CN116166906A - Search page optimization layout analysis system and method based on cloud computing - Google Patents

Abstract

The invention discloses a search page optimization layout analysis system and method based on cloud computing, which belongs to the field of page layout. The optimization layout analysis system includes a device identification module, a user data module, a search page optimization module and a result page optimization module. The device identification module identifies the current device to adjust the appropriate page size and fit the display screen, and loads the user's search page settings from the cloud through the user data module, so that the settings of different devices of the user are connected, and multi-platform linkage is used to load the user's profile on the current device Personalized pages and search schemes, realize differentiated and personalized page layouts from the search page optimization module, switch between different modes, highlight high-quality recommended content, always present the current reading content when the page is zoomed in, and realize search results from the result page optimization module High-quality presentation, blocking spam websites, optimizing search results, and using cloud computing to iteratively optimize based on user feedback.

Description

A cloud computing-based retrieval page optimization layout analysis system and method

technical field

The invention relates to the field of retrieval pages, in particular to a cloud computing-based retrieval page optimization layout analysis system and method.

Background technique

The search page is a page on which you can find the materials or information you need from various information collections. Users input content through the search box on the search page to get relevant information.

The search method determines the search results. Usually, the search method only presents as much relevant content as possible for users to choose. The layout of the search page determines the presentation of the information. The layout design of the search page is designed to display clearly and concisely Relevant content captures the user's attention, the existing search page information is redundant, the display of search results is not clear enough, and each search is based on the same logic, each search result is the same, there is no difference, and it is impossible to personalize for individual users Optimize the page layout and improve the user's retrieval and reading experience. When the page is zoomed, the current reading content is hidden, and the user cannot read on the same screen, especially when reading remote sensing maps, data layers and area information, the current reading information Relocation takes more time again.

Therefore, a cloud computing-based search page optimization layout analysis system and method are proposed to solve the above problems.

Contents of the invention

The purpose of the present invention is to provide a system and method for optimizing the layout analysis of retrieval pages based on cloud computing, so as to solve the problems raised in the above-mentioned background technology.

In order to achieve the above object, the present invention provides the following technical solutions: a system and method for optimizing the layout analysis of search pages based on cloud computing, characterized in that: the system for optimizing the layout analysis of search pages includes a device identification module, a user data module, and a search page optimization system. modules and results page optimization modules;

The device identification module is used to detect the operating platform, identify the device to match the appropriate page, and provide page mode selection;

Make it possible to identify the current device and match the corresponding page, provide a variety of modes for users to choose, and initially optimize the layout of the search page;

The user data module is used to identify the identity of the user, read the user's historical data and usage habits, and synchronize to the current device;

Obtain user personalized data, load the user's frequently used websites, and obtain the user's preferences and reading habits;

The retrieval page optimization module optimizes the layout of the current retrieval main page through cloud computing;

Make the recommended content of the current main page accurate and rich, users can browse favorite content, find commonly used websites and links, and obtain a good user experience;

The result optimization module is used to optimize the layout of the retrieval result page, highlighting the retrieval results required by the user;

Make the high-value links and pages in the search results clearly displayed, and get the user's attention as quickly as possible;

According to the above technical solution, the device identification module includes a device detection unit and a page adaptation unit;

The device detection unit identifies the device used by the user by detecting the current device identification code;

The page adaptation unit is used to adjust the page size according to the device specifications;

According to the above technical solution, the user data module includes a data loading unit, a localization configuration unit and a data uploading unit;

The data loading unit is used to download relevant data of the logged-in user from the cloud server, and the download and transmission of the data can be quickly completed by utilizing the performance of the cloud server;

The localization configuration unit stores the loaded data locally, and generates a cache file on the current device, which is convenient to call. At the same time, the generated file occupies a small memory and will not cause a large load on the current device. According to the data, the current device initially Personalize the user's retrieval page;

The data upload unit uploads the user's usage data to the cloud server, updates the user's usage data, facilitates the server to perform cloud computing, generates more matching recommended content, and adapts to the user's usage and reading habits;

According to the above technical solution, the retrieval page optimization module includes a content optimization unit, a page element optimization unit and a personalization unit;

The content optimization unit is used to recommend user-favorite content according to user data, arrange user-preferred information patterns according to user reading habits, and set appropriate information density so that users can return to reading preferences and optimize user experience;

