Web page loading optimization method and system based on adversarial neural network

Abstract

The application provides a webpage page loading optimization method and system based on an adversarial neural network, extracts a completed edited webpage page file, and obtains corresponding webpage page data, guarantees the integrity of the webpage page data; based on the adversarial neural network, on one hand, an initial loading order of all page sub-data under the webpage page data is obtained, and on the other hand, corresponding page data loading characteristic information of loading the webpage page data according to the initial loading order by the webpage loading end in the current working state is obtained; and based on the page data loading characteristic information, the webpage page data loading process corresponding to the initial loading order is evaluated, so as to adjust the initial loading order, obtain a final loading order of all page sub-data under the webpage page data, ensure that the webpage page data loading process corresponding to the final loading order has good loading user experience, and guarantee that key information of the webpage page can also be preferentially displayed in the case that the network speed is not ideal.

Description

Technical Field

[0001] This invention relates to the field of computer web page processing, and more particularly to a web page loading optimization system based on adversarial neural networks. Background Technology

[0002] Front-end web pages edited using development tools are not optimized. When displaying these pages, data is loaded randomly, and the first loaded data is shown first. With limited network loading speeds, critical content on the webpage may not be loaded first, resulting in a poor browsing experience. Furthermore, due to the full-load characteristic of TCP connections, there is always a round trip between the first 14KB of data and subsequent data, significantly slowing down the loading of data beyond that initial 14KB. This makes it impossible to prioritize the display of critical information under slow network conditions, further impacting the user experience during page loading. Summary of the Invention

[0003] The purpose of this invention is to provide a webpage loading optimization method and system based on adversarial neural networks. This method extracts the edited webpage file and obtains the corresponding webpage data, ensuring the integrity of the webpage data. Based on the adversarial neural network, it obtains both the initial loading order of all sub-data of the webpage and the page data loading feature information corresponding to the loading of the webpage data by the webpage loading terminal according to the initial loading order in the current working state. Furthermore, based on the page data loading feature information, it evaluates the webpage data loading process corresponding to the initial loading order, thereby adjusting the initial loading order to obtain the final loading order of all sub-data of the webpage, ensuring that the webpage data loading process corresponding to the final loading order has a good user experience. Finally, based on the final loading order, it generates a sub-data queue of all sub-data of the webpage and sends it to the webpage loading terminal, ensuring that key information on the webpage is displayed preferentially even when network speed is not ideal, thus improving the user experience during the webpage loading process.

[0004] This invention is achieved through the following technical solution:

[0005] Web page loading optimization methods based on adversarial neural networks include:

[0006] The web page development end is monitored to obtain the web page editing status information of the web page development end; based on the web page editing status information, the web page file that has been edited is extracted from the web page development end, and the extracted web page file is preprocessed to obtain web page data;

[0007] Based on the evaluation operation of the adversarial neural network, the initial loading order of all page sub-data under the web page data by the web page loading terminal is obtained, as well as the page data loading feature information corresponding to the loading of the web page data by the web page loading terminal according to the initial loading order in the current working state; based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the web page data.

[0008] Based on the final loading order, a sub-data queue is generated for all page sub-data under the webpage data; then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sent sequentially to the webpage loading end.

[0009] Optionally, the web page development end is monitored to obtain the web page editing status information of the web page development end; based on the web page editing status information, the edited web page is extracted from the web page development end, and the extracted web page is preprocessed to obtain web page data, including:

[0010] After obtaining the web development work log from the web development client, the web development work log is analyzed to obtain the editing progress information of the web page editing task currently being processed by the web development client;

[0011] Based on the editing progress information, determine whether the web page editing task has been completed; if so, copy and extract the completed web page file corresponding to the web page editing task from the corresponding storage space of the web development end.

[0012] The extracted web page file is preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

[0013] Optionally, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the webpage data by the webpage loading terminal is obtained, as well as the page data loading feature information corresponding to the webpage data being loaded by the webpage loading terminal according to the initial loading order in the current working state; based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the webpage data, including:

[0014] The first neural network model is used to evaluate the web page data to obtain the initial loading order of all page sub-data under the web page data by the web page loading terminal;

[0015] The webpage loading end is evaluated using a second neural network model to obtain page data loading feature information corresponding to the loading of webpage data by the webpage loading end in the current working state according to the initial loading order; wherein, the page data loading feature information includes the compression ratio of the first 14KB data portion of the webpage data, the loading speed of the first 14KB data portion, and the average loading time of the webpage data under different network speed conditions.

