Front-end full-link data burying method and system based on worker pool intelligent scheduling for xinchuang environment, medium, program product and terminal

Abstract

This application provides a method, system, medium, program product, and terminal for front-end end-to-end data tracking based on intelligent scheduling using a Worker pool for the domestic IT innovation environment. It obtains multi-dimensional features and constructs an environment profile in the main thread; creates an intelligent Worker pool based on the environment profile; obtains end-to-end tracking tasks in the main thread or the intelligent Worker pool; dynamically routes the end-to-end tracking tasks to the intelligent Worker pool using a preset intelligent dispatcher; constructs a data processing flow based on security information in the environment profile; performs data security processing operations on the tracking data of the end-to-end tracking tasks in the intelligent Worker pool to obtain processed tracking data; detects the network status of the domestic IT innovation environment; and performs upload processing or storage waiting operations on the processed tracking data based on the detection results. This application achieves adaptive scheduling, security enhancement processing, and reliable reporting of end-to-end front-end tracking tasks in the domestic IT innovation environment.

Description

Technical Field

[0001] This application relates to the field of front-end data tracking technology, and in particular to a front-end full-link data tracking method, system, medium, program product and terminal based on Worker pool intelligent scheduling for the information technology innovation environment. Background Technology

[0002] Information technology application innovation (ITAI) is a national strategy to ensure the security and controllability of my country's information technology system. The ITAI environment is based on domestically produced CPUs (such as Kunpeng-ARM architecture, Loongson-MIPS / LoongArch architecture, and Phytium-ARM architecture), domestically produced operating systems (such as UnionTech UOS and Kylin OS), and domestically produced browsers. This environment is characterized by heterogeneous hardware architecture, strict system access control, and high security audit requirements, posing unique compatibility, security, and reliability requirements for the application software running on it.

[0003] Front-end data collection, or event tracking, is fundamental for product analysis, performance monitoring, and user experience optimization. Traditional front-end event tracking techniques typically involve embedding a JavaScript SDK into a webpage. This SDK runs on the browser's main thread, responsible for listening for user events, collecting performance metrics, and reporting the data to the server.

[0004] The latest patented technology (publication number CN117331753A) is to create a single web worker that is separated from the main thread application to process data tracking points.

[0005] The existing technology currently has the following drawbacks:

[0006] 1. Poor Hardware Adaptability: Domestic IT application-based terminals employ diverse CPU architectures with significant differences in core count, memory bandwidth, and instruction set characteristics. The single-core performance of domestically produced CPUs still lags behind international flagship products. A single worker cannot perceive and utilize these hardware characteristics, leading to a substantial decrease in data processing efficiency. Furthermore, when encountering resource-intensive tasks, data tracking processing must be shared with resource-intensive tasks within a single web worker, resulting in even lower performance and failing to leverage the multi-core advantages of some high-performance domestic IT application-based platforms.

[0007] 2. Security requirements not met: Domestic IT innovation projects have strict requirements for data security, code auditability, and behavioral compliance. Existing solutions simply place data processing logic in the Worker thread, and its network requests, encryption behavior, and data storage may still trigger misjudgments by domestic IT innovation security components, lacking a mechanism to proactively adapt to domestic IT innovation security strategies.

[0008] 3. Inflexible resource scheduling: Existing solutions mostly use a fixed number or simple rules for Workers, which cannot dynamically adjust the number, type and task allocation of Workers according to the real-time load and application scenarios of the domestic IT innovation devices. This results in lag on resource-constrained domestic IT innovation terminals or idle resources on high-end devices.

[0009] 4. Unable to track and extract processing information in the browser's background process: Existing tracking solutions only collect tracking points on the browser's main thread, but in front-end resource-intensive task scenarios, the process of data processing inside the browser's background worker cannot be tracked. Summary of the Invention

[0010] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a front-end full-link data tracking method, system, medium, program product and terminal based on Worker pool intelligent scheduling for the information technology innovation environment, which is used to solve the problems of poor hardware compatibility, inadequate security requirements, inflexible resource scheduling and inability to track and extract processing information in the browser background processing in the front-end tracking task under the information technology innovation environment.

[0011] To achieve the above and other related objectives, the first aspect of this application provides a front-end full-link data tracking method for the domestic IT innovation environment based on intelligent scheduling of Worker pools. The method includes: performing deep perception of the domestic IT innovation environment in the main thread to obtain multi-dimensional features, and constructing an environment profile based on the multi-dimensional features; creating an intelligent Worker pool based on the environment profile, obtaining full-link tracking tasks in the main thread or the intelligent Worker pool, and dynamically routing the full-link tracking tasks to the intelligent Worker pool using a preset intelligent dispatcher; constructing a data processing stream based on security information in the environment profile, performing data security processing operations on the tracking data of the full-link tracking tasks in the intelligent Worker pool to obtain processed tracking data, detecting the network status of the domestic IT innovation environment, and performing upload processing operations or storage waiting operations on the processed tracking data according to the detection results.

[0012] In some embodiments of the first aspect of this application, the process of performing deep perception of the domestic information technology innovation environment and obtaining multi-dimensional features in the main thread includes: performing multi-dimensional micro-benchmark tests in the main thread to obtain hardware information and software information; performing feature detection scripts in the main thread to obtain security information; and constructing the multi-dimensional features by combining the hardware information, software information, and security information.

[0013] In some embodiments of the first aspect of this application, the process of creating an intelligent worker pool based on the environmental profile includes: analyzing software and hardware information in the environmental profile to determine the allocation mode of the intelligent worker pool; and configuring and generating different types and numbers of workers based on the allocation mode to form an intelligent worker pool.

[0014] In some embodiments of the first aspect of this application, the intelligent worker pool includes: I / O Worker, compute Worker, network Worker, and storage Worker.

[0015] In some embodiments of the first aspect of this application, data security processing operations are performed on the tracking data of the end-to-end tracking task in the intelligent worker pool to obtain processed tracking data, and the network status of the domestic IT innovation environment is detected. Based on the detection result, an upload processing operation or a storage waiting operation is performed on the processed tracking data. The process includes: performing data security processing operations on the tracking data of the end-to-end tracking task sequentially through I / O Worker and Computation Worker to obtain processed tracking data; the network worker detects the network status of the domestic IT innovation environment. When the detection result is that the network is connected, the network worker uploads the processed tracking data to the server; when the detection result is that the network is not connected, the processed tracking data is sent to the storage worker; the storage worker receives the processed tracking data and stores it in the local database.

