An implementation method for a browser to process complex algorithm calculation without blocking the browsing main process
By integrating Web Workers with the Vue3 reactive system, a parallel computing framework was built, which solved the problem of main process blocking caused by browsers processing complex algorithms. It achieved efficient, stable and easy-to-use parallel computing, improving user experience and computing efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HAINA ZHIYUAN DIGITAL TECH (SHANGHAI) CO LTD
- Filing Date
- 2026-01-06
- Publication Date
- 2026-05-29
AI Technical Summary
The browser's single-threaded execution model causes the main thread to be occupied when processing complex algorithms, resulting in user interface freezing, inability to utilize multi-core processor performance, logical fragmentation caused by asynchronous processing, difficulties in managing complex states, and memory management issues when processing big data, thus limiting the processing capabilities of web applications.
By deeply integrating Web Workers with the Vue3 reactive system, a parallel computing framework is built to achieve complete decoupling between computationally intensive tasks and UI rendering, including mechanisms such as Worker manager, reactive state integration, secure data transmission, task progress monitoring and feedback, and error isolation and recovery.
It achieves a non-blocking user interface, improves computing efficiency and user experience, reduces memory usage, simplifies the development process, and enhances concurrency processing capabilities and system stability.
Smart Images
Figure CN122111644A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computer technology, specifically to a method for processing complex algorithm calculations in a browser without blocking the main browsing process, which is particularly suitable for web front-end application scenarios that require high real-time performance and high interactivity. Background Technology
[0002] Modern web front-end applications have evolved from simple page displays to complex, feature-rich systems that require processing large amounts of data and executing intricate algorithms on the browser side. However, the browser's single-threaded execution model limits the performance of these applications. When an application performs time-consuming computational tasks, the main thread is occupied, causing the user interface to freeze and become unresponsive, severely impacting the user experience.
[0003] Key pain points include: user interface freezing issues, performance bottlenecks due to the inability to utilize multi-core processors, logical fragmentation caused by asynchronous processing, challenges in managing complex states, and memory management issues during big data processing. These challenges limit the ability of web applications to handle complex computational tasks and impact product competitiveness. Summary of the Invention
[0004] To address the problems existing in the prior art, this invention provides a method for handling complex algorithm calculations in the browser without blocking the main browsing process. By deeply integrating Web Workers with the Vue3 reactive system, a complete parallel computing framework is built, achieving complete decoupling between computationally intensive tasks and UI rendering.
[0005] Definitions:
[0006] Vue 3: The current major version of Vue.js, it is a progressive JavaScript framework for building user interfaces. It introduces a composable API and offers better performance, improved TypeScript support, and more flexible code organization compared to Vue 2.
[0007] API: Application Programming Interface. It is a set of well-defined rules and tools that allow different software applications or components to communicate with each other and invoke functions without needing to know the internal implementation details.
[0008] UI: User Interface. Refers to the visual and operational layers through which users interact with digital products (such as websites and apps), including all visible elements such as layout, buttons, and text. The core goal is to provide a clear and smooth user experience.
[0009] Ref: One of Vue 3's reactive APIs. Used to create a reactive reference object containing a .value property, primarily handling reactive updates of basic data types (such as strings and numbers), or referencing DOM elements in templates.
[0010] Reactive: Vue 3's core reactive API. It's used to transform a regular JavaScript object into a deeply reactive object, where all nested properties automatically become reactive, and simply modifying property values triggers view updates.
[0011] The technical solution of this invention is: a method for handling complex algorithm calculations in a browser without blocking the main browsing process, comprising the following steps:
[0012] Step S1: Initialize the Worker Manager;
[0013] Create dynamic Worker instances, bind lifecycle management, and support task scheduling, state synchronization, and resource reclamation;
[0014] Step S2: Define the reactive state integration layer;
[0015] By using Vue3's reactive API, the worker task state is two-way bound to the UI component, enabling real-time synchronization between the calculation process and the interface display.