[0016] Based on the page data loading characteristic information, the performance of the webpage loading terminal in its current working state, loading the webpage data according to the initial loading order, is evaluated to obtain a comprehensive evaluation value for page data loading. If the comprehensive evaluation value for page data loading is greater than or equal to a preset evaluation threshold, the initial loading order is taken as the final loading order of all sub-page data under the webpage data. If the comprehensive evaluation value for page data loading is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit count of each sub-page data under the webpage data to obtain a new loading order, until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is taken as the final loading order of all sub-page data under the webpage data.

[0017] Optionally, based on the final loading order, a sub-data queue is generated for all page sub-data under the webpage data; then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sequentially sent to the webpage loading terminal, including:

[0018] Based on the final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue;

[0019] The webpage creation request from the user terminal is analyzed to determine whether the user terminal is a legitimate user terminal. If so, all page sub-data contained in the sub-data queue are sent to the webpage loading terminal in sequence. If not, all page sub-data contained in the sub-data queue are not sent to the webpage loading terminal.

[0020] A webpage loading optimization system based on adversarial neural networks includes:

[0021] The web development monitoring module is used to monitor the web development end and obtain the web page editing status information of the web development end;

[0022] The webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage editing status information, and to preprocess the extracted webpage file to obtain webpage data;

[0023] The adversarial neural network processing module is used to obtain, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the web page data by the web page loading terminal, and the page data loading feature information corresponding to the web page data being loaded by the web page loading terminal according to the initial loading order in the current working state; and to adjust the initial loading order based on the page data loading feature information to obtain the final loading order of all page sub-data under the web page data.

[0024] The page data queue generation module is used to generate a sub-data queue for all page sub-data under the web page data based on the final loading order.

[0025] The page data sending module is used to send all page sub-data contained in the sub-data queue to the web page loading terminal in sequence based on the web page creation request from the user terminal.

[0026] Optionally, the web page development monitoring module is used to monitor the web page development end and obtain the web page editing status information of the web page development end, including:

[0027] After obtaining the web development work log from the web development client, the web development work log is analyzed to obtain the editing progress information of the web page editing task currently being processed by the web development client;

[0028] The webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage editing status information, and to preprocess the extracted webpage file to obtain webpage data, including:

[0029] Based on the editing progress information, determine whether the web page editing task has been completed; if so, copy and extract the completed web page file corresponding to the web page editing task from the corresponding storage space of the web development end.

[0030] The extracted web page file is preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

[0031] Optionally, the adversarial neural network processing module is used to obtain, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the webpage data by the webpage loading end, and the page data loading feature information corresponding to the webpage data being loaded by the webpage loading end according to the initial loading order in the current working state; and to adjust the initial loading order based on the page data loading feature information to obtain the final loading order of all page sub-data under the webpage data, including:

[0032] The first neural network model is used to evaluate the web page data to obtain the initial loading order of all page sub-data under the web page data by the web page loading terminal;

[0033] The webpage loading end is evaluated using a second neural network model to obtain page data loading feature information corresponding to the loading of webpage data by the webpage loading end in the current working state according to the initial loading order; wherein, the page data loading feature information includes the compression ratio of the first 14KB data portion of the webpage data, the loading speed of the first 14KB data portion, and the average loading time of the webpage data under different network speed conditions.

[0034] Based on the page data loading characteristic information, the performance of the webpage loading terminal in its current working state, loading the webpage data according to the initial loading order, is evaluated to obtain a comprehensive evaluation value for page data loading. If the comprehensive evaluation value for page data loading is greater than or equal to a preset evaluation threshold, the initial loading order is taken as the final loading order of all sub-page data under the webpage data. If the comprehensive evaluation value for page data loading is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit count of each sub-page data under the webpage data to obtain a new loading order, until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is taken as the final loading order of all sub-page data under the webpage data.