The page element optimization unit is used for typesetting and zooming each element in the page, optimizing the layout ratio between each element, and can always display the current reading content when the page is zoomed;

The personalization unit is used to set the elements of the search page, to set the recommended content, the search box and the page mode in a more detailed manner, to optimize the layout, and to meet the differentiated needs of users;

According to the above technical solution, the result page optimization module includes a sorting unit, a shielding unit and an evaluation feedback unit;

The sorting unit sorts the links of related searches according to certain rules, highlighting more relevant high-quality links;

The shielding unit intelligently screens and shields dangerous websites to protect the safety of users and improve the quality of retrieval;

The evaluation feedback unit is used to collect user feedback, record user data, and optimize recommendation algorithms; use cloud computing to optimize user usage and reading habits, and block website links reported by users.

A cloud computing-based search page optimization layout analysis method, the method includes the following steps:

S1. The device identification module reads the device information and matches the adaptation page;

S2. The user data module reads user data, performs personalized layout optimization, and completes data transmission;

S3. The search page optimization module optimizes the content recommendation layout and page element layout of the search page and matches the individual needs of users;

S4. The result page optimization module optimizes the link layout of the search, records the user's feedback content, and optimizes the recommendation algorithm;

According to the above technical solution, in S1, the device identification module is used to read the device information, and match the appropriate page according to the device; the user uses different devices in different scenarios, and usually uses a personal computer when working in the office area. In field research or agricultural research, mobile devices such as mobile phones, tablets or laptops are usually used to retrieve information, and it is necessary to switch between different devices. The device detection unit reads the user-agent sent by the current device. key to identify the current device, and at the same time read the device size and size of the user terminal, whether it touches the screen, put the information into the cookie, and use the comprehensive judgment of cookie and user-agent to obtain more information and identify the current device; The matching unit selects the appropriate page from the alternative library according to the size of the device to adapt to the display size of the current device. This module realizes the synchronization of different devices, making it easy for users to search in different usage scenarios;

According to the above technical solution, in S2, the user data module is used to process user-related data, perform personalized page layout, and simultaneously complete iterative optimization of user-related data; wherein the data loading unit loads user data from the cloud server, such as user Commonly used map links, agricultural information websites and remote sensing image websites transmit users' personal information, reading habits, search habits, content preferences and search page layout, enabling agricultural professionals to conduct professional searches; localization configuration units generate cache files and record user configurations information, optimize the page layout on the current device according to the configuration information, generate the user’s frequently used pages, and change them to the user’s applicable search settings; the data upload unit records the user’s operations in real time, including changes to the search page, search settings, and search records Updates and preferences for search results, and upload them to the cloud server in real time to realize the iterative update of user data, and transmit the data to the cloud server center, use cloud computing to analyze the search and reading preferences of all users, and classify user groups according to the content. Obtain user group portraits, correlate related groups, associate high-frequency search agriculture-related groups, and capture the search preferences of agricultural groups;

