Page update data processing method, system and device based on RN and H5

Abstract

The present invention relates to the field of Internet application technology, and specifically discloses a page update data processing method, system and device based on RN and H5, the method comprising: obtaining page update data, the page update data comprising page history access data and page loading data; processing the page history access data to obtain an access frequency index of each page, processing the page loading data to obtain a data generation complexity index of each page, and comprehensively analyzing to obtain a cache demand evaluation value of each page; caching each content module of each page to be cached according to the content update cycle of each content module of each page to be cached. The present invention can improve the overall response speed of the system and the response time of data access by evaluating the page cache demand evaluation value, and can reasonably allocate cache resources according to actual needs to avoid resource waste, thereby helping to improve the competitiveness of the page and achieve sustainable development.

Description

Technical Field

[0001] The present invention relates to the field of Internet application technology, and specifically to a page update data processing method, system and device based on RN and H5. Background Art

[0002] Currently, page update data processing is a very important part of the field of Internet application technology. With the advancement of technology and the growth of market demand, providing real-time and accurate page data has become the norm. Efficient and comprehensive page update data processing methods are of great significance for improving user experience and promoting technological innovation.

[0003] For example, the invention patent with the public account CN111857773B is a method for automatically updating pages based on the similarities and differences of elements within a web page, belonging to the field of computer interface design technology. The method includes: marking and saving the modifications of page m in template A, and publishing the modified template A; after the template A is published, updating the version number of template A and the version number of the modified page m to obtain the actual version number of page m; comparing the actual version number of page m with the recorded version number and displaying the update mark of template A; the recorded version number is the version number recorded on page n that references page m in template A; determining whether to initiate a page upgrade based on the update mark of template A displayed by the system; and, if it is determined that the page upgrade is initiated, updating page n based on the similarities and differences of the elements of page m after the updated template A. For example, the invention patent with the public number CN109857403B is a page update, page processing method and device. The page update method includes: when compiling based on the local code of the target project, using the isomorphic compilation plug-in to perform isomorphic compilation conversion of the local code to the cross-platform mobile application development framework RN to obtain the RN executable file; storing the RN executable file in the offline file where the local offline resource file of the target project is located; updating the local business page based on the RN executable file and the local offline resource file in the offline file.

[0004] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: At present, page update data processing has made significant progress in improving page synchronization and automation. For example, by marking page modifications in the template and saving them, and then publishing updates and automatically comparing version numbers to display update signs, the synchronous upgrade of pages referencing the template is achieved. However, when processing updates of multiple pages and complex templates, the efficiency of outputting page update data is limited, and user experience is difficult to guarantee. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention provides a page update data processing method, system, and device based on RN and H5, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a page update data processing method based on RN and H5, including: obtaining page update data, the page update data including page history access data and page loading data.

[0007] The page historical access data is processed to obtain the access frequency index of each page, the page loading data is processed to obtain the data generation complexity index of each page, and the cache demand evaluation value of each page is obtained through comprehensive analysis.

[0008] Each page is screened according to the cache demand evaluation value to obtain each page to be cached, and each page to be cached is divided into modules and marked as each content module of each page to be cached.

[0009] The content update period of each content module of each to-be-cached page is obtained, and the content modules of each to-be-cached page are cached according to the content update period of each content module of each to-be-cached page.

[0010] As a further method, the page historical access data is processed to obtain the access frequency index of each page. The specific processing process is: the page historical access data includes the cumulative number of visits to each page at each time node, user access frequency and user access duration.

[0011] The critical cumulative number of visits, critical user access frequency and critical user access duration are extracted from the page update database, and the access frequency index of each page is obtained through comprehensive analysis.

[0012] As a further method, the page loading data is processed to obtain the data generation complexity index of each page. The specific processing process is: the page loading data specifically includes the number of page elements, page loading speed and static content ratio of each page.

[0013] The critical number of page elements, critical page loading speed and critical static content ratio are extracted from the page update database, and the data generation complexity index of each page is obtained through comprehensive analysis.

[0014] As a further method, the comprehensive analysis obtains the cache demand evaluation value of each page, and the specific analysis process is: extracting the critical page access frequency index and the critical page data generation complexity index from the page update database.

[0015] Based on the access frequency index of each page and the data generation complexity index, a comprehensive analysis is conducted to obtain the cache demand assessment value of each page.

[0016] As a further method, each page is screened according to the cache requirement evaluation value to obtain each page to be cached. The specific screening process is: extracting the cache requirement evaluation threshold from the page update database, comparing the cache requirement evaluation value of each page with the cache requirement evaluation threshold, if the cache requirement evaluation value of a page is greater than or equal to the cache requirement evaluation threshold, then the page is marked as a page to be cached, if the cache requirement evaluation value of a page is less than the cache requirement evaluation threshold, then the page is marked as a non-cached page.

