Browser data lazy loading method, device and equipment and storage medium

Abstract

The invention discloses a browser data lazy loading method and device, equipment and a storage medium, and relates to the technical field of network resource loading optimizing.The method comprises the steps that a target point burying demand is determined, a target point burying project is created according to the target point burying demand, and behavior feature information of a target user is collected through the target point burying project; the behavior feature information is uploaded to a target data platform, so that the target data platform generates a target rendering strategy corresponding to the behavior feature information, and the target rendering strategy is returned to the browser side; and receiving the returned target rendering strategy, determining a loading mode of local browser data according to the target rendering strategy, and loading the browser data according to the loading mode. According to the method, the corresponding target rendering strategy is generated according to the behavior feature information of the target user, and the loading mode of the local browser data is determined according to the target rendering strategy, so that the accuracy of data loading is improved, and the waiting time of the user is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of web page resource loading optimization, and in particular to a browser data lazy loading method, device, equipment and storage medium. Background Art

[0002] The interaction between users and browsers is the main channel for users to obtain information from the Internet. The web page interaction between users and browsers occupies an important position in various industries. Due to the limited network loading speed, it is usually impossible to load all the data at once during the browser data loading process.

[0003] In modern Internet applications, web page interaction and loading speed are the most important factors affecting user experience. Traditional front-end resource loading methods usually rely on static rules to obtain all resources at once when the initial page is loaded. This method of loading web page resources uniformly has the problem of slow resource loading speed due to loading too many resources at one time, which leads to long waiting time for users and poor user experience, especially in poor network environment. Therefore, it is becoming more and more important to optimize rendering resources and user experience based on user behavior. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a browser data lazy loading method, device, equipment and storage medium, which can generate a corresponding target rendering strategy according to the behavior characteristic information of the target user, and determine the loading method of the local browser data according to the target rendering strategy, thereby reducing the user's waiting time. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a browser data lazy loading method, which is applied to a browser side, comprising:

[0006] Determine a target tracking point requirement corresponding to the behavior characteristic information of any target user, create a target tracking point project according to the target tracking point requirement, and use the target tracking point project to collect the behavior characteristic information of the target user;

[0007] Uploading the behavior feature information to the target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser end;

[0008] Receive the target rendering strategy returned by the target data platform, determine the loading method of the local browser data according to the target rendering strategy, and load the browser data according to the loading method.

[0009] Optionally, the collecting behavior feature information of target users by using the target embedding project includes:

[0010] According to actual application requirements, a target burying method is determined from different preset burying methods, and the target burying project and the target burying method are used to collect the behavioral feature information of the target user locally.

[0011] Optionally, uploading the behavior characteristic information to a target data platform includes:

[0012] Determining a target upload format of the behavior characteristic information, and performing format conversion on the behavior characteristic information according to the target upload format to obtain the behavior characteristic information in a data format that is the target upload format;

[0013] A network communication protocol connection is established between the target data platform and the browser end, and the behavior feature information in the target upload format is uploaded to the target data platform using the network communication protocol connection.

[0014] Optionally, determining a loading method of local browser data according to the target rendering strategy includes:

[0015] Obtain the return type of the target rendering strategy returned from the target data platform, and determine the loading method of the local browser data according to the return type and the target parameters included in the return type.

[0016] In a second aspect, the present application provides a browser data lazy loading method, which is applied to a target data platform, including:

[0017] Receive the behavior feature information of the target user uploaded by the browser, and generate a target rendering strategy corresponding to the behavior feature information according to a local preset prediction algorithm model and a preset simulated annealing algorithm;

[0018] The target rendering strategy is transmitted back to the browser end, so that the browser end receives the transmitted target rendering strategy, determines the loading method of the local browser data according to the target rendering strategy, and loads the browser data according to the loading method.

