Front-end performance optimization method based on machine learning

Abstract

The invention discloses a front-end performance optimization method based on machine learning, and the method comprises the steps: collecting data; training a machine learning model; generating a performance optimization strategy; dynamically adjusting the optimization strategy; and integrating and monitoring the system. According to the method, multivariate data such as the user behavior data, the resource loading condition and the network condition are collected, the user behavior mode is analyzed through the machine learning algorithm, the front-end performance optimization strategy is dynamically adjusted according to the analysis result, and continuous optimization of the front-end performance is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of machine learning, and specifically is a front-end performance optimization method based on machine learning. Background Art

[0002] In web applications, front-end performance is crucial to user experience and the overall performance of the website. However, due to the diversity of user devices, the complexity of the network environment, and the uncertainty of user behavior, front-end performance optimization faces many challenges. There are the following shortcomings:

[0003] Traditional optimization methods often rely on fixed optimization strategies and parameter settings, with a low degree of automation; they cannot be flexibly adjusted according to actual conditions, are difficult to meet complex and changing application scenarios, and have weak scalability; they mainly rely on manual analysis and adjustment by developers, are inefficient and difficult to adapt to rapidly changing front-end environments. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a front-end performance optimization method based on machine learning that overcomes the above problems or at least partially solves the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A front-end performance optimization method based on machine learning, the method comprising:

[0007] Collect data;

[0008] Train machine learning models;

[0009] Generate performance optimization strategies;

[0010] Dynamically adjust optimization strategies;

[0011] System integration and monitoring.

[0012] Optionally, the collected data includes:

[0013] Collect user behavior data and record user interaction behaviors through tracking technology;

[0014] Collect resource loading information and record the loading time, loading order, and failure rate of resources during page loading;

[0015] Collect network status information, including network delay, bandwidth information and packet loss rate;

[0016] The data is sent to the server in real time for storage and analysis.

[0017] Optionally, the training of the machine learning model includes:

[0018] Select a machine learning algorithm;

[0019] Use historical data and labeled data to train models;

[0020] Perform model iterations and optimize model parameters.

[0021] Optionally, the machine learning algorithm includes decision tree, random forest and neural network.

[0022] Optionally, the generation performance optimization strategy includes:

[0023] Generate corresponding performance optimization strategies for different user behavior patterns based on the analysis results of the machine learning model;

[0024] For users with high network latency, optimize resource loading strategies;

[0025] For users who frequently jump to other pages, optimize the user page loading logic.

[0026] Optionally, the dynamic adjustment optimization strategy includes:

[0027] Apply the generated performance optimization strategy to the web application and verify the effectiveness of the strategy through A / B testing method;

[0028] Dynamically adjust the optimization strategy based on the collected data;

[0029] If you find that an optimization strategy causes an increase in user churn, adjust or revoke the strategy immediately.

[0030] Optionally, the system integration and monitoring includes:

[0031] Integrate with the front-end development framework and performance monitoring tools of Web applications to achieve automation and intelligence of performance optimization;

[0032] Set performance monitoring indicators to monitor the performance changes of the front end in real time;

[0033] Based on the monitoring results, problems are discovered and adjustments and optimizations are made.

[0034] Optionally, the performance monitoring indicators include page loading time, resource loading efficiency and user satisfaction.

[0035] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0036] The present invention collects multiple data such as user behavior data, resource loading status, network status, etc., uses machine learning algorithms to analyze user behavior patterns, and dynamically adjusts the front-end performance optimization strategy according to the analysis results, thereby achieving continuous optimization of front-end performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a front-end performance optimization method based on machine learning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] See also Figure 1 This embodiment provides a front-end performance optimization method based on machine learning, including:

[0040] S1. Collect data.

[0041] The collected data includes:

[0042] Collect user behavior data and record user clicks, scrolling, dwell time and other interactive behaviors through tracking technology.

[0043] Collect resource loading status and record the loading time, loading order and failure rate of resources (such as HTML, CSS, JavaScript, images, etc.) during page loading.

[0044] Collect network status information, including network performance indicators such as network delay, bandwidth information and packet loss rate.

[0045] The data is sent to the server in real time for storage and analysis.