According to the above technical solution, in S3, the retrieval page optimization module is used to optimize the layout of the retrieval page; wherein the content optimization unit is used to optimize the display of recommended content; with the search box as the center, the recommended content is expanded in sequence, and the area near the search box It is the attention area that users focus on, which is conducive to the attention of recommended content. The recommended content includes text, pictures and videos. The recommendation degree of the content is graded, and the weight is divided according to the user's preference. T _i ＝W _p -W _q +α*W _t +β*W _u , each recommended content has a tag, W _p represents the number of times the tag is clicked by the user, W _q represents the number of times the tag is clicked by the user and W _t represents the content in n hours The number of times the content has been read and browsed by everyone, Wu _u represents the number of clicks of similar users on the content, where α and β are proportional parameters, which are used to limit the weight ratio of W _t and Wu _u . According to the current page size, the recommended content area It is divided into m*n block areas, using purity contrast to highlight the content, and the high-saturation area is used to display high-weight content. At the same time, the size of the high-saturation area is expanded to expand the display range of the content. This display method ensures information The richness of recommendations also highlights the content that users are more concerned about, reflecting the degree of content differentiation; at the same time, a hard-attention mode is introduced to simplify content recommendation. Users can select p tags, and set the content probability of displaying these tags as 1, the other probabilities are 0, that is, _Bi = [0,1], the current page only displays the tag content with a _Bi value of 1, and the rest are not displayed, so as to simplify the recommended content and make users focus on what they care about Content; the page element optimization unit is used to flexibly modify the size of each element in the page. When the page is reduced, the current reading content is always displayed, and can be fully displayed on the current screen without any operation. When the page is enlarged, the current reading content is always displayed in the center ;Read the size of the main page, which is M*N, M is the length of the main page, N is the width of the main page, and each element A _i currently displayed, when zooming in and out, set a floating frame to frame the current reading content and Highlight, read the size of the floating box as m*n, m is the length of the floating box, n is the width of the floating box, the size of the elements in the floating box is m _i *n _i , m _i is the length of the element A _i , and n _i is the element The width of A _i , when zooming in, the size is Y _i *(m*n), the size of the elements in the floating frame is Y _i *(m _i *n _i ), Y _i is the proportional coefficient, and the position is relatively unchanged; when When the page shrinks, the floating frame shrinks accordingly. The size of the floating frame is Z _i *(m*n), Z _i is the proportional coefficient, and the position is relatively unchanged; the minimum page threshold is j*k, j is the minimum page length, and k is The minimum page width, when the size of the main page is reduced to j*k, the elements in the floating frame are no longer reduced, and the elements in the floating frame are arranged in proportion to ensure readability. When the elements outside the floating frame are reduced, they do not occupy the display page, and when they are enlarged, they do not occupy In the middle of the page, this method makes the content of the floating box always displayed on the screen, and when zoomed in, it is displayed in the center, especially when retrieving remote sensing maps, locate the currently displayed position. When the size of the retrieved page changes, this method makes the rest of the map automatically Adapt to scaling, while the current position is still displayed within the screen range, and will not be lost due to the retrieval of the scaling information of the main page; the personalization unit is used to meet the different needs of users, and uses the personalized setting button to provide users with the ability to change the current page Background, color, font, and text size options. At the same time, users can typesetting page elements to make them conform to usage habits. This change will be saved to the cloud, and users can still use it when switching between different devices. Users can also customize the layout mode and Naming and saving various patterns to suit various retrieval needs;

According to the above scheme, in S4, the result page optimization module is used to optimize the layout of the search results to more clearly display the high-quality search content; wherein the sorting unit is designed to fine-tune the sorting results of the search engine according to the user experience, through Read the number of times the user reads the text, the frequency of watching the video, and the number of times the picture is viewed in the user data to judge the user's preference for the form of information, and at the same time, according to the length of time the user reads the text, the time of watching the video and the time of browsing the picture in different modes To deeply compare the user's choices in different search situations, pre-position the link containing high-frequency browsing information, and highlight the content of the information at the same time to attract the user's attention. The content, remote sensing images, and graphic information of animals and plants are more sensitive, and agricultural personnel can obtain useful information faster; the shielding unit is used to shield inferior websites, shield links in the blacklist of URLs, and set Shield button, after the user clicks, the website will be added to the user blacklist, and the URL will never be associated, so that the user will not search for the website until the user removes the website from the user blacklist and optimizes the search in the cloud server The engine reduces the correlation between the link and the search keyword; the evaluation feedback unit performs screening processing according to the user feedback content, selects useful information to add to the negative feedback adjustment mechanism, and optimizes the algorithm and page layout;

Compared with the prior art, the beneficial effects achieved by the present invention are:

1. The present invention is based on cloud server and cloud computing, data transmission is fast, a large amount of user data is stored, and personalized experience is provided.

2. The present invention optimizes the layout of the search page, highlights the high-quality and highly relevant recommended content, and ensures that the current reading content can still be displayed when the page is zoomed in; different reading modes are set to facilitate users in different situations.

3. The present invention optimizes the layout of the search results, combines the user's personal preference, pre-installs information forms that are more acceptable to the user, and combines two blacklist mechanisms to better shield inferior websites. At the same time, the feedback mechanism is used to The algorithm and layout are optimized by using cloud computing.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the description, and are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the attached picture:

Fig. 1 is a schematic diagram of the module composition structure of a search page optimization layout analysis system based on cloud computing in the present invention;

Fig. 2 is a schematic flow chart of the steps of a cloud computing-based search page optimization layout analysis method of the present invention;

Fig. 3 is a search page scaling display diagram of a search page optimization layout analysis method based on cloud computing in the present invention;

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

As shown in Figures 1 to 3, the present invention provides the following technical solutions, a search page optimization layout analysis system and method based on cloud computing, characterized in that: the search page optimization layout analysis system includes a device identification module and a user data module , a search page optimization module and a result page optimization module;