[0019] Optionally, generating a target rendering strategy corresponding to the behavior feature information according to a local preset prediction algorithm model and a preset simulated annealing algorithm includes:

[0020] Generating different clusters according to the local preset prediction algorithm model and the preset simulated annealing algorithm, and converting the behavior feature information into different vector sets;

[0021] An optimization target of the preset simulated annealing algorithm is determined based on the different clusters and the different vector sets, so as to generate the target rendering strategy corresponding to the behavior feature information based on the optimization target.

[0022] Optionally, the generating the target rendering strategy corresponding to the behavior feature information based on the optimization target includes:

[0023] Determine other behavior characteristic information that meets the same preset similarity condition as the behavior characteristic information from different behavior characteristic information using a preset similarity measurement method;

[0024] The target rendering strategy corresponding to the behavior feature information is generated according to the other behavior feature information and the optimization target.

[0025] In a third aspect, the present application provides a browser data lazy loading device, which is applied to a browser side, including:

[0026] An information collection module, used to determine a target embedding requirement corresponding to the behavior characteristic information of any target user, create a target embedding project according to the target embedding requirement, and use the target embedding project to collect the behavior characteristic information of the target user;

[0027] An information uploading module is used to upload the behavior feature information to a target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser end;

[0028] A strategy receiving module is used to receive the target rendering strategy returned by the target data platform, determine the loading method of the local browser data according to the target rendering strategy, and load the browser data according to the loading method.

[0029] In a fourth aspect, the present application provides an electronic device, including:

[0030] Memory, used to store computer programs;

[0031] A processor is used to execute the computer program to implement the aforementioned browser data lazy loading method.

[0032] In a fifth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned browser data lazy loading method when executed by a processor.

[0033] In this application, the browser side first determines the target tracking demand corresponding to the behavioral characteristic information of any target user, creates a target tracking project according to the target tracking demand, and uses the target tracking project to collect the behavioral characteristic information of the target user, and then uploads the behavioral characteristic information to the target data platform so that the target data platform receives the behavioral characteristic information, generates a target rendering strategy corresponding to the behavioral characteristic information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser side, and finally receives the target rendering strategy returned by the target data platform, determines the loading method of the local browser data according to the target rendering strategy, and loads the browser data according to the loading method. It can be seen that this application avoids the browser side relying on static rules to load browser data using a loading method different from the user's usage habits by generating a corresponding rendering method using user-specific behavioral characteristic information; by loading browser data using a rendering method corresponding to the user's behavioral characteristic information, the accuracy of the loading strategy is improved, and the user's preference data can be loaded first, thereby reducing the waiting time of the user when interacting with the web page. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 A flow chart of a browser data lazy loading method provided for this application;

[0036] Figure 2 A flowchart of a specific method for lazy loading of browser data provided by this application;

[0037] Figure 3 A flow chart of a browser data lazy loading method provided for this application;

[0038] Figure 4 A schematic diagram of the structure of a strategy output device provided in this application;

[0039] Figure 5 A schematic diagram of the structure of a browser data lazy loading device provided by this application;

[0040] Figure 6 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The current front-end resource loading method relies on static rules to obtain all resources at once when the initial page is loaded. This method has the problem that the data loading order is inconsistent with user habits, resulting in a long waiting time for users. To this end, the present application provides a browser data lazy loading method, which improves the accuracy of data loading and reduces the waiting time of users by generating a corresponding target rendering strategy based on the behavioral feature information of the target user.

[0043] See also Figure 1 As shown, an embodiment of the present invention discloses a browser data lazy loading method, which is applied to a browser side and includes:

[0044] Step S11, determine the target embedding demand corresponding to the behavior characteristic information of any target user, create a target embedding project according to the target embedding demand, and use the target embedding project to collect the behavior characteristic information of the target user.

