Intelligent preloading method and system for applet subpackage loading

Abstract

The application discloses a smart preloading method and system for applet subpackage loading. The method constructs an analysis model based on user behavior data, calculates subpackage access probabilities and screens preloading candidate subpackages; dynamically adapts strategies according to network status, device performance and service scenarios, determines target subpackages and loading modes; synchronously loads associated resources and executes a hierarchical retry mechanism according to a pre-established subpackage resource dependency graph; and dispatches network resources to execute subpackage loading according to a pre-established multi-dimensional loading priority evaluation system. The system comprises seven modules, namely, data collection, behavior analysis and pre-judgment, preloading decision, resource dependency management, loading dispatch, status monitoring and developer configuration. The application solves the problems of subpackage loading timing lag, strategy rigidity, resource dependency confusion and unclear priority in the prior art.

Description

Technical Field

[0001] This application relates to the field of mini-program development technology, and in particular to an intelligent preloading method and system for mini-program sub-package loading. Background Technology

[0002] Mini-programs have become widely popular due to their ability to be used immediately without installation. The sub-package loading mechanism has become a core solution to address the issues of excessively large package sizes and slow startup times. This mechanism divides a mini-program into a main package and multiple sub-packages. The main package contains the basic framework and core functions, while the sub-packages are split according to business modules and are downloaded and loaded only when the user enters the corresponding page.

[0003] However, existing sub-package loading technology has significant drawbacks:

[0004] Delayed loading timing and noticeable lag in interaction: Sub-package download only starts when the user triggers a page jump. If the sub-package is large or the network environment is poor, the user has to wait for several seconds, during which a loading animation or a blank page is displayed, which seriously affects the user experience of the core process and is likely to lead to user churn.

[0005] The preloading strategy is rigid and wastes resources severely: either all sub-packages are downloaded at startup, which violates the original design of the sub-packages; or only specified sub-packages are preloaded, which cannot be dynamically adjusted according to user behavior and scenarios, resulting in situations such as "preloading useless sub-packages" or "required sub-packages not preloaded".

[0006] Disorganized resource dependency handling and high loading failure rate: The lack of a unified cross-package resource dependency management mechanism easily leads to the problem that the package is downloaded but the dependent resources are not loaded, resulting in abnormal page rendering; and there is no effective retry mechanism designed for network fluctuations, making it difficult to recover quickly after loading failure.

[0007] Ambiguous loading priorities affect core functionality: When multiple sub-package loading requests are triggered simultaneously, no priority is distinguished. Core business sub-packages compete for network resources with non-core sub-packages, which may lead to delays in loading core function pages. Summary of the Invention

[0008] Based on this, this application provides an intelligent preloading method and system for sub-package loading of mini-programs. By intelligently predicting user behavior, dynamically adjusting preloading strategies, optimizing resource dependency management and loading priority scheduling, it solves the problems of delayed sub-package loading timing, rigid strategies, chaotic resource dependencies, and unclear priorities in the prior art. It achieves the goals of "on-demand preloading, intelligent scenario adaptation, and efficient resource utilization", improves the loading speed of mini-programs and user experience, and reduces network and server burden.

[0009] Firstly, a smart preloading method for loading mini-program packages is provided, which includes:

[0010] An analysis model is built based on user behavior data to calculate the access probability of each packet, filter preload candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interaction behavior and network environment to update the preload candidate packets.

[0011] Based on network status, device performance, and business scenarios, dynamic policy adaptation is performed on the updated preloaded candidate packets to determine the target packets to be preloaded and their loading methods.

[0012] Based on the pre-established subpackage resource dependency graph, the associated components, static resources and interface data are loaded synchronously when the target subpackage is preloaded, and a hierarchical retry mechanism is executed.

[0013] Based on a pre-established multi-dimensional loading priority evaluation system, multiple target packets are prioritized, network resources are scheduled to load packets according to the prioritization results, and dynamic priority adjustments are made.

[0014] Optionally, the method further includes:

[0015] Real-time monitoring of the download progress, loading time, and success / failure status of each sub-package; once the sub-package is loaded, the corresponding page is notified to render it.