The device identification module is used to detect the operating platform, identify the device to match the appropriate page, and provide page mode selection;

The user data module is used to identify the user identity, read the user's historical data and usage habits, and synchronize to the current device; obtain user personalized data, load the user's common website, and obtain the user's preferences and reading habits;

The retrieval page optimization module optimizes the layout of the current retrieval main page through cloud computing;

The result optimization module is used to optimize the layout of the retrieval result page, highlighting the retrieval results required by the user;

The device identification module includes a device detection unit and a page adaptation unit;

The device detection unit identifies the device used by the user by detecting the current device identification code;

The page adaptation unit is used to adjust the page size according to the device specifications;

The user data module includes a data loading unit, a localization configuration unit and a data uploading unit;

The data loading unit is used to download relevant data of the logged-in user from the cloud server, and the download and transmission of the data can be quickly completed by utilizing the performance of the cloud server;

The localization configuration unit stores the loaded data locally, and generates a cache file on the current device, which is convenient to call. At the same time, the generated file occupies a small memory and will not cause a large load on the current device. According to the data, the current device initially Personalize the user's retrieval page;

The data upload unit uploads the user's usage data to the cloud server, updates the user's usage data, facilitates the server to perform cloud computing, generates more matching recommended content, and adapts to the user's usage and reading habits;

The retrieval page optimization module includes a content optimization unit, a page element optimization unit and a personalization unit;

The content optimization unit is used to recommend user-favorite content according to user data, arrange user-preferred information patterns according to user reading habits, and set appropriate information density so that users can return to reading preferences and optimize user experience;

The page element optimization unit is used for typesetting and zooming each element in the page, optimizing the layout ratio between each element, and always displaying the current reading content;

The personalization unit is used to set the elements of the search page, to set the recommended content, the search box and the page mode in a more detailed manner, to optimize the layout, and to meet the differentiated needs of users;

The result page optimization module includes a sorting unit, a shielding unit and an evaluation feedback unit;

The sorting unit sorts the links of related searches according to certain rules, highlighting more relevant high-quality links;

The shielding unit intelligently screens and shields dangerous websites to protect the safety of users and improve the quality of retrieval;

The evaluation feedback unit is used to collect user feedback, record user data, and optimize recommendation algorithms; use cloud computing to optimize user usage and reading habits, and block website links reported by users.

A cloud computing-based search page optimization layout analysis method, the method includes the following steps:

S1. The device identification module reads the device information and matches the adaptation page;

S2. The user data module reads user data, performs personalized layout optimization, and completes data transmission;

S3. The search page optimization module optimizes the content recommendation layout and page element layout of the search page and matches the individual needs of users;

S4. The result page optimization module optimizes the link layout of the search, records the user's feedback content, and optimizes the recommendation algorithm;

In S1, the device identification module is used to read the device information and match the appropriate page according to the device; wherein the device detection unit identifies the current device by reading the user-agent key issued by the current device, and at the same time reads the user terminal The size and size of the device, touch screen or not, put the information into the cookie, and use the comprehensive judgment of cookie and user-agent to obtain more information and identify the current device; the page adaptation unit selects from the alternative library according to the size of the device Appropriate page, adapting to the display size of the current device;

In S2, the user data module is used to process user-related data, perform personalized page layout, and simultaneously complete iterative optimization of user-related data; wherein the data loading unit loads user data from the cloud server, transmits user personal information, and reads Habits, search habits, content preferences, and search page layout; the localization configuration unit generates cache files, records user configuration information, and optimizes the page layout on the current device according to the configuration information, generates pages commonly used by users, and changes them to search settings applicable to users; The data upload unit records user operations in real time, including changes to search pages, changes to search settings, updates to search records, and preferences for search results, and uploads them to the cloud server in real time to realize iterative updates of user data and transmit data Go to the cloud server center and use cloud computing to analyze the search and reading preferences of all users;