[0017] As a further method, the access frequency index of each page is a quantitative indicator obtained by analyzing the cumulative number of visits to each page at each time node, user access frequency and user access duration, and is used to quantify the access frequency of the page.

[0018] As a further method, the access frequency index of each page is expressed as follows:

[0019]

[0020] Among them, B i represents the access frequency index of the i-th page, e represents a natural constant, A ij represents the cumulative number of visits to the i-th page at the j-th time node, A0 represents the set critical cumulative number of visits, and p ij represents the user access frequency of the i-th page at the j-th time node, p0 represents the set critical user access frequency, Q i represents the user access time of the i-th page, Q0 represents the set critical user access time, ∈1 represents the page access frequency assessment impact factor corresponding to the set cumulative number of visits, ∈2 represents the page access frequency assessment impact factor corresponding to the set user access frequency, ∈3 represents the page access frequency assessment impact factor corresponding to the set user access time, i represents the page number, i = 1, 2, 3, ..., m, m represents the total number of page numbers, j represents the time node number, j = 1, 2, 3, ..., n, n represents the total number of time node numbers.

[0021] As a further method, the data of each page generates a complexity index, the specific numerical expression is:

[0022]

[0023] Among them, C i Indicates the complexity index of page data generation for the i-th page, si Indicates the number of page elements of the i-th page, s0 indicates the set critical number of page elements, d i represents the page loading speed of the i-th page, d0 represents the set critical page loading speed, f i represents the static content ratio of the i-th page, f0 represents the set critical static content ratio, ρ1 represents the page data generation complexity assessment impact factor corresponding to the set number of page elements, ρ2 represents the page data generation complexity assessment impact factor corresponding to the set page loading speed, ρ3 represents the page data generation complexity assessment impact factor corresponding to the set static content ratio, i represents the page number, i = 1, 2, 3, ..., m, m represents the total number of page numbers.

[0024] The second aspect of the present invention provides a page update data processing system based on RN and H5, including: a page update data acquisition module for acquiring page update data, wherein the page update data includes page history access data and page loading data.

[0025] The page cache demand evaluation value analysis module is used to process the page historical access data to obtain the access frequency index of each page, process the page loading data to obtain the data generation complexity index of each page, and comprehensively analyze to obtain the cache demand evaluation value of each page.

[0026] The to-be-cached page screening module is used to screen each page according to the cache demand evaluation value to obtain each to-be-cached page, and divide each to-be-cached page into modules, marking each to-be-cached page as each content module.

[0027] The cache processing module is used to obtain the content update cycle of each content module of each page to be cached, and perform cache processing on each content module of each page to be cached according to the content update cycle of each content module of each page to be cached.

[0028] The page update database is used to store page update related data, including the page access frequency assessment impact factor corresponding to the set cumulative number of visits, the page access frequency assessment impact factor corresponding to the set user access frequency, the page access frequency assessment impact factor corresponding to the user access time, the critical page loading speed, the critical page loading speed, the critical static content ratio, the page cache demand assessment impact factor corresponding to the page data generation complexity index, the page cache demand assessment impact factor corresponding to the page access frequency index and the cache demand assessment threshold and other indicators.

[0029] The third aspect of the present invention provides a page update data processing device based on RN and H5, including: a processor and a memory and a network interface connected to the processor; the network interface is connected to the non-volatile memory in the server; the processor calls the computer program from the non-volatile memory through the network interface during operation, and runs the computer program through the memory to execute any one of the methods described in the present invention.

[0030] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0031] (1) The present invention can improve the overall response speed of the system and the response time of data access by evaluating the page cache demand evaluation value, and can reasonably allocate cache resources according to actual needs to avoid resource waste. At the same time, it can improve user experience and reduce the risk of network congestion, which helps to improve the competitiveness of the page, meet user needs, and achieve sustainable development.

[0032] (2) By evaluating the page access frequency index, the present invention can gain a deeper understanding of user needs and interests, optimize website structure and navigation, formulate precise marketing strategies, improve page performance and stability, and evaluate page value and influence, which helps to better grasp user behavior and market trends and achieve page optimization.

[0033] (3) By evaluating the complexity index of page data generation, the present invention can find out which parts are most time-consuming or occupy the most resources, and then perform targeted optimization. By optimizing page elements with high complexity, the loading time can be reduced, the user experience can be improved, and the page can be ensured to be responsive and easy to use, thereby improving user satisfaction. At the same time, it helps to predict page cache requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without inventive effort.

[0035] Figure 1 Schematic diagram of the method of the present invention.

[0036] Figure 2 This is a schematic diagram of system module connections of the present invention.

