WEB access speed increasing method based on user behavior and network state

Abstract

The invention relates to the technical field of network improvement, and particularly discloses a WEB access speed improvement method based on user behaviors and network states, and the method comprises the steps: detecting the performances of user equipment through an equipment performance monitoring module, including the CPU core number and the memory size, and deciding whether to execute a resource degradation strategy or not; by monitoring the performance of the equipment and the network condition, resource loading can be dynamically adjusted, it is ensured that users under the equipment and the network with different performances can enjoy smooth page experience, the resource loading sequence is optimized and resource degradation is carried out for the users with low-performance equipment and poor network condition, and the user experience is improved. According to the embodiment of the invention, the user is ensured to obtain smooth use experience, and by analyzing the operation behavior of the user, predicting the next operation of the user and loading related resources in advance, personalized optimization schemes can be provided for different users, the loading speed and interaction experience of the webpage are improved, and the user experience is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of network improvement, and particularly to a method for improving WEB access speed based on user behavior and network status. Background Art

[0002] As an important information technology and platform, the Web enables people to quickly find the information they need through search engines. Whether for academic research, work requirements, or entertainment and leisure, the Web has become an important way for people to obtain information.

[0003] In current Web development, front-end performance optimization has become an important part of improving user experience. Due to the increasing complexity of Web applications, page loading speed, response time, and user interaction fluency have become important indicators of user satisfaction. Therefore, in order to optimize the Web, front-end performance optimization methods are usually used, mainly including operations such as static resource optimization, code compression, and caching strategies, so as to improve page performance.

[0004] In the prior art, front-end performance optimization in Web development usually focuses on means such as static resource optimization, code compression, and caching strategies. Although these methods have improved page performance to a certain extent, they lack dynamic adjustment of device performance, network conditions, and user real-time interaction behavior, and cannot achieve dynamic optimization of resource loading and resource preloading and other operations, thus affecting user experience. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for improving WEB access speed based on user behavior and network status, and solve the following technical problems:

[0006] How to achieve dynamic optimization of resource loading and resource preloading to improve user experience.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for improving WEB access speed based on user behavior and network status, the method comprising:

[0009] S1: Detect the performance of the user device through the device performance monitoring module, including the number of CPU cores and the memory size, and decide whether to execute the resource degradation strategy;

[0010] S2: Detect the current network bandwidth and network latency of the user through the network condition monitoring module, optimize the resource loading method, and ensure that key resources are preferentially loaded in the case of low bandwidth or high latency;

[0011] S3: Monitor the user's behaviors such as scrolling, clicking, and hovering through the user behavior monitoring module, predict the user's next operation, and pre-load relevant resources;

[0012] S4: Through the dynamic resource optimization module, comprehensively integrate the device performance monitoring, network status monitoring, and user behavior monitoring data, and intelligently optimize the resource loading order.

[0013] Furthermore, the working process of the device performance monitoring module in S1 includes:

[0014] S11: First, obtain the number of cores of the device CPU through navigator.hardwareConcurrency, and detect the memory size of the device through navigator.deviceMemory;

[0015] S12: When it is detected that the number of CPU cores of the user device is less than 4 or the memory is less than 2GB, the system automatically activates the resource degradation strategy to reduce the loading of high-load resources;

[0016] S13: Re-evaluate the performance of the device at fixed intervals and dynamically adjust the resource loading strategy according to the real-time device performance.

[0017] Furthermore, the evaluation process in S13 includes:

[0018] Obtain the device running state influence coefficient r at the i-th time point through the formula ; i ;

[0019] where i is the time point of any data collection at fixed time intervals, cu i is the CPU utilization rate at the i-th time point, cu y is the preset CPU utilization rate, nc i is the memory occupancy at the i-th time point, nc y is the preset memory occupancy, xk i is the graphics card occupancy at the i-th time point, xk y is the preset graphics card occupancy, ρ is the error correction coefficient, set by empirical fitting, dx i is the device read / write speed at the i-th time point, dx y is the preset device read / write speed, dx b is the standard value of dx i , f c (x) is a defined function. If f c (x) ≥ 0, then let f c (x) = x, otherwise, let f c (x) = 0, and x1 and x2 are weight coefficients.

