Website performance monitoring method and system based on cloud

Abstract

The invention discloses a cloud-based website performance monitoring method and system, and relates to the technical field of website performance monitoring, and the method comprises the steps: packaging a monitoring node into a lightweight container, and selecting an optimal operation node for the lightweight container in a mixed cloud node pool; the method comprises the following steps: sending a request to a website through a monitoring node self-adaptive simulation client, and capturing multi-dimensional response data of a target website; quantifying each running state of the target website in real time based on the captured multi-dimensional response data; calculating a performance score according to the quantitative result of each running state of the target website; and visually displaying the performance score of each website on the time sequence, and sending out an alarm notification when the performance score is lower than a threshold value. Flexible deployment of the monitoring nodes can be realized by increasing or decreasing the cloud nodes; the method can adapt to the increasingly updated Web technology; and multi-dimensional response data monitoring is supported, and more accurate health detection information can be generated.

Description

Technical Field

[0001] The present invention relates to the technical field of website performance monitoring, and in particular to a cloud-based website performance monitoring method and system. Background Art

[0002] With the rapid development of Internet technology, website system architecture has become increasingly complex, showing features such as distributed services, multi-CDN acceleration, and dynamic content loading. In this context, website performance monitoring technology has become a key link in ensuring user experience and maintaining service stability. However, existing technical solutions mostly adopt a localized deployment model, relying on monitoring nodes in fixed locations (such as internal enterprise servers), resulting in the monitoring scope being limited to the geographical distribution of preset nodes; current methods also focus on the collection of basic performance indicators (such as response time, HTTP status code), and lack the ability to adapt to new Web technologies. In response to the above technical shortcomings, there is an urgent need to develop a new cloud-based monitoring method to achieve dynamic and scalable monitoring node deployment, adapt to the increasingly updated Web technologies, and thus build a more accurate and efficient website performance monitoring system. Summary of the Invention

[0003] The present invention provides a cloud-based website performance monitoring method, comprising:

[0004] Step 1: Encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool;

[0005] Step 2: Use the monitoring node to adaptively simulate the client sending a request to the website and capture the multi-dimensional response data of the target website;

[0006] Step 3: Quantify the various operating states of the target website in real time based on the captured multi-dimensional response data;

[0007] Step 4. Calculate the performance score based on the quantitative results of each operating status of the target website;

[0008] Step 5: Visualize the performance scores of each website in time series and issue an alarm notification when the performance score falls below the threshold.

[0009] The cloud-based website performance monitoring method described above encapsulates the monitoring node as a lightweight container and selects the optimal running node for it in the hybrid cloud node pool. The specific sub-steps are as follows:

[0010] Build a basic image based on the latest stable version of Chromium, and package the monitoring scripts and configuration files of the target website into the image to generate a packaged lightweight container;

[0011] By deploying multi-cloud vendor nodes and edge computing nodes, a hybrid cloud node pool is formed;

[0012] Dynamically select the optimal running node for the packaged lightweight container based on the user's geographical distribution, the target website's CDN configuration, and historical monitoring data.

[0013] The cloud-based website performance monitoring method described above, wherein a monitoring node adaptively simulates a client sending a request to a website to capture multi-dimensional response data of the target website, is specifically divided into the following sub-steps:

[0014] Build a browser environment based on the web technology stack used by the target website;

[0015] Dynamically adjust request parameters and use the built browser environment to send requests to the target website;

[0016] Extract and record the multi-dimensional response data returned by the target website into a separate data table.

[0017] The cloud-based website performance monitoring method described above, wherein various operating states of the target website are quantified in real time based on the captured multi-dimensional response data, is specifically divided into the following sub-steps:

[0018] Standardize the captured multi-dimensional response data;

[0019] The pre-model is used to extract the latent variables of the multidimensional response data, and the multidimensional response data is reconstructed according to the latent variables;

[0020] The reconstruction error, latent space characteristics and dynamic adjustment factors of multi-dimensional response data are integrated to quantify the various operating states of the target website.