[0016] Step S3: Submit the parallel computing task;
[0017] Submit the task type via a standardized API, generate a unique task ID, and send it to the Worker thread for execution;
[0018] Step S4: Secure data transmission;
[0019] It adopts an intelligent serialization mechanism to support cross-thread transmission of complex data types and avoid data cloning errors;
[0020] Step S5: Task progress monitoring and feedback;
[0021] Worker threads process data in batches, periodically send progress information to the main thread, and update the UI through reactive states;
[0022] Step S6: Error isolation and recovery;
[0023] The Worker thread captures runtime errors and reports them to the main thread, automatically restarting the abnormal Worker instance to ensure the stability of the main application;
[0024] Step S7: Processing and displaying task results;
[0025] After the task is completed, the main thread receives the result data and updates the UI, supporting concurrent processing of multiple tasks and association with the results.
[0026] Further, in step S8, release the Worker resources;
[0027] After a component is destroyed or a task is completed, the Worker instance is terminated to reclaim system resources and prevent memory leaks.
[0028] Furthermore, step S2 achieves state synchronization through Vue3's ref and reactive methods.
[0029] Furthermore, step S4 supports serialization and deserialization of Date objects and circular reference structures.
[0030] Furthermore, the progress feedback in step S5 includes the percentage of completion, the estimated remaining time, and the interim results.
[0031] Furthermore, in step S3, the types of tasks submitted via standardized APIs include data analysis and image processing.
[0032] Furthermore, the Worker task status in step S2 includes progress, result, and error.
[0033] The beneficial effects of this invention are:
[0034] 1. Worker Lifecycle Management Innovation
[0035] This solution implements a full lifecycle management mechanism for Web Worker instances, providing the ability to dynamically create and destroy Worker instances and allocate computing resources according to actual needs. The manager is integrated with the component lifecycle, automatically terminating related Workers when a component is destroyed to prevent memory leaks.
[0036] In addition, the solution implements an error recovery mechanism for Worker instances. When a Worker terminates abnormally, it can automatically recreate the Worker instance and restore the task queue, ensuring system stability and continuity.
[0037] 2. Task ID and Callback Mapping Mechanism
[0038] The innovative task ID-based callback mapping mechanism solves the multi-task concurrency problem of traditional Web Workers. When an application submits a task, a unique task ID is generated and stored in a mapping table along with the callback function. When the worker completes its execution and returns a result, the system uses the ID to find the corresponding callback function to process the result.
[0039] This mechanism enables precise pairing of task requests and responses, allowing the system to handle multiple concurrent tasks simultaneously. The results of each task are accurately transmitted to the corresponding processing logic, greatly enhancing concurrent processing capabilities.
[0040] 3. Data security serialization mechanism
[0041] To address the limitations of Web Workers communication on data types, the solution incorporates a secure data serialization mechanism. This mechanism recursively analyzes and processes data structures, automatically detecting and converting data types that cannot be directly cloned, such as Date objects and circular references.
[0042] This transformation is transparent to developers, allowing them to freely transmit various complex data structures without worrying about serialization errors. At the same time, this mechanism optimizes serialization performance, reduces unnecessary data copying, and improves the efficiency of transmitting large datasets.
[0043] 4. Responsive State Integration Innovation
[0044] The most innovative part of the solution is the seamless integration of Worker state with the Vue 3 reactive system. By creating reactive state objects in the Worker manager and exposing these objects as public interfaces to components, automatic state synchronization is achieved.
[0045] When a Worker's state changes, the manager updates the reactive state. Vue's reactive system automatically detects the change and triggers a UI re-render, requiring no manual intervention from the developer. This approach not only results in concise code but also ensures the reliability and consistency of state updates.
[0046] 5. Standardized progress reporting mechanism
[0047] The solution employs a standardized progress reporting mechanism, dividing large data processing tasks into multiple smaller batches, and reporting progress after each batch is completed. Progress information includes percentage completion, status description, and interim results, which are automatically updated to the UI interface through responsive status updates.
[0048] In addition to the basic percentage progress, the mechanism also supports rich progress information, such as the currently processed data items, estimated remaining time, and processing speed, providing users with a transparent view of the calculation process and significantly improving the user experience.
[0049] 6. User experience improvements
[0050] In tests involving large amounts of data, traditional methods caused the interface to freeze completely, while this solution maintained a smooth and responsive interface. Users can perform other operations simultaneously while the computation task is being executed, greatly improving work efficiency.
[0051] User testing shows that using this solution reduces the average time to complete the same task by about one-third, increases user satisfaction by about half, and directly enhances the product's competitiveness.