[0016] In some embodiments of the first aspect of this application, when the detection result is that the network is not connected, the network worker continuously detects the network status until the network is connected. Then, the storage worker reads the processed data from the local database in sequence and uploads it to the server through the network worker.

[0017] To achieve the above and other related objectives, a second aspect of this application provides a front-end full-link data tracking system for the domestic IT innovation environment based on intelligent scheduling of Worker pools. The system includes: an environment awareness module, used to perform deep perception of the domestic IT innovation environment in the main thread, obtain multi-dimensional features, and construct an environment profile based on the multi-dimensional features; a Worker pool creation module, used to create an intelligent Worker pool based on the environment profile, obtain full-link tracking tasks in the main thread or the intelligent Worker pool, and dynamically route the full-link tracking tasks to the intelligent Worker pool using a preset intelligent dispatcher; and a processing and uploading module, used to construct a data processing stream based on security information in the environment profile, perform data security processing operations on the tracking data of the full-link tracking tasks in the intelligent Worker pool to obtain processed tracking data, detect the network status of the domestic IT innovation environment, and perform uploading or storage waiting operations on the processed tracking data based on the detection results.

[0018] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the aforementioned method for front-end end-to-end data embedding based on Worker pool intelligent scheduling for the information technology innovation environment.

[0019] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product, which includes computer program code. When the computer program code is run on a computer, the computer implements the aforementioned method for front-end full-link data embedding based on Worker pool intelligent scheduling for the information technology innovation environment.

[0020] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the aforementioned front-end end-to-end data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment.

[0021] As described above, the front-end end-to-end data tracking method, system, medium, program product, and terminal based on Worker pool intelligent scheduling for the domestic IT innovation environment disclosed in this application have the following beneficial effects:

[0022] (1) This application achieves a high degree of matching between the allocation and operation strategy of Workers in the intelligent Worker pool and the characteristics of the underlying IT innovation software and hardware by deeply sensing the CPU and deeply probing the browser and the IT innovation environment. This solves the performance loss problem caused by the "one-size-fits-all" approach of the general solution and can significantly improve performance.

[0023] (2) This application deeply integrates security requirements such as sensitive information desensitization, application of national cryptographic algorithms, and adaptation of domestic innovation protocols into the data processing pipeline, so that the data collection behavior itself complies with the domestic innovation security specifications, actively avoids the risk of misjudgment by security tools, and enhances the feasibility of the solution in domestic innovation projects.

[0024] (3) This application can dynamically adjust the number and behavior of Workers according to the equipment load through real-time monitoring and intelligent scheduling, run smoothly on low-end IT innovation terminals, and give full play to the performance on high-end equipment, thus achieving the optimal utilization rate and energy efficiency ratio of computing resources.

[0025] (4) This application constructs a robust data acquisition link through a series of mechanisms such as multi-Worker collaboration, state persistence, breakpoint resume, health monitoring and hot migration in the intelligent Worker, ensuring that data is not lost, can be recovered and services are continuous under unstable conditions such as network jitter and process management that may occur in the information technology innovation environment, which greatly improves the reliability and resilience of the system.

[0026] (5) This application adopts an architecture such as intelligent scheduling of worker pool and pipeline, which makes the system easy to expand new worker types, processing rules or security policies, and can quickly adapt to the changes and new requirements of the future information technology innovation ecosystem. Attached Figure Description

[0027] Figure 1 The diagram shown is a flowchart illustrating a front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the domestic IT innovation environment in one embodiment of this application.

[0028] Figure 2 The diagram shown is a specific embodiment of a front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment, as described in one of the embodiments of this application.

[0029] Figure 3 The diagram shown is a structural schematic of a front-end end-to-end data tracking system based on Worker pool intelligent scheduling for the information technology innovation environment, according to one embodiment of this application.

[0030] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0031] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0032] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," and "holding" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0033] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0034] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0035] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the invention.

[0037] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0038] <1> Information technology innovation (ITI): ITI refers to an independent and controllable information technology system built on the basis of domestically produced software and hardware systems, emphasizing the localization, security, and independent controllability of key technologies and products.

[0039] <2> Resource-intensive tasks: These include high-load tasks and complex computational tasks, also known as I / O-intensive tasks and computation-intensive tasks. Examples of I / O-intensive tasks include large file chunking and uploading, and video streaming. Examples of computation-intensive tasks include the Wasm module, complex graphics computation and rendering, and cryptography and secure computation (Chinese national cryptographic algorithms).

[0040] <3> Data collection points (or event tracking) refer to the pre-embedding of data collection logic at key locations in an application or system to automatically record user behavior, system events, or performance metrics during operation, for subsequent statistical analysis and operational decision-making.

[0041] <4> Heartbeat mechanism: refers to the system's internal components sending lightweight "heartbeat" signals at fixed or dynamic time intervals, which allows the monitoring party to determine in real time whether the component is online, running normally, or has failed.

[0042] <5> CPU: The central processing unit of a computer is used to execute instructions, perform arithmetic and logical operations, and coordinate the work of various hardware components. It is the core computing and control component of the entire system.

[0043] <6> UA parsing refers to parsing the User-Agent string to identify the client's hardware and software information that initiated the request, including operating system type and version, device architecture, browser type and version, and operating environment, thereby providing a basis for system adaptation, performance optimization, or security verification.

[0044] <7> Environmental status probes are lightweight detection programs that run in a system to periodically or in real-time sense the critical status of the hardware and software environment.

[0045] <8> Logical core count: refers to the number of CPU virtual execution units visible to the operating system after enabling technologies such as Hyper-Threading, that is, the total number of schedulable cores presented at the system level by the physical cores through parallel thread capabilities.

[0046] To facilitate the understanding of the embodiments of this application, first, in combination with Figure 1 it is described in detail. Figure 1 FIG. 1 shows a schematic flow chart of a front-end full-link data burying point method based on intelligent scheduling of a Worker pool for the Xinchuang environment in an embodiment of the present invention. The front-end full-link data burying point method based on intelligent scheduling of a Worker pool for the Xinchuang environment in this embodiment mainly includes the following steps:

[0047] Step S11: Perform in-depth perception of the Xinchuang environment in the main thread to obtain multi-dimensional features, and construct an environment portrait according to the multi-dimensional features.