[0021] The present invention also provides a cloud-based website performance monitoring system, comprising: a monitoring node deployment module, a request simulation module, an operation status quantification module, a performance score generation module, and a performance score visualization module;

[0022] The monitoring node deployment module is used to encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool;

[0023] The request simulation module is used to send requests to the website through the monitoring node adaptively simulating the client and capture the multi-dimensional response data of the target website;

[0024] The operation status quantification module is used to quantify the operation status of the target website in real time based on the captured multi-dimensional response data;

[0025] A performance score generation module is used to calculate the performance score based on the quantitative results of various operating states of the target website;

[0026] The performance score visualization module is used to visualize the performance score of each website in time series and issue an alarm notification when the performance score is lower than the threshold.

[0027] The beneficial effects achieved by the present invention are as follows: flexible deployment of monitoring nodes can be achieved by adding or reducing cloud nodes; it can adapt to increasingly updated Web technologies; it supports multi-dimensional response data monitoring and can generate more accurate health detection information. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 This is a flow chart of a cloud-based website performance monitoring method provided in Example 1 of the present application;

[0030] Figure 2 This is a schematic diagram of a cloud-based website performance monitoring system provided in Example 2 of the present application. DETAILED DESCRIPTION

[0031] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, the first embodiment of the present application provides a cloud-based website performance monitoring method, including:

[0034] Step S10: Encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool;

[0035] Encapsulating monitoring nodes as lightweight containers can improve resource utilization. Deploying them to cloud nodes enables dynamic expansion and flexible deployment of monitoring nodes. Specifically:

[0036] Step S11: Build a basic image based on the latest stable version of Chromium, and package the monitoring script and configuration file of the target website into the image to generate a packaged lightweight container;

[0037] The monitoring script includes script files for initializing the monitoring node, such as importing dependent libraries and loading configurations. The configuration file includes monitoring target configuration, multi-region configuration, and compliance configuration. It is recommended to automatically build images through the CI / CD pipeline to speed up the system initialization time.

[0038] Step S12: Form a hybrid cloud node pool by deploying multiple cloud vendor nodes and edge computing nodes;

[0039] This hybrid cloud node pool allows monitoring nodes to be deployed close to users, reducing network latency while leveraging the redundancy of multi-cloud resources to improve system availability.

[0040] Step S13: Dynamically select the optimal running node for the packaged lightweight container based on the user's geographical distribution, the target website's CDN configuration, and historical monitoring data;

[0041] The applicability index of each cloud node for the current container is calculated every 5 seconds, and the cloud node with the highest applicability index is dynamically selected to run the current container. If the load of the node with the highest index exceeds 80%, the next highest cloud node is selected to calculate the cloud node N. k The formula for the applicable index Z of the current container is expressed as:

[0042]

[0043] Among them, ω1, ω2, ω3 are adjustable weights, Latency(u,N k ) indicates the speed from user u to cloud node N obtained through IP speed test k Round trip time, CDNTier(N k ) represents cloud node N k The role assignment in the target website CDN (such as edge node = 1, regional relay = 2, source station = 3), max(CDNTier) represents the maximum role assignment of the cloud node, α and β are adjustable resolution coefficients, AvgRTT(N k ) represents cloud node N k The average response time of requesting the target website in the historical window (such as the past 1 hour), ER (N k ) represents cloud node N k The probability of the target website returning a server error in the historical window, RTT max , ER max are the maximum response time and maximum server error rate among all cloud nodes, respectively.

[0044] Step S20: The monitoring node adaptively simulates the client sending a request to the website to capture the multi-dimensional response data of the target website;

[0045] Adaptive simulation of a client sending a request to a website means that the monitoring node can dynamically build a browser environment based on the technology stack used by the website to simulate the client sending a request to the website, in order to adapt to the ever-changing web technologies. The specific steps are as follows:

[0046] Step S21: Building a browser environment based on the web technology stack used by the target website;

[0047] Separate the browser core and plug-in components, automatically build the pipeline daily to synchronize Chromium upstream updates, ensure that the kernel version lags no more than 72 hours, and load the appropriate plug-in components on demand based on the Web technology stack used by the target website. For example, by parsing the HTML of the web page and detecting the feature tags of the front-end framework; extracting the JS / CSS file URL, detecting the file name hash or path pattern and other methods to identify the Web technology stack, and then querying the plug-in components required for the corresponding technology stack from the library and loading them into the current browser environment.