[0052] 7. Improved computational efficiency
[0053] By fully utilizing the capabilities of multi-core processors, this solution significantly improves the execution efficiency of computationally intensive tasks. Tests show that the execution time for large data processing tasks is reduced by approximately half compared to traditional methods.
[0054] The solution executes computational tasks entirely within Worker threads, avoiding frequent context switching and reducing overhead. Efficiency improvements vary depending on the type of computation, ranging from one-third to half, enabling complex calculations that previously could only be performed on the server side to now be completed directly in the browser.
[0055] 8. Memory usage optimization
[0056] Through sophisticated memory management and batch processing mechanisms, this solution maintains low memory usage when processing large amounts of data. Tests show that peak memory usage is reduced by about one-third compared to traditional methods, thus reducing the risk of browser crashes.
[0057] The optimization measures include batch processing of data, timely release of intermediate results, and avoidance of unnecessary data copying, forming an efficient memory management strategy that enables applications to stably handle large-scale datasets that exceed the capabilities of traditional methods.
[0058] 9. Improved development efficiency
[0059] This solution encapsulates complex thread communication details and provides a concise and unified API, significantly reducing the difficulty of implementing parallel computing. Project evaluation shows that developers using this solution reduce the amount of code by about half, shorten development time by about one-third, and reduce maintenance costs by about half.
[0060] The robust error capture and reporting mechanism simplifies the debugging process, enabling developers to easily locate and resolve issues in Worker threads, thus avoiding the "black box" debugging dilemma of traditional Web Workers development.
[0061] This patented technical solution innovatively combines Web Workers with the Vue3 reactive system, successfully solving the problem of browser main process blocking caused by executing complex algorithms. The solution provides a complete technical architecture and implementation method, enabling developers to achieve true parallel computing in a concise way while maintaining an excellent user experience.
[0062] Core innovations include Worker lifecycle management, task ID and callback mapping, secure data serialization, reactive state integration, and standardized progress reporting. These innovations together constitute an efficient, reliable, and easy-to-use parallel computing framework, opening up new possibilities for web applications.
[0063] This technical solution provides a new paradigm for complex computations in web applications, driving the evolution of web front-ends from the presentation layer to the computation layer and becoming a crucial technological foundation for interaction-intensive web applications. As web platforms evolve and browser capabilities improve, the parallel computing framework based on this solution will play an increasingly important role, delivering a smoother, more efficient, and feature-rich web application experience. Attached Figure Description
[0064] Figure 1 Initialization flowchart;
[0065] Figure 2 This is a flowchart of the task execution process;
[0066] Figure 3 This is a diagram illustrating the Worker Manager and task processing flow.
[0067] Figure 4 This is a diagram illustrating the binding of the responsive state integration layer to UI components;
[0068] Figure 5 Process the flowchart for the worker;
[0069] Figure 6 To use flowcharts;
[0070] Figure 7 This is the overall architecture diagram. Detailed Implementation
[0071] The present invention will now be further described with reference to the accompanying drawings.
[0072] I. Core Technology Architecture
[0073] The architecture comprises four closely cooperating core modules:
[0074] 1. Worker Task Processor: A dedicated script that runs in an independent thread to perform computationally intensive tasks. It supports multiple types of computation and is equipped with a standardized interface and progress reporting mechanism.
[0075] 2. Worker Manager: The core hub connecting the main thread and the worker thread, responsible for worker instance lifecycle management, task scheduling, state synchronization and resource reclamation, and providing a unified API interface.
[0076] 3. Responsive State Integration Layer: By leveraging Vue3's Composition API, a two-way binding is established between the Worker's execution state and UI components, enabling real-time synchronization between the calculation process and the interface display.
[0077] 4. Data security transmission mechanism: It solves the problem of transmitting complex data structures between different execution environments and processes data types that cannot be directly serialized through intelligent data conversion algorithms.
[0078] II. Technological Advantages
[0079] This solution implements a completely non-blocking user interface that remains responsive even when processing large amounts of data; seamless responsive integration allows developers to use Worker results just like they would use regular responsive data; standardized task management simplifies the parallel computing development process; a real-time progress feedback mechanism enhances the user experience; and error isolation and recovery mechanisms improve application stability.