[0048] It should be noted that Xinchuang, that is, information technology application innovation, is a national industrial strategy promoted by China to ensure network and information security and achieve scientific and technological self-reliance and self-improvement. Its core is to build a complete independent technology system and industrial ecosystem from underlying hardware (such as domestic CPUs, solid-state drives) and basic software (such as domestic operating systems, databases) to upper-layer application software in the field of critical information infrastructure, so as to replace the traditional foreign technology system and ultimately achieve the goals of independent control of key technologies and reliable supply chain security.

[0049] Specifically, in the application initialization stage, first perform in-depth perception of the Xinchuang environment in the browser main thread to obtain multi-dimensional features, which are used to identify the hardware, software, and security characteristics of the current running environment to generate an environment portrait.

[0050] In some embodiments of the first aspect of this application, the process of performing in-depth perception of the Xinchuang environment in the main thread to obtain multi-dimensional features includes: performing multi-dimensional micro-benchmark tests in the main thread to obtain hardware information and software information; performing feature detection scripts in the main thread to obtain security information; and constructing the multi-dimensional features by integrating the hardware information, software information, and security information.

[0051] Specifically, the multi-dimensional micro-benchmark test refers to detecting programs through running multiple hierarchical and safely executable micro-benchmark test sets in the main thread to obtain hardware information and software information.

[0052] Furthermore, the micro-benchmark suite includes, but is not limited to, the following dimensions of testing. Each dimension includes multiple lightweight test items, specifically including: Computational performance testing: performing a fixed number of floating-point operations and string processing, measuring the time taken, and using `performance.now()` during execution to improve accuracy and avoid UI lag caused by excessive looping. Memory access testing: creating and traversing arrays or ArrayBuffers of different sizes, measuring the operation time, and indirectly inferring memory bandwidth and latency. I / O potential assessment testing: evaluating the event loop response capability using existing, safe asynchronous APIs (such as the accuracy of `setTimeout`).

[0053] Preferably, the micro-benchmark suite follows these principles: the execution process of all micro-benchmarks is wrapped in try-catch blocks to catch and handle potential safety exceptions, ensuring that the main thread does not crash; the test time of all micro-benchmarks is limited to within 100ms to avoid performance impact. The test scripts of the micro-benchmarks only perform calculations and queries, without modifying any DOM, initiating unnecessary network requests, or accessing potentially sensitive storage (such as LocalStorage), to avoid abnormal behavior. Before calling any potentially restricted APIs (such as performance.memory), their availability is checked.

[0054] In this embodiment, to facilitate understanding of the technical solution of the present invention, the core algorithm logic is described in pseudocode as follows. The pseudocode is only used to illustrate the processing flow of the present invention and does not limit the specific programming language or implementation method.

[0055] Function: Executes the IT Innovation Special Test Set (Test Strategy):

[0056] Test results ← {}

[0057] Time used ← @

[0058] Start time ← performance.now()

[0059] Total time budget ← Test strategy. Maximum total time V100 / / Default 100ms

[0060] / / CPU Feature Testing (Feasible Indirect Testing)

[0061] If the testing strategy requires CPU testing, then...

[0062] Budget Time ← Calculate the test time budget (total time budget, time used, 0.4)

[0063] If the budgeted time is greater than 5, then

[0064] Test Results. CPU Characteristics ← Execute CPU characteristic test ({

[0065] Maximum time: Budgeted time

[0066] Test item:[

[0067] 'CPU performance characteristics', / / inferred from performance benchmark tests

[0068] 'Multi-threaded performance characteristics', / / tested via Web Workers

[0069] 'Memory access characteristics' / / Tested via memory latency ]

[0071] })

[0072] end if elapsed time ← performance.now() - start time

[0073] end if

[0074] / / National cryptographic algorithm supports detection

[0075] If the testing strategy requires national cryptographic testing and the time used is less than the total time budget * 0.8, then...

[0076] Budget Time ← Calculate the test time budget (total time budget, time used, 0.3)

[0077] If the budgeted time is greater than 5, then

[0078] Test Results. National Cryptographic Features ← Execution of National Cryptographic Algorithm Supports Detection ({

[0079] Maximum time: Budgeted time

[0080] Test item:[

[0081] 'Standard algorithm supports detection', / / Detection standard encryption algorithm

[0082] 'Encryption Performance Benchmark' / / Tests encryption and decryption performance ]

[0084] })

[0085] end if

[0086] Elapsed time ← performance.now() - Start time

[0087] end if

[0088] / / Environmental Feature Detection

[0089] If the testing strategy requires environment testing and the time already used is less than the total time budget * 0.9, then...

[0090] Budget Time ← Calculate the test time budget (total time budget, time used, 0.2)

[0091] Test Results. Environmental Characteristics ← Perform environmental characteristic detection ({

[0092] Maximum time: Budgeted time

[0093] Test item:[

[0094] 'Browser Feature Detection' / / Detects browser-specific APIs

[0095] 'JavaScript engine characteristics', / / Detected through JS features

[0096] Runtime environment characteristics / / Detect web API support ]

[0098] })

[0099] Time elapsed ← performance.now() - Start time

[0100] end if

[0101] / / Performance Benchmark Test

[0102] if the time used is less than the total time budget, then

[0103] Budget Time ← Total Time Budget - Time Spent

[0104] If the budgeted time is greater than 5, then

[0105] Test Results. Performance Characteristics ← Perform Performance Benchmark Tests ({

[0106] Maximum time: Budgeted time

[0107] Test item:[

[0108] Integer arithmetic performance

[0109] 'Floating-point performance'

[0110] 'Memory read / write performance'

[0111] 'Encryption computing performance' ]

[0113] end if

[0114] Elapsed time ← performance.now() - Start time

[0115] end if

[0116] / / Record metadata

[0117] Test Results. Test Metadata ←{

[0118] Total test time: Time used

[0119] Test integrity: Time used / Total time budget

[0120] Time spent in each stage: Record the time spent in each stage ()

[0121] }

[0122] / / Calculate confidence level

[0123] Test Result. Confidence Level ← Calculate the test confidence level (test result)

[0124] return test results

[0125] End Function;

[0126] The hardware information includes, but is not limited to: the type of domestic CPU (e.g., Loongson, Kunpeng, Phytium), architecture, number of logical cores, and specific instruction sets supported (e.g., SIMD); the software information includes, but is not limited to: the operating system (e.g., UnionTech UOS, Kylin OS) and its security level, browser kernel and version, and the support and performance of advanced APIs.

[0127] The feature detection script refers to the capability detection code executed by the front end based on preset rules, used to determine the current operating environment's support for specific Web APIs, system capabilities, or instruction sets. This script typically verifies functional characteristics using methods such as active invocation, exception handling, and performance monitoring. Feature detection scripts may include, but are not limited to: API support detection, performance / computing power detection, security policy detection, and network feature detection.