In S3, the search page optimization module is used to optimize the layout of the search page; wherein the content optimization unit is used to optimize the display of recommended content; with the search box as the center, the recommended content is expanded in turn, and the area near the search box is the focus of the user Area, which is conducive to the attention of the recommended content. The recommended content includes text, pictures and videos. The recommendation degree of the content is graded, and the weight is divided according to the user's preference. T _i = W _p -W _q +α*W _t +β*W _u , each recommended content has a tag, W _p means the number of times the tag is clicked by the user, W _q means the number of times the tag is clicked and disliked by the user, W _t means the content is read by everyone within n hours The number of browsing times, Wu _u represents the number of clicks of similar users on the content, where α and β are proportional parameters, which are used to limit the weight ratio of W _t and Wu _u , and divide the recommended content area into m*n according to the current page size In the block area, the purity contrast is used to highlight the content. The high-saturation area is used to display high-weight content. At the same time, the size of the high-saturation area is enlarged to expand the display range of the content. This display method ensures the richness of information recommendations. At the same time, it highlights the content that users are more concerned about, reflecting the differentiation of content; at the same time, it introduces the hard-attention mode to simplify content recommendation. Users can select p tags, and set the content probability of displaying these tags to 1, and the remaining probabilities are 1. is 0, that is, _Bi = [0,1], the current page only displays the tag content with a _Bi value of 1, and the rest are not displayed, so as to simplify the recommended content and allow users to focus on the content they care about; the page The element optimization unit is used to flexibly modify the size of each element in the page. When the page is zoomed out, the current reading content is always displayed, and can be fully displayed on the current screen without any operation. When the page is enlarged, the current reading content is always displayed in the center; read the main page The size is M*N, M is the length of the main page, N is the width of the main page, and each element A _i currently displayed, when zooming in and out, set a floating frame to frame the current reading content and highlight it, read Take the size of the floating box as m*n, m is the length of the floating box, n is the width of the floating box, the size of the elements in the floating box is m _i *n _i , m _i is the length of the element A _i , and n _i is the width of the element A _i , when When zooming in, the size is Y _i *(m*n), the size of the elements in the floating frame is Y _i *(m _i *n _i ), Y _i is the proportional coefficient, and the position is relatively unchanged; when the page is reduced, the floating The frame shrinks accordingly, the size of the floating frame is Z _i *(m*n), _Zi is the proportional coefficient, and the position is relatively unchanged; the minimum page threshold is j*k, j is the minimum page length, k is the minimum page width, when the main The page size is reduced to j*k, and the elements in the floating frame are no longer reduced. The elements in the floating frame are arranged in proportion to ensure readability. When the elements outside the floating frame are reduced, they do not occupy the display page, and when they are enlarged, they do not occupy the middle position of the page. The method makes the content of the floating frame always displayed on the screen, and is displayed in the center when enlarged; the personalized unit is used to meet the different needs of users, and provides users with the ability to change the background, color, font, and text of the current page by using the personalized setting button At the same time, the user can layout the page elements to make it conform to the usage habits. The change will be saved to the cloud, and the user can still use it when switching between different devices. The user can also customize the layout mode and name it, and save various modes. To meet various retrieval needs;

In S4, the result page optimization module is used to optimize the layout of the search results to more clearly display the high-quality search content; wherein the sorting unit is designed to fine-tune the sorting results of the search engine according to the user experience, by reading the user data The number of times the user reads text, the frequency of watching videos, and the number of times they view pictures are used to judge the user's preferences for information forms. At the same time, users are compared in depth based on the time spent reading text, watching videos, and viewing pictures in different modes. The selection under different search conditions will include high-frequency browsing information in front of the link, and at the same time highlight the content of the information to attract the user's attention; the shielding unit is used to shield inferior websites and blacklist the links in the URL Blocking, for each searched link, set a blocking button. After the user clicks, the website will be added to the user blacklist, and the URL will never be associated. The website will not be searched and displayed until the user removes this website from the user blacklist and optimize the search engine in the cloud server to reduce the correlation between the link and the search keyword; the evaluation feedback unit performs screening processing according to the user feedback content, selects useful information to add to the negative feedback adjustment mechanism, optimizes the algorithm and the page layout.

Example 1

In step S1, it is read that the current device is a tablet computer with a size of 1080*768, and the page is adapted according to the current page size. In step S2, the current tablet device loads user data, optimizes the page layout according to user historical data, and Record and transmit the user's operation data in real time, read that the user often uses the professional search mode, and has a high demand for retrieval of remote sensing maps, load the current page as the professional search mode, and pre-install the map search function.