[0080] III. Implementation of Key Technologies
[0081] 1. Worker Manager Implementation
[0082] The Worker Manager acts as the core hub, providing comprehensive control over the lifecycle of Worker instances and the ability to schedule tasks. During initialization, it creates reactive state variables to reflect the real-time execution status of the Workers; these state variables can be directly bound to Vue components.
[0083] The manager dynamically creates Worker instances via the `init` method and sets message handling functions to receive Worker messages. The core functionality, the `runTask` method, receives the task type, data, and callback function, generates a unique task ID, and sends the task information to the Worker for execution. Before a task is submitted, the manager uses a data security serialization mechanism to handle objects that cannot be directly cloned.
[0084] In addition, the manager provides task cancellation functionality and a worker termination method to ensure proper resource reclamation and prevent memory leaks. The error handling mechanism can capture worker exceptions and notify the application layer, improving system stability.
[0085] Initialization flowchart as follows Figure 1 As shown.
[0086] 2. Implementation of Worker Task Processor
[0087] The Worker processor runs in an independent thread, receiving computation tasks from the main thread, executing the specific computation logic, and returning results and progress information. It sets up a message listener to receive tasks and calls the appropriate processing function based on the task type.
[0088] The processor employs a batch processing strategy, dividing data into multiple batches for processing. The progress is updated after each batch is processed, providing smooth progress feedback while reducing peak memory usage. For different types of computing tasks, the processor implements specialized processing functions, such as big data processing, statistical analysis, and data filtering, ensuring optimal performance and accuracy.
[0089] Task execution flowchart as follows Figure 2 As shown.
[0090] 3. Vue3 component integration implementation
[0091] The integration of Vue 3 components with the Worker manager is achieved through a reactive system. Within a component, developers create a Worker manager instance, define the component's own reactive state, and map the Worker manager's reactive state to the component's internals using computed properties.
[0092] When a user triggers a calculation operation, the component calls the task processing method of the manager to submit the task and processes the result through a callback function. The component is responsible for managing the lifecycle of the Worker, initializing the Worker when it is mounted and terminating the Worker when it is destroyed, ensuring that resources are correctly allocated and released.
[0093] This encapsulation simplifies and standardizes the use of Workers, allowing developers to utilize their computational capabilities just like ordinary component states, significantly reducing development complexity.
[0094] A diagram illustrating the Worker Manager and task processing flow is shown below. Figure 3 As shown in the diagram, the responsive state integration layer is bound to UI components. Figure 4 As shown.
[0095] IV. Examples
[0096] Example 1: Data Visualization Platform
[0097] 1. Initialize the Worker manager and bind the Vue component lifecycle.
[0098] 2. A user submits a task to collect millions of data points. The task ID is TASK_001.
[0099] 3. Worker threads process data in batches, sending an update to the main thread every 10% of the progress completed.
[0100] 4. The UI displays a progress bar and intermediate statistical results (such as average and standard deviation) in real time.
[0101] 5. Once the task is completed, render an interactive chart and release the Worker resources.
[0102] Example 2: Online Image Editor
[0103] 1. Users upload images and apply filter tasks (such as Gaussian blur).
[0104] 2. The Worker thread segments the image into blocks for parallel processing and provides real-time feedback on the block processing progress.
[0105] 3. The main thread updates the image preview; users can adjust parameters or cancel the task midway.
[0106] 4. After the task is completed, memory will be automatically released and the processing results will be saved.
[0107] The worker processing flowchart is as follows: Figure 5 As shown.
[0108] V. Application Scenario Analysis
[0109] This technical solution is applicable to various scenarios that require a large amount of computation but also need to maintain UI responsiveness:
[0110] Data visualization and analysis platform: Users can perform various complex processing operations on large amounts of data, such as filtering, aggregation, classification, regression analysis, etc., while maintaining a smooth interface, viewing progress in real time, and even submitting multiple analysis tasks at the same time.
[0111] Online image editing application: Image processing algorithms are executed on worker threads, the user interface remains responsive and smooth, and the processing progress can be viewed in real time. Other parameters can be adjusted during operation.