[0020] Furthermore, the evaluation process in S13 also includes:

[0021] By comparing the device operation status influence coefficient r at the i-th time point i with the preset operation status influence coefficient threshold r 01 for comparison;

[0022] If r i ≥r 01 , it is determined that the device's operation status at this time point is poor, the device's performance has declined, and the resource degradation strategy is started;

[0023] If r i <r 01 , it is determined that the device's operation status at this time point is good, the device's performance has not significantly declined, and the full loading of resources can be gradually restored.

[0024] Furthermore, the degradation strategy in S12 includes: picture quality degradation, disabling animations, reducing video preloading, and loading lightweight resources.

[0025] Furthermore, the working process of the network status monitoring module in S2 includes:

[0026] S21: Use navigator.connection.effectiveType to detect the current user's network type, determine whether the user is in a low-bandwidth network or a high-speed network. If it is a high-speed network, no processing is required. If it is a low-bandwidth network, proceed to step S22:

[0027] S22: When it is determined that the user's network type is a low-bandwidth network, prioritize loading key texts and low-resolution pictures, and delay or skip the loading of large files such as videos and audios;

[0028] S23: In a low-bandwidth environment, disable or reduce large scripts and animation effects on the page to reduce bandwidth and page rendering pressure;

[0029] S24: Detect network latency through Ping tests, including sending a small picture request to the server and judging the network latency level based on the response time. If the latency is low, no processing is required. If the latency is high, proceed to step S25;

[0030] S25: When it is determined that the network latency is high, cancel the preloading of picture and video resources, and only load these resources when the user actually needs them, and replace high-resolution content with low-resolution pictures and low-quality videos to reduce bandwidth pressure.

[0031] Furthermore, the working process of the user behavior monitoring module in S3 includes:

[0032] S31: Detect the user's scrolling behavior through the scroll event or IntersectionObserver;

[0033] S32: By listening to the user's click behavior, the system records the area and frequency of the user's clicks, and predicts the user's possible next actions based on this behavior data;

[0034] S33: By listening to the mouse hover event, when the user hovers the mouse over a link or button, preload the target page in advance;

[0035] S34: Analyze the user's scrolling speed, dwell time, and click frequency through the behavior prediction model, intelligently predict the user's next interaction behavior, and preload resources in advance based on these predictions.

[0036] Furthermore, the working process of the dynamic resource optimization module in S4 includes:

[0037] S41: Through comprehensive feedback analysis by combining device performance, network conditions, and user behavior data, dynamically adjust the resource loading order in real time. When the device performance is poor or the network conditions are poor, prioritize the loading of key resources and postpone the loading of secondary resources;

[0038] S42: Dynamically adjust the preloading order through intelligent resource loading adjustment methods based on the prediction data of the user behavior module to ensure that the resources that the user may need are loaded first;

[0039] S43: Through the resource recovery mechanism, gradually resume the full resource loading after detecting an improvement in device performance or a recovery in network conditions.

[0040] Advantages of the present invention:

[0041] (1) By monitoring the performance of the device and the network conditions, the present invention can dynamically adjust the resource loading, ensuring that users under different performance devices and networks can enjoy a smooth page experience. For users with low-performance devices and poor network conditions, optimize the resource loading order and perform resource degradation to ensure that users obtain a smooth usage experience. By analyzing the user's operation behavior, predict the user's next action, and preload relevant resources in advance. Through such settings, personalized optimization solutions can be provided for different users, improving the loading speed and interaction experience of the web page, and thus enhancing the user experience.

[0042] (2) The present invention formulates different resource loading strategies according to the device performance of different users, and when it is determined that the device performance of the user is poor, by starting a resource degradation strategy, the loading of high-load resources is reduced, so as to ensure the smooth operation of low-performance devices. And by re-evaluating the performance of the device at fixed intervals, real-time monitoring can ensure that resource consumption is reduced when the device performance drops, and when the performance recovers, the complete loading of resources can be gradually restored, so as to realize the dynamic adjustment of resource loading, and further ensure that users under devices with different performances can enjoy a smooth page experience.

