CN117556160A - Front-end webpage performance automatic optimization method, device, equipment and readable storage medium - Google Patents
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Abstract
A front-end webpage performance automatic optimization method, device, equipment and readable storage medium relate to the technical field of Internet, and comprise the steps of acquiring webpage data of a target webpage, wherein the webpage data comprise webpage state data and user feedback data; performing webpage performance analysis on the webpage data based on a preset deep learning network model, and generating a webpage performance optimization strategy based on a webpage performance analysis result; and optimizing and adjusting the target webpage based on the webpage performance optimization strategy. According to the method and the device for optimizing the webpage performance, objective optimization of the webpage performance can be achieved, and the optimization efficiency of the webpage performance is improved.
Description
Technical Field
The application relates to the technical field of internet, in particular to a method, a device, equipment and a readable storage medium for automatically optimizing the performance of a front-end webpage.
Background
In the traditional front-end development, the optimization of the webpage performance is often carried out through manual tuning and optimization work. However, as the web page becomes more and more complex and has rich functions, the problem of low optimization efficiency exists by manually optimizing the performance of the front-end web page, and the subjectivity is strong, so that the user requirements cannot be met practically. Therefore, how to efficiently and objectively implement the performance optimization of the front-end web page is a current urgent problem to be solved.
Disclosure of Invention
The application provides a front-end webpage performance automatic optimization method, device and equipment and a readable storage medium, which can solve the technical problems of low webpage performance optimization efficiency and strong subjectivity in the prior art.
In a first aspect, an embodiment of the present application provides a method for automatically optimizing performance of a front-end web page, where the method for automatically optimizing performance of a front-end web page includes:
acquiring webpage data of a target webpage, wherein the webpage data comprises webpage state data and user feedback data;
performing webpage performance analysis on the webpage data based on a preset deep learning network model, and generating a webpage performance optimization strategy based on a webpage performance analysis result;
and optimizing and adjusting the target webpage based on the webpage performance optimization strategy.
With reference to the first aspect, in one implementation manner, the web page status data includes a web page loading time, a resource loading number, a network request number and an error log; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
With reference to the first aspect, in an implementation manner, the performing, based on a preset deep learning network model, web performance analysis on the web data includes:
performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
With reference to the first aspect, in an embodiment, the method further includes:
performing webpage quality analysis on the user feedback data and the error log based on the deep learning network model, and generating a webpage quality optimization strategy based on a webpage quality analysis result;
and optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
In a second aspect, an embodiment of the present application provides a front-end web page performance automatic optimization device, where the front-end web page performance automatic optimization device includes:
the data acquisition module is used for acquiring webpage data of a target webpage, wherein the webpage data comprise webpage state data and user feedback data;
the performance analysis module is used for carrying out webpage performance analysis on the webpage data based on a preset deep learning network model and generating a webpage performance optimization strategy based on a webpage performance analysis result;
and the performance optimization module is used for optimally adjusting the target webpage based on the webpage performance optimization strategy.
With reference to the second aspect, in one embodiment, the web page status data includes a web page loading time, a resource loading time, a number of resource loading times, a number of network requests, and an error log; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
With reference to the second aspect, in one embodiment, the performance analysis module is specifically configured to:
performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
With reference to the second aspect, in one implementation manner, the performance analysis module is further configured to perform a web quality analysis on the user feedback data and the error log based on the deep learning network model, and generate a web quality optimization policy based on a web quality analysis result;
the performance optimization module is also used for optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
In a third aspect, an embodiment of the present application provides a front-end web page performance automatic optimization apparatus, where the front-end web page performance automatic optimization apparatus includes a processor, a memory, and a front-end web page performance automatic optimization program stored on the memory and executable by the processor, where the front-end web page performance automatic optimization program, when executed by the processor, implements the steps of the front-end web page performance automatic optimization method as described above.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where a front-end web page performance automatic optimization program is stored on the computer readable storage medium, where the front-end web page performance automatic optimization program, when executed by a processor, implements the steps of the front-end web page performance automatic optimization method as described above.