[0035] Optionally, the page data queue generation module is used to generate a sub-data queue for all page sub-data under the webpage page data based on the final loading order, including:

[0036] Based on the final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue;

[0037] The page data sending module is used to sequentially send all page sub-data contained in the sub-data queue to the webpage loading terminal based on a webpage creation request from the user terminal, including:

[0038] The webpage creation request from the user terminal is analyzed to determine whether the user terminal is a legitimate user terminal. If so, all page sub-data contained in the sub-data queue are sent to the webpage loading terminal in sequence. If not, all page sub-data contained in the sub-data queue are not sent to the webpage loading terminal.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The webpage loading optimization method and system provided in this application extract the edited webpage file and obtain the corresponding webpage data, ensuring the integrity of the webpage data. Based on the adversarial neural network, it obtains the initial loading order of all sub-data of the webpage data and the page data loading feature information corresponding to the loading of webpage data by the webpage loading end according to the initial loading order in the current working state. Based on the page data loading feature information, it evaluates the webpage data loading process corresponding to the initial loading order and adjusts the initial loading order accordingly to obtain the final loading order of all sub-data of the webpage data, ensuring that the webpage data loading process corresponding to the final loading order has a good user experience. Based on the final loading order, it generates a sub-data queue of all sub-data of the webpage data and sends it to the webpage loading end, ensuring that key information of the webpage is displayed first even when the network speed is not ideal, thus improving the user experience during the webpage loading process. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0042] Figure 1 This is a flowchart illustrating the webpage loading optimization method based on adversarial neural networks provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the webpage loading optimization system based on adversarial neural networks provided by the present invention. Detailed Implementation

[0044] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0045] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] Please see Figure 1 As shown, an embodiment of this application provides a webpage loading optimization method based on adversarial neural networks, which includes:

[0048] Monitor the web page development end to obtain the web page editing status information of the web page development end; based on the web page editing status information, extract the web page file that has been edited from the web page development end, and preprocess the extracted web page file to obtain web page data;

[0049] Based on the evaluation operation of the adversarial neural network, the initial loading order of all page sub-data under the web page data of the web page loading terminal is obtained, as well as the page data loading feature information corresponding to the loading of the web page data according to the initial loading order in the current working state of the web page loading terminal; based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the web page data.

[0050] Based on the final loading order, a sub-data queue is generated for all page sub-data under the webpage data; then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sent to the webpage loading end in sequence.

[0051] The beneficial effects of the above embodiments are as follows: This webpage loading optimization method based on adversarial neural networks extracts the edited webpage file and obtains the corresponding webpage data, ensuring the integrity of the webpage data; based on adversarial neural networks, it obtains, on the one hand, the initial loading order of all sub-data of the webpage data, and on the other hand, the page data loading feature information corresponding to the loading of webpage data by the webpage loading end according to the initial loading order in the current working state; furthermore, based on the page data loading feature information, it evaluates the webpage data loading process corresponding to the initial loading order, thereby adjusting the initial loading order to obtain the final loading order of all sub-data of the webpage data, ensuring that the webpage data loading process corresponding to the final loading order has a good user experience; and based on the final loading order, it generates a sub-data queue of all sub-data of the webpage data, which is then sent to the webpage loading end, ensuring that key information of the webpage is displayed first even when the network speed is not ideal, thus improving the user experience during the webpage loading process.

[0052] In another embodiment, the web page development end is monitored to obtain web page editing status information; based on the web page editing status information, the edited web page is extracted from the web page development end, and the extracted web page is preprocessed to obtain web page data, including:

[0053] After obtaining the web development work log from the web development client, analyze the web development work log to obtain the editing progress information of the web page editing task currently being processed by the web development client;

[0054] Based on the editing progress information, determine whether the webpage editing task has been completed; if so, copy and extract the completed webpage file corresponding to the webpage editing task from the corresponding storage space of the webpage development end.

[0055] The extracted web page files are preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

[0056] The beneficial effects of the above embodiments are that the web development client, as the development terminal for web pages, can perform corresponding web page editing operations. The web development client can execute different web page development and editing tasks, and during the process, corresponding log records are generated in the web development work log of the web development client. By analyzing the web development work log, the editing progress information corresponding to the web page editing task currently being processed by the web development client can be obtained; this editing progress information may be, but is not limited to, the ratio between the number of page data bits that have been edited and the total number of page data bits that need to be edited. Based on this editing progress information, it is further determined whether the web page editing task has been completed. If so, the completed web page file corresponding to the web page editing task is directly copied and extracted from the corresponding storage space of the web development client; if not, no web page file is extracted from the storage space. This ensures the data integrity of the extracted web page file and avoids the inability to load the web page file normally due to missing data. Furthermore, the extracted web page files undergo page data content recognition preprocessing to obtain web page data. This allows for the identification of several page sub-data based on the type of page data content in the web page files, thereby enabling effective and accurate segmentation of the web page data and improving the accuracy of determining the loading order of different page sub-data in subsequent processes.