[0043] (3) The present invention compares the device operation state influence coefficient r at the i-th time point i with the preset operation state influence coefficient threshold r 01 Through this comparison method, the operation state of the device at different time points can be analyzed, and the performance of the device can be further analyzed according to the analysis results. By setting like this, the dynamic monitoring of the device performance can be realized, and the device can be controlled to reduce the loading of high-load resources when the performance drops, and gradually restore the complete loading of resources when the performance recovers, realizing the dynamic adjustment of resource loading, and ensuring that users can enjoy a smooth page experience under different performances.

[0044] (4) The present invention detects the network type of the current user through the network status monitoring module, can judge whether the user is in a low-bandwidth network or a high-speed network, and when it is judged that the network type of the user is a low-bandwidth network, reduces the loading of resources, large scripts and animation effects to reduce bandwidth pressure. Then, by detecting the network latency, when it is judged that the user's network latency is high, the preloading of picture and video resources can be cancelled, so as to reduce bandwidth pressure and ensure that the user can enjoy a smooth page experience and improve the user's experience.

[0045] (5) After the present invention conducts comprehensive feedback analysis by combining device performance, network status and user behavior data, it can optimize the resource loading order and perform resource degradation for low-performance devices and users with poor network conditions, ensure that users obtain a smooth usage experience, and through the problem of dynamically adjusting resource loading, it can ensure that users under different performance devices and networks can enjoy a smooth page experience. Finally, by analyzing the operation behavior of the user, the next operation of the user can be predicted, relevant resources can be preloaded in advance, and the page waiting time can be reduced, thereby improving the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 is a flowchart of a method for improving WEB access speed based on user behavior and network status in the present invention;

[0048] Figure 2 It is a flowchart of the working process of the device performance monitoring module in the present invention;

[0049] Figure 3 It is a schematic diagram of the working overview of the device performance monitoring module in the present invention;

[0050] Figure 4 It is a flowchart of the working process of the network condition monitoring module in the present invention;

[0051] Figure 5 It is a schematic diagram of the working overview of the network condition monitoring module in the present invention;

[0052] Figure 6 It is a flowchart of the working process of the user behavior monitoring module in the present invention;

[0053] Figure 7 It is a schematic diagram of the working overview of the user behavior monitoring module in the present invention;

[0054] Figure 8 It is a flowchart of the working process of the dynamic resource optimization module in the present invention;

[0055] Figure 9 It is a schematic diagram of the working overview of the dynamic resource optimization module in the present invention. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0057] Please refer to Figure 1 As shown, in one embodiment, the present application provides a method for improving the WEB access speed based on user behavior and network status. The method includes:

[0058] S1: Detect the performance of the user device through the device performance monitoring module, including the number of CPU cores and the memory size, and decide whether to execute the resource degradation strategy;

[0059] S2: Detect the current network bandwidth and network latency of the user through the network condition monitoring module, optimize the resource loading method, and ensure that key resources are preferentially loaded in the case of low bandwidth or high latency;

[0060] S3: Monitor the user's behavior, such as scrolling, clicking, and hovering, through the user behavior monitoring module, predict the user's next operation, and pre-load relevant resources;

[0061] S4: The dynamic resource optimization module synthesizes the data of device performance monitoring, network condition monitoring, and user behavior monitoring, and intelligently optimizes the resource loading order.

[0062] Through the above technical solutions, this embodiment provides a method for improving the WEB access speed based on user behavior and network status. First, the device performance monitoring module detects the performance of the user device, including the number of CPU cores and the memory size, and decides whether to execute the resource degradation strategy. Then, the network condition monitoring module detects the current network bandwidth and network latency of the user, optimizes the resource loading method, ensures that key resources are preferentially loaded in the case of low bandwidth or high latency, and the user behavior monitoring module monitors the user's behaviors such as scrolling, clicking, and hovering, predicts the user's next operation, and pre-loads relevant resources. Finally, the dynamic resource optimization module synthesizes the data of device performance monitoring, network condition monitoring, and user behavior monitoring, and intelligently optimizes the resource loading order.

[0063] By setting like this, by monitoring the performance of the device and the network condition, it is possible to dynamically adjust the resource loading, ensure that users under different performance devices and networks can enjoy a smooth page experience, and for users with low-performance devices and poor network conditions, optimize the resource loading order and perform resource degradation to ensure that users obtain a smooth usage experience. And by analyzing the user's operation behaviors, predicting the user's next operation, and pre-loading relevant resources, by setting like this, it is possible to provide personalized optimization solutions for different users, improve the loading speed and interaction experience of the web page, and thus improve the user experience.