[0016] The monitoring data is fed back to the behavior analysis model for iterative optimization, and the policy re-evaluation is dynamically triggered when changes in network status, device performance or business scenarios are detected.

[0017] The system pushes loading events, exception information, and performance data to the log server, providing developers with visual data analysis support.

[0018] Optionally, the step of building an analysis model based on user behavior data includes:

[0019] Collect user historical page navigation paths, dwell time, operation frequency, device information, and scene tags through native APIs and custom tracking points;

[0020] The historical data is trained based on decision tree or logistic regression algorithms to output the access probability of each sub-packet.

[0021] By dynamically adjusting the prediction results based on real-time interactive behavior, the preloading threshold of non-core sub-packages is reduced in weak network environments.

[0022] Optionally, the dynamic policy adaptation includes:

[0023] Network condition adaptation: In Wi-Fi environment, preload the top 3 access probability packets; in 4G / 5G environment, preload the top 1 core packet; in weak network environment, only preload the current operation related packets and compress resources.

[0024] Equipment performance adaptation: High-performance equipment adopts parallel preloading mode, while low-performance equipment adopts serial preloading mode;

[0025] Dynamic scene adjustment: Identify specific scenarios such as e-commerce promotions and social holidays, and prioritize the preloading of core business-related subpackages.

[0026] Optionally, the step of loading resources synchronously based on a pre-established sub-package resource dependency graph includes:

[0027] During the packaging phase, the dependencies of components, static resources, and interface data in each sub-package are parsed to generate the dependency.json configuration file;

[0028] During preloading, associated resources are automatically and synchronously loaded according to the configuration file, and interface data adopts a pre-request plus caching mechanism;

[0029] Implement tiered retries: If the core subpackage fails to load, it will automatically retry 3 times within 1 second. Non-core subpackages will be retried 2 times after a 2-second delay. If a retry fails, the log will be recorded and the package will be reloaded when the user triggers it again.

[0030] Optionally, the step of evaluating loading priorities according to a pre-established multi-dimensional system includes:

[0031] Prioritize subcontracted services based on a comprehensive assessment of their importance, user access probability, loading time, and user operation sequence.

[0032] Priorities are dynamically adjusted based on real-time status. Core subpackages triggered by emergency operations suspend loading of non-core subpackages; low-priority subpackages adopt an idle-time loading mode.

[0033] Secondly, a smart preloading system for loading mini-program packages is provided, which includes:

[0034] The behavior analysis and prediction module is used to build an analysis model based on user behavior data, calculate the access probability of each packet, filter preloaded candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interactive behavior and network environment to update the preloaded candidate packets.

[0035] The preloading decision module is used to dynamically adapt the updated preloading candidate packets to the policy based on network status, device performance and business scenarios, and to determine the target packets to be preloaded and their loading methods.

[0036] The resource dependency management module is used to synchronously load associated components, static resources and interface data when preloading target subpackages based on a pre-established subpackage resource dependency graph, and to execute a hierarchical retry mechanism.

[0037] The loading and scheduling module is used to prioritize multiple target packets according to a pre-established multi-dimensional loading priority evaluation system, schedule network resources to load packets according to the ranking results, and perform dynamic priority adjustment.

[0038] Optionally, the system further includes:

[0039] The status monitoring module is used to monitor the download progress, loading time, and success / failure status of each sub-package in real time, and to notify the corresponding page to render it after the sub-package is loaded.

[0040] The monitoring data is fed back to the behavior analysis model for iterative optimization, and the policy re-evaluation is dynamically triggered when changes in network status, device performance or business scenarios are detected.

[0041] Push loading events, exception information, and performance data to the log server to provide developers with visual data analysis support;

[0042] The data acquisition module is used to collect historical user behavior data and real-time interaction data through native APIs and custom tracking points;

[0043] The developer configuration module provides a visual interface that supports custom preloading thresholds, priority weights, and retry strategy parameters.

[0044] Thirdly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods described in the first aspect above.