[0048] Step S22: dynamically adjust the request parameters and use the constructed browser environment to send a request to the target website;

[0049] Request parameters that need to be dynamically adjusted include: user region, device fingerprint, and request frequency. Switch the exit IP through the proxy pool, or use the GeoIP database to match the target website CDN node to simulate different user regions. By modifying the User-Agent header, you can simulate different device fingerprints such as PCs, mobile terminals, and crawlers. Dynamically adjust the request frequency based on the token bucket algorithm to avoid triggering the website's anti-crawling mechanism. After the configuration is complete, you can use the constructed browser environment to send requests to the target website.

[0050] Step S23: extract and record the multi-dimensional response data returned by the target website into a separate data table;

[0051] Multi-dimensional response data includes data from three dimensions: basic indicators, front-end indicators, and API monitoring indicators. Basic indicators include basic performance indicators such as network latency (RTT), TCP connection time, TLS handshake time, and server response time (TTFB); front-end indicators refer to key front-end performance indicators captured by the monitoring node during page loading by executing JavaScript code, including first contentful paint (FCP), maximum contentful paint (LCP), cumulative layout shift (CLS), etc.; API monitoring indicators refer to the API interface provided by the target website. The monitoring node sends simulated requests and records the response time, success rate, and correctness of the returned data of the interface.

[0052] Step S30: quantifying various operating states of the target website in real time based on the captured multi-dimensional response data, which is specifically divided into the following sub-steps:

[0053] Step S31: standardizing the captured multi-dimensional response data;

[0054] If the response data has a high dimension (e.g., more than 10 dimensions), it can also be reduced by PCA or t-SNE to retain 95% of the variance.

[0055] Step S32: extracting latent variables of the multidimensional response data using the pre-model, and reconstructing the multidimensional response data according to the latent variables;

[0056] The standardized multi-dimensional response data are input into the front model to obtain the reconstructed data set. The front model contains three parts: encoder, sampler and decoder. The encoder is used to map the input data X to the latent space, which contains two neural networks f μ and f σ (both fully connected layers + ReLU activation), which are used to return the latent variable mean μ and logarithmic variance logσ generated after X is mapped to the latent space 2 ; The sampler is used to use the formula: Complete the sampling of the latent variable, where b is the returned latent variable, ∈ is the standard normal distribution noise, is the element-wise product; the decoder is used to reconstruct the latent variable b returned by the sampler into the output The decoder neural network is a fully connected layer + Sigmoid activation.

[0057] Step S33: quantifying various operating states of the target website by integrating the reconstruction error, latent space characteristics and dynamic adjustment factors of the multi-dimensional response data;

[0058] The quantitative formula for the i-th operating status of the target website at time t is expressed as:

[0059]

[0060] Among them H it is the quantitative result of the i-th operating status of the target website at time t, is an adjustable weight, is the reconstruction error term, is the latent space feature term, (1+φ·e -λt )and is the dynamic adjustment factor term, X it is the value of the response data of dimension i at time t, For X it The reconstruction value, b it is the potential variable sampled by the pre-model at time t for the i-th dimension response data, μ b 、 They represent the mean and covariance matrix of all potential variables generated by the i-th dimension response data in the latent space, φ is the decay factor, λ is the decay rate, γ is the sensitivity coefficient of special events, It is an indicator of whether there is a special event (such as a promotion on an e-commerce website) at time t. If yes, the value is 1, otherwise it is 0.

[0061] Step S40: Calculating a performance score based on the quantitative results of various operating states of the target website;

[0062] Performance score S t It is used to reflect the overall health status of the website at time t. The calculation formula is:

[0063] in Indicates the target website's emphasis on the i-th item's operating status, H it It represents the quantitative result of the i-th running status of the target website at time t, H i0 represents the quantitative result of the target website's i-th running state under the standard environment, τ i is the maximum deviation that the target website can tolerate for the i-th operating status, i ranges from 1 to n, and n is the total number of operating status items.