[0037] Figure 3 Schematic diagram of the functional relationship between the page cache demand evaluation value and the page access frequency index of the present invention. DETAILED DESCRIPTION

[0038] 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 embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Figure 1 As shown, the first aspect of the present invention provides a page update data processing method based on RN and H5, including: obtaining page update data, wherein the page update data includes page history access data and page loading data.

[0039] The page historical access data is processed to obtain the access frequency index of each page, the page loading data is processed to obtain the data generation complexity index of each page, and the cache demand evaluation value of each page is obtained through comprehensive analysis.

[0040] Each page is screened according to the cache demand evaluation value to obtain each page to be cached, and each page to be cached is divided into modules and marked as each content module of each page to be cached.

[0041] The content update period of each content module of each to-be-cached page is obtained, and the content modules of each to-be-cached page are cached according to the content update period of each content module of each to-be-cached page.

[0042] Specifically, each page to be cached is divided into modules and marked as content modules of each page to be cached. The specific division process is: first identify which content in the page needs to be cached and which content is dynamically changing, which usually includes static text, pictures, videos, JavaScript files, CSS style sheets, etc., and then use RN and H5 to divide the page into different modules according to the page structure and content logic. These modules can be functional areas such as navigation bars, sidebars, main content areas, components such as buttons, forms, carousels or page fragments such as article paragraphs and product lists. Each module should have relatively independent functions and data to facilitate subsequent cache management and updates.

[0043] In a specific embodiment, the content update period of each content module of each page to be cached can be obtained through RN and H5. A longer cache validity period is set for content modules with a longer update period, a shorter cache validity period is set for content modules with a shorter update period, and a moderate cache validity period is set for content modules with a moderate update period. For content modules with extremely high real-time requirements and which require the latest data almost every time they are accessed, you can choose not to cache them. Once the validity period expires, new data is requested and the cache is updated.

[0044] Specifically, the page historical access data is processed to obtain the access frequency index of each page. The specific processing process is: the page historical access data includes the cumulative number of visits to each page at each time node, user access frequency and user access duration.

[0045] The critical cumulative number of visits, critical user access frequency and critical user access duration are extracted from the page update database, and the access frequency index of each page is obtained through comprehensive analysis.

[0046] It should be explained that in this embodiment, the cumulative number of visits refers to the total number of times users visit a page in a time node, the user visit frequency refers to the ratio of the number of times users visit a page in a time node to the length of this time period, and the user visit duration refers to the total time users stay on the page. The cumulative number of visits, user visit frequency and user visit duration of each page at each time node can be obtained through RN and H5. By monitoring the cumulative number of visits, it is helpful to measure the user's attention to the page. By monitoring the user visit frequency, it is possible to measure user activity and stickiness, thereby optimizing the user experience. If the visit frequency of a page suddenly drops, it may mean that the content is outdated, the function fails, or there are other problems. By promptly discovering problems and taking measures to resolve them, user satisfaction and loyalty can be maintained. By monitoring the user visit duration, it is helpful to measure the attractiveness of the content and evaluate the value of the page.

[0047] Specifically, the critical cumulative number of visits refers to the maximum cumulative number of visits that the page can withstand within a time node, the critical user access frequency refers to the maximum number of requests initiated by users to the page within a time node, and the critical user access duration refers to the longest time that a user can continuously access the page without triggering any timeout or limit.

[0048] Specifically, the access frequency index of each page is expressed as follows:

[0049]

[0050] Among them, B i represents the access frequency index of the i-th page, e represents a natural constant, A ij represents the cumulative number of visits to the i-th page at the j-th time node, A0 represents the set critical cumulative number of visits, and p ij represents the user access frequency of the i-th page at the j-th time node, p0 represents the set critical user access frequency, Q irepresents the user access time of the i-th page, Q0 represents the set critical user access time, ∈1 represents the page access frequency assessment impact factor corresponding to the set cumulative number of visits, ∈2 represents the page access frequency assessment impact factor corresponding to the set user access frequency, ∈3 represents the page access frequency assessment impact factor corresponding to the set user access time, i represents the page number, i = 1, 2, 3, ..., m, m represents the total number of page numbers, j represents the time node number, j = 1, 2, 3, ..., n, n represents the total number of time node numbers.

[0051] The algorithm of this embodiment combines the cumulative number of visits, user visit frequency, and user visit duration of each page at each time node, and comprehensively analyzes to obtain the dependent variable. There is usually a positive correlation between the cumulative number of visits and the user visit frequency, that is, users with a large number of cumulative visits tend to have a higher visit frequency. This is because high-frequency visits naturally lead to an increase in the cumulative number of user visits. There may also be a positive correlation between the user visit duration and the cumulative number of visits. In particular, when users have strong interests or strong needs, a long stay may prompt users to make multiple visits, thereby increasing the cumulative number of visits. The extension of user visit duration may also affect the user's visit frequency. If users have a good experience and find valuable content in a certain visit, they may be more inclined to visit the page again. Comprehensive analysis can obtain a more comprehensive, accurate, and in-depth page visit frequency index.