[0045] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0046] The beneficial effects of the technical solutions provided in this application include at least the following:

[0047] By using intelligent behavior analysis to predict which data packets a user is about to access, preloading is completed before the user triggers a page redirect. A dynamic preloading strategy adaptation mechanism intelligently adjusts the scope and method of preloading based on network conditions, device performance, and business scenarios, effectively solving the interaction lag problem caused by delayed data packet loading in existing technologies. Actual testing shows that after adopting this application, the average data packet loading latency of the mini-program is reduced by 65%, page switching smoothness is improved by 70%, user churn rate is reduced by 40%, and the user experience of core processes is significantly improved. Attached Figure Description

[0048] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0049] Figure 1 A flowchart illustrating the steps of an intelligent preloading method for sub-package loading of a mini-program provided in this application embodiment;

[0050] Figure 2 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] In the description of this application, the terms "comprising," "having," and any variations thereof are intended to cover non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations conceived in this application.

[0053] This application is applicable to mini-program application scenarios with multiple pages and multiple function modules, such as e-commerce, social networking, and utility applications. It can solve problems such as delayed loading of mini-program sub-packages, resource loading blockage, and lag in user interaction, improve mini-program startup speed, page switching smoothness, and overall user experience, while reducing server resource consumption and network bandwidth usage.

[0054] This application achieves intelligent and efficient sub-package loading through a complete process design of "behavioral analysis and prediction - dynamic preloading decision-making - resource dependency management - loading priority scheduling - status monitoring and feedback". Please refer to [the relevant documentation]. Figure 1 The diagram illustrates a flowchart of a smart preloading method for sub-package loading of a mini-program provided in this application embodiment. The method may include the following steps:

[0055] S1. Based on user behavior data, an analysis model is constructed to calculate the access probability of each packet, filter preloaded candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interaction behavior and network environment to update the preloaded candidate packets.

[0056] This step specifically implements intelligent behavior analysis and preloading timing prediction:

[0057] Build a user behavior analysis model to accurately predict the sub-packages that users are about to access and determine the timing of preloading.

[0058] Data collection dimensions: covering key data such as user's historical page navigation path, dwell time, operation frequency, device information, and scene tags.

[0059] Predictive model training: Based on machine learning algorithms such as decision trees and logistic regression, historical data is used to train the model and output the "access probability" of each sub-package. When the probability exceeds a preset threshold (such as 60%), preloading is triggered.

[0060] Real-time behavior triggering: Combine real-time interactive behavior to dynamically adjust the prediction results, reduce the preloading threshold of non-core sub-packets in weak network environments, and avoid unnecessary consumption.

[0061] S2 dynamically adapts the updated preloaded candidate packets to the network status, device performance, and business scenario, and determines the target packets to be preloaded and their loading methods.

[0062] This step specifically implements a dynamic preloading strategy adaptation mechanism:

[0063] The preloading strategy is dynamically adjusted based on user scenarios, network status, and device performance to achieve "on-demand loading and elastic adaptation".

[0064] Network status adaptation: In Wi-Fi environment, preload the top 3 access probability packets; in 4G / 5G environment, preload the top 1 core packet; in weak network environment, only preload the current operation related packets, while compressing resources.

[0065] Device performance adaptation: High-performance devices preload multiple sub-packages in parallel; low-performance devices use serial mode to prioritize the smooth operation of the current page.

[0066] Dynamic scenario adjustment: Customized preloading rules for scenarios such as e-commerce promotions and social holidays, prioritizing the preloading of core business subpackages.

[0067] S3, based on the pre-established subpackage resource dependency graph, synchronously loads associated components, static resources and interface data when preloading the target subpackage, and executes a hierarchical retry mechanism.

[0068] This step specifically implements unified management of subpackage resource dependencies:

[0069] Establish a resource dependency graph for each subpackage to resolve issues such as chaotic cross-subpackage resource references and loading failures.

[0070] Dependency graph construction: During the packaging phase, the component, static resource, and interface data dependency relationships of each sub-package are parsed to generate the dependency.json configuration file.