Example 2

In step S2, the layout of the recommended content is optimized. According to T _i =W _p -W _q +α*W _t +β*W _u , when there is popular search content, W _t is 234k, and the value of T _i is large. Prioritize and highlight the content with the popular tag, and highlight the content through purity comparison, and the content area becomes larger. When the user clicks the dislike button, the value of W _q becomes larger, the value of T _i becomes smaller, and the recommendation degree decreases. In the recommended content, the weight of the tag content is reduced, and the purity comparison is weakened, and the content area of the tag is reduced. According to the user click rate, the user clicks more on the remote sensing image tag content, the W _p value is high, and the weight value is high. Recommend more content with the tag of remote sensing image, and use the purity comparison to highlight. According to the value of user group Wu _u , similar user groups also click more on the content with the animal husbandry tag, recommend it to the current user, and the user clicks no Like, the W _q value is high and the weight is low, that is, the user has no preference for animal husbandry, so the recommendation is reduced. When the user performs the page zoom operation, the current page size is 1080*768, and the displayed content is the A area and B area on the remote sensing map, which are elements A ₁₄ and A ₁₅ , and put the current element into the floating frame, and the size of the floating frame is 600* 384, when the page is reduced to 256*256, the floating frame is scaled down to 128*56, the elements in the frame are correspondingly reduced, and the relative positions remain unchanged, still displaying areas A and B on the remote sensing map.