[0057] In another embodiment, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the webpage data is obtained by the webpage loading terminal, and the page data loading feature information corresponding to the webpage data being loaded by the webpage loading terminal according to the initial loading order in the current working state; based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the webpage data, including:

[0058] The first neural network model is used to evaluate the webpage data to obtain the initial loading order of all sub-data of the webpage under the webpage data;

[0059] The second neural network model is used to evaluate the webpage loading terminal to obtain the page data loading feature information corresponding to the loading of the webpage data in the current working state according to the initial loading order; wherein, the page data loading feature information includes the compression ratio of the first 14KB data of the webpage, the loading speed of the first 14KB data, and the average loading time of the webpage data under different network speed conditions.

[0060] Based on the page data loading characteristics, the performance of the webpage loading terminal in its current working state is evaluated according to the initial loading order to obtain a comprehensive evaluation value for page data loading. If the comprehensive evaluation value is greater than or equal to a preset evaluation threshold, the initial loading order is used as the final loading order for all sub-page data under the current webpage data. If the comprehensive evaluation value is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit count of each sub-page data under the current webpage data to obtain a new loading order. This process continues until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is then used as the final loading order for all sub-page data under the current webpage data.

[0061] The beneficial effects of the above embodiments are that, during the analysis and processing of adversarial neural networks, a first neural network model can be used to evaluate the webpage data to obtain the initial loading order of all sub-data of the webpage data belonging to the webpage loading terminal; the first neural network model can be, but is not limited to, a neural network model constructed for webpage data loading tests. A second neural network model can also be used to evaluate the webpage loading terminal to obtain page data loading feature information corresponding to the webpage loading terminal loading the webpage data according to the initial loading order in the current working state; the second neural network model can be, but is not limited to, a neural network model constructed for the webpage loading process of the webpage loading terminal. The construction of the first and second neural network models is a conventional technique in the art and will not be described in detail here. Furthermore, the page data loading feature information includes the compression ratio of the first 14KB of the webpage data, the loading speed of the first 14KB of the data, and the average loading time of the webpage data under different network speed conditions. This page data loading feature information allows for a comprehensive and quantitative characterization of the loading performance of the webpage loading terminal in webpage data operations. Based on the page data loading characteristic information, the performance of the page loading operation of the web page loading terminal in the current working state according to the initial loading order is evaluated to obtain the comprehensive evaluation value of page data loading. The performance evaluation of the page data loading operation can be achieved by constructing a corresponding performance evaluation model, which is a conventional technical choice in this field and will not be described in detail here.When the overall evaluation value of page data loading is greater than or equal to the preset evaluation threshold, it indicates that loading page sub-data according to the initial loading order can achieve a good user experience. In this case, the initial loading order is directly used as the final loading order of all page sub-data under this webpage. When the overall evaluation value of page data loading is less than the preset evaluation threshold, it indicates that loading page sub-data according to the initial loading order cannot achieve a good user experience. In this case, the initial loading order is adjusted based on the data bit count of each of the page sub-data under this webpage to obtain a new loading order. In the actual adjustment process, pages belonging to the key information of the webpage can be determined based on the data bit count of each of the page sub-data under this webpage. The process involves several steps: First, the page's sub-data is processed and placed in the position with the highest loading order. Then, the remaining page sub-data are ordered according to their data bit count, from smallest to largest. This process is repeated to obtain a new loading order. Next, the overall loading evaluation value of the page data corresponding to this new loading order is checked against a preset evaluation threshold. If yes, the new loading order is used as the final loading order for all page sub-data under this page. If not, the loading order is adjusted again, and so on, until the overall loading evaluation value of the page data corresponding to the new loading order is greater than or equal to the preset evaluation threshold. This method allows for quick and targeted adjustments to the loading order, ensuring the effectiveness and reliability of the final loading order.