[0064] Step S50: Visually display the performance score of each website in time series, and issue an alarm notification when the performance score is lower than the threshold;

[0065] After obtaining the performance scores of each website in time series, they need to be displayed in a visual way so that operation and maintenance personnel can intuitively obtain the monitoring results. When the performance score falls below the threshold, an alarm notification needs to be issued to the corresponding operation and maintenance personnel in a timely manner. Specifically:

[0066] Step S51: Generate a single website visual monitoring table with time as the horizontal axis and the performance score of the target website as the vertical axis;

[0067] Use Python libraries such as Matplotlib and Plotly for real-time plotting. Select appropriate chart types, such as line charts and area charts, as needed to show how performance scores change over time. Then, through scheduled tasks or event-driven mechanisms, regularly obtain the latest score data to update the visualization monitoring table to ensure real-time visualization.

[0068] Step S52: Synchronize monitoring timestamps of multiple websites and generate a multi-website visual monitoring table;

[0069] If multiple websites are selected when generating a visual monitoring table, you first need to synchronize the monitoring timestamps of each website and then plot them into the same table.

[0070] Step S53: Add a threshold line to the visual monitoring table. When it is detected that the performance score of a website is lower than the threshold, an alarm mechanism is triggered;

[0071] Add a threshold line to visually see whether the performance score is lower than the preset threshold. The threshold is reasonably set according to business needs and historical performance score data. Every time the chart is updated, the performance score data is also detected. When the performance score of a website is found to be lower than the threshold, the alarm mechanism is triggered. The alarm notification is sent via email, SMS, pop-up window, etc., which should include key information such as website name, timestamp, performance score, etc., so that the problem can be quickly located.

[0072] Example 2

[0073] like Figure 2 As shown, the second embodiment of the present application provides a cloud-based website performance monitoring system, including: a monitoring node deployment module 21, a request simulation module 22, an operation status quantification module 23, a performance score generation module 24, and a performance score visualization module 25;

[0074] The monitoring node deployment module 21 is used to encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool; specifically includes: a container encapsulation submodule, a hybrid cloud node pool deployment submodule, and a running node dynamic selection submodule;

[0075] 1. The container encapsulation submodule is used to build a basic image based on the latest stable version of Chromium, and package the monitoring scripts and configuration files of the target website into the image to generate a packaged lightweight container;

[0076] The monitoring script includes script files for initializing the monitoring node, such as importing dependent libraries and loading configurations. The configuration file includes monitoring target configuration, multi-region configuration, and compliance configuration. It is recommended to automatically build images through the CI / CD pipeline to speed up the system initialization time.

[0077] 2. Hybrid cloud node pool deployment submodule, which is used to form a hybrid cloud node pool by deploying multi-cloud vendor nodes and edge computing nodes;

[0078] This hybrid cloud node pool allows monitoring nodes to be deployed close to users, reducing network latency while leveraging the redundancy of multi-cloud resources to improve system availability.

[0079] 3. The dynamic running node selection submodule is used to dynamically select the optimal running node for the packaged lightweight container based on the user's geographical distribution, the target website's CDN configuration, and historical monitoring data;

[0080] The applicability index of each cloud node for the current container is calculated every 5 seconds, and the cloud node with the highest applicability index is dynamically selected to run the current container. If the load of the node with the highest index exceeds 80%, the next highest cloud node is selected to calculate the cloud node N. k The formula for the applicable index Z of the current container is expressed as:

[0081]

[0082] Among them, ω1, ω2, ω3 are adjustable weights, Latency(u,N k ) indicates the speed from user u to cloud node N obtained through IP speed test k Round trip time, CDNTier(N k ) represents cloud node N k The role assignment in the target website CDN (such as edge node = 1, regional relay = 2, source station = 3), max(CDNTier) represents the maximum role assignment of the cloud node, α and β are adjustable resolution coefficients, AvgRTT(N k ) represents cloud node N k The average response time of requesting the target website in the historical window (such as the past 1 hour), ER (N k ) represents cloud node N k The probability of the target website returning a server error in the historical window, RTT max , ER max are the maximum response time and maximum server error rate among all cloud nodes, respectively.