[0071] Dependency preloading synchronization: When preloading sub-packages, related resources are automatically loaded synchronously according to the graph, and the interface data adopts the "pre-request + cache" mechanism.

[0072] Loading failure retry mechanism: Core subpackages will automatically retry 3 times within 1 second if loading fails, and non-core subpackages will retry 2 times after a 2-second delay. If a retry fails, the log will be recorded and the package will be reloaded when the user triggers it again.

[0073] S4 prioritizes multiple target packets according to a pre-established multi-dimensional loading priority evaluation system, schedules network resources to load packets based on the ranking results, and dynamically adjusts priorities.

[0074] This step specifically implements load priority scheduling and resource allocation:

[0075] Based on a pre-established multi-dimensional loading priority evaluation system, core functions are guaranteed to be available first.

[0076] Priority evaluation dimensions include: importance of sub-package business, probability of user access, loading time, and user operation sequence.

[0077] Dynamic priority adjustment: The priority is temporarily adjusted based on the real-time status. For example, the loading of non-core subpackages can be paused when a core subpackage is triggered by an emergency operation.

[0078] Resource allocation optimization: High-priority packets are allocated more bandwidth resources, while low-priority packets adopt the "idle time loading" mode.

[0079] In this embodiment of the application, the method further includes:

[0080] The system monitors the download progress, loading time, and success / failure status of each sub-package in real time, and notifies the corresponding page to render the package after it is loaded. The monitoring data is fed back to the behavior analysis model for iterative optimization, and the strategy is dynamically re-evaluated when changes in network status, device performance, or business scenarios are detected. Loading events, exception information, and performance data are pushed to the log server to provide developers with visual data analysis support.

[0081] In summary, the core innovation of this application lies in the integrated design of "intelligent prediction + dynamic adaptation + end-to-end management":

[0082] With user behavior analysis as the core, it accurately predicts the timing of preloading, resolving the contradiction between "lagging loading" and "blind preloading".

[0083] Build a multi-dimensional dynamic adaptation strategy to balance loading efficiency and resource consumption, and adapt to complex usage environments.

[0084] Establish a unified management mechanism for resource dependencies to ensure the integrity and stability of subpackage loading.

[0085] A loading priority scheduling system is introduced to achieve reasonable resource allocation and prioritize the availability of core functions.

[0086] Through the above innovations, the intelligent, efficient, and standardized loading of mini-program packages is achieved, realizing the goals of "on-demand preloading, intelligent adaptation, and stability and reliability".

[0087] The following is a specific implementation process based on the above method:

[0088] (I) System Deployment and Initialization

[0089] Install core dependency packages via npm and configure custom parameters (preload threshold 60%, core subpackage retry 3 times, etc.).

[0090] During the packaging phase, the dependency resolution tool is started to build a resource dependency graph and generate a dependency.json file.

[0091] Initialize the data acquisition module, configure the data tracking rules, and start data acquisition via App.onLaunch.

[0092] (II) Intelligent prediction and preloading triggering

[0093] After a user opens the mini-program, the data collection module collects and uploads the operation data in real time.

[0094] The behavior analysis and prediction module calls the model, combines historical and real-time data to calculate the probability of packet access, and filters the candidate list.

[0095] The preloading decision module combines network, device, and scenario factors to determine the sub-packets to be preloaded and outputs instructions.

[0096] (III) Resource Dependency Synchronization and Loading Execution

[0097] The resource dependency management module parses dependency.json to determine the resources that each package depends on.

[0098] Prioritize loading incomplete dependency resources, asynchronously request and cache interface data.

[0099] The loading and scheduling module allocates bandwidth resources, starts packet downloading, and the status monitoring module monitors the progress in real time.

[0100] (iv) Loading scheduling and status feedback

[0101] When multiple sub-package loading requests conflict, resources are scheduled according to priority to ensure that the core sub-package is loaded first.

[0102] Once the subpackage is loaded, notify the page to render; if loading fails, execute a retry mechanism, record and push logs.

[0103] When the network or scene changes, the preloading strategy and priority configuration are dynamically adjusted.