[0052] It should be explained that this embodiment takes into account three key factors: the cumulative number of user visits, the frequency of user visits, and the duration of user visits. This helps understand user engagement and measure user interest in pages. At the same time, businesses can discover which pages or features are most popular with users and which are less frequently visited. This helps businesses optimize the layout, navigation, and content of pages to provide a smoother and more engaging user experience. Businesses can also provide users with more personalized recommended content and services. For example, for users who visit frequently, more in-depth and professional information or offers can be provided, while for users who visit less frequently, relevant content or activities can be pushed to re-attract their attention. By standardizing the cumulative number of user visits, user visit frequency, and user visit duration at different time points on different pages, ensuring that they are compared at the same level, the fairness and comparability of the evaluation are improved. At the same time, it can reveal the active time periods of users. For example, some pages may have higher visits in the evening or on weekends, while other pages may be more popular during specific time periods on weekdays. This is crucial for developing targeted marketing strategies and operational plans. By weighting the impact of cumulative user visits, user visit frequency, and user visit duration, the relative importance of these factors in the evaluation index is reflected. The weights of these factors can be adjusted to suit different needs, making the model highly adaptable. It's easy to see that the greater the cumulative user visits, user visit frequency, or user visit duration, the greater the page visit frequency index. By evaluating the page visit frequency index, we can gain a deeper understanding of user needs and interests, optimize website structure and navigation, develop precise marketing strategies, improve page performance and stability, and assess the value and influence of pages. This helps us better understand user behavior and market trends, and achieve continuous development and optimization of pages.

[0053] It should be understood that in this embodiment, ∈1 is the page access frequency assessment influence factor corresponding to the cumulative number of visits preset in the page update database, which represents the numerical value of the influence of the cumulative number of visits on the frequency of visits of each page. When used, the page access frequency assessment influence factor corresponding to the cumulative number of visits can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the cumulative number of visits at each time node and the page access frequency assessment influence factor corresponding to the cumulative number of visits preset in the page update database form a mapping set, and the real-time cumulative number of visits is input into the mapping set to obtain the page access frequency assessment influence factor corresponding to the cumulative number of visits. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1; ∈2 is the page access frequency assessment influence factor corresponding to the user access frequency preset in the page update database, which represents the numerical value of the influence of the user access frequency on the frequency of visits of each page. When used, the page access frequency assessment influence factor corresponding to the user access frequency can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the user access frequency of each time node and the page access frequency assessment influence factor corresponding to the user access frequency preset in the page update database form a mapping set, and the real-time user access frequency is input into the mapping set to obtain the page access frequency assessment influence factor corresponding to the user access frequency, wherein the mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1; ∈3 is the page access frequency assessment influence factor corresponding to the user access time preset in the page update database, which represents the numerical value of the influence of the user access time on the access frequency of each page. When used, the page access frequency assessment influence factor corresponding to the user access time can be directly obtained from the page update database. The corresponding relationship can be a preset mapping relationship. For example, the user access time and the page access frequency assessment influence factor corresponding to the user access time preset in the page update database form a mapping set, and the real-time user access time is input into the mapping set to obtain the page access frequency assessment influence factor corresponding to the user access time. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1.

[0054] It should be explained that the access frequency index of each page in this embodiment is a quantitative indicator obtained by analyzing the cumulative number of visits, user access frequency and user access duration of each page at each time node, and is used to quantify the access frequency of the page.

[0055] Specifically, the page loading data is processed to obtain the data generation complexity index of each page, and the specific processing process is: the page loading data specifically includes the number of page elements, page loading speed and static content ratio of each page.

[0056] The critical number of page elements, critical page loading speed and critical static content ratio are extracted from the page update database, and the data generation complexity index of each page is obtained through comprehensive analysis.

[0057] It should be explained that in this embodiment, the number of page elements refers to the number of elements returned from the server during the browsing of a page. These elements include but are not limited to images, scripts, CSS files, Flash objects, etc., which together constitute the content and functionality of the page. The page loading speed refers to the time required for the web page to be fully displayed on the browser after the user initiates a request. The static content ratio refers to the proportion of static resources in the web page. Static resources refer to files with the same data accessed in different requests, such as images, videos, CSS files, JavaScript files, etc. The number of page elements, page loading speed and static content ratio of each page can be obtained through RN and H5. By considering the number of page elements, unnecessary elements can be reduced, making the page structure clearer, which is conducive to search engine crawling and ranking. Too many page elements often lead to a cluttered page layout, affecting the user's reading experience and ease of operation. By streamlining the number of elements, the page can be made more concise and clear, improving user satisfaction and retention. By considering the page loading speed, user satisfaction and loyalty can be significantly improved, while also increasing page conversion rate. By considering the static content ratio, loading speed can be improved, which helps protect the security and stability of the website and reduce losses and risks caused by hacker attacks.