[0112] Scientific computing and simulation systems: High-computational tasks such as solving complex mathematical models and iterative calculations can be performed directly in the browser. Researchers can view the calculation progress and intermediate results in real time and adjust parameters as needed.
[0113] Real-time search and filtering system: The search and filtering logic is executed on the worker thread. Even when processing millions of data points, the interface remains responsive, and the results are returned and updated in batches.
[0114] Large enterprise reporting system: The report generation logic is executed in the Worker thread. The system displays the generation progress, and users can adjust parameters or prepare other reports while waiting.
[0115] VI. Key Innovations in Technical Implementation
[0116] 1. Worker Lifecycle Management Innovation
[0117] This solution implements a full lifecycle management mechanism for Web Worker instances, providing the ability to dynamically create and destroy Worker instances and allocate computing resources according to actual needs. The manager is integrated with the component lifecycle, automatically terminating related Workers when a component is destroyed to prevent memory leaks.
[0118] In addition, the solution implements an error recovery mechanism for Worker instances. When a Worker terminates abnormally, it can automatically recreate the Worker instance and restore the task queue, ensuring system stability and continuity.
[0119] 2. Task ID and Callback Mapping Mechanism
[0120] The innovative task ID-based callback mapping mechanism solves the multi-task concurrency problem of traditional Web Workers. When an application submits a task, a unique task ID is generated and stored in a mapping table along with the callback function. When the worker completes its execution and returns a result, the system uses the ID to find the corresponding callback function to process the result.
[0121] This mechanism enables precise pairing of task requests and responses, allowing the system to handle multiple concurrent tasks simultaneously. The results of each task are accurately transmitted to the corresponding processing logic, greatly enhancing concurrent processing capabilities.
[0122] 3. Data security serialization mechanism
[0123] To address the limitations of Web Workers communication on data types, the solution incorporates a secure data serialization mechanism. This mechanism recursively analyzes and processes data structures, automatically detecting and converting data types that cannot be directly cloned, such as Date objects and circular references.
[0124] This transformation is transparent to developers, allowing them to freely transmit various complex data structures without worrying about serialization errors. At the same time, this mechanism optimizes serialization performance, reduces unnecessary data copying, and improves the efficiency of transmitting large datasets.
[0125] 4. Responsive State Integration Innovation
[0126] The most innovative part of the solution is the seamless integration of Worker state with the Vue 3 reactive system. By creating reactive state objects in the Worker manager and exposing these objects as public interfaces to components, automatic state synchronization is achieved.
[0127] When a Worker's state changes, the manager updates the reactive state. Vue's reactive system automatically detects the change and triggers a UI re-render, requiring no manual intervention from the developer. This approach not only results in concise code but also ensures the reliability and consistency of state updates.
[0128] 5. Standardized progress reporting mechanism
[0129] The solution employs a standardized progress reporting mechanism, dividing large data processing tasks into multiple smaller batches, and reporting progress after each batch is completed. Progress information includes percentage completion, status description, and interim results, which are automatically updated to the UI interface through responsive status updates.
[0130] In addition to the basic percentage progress, the mechanism also supports rich progress information, such as the currently processed data items, estimated remaining time, and processing speed, providing users with a transparent view of the calculation process and significantly improving the user experience.
[0131] Use flowcharts as follows Figure 6 As shown.
[0132] VII. Solution Effects and Performance Advantages
[0133] 1. User experience improvement
[0134] In tests involving large amounts of data, traditional methods caused the interface to freeze completely, while this solution maintained a smooth and responsive interface. Users can perform other operations simultaneously while the computation task is being executed, greatly improving work efficiency.
[0135] User testing shows that using this solution reduces the average time to complete the same task by about one-third, increases user satisfaction by about half, and directly enhances the product's competitiveness.
[0136] 2. Improved computational efficiency
[0137] By fully utilizing the capabilities of multi-core processors, this solution significantly improves the execution efficiency of computationally intensive tasks. Tests show that the execution time for large data processing tasks is reduced by approximately half compared to traditional methods.
[0138] The solution executes computational tasks entirely within Worker threads, avoiding frequent context switching and reducing overhead. Efficiency improvements vary depending on the type of computation, ranging from one-third to half, enabling complex calculations that previously could only be performed on the server side to now be completed directly in the browser.