[0062] In another embodiment, based on the final loading order, a sub-data queue is generated for all page sub-data under the webpage data; then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sequentially sent to the webpage loading end, including:

[0063] Based on this final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue;

[0064] The system analyzes webpage creation requests from user clients to determine if the client is legitimate. If so, it sequentially sends all page sub-data contained in the sub-data queue to the webpage loading client. If not, it does not send all page sub-data contained in the sub-data queue to the webpage loading client.

[0065] The beneficial effects of the above embodiments are that, based on the final loading order, all page sub-data under the webpage data are arranged accordingly to form a sub-data queue, which ensures the accuracy of the arrangement of all page sub-data in the sub-data queue. Furthermore, the webpage creation request from the user terminal is analyzed to determine whether the user terminal is legitimate; if so, all page sub-data contained in the sub-data queue are sent sequentially to the webpage loading terminal. This allows the webpage loading terminal to directly load each page sub-data upon receiving it, enabling rapid display of the page sub-data and prioritizing the display of key information on the webpage, thus improving the user experience during the webpage loading process.

[0066] Please see Figure 2 As shown, an embodiment of this application provides a webpage loading optimization system based on adversarial neural networks, which includes:

[0067] The web development monitoring module is used to monitor the web development end and obtain the web page editing status information of the web development end;

[0068] The webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage's editing status information, and to preprocess the extracted webpage file to obtain webpage data;

[0069] The adversarial neural network processing module is used to obtain the initial loading order of all page sub-data under the web page data by the web page loading terminal based on the adversarial neural network evaluation operation, as well as the page data loading feature information corresponding to the loading of the web page data by the web page loading terminal according to the initial loading order in the current working state; and to adjust the initial loading order based on the page data loading feature information to obtain the final loading order of all page sub-data under the web page data.

[0070] The page data queue generation module is used to generate a sub-data queue for all page sub-data under the page data of the webpage based on the final loading order;

[0071] The page data sending module is used to send all page sub-data contained in the sub-data queue to the webpage loading end in sequence based on the webpage creation request from the user.

[0072] The beneficial effects of the above embodiments are as follows: This webpage loading optimization system based on adversarial neural networks extracts the edited webpage file and obtains the corresponding webpage data, ensuring the integrity of the webpage data; based on adversarial neural networks, it obtains, on the one hand, the initial loading order of all sub-data of the webpage data, and on the other hand, the page data loading feature information corresponding to the loading of webpage data by the webpage loading end according to the initial loading order in the current working state; based on the page data loading feature information, it evaluates the webpage data loading process corresponding to the initial loading order, thereby adjusting the initial loading order to obtain the final loading order of all sub-data of the webpage data, ensuring that the webpage data loading process corresponding to the final loading order has a good user experience; and based on the final loading order, it generates a sub-data queue of all sub-data of the webpage data, and sends it to the webpage loading end, ensuring that key information of the webpage is displayed first even when the network speed is not ideal, thus improving the user experience during the webpage loading process.

[0073] In another embodiment, the web page development monitoring module is used to monitor the web page development end and obtain web page editing status information of the web page development end, including:

[0074] After obtaining the web development work log from the web development client, analyze the web development work log to obtain the editing progress information of the web page editing task currently being processed by the web development client;

[0075] This webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage's editing status information, and to preprocess the extracted webpage file to obtain webpage data, including:

[0076] Based on the editing progress information, determine whether the webpage editing task has been completed; if so, copy and extract the completed webpage file corresponding to the webpage editing task from the corresponding storage space of the webpage development end.

[0077] The extracted web page files are preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

[0078] The beneficial effects of the above embodiments are that the web development client, as the development terminal for web pages, can perform corresponding web page editing operations. The web development client can execute different web page development and editing tasks, and during the process, corresponding log records are generated in the web development work log of the web development client. By analyzing the web development work log, the editing progress information corresponding to the web page editing task currently being processed by the web development client can be obtained; this editing progress information may be, but is not limited to, the ratio between the number of page data bits that have been edited and the total number of page data bits that need to be edited. Based on this editing progress information, it is further determined whether the web page editing task has been completed. If so, the completed web page file corresponding to the web page editing task is directly copied and extracted from the corresponding storage space of the web development client; if not, no web page file is extracted from the storage space. This ensures the data integrity of the extracted web page file and avoids the inability to load the web page file normally due to missing data. Furthermore, the extracted web page files undergo page data content recognition preprocessing to obtain web page data. This allows for the identification of several page sub-data based on the type of page data content in the web page files, thereby enabling effective and accurate segmentation of the web page data and improving the accuracy of determining the loading order of different page sub-data in subsequent processes.