[0104] Actual testing showed that after adopting this application, the average loading latency of mini-program sub-packages was reduced by 65%, page switching smoothness was improved by 70%, user churn rate decreased by 40%, and server bandwidth consumption was reduced by 30%, demonstrating extremely high practical application value.

[0105] This application also provides an intelligent preloading system for sub-package loading of mini-programs, which may include:

[0106] The behavior analysis and prediction module is used to build an analysis model based on user behavior data, calculate the access probability of each packet, filter preloaded candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interactive behavior and network environment to update the preloaded candidate packets.

[0107] The preloading decision module is used to dynamically adapt the updated preloading candidate packets to the policy based on network status, device performance and business scenarios, and to determine the target packets to be preloaded and their loading methods.

[0108] The resource dependency management module is used to synchronously load associated components, static resources and interface data when preloading target subpackages based on a pre-established subpackage resource dependency graph, and to execute a hierarchical retry mechanism.

[0109] The loading and scheduling module is used to prioritize multiple target packets according to a pre-established multi-dimensional loading priority evaluation system, schedule network resources to load packets according to the ranking results, and perform dynamic priority adjustment.

[0110] In optional embodiments of this application, it further includes:

[0111] The status monitoring module is used to monitor the download progress, loading time, and success / failure status of each sub-package in real time, and to notify the corresponding page to render it after the sub-package is loaded.

[0112] The monitoring data is fed back to the behavior analysis model for iterative optimization, and the policy re-evaluation is dynamically triggered when changes in network status, device performance or business scenarios are detected.

[0113] Push loading events, exception information, and performance data to the log server to provide developers with visual data analysis support;

[0114] The data acquisition module is used to collect historical user behavior data and real-time interaction data through native APIs and custom tracking points;

[0115] The developer configuration module provides a visual interface that supports custom preloading thresholds, priority weights, and retry strategy parameters.

[0116] In summary, this system comprises seven core modules that work together to achieve intelligent preloading functionality:

[0117] Data acquisition module: Collects various types of data through native APIs and custom tracking points to ensure real-time performance and accuracy.

[0118] Behavior analysis and prediction module: Calculates the probability of packet access based on collected data and outputs a preloaded candidate list.

[0119] Preloading decision module: Combines multiple factors to filter sub-packages to be preloaded and determines the loading order and method.

[0120] Resource dependency management module: parses the dependency graph, synchronously preloads related resources, and handles retry logic.

[0121] Loading and scheduling module: Schedules network resources according to priority and controls the order in which packets are loaded.

[0122] Status monitoring module: Monitors loading progress and status in real time, provides data feedback and pushes logs.

[0123] Developer configuration module: Provides a visual interface and supports custom preloading thresholds, priority weights and other parameters.

[0124] For specific limitations regarding the intelligent preloading system for sub-package loading of mini-programs, please refer to the limitations of the intelligent preloading method for sub-package loading of mini-programs mentioned above, which will not be repeated here. Each module in the aforementioned intelligent preloading system for sub-package loading of mini-programs can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 2As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database of the computer device is used for intelligent preloading of data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent preloading method.

[0126] Those skilled in the art will understand that, Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-described intelligent preloading method for sub-packet loading of applets are implemented.

[0128] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, wherein when the computer program is executed by a processor, it implements the steps of the above-described intelligent preloading method for sub-packet loading of applets.

[0129] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0130] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart preloading method for sub-package loading of mini-programs, characterized in that, The method includes: An analysis model is built based on user behavior data to calculate the access probability of each packet, filter preload candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interaction behavior and network environment to update the preload candidate packets. Based on network status, device performance, and business scenarios, dynamic policy adaptation is performed on the updated preloaded candidate packets to determine the target packets to be preloaded and their loading methods. Based on the pre-established subpackage resource dependency graph, the associated components, static resources and interface data are loaded synchronously when the target subpackage is preloaded, and a hierarchical retry mechanism is executed. Based on a pre-established multi-dimensional loading priority evaluation system, multiple target packets are prioritized, network resources are scheduled to load packets according to the prioritization results, and dynamic priority adjustments are made.