[0058] It should be explained that in this embodiment, the critical number of page elements refers to the maximum number of elements that can be accommodated on the page, the critical page loading speed refers to the upper limit of the page loading time that the user can accept, and the critical static content ratio refers to the maximum proportion of static content in a page.

[0059] Specifically, the data generation complexity index of each page is expressed as follows:

[0060]

[0061] Among them, C i Indicates the complexity index of page data generation for the i-th page, s i Indicates the number of page elements of the i-th page, s0 indicates the set critical number of page elements, d i represents the page loading speed of the i-th page, d0 represents the set critical page loading speed, f irepresents the static content ratio of the i-th page, f0 represents the set critical static content ratio, ρ1 represents the page data generation complexity assessment impact factor corresponding to the set number of page elements, ρ2 represents the page data generation complexity assessment impact factor corresponding to the set page loading speed, ρ3 represents the page data generation complexity assessment impact factor corresponding to the set static content ratio, i represents the page number, i = 1, 2, 3, ..., m, m represents the total number of page numbers.

[0062] The algorithm of this embodiment combines the number of page elements, page loading speed and static content ratio of each page, and conducts a comprehensive analysis to obtain the dependent variable. Page loading speed refers to the page loading time. An increase in the number of page elements usually increases the page loading time. At the same time, increasing the static content ratio usually helps to reduce the page loading time. By increasing the static content ratio, the server load can be reduced and the page rendering speed can be accelerated. The number of page elements does not directly determine the static content ratio, but there is a mutual influence between the two. On the one hand, if the number of page elements is too large, it may contain a large amount of dynamic content, which will reduce the static content ratio. On the other hand, by optimizing the page structure, reducing unnecessary elements and merging resource files, the number of page elements can be reduced without reducing the page functionality, and the static content ratio may be indirectly increased. Comprehensive analysis can obtain a more comprehensive, accurate and in-depth page data generation complexity index.

[0063] Table 1 Example of page data generation complexity index data

[0064]

[0065]

[0066] As shown in Table 1, the page data generation complexity index is determined by the number of page elements, page loading speed, and the proportion of static content on the page. In a specific embodiment, i = 1, the critical number of page elements is 500, the critical page loading speed is 4 seconds, and the critical proportion of static content on the page is 80%. The impact factor of the page data generation complexity assessment corresponding to the set number of page elements is 0.3, the impact factor of the page data generation complexity assessment corresponding to the set page loading speed is 0.4, and the impact factor of the page data generation complexity assessment corresponding to the set static content proportion is 0.3. This formula considers three key factors: the number of page elements, page loading speed, and the proportion of static content on the page. It can significantly improve page performance and user experience, while also helping to enhance page security. By optimizing these factors, faster, simpler, and more secure pages can be created. By standardizing the number of page elements, page loading speed, and proportion of static content on different pages, ensuring that they are compared on the same scale, the fairness and comparability of the evaluation are improved. By weighting the impact of the number of page elements, page loading speed, and the proportion of static content on the page, their relative importance in the evaluation index is reflected. The weights of different factors can be adjusted according to different needs, making the model highly adaptable. It is not difficult to see that the greater the number of page elements, the faster the page loading speed, or the smaller the proportion of static content on the page, the greater the page data generation complexity index. By evaluating the page data generation complexity index, it is possible to discover which parts are most time-consuming or resource-intensive to generate, and then perform targeted optimizations. By optimizing highly complex page elements, loading time can be reduced, user experience can be improved, and page responsiveness and ease of use can be ensured, thereby increasing user satisfaction. This also helps predict page caching needs.

[0067] It should be understood that in this embodiment, ρ1 is the page data generation complexity assessment influence factor corresponding to the number of page elements preset in the page update database, which represents the numerical value of the influence of the number of page elements on the complexity of each page data generation. When used, the page data generation complexity assessment influence factor corresponding to the number of page elements can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the number of page elements and the page data generation complexity assessment influence factor corresponding to the number of page elements preset in the page update database form a mapping set, and the real-time number of page elements is input into the mapping set to obtain the page data generation complexity assessment influence factor corresponding to the number of page elements. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1; ρ2 is the page data generation complexity assessment influence factor corresponding to the page loading speed preset in the page update database, which represents the numerical value of the influence of the page loading speed on the complexity of each page data generation. When used, the page data generation complexity assessment influence factor corresponding to the page loading speed can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the page loading speed can be The page data generation complexity assessment influence factor corresponding to the page loading speed preset in the page update database is formed into a mapping set, and the real-time page loading speed is input into the mapping set to obtain the page data generation complexity assessment influence factor corresponding to the page loading speed, wherein the mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1; ρ3 is the page data generation complexity assessment influence factor corresponding to the page static content ratio preset in the page update database, which represents the numerical value of the influence of the page static content ratio on the data generation complexity of each page. When used, the page data generation complexity assessment influence factor corresponding to the page static content ratio can be directly obtained from the page update database. The corresponding relationship can be a preset mapping relationship. For example, the page static content ratio and the page data generation complexity assessment influence factor corresponding to the page static content ratio preset in the page update database form a mapping set, and the real-time page static content ratio is input into the mapping set to obtain the page data generation complexity assessment influence factor corresponding to the page static content ratio. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is between 0 and 1.