[0045] In this embodiment, the process of performing page embedding is as follows: Figure 2As shown, the process is generally divided into three parts: product, research and development, and algorithm. Among them, the process of using the target tracking project to collect the behavior feature information of the target user specifically includes: determining the target tracking method from different tracking methods according to actual application requirements, and using the target tracking project and the target tracking method to collect the behavior feature information of the target user from the local; it should be noted that the common tracking methods include manual tracking method and automatic tracking method. In this embodiment, the manual tracking method is determined as the target tracking method; the advantage of using manual tracking is that it allows users to easily set custom attributes and custom events. When data drilling down and refined analysis are required, manual tracking will be more appropriate; specifically, create an npm (a software package management tool) tracking project that uses rollup (a module packaging tool) for packaging and uploading, and use the front-end manual tracking method to collect each DOM (Document Object Model, i.e., document object model) node on the browser side in turn using a number of tracking points to obtain the user's dynamic behavior feature information, i.e., the above-mentioned behavior feature information. It can be understood that the user's behavior feature information may include multiple types, such as user browsing history, user behavior, and other custom information, and only the required information can be collected through the manual tracking method. The core code of the above process, such as uploading tracking information, that is, the method of dynamic behavior feature information (user number, transmission address, type, version, page information, SDK version, etc.), is as follows: code: report_track (type, data, custom) {const reportData = {

[0046] type, / / Transmission type, page stage, user behavior, browsing history, etc.

[0047] data, / / Transmitted data, node information, page information, device information, network bandwidth, etc.

[0048] sdk: this.sdkVer, / / version information

[0049] …this.getPageDetail(), / / Page information

[0050] ...custom, / / Custom information

[0051] }

[0052] const imgReq = new Image();

[0053] imgReq.src=`${this.reportUrl}?params=JSON.stringify(reportData)&t=${new Date().getTime()}`;

[0054] }

[0055] The above-mentioned behavior characteristic information collection method is a method in the preset tracking point library adopted in this embodiment. It should be noted that this embodiment can also use another method in the preset tracking point library to obtain all DOM elements that need to be counted on the browser side at one time, and obtain the user's behavior characteristic information. The process is as follows: first obtain all DOM elements that need to be counted, and specify the tracking point standards, and then use the MutationObserver API (Application ProgrammingInterface, an application programming interface) to obtain the changes of all DOM nodes that need to be counted on the browser side, and obtain the user's behavior characteristic information based on the changes of the DOM nodes. The key code part of the above process is as follows: code: const mo_track_trigger = new MutationObserver(function (mutations,observer) {mutations.forEach(function (mutation) {this.report_track(`nodesChange`,{tagName: mutation.target.nodeName,tagText:mutation.target.innerText,…})});});It should be noted that the MutationObserver API provides the ability to increase and decrease DOM nodes and detect attribute changes. The API is asynchronously triggered, that is, it will not be triggered until all current DOM operations are completed, avoiding performance loss caused by frequent DOM changes. Therefore, it can be used to monitor DOM structure changes. This can be used in the initial loading scenario to prevent high frequency. By using manual tracking to collect the required user behavior feature information, the collected user behavior feature information is targeted, and the indiscriminate collection of all user behavior feature information is avoided, resulting in a waste of computing resources.

[0056] Step S12: upload the behavior feature information to the target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser.

[0057] In this embodiment, the process of uploading the target user's behavior characteristic information to the target data platform specifically includes: determining the target upload format of the behavior characteristic information, converting the format of the behavior characteristic information according to the target upload format to obtain the behavior characteristic information with the data format of the target upload format; building a network communication protocol connection between the target data platform and the browser end, and uploading the behavior characteristic information with the data format of the target upload format to the target data platform using the network communication protocol connection; for example, the GIF (Graphics Interchange Format) format can be used as the target upload format, and the user's behavior characteristic information is converted into the GIF format to upload the data in the GIF format; reporting data in the form of requesting pictures can prevent cross-domain and page blocking. Another advantage of using GIF pictures is that they are small in size. Using 1*1 pixel transparent GIFs for reporting can reduce server pressure; when the project is mounted in main.js, the front end establishes a websocket connection, that is, a network communication protocol connection, and uses the network communication protocol connection to send to the server, that is, the behavior characteristic information corresponding to the target data platform and various point-of-sale related data. By converting the format of the behavior feature information and uploading the converted behavior feature information to the target data platform, the pressure on the server is reduced and page blocking is avoided.