The beneficial effects that technical scheme that this application embodiment provided include:
objective webpage performance analysis is carried out on webpage data comprising webpage state data and user feedback data based on a preset deep learning network model by acquiring webpage data of a target webpage, and then a webpage performance optimization strategy is generated according to webpage performance analysis results; and then, the target webpage is automatically optimized and adjusted through a webpage performance optimization strategy, so that the optimization efficiency of the webpage performance is effectively improved, and objective analysis of the webpage performance is performed by combining user feedback, so that the user requirements are practically met, and the technical problems of low webpage performance optimization efficiency and strong subjectivity in the related technology are solved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a method for automatically optimizing the performance of a front-end web page;
FIG. 2 is a schematic diagram of a refinement flow chart of step S20 in FIG. 1 of the present application;
FIG. 3 is a schematic diagram of a functional module of an embodiment of an apparatus for automatically optimizing performance of a front-end web page according to the present application;
fig. 4 is a schematic hardware structure diagram of a front-end web page performance automatic optimization device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In a first aspect, an embodiment of the present application provides a method for automatically optimizing performance of a front-end web page.
In an embodiment, referring to fig. 1, fig. 1 is a flowchart of an embodiment of a method for automatically optimizing performance of a front-end web page according to the present application. As shown in fig. 1, the automatic optimization method for the performance of the front-end webpage comprises the following steps:
step S10: acquiring webpage data of a target webpage, wherein the webpage data comprise webpage state data and user feedback data, and the webpage state data comprise webpage loading time, resource loading times, network request quantity and error logs; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
In this embodiment, the page resource and performance data of the target web page to be optimized and feedback data such as problems and satisfaction degree of the user on the target web page are obtained to form web page data. The webpage state data comprise, but are not limited to, webpage loading time, resource loading times, network request quantity and error logs, wherein the webpage loading time represents the time required by the webpage from the user to the complete loading; the resource loading time characterizes the loading time of various resources such as pictures, CSS (Cascading Style Sheets ), javaScript (a lightweight interpreted or just-in-time programming language with function priority) and the like; the number of resource loading represents the number of times various resources are loaded; the network request quantity characterizes the network request times sent in the webpage loading process, the network request times comprise resource requests and the like, and in the same period, the larger the network request quantity is, the more bandwidth is occupied, the greater the server pressure is, and other active requests of a user cannot be processed and fed back in time; the error log characterizes errors that occur during the loading of the web page, such as 404 errors, network connection problems, etc.
The user feedback data comprises at least 1 of performance problem data, satisfaction data and improvement suggestion data, wherein the performance problem data characterizes problems of a target webpage in the aspects of loading speed, response speed and the like, such as slow loading speed, slow response speed and the like, which are considered by a user; the satisfaction data represents scores used for the aspects of loading speed, response speed and the like of the target webpage; the improvement suggestion data characterizes improvement suggestions made by users on the loading speed, the response speed and the like of the target webpage, for example, the improvement suggestions are used for greatly accelerating the loading speed.
Step S20: and carrying out webpage performance analysis on the webpage data based on a preset deep learning network model, and generating a webpage performance optimization strategy based on a webpage performance analysis result.
In this embodiment, the deep learning network model may be an image processing algorithm, a text analysis algorithm, or other algorithms capable of identifying and extracting key features of a web page, such as a web page loading time, a resource loading number, a network request number, and an error log; for example, the text analysis algorithm is used as a deep learning network model, and the text analysis algorithm may be a convolutional neural network CNN, a cyclic neural network RNN, or other neural network models such as decision trees, random forests, etc., which may be specifically determined according to actual requirements, and is not limited herein.
Before the web page performance analysis is performed by using the deep learning network model, a machine learning model is constructed and trained to form the deep learning network model. The specific training process may be: automatically acquiring data such as HTML (Hypertext Transfer Protocol ), CSS codes, characteristic information and the like of each webpage through web crawler and the like, or directly acquiring an existing open source data set and storing the data set in a data warehouse; the feature information includes structural features, element features, style features, text features, performance features (such as webpage loading time, resource loading times, network request quantity, error log and the like), user behavior features (such as clicking of a user, mouse pointer rolling and stay time distribution, activity paths of the user on a webpage, access source and equipment information of the user and the like), user feedback features (such as problems, satisfaction and improvement suggestions) and the like, and the feature information of each webpage is extracted as a feature vector of a data set.