[0068] It should be explained that the data generation complexity index of each page in this embodiment is a quantitative indicator obtained by analyzing the number of page elements, page loading speed and static content ratio of each page, and is used to quantify the data generation complexity of the page.

[0069] Specifically, a comprehensive analysis is performed to obtain a cache demand evaluation value for each page. The specific analysis process is as follows: extracting a critical page access frequency index and a critical page data generation complexity index from a page update database.

[0070] Based on the access frequency index of each page and the data generation complexity index, a comprehensive analysis is conducted to obtain the cache demand assessment value of each page.

[0071] Specifically, the cache demand evaluation value of each page is expressed as follows:

[0072] D i =tanh[θ1*C i +θ2*B i ];

[0073] Among them, D i represents the page cache demand evaluation value of the i-th page, C i represents the complexity index of page data generation for the i-th page, B i represents the page access frequency index of the i-th page, θ1 represents the page cache demand assessment impact factor corresponding to the set page data generation complexity index, θ2 represents the page cache demand assessment impact factor corresponding to the set page access frequency index, i represents the page number, i = 1, 2, 3, ..., m, m represents the total number of pages.

[0074] In a specific embodiment, the algorithm of this embodiment combines the data generation complexity index and access frequency index of each page to comprehensively analyze and obtain a page cache demand assessment value. In some cases, pages with high data generation complexity may attract a large number of users due to the provision of high-quality, valuable content or services, thereby exhibiting a high access frequency. However, not all pages with high data generation complexity will receive high visits. If the page content is too professional, obscure, or does not meet the needs of the general public, it may not be visited even if the data processing is complex. Conversely, some pages that are simple, easy to understand, and highly practical may be more likely to attract users and be frequently visited. In certain specific scenarios, there may not be a significant correlation between data generation complexity and access frequency. For example, some pages such as internal management systems or backend databases may have high data generation complexity, but because they are only used by internal personnel and have limited access rights, they are not frequently visited. Comprehensive analysis can produce a more accurate, comprehensive, and reliable page cache demand assessment value.

[0075] like Figure 3 As shown, in a specific embodiment, i=1, θ1=0.5, θ2=0.3. i= 0.1, the functional relationship between the page cache demand evaluation value and the page access frequency index is shown in curve a; when C i = 0.5, the functional relationship between the page cache demand evaluation value and the page access frequency index is shown in curve b; when C i When =1, the functional relationship between the page cache demand evaluation value and the page access frequency index is shown as curve c.

[0076] It should be explained that in this embodiment, two key factors, namely the page data generation complexity index and the page access frequency index, are comprehensively considered, which can provide an in-depth understanding of user behavior and demand characteristics, provide strong support for data analysis and decision-making, further optimize the website structure and functional layout, and improve the overall performance of the page and user experience. By weighting the impact of the page data generation complexity index and the page access frequency index, their relative importance in the evaluation index is reflected, and the weights of different factors can be adjusted according to different needs, so that the model has good adaptability. It is not difficult to see that the larger the page data generation complexity index or the page access frequency index, the larger the page cache demand evaluation value. By evaluating the page cache demand evaluation value, the overall response speed of the system and the response time of data access can be improved, and cache resources can be reasonably allocated according to actual needs to avoid resource waste. At the same time, the user experience can be improved, and the risk of network congestion can be reduced, which helps to enhance the competitiveness of the page, meet user needs, and achieve sustainable development.