[0058] Step S13: receiving the target rendering strategy transmitted back by the target data platform, determining a loading method of the local browser data according to the target rendering strategy, and loading the browser data according to the loading method.

[0059] In this embodiment, the process of determining the browser data loading method of the browser side according to the target rendering strategy returned by the target data platform specifically includes: obtaining the return type of the target rendering strategy returned from the target data platform, and determining the local browser data loading method according to the return type and the target parameters contained in the return type; specifically, when receiving the rendering strategy for user behavior, page module, and hot spot area optimization returned by the server, executing the relevant methods of the browser side to implement it, and some interfaces will also include batch node and batch component information; in a specific implementation, if the return type of the rendering strategy is pageMount, it represents the page initialization strategy, and the modules frequently used by the user can be obtained according to the parameter hotAreaIds in the return type for priority loading. ; In another specific implementation, if the return type of the rendering strategy is pageScroll, it represents the user's scrolling behavior, and controls the start and stop of the function according to whether ifOpen is turned on; controls the volume of data requested at a time according to loadPage; realizes early data rendering according to the height of lazyHeight from the bottom, etc.; In the third specific implementation, if the return type of the rendering strategy is a specific component node, the local loading function is enabled according to the corresponding attribute lazy of the node; policy is used to control the loading strategy; debounce is used to control the loading frequency; In the fourth specific implementation, if the return type of the rendering strategy is localStorage, the front end performs appropriate caching according to the modules that need to be stored according to the prediction. In this embodiment, the browser establishes a websocket connection and sends the corresponding user information and various embedded point data to the server. The back end stores the user information and various embedded point data in the database, and regularly cleans and analyzes them through the model, and finally generates a strategy and sends it back to the front end, and executes the strategy on the front end. By determining different types of loading methods on the browser side according to the feedback type and target parameters corresponding to the feedback strategy, and loading the browser data according to the corresponding loading method, it is ensured that the loading method executed by the front end is the loading method corresponding to the user's behavioral characteristic information, thereby reducing the user's waiting time during the data loading process.

[0060] It can be seen that the embodiment of the present application collects the required user behavior feature information by utilizing manual tracking, thereby ensuring the pertinence of the collected user behavior feature information; by converting the format of the behavior feature information and uploading the format-converted behavior feature information to the target data platform, the pressure on the server is reduced and page blocking is avoided; by determining different types of loading methods on the browser side according to the return type and target parameters corresponding to the return strategy, it is ensured that the loading method executed by the front end is the loading method corresponding to the user's behavior feature information, thereby reducing the user's waiting time during the data loading process.

[0061] Based on the previous embodiment, it can be seen that the present application describes the corresponding process of the data lazy loading method on the browser side. In order to make the technical solution of the present application more complete, the present application will next describe the data lazy loading process of the target data platform. Figure 3 As shown, the embodiment of the present application discloses a data lazy loading method, which is applied to a target data platform, including:

[0062] Step S21 : receiving the behavior characteristic information of the target user uploaded by the browser, and generating a target rendering strategy corresponding to the behavior characteristic information according to a local preset prediction algorithm model and a preset simulated annealing algorithm.