[0083] The request simulation module 22 is used to simulate the client sending a request to the website through the monitoring node adaptively, and capture the multi-dimensional response data of the target website; specifically, it includes: a browser environment construction submodule, a request parameter adjustment submodule, and a multi-dimensional response data capture submodule;

[0084] 1. Browser environment construction submodule, used to build a browser environment based on the web technology stack used by the target website;

[0085] Separate the browser core and plug-in components, automatically build the pipeline daily to synchronize Chromium upstream updates, ensure that the kernel version lags no more than 72 hours, and load the appropriate plug-in components on demand based on the Web technology stack used by the target website. For example, by parsing the HTML of the web page and detecting the feature tags of the front-end framework; extracting the JS / CSS file URL, detecting the file name hash or path pattern and other methods to identify the Web technology stack, and then querying the plug-in components required for the corresponding technology stack from the library and loading them into the current browser environment.

[0086] 2. Request parameter adjustment submodule, used to dynamically adjust request parameters and use the constructed browser environment to send requests to the target website;

[0087] Request parameters that need to be dynamically adjusted include: user region, device fingerprint, and request frequency. Switch the exit IP through the proxy pool, or use the GeoIP database to match the target website CDN node to simulate different user regions. By modifying the User-Agent header, you can simulate different device fingerprints such as PCs, mobile terminals, and crawlers. Dynamically adjust the request frequency based on the token bucket algorithm to avoid triggering the website's anti-crawling mechanism. After the configuration is complete, you can use the constructed browser environment to send requests to the target website.

[0088] 3. Multi-dimensional response data capture submodule, used to extract and record the multi-dimensional response data returned by the target website into a separate data table;

[0089] Multi-dimensional response data includes data from three dimensions: basic indicators, front-end indicators, and API monitoring indicators. Basic indicators include basic performance indicators such as network latency (RTT), TCP connection time, TLS handshake time, and server response time (TTFB); front-end indicators refer to key front-end performance indicators captured by the monitoring node during page loading by executing JavaScript code, including first contentful paint (FCP), maximum contentful paint (LCP), cumulative layout shift (CLS), etc.; API monitoring indicators refer to the API interface provided by the target website. The monitoring node sends simulated requests and records the response time, success rate, and correctness of the returned data of the interface.

[0090] The operation status quantification module 23 is used to quantify the various operation statuses of the target website in real time based on the captured multi-dimensional response data, and specifically includes: a multi-dimensional response data pre-processing sub-module, a multi-dimensional response data reconstruction sub-module, and an operation status calculation sub-module;

[0091] 1. Multi-dimensional response data pre-processing sub-module, used to standardize the captured multi-dimensional response data;

[0092] If the response data has a high dimension (e.g., more than 10 dimensions), it can also be reduced by PCA or t-SNE to retain 95% of the variance.

[0093] 2. Multidimensional response data reconstruction submodule: uses the pre-model to extract the latent variables of the multidimensional response data and reconstructs the multidimensional response data based on the latent variables;

[0094] The standardized multi-dimensional response data are input into the front model to obtain the reconstructed data set. The front model contains three parts: encoder, sampler and decoder. The encoder is used to map the input data X to the latent space, which contains two neural networks f μ and f σ(both fully connected layers + ReLU activation), which are used to return the latent variable mean μ and logarithmic variance logσ generated after X is mapped to the latent space 2 ; The sampler is used to use the formula: Complete the sampling of the latent variable, where b is the returned latent variable, ∈ is the standard normal distribution noise, is the element-wise product; the decoder is used to reconstruct the latent variable b returned by the sampler into the output The decoder neural network is a fully connected layer + Sigmoid activation.