Wherein the structural features represent the presence and number of different parts (such as header, navigation, sidebar, text, footer, etc.) in the page; element features represent the number and type of images on a page, such as the presence and number of video, audio, or other multimedia elements, the type and location of buttons, forms, and interactive elements; style characteristics represent the color theme of the page, the font and font size used, the size and complexity of the background image and CSS style sheets, etc.; text features represent text content in a page, including, for example, titles, paragraphs, links and lists, frequency and distribution of occurrence of keywords, and language and subject matter of the text.
And then preprocessing the acquired data with missing values, normalized or standardized numerical characteristics, coding classification characteristics and the like to form a data set so as to ensure that the data is suitable for a machine learning model. It will be appreciated that for supervised learning tasks, a tag (i.e., a target variable) needs to be defined, so this embodiment will define a tag for web page performance optimization, e.g., scoring, classification of user behavior (e.g., click or no click), or other target that needs to be predicted may be used as the web page performance optimization tag.
Dividing the data set into a training set and a testing set, wherein the training set is used for training a machine learning model, and the testing set is used for evaluating the performance of the model; selecting an appropriate machine learning model, which may be a decision tree, random forest, neural network, etc., based on the problem type and data characteristics, and then training the machine learning model using a training set to learn how to map input features to output labels for prediction on unseen data; and then using the test set to evaluate the performance of the model, wherein the performance of the model can be evaluated by adopting common performance indexes including accuracy, precision, recall, F1 score and the like, and the selection of the specific performance index depends on the type of the problem.
If the performance of the model obtained by training does not meet the requirements, the model is optimized, such as adjusting model parameters, adding features, trying different model algorithms and the like; and if the model performance once meets the performance standard, deploying the trained model into practical application to form a deep learning network model for automatic webpage optimization or other tasks.
In this embodiment, feature recognition and analysis are performed on the webpage loading time, the resource loading times, the network request quantity, the error log, the performance problem data, the satisfaction data, the improvement suggestion data and the like based on the trained deep learning network model, so as to obtain a webpage performance analysis result, and it should be understood that the webpage performance grade may be used as the webpage performance analysis result, the webpage performance normalization value may be used as the webpage performance analysis result, and the webpage performance normalization value may be specifically determined according to actual requirements, which is not limited herein; and then, generating webpage performance optimization strategies objectively according to the webpage performance analysis result, wherein if the webpage performance analysis result is that the webpage performance grade is poor, namely the loading performance or the response performance of the target webpage is poor, the adopted strategies can be optimized image compression, HTTP request quantity reduction, delayed loading, merging and compression CSS, javaScript files and the like. It should be noted that, the specific classification of the performance level of the web page may be determined according to the actual requirement, for example, the classification from good to bad is: very good, better, general, worse, very poor and very poor
Step S30: and optimizing and adjusting the target webpage based on the webpage performance optimization strategy.
Exemplary, in this embodiment, the performance of the target web page is automatically optimized and adjusted through the web page performance optimization strategy without manual participation, so that the efficiency of web page performance optimization is effectively improved. When the web page optimization adjustment is performed, distributed computation can be adopted to provide high-performance and real-time web page optimization service, and technical means such as caching, load balancing and the like can be used to improve response speed and fault tolerance.
Further, referring to fig. 2, in an embodiment, the performing, based on the preset deep learning network model, web performance analysis on the web data includes:
step S201: performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
step S202: performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
step S203: and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
In this embodiment, performance analysis is performed on web page state data such as web page loading time, resource loading times, network request quantity, and error log through the deep learning network model, so as to identify information such as page access amount, loading speed, high load period, and jump rate of a target web page.