[0063] In this embodiment, the target data platform includes a strategy output device that integrates a simulated annealing algorithm and a statistical prediction model, which is specifically used to implement the page lazy loading function. Figure 4As shown in the figure, before using the model to analyze and process the user's behavioral feature information, it is necessary to generate training data in the offline stage to train the model so that the trained model can have the corresponding functions; the device consists of a clustering module and an output module, and its core is a GBTs (Gradient Boosting Tree) model optimized based on a simulated annealing algorithm. This solution uses XGBoost (eXtremeGradient Boosting, an algorithm toolkit) as the implementation framework of GBTs, and develops a lightweight XGBoost inference engine to improve the operation efficiency of the model; it should be noted that the core parameters of the simulated annealing algorithm include initial temperature, end temperature, cooling factor, Markov chain length, etc. The application process of the algorithm is as follows: first, determine the number of clusters, and use the simulated annealing algorithm to generate the corresponding number of cluster centers; second, convert the user propensity matrix into an equal number of user vector sets; finally, set the optimization goal to maximize the sum of the similarities between each cluster center and each user vector in the user vector set, and use this as the optimization criterion of the simulated annealing algorithm, that is, the optimization goal, and generate a target rendering strategy based on the above optimization criterion; accordingly, the process of generating a target rendering strategy corresponding to the user's behavioral feature information according to the prediction algorithm model of the target data platform and the simulated annealing algorithm specifically includes: generating different clusters according to the local preset prediction algorithm model and the preset simulated annealing algorithm, and converting the behavioral feature information into different vector sets; determining the optimization goal of the preset simulated annealing algorithm based on different clusters and different vector sets, so as to generate a target rendering strategy corresponding to the behavioral feature information based on the optimization goal. Specifically, first, the number of clusters is set through the first submodule of the above-mentioned strategy output device, and the corresponding number of cluster centers are generated by the simulated annealing algorithm, and the cluster centers are multidimensional vectors; and the user browsing data, that is, the behavioral feature information is converted into a set number of vector sets of access module tendencies using a re-conversion submodule, each user vector set includes multiple user vectors, each cluster center corresponds to a user vector set, and each user vector represents the preset user's tendency to each module; then, the second setting submodule is used to set the maximum sum of the similarities between each cluster center and each user vector in the corresponding user vector set as the optimization target of the simulated annealing algorithm; and the target rendering strategy is generated according to the optimization target.Among them, the process of generating a target rendering strategy corresponding to the behavior feature information based on the optimization goal specifically includes: using a preset similarity measurement method to determine other behavior feature information that meets the same preset similarity condition as the behavior feature information from different behavior feature information; generating a target rendering strategy corresponding to the behavior feature information based on other behavior feature information and the optimization goal; specifically, using a clustering submodule to continuously cluster the behavior feature information of preset users according to the optimization goal of the simulated annealing algorithm and the similarity between the behavior feature information of each user and each cluster center, and continuously adjusting the cluster center according to the clustering results, so that each user in each clustering The cluster objects in the cluster sets have the greatest similarity with the cluster centers, until each cluster center no longer changes, and a set number of cluster sets are obtained; wherein each cluster set includes behavioral feature information of multiple preset users; then, according to a preset similarity measurement method, multiple behavioral feature information similar to the behavioral feature information of the target user are extracted from the cluster set where the behavioral feature information of the target user is located, that is, the other behavioral feature information mentioned above, and the functions that the target user browses frequently are predicted according to the tendency of each behavioral feature information to each preset module, and a target rendering strategy corresponding to the target user is generated, so as to load or throttle the corresponding functions for the target user according to the target rendering strategy.

[0064] It should be noted that the browser data lazy loading method in this example first generates a preset number of cluster centers. When new behavior feature information is obtained, the new behavior feature information is matched with the cluster with the highest similarity in the above preset clusters, and the corresponding target rendering method is generated based on the cluster. When the obtained behavior feature information reaches a certain amount, the number of clusters needs to be adjusted to make the cluster division more reasonable. For example, there are 4 preset clusters in the system. When the obtained behavior feature information reaches a certain amount, in order to make the cluster division more reasonable, the number of clusters can be increased or decreased according to the actual situation.