[0064] Please refer to Figure 2 and Figure 3 As shown, the working process of the device performance monitoring module in S1 includes:

[0065] S11: First, obtain the number of cores of the device CPU through navigator.hardwareConcurrency, and detect the memory size of the device through navigator.deviceMemory;

[0066] S12: When it is detected that the number of CPU cores of the user device is less than 4 or the memory is less than 2GB, the system automatically starts the resource degradation strategy to reduce the loading of high-load resources;

[0067] S13: Re-evaluate the performance of the device at fixed intervals, and dynamically adjust the resource loading strategy according to the real-time device performance;

[0068] Through the above technical solution, this embodiment provides the working process of the device performance monitoring module. First, the number of cores of the device CPU is obtained through navigator.hardwareConcurrency, and the memory size of the device is detected through navigator.deviceMemory. When it is detected that the number of CPU cores of the user device is less than 4 or the memory is less than 2GB, the system automatically activates the resource degradation strategy to reduce the loading of high-load resources. Then, the performance of the device is re-evaluated at fixed intervals, and the resource loading strategy is dynamically adjusted according to the real-time device performance.

[0069] By setting like this, different resource loading strategies can be formulated according to the device performance of different users. When it is judged that the device performance of the user is poor, the resource degradation strategy is activated to reduce the loading of high-load resources, so as to ensure that low-performance devices can run smoothly. And by re-evaluating the performance of the device at fixed intervals, real-time monitoring can ensure that resource consumption decreases when the device performance drops, and the full loading of resources can be gradually restored when the performance recovers, so as to realize the dynamic adjustment of resource loading, and further ensure that users under different performance devices can enjoy a smooth page experience.

[0070] The evaluation process in S13 includes:

[0071] Through the formula Calculate the device running state influence coefficient r at the i-th time point i ;

[0072] where i is the time point of any data collection at a fixed time interval, cu i is the CPU utilization rate at the i-th time point, cu y is the preset CPU utilization rate, nc i is the memory occupancy at the i-th time point, nc y is the preset memory occupancy, xk i is the graphics card occupancy at the i-th time point, xk y is the preset graphics card occupancy, ρ is the error correction coefficient, set by empirical fitting, dx i is the device read / write speed at the i-th time point, dx y is the preset device read / write speed, dx b is the standard value of dx i The above standard value can be selected and set according to the allowable error in empirical data, f c (x) is a defined function. If f c (x)≥0, then let f c (x) = x, otherwise, let f c(x) = 0, where x1 and x2 are weighting coefficients set by empirical fitting;

[0073] Through the above technical solution, this embodiment provides the device operation state influence coefficient r at the i-th time point i , which can be obtained by the formula Obviously, when the CPU utilization rate, memory occupancy, and graphics card occupancy at the i-th time point are higher, and the read / write speed of the device is slower, then the device operation state influence coefficient r at the i-th time point i is larger, indicating that the performance of the device has declined at the current time point. On the contrary, when the CPU utilization rate, memory occupancy, and graphics card occupancy at the i-th time point are lower, and the read / write speed of the device is faster, then the device operation state influence coefficient r at the i-th time point i is smaller, indicating that the performance of the device is good and has not declined at the current time point. Through this calculation method, the calculation result can reflect the performance change of the device from the side. By calculating the device operation state influence coefficient r i at fixed time intervals, the performance change of the device at different time points can be understood, thus providing strong data support for subsequent real-time dynamic adjustment of resource loading.

[0074] The evaluation process in S13 further includes:

[0075] By comparing the device operation state influence coefficient r i at the i-th time point with the preset operation state influence coefficient threshold r 01 ;

[0076] If r i ≥ r 01 , it is determined that the operation state of the device at this time point is poor, the performance of the device has declined, and the resource degradation strategy is started;

[0077] If r i < r 01 , it is determined that the operation state of the device at this time point is good, the performance of the device has not significantly declined, and the full loading of resources can be gradually restored;

[0078] Through the above technical solution, in this embodiment, by comparing the device operation state influence coefficient r i at the i-th time point with the preset operation state influence coefficient threshold r 01Compare. Through this comparison method, the operating states of the device at different time points can be analyzed, and based on the analysis results, the performance of the device can be further analyzed. By setting it like this, dynamic monitoring of the device performance can be achieved, and when the performance drops, the loading of high-load resources can be reduced, and when the performance recovers, the full loading of resources can be gradually restored, realizing dynamic adjustment of resource loading to ensure that users can enjoy a smooth page experience under different performance levels.

