtered out; Achieve periodic self-update cycle, and update and re-analyze the periodic comprehensive adaptation advertising recommendation score interval by allowing users to set the update time.

6. An intelligent matching system for advertising visual elements based on big data, characterized by: The system includes an advertisement record acquisition module, a texture element feature processing module, an advertisement adaptation recommendation analysis module and a feedback update module; The advertising record acquisition module acquires the user's historical advertising viewing record data, determines the analysis period, constructs the corresponding period advertising viewing data set, and determines the period user characteristic advertising data set based on the user personalized screening analysis; the texture element feature processing module extracts the corresponding advertising texture element features based on the period user characteristic advertising data set, constructs the texture element feature path of the corresponding period user characteristic advertising data, analyzes the corresponding period user characteristic advertising data score, and determines the period best user characteristic advertising data; the advertising adaptation recommendation analysis module uses the texture element feature path nodes of the period best user characteristic advertising data as reference nodes, performs clustering processing on the texture element feature path nodes corresponding to the period user characteristic advertising data, analyzes the adaptation texture element features of the texture element feature path nodes of each best user characteristic advertising data, and comprehensively determines the corresponding user's adaptation advertising data recommendation score interval; the feedback update module makes a push judgment on the push advertisement based on the adaptation advertising data recommendation score interval, and performs a cyclic update processing on the period adaptation advertising data recommendation score interval.

7. The intelligent matching system for advertising visual elements based on big data according to claim 6 is characterized by: The advertisement record acquisition module includes an advertisement data acquisition unit and an advertisement data personalization processing unit; The advertisement data acquisition unit determines the analysis period window by calling the user's historical advertisement viewing record database, retrieves the user's historical advertisement viewing record data in the corresponding period, and constructs a corresponding period advertisement viewing data set; the period advertisement viewing data set includes the viewing time data of the corresponding advertisement user, the advertisement type data, and the corresponding advertisement number data; The advertisement data personalization processing unit performs personalized screening analysis according to the average viewing time of each advertisement data in the periodic advertisement viewing data set of the corresponding user; and determines the periodic user characteristic advertisement data set after the personalized screening analysis.

8. The intelligent matching system for advertising visual elements based on big data according to claim 7 is characterized by: The texture element feature processing module includes an advertisement texture element path construction unit and an advertisement data scoring analysis unit; The advertisement texture element path construction unit extracts texture element features of the corresponding periodic user characteristic advertisement data according to each periodic user characteristic advertisement data in the periodic user characteristic advertisement data set, constructs corresponding texture element feature nodes, and constructs texture element feature paths of the corresponding periodic user characteristic advertisement data by connecting the nodes in series; The advertising data scoring and analysis unit performs scoring and analysis on the advertising data with user characteristics of each period respectively according to the texture element feature path corresponding to the advertising data with user characteristics of each period; it constructs the corresponding texture element feature node feature matrix for the texture element feature nodes of the advertising data with user characteristics of each period respectively; the texture element feature node feature matrix is ​​composed of the advertising data proportion weight and type experience score combination of the corresponding texture element feature; based on the texture element feature node feature matrix corresponding to the advertising data with user characteristics of each period, a texture element feature node scoring analysis is performed; based on the texture element feature node scoring data corresponding to the advertising data with user characteristics of each period, the node scores on the texture element feature path corresponding to the advertising data with user characteristics of each period are summed up and calculated to determine the scoring data corresponding to the advertising data with user characteristics of each period.

9. The intelligent matching system for advertising visual elements based on big data according to claim 8 is characterized by: The advertisement adaptation recommendation analysis module includes an advertisement feature node clustering processing unit and an advertisement adaptation recommendation analysis unit; The advertising feature node clustering processing unit outputs the maximum value of the scoring data as the advertising data with the best user characteristics for the period based on the scoring data of the advertising data with the user characteristics of each period; then, each texture element feature node on the texture element feature path corresponding to the advertising data with the best user characteristics for the period is used as a reference node, and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period in the advertising data set with the user characteristics of each period are used for node clustering processing; according to the node clustering processing result, the clustering adaptation state of the texture element feature node corresponding to the advertising data with the best user characteristics for the period corresponding to each reference node and the texture element feature nodes corresponding to the remaining advertising data with the user characteristics of each period is determined; The advertisement adaptation recommendation analysis unit constructs concentric circles with each reference node as the center, and respectively performs density analysis on the texture element feature nodes corresponding to the remaining periodic user characteristic advertisement data contained in the concentric circles; by gradually increasing the radius of the concentric circles, and respectively calculating the ratio of the number of nodes to the area in the concentric circles corresponding to each radius, the node density value of the concentric circles corresponding to each radius is determined, and the concentric circle corresponding to the maximum node density value is taken as the best adaptation node concentric circle corresponding to the reference node; based on the best adaptation node concentric circles corresponding to each texture element feature node of the periodic best user characteristic advertisement data, the type and score of each texture element feature node in the concentric circle of the best adaptation node are comprehensively divided; if there are two or more texture element feature nodes of the same type, the texture element feature nodes of the same type are classified and screened out, and the maximum and minimum scores of the texture element feature nodes of the same type are respectively retained; in the best adaptation node concentric circles where each texture element feature node of the periodic best user characteristic advertisement data is located, A full traversal of different types of texture element feature nodes is performed starting from the center of the circle. For texture element feature nodes of the same type, branch traversals are performed respectively, and the maximum value node traversal path and the minimum value node traversal path in the concentric circle of the current best adaptation node are output; the node scores of the maximum value node traversal path and the minimum value node traversal path are calculated and summed respectively, and the upper and lower limits of the adaptation advertisement recommendation score of each texture element feature node corresponding to the best user characteristic advertising data of the current period are determined respectively, and the adaptation advertisement recommendation score interval corresponding to the current texture element feature node is constructed; the adaptation advertisement recommendation score interval of each node on the texture element feature node path corresponding to the best user characteristic advertising data of the current period is integrated, and the upper and lower limits of each interval are summed and averaged, and the upper and lower limits of the upper limits are output as the upper limit of the period comprehensive adaptation advertisement recommendation score interval, and the lower limit is output as the lower limit of the period comprehensive adaptation advertisement recommendation score interval, and then the comprehensive adaptation advertisement recommendation score interval corresponding to the current user period is constructed.

10. The intelligent matching system of advertising visual elements based on big data according to claim 9, characterized in that: The feedback update module includes an advertisement push judgment unit and a data self-update unit; The advertising push judgment unit calculates and screens the push advertising data based on the comprehensive adaptation advertising recommendation score interval within the user cycle; The score of the pushed advertising data is placed in the comprehensive adaptation advertising recommendation score area for user push; Otherwise, the advertising data will be filtered out; The data self-update unit implements a periodic self-update cycle, and updates and re-analyzes the periodic comprehensive adaptation advertisement recommendation score interval through the user setting the update time.

Images