Then, the performance quality of the target webpage can be determined through the size relation between the loading speed and the loading speed threshold value, the size relation between the page access quantity and the access quantity threshold value and the like; if the loading speed is smaller than the loading speed threshold, judging that the performance grade of the target webpage is general, namely, webpage performance optimization is needed, and taking the performance grade as a webpage performance result, so that optimization information such as image compression ratio, reduction of HTTP request quantity, delay loading time and the like can be determined according to the difference value between the loading speed and the loading speed threshold; meanwhile, the image compression ratio corresponding to the high load period can be further improved, the reduction amount of the number of HTTP requests and the delay loading time length can be reduced, and the like. The specific value settings of the loading speed threshold and the access amount threshold may be determined according to actual requirements, and are not limited herein.
In this embodiment, the aggregation processing is further performed on the user feedback data corresponding to each user through the deep learning network model, so as to obtain a performance aggregation result. For example, if the satisfaction degree corresponding to the user a is 6 minutes (fully divided into 10), the performance problem data is a little slower in the loading number and the improvement proposal is that the loading speed is a little faster, the satisfaction degree corresponding to the user B is 5 minutes, the performance problem data is a very slower loading speed and the improvement proposal is that the loading speed is greatly faster, the deep learning network model will recognize and analyze the user feedback data of the users a and B through the natural language processing technology such as NLP to obtain the information; and then respectively weighting the data of the user A and the data of the user B, and then taking an average value to obtain a performance aggregation result.
Wherein, the weight of satisfaction can be set to be 0.6, and the weight of performance problem data and improvement suggestions is 0.2; the normalization processing is performed on the performance problem data and the improvement suggestion, and it is to be noted that how to perform the normalization processing on the text data is common knowledge in the art, and is not described herein in detail, and the weights are only presented in embodiments, and can be adjusted according to actual requirements; for user a, the performance score is s1= (satisfaction×0.6+ normalized performance problem data×0.2+ normalized improvement suggestion×0.2), and for user B, the performance score is s2= (satisfaction×0.6+ normalized performance problem data×0.2+ normalized improvement suggestion×0.2); then judging whether the average value of S1 and S2 is lower than an average value threshold value, if not, indicating that the webpage performance is better; if so, it is indicated that the web page performance is poor, wherein the specific web page performance level may be determined according to the difference between the average value and the average value threshold. For example, if the performance level of the web page is poor, the performance level of the web page is taken as a performance aggregation result.
And then updating the page performance results with the general page performance grades according to the performance aggregation results with the poor page performance grades, and obtaining the page performance grade difference as a page performance analysis result.
Further, in an embodiment, the method further comprises:
performing webpage quality analysis on the user feedback data and the error log based on the deep learning network model, and generating a webpage quality optimization strategy based on a webpage quality analysis result;
and optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
Exemplary, in this embodiment, the quality of HTML code, CSS code, and JavaScript code in the target web page may be determined through the error log in the web page status data, so as to determine the quality of structural features, element features, style features, and text features of the target web page; and can determine whether the content, navigation, layout, and interaction of the target web page need improvement through the user feedback data. Therefore, the webpage quality analysis is carried out on the user feedback data and the error log through the deep learning network model, so that the quality of the target webpage can be determined, and further, the parts of the target webpage, which have problems, need to be improved; then generating a webpage quality optimization strategy according to the quality and the improvement suggestion of the user; and finally, optimizing and adjusting the target webpage according to the webpage quality optimizing strategy so as to really meet the user requirements.
It should be appreciated that the above described web page quality optimization policies include, but are not limited to, content improvement policies, navigation and layout optimization policies, and interaction improvement policies. For example, updating or expanding the content of the website to ensure information accuracy and relevance; redesigning a navigation menu and adjusting page layout to provide better user guidance and usability; form design and button positions are optimized to improve usability of user interaction.
In addition, the deep learning network model can analyze the personalized requirements of the user according to the user behavior data including the click of the user, the rolling and stay time distribution of the mouse pointer, the moving path of the user on the page, the access source and equipment information of the user and the like, and further generate a personalized suggestion strategy so as to provide personalized content or product suggestion for the user.