[0079] The degradation strategy in S12 includes: picture quality degradation, disabling animations, reducing video preloading, and loading lightweight resources.

[0080] Through the above technical solution, this embodiment provides the main ways of the degradation strategy, including picture quality degradation, disabling animations, reducing video preloading, and loading lightweight resources. Among them, picture quality degradation means loading pictures with low resolution to reduce the resource consumption of high-resolution pictures. Disabling animations means disabling the dynamic effects on the page, such as CSS animations and JavaScript animations, to reduce CPU and memory usage. Reducing video preloading means not preloading the entire video file, but only loading the necessary metadata to avoid excessive bandwidth consumption. Loading lightweight resources means preferentially loading text content and reducing graphical presentation or complex layout effects.

[0081] By setting it like this, the consumption of resources, as well as the use of bandwidth, CPU, and memory, can be greatly reduced, thereby ensuring the smooth operation of low-performance devices.

[0082] Please refer to Figure 4 and Figure 5 As shown, the working process of the network status monitoring module in S2 includes:

[0083] S21: Use navigator.connection.effectiveType to detect the current user's network type, and judge whether the user is in a low-bandwidth network or a high-speed network. If it is a high-speed network, no processing is required. If it is a low-bandwidth network, proceed to step S22:

[0084] S22: When it is judged that the user's network type is a low-bandwidth network, preferentially load key texts and low-resolution pictures, and delay or skip the loading of large files such as videos and audios.

[0085] S23: Under low bandwidth, disable or reduce large scripts and animation effects on the page to reduce the pressure on bandwidth and page rendering.

[0086] S24: Detect network latency through Ping tests, including sending a small picture request to the server and judging the network latency level according to the response time. If the latency is low, no processing is required. If the latency is high, proceed to step S25;

[0087] S25: When it is determined that the network latency is high, cancel the preloading of image and video resources, and load these resources only when the user actually needs them. Replace high-resolution content with low-resolution images and low-quality videos to reduce bandwidth pressure;

[0088] Through the above technical solutions, this embodiment provides the working process of the network status monitoring module. First, use navigator.connection.effectiveType to detect the current user's network type and determine whether the user is in a low-bandwidth network or a high-speed network. When it is determined that the user's network type is a low-bandwidth network, preferentially load key texts and low-resolution images, delay or skip the loading of large files such as videos and audios, and disable or reduce large scripts and animation effects on the page under low bandwidth to reduce the pressure on bandwidth and page rendering. Then, detect the network latency through Ping tests, including sending a small image request to the server and judging the network latency level according to the response time. When it is determined that the network latency is high, cancel the preloading of image and video resources, and load these resources only when the user actually needs them. Replace high-resolution content with low-resolution images and low-quality videos to reduce bandwidth pressure;

[0089] By setting like this, the network status monitoring module can detect the current user's network type, make a judgment on whether the user is in a low-bandwidth network or a high-speed network, and when it is determined that the user's network type is a low-bandwidth network, reduce the loading of resources and large scripts and animation effects to reduce bandwidth pressure. Then, by detecting the network latency, when it is determined that the user's network latency is high, cancel the preloading of image and video resources, thereby reducing bandwidth pressure and ensuring that the user can enjoy a smooth page experience and improving the user's experience.