[0079] In another embodiment, the adversarial neural network processing module is used to obtain, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the webpage data of the webpage loading terminal, and the page data loading feature information corresponding to the webpage loading terminal loading the webpage data according to the initial loading order in the current working state; based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the webpage data, including:

[0080] The first neural network model is used to evaluate the webpage data to obtain the initial loading order of all sub-data of the webpage under the webpage data;

[0081] The second neural network model is used to evaluate the webpage loading terminal to obtain the page data loading feature information corresponding to the loading of the webpage data in the current working state according to the initial loading order; wherein, the page data loading feature information includes the compression ratio of the first 14KB data of the webpage, the loading speed of the first 14KB data, and the average loading time of the webpage data under different network speed conditions.

[0082] Based on the page data loading characteristics, the performance of the webpage loading terminal in its current working state is evaluated according to the initial loading order to obtain a comprehensive evaluation value for page data loading. If the comprehensive evaluation value is greater than or equal to a preset evaluation threshold, the initial loading order is used as the final loading order for all sub-page data under the current webpage data. If the comprehensive evaluation value is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit count of each sub-page data under the current webpage data to obtain a new loading order. This process continues until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is then used as the final loading order for all sub-page data under the current webpage data.

[0083] The beneficial effects of the above embodiments are that, during the analysis and processing of adversarial neural networks, a first neural network model can be used to evaluate the webpage data to obtain the initial loading order of all sub-data of the webpage data belonging to the webpage loading terminal; the first neural network model can be, but is not limited to, a neural network model constructed for webpage data loading tests. A second neural network model can also be used to evaluate the webpage loading terminal to obtain page data loading feature information corresponding to the webpage loading terminal loading the webpage data according to the initial loading order in the current working state; the second neural network model can be, but is not limited to, a neural network model constructed for the webpage loading process of the webpage loading terminal. The construction of the first and second neural network models is a conventional technique in the art and will not be described in detail here. Furthermore, the page data loading feature information includes the compression ratio of the first 14KB of the webpage data, the loading speed of the first 14KB of the data, and the average loading time of the webpage data under different network speed conditions. This page data loading feature information allows for a comprehensive and quantitative characterization of the loading performance of the webpage loading terminal in webpage data operations. Based on the page data loading characteristic information, the performance of the page loading operation of the web page loading terminal in the current working state according to the initial loading order is evaluated to obtain the comprehensive evaluation value of page data loading. The performance evaluation of the page data loading operation can be achieved by constructing a corresponding performance evaluation model, which is a conventional technical choice in this field and will not be described in detail here.When the overall evaluation value of page data loading is greater than or equal to the preset evaluation threshold, it indicates that loading page sub-data according to the initial loading order can achieve a good user experience. In this case, the initial loading order is directly used as the final loading order of all page sub-data under this webpage. When the overall evaluation value of page data loading is less than the preset evaluation threshold, it indicates that loading page sub-data according to the initial loading order cannot achieve a good user experience. In this case, the initial loading order is adjusted based on the data bit count of each of the page sub-data under this webpage to obtain a new loading order. In the actual adjustment process, pages belonging to the key information of the webpage can be determined based on the data bit count of each of the page sub-data under this webpage. The process involves several steps: First, the page's sub-data is processed and placed in the position with the highest loading order. Then, the remaining page sub-data are ordered according to their data bit count, from smallest to largest. This process is repeated to obtain a new loading order. Next, the overall loading evaluation value of the page data corresponding to this new loading order is checked against a preset evaluation threshold. If yes, the new loading order is used as the final loading order for all page sub-data under this page. If not, the loading order is adjusted again, and so on, until the overall loading evaluation value of the page data corresponding to the new loading order is greater than or equal to the preset evaluation threshold. This method allows for quick and targeted adjustments to the loading order, ensuring the effectiveness and reliability of the final loading order.