[0065] In order to calculate the similarity between the behavioral feature information of different users, the present invention adopts a cosine-based similarity measurement method. The inclination of the preset user for each preset module is regarded as an n-dimensional vector. If the preset user has not browsed the preset item, the user's inclination for the preset item is set to 0. The similarity between the preset users is measured by the cosine angle between the vectors. Assuming that the inclination of the preset user i and the preset user j in the n-dimensional space are respectively represented as vectors i and j, the calculation formula of the similarity sim (i, j) between the preset user i and the preset user j is as follows:

[0066] ;

[0067] Among them, i⋅j represents the dot product of vectors i and j, and and Represent the modulus of vectors i and j respectively. By using the target annealing algorithm and clustering, the user's browsing preference can be predicted, thereby generating a corresponding rendering strategy, and using the above rendering strategy to preload the functional modules and pages that the user may be interested in, thereby significantly reducing the burden of page loading and server requests, and significantly improving the user's browsing experience, achieving fast response and smooth interaction.

[0068] Step S22: transmit the target rendering strategy back to the browser end, so that the browser end receives the transmitted target rendering strategy, determines the loading method of the local browser data according to the target rendering strategy, and loads the browser data according to the loading method.

[0069] In this embodiment, the generated target rendering strategy is transmitted back to the browser side through the above-mentioned websocket connection, and the browser side can use the target rendering strategy to load local browser data.

[0070] It can be seen that this embodiment can predict the user's browsing preferences by utilizing the target annealing algorithm and clustering, thereby generating a corresponding rendering strategy, and using the above rendering strategy to load the functional modules and pages that the user may be interested in in advance, thereby significantly reducing the burden of page loading and server requests, and at the same time reducing the user's waiting time when interacting with the browser.

[0071] See also Figure 5 As shown, the embodiment of the present invention discloses a browser data lazy loading device, which is applied to a browser side and includes:

[0072] The information collection module 11 is used to determine the target embedding demand corresponding to the behavior characteristic information of any target user, create a target embedding project according to the target embedding demand, and use the target embedding project to collect the behavior characteristic information of the target user;

[0073] An information uploading module 12 is used to upload the behavior feature information to a target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser end;

[0074] The strategy receiving module 13 is used to receive the target rendering strategy sent back by the target data platform, determine the loading method of the local browser data according to the target rendering strategy, and load the browser data according to the loading method.

[0075] It can be seen that the present application generates a corresponding rendering method by utilizing user-specific behavioral feature information, thereby avoiding the browser side relying on static rules to load browser data using a loading method that is different from the user's usage habits; by loading browser data using a rendering method corresponding to the user's behavioral feature information, the accuracy of the loading strategy is improved, and the user's preference data can be loaded first, thereby reducing the user's waiting time when interacting with the web page.

[0076] In some specific implementations, the information collection module 11 specifically includes:

[0077] An information collection unit is used to determine a target burying method from different preset burying methods according to actual application requirements, and use the target burying project and the target burying method to collect the behavioral feature information of the target user locally.

[0078] In some specific implementations, the information uploading module 12 specifically includes:

[0079] a format conversion unit, configured to determine a target upload format of the behavior characteristic information, and perform format conversion on the behavior characteristic information according to the target upload format to obtain the behavior characteristic information in a data format of the target upload format;

[0080] An information uploading unit is used to establish a network communication protocol connection between the target data platform and the browser end, and use the network communication protocol connection to upload the behavior feature information in the target upload format to the target data platform.

[0081] In some specific implementations, the policy receiving module 13 specifically includes:

[0082] A loading method determination unit is used to obtain the return type of the target rendering strategy returned from the target data platform, and determine the local loading method of the browser data according to the return type and the target parameters contained in the return type.

[0083] Furthermore, the present application also discloses an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0084] Figure 6The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the browser data lazy loading method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0085] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0086] In addition, the memory 22 as a carrier for storing resources may be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0087] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the browser data lazy loading method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0088] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned disclosed method for lazy loading of browser data. The specific steps of the method can be referred to the corresponding contents disclosed in the aforementioned embodiments, and will not be described in detail here.