[0095] 3. Comprehensive quantification submodule, which is used to quantify the various operating states of the target website by integrating the reconstruction error, latent space characteristics and dynamic adjustment factors of multi-dimensional response data;

[0096] The quantitative formula for the i-th operating status of the target website at time t is expressed as:

[0097]

[0098] Among them H it is the quantitative result of the i-th operating status of the target website at time t, is an adjustable weight, is the reconstruction error term, is the latent space feature term, (1+φ·e -λt )and is the dynamic adjustment factor term, X it is the value of the response data of dimension i at time t, For X it The reconstruction value, b it is the potential variable sampled by the pre-model at time t for the i-th dimension response data, μ b 、 They represent the mean and covariance matrix of all potential variables generated by the i-th dimension response data in the latent space, φ is the decay factor, λ is the decay rate, γ is the sensitivity coefficient of special events, It is an indicator of whether there is a special event (such as a promotion on an e-commerce website) at time t. If yes, the value is 1, otherwise it is 0.

[0099] The performance score generating module 24 is used to calculate the performance score based on the quantitative results of various operating states of the target website;

[0100] Performance score S t It is used to reflect the overall health status of the website at time t. The calculation formula is:

[0101] in Indicates the target website's emphasis on the i-th item's operating status, H itIt represents the quantitative result of the i-th running status of the target website at time t, H i0 represents the quantitative result of the target website's i-th running state under the standard environment, τ i is the maximum deviation that the target website can tolerate for the i-th operating status, i ranges from 1 to n, and n is the total number of operating status items.

[0102] The performance score visualization module 25 is used to visualize the performance scores of each website in time series and issue an alarm notification when the performance score is lower than the threshold. It specifically includes: a visualization monitoring table generation submodule and an alarm triggering submodule;

[0103] 1. Visual monitoring table generation submodule, used to generate a visual monitoring table for a single website or multiple websites with time as the horizontal axis and the performance score of the target website as the vertical axis;

[0104] Use Python libraries like Matplotlib and Plotly for real-time plotting. Select appropriate chart types, such as line charts and area charts, to display performance score changes over time. Then, through scheduled tasks or event-driven mechanisms, regularly obtain the latest score data to update the visualization monitoring table to ensure real-time visualization. If multiple websites are selected when generating the visualization monitoring table, the monitoring timestamps of each website must be synchronized before they are plotted into the same table.

[0105] 2. The alarm trigger submodule is used to add a threshold line in the visual monitoring table. When the performance score of a website is detected to be lower than the threshold, the alarm mechanism is triggered;

[0106] Add a threshold line to visually see whether the performance score is lower than the preset threshold. The threshold is reasonably set according to business needs and historical performance score data. Every time the chart is updated, the performance score data is also detected. When the performance score of a website is found to be lower than the threshold, the alarm mechanism is triggered. The alarm notification is sent via email, SMS, pop-up window, etc., which should include key information such as website name, timestamp, performance score, etc., so that the problem can be quickly located.

[0107] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor;

[0108] The memory is used to store one or more program instructions;

[0109] The processor is configured to run one or more program instructions to execute a cloud-based website performance monitoring method.

[0110] Corresponding to the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute a cloud-based website performance monitoring method.

[0111] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned cloud-based website performance monitoring method.

[0112] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0113] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0114] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0115] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0116] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0117] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0118] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0119] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cloud-based website performance monitoring method, characterized in that: include: Step 1: Encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool; Step 2: Use the monitoring node to adaptively simulate the client sending a request to the website and capture the multi-dimensional response data of the target website; Step 3: Quantify the various operating states of the target website in real time based on the captured multi-dimensional response data; Step 4. Calculate the performance score based on the quantitative results of each operating status of the target website; Step 5: Visualize the performance scores of each website in time series and issue an alarm notification when the performance score falls below the threshold.

2. A cloud-based website performance monitoring method according to claim 1, characterized in that: Encapsulate the monitoring node as a lightweight container and select the optimal running node for it in the hybrid cloud node pool. The specific steps are as follows: Build a basic image based on the latest stable version of Chromium, and package the monitoring scripts and configuration files of the target website into the image to generate a packaged lightweight container; By deploying multi-cloud vendor nodes and edge computing nodes, a hybrid cloud node pool is formed; Dynamically select the optimal running node for the packaged lightweight container based on the user's geographical distribution, the target website's CDN configuration, and historical monitoring data.