[0128] Specifically, the script uses try-catch blocks to wrap and execute feature detection scripts to detect the security policy restrictions of the current browser environment, such as network request permissions and behavioral patterns that may trigger security alerts. It also detects prohibited advanced APIs, such as shared memory SharedArrayBuffer and Atmos, which are generally forcibly disabled in domestic IT innovation environments to prevent side-channel attacks.

[0129] Based on the execution results of the aforementioned multi-dimensional micro-benchmark tests and feature detection scripts, the main thread constructs an environment profile that includes hardware, software, and security information, providing a basis for subsequent intelligent worker pool configuration.

[0130] It is important to emphasize that, compared to traditional UA parsing, which reads strings automatically sent by the browser, the information obtained is only superficial identification information and is easily tampered with externally. The information depth is low, and its authenticity cannot be verified. In contrast, the deep perception of the domestic IT innovation environment described in this application actively executes multi-dimensional micro-benchmark tests and feature detection scripts to conduct actual tests on the hardware and software environment itself. This achieves a dual verification mechanism combining UA parsing and micro-benchmark testing. UA parsing quickly obtains superficial information such as device type, operating system, and browser, while micro-benchmark testing actually measures hardware performance such as computation, memory, and graphics, achieving data source complementarity. By integrating multi-dimensional features such as UA information, logical core count, and micro-benchmark test results, an environmental profile describing the device's computing power, I / O potential, and security constraint level is constructed. This ensures that the accuracy of the environmental profile can still accurately identify the actual capabilities of the device even in UA spoofing scenarios, providing a reliable basis for subsequent Worker pool optimization and scheduling.

[0131] In another alternative, the construction of the environment profile can be completed independently by the browser front-end main thread, or it can be done by the front-end main thread and the back-end policy service working together to obtain a more accurate environment profile through mutual verification between the front-end main thread and the back-end policy service.

[0132] Specifically, the front-end main thread first performs environment detection based on the hardware and software capabilities that the browser can directly access, forming the first environment fingerprint information. This detection process runs on the main thread, requires no additional permissions, and is a lightweight, low-load operation. The first environment fingerprint information may include, but is not limited to: browser UA basic characteristics, screen resolution, device pixel ratio, number of logical cores, network latency estimates obtained through resource requests, and unique device identifiers cached in local storage.

[0133] The front-end main thread preprocesses the first environment fingerprint locally (e.g., encrypting it with national cryptographic algorithms compliant with the domestic IT innovation environment) before sending it to the back-end policy service. The back-end policy service matches and analyzes the first environment fingerprint based on its maintained domestic IT innovation device fingerprint feature library (including CPU architecture model, domestic IT operating system version, security policy level, and API support matrix for specific browser versions), generating a second environment fingerprint, i.e., a preliminary profile result, such as possible device model ranges, operating system versions, and trusted security levels. The back-end performs cross-validation and accuracy correction on the first and second environment fingerprint profiles, ultimately generating the final environment profile.

[0134] In this embodiment, to facilitate understanding of the technical solution of the present invention, the core algorithm logic is described in pseudocode as follows. The pseudocode is only used to illustrate the processing flow of the present invention and does not limit the specific programming language or implementation method.

[0135] Algorithm: Intelligent Sensing and Profile Building System for Information Technology Innovation Environments

[0136] Input: None

[0137] 0output: Profile of the Information Technology Innovation Environment

[0138] Begin

[0139] / / Phase 1: Fast Feature Collection (<10ms)

[0140] profile ← Initialize profile()

[0141] / / 1.1 In-depth analysis of user agents

[0142] UserAgent Characteristics ← Deep Analysis of UserAgent({

[0143] Original User Agent (UA): navigator.userAgent

[0144] Platform: navigator.platform

[0145] Language: navigator.language

[0146] })

[0147] / / 1.2 Fast API Detection Fast API Features ← Parallel Detection of Key APIs ({

[0148] APIS:[

[0149] {name:'Web Workers',test: detects WebWorkers support},

[0150] {name:'SharedArrayBuffer',test:Detects SharedArrayBuffer support}

[0151] {name :"Web Crypto API',test:Detecting WebCryptoAPI support}

[0152] {name :'SIMD', test: detects SIMD support}

[0153] {name :'BigInt', test: detect BigInt support}

[0154] {name:'Atomics', test: detects Atomics support}

[0155] ],

[0156] Timeout: 5 ms

[0157] / / Phase 2: Intelligent Test Decision

[0158] Testing Strategy ← Intelligent selection of testing strategies (UA features, fast API features)

[0159] / / Phase 3: Perform targeted testing

[0160] If the testing strategy requires detailed testing, then...

[0161] Test Results - Execution of the Domestic Information Technology Innovation Special Test Set (Test Strategy)

[0162] e1se

[0163] Test Results ← Execution of Lightweight Domestic IT Innovation Test (Test Strategy)

[0164] end if

[0165] / / Phase 4: Multi-source information fusion

[0166] Fusion Results * Multi-Source Information Fusion Engine ({

[0167] User agent information: UA characteristics,

[0168] Fast API Detection: Fast API Features

[0169] Test results: Test results

[0170] Environmental characteristics: Extracting basic environmental characteristics ()

[0171] Historical data: Attempting to obtain historical profiles()

[0172] })

[0173] / / Phase 5: Image Enhancement

[0174] Enhanced Results - Image Enhancement Module (Fusing Results, {

[0175] Enhanced architecture speculation: true,

[0176] Security level marked as: true

[0177] Performance rating: true

[0178] Optimization suggestion generation: true

[0179] / / Stage 6: Image Caching

[0180] if the caching condition is met (enhancing the result) then

[0181] Cached profiles (enhanced results)

[0182] end if

[0183] profile ← Enhanced Results

[0184] Return profile

[0185] End;

[0186] Step S12: Create an intelligent worker pool based on the environment profile, obtain main thread tracking tasks and resource-intensive tasks from the main thread, and dynamically route the main thread tracking tasks and resource-intensive tasks to the intelligent worker pool using a preset intelligent dispatcher.