[0046] S2. Train the machine learning model.

[0047] The training of the machine learning model comprises:

[0048] Select a machine learning algorithm, including decision trees, random forests, and neural networks.

[0049] Use historical data and labeled data to train the model; enabling it to identify user behavior patterns and predict future behavior trends.

[0050] Perform model iterations and optimize model parameters to improve prediction accuracy.

[0051] S3. Generate performance optimization strategy.

[0052] The generation performance optimization strategy includes:

[0053] Based on the analysis results of the machine learning model, corresponding performance optimization strategies are generated for different user behavior patterns.

[0054] For users with high network latency, optimize resource loading strategies, such as using CDN acceleration and compressing resource files.

[0055] For users who frequently jump to other pages, optimize the user page loading logic.

[0056] S4. Dynamically adjust the optimization strategy.

[0057] The dynamic adjustment optimization strategy includes:

[0058] The generated performance optimization strategy is applied to the Web application, and the effectiveness of the strategy is verified through the A / B testing method.

[0059] Based on the collected data, the optimization strategy is dynamically adjusted.

[0060] If you find that an optimization strategy causes an increase in user churn, adjust or revoke the strategy immediately.

[0061] S5. System integration and monitoring.

[0062] The system integration and monitoring includes:

[0063] This embodiment provides a front-end performance optimization method based on machine learning, which is integrated with the front-end development framework and performance monitoring tools of Web applications to achieve automation and intelligence of performance optimization.

[0064] Set performance monitoring indicators to monitor the performance changes of the front-end in real time; performance monitoring indicators include page loading time, resource loading efficiency and user satisfaction.

[0065] Based on the monitoring results, problems are discovered and adjustments and optimizations are made.

[0066] This embodiment collects multiple data such as user behavior data, resource loading status, network status, etc., uses machine learning algorithms to analyze user behavior patterns, and dynamically adjusts the front-end performance optimization strategy based on the analysis results, thereby achieving continuous optimization of front-end performance.

[0067] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A front-end performance optimization method based on machine learning, characterized in that: The method comprises: Collect data; Train machine learning models; Generate performance optimization strategies; Dynamically adjust optimization strategies; System integration and monitoring.

2. A front-end performance optimization method based on machine learning as claimed in claim 1, characterized in that: The collected data includes: Collect user behavior data and record user interaction behaviors through tracking technology; Collect resource loading information and record the loading time, loading order, and failure rate of resources during page loading; Collect network status information, including network delay, bandwidth information and packet loss rate; The data is sent to the server in real time for storage and analysis.

3. A front-end performance optimization method based on machine learning as claimed in claim 1, characterized in that: The training of the machine learning model comprises: Select a machine learning algorithm; Use historical data and labeled data to train models; Perform model iterations and optimize model parameters.

4. A front-end performance optimization method based on machine learning as claimed in claim 3, characterized in that: The machine learning algorithms include decision trees, random forests, and neural networks.

5. The front-end performance optimization method based on machine learning according to claim 1, characterized in that: The generation performance optimization strategy includes: Generate corresponding performance optimization strategies for different user behavior patterns based on the analysis results of the machine learning model; For users with high network latency, optimize resource loading strategies; For users who frequently jump to other pages, optimize the user page loading logic.

6. A front-end performance optimization method based on machine learning as claimed in claim 1, characterized in that: The dynamic adjustment optimization strategy includes: Apply the generated performance optimization strategy to the web application and verify the effectiveness of the strategy through A / B testing method; Dynamically adjust the optimization strategy based on the collected data; If you find that an optimization strategy causes an increase in user churn, adjust or revoke the strategy immediately.

7. A front-end performance optimization method based on machine learning as claimed in claim 1, characterized in that: The system integration and monitoring includes: Integrate with the front-end development framework and performance monitoring tools of Web applications to achieve automation and intelligence of performance optimization; Set performance monitoring indicators to monitor the performance changes of the front end in real time; Based on the monitoring results, problems are discovered and adjustments and optimizations are made.

8. A front-end performance optimization method based on machine learning as claimed in claim 7, characterized in that: The performance monitoring indicators include page loading time, resource loading efficiency and user satisfaction.

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