[0077] It should be understood that, in this embodiment, θ1 is the page cache demand assessment impact factor corresponding to the page data generation complexity index preset in the page update database, which represents the numerical value of the degree of influence of the page data generation complexity index on the page cache demand assessment value. When used, the page cache demand assessment impact factor corresponding to the page data generation complexity index can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the page data generation complexity index and the page cache demand assessment impact factor corresponding to the page data generation complexity index preset in the page update database form a mapping set, and the real-time page data generation complexity index is input into the mapping set to obtain the page cache demand assessment impact factor corresponding to the page data generation complexity index. The mapping relationship can be one-to-one or many-to-one. In this example, Its value range is between 0 and 1; θ2 is the page cache demand assessment impact factor corresponding to the page access frequency index preset in the page update database, which represents the numerical value of the influence of the page access frequency index on the page cache demand assessment value. When used, the page cache demand assessment impact factor corresponding to the page access frequency index can be directly obtained from the page update database, and the corresponding relationship can be a preset mapping relationship. For example, the page access frequency index and the page cache demand assessment impact factor corresponding to the page access frequency index preset in the page update database form a mapping set, and the real-time page access frequency index is input into the mapping set to obtain the page cache demand assessment impact factor corresponding to the page access frequency index. The mapping relationship can be one-to-one or many-to-one. In this instance, its value range is between 0 and 1.

[0078] In a specific embodiment, the page cache demand evaluation value is a quantitative index obtained by analyzing the page data generation complexity index and the page access frequency index, and is used to quantify the cache demand of the page.

[0079] In a specific embodiment, each page is screened according to the cache requirement evaluation value to obtain each page to be cached. The specific screening process is: extracting the cache requirement evaluation threshold from the page update database, comparing the cache requirement evaluation value of each page with the cache requirement evaluation threshold, if the cache requirement evaluation value of a page is greater than or equal to the cache requirement evaluation threshold, then the page is marked as a page to be cached, if the cache requirement evaluation value of a page is less than the cache requirement evaluation threshold, then the page is marked as a non-cached page.

[0080] Reference Figure 2 As shown, the second aspect of the present invention provides a page update data processing system based on RN and H5, including: a page update data acquisition module for acquiring page update data, wherein the page update data includes page history access data and page loading data.

[0081] The page cache demand evaluation value analysis module is used to process the page historical access data to obtain the access frequency index of each page, process the page loading data to obtain the data generation complexity index of each page, and comprehensively analyze to obtain the cache demand evaluation value of each page.

[0082] The to-be-cached page screening module is used to screen each page according to the cache demand evaluation value to obtain each to-be-cached page, and divide each to-be-cached page into modules, marking each to-be-cached page as each content module.

[0083] The cache processing module is used to obtain the content update cycle of each content module of each page to be cached, and perform cache processing on each content module of each page to be cached according to the content update cycle of each content module of each page to be cached.

[0084] The page update database is used to store page update related data, including the page access frequency assessment impact factor corresponding to the set cumulative number of visits, the page access frequency assessment impact factor corresponding to the set user access frequency, the page access frequency assessment impact factor corresponding to the user access time, the critical page loading speed, the critical page loading speed, the critical static content ratio, the page cache demand assessment impact factor corresponding to the page data generation complexity index, the page cache demand assessment impact factor corresponding to the page access frequency index and the cache demand assessment threshold and other indicators. The data in the page update database is obtained by collecting data from multiple page update experiments.

[0085] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A page update data processing method based on RN and H5, characterized in that: include: Obtaining page update data, wherein the page update data includes page history access data and page loading data; The page historical access data is processed to obtain the access frequency index of each page, the page loading data is processed to obtain the data generation complexity index of each page, and the cache demand assessment value of each page is obtained through comprehensive analysis; Screen each page according to the cache demand evaluation value to obtain each page to be cached, and divide each page to be cached into modules, marking each content module of each page to be cached; Obtaining a content update cycle of each content module of each to-be-cached page, and performing cache processing on each content module of each to-be-cached page according to the content update cycle of each content module of each to-be-cached page; The page loading data is processed to obtain the data generation complexity index of each page. The specific processing process is as follows: Extracting the critical number of page elements, critical page loading speed, and critical static content ratio from the page update database; The critical number of page elements refers to the maximum number of elements that can be accommodated on a page. The critical page loading speed refers to the upper limit of the page loading time that users can accept. The critical static content ratio refers to the maximum proportion of static content on a page. The page loading data specifically includes the number of page elements, page loading speed and static content ratio of each page; Comprehensive analysis is performed to obtain the data generation complexity index of each page.

2. The page update data processing method based on RN and H5 according to claim 1 is characterized in that: The page history access data is processed to obtain the access frequency index of each page. The specific processing process is as follows: Extracting critical cumulative access times, critical user access frequency, and critical user access duration from a page update database; The critical cumulative number of visits refers to the maximum cumulative number of visits that a page can withstand within a time node. The critical user access frequency refers to the maximum number of requests initiated by users to a page within a time node. The critical user access duration refers to the maximum time a user can continuously access a page without triggering any timeouts or restrictions. The page access history data includes the cumulative number of visits to each page at each time point, user access frequency, and user access duration; The access frequency index of each page is obtained through comprehensive analysis, and the access frequency index of each page is used to quantify the access frequency of the page.