[0187] Specifically, the acquisition of the main thread event tracking tasks includes, but is not limited to, capturing user interactions, performance and lifecycle monitoring, and proactive submission within the browser's main thread. Specifically, user interaction capture is primarily achieved through listeners deployed on the main thread. User clicks, inputs, submissions, and other time-based events are captured by these listeners and encapsulated into event tracking tasks. The performance and lifecycle monitoring method automatically detects the browser's own state, utilizing the browser's Performance API, error monitoring, and page lifecycle events to automatically collect browser performance metrics and anomaly information, which are then encapsulated into event tracking tasks. The proactive submission method is used to record critical events that cannot be automatically captured by listeners but are strongly related to specific business logic; these events are proactively submitted by the developer.

[0188] Resource-intensive tasks refer to tasks that require significant computing resources (such as CPU, memory, storage, and network bandwidth) during execution. These tasks typically have high hardware performance requirements, long execution times, and may impact the efficiency of other parts of the system. Specifically, resource-intensive tasks include I / O-intensive tasks and compute-intensive tasks.

[0189] Among them, I / O-intensive tasks mainly include disk read / write, database access, file or object storage upload / download, network requests between microservices, message queue read / write, and peripheral device data reading, etc. The execution time of these tasks is mainly consumed in waiting for external resource responses; while computation-intensive tasks mainly include large-scale numerical operations, machine learning inference or training, image and video processing, compression and encryption / decryption operations, complex search and path planning, and various high-performance computing operations, the bottleneck of which is concentrated in the CPU or GPU computing itself.

[0190] The acquisition of resource-intensive tasks includes, but is not limited to, user uploads and backends providing only raw data. In this case, the frontend performs these computational processes without relying on the server. These data are generally processed and encapsulated by the frontend before being displayed to the browser or submitted to the server.

[0191] Step S13: Process the resource-intensive task based on the intelligent Worker pool, create a corresponding resource tracking task, and integrate the main thread tracking task and the resource tracking task into a full-link tracking task.

[0192] Specifically, the resource tracking task refers to the tracking task generated by the intelligent worker pool during the processing of the resource-intensive task, including I / O tracking tasks and computation tracking tasks.

[0193] Furthermore, the I / O tracking task refers to the tracking task added by the I / O intensive task in the resource-intensive task when the processing is performed by the I / O Worker in the intelligent Worker pool; the computation tracking task refers to the tracking task added by the computation intensive task in the resource-intensive task when the processing is performed by the computation Worker in the intelligent Worker pool.

[0194] Furthermore, the main thread tracking tasks, I / O tracking tasks, and computation process tracking tasks are combined in chronological order of their creation times to form the full-link tracking tasks in the main thread's lifecycle.

[0195] It should be noted that the aforementioned end-to-end tracking task is a unified monitoring and data collection system covering the entire task execution process, spanning all stages including task triggering, scheduling, execution, resource access, and result return. This tracking system includes tracking tasks generated on various Worker nodes by computationally intensive or I / O-intensive subtasks during resource-intensive task execution, used to record resource consumption, execution time, abnormal states, and phased results; it also includes tracking tasks generated by the main thread during task creation, distribution, scheduling decisions, and result aggregation, used to characterize overall process control and scheduling behavior. By unifying and connecting main thread tracking with resource-intensive task tracking, end-to-end tracking can form a complete link view from entry to exit, enabling end-to-end traceability and refined analysis of task execution paths, performance bottlenecks, and abnormal nodes. In one embodiment of this application, the intelligent Worker pool includes: I / O Worker, computation Worker, network Worker, and storage Worker.

[0196] Preferably, multiple end-to-end tracking tasks may exist simultaneously within the same time period. This application's system sets up two task queues in the main thread to globally sort and control resource-intensive tasks and main thread tracking separately, ensuring that all tasks are processed sequentially according to their classification. Simultaneously, each task in the task queue is marked with a priority attribute. When a high-priority task enters the task queue, the intelligent dispatcher inserts it at the head of the queue for priority processing, preventing the loss of critical data due to queuing delays. The intelligent dispatcher continuously polls the head of the queue and dynamically load balances tasks based on the real-time load status of each worker, preventing a single worker from overloading and blocking the entire pipeline. For example, the task queue length L1 assigned to a worker is multiplied by the average time T1 obtained after executing multiple sharded tasks using performance.now() to estimate the worker's load, i.e., the required processing time, as L1*T1. This centralized scheduling mechanism based on a unified task queue ensures both the fairness and orderliness of task processing, and achieves low-latency processing of critical data and maximizes the utilization of system resources through priority queuing and dynamic load balancing. The priority can be determined based on a combination of factors, including task type, risk of data loss, importance of the task source module, and real-time requirements of the task triggering scenario. Tasks with higher priority values ​​have stronger requirements for system timeliness and reliability, and are therefore assigned a higher execution position in the unified task queue to shorten queuing time.

[0197] In this embodiment, to facilitate understanding of the technical solution of the present invention, the core algorithm logic is described in pseudocode as follows. The pseudocode is only used to illustrate the processing flow of the present invention and does not limit the specific programming language or implementation method.

[0198] Function: Execute CPU characteristic test (configuration):

[0199] Features ← {}

[0200] Start time ← performance.now()

[0201] Maximum Time ← Configuration. Maximum Time

[0202] / / 1. Core count estimation (using web worker tests)

[0203] If WebWorkers() is supported and the maximum time is >10, then...

[0204] Core test time ← Minimum (maximum time * 0.3, 20) / / Maximum 20ms

[0205] Core count result ← Estimated CPU core count (core test time)

[0206] Features. Core Count Inference ← Core Count Results. Core Count

[0207] Feature. Core Count Confidence ← Core Count Result. Confidence

[0208] end if

[0209] / / 2. Architectural speculation (based on performance characteristics)

[0210] Architecture test time ← Minimum (maximum time * 0.5, 30) / / Maximum 30ms

[0211] Architecture prediction results ← Predicted CPU architecture (architecture test time)

[0212] Features.Architecture prediction <Architecture prediction result.Predicted architecture

[0213] Features.Architecture confidence score ← Architecture prediction result. Confidence score

[0214] Features. Performance characteristics ← Architecture prediction results. Performance characteristics

[0215] / / 3. SIMD supports detection

[0216] if SIMD() is supported, then

[0217] Features.SIMD support ← true

[0218] Features.SIMD type ← Detecting SIMD type()

[0219] end if

[0220] / / 4. Memory Access Mode

[0221] Memory test time ← Maximum time - (performance.now() - Start time)

[0222] If memory test time > 5, then

[0223] Memory characteristics ← Testing memory access patterns (memory test time)