[0090] Please refer to Figure 6 and Figure 7 As shown, the working process of the user behavior monitoring module in S3 includes:

[0091] S31: Detect the user's scrolling behavior through the scroll event or IntersectionObserver;

[0092] S32: By listening to the user's click behavior, the system records the area and frequency of the user's clicks, and predicts the user's possible next operation based on these behavior data;

[0093] S33: By listening to the mouse hover event, when the user hovers the mouse over a link or button, preload the target page;

[0094] S34: Analyze the user's scrolling speed, dwell time, and click frequency through the behavior prediction model, intelligently predict the user's next interaction behavior, and preload resources in advance based on these predictions;

[0095] Through the above technical solution, this embodiment provides the working process of the user behavior monitoring module. First, detect the user's scrolling behavior through the scroll event or IntersectionObserver, and by listening to the user's click behavior, the system records the area and frequency of the user's clicks. Based on these behavior data, predict the user's possible next operation. Then, by listening to the mouse hover event, when the user hovers the mouse over a link or button, preload the target page in advance, and finally analyze the user's scrolling speed, dwell time, and click frequency through the behavior prediction model, intelligently predict the user's next interaction behavior, and preload resources in advance based on these predictions;

[0096] By setting it like this, when listening to the user's scrolling behavior, when the user approaches the bottom of the page or a specific area, it is speculated that the user may continue to scroll, so as to preload the next content in advance. By listening to the user's click behavior, the possible next operation of the user can be predicted based on these behavior data, and by listening to the mouse hover event, when the user hovers the mouse over a link or button, preload the target page in advance. Finally, based on the behavior prediction model, the user's next operation can be predicted by analyzing the user's operation behavior, preload relevant resources in advance, reduce the page waiting time, and ensure that the user obtains a smooth usage experience.

[0097] Please refer to Figure 8 and Figure 9 As shown, the working process of the dynamic resource optimization module in S4 includes:

[0098] S41: Through comprehensive feedback analysis by combining device performance, network conditions, and user behavior data, dynamically adjust the resource loading order in real time. When the device performance is poor or the network conditions are poor, give priority to loading critical resources and postpone the loading of secondary resources;

[0099] S42: Dynamically adjust the preloading order through an intelligent resource loading adjustment method based on the prediction data of the user behavior module to ensure that the resources that the user may need are loaded first;

[0100] S43: Through the resource recovery mechanism, gradually resume the full resource loading after detecting an improvement in device performance or a recovery in network conditions;

[0101] Through the above technical solution, this embodiment provides the working process of the dynamic resource optimization module. First, through comprehensive feedback analysis by combining device performance, network conditions, and user behavior data, the resource loading order is adjusted dynamically in real time. When the device performance is poor or the network conditions are poor, key resources are loaded first, and the loading of secondary resources is postponed. Then, through an intelligent resource loading adjustment method, based on the prediction data of the user behavior module, the preloading order is adjusted dynamically to ensure that the resources that the user may need are loaded first. And through the resource recovery mechanism, after detecting an improvement in device performance or a recovery of network conditions, the complete resource loading is gradually restored;

[0102] By setting it like this, after comprehensive feedback analysis by combining device performance, network conditions, and user behavior data, the resource loading order can be optimized and resource degradation can be performed for users with low-performance devices and poor network conditions, ensuring a smooth user experience. And through the problem of dynamically adjusting resource loading, it can be ensured that users under different performance devices and networks can enjoy a smooth page experience. Finally, by analyzing the user's operation behavior, the user's next operation can be predicted, relevant resources can be loaded in advance, and the page waiting time can be reduced, thereby improving the user's experience.

[0103] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as used to limit the scope of implementation of the present invention. All equivalent changes and improvements made in accordance with the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for improving WEB access speed based on user behavior and network status, characterized in that: The method comprises: S1: Detect the performance of the user's device through the device performance monitoring module, including the number of CPU cores and memory size, and decide whether to implement the resource degradation strategy; S2: Detect the user's current network bandwidth and network latency through the network status monitoring module, optimize the resource loading method, and ensure that key resources are loaded first in the case of low bandwidth or high latency; S3: Monitor user behaviors such as scrolling, clicking, and hovering through the user behavior monitoring module, predict the user's next action, and load related resources in advance; S4: The dynamic resource optimization module integrates device performance monitoring, network status monitoring, and user behavior monitoring data to intelligently optimize the resource loading order.