It should be noted that, for the target web page, only one optimization strategy may be output, or multiple optimization strategies may be output; if multiple optimization strategies are output, the effect of each optimization strategy can be evaluated through an A/B test, so that more effective optimization strategies which meet the user requirements are screened out to perform optimization adjustment on the target webpage, and further the optimized target webpage can be ensured to improve user experience and webpage performance as expected.
In a second aspect, the embodiment of the application also provides an automatic optimization device for the performance of the front-end webpage.
In an embodiment, referring to fig. 3, fig. 3 is a schematic functional module diagram of an embodiment of an apparatus for automatically optimizing performance of a front-end web page in the present application. As shown in fig. 3, the front-end web page performance automatic optimizing apparatus includes:
the data acquisition module is used for acquiring webpage data of a target webpage, wherein the webpage data comprise webpage state data and user feedback data;
the performance analysis module is used for carrying out webpage performance analysis on the webpage data based on a preset deep learning network model and generating a webpage performance optimization strategy based on a webpage performance analysis result;
and the performance optimization module is used for optimally adjusting the target webpage based on the webpage performance optimization strategy.
Further, in an embodiment, the web page status data includes a web page loading time, a resource loading frequency, a network request number and an error log; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
Further, in an embodiment, the performance analysis module is specifically configured to:
performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
Further, in an embodiment, the performance analysis module is further configured to perform a web quality analysis on the user feedback data and the error log based on the deep learning network model, and generate a web quality optimization policy based on a web quality analysis result;
the performance optimization module is also used for optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
The function implementation of each module in the front-end webpage performance automatic optimization device corresponds to each step in the front-end webpage performance automatic optimization method embodiment, and the function and implementation process of each module are not described in detail herein.
In a third aspect, an embodiment of the present application provides a front-end web performance automatic optimization device, which may be a device with a data processing function, such as a personal computer (personal computer, PC), a notebook computer, a server, or the like.
Referring to fig. 4, fig. 4 is a schematic hardware structure diagram of a front-end web page performance automatic optimization device according to an embodiment of the present application. In the embodiment of the application, the front-end webpage performance automatic optimization device may include a processor, a memory, a communication interface and a communication bus.
The communication bus may be of any type for implementing the processor, memory, and communication interface interconnections.
The communication interfaces include input/output (I/O) interfaces, physical interfaces, logical interfaces, and the like for implementing the interconnection of devices inside the front-end web page performance automatic optimization apparatus, and interfaces for implementing the interconnection of the front-end web page performance automatic optimization apparatus with other apparatuses (e.g., other computing apparatuses or user apparatuses). The physical interface may be an ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device may be a Display, a Keyboard (Keyboard), or the like.
The memory may be various types of storage media such as random access memory (randomaccess memory, RAM), read-only memory (ROM), nonvolatile RAM (non-volatileRAM, NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (electrically erasable PROM, EEPROM), and the like.
The processor may be a general-purpose processor, and the general-purpose processor may call the front-end webpage performance automatic optimization program stored in the memory, and execute the front-end webpage performance automatic optimization method provided by the embodiment of the present application. For example, the general purpose processor may be a central processing unit (central processing unit, CPU). The method executed when the front-end web page performance automatic optimization program is invoked may refer to various embodiments of the front-end web page performance automatic optimization method of the present application, which are not described herein again.
Those skilled in the art will appreciate that the hardware configuration shown in fig. 4 is not limiting of the application and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium.
The readable storage medium stores a front-end webpage performance automatic optimization program, wherein when the front-end webpage performance automatic optimization program is executed by a processor, the steps of the front-end webpage performance automatic optimization method are realized.
The method implemented when the front-end web page performance automatic optimization program is executed may refer to various embodiments of the front-end web page performance automatic optimization method of the present application, which are not described herein again.
It should be noted that, the foregoing embodiment numbers are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments.
The terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the foregoing drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus. The terms "first," "second," and "third," etc. are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order, and are not limited to the fact that "first," "second," and "third" are not identical.