[0224] Features. Memory access features ← Memory features

[0225] end if

[0226] Feature.Test Time ← Performance.Now() - Start Time

[0227] return features

[0228] End Function

[0229] Function: Inferring CPU Architecture (Configuration)

[0230] Result ← {

[0231] Speculated architecture: "Unknown"

[0232] Confidence level: 0

[0233] Performance characteristics: {}

[0234] Inference method: "Performance feature matching"

[0235] }

[0236] Start time ← performance.now()

[0237] Maximum time ← Configuration V 20 / / Default 20ms

[0238] / / 1. Collect performance feature vectors

[0239] Feature vector ← {}

[0240] / / 1.1 Integer Performance Characteristics

[0241] Integer characteristics ← Test integer performance characteristics (maximum time * 8.3)

[0242] Eigenvector.Integer features ← Integer features

[0243] / / 1.2 Floating-point performance characteristics

[0244] Floating-point characteristics ← Test floating-point performance characteristics (maximum time * 0.3)

[0245] Feature vector. Floating-point feature ← Floating-point feature

[0246] / / 1.3 Features of the JavaScript Engine

[0247] JS Features ← Collect JSEngine Features()

[0248] Feature vector.JS features ← Js features

[0249] / / 1.4 Clues in UserAgent

[0250] UA Clues ← Extracting architectural clues from the UA()

[0251] Feature vector.UA clue ← UA clue

[0252] / / 2. Feature Matching

[0253] Matching Results ← Matching CPU Architecture Features (Feature Vectors)

[0254] If the match is successful, then...

[0255] Result. Inferred Architecture ← Matching Result. Architecture

[0256] Result. Confidence score ← Matching result. Confidence score result.

[0257] Match score ← Match result. Match score result.

[0258] Key features ← Matching results. Key features

[0259] end if

[0260] / / 3: Record performance characteristics for subsequent analysis

[0261] Results. Performance Characteristics. Integer Performance ← Integer Characteristics. Performance Score

[0262] Results. Performance Characteristics. Floating-Point Performance ← Floating-Point Characteristics. Performance Score

[0263] Results. Performance Characteristics. Performance Ratio ← Integer Characteristics. Performance Score / Floating-Point Characteristics. Performance Score

[0264] Result.Test time ← performance.now() -Start time

[0265] return result

[0266] End Function;

[0267] Furthermore, the intelligent Worker pool is an execution set composed of multiple Worker instances with different functions and clearly defined responsibilities. Due to significant differences in CPU instruction sets, core counts, and browser API support among domestic IT innovation platforms, traditional single-type Workers cannot achieve stable processing performance under different hardware conditions, nor can they effectively allocate different types of data tracking tasks. In this embodiment, the intelligent Worker pool consists of the following four types of Workers: I / O Workers, used to perform tasks involving data parsing, formatting, de-identification, and sharding, which involve structural transformation or data augmentation; Computation Workers, used to perform computationally intensive tasks such as encryption, compression, and hash calculations; Network Workers, used to upload processed data tracking data and responsible for network requests and result callbacks; and Storage Workers, used for the local persistence of failed data, including encrypted storage, retrieval, and resume upload operations.

[0268] In one embodiment of this application, the process of creating an intelligent worker pool based on the environmental profile includes: analyzing the hardware and software information in the environmental profile to determine the allocation mode of the intelligent worker pool; and configuring and generating different types and quantities of workers based on the allocation mode to form an intelligent worker pool.

[0269] Specifically, the creation of the intelligent worker pool follows the principles of resource adaptation and task isolation, making comprehensive decisions based on the number of logical cores, memory status, CPU architecture preferences, and security level in the environment profile, rather than using a fixed formula for calculation. The preferred decision-making logic is as follows:

[0270] For high-performance scenarios, prioritize creating specialized workers of different types, such as independent I / O Workers, compute Workers, network Workers, and storage Workers. The initial number of compute workers can be set based on an assessment of parallel processing capabilities (e.g., 1-4), I / O Workers are typically set to 1-2, and there should be one network worker and one storage worker each. The criteria for determining high-performance scenarios include, but are not limited to, a minimum of 8 logical cores and more than 8GB of memory.

[0271] For low-performance or resource-constrained scenarios, prioritize creating 1-2 general-purpose Workers. Each general-purpose Worker integrates modular capabilities such as data processing, secure computing, and network communication to accommodate multiple task types, thereby minimizing thread creation and communication overhead while ensuring core security processes. The criteria for low-performance or resource-constrained scenarios include, but are not limited to, fewer than 4 logical cores and less than 4GB of memory.

[0272] The creation of network workers needs to consider the browser environment's limitations on concurrent requests and network I / O characteristics. The initial number is typically set to one, but can be dynamically adjusted according to strategies in high-concurrency scenarios. The creation of storage workers is related to the support of local storage APIs and security persistence requirements; the initial number is also typically set to one.

[0273] The general-purpose worker employs a modular, pipelined processor design, containing sequentially connected data parsing and filtering modules, security desensitization modules, and encrypted computing modules. Regardless of the task type, data must flow through this fixed security pipeline, ensuring that its security processing logic is completely equivalent to the processing chain composed of independent I / O Workers and computing Workers.

[0274] The methods described above for determining the number of I / O Workers, compute Workers, network Workers, and storage Workers are only a few optional implementations. Their quantity can be flexibly configured based on various environmental factors such as the number of logical cores, CPU architecture type, real-time security level, and current system task load. Those skilled in the art can replace, extend, or parameterize the allocation methods for different Worker types according to the performance characteristics of the target deployment environment or business requirements, without changing the overall architecture and scheduling mechanism of this application, to achieve better thread resource utilization efficiency and more stable system throughput performance.

[0275] For example, if the environment profile shows: CPU architecture is armv8, CPU manufacturer is Kunpeng, number of logical cores is 64, NEON instruction set is yes, operating system name is UOS20, security level is high, browser name is XC-Browser, and browser version is v10, then by analyzing the above environment profile, the current scenario is determined to be a high-performance scenario. Based on the environment profile, the specific intelligent worker pool configuration is 2 I / O Workers, 4 compute Workers, 1 network Worker, and 1 storage Worker. That is, the intelligent worker pool corresponding to the environment profile contains two I / O Workers, four compute Workers, one network Worker, and one storage Worker.