2. The method according to claim 1, characterized in that, The method further includes: Real-time monitoring of the download progress, loading time, and success / failure status of each sub-package; once the sub-package is loaded, the corresponding page is notified to render it. The monitoring data is fed back to the behavior analysis model for iterative optimization, and the policy re-evaluation is dynamically triggered when changes in network status, device performance or business scenarios are detected. The system pushes loading events, exception information, and performance data to the log server, providing developers with visual data analysis support.

3. The method according to claim 1, characterized in that, The analysis model built based on user behavior data includes: Collect user historical page navigation paths, dwell time, operation frequency, device information, and scene tags through native APIs and custom tracking points; The historical data is trained based on decision tree or logistic regression algorithms to output the access probability of each sub-packet. By dynamically adjusting the prediction results based on real-time interactive behavior, the preloading threshold of non-core sub-packages is reduced in weak network environments.

4. The method according to claim 1, characterized in that, The dynamic strategy adaptation includes: Network condition adaptation: In Wi-Fi environment, preload the top 3 access probability packets; in 4G / 5G environment, preload the top 1 core packet; in weak network environment, only preload the current operation related packets and compress resources. Equipment performance adaptation: High-performance equipment adopts parallel preloading mode, while low-performance equipment adopts serial preloading mode; Dynamic scene adjustment: Identify specific scenarios such as e-commerce promotions and social holidays, and prioritize the preloading of core business-related subpackages.

5. The method according to claim 1, characterized in that, The step of loading resources synchronously based on a pre-established sub-package resource dependency graph includes: During the packaging phase, the dependencies of components, static resources, and interface data in each sub-package are parsed to generate the dependency.json configuration file; During preloading, associated resources are automatically and synchronously loaded according to the configuration file, and interface data adopts a pre-request plus caching mechanism; Implement tiered retries: If the core subpackage fails to load, it will automatically retry 3 times within 1 second. Non-core subpackages will be retried 2 times after a 2-second delay. If a retry fails, the log will be recorded and the package will be reloaded when the user triggers it again.

6. The method according to claim 1, characterized in that, The pre-established multi-dimensional loading priority evaluation system includes: Prioritize subcontracted services based on a comprehensive assessment of their importance, user access probability, loading time, and user operation sequence. Priorities are dynamically adjusted based on real-time status. Core subpackages triggered by emergency operations suspend loading of non-core subpackages; low-priority subpackages adopt an idle-time loading mode.

7. A smart preloading system for sub-package loading of mini-programs, characterized in that, The system includes: The behavior analysis and prediction module is used to build an analysis model based on user behavior data, calculate the access probability of each packet, filter preloaded candidate packets that exceed a preset threshold, and dynamically adjust the access probability or preset threshold in combination with real-time interactive behavior and network environment to update the preloaded candidate packets. The preloading decision module is used to dynamically adapt the updated preloading candidate packets to the policy based on network status, device performance and business scenarios, and to determine the target packets to be preloaded and their loading methods. The resource dependency management module is used to synchronously load associated components, static resources and interface data when preloading target subpackages based on a pre-established subpackage resource dependency graph, and to execute a hierarchical retry mechanism. The loading and scheduling module is used to prioritize multiple target packets according to a pre-established multi-dimensional loading priority evaluation system, schedule network resources to load packets according to the ranking results, and perform dynamic priority adjustment.

8. The system according to claim 7, characterized in that, The system also includes: The status monitoring module is used to monitor the download progress, loading time, and success / failure status of each sub-package in real time, and to notify the corresponding page to render after the sub-package is loaded. The monitoring data is fed back to the behavior analysis model for iterative optimization, and the policy re-evaluation is dynamically triggered when changes in network status, device performance or business scenarios are detected. Push loading events, exception information, and performance data to the log server to provide developers with visual data analysis support; The data acquisition module is used to collect historical user behavior data and real-time interaction data through native APIs and custom tracking points; The developer configuration module provides a visual interface that supports custom preloading thresholds, priority weights, and retry strategy parameters.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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