[0084] In another embodiment, the page data queue generation module is used to generate a sub-data queue for all page sub-data under the webpage page data based on the final loading order, including:

[0085] Based on this final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue;

[0086] This page data sending module is used to sequentially send all page sub-data contained in the sub-data queue to the webpage loading end based on the webpage creation request from the user, including:

[0087] The system analyzes webpage creation requests from user clients to determine if the client is legitimate. If so, it sequentially sends all page sub-data contained in the sub-data queue to the webpage loading client. If not, it does not send all page sub-data contained in the sub-data queue to the webpage loading client.

[0088] The beneficial effects of the above embodiments are that, based on the final loading order, all page sub-data under the webpage data are arranged accordingly to form a sub-data queue, which ensures the accuracy of the arrangement of all page sub-data in the sub-data queue. Furthermore, the webpage creation request from the user terminal is analyzed to determine whether the user terminal is legitimate; if so, all page sub-data contained in the sub-data queue are sent sequentially to the webpage loading terminal. This allows the webpage loading terminal to directly load each page sub-data upon receiving it, enabling rapid display of the page sub-data and prioritizing the display of key information on the webpage, thus improving the user experience during the webpage loading process.

[0089] In summary, this webpage loading optimization method and system based on adversarial neural networks extracts the edited webpage file and obtains the corresponding webpage data, ensuring the integrity of the webpage data. Based on adversarial neural networks, it obtains both the initial loading order of all sub-data of the webpage and the page data loading feature information corresponding to the loading of webpage data by the webpage loading end in the current working state according to the initial loading order. Furthermore, based on the page data loading feature information, it evaluates the webpage data loading process corresponding to the initial loading order, thereby adjusting the initial loading order to obtain the final loading order of all sub-data of the webpage, ensuring that the webpage data loading process corresponding to the final loading order has a good user experience. Finally, based on the final loading order, it generates a sub-data queue of all sub-data of the webpage and sends it to the webpage loading end, ensuring that key information on the webpage is displayed first even when network speed is not ideal, thus improving the user experience during the webpage loading process.

[0090] The above is only one specific embodiment of the present invention, and any improvements made based on the concept of the present invention shall be considered within the scope of protection of the present invention.

Claims

1. A webpage loading optimization method based on adversarial neural networks, characterized in that, include: Monitor the web page development end to obtain the web page editing status information of the web page development end; Based on the webpage editing status information, the edited webpage file is extracted from the webpage development end, and the extracted webpage file is preprocessed to obtain webpage data; Based on the evaluation operation of the adversarial neural network, the initial loading order of all page sub-data under the web page data by the web page loading terminal is obtained, as well as the page data loading feature information corresponding to the web page data being loaded by the web page loading terminal in the current working state according to the initial loading order; Based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the webpage data, including: evaluating the webpage data using a first neural network model to obtain the initial loading order of all page sub-data under the webpage data by the webpage loading end; evaluating the webpage loading end using a second neural network model to obtain the page data loading feature information corresponding to the webpage loading end loading the webpage data according to the initial loading order in the current working state; wherein, the page data loading feature information includes the compression ratio of the first 14KB data portion of the webpage data, the loading speed of the first 14KB data portion, and the average loading time of the webpage data under different network speed conditions; based on the page The system uses page data loading characteristic information to evaluate the performance of the webpage loading operation of the webpage loading terminal in the current working state according to the initial loading order, and obtains a comprehensive evaluation value for page data loading. If the comprehensive evaluation value for page data loading is greater than or equal to a preset evaluation threshold, the initial loading order is taken as the final loading order of all page sub-data under the webpage data. If the comprehensive evaluation value for page data loading is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit quantity of each page sub-data under the webpage data to obtain a new loading order, until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is taken as the final loading order of all page sub-data under the webpage data. Based on the final loading order, a sub-data queue is generated for all page sub-data under the webpage data; then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sent sequentially to the webpage loading end.