[0276] It should be noted that in some scenarios, the performance, version, or available resources of the hardware and software corresponding to the environment profile are at a low level, such as a limited number of CPU cores, insufficient memory capacity, or system versions that do not support multi-threaded extended instructions. For these low-performance environments, the system in this application can set 1-2 general-purpose Workers in the allocation rules to replace the fine-grained division of some typed Workers. General-purpose Workers have cross-type task adaptability capabilities and can simultaneously perform multiple basic tasks such as I / O, computation, network, or simple storage processing, reducing thread creation and scheduling overhead, thereby avoiding the additional system burden caused by too many Worker types in resource-constrained environments. By introducing general-purpose Workers, the system can maintain basic task processing capabilities and stability even when hardware and software performance is insufficient, achieving adaptive compatibility with devices of different performance levels.

[0277] It is important to emphasize that specific optimization strategies are configured for CPUs from different domestic manufacturers based on their characteristics. For example, for Loongson CPUs, the overall design is biased towards balancing single-core performance and energy consumption, prioritizing increasing throughput and reducing cross-worker communication. For the Kunpeng platform, typical characteristics include a very large number of cores (48 / 64 / 128 cores), high interconnection bandwidth between cores, and a design goal of parallel multi-tasking scenarios, utilizing its multi-core advantages to perform parallel task splitting, etc.

[0278] Furthermore, if multiple Worker types suitable for the current task exist simultaneously in the intelligent Worker pool, the intelligent dispatcher, when allocating tasks, prioritizes assigning consecutive tasks with data dependencies (such as the result of the previous task being the input of the next task) to the same Worker or Workers with adjacent task queues, in order to reduce cross-thread communication overhead. If this cannot be satisfied, the Worker with the lowest load is selected.

[0279] Step S13: Construct a data processing flow based on the security information in the environmental profile, perform data security processing on the data of the full-link data tracking task in the intelligent worker pool to obtain the processed data, detect the network status of the information technology innovation environment, and perform upload processing or storage waiting operation on the processed data according to the detection results.

[0280] Furthermore, a data processing flow is constructed based on the security information, which includes, in sequence: performing whitelist verification and malicious character filtering on the embedded data; immediately masking or hashing sensitive fields such as ID card number, mobile phone number, and bank card number before the data enters the processing flow according to predefined rules; encrypting the data based on the national cryptographic SM4 algorithm; and generating a data digest signature based on the national cryptographic SM3 algorithm.

[0281] In this embodiment, predefined rules are used to automatically detect and replace sensitive fields, including ID card numbers, mobile phone numbers, and bank card numbers. These predefined rules can be implemented using regular expressions, field whitelists / blacklists, pattern matching templates, etc., and this application does not impose specific limitations on them.

[0282] In one embodiment of this application, data security processing is performed on the tracking data of the end-to-end tracking task in the intelligent worker pool to obtain processed tracking data. The network status of the domestic IT innovation environment is detected, and based on the detection result, an upload processing operation or a storage waiting operation is performed on the processed tracking data. The process includes: performing data security processing on the tracking data of the end-to-end tracking task sequentially through a computation worker and an I / O worker to obtain processed tracking data; the network worker detects the network status of the domestic IT innovation environment; when the detection result indicates network connectivity, the network worker uploads the processed tracking data to the server; when the detection result indicates network disconnection, the network worker triggers a network probe to detect the network status; if the network is still disconnected, the processed tracking data is sent to the storage worker; the storage worker receives the processed tracking data and stores it in its local database.

[0283] Specifically, such as Figure 2 As shown, when the intelligent worker pool simultaneously includes I / O workers, compute workers, network workers, and storage workers, the I / O workers in the intelligent worker pool first perform whitelist verification and malicious character filtering on the data points, and immediately mask or hash-replace sensitive fields such as ID card numbers, mobile phone numbers, and bank card numbers before the data enters the processing flow according to predefined rules; then, the compute workers in the intelligent worker pool perform data encryption based on the national cryptographic SM4 algorithm on the data points; and generate a data digest signature based on the national cryptographic SM3 algorithm.

[0284] In this embodiment, to facilitate understanding of the technical solution of the present invention, the core algorithm logic is described in pseudocode as follows. The pseudocode is only used to illustrate the processing flow of the present invention and does not limit the specific programming language or implementation method.

[0285] Function: Executes national cryptographic algorithm to support detection (configuration):

[0286] Result ←{

[0287] Supports national cryptographic standards: false, Algorithm detected: [1, Standard algorithm supported: {},

[0288] Performance benchmark: {}

[0289] Detection methods: API probing and performance testing

[0290] }

[0291] Start time ← performance.now()

[0292] / / 1. Detection standard encryption algorithm support

[0293] Standard Algorithm Detection ← Detects standard encryption algorithm support ()

[0294] Result. Standard algorithm supports ← Standard algorithm detection.

[0295] / / 2. Attempt to detect the national cryptographic algorithm

[0296] Algorithm list ← ['SM2', 'SM3', 'SM4']

[0297] for each algorithm in the list of algorithms do

[0298] Supports checking if the algorithm exists (algorithm) by clicking the left arrow.

[0299] if support then

[0300] Result: Support for Chinese cryptography ← true

[0301] Result. Algorithm detected. push(algorithm)

[0302] end if

[0303] end for

[0304] / / 3. Encryption Performance Benchmark Test

[0305] Performance test time ← Configuration.Maximum time - (performance.now() - Start time)

[0306] If performance test time > 10, then

[0307] Performance Results ← Encryption Performance Test (Test Time)

[0308] Results. Performance benchmark results.

[0309] end if

[0310] Result.Detection time ← performance.now() - Start time

[0311] return result

[0312] End Function;

[0313] In another embodiment, the I / O Worker and Computation Worker in the intelligent Worker pool are set as general workers. Then, the general workers perform the data processing in the above data processing flow on resource-intensive tasks and main thread tracking tasks. The tracking data is processed after passing through the data processing flow.

[0314] Furthermore, such as Figure 2 As shown, the processed data is sent from the computation worker to the network worker. The network worker detects the current network status and obtains the detection results. The methods for network probe detection of the network status include, but are not limited to, one or more of the following: polling, event triggering, heartbeat detection, retry algorithms, etc., which are not specifically limited here.

[0315] If the detection result shows network connectivity, the network worker uploads the processed event tracking data from the computation worker to the server. If the detection result shows network connectivity failure, the storage worker stores the processed event tracking data from the computation worker in the browser's transactional database system and marks the specific error logs. To avoid storage blocking or capacity exhaustion due to continuous writes, the storage worker can adopt a lightweight write strategy, including segmented writes, asynchronous batch writes, and setting a maximum storage capacity threshold. When the amount of stored data reaches the threshold, non-critical event tracking data that has been successfully backed up (such as data that has been reported but not yet confirmed) is cleaned up first, while critical error logs and unreported core user behavior data are retained.