3. A cloud-based website performance monitoring method according to claim 1, characterized in that: The monitoring node adaptively simulates the client sending requests to the website and captures the multi-dimensional response data of the target website. The specific steps are as follows: Build a browser environment based on the web technology stack used by the target website; Dynamically adjust request parameters and use the built browser environment to send requests to the target website; Extract and record the multi-dimensional response data returned by the target website into a separate data table.

4. A cloud-based website performance monitoring method according to claim 1, characterized in that: Quantify the various operating states of the target website in real time based on the captured multi-dimensional response data. This is divided into the following sub-steps: Standardize the captured multi-dimensional response data; The pre-model is used to extract the latent variables of the multidimensional response data, and the multidimensional response data is reconstructed according to the latent variables; The reconstruction error, latent space characteristics and dynamic adjustment factors of multi-dimensional response data are integrated to quantify the various operating states of the target website.

5. A cloud-based website performance monitoring method according to claim 1, characterized in that: Visualize the performance scores of each website in time series and issue an alarm notification when the performance score falls below the threshold. The specific steps are as follows: With time as the horizontal axis and the performance score of the target website as the vertical axis, a single website visual monitoring table is generated; Synchronize monitoring timestamps of multiple websites and generate multi-website visual monitoring tables; Add a threshold line in the visual monitoring table. When a website's performance score is detected to be lower than the threshold, an alarm mechanism is triggered.

6. A cloud-based website performance monitoring system, characterized in that: include: Monitoring node deployment module, request simulation module, operation status quantification module, performance score generation module, and performance score visualization module; The monitoring node deployment module is used to encapsulate the monitoring node into a lightweight container and select the optimal running node for it in the hybrid cloud node pool; The request simulation module is used to send requests to the website through the monitoring node adaptively simulating the client and capture the multi-dimensional response data of the target website; The operation status quantification module is used to quantify the operation status of the target website in real time based on the captured multi-dimensional response data; A performance score generation module is used to calculate the performance score based on the quantitative results of various operating states of the target website; The performance score visualization module is used to visualize the performance score of each website in time series and issue an alarm notification when the performance score is lower than the threshold.

7. A cloud-based website performance monitoring system according to claim 6, characterized in that: The monitoring node deployment module specifically includes: container encapsulation submodule, hybrid cloud point pool deployment submodule, and running node dynamic selection submodule; The container encapsulation submodule is used to build a basic image based on the latest stable version of Chromium, and package the monitoring scripts and configuration files of the target website into the image to generate a packaged lightweight container; The hybrid cloud node pool deployment submodule is used to form a hybrid cloud node pool by deploying multi-cloud vendor nodes and edge computing nodes; The running node dynamic selection submodule is used to dynamically select the optimal running node for the encapsulated lightweight container based on the user's geographical distribution, the target website CDN configuration, and historical monitoring data.

8. A cloud-based website performance monitoring system according to claim 6, characterized in that: The request simulation module specifically includes: browser environment construction submodule, request parameter adjustment submodule, and multi-dimensional response data capture submodule; The browser environment construction submodule is used to build a browser environment based on the web technology stack used by the target website; The request parameter adjustment submodule is used to dynamically adjust the request parameters and use the constructed browser environment to send requests to the target website; The multi-dimensional response data capture sub-module is used to extract and record the multi-dimensional response data returned by the target website into a separate data table.

9. A cloud-based website performance monitoring system according to claim 6, characterized in that: The operation status quantification module specifically includes: a multi-dimensional response data preprocessing submodule, a multi-dimensional response data reconstruction submodule, and an operation status calculation submodule; The multi-dimensional response data pre-processing sub-module is used to standardize the captured multi-dimensional response data; The multidimensional response data reconstruction submodule uses the pre-model to extract the latent variables of the multidimensional response data and reconstructs the multidimensional response data based on the latent variables; The comprehensive quantification submodule is used to quantify the various operating states of the target website by integrating the reconstruction error, latent space characteristics and dynamic adjustment factors of the multi-dimensional response data.

10. A computer storage medium, characterized in that include: at least one memory and at least one processor; a memory for storing one or more program instructions; A processor, configured to run one or more program instructions to execute a cloud-based website performance monitoring method as described in any one of claims 1 to 5.

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