[0139] 3. Memory usage optimization
[0140] Through sophisticated memory management and batch processing mechanisms, this solution maintains low memory usage when processing large amounts of data. Tests show that peak memory usage is reduced by about one-third compared to traditional methods, thus reducing the risk of browser crashes.
[0141] The optimization measures include batch processing of data, timely release of intermediate results, and avoidance of unnecessary data copying, forming an efficient memory management strategy that enables applications to stably handle large-scale datasets that exceed the capabilities of traditional methods.
[0142] 4. Improved development efficiency
[0143] This solution encapsulates complex thread communication details and provides a concise and unified API, significantly reducing the difficulty of implementing parallel computing. Project evaluation shows that developers using this solution reduce the amount of code by about half, shorten development time by about one-third, and reduce maintenance costs by about half.
[0144] The robust error capture and reporting mechanism simplifies the debugging process, enabling developers to easily locate and resolve issues in Worker threads, thus avoiding the "black box" debugging dilemma of traditional Web Workers development.
[0145] VIII. Summary and Technological Outlook
[0146] This patented technical solution innovatively combines Web Workers with the Vue3 reactive system, successfully solving the problem of browser main process blocking caused by executing complex algorithms. The solution provides a complete technical architecture and implementation method, enabling developers to achieve true parallel computing in a concise way while maintaining an excellent user experience.
[0147] Core innovations include Worker lifecycle management, task ID and callback mapping, secure data serialization, reactive state integration, and standardized progress reporting. These innovations together constitute an efficient, reliable, and easy-to-use parallel computing framework, opening up new possibilities for web applications.
[0148] Future research directions include: dynamic management mechanisms for worker thread pools, automatically adjusting the number of workers based on load and device capabilities; cross-worker data sharing technology to enable direct data sharing between workers; hybrid computing strategies, combining GPU computing technologies such as WebGL / WebGPU; collaborative models between front-end workers and back-end server computing to establish a flexible distributed computing architecture; and integration with Service Workers to achieve complex computing capabilities in offline environments.
[0149] This technical solution provides a new paradigm for complex computations in web applications, driving the evolution of web front-ends from the presentation layer to the computation layer and becoming a crucial technological foundation for interaction-intensive web applications. As web platforms evolve and browser capabilities improve, the parallel computing framework based on this solution will play an increasingly important role, delivering a smoother, more efficient, and feature-rich web application experience.
[0150] Overall architecture diagram as followsFigure 7 As shown.
[0151] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for implementing complex algorithm calculations in a browser without blocking the main browsing process, characterized in that, Includes the following steps: Step S1: Initialize the Worker Manager; Create dynamic Worker instances, bind lifecycle management, and support task scheduling, state synchronization, and resource reclamation; Step S2: Define the reactive state integration layer; By using Vue3's reactive API, the worker task state is two-way bound to the UI component, enabling real-time synchronization between the calculation process and the interface display. Step S3: Submit the parallel computing task; Submit the task type via a standardized API, generate a unique task ID, and send it to the Worker thread for execution; Step S4: Secure data transmission; It adopts an intelligent serialization mechanism to support cross-thread transmission of complex data types and avoid data cloning errors; Step S5: Task progress monitoring and feedback; Worker threads process data in batches, periodically send progress information to the main thread, and update the UI through reactive states; Step S6: Error isolation and recovery; The Worker thread captures runtime errors and reports them to the main thread, automatically restarting the abnormal Worker instance to ensure the stability of the main application; Step S7: Processing and displaying task results; After the task is completed, the main thread receives the result data and updates the UI, supporting concurrent processing of multiple tasks and association with the results.
2. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: Step S8: Release Worker resources; After a component is destroyed or a task is completed, the Worker instance is terminated to reclaim system resources and prevent memory leaks.
3. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: Step S2 achieves state synchronization through Vue3's ref and reactive functions.
4. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: Step S4 supports serialization and deserialization of Date objects and circular reference structures.
5. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: The progress feedback in step S5 includes the percentage of completion, the estimated remaining time, and the interim results.
6. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: In step S3, the types of tasks submitted via standardized APIs include data analysis and image processing.
7. The method for implementing complex algorithm calculations in a browser without blocking the main browsing process, as described in claim 1, is characterized in that: The Worker task status in step S2 includes progress, result, and error.