Finally, it should be noted that: the above is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it still The technical solutions recorded in the foregoing embodiments may be modified, or part of the technical features may be replaced. All modifications, replacements and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A retrieval page optimization layout analysis system based on cloud computing, characterized in that: the retrieval page optimization layout analysis system includes a device identification module, a user data module, a retrieval page optimization module and a result page optimization module; The device identification module is used to detect the operating platform, identify the device to match the appropriate page, and provide page mode selection; The user data module is used to identify the identity of the user, read the user's historical data and usage habits, and synchronize to the current device; The retrieval page optimization module optimizes the layout of the current retrieval main page through cloud computing; The result optimization module is used to optimize the layout of the retrieval result page, highlighting the retrieval results required by the user. 2. A cloud computing-based retrieval page optimization layout analysis system according to claim 1, characterized in that: the device identification module includes a device detection unit and a page adaptation unit; The device detection unit detects the current device and identifies the device used by the user; The page adaptation unit is used to adjust the page size according to device specifications. 3. A kind of retrieval page optimization layout analysis system based on cloud computing according to claim 1, characterized in that: said user data module comprises a data loading unit, a localization configuration unit and a data uploading unit; The data loading unit is used to download relevant data of the user from the cloud server; The localized configuration unit stores the loaded data locally, generates a cache file call in the current device, and initially configures the user's retrieval page in the current device according to the data; The data uploading unit uploads the usage data of the user to the cloud server, updates the usage data of the user, facilitates the server to perform cloud computing, and generates more matching recommendation content. 4. A kind of retrieval page optimization layout analysis system based on cloud computing according to claim 1, characterized in that: the retrieval page optimization module includes a content optimization unit, a page element optimization unit and a personalization unit; The content optimization unit is used to recommend user-favorite content according to user data, arrange user-preferred information patterns according to user reading habits, and set appropriate information density; The page element optimization unit is used for typesetting and zooming each element in the page, optimizing the layout ratio between each element, and can always display the current reading content when the page is zoomed; The personalization unit is used to set each element of the search page, perform more detailed settings on the recommended content, the search box and the page mode, and differentiate the layout. 5. A cloud computing-based search page optimization layout analysis system according to claim 1, characterized in that: said result page optimization module includes a sorting unit, a shielding unit and an evaluation feedback unit; The sorting unit sorts the links of related searches according to certain rules, highlighting more relevant high-quality links; The shielding unit intelligently screens and shields inferior websites; The evaluation feedback unit is used to collect user feedback and record user data to optimize the recommendation algorithm; utilize cloud computing to optimize the user's usage and reading habits, and shield the user's website links. 6. A search page optimization layout analysis method based on cloud computing, the method comprising the following steps: S1. The device identification module reads the device information and matches the adaptation page; S2. The user data module reads user data, performs personalized layout optimization, and completes data transmission; S3. The search page optimization module optimizes the content recommendation layout and page element layout of the search page and matches the individual needs of users; S4. The result page optimization module optimizes the link layout of the search, records the user's feedback content, and optimizes the recommendation algorithm. 7. A cloud computing-based search page optimization layout analysis method according to claim 6, characterized in that: in S1, the device identification module is used to read device information and match appropriate pages according to the device; wherein The device detection unit identifies the current device, and at the same time reads the device size and size of the user terminal, whether it touches the screen, obtains more information, and identifies the current device; the page adaptation unit selects a suitable page from the candidate database according to the size of the device, Adapt to the display size of the current device. 8. A kind of retrieval page optimization layout analysis method based on cloud computing according to claim 6, is characterized in that: in S2, described user data module is used for processing user's relevant data, carries out personalized page layout, simultaneously Complete the iterative optimization of user-related data; the data loading unit loads user data from the cloud server and transmits user data; the localization configuration unit generates cache files, records user configuration information, and optimizes the page layout according to the configuration information on the current device to generate user data. Commonly used pages, change the search settings; the data upload unit records the user's operations in real time, uploads them to the cloud server in real time, and transmits the data to the cloud server center for cloud computing analysis. 9. A cloud computing-based retrieval page optimization layout analysis method according to claim 6, characterized in that: in S3, the retrieval page optimization module is used to optimize the layout of the retrieval page; wherein the content optimization unit is used to Optimize the display of recommended content; with the search box as the center, the recommended content is expanded in turn. The area near the search box is the focus area of the user, which is conducive to the recommended content being followed. The recommended content includes text, pictures and videos. The degree of recommendation of the content is adjusted Grading, divide the weight according to the user's preferences, divide the recommended content area into m*n block areas according to the current page size, use the purity comparison to highlight the content, and the high saturation area is used to display high weight content, and at the same time expand the high saturation The size of the area can be adjusted to expand the display range of the content; at the same time, the hard-attention mode is introduced to simplify the content recommendation; the page element optimization unit is used to flexibly modify the size of each element in the page. When the page is reduced, the current reading content is always displayed. It can be fully displayed on the current screen without any operation. When the page is enlarged, the current reading content is always displayed in the center; read the size of the main page, which is M*N, where M is the length of the main page, N is the width of the main page, and each currently displayed Element A _i , when zooming in and out, set a floating box to frame the current reading content and highlight it. The size of the read floating box is m*n, m is the length of the floating box, n is the width of the floating box, inside the floating box The element size is m _i *n _i , m _i is the length of element A _i , and n _i is the width of element A _i . When zooming in, the size is Y _i *(m*n), and the size of the element in the floating frame is Y _i *(m _i *n _i ), Y _i is the proportional coefficient, and the position is relatively unchanged; when the page shrinks, the floating frame shrinks accordingly, the size of the floating frame is Z _i *(m*n), Z _i is the proportional coefficient, The position is relatively unchanged; the minimum page threshold is j*k, j is the minimum page length, and k is the minimum page width. When the size of the main page is reduced to j*k, the elements in the floating frame will no longer shrink, and the elements in the floating frame will be arranged in proportion. To ensure readability, the elements outside the floating frame do not occupy the display page when zooming out, and do not occupy the middle position of the page when zooming in; the personalized unit is used to meet the different needs of users, and uses the personalized setting button to provide users with the ability to change the current Page background, color, font, and text size options. At the same time, users can typesetting page elements. This change will be saved to the cloud, and it can still be used after switching between different devices. Users can also customize the layout mode and name it, and save various modes . 10. A cloud computing-based search page optimization layout analysis method according to claim 6, characterized in that: in S4, the result page optimization module is used to optimize the search result layout; wherein the sorting unit is designed to The sorting results of the search engine are fine-tuned according to the user experience. By reading the user data, the number of times the user reads the text, the frequency of the video, and the number of the picture are used to judge the user's preference for the form of information. At the same time, according to the user's reading in different modes The duration of the text, the time of watching the video and the time of browsing the picture are used to deeply compare the user's choices in different search situations, and the link containing the high-frequency browsing information form is front-loaded, and the information content is highlighted at the same time; the shielding unit uses To block inferior websites, block the links in the URL blacklist, and set a shield button for each searched link. After the user clicks, the website will be added to the user blacklist, and the URL will never be associated. The website will not pass the search present until the user removes the website from the user blacklist, and optimizes the search engine in the cloud server to reduce the correlation between the link and the search keyword; the evaluation feedback unit performs screening processing according to the user feedback content, and selects Useful information is added to the negative feedback adjustment mechanism to optimize the recommendation algorithm and page layout.

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