2. A method for improving WEB access speed based on user behavior and network status according to claim 1, characterized in that: The working process of the equipment performance monitoring module in S1 includes: S11: First, obtain the number of CPU cores of the device through navigator.hardwareConcurrency, and detect the memory size of the device through navigator.deviceMemory; S12: When it is detected that the number of CPU cores of the user device is less than 4, or the memory is less than 2GB, the system automatically starts the resource degradation strategy to reduce the loading of high-load resources; S13: Re-evaluate the performance of the device at regular intervals and dynamically adjust the resource loading strategy based on the real-time device performance.

3. A method for improving WEB access speed based on user behavior and network status according to claim 2, characterized in that: The evaluation process in S13 includes: By formula Calculate the equipment operation status influence coefficient r at the i-th time point i ; Where i is the time point of any data collection at a fixed time interval, cu i is the CPU utilization at the i-th time point, cu y The preset utilization of the CPU, nc i is the memory usage at the i-th time point, nc y is the preset memory usage, xk i is the graphics card usage at the i-th time point, xk y is the preset graphics card usage, ρ is the error correction coefficient, which is set based on empirical fitting, and dx i is the device read and write speed at the i-th time point, dx y The preset read and write speed for the device, dx b dx i The standard value, f c (x) is a defined function, if f c (x)≥0, then let f c (x) = x, otherwise, let f c (x)=0, x1 and x2 are weight coefficients.

4. A method for improving WEB access speed based on user behavior and network status according to claim 3, characterized in that: The evaluation process in S13 further includes: By calculating the equipment operation status influence coefficient r at the i-th time point i The preset operating status influence coefficient threshold r 01 Make a comparison; If r i ≥r 01 , it is determined that the running status of the device at this time point is poor, the performance of the device has declined, and the resource degradation strategy is initiated; If r i <r 01 , judging that the device is in good operating condition at that point in time, the performance of the device has not significantly declined, and the complete loading of resources can be gradually restored.

5. A method for improving WEB access speed based on user behavior and network status according to claim 2, characterized in that: The degradation strategy in S12 includes: image quality degradation, animation disabling, video preloading reduction, and lightweight resource loading.

6. A method for improving WEB access speed based on user behavior and network status according to claim 1, characterized in that: The working process of the network status monitoring module in S2 includes: S21: Use navigator.connection.effectiveType to detect the network type of the current user, and determine whether the user is in a low-bandwidth network or a high-speed network. If it is a high-speed network, no processing is performed; if it is a low-bandwidth network, proceed to step S22: S22: When it is determined that the user's network type is a low-bandwidth network, key texts and low-resolution images are loaded first, and the loading of large files such as video and audio is delayed or skipped; S23: Under low bandwidth, disable or reduce large scripts and animation effects on the page to reduce bandwidth and page rendering pressure; S24: Detecting network delay through Ping test, including sending a small picture request to the server, and judging the network delay according to the response time. If it is low delay, no processing is performed; if it is high delay, proceeding to step S25; S25: When it is determined that the network delay is high, cancel the pre-loading of image and video resources, load these resources only when the user actually needs them, and replace high-resolution content with low-resolution images and low-quality videos to reduce bandwidth pressure.

7. A method for improving WEB access speed based on user behavior and network status according to claim 1, characterized in that: The working process of the user behavior monitoring module in S3 includes: S31: Detect user scrolling behavior through scroll events or IntersectionObserver; S32: By monitoring the user's click behavior, the system records the area and frequency of the user's clicks, and predicts the user's possible next operation based on these behavior data; S33: By listening to the mouse hover event, when the user hovers the mouse over a link or button, the target page is loaded in advance; S34: Analyze the user's scrolling speed, dwell time, and click frequency through the behavior prediction model, intelligently predict the user's next interactive behavior, and load resources in advance based on these predictions.

8. A method for improving WEB access speed based on user behavior and network status according to claim 1, characterized in that: The working process of the dynamic resource optimization module in S4 includes: S41: By combining device performance, network conditions and user behavior data for comprehensive feedback analysis, the loading order of resources is dynamically adjusted in real time. When the device performance or network conditions are poor, key resources are loaded first and the loading of secondary resources is postponed; S42: dynamically adjusting the preloading order based on the prediction data of the user behavior module through an intelligent resource loading adjustment method to ensure that resources that the user may need are loaded first; S43: After detecting that the device performance has improved or the network condition has recovered, the complete resource loading is gradually restored through the resource recovery mechanism.

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