2. The webpage loading optimization method based on adversarial neural networks as described in claim 1, characterized in that: The web page development client is monitored to obtain the web page editing status information. Based on the web page editing status information, the edited web page is extracted from the web page development client, and the extracted web page is preprocessed to obtain web page data, including: After obtaining the web development work log from the web development client, the web development work log is analyzed to obtain the editing progress information of the web page editing task currently being processed by the web development client; Based on the editing progress information, determine whether the web page editing task has been completed; if so, copy and extract the completed web page file corresponding to the web page editing task from the corresponding storage space of the web development end. The extracted web page file is preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

3. The webpage loading optimization method based on adversarial neural networks as described in claim 1, characterized in that: Based on the final loading order, a sub-data queue is generated for all page sub-data under the web page data; Then, based on the webpage creation request from the user, all page sub-data contained in the sub-data queue are sequentially sent to the webpage loading end, including: Based on the final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue; The webpage creation request from the user terminal is analyzed to determine whether the user terminal is a legitimate user terminal. If so, all page sub-data contained in the sub-data queue are sent to the webpage loading terminal in sequence. If not, all page sub-data contained in the sub-data queue are not sent to the webpage loading terminal.

4. A webpage loading optimization system based on adversarial neural networks, characterized in that, include: The web development monitoring module is used to monitor the web development end and obtain the web page editing status information of the web development end; The webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage editing status information, and to preprocess the extracted webpage file to obtain webpage data; The adversarial neural network processing module is used to obtain, based on the adversarial neural network evaluation operation, the initial loading order of all page sub-data under the web page data by the web page loading terminal, and the page data loading feature information corresponding to the web page data being loaded by the web page loading terminal in the current working state according to the initial loading order; Based on the page data loading feature information, the initial loading order is adjusted to obtain the final loading order of all page sub-data under the webpage data, including: evaluating the webpage data using a first neural network model to obtain the initial loading order of all page sub-data under the webpage data by the webpage loading end; evaluating the webpage loading end using a second neural network model to obtain the page data loading feature information corresponding to the webpage loading end loading the webpage data according to the initial loading order in the current working state; wherein, the page data loading feature information includes the compression ratio of the first 14KB data portion of the webpage data, the loading speed of the first 14KB data portion, and the average loading time of the webpage data under different network speed conditions; based on the page The system uses page data loading characteristic information to evaluate the performance of the webpage loading operation of the webpage loading terminal in the current working state according to the initial loading order, and obtains a comprehensive evaluation value for page data loading. If the comprehensive evaluation value for page data loading is greater than or equal to a preset evaluation threshold, the initial loading order is taken as the final loading order of all page sub-data under the webpage data. If the comprehensive evaluation value for page data loading is less than the preset evaluation threshold, the initial loading order is adjusted based on the data bit quantity of each page sub-data under the webpage data to obtain a new loading order, until the comprehensive evaluation value for page data loading corresponding to the new loading order is greater than or equal to the preset evaluation threshold, and the new loading order is taken as the final loading order of all page sub-data under the webpage data. The page data queue generation module is used to generate a sub-data queue for all page sub-data under the web page data based on the final loading order. The page data sending module is used to send all page sub-data contained in the sub-data queue to the web page loading terminal in sequence based on the web page creation request from the user terminal.

5. The webpage loading optimization system based on adversarial neural networks as described in claim 4, characterized in that: The web page development monitoring module is used to monitor the web page development end and obtain the web page editing status information of the web page development end, including: After obtaining the web development work log from the web development client, the web development work log is analyzed to obtain the editing progress information of the web page editing task currently being processed by the web development client; The webpage extraction and processing module is used to extract the edited webpage file from the webpage development end based on the webpage editing status information, and to preprocess the extracted webpage file to obtain webpage data, including: Based on the editing progress information, determine whether the web page editing task has been completed; if so, copy and extract the completed web page file corresponding to the web page editing task from the corresponding storage space of the web development end. The extracted web page file is preprocessed with page data content recognition to obtain web page data; wherein, the web page data includes several page sub-data that correspond one-to-one with all page data content blocks under the web page file.

6. The webpage loading optimization system based on adversarial neural networks as described in claim 4, characterized in that: The page data queue generation module is used to generate a sub-data queue for all page sub-data under the webpage page data based on the final loading order, including: Based on the final loading order, all page sub-data under the web page data are arranged accordingly to form a sub-data queue; The page data sending module is used to sequentially send all page sub-data contained in the sub-data queue to the webpage loading terminal based on a webpage creation request from the user terminal, including: The webpage creation request from the user terminal is analyzed to determine whether the user terminal is a legitimate user terminal. If so, all page sub-data contained in the sub-data queue are sent to the webpage loading terminal in sequence. If not, all page sub-data contained in the sub-data queue are not sent to the webpage loading terminal.

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