[0089] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0090] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0092] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0093] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A browser data lazy loading method, characterized in that: Applied to the browser side, including: Determine a target tracking point requirement corresponding to the behavior characteristic information of any target user, create a target tracking point project according to the target tracking point requirement, and use the target tracking point project to collect the behavior characteristic information of the target user; Uploading the behavior feature information to the target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser end; Receive the target rendering strategy returned by the target data platform, determine the loading method of the local browser data according to the target rendering strategy, and load the browser data according to the loading method.

2. The browser data lazy loading method according to claim 1, characterized in that: The method of collecting the target user's behavior feature information by using the target embedding project includes: According to actual application requirements, a target burying method is determined from different preset burying methods, and the target burying project and the target burying method are used to collect the behavioral feature information of the target user locally.

3. The browser data lazy loading method according to claim 1, characterized in that: The uploading of the behavior characteristic information to the target data platform includes: Determining a target upload format of the behavior characteristic information, and performing format conversion on the behavior characteristic information according to the target upload format to obtain the behavior characteristic information in a data format that is the target upload format; A network communication protocol connection is established between the target data platform and the browser end, and the behavior feature information in the target upload format is uploaded to the target data platform using the network communication protocol connection.

4. The browser data lazy loading method according to any one of claims 1 to 3, characterized in that: Determining the loading method of the local browser data according to the target rendering strategy includes: Obtain the return type of the target rendering strategy returned from the target data platform, and determine the loading method of the local browser data according to the return type and the target parameters included in the return type.

5. A browser data lazy loading method, characterized in that: Applied to the target data platform, including: Receive the behavior feature information of the target user uploaded by the browser, and generate a target rendering strategy corresponding to the behavior feature information according to a local preset prediction algorithm model and a preset simulated annealing algorithm; The target rendering strategy is transmitted back to the browser end, so that the browser end receives the transmitted target rendering strategy, determines the loading method of the local browser data according to the target rendering strategy, and loads the browser data according to the loading method.

6. The browser data lazy loading method according to claim 5, characterized in that: The generating a target rendering strategy corresponding to the behavior feature information according to a local preset prediction algorithm model and a preset simulated annealing algorithm includes: Generating different clusters according to the local preset prediction algorithm model and the preset simulated annealing algorithm, and converting the behavior feature information into different vector sets; An optimization target of the preset simulated annealing algorithm is determined based on the different clusters and the different vector sets, so as to generate the target rendering strategy corresponding to the behavior feature information based on the optimization target.

7. The browser data lazy loading method according to claim 6, characterized in that: The generating the target rendering strategy corresponding to the behavior feature information based on the optimization target includes: Determine other behavior characteristic information that meets the same preset similarity condition as the behavior characteristic information from different behavior characteristic information using a preset similarity measurement method; The target rendering strategy corresponding to the behavior feature information is generated according to the other behavior feature information and the optimization target.

8. A browser data lazy loading device, characterized in that: Applied to the browser side, including: An information collection module is used to determine a target embedding requirement corresponding to the behavior characteristic information of any target user, create a target embedding project according to the target embedding requirement, and use the target embedding project to collect the behavior characteristic information of the target user; An information uploading module is used to upload the behavior feature information to a target data platform so that the target data platform receives the behavior feature information, generates a target rendering strategy corresponding to the behavior feature information according to a preset prediction algorithm model and a preset simulated annealing algorithm, and transmits the target rendering strategy back to the browser end; A strategy receiving module is used to receive the target rendering strategy returned by the target data platform, determine the loading method of the local browser data according to the target rendering strategy, and load the browser data according to the loading method.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the browser data lazy loading method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the browser data lazy loading method as described in any one of claims 1 to 7.

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