In the description of embodiments of the present application, "exemplary," "such as," or "for example," etc., are used to indicate an example, instance, or illustration. Any embodiment or design described herein as "exemplary," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, unless otherwise indicated, "/" means or, for example, a/B may represent a or B; the text "and/or" is merely an association relation describing the associated object, and indicates that three relations may exist, for example, a and/or B may indicate: the three cases where a exists alone, a and B exist together, and B exists alone, and in addition, in the description of the embodiments of the present application, "plural" means two or more than two.
In some of the processes described in the embodiments of the present application, a plurality of operations or steps occurring in a particular order are included, but it should be understood that these operations or steps may be performed out of the order in which they occur in the embodiments of the present application or in parallel, the sequence numbers of the operations merely serve to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the processes may include more or fewer operations, and the operations or steps may be performed in sequence or in parallel, and the operations or steps may be combined.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising several instructions for causing a terminal device to perform the method described in the various embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (10)
1. The front-end webpage performance automatic optimization method is characterized by comprising the following steps of:
acquiring webpage data of a target webpage, wherein the webpage data comprises webpage state data and user feedback data;
performing webpage performance analysis on the webpage data based on a preset deep learning network model, and generating a webpage performance optimization strategy based on a webpage performance analysis result;
and optimizing and adjusting the target webpage based on the webpage performance optimization strategy.
2. The method for automatically optimizing the performance of a front-end web page according to claim 1, wherein: the webpage state data comprise webpage loading time, resource loading times, network request quantity and error logs; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
3. The method for automatically optimizing the performance of a front-end web page according to claim 2, wherein the performing web page performance analysis on the web page data based on a preset deep learning network model comprises:
performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
4. The method for automatically optimizing the performance of a front-end web page of claim 2, wherein the method further comprises:
performing webpage quality analysis on the user feedback data and the error log based on the deep learning network model, and generating a webpage quality optimization strategy based on a webpage quality analysis result;
and optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
5. The front-end webpage performance automatic optimizing device is characterized by comprising:
the data acquisition module is used for acquiring webpage data of a target webpage, wherein the webpage data comprise webpage state data and user feedback data;
the performance analysis module is used for carrying out webpage performance analysis on the webpage data based on a preset deep learning network model and generating a webpage performance optimization strategy based on a webpage performance analysis result;
and the performance optimization module is used for optimally adjusting the target webpage based on the webpage performance optimization strategy.
6. The front-end web page performance automatic optimization apparatus of claim 5, wherein: the webpage state data comprise webpage loading time, resource loading times, network request quantity and error logs; the user feedback data includes at least 1 of performance issue data, satisfaction data, and improvement advice data.
7. The apparatus for automatically optimizing performance of a front-end web page according to claim 6, wherein the performance analysis module is specifically configured to:
performing performance analysis on the webpage loading time, the resource loading times, the network request quantity and the error log based on the deep learning network model to obtain a page performance result;
performing aggregation processing on the performance problem data, the satisfaction data and/or the improvement suggestion data corresponding to each user based on the deep learning network model to obtain a performance aggregation result;
and updating the page performance result based on the performance aggregation result to obtain a webpage performance analysis result.
8. The front-end web page performance automatic optimization apparatus of claim 6, wherein:
the performance analysis module is further used for carrying out webpage quality analysis on the user feedback data and the error log based on the deep learning network model, and generating a webpage quality optimization strategy based on webpage quality analysis results;
the performance optimization module is also used for optimizing and adjusting the target webpage based on the webpage quality optimization strategy.
9. A front-end web page performance automatic optimization apparatus, characterized in that the front-end web page performance automatic optimization apparatus comprises a processor, a memory, and a front-end web page performance automatic optimization program stored on the memory and executable by the processor, wherein the front-end web page performance automatic optimization program, when executed by the processor, implements the steps of the front-end web page performance automatic optimization method according to any one of claims 1 to 4.
10. A computer readable storage medium, wherein a front-end web page performance automatic optimization program is stored on the computer readable storage medium, wherein the front-end web page performance automatic optimization program, when executed by a processor, implements the steps of the front-end web page performance automatic optimization method according to any one of claims 1 to 4.
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