3. The page update data processing method based on RN and H5 according to claim 1 is characterized in that: The comprehensive analysis obtains the cache demand evaluation value of each page. The specific analysis process is as follows: Extracting the critical page access frequency index and the critical page data generation complexity index from the page update database; Based on the access frequency index of each page and the data generation complexity index, a comprehensive analysis is conducted to obtain the cache demand assessment value of each page; The page cache demand assessment impact factor corresponding to the preset page data generation complexity index represents the numerical value of the influence of the page data generation complexity index on the page cache demand assessment value; the page cache demand assessment impact factor corresponding to the preset page access frequency index represents the numerical value of the influence of the page access frequency index on the page cache demand assessment value.

4. The page update data processing method based on RN and H5 according to claim 1 is characterized in that: The pages are screened according to the cache requirement evaluation value to obtain the pages to be cached. The specific screening process is as follows: The cache requirement evaluation threshold is extracted from the page update database, and the cache requirement evaluation value of each page is compared with the cache requirement evaluation threshold. If the cache requirement evaluation value of a page is greater than or equal to the cache requirement evaluation threshold, the page is marked as a page to be cached. If the cache requirement evaluation value of a page is less than the cache requirement evaluation threshold, the page is marked as a non-cached page.

5. The page update data processing method based on RN and H5 according to claim 2 is characterized in that: The access frequency index of each page is expressed as follows: Among them, B i represents the access frequency index of the i-th page, e represents a natural constant, A ij represents the cumulative number of visits to the i-th page at the j-th time node, A0 represents the set critical cumulative number of visits, and p ij represents the user access frequency of the i-th page at the j-th time node, p0 represents the set critical user access frequency, Q i represents the user access time of the i-th page, Q0 represents the set critical user access time, ∈1 represents the page access frequency assessment impact factor corresponding to the set cumulative number of visits, ∈2 represents the page access frequency assessment impact factor corresponding to the set user access frequency, ∈3 represents the page access frequency assessment impact factor corresponding to the set user access time, i represents the page number, i=1,2,3,...,m, m represents the total number of page numbers, j represents the time node number, j=1,2,3,...,n, n represents the total number of time node numbers, and the page access frequency assessment impact factors corresponding to the set cumulative number of visits, user access frequency and user access time range from 0 to 1.

6. The page update data processing method based on RN and H5 according to claim 1 is characterized in that: The data generation complexity index of each page is expressed as follows: Among them, C i Indicates the complexity index of page data generation for the i-th page, s i Indicates the number of page elements of the i-th page, s0 indicates the set critical number of page elements, d i represents the page loading speed of the i-th page, d0 represents the set critical page loading speed, f i represents the static content ratio of the i-th page, f0 represents the set critical static content ratio, ρ1 represents the page data generation complexity assessment impact factor corresponding to the set number of page elements, ρ2 represents the page data generation complexity assessment impact factor corresponding to the set page loading speed, ρ3 represents the page data generation complexity assessment impact factor corresponding to the set static content ratio, i represents the page number, i=1,2,3,...,m, m represents the total number of page numbers, and the page data generation complexity assessment impact factors corresponding to the set number of page elements, page loading speed and static content ratio range from 0 to 1.

7. A page update data processing system based on RN and H5, applying the page update data processing method based on RN and H5 according to any one of claims 1 to 6, characterized in that: include: A page update data acquisition module is used to obtain page update data, wherein the page update data includes page history access data and page loading data; The page cache demand evaluation value analysis module is used to process the page historical access data to obtain the access frequency index of each page, process the page loading data to obtain the data generation complexity index of each page, and conduct comprehensive analysis to obtain the cache demand evaluation value of each page; The to-be-cached page screening module is used to screen each page according to the cache demand evaluation value to obtain each to-be-cached page, and divide each to-be-cached page into modules, marking each to-be-cached page as a content module; A cache processing module is used to obtain the content update cycle of each content module of each page to be cached, and cache the content modules of each page to be cached according to the content update cycle of each content module of each page to be cached; The page update database is used to store page update related data, including the page access frequency assessment impact factor corresponding to the set cumulative number of visits, the page access frequency assessment impact factor corresponding to the set user access frequency, the page access frequency assessment impact factor corresponding to the set user access duration, the page data generation complexity assessment impact factor corresponding to the set number of page elements, the page data generation complexity assessment impact factor corresponding to the set page loading speed, the page data generation complexity assessment impact factor corresponding to the set static content ratio, the page cache requirement assessment impact factor corresponding to the set page data generation complexity index, the page cache requirement assessment impact factor corresponding to the set page access frequency index and the cache requirement assessment threshold and other indicators.

8. A data processing device for page updates based on RN and H5, characterized by: include: Processors and the memory and network interfaces connected to them; The network interface is connected to the non-volatile memory in the server; When running, the processor retrieves a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the method according to any one of claims 1 to 6.

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