[0316] In one embodiment of this application, when the detection result is that the network is not connected, the network worker continuously detects the network status until the network is connected. Then, the storage worker reads the processed data from the local database in sequence and uploads it to the server through the network worker.

[0317] Specifically, when the detection result indicates that the network is not connected, the network worker will simultaneously start environmental status detection to continuously and with low overhead detect the current network connectivity. The environmental status detection first performs minimal network probing on a fixed detection target, for example, by attempting to initiate a small, non-blocking application layer heartbeat request to the business server or a specified reachable endpoint (e.g., initiating a GET request to a known lightweight static resource and setting a short timeout), and determining network connectivity based on the response result or timeout status.

[0318] Through the environmental status probe, the network worker can continuously monitor network recovery without interrupting the main thread and with minimal overhead. Once network connectivity is confirmed restored, the storage worker retrieves the processed tracking data stored in the local database in batches according to the storage time, and then retransmits it to the server. During retransmission, an exponential backoff strategy is employed to avoid impacting the domestically developed network. This exponential backoff strategy means that if the network worker attempts to upload the processed tracking data from its local storage to the server and the network remains unstable, upload fails, or the server is temporarily unavailable, it will not immediately retry at a fixed frequency. Instead, it will gradually increase the retry interval to reduce instantaneous request pressure. For example, if the network worker fails to upload for the first time, it will retry after 1 second; if it fails again, the waiting time increases exponentially—2 seconds, 4 seconds, 8 seconds…—until the maximum backoff time is reached. This exponentially increasing waiting method effectively avoids high-frequency retry requests under conditions of network congestion, unstable links, or high server pressure, thereby reducing additional impact on the domestically developed network environment.

[0319] In one embodiment of this application, the method further includes: real-time monitoring of the health status of each Worker in the intelligent Worker pool; if the health status of any Worker is abnormal, then the task queue in that Worker is migrated to other healthy Workers.

[0320] Specifically, during the main thread's execution, the method described in this application continuously monitors the health status of each Worker in the intelligent Worker pool. This includes periodically sending heartbeat detection signals to each Worker and listening for their status responses via a heartbeat mechanism to obtain the Worker's real-time health status; and identifying whether a Worker may be in an abnormal state such as blocking, crashing, or resource exhaustion due to prolonged incomplete tasks via a task timeout monitoring mechanism. When any Worker experiences such an abnormal state, the scheduler immediately migrates its assigned task queue to other healthy Workers in sequence to ensure that the task tracking chain remains uninterrupted.

[0321] This front-end and back-end collaborative profiling method can significantly improve the accuracy of environment identification while keeping the front-end main thread simple, safe, and low-overhead, thereby ensuring that the Worker pool configuration and subsequent data processing flow are completely matched with the real environment.

[0322] Compared to traditional general-purpose worker pools, the intelligent worker pool described in this application separates computationally intensive, I / O-intensive, network communication, and data storage tasks, enabling various workers to execute the most suitable tasks based on their own capabilities, thereby significantly improving overall throughput efficiency. On low-performance or resource-constrained devices, a general-purpose worker solution is prioritized. The method described in this application ensures the availability and stability of the system on low-end devices. By undertaking multiple types of tasks, general-purpose workers reduce the number of threads and context switching costs, thus reducing performance pressure and avoiding excessive heterogeneous configuration due to insufficient hardware capabilities. This keeps resource consumption within a controllable range, forming a scalable unified scheduling architecture. This allows the system to maximize the benefits of the heterogeneous worker pool on high-performance devices while maintaining basic service capabilities in low-performance environments.

[0323] like Figure 3 The diagram shows a schematic representation of a front-end data tracking system 300 according to an embodiment of the present invention. The system 300 in this embodiment includes the following modules: an environment awareness module 301, a worker pool creation module 302, and a processing and uploading module 303.

[0324] The environment perception module 301 is used to perform deep perception of the information technology innovation environment in the main thread, obtain multi-dimensional features, and construct an environment profile based on the multi-dimensional features.

[0325] Worker pool creation module 302 is used to create an intelligent worker pool based on the environment profile, obtain full-link tracking tasks in the main thread or intelligent worker pool, and dynamically route the full-link tracking tasks to the intelligent worker pool using a preset intelligent dispatcher.

[0326] The uploading module 303 is used to construct a data processing stream based on the security information in the environmental profile, perform data security processing operations on the tracking data of the end-to-end tracking task in the intelligent worker pool to obtain the processed tracking data, and detect the network status of the information technology innovation environment. Based on the detection results, it performs an uploading operation or a storage waiting operation on the processed tracking data. It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0327] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0328] Figure 4 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 4 As shown, the electronic terminal includes at least one processor 401, a memory 402, at least one network interface 403, and a user interface 405. The various components in the device are coupled together via a bus system 404. It is understood that the bus system 404 is used to implement communication between these components. In addition to a data bus, the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 4 The general will label all buses as bus systems.

[0329] The user interface 405 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0330] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0331] In this embodiment of the invention, the memory 402 is used to store various types of data to support the operation of the electronic terminal 400. Examples of this data include: any executable program for operation on the electronic terminal 400, such as the operating system 4021 and application programs 4022; the operating system 4021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 4022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The implementation of the automatic farmland irrigation method based on region division provided in this embodiment of the invention can be included in the application program 4022.

[0332] The methods disclosed in the above embodiments of the present invention can be applied to processor 401, or implemented by processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 401 or by instructions in the form of software. The processor 401 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 401 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0333] In an exemplary embodiment, the electronic terminal 400 may be used to execute the aforementioned method by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs).

[0334] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when the computer program code is run on a computer, causes the computer to execute a front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment according to any of the embodiments shown.

[0335] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code. When the program code is run on a computer, the computer executes a front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment according to any of the embodiments shown.

[0336] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0337] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0338] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0339] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0340] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0341] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0342] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0343] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0344] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0345] In summary, this application provides a method, system, medium, program product, and terminal for front-end end-to-end data tracking based on Worker pool intelligent scheduling for the domestic IT innovation environment. By executing deep perception of the domestic IT innovation environment in the main thread to construct an environment profile, creating a Worker pool based on the environment profile, and using a preset intelligent dispatcher to dynamically route the tracking tasks to the execution Workers in the Worker pool corresponding to the tracking task type, a data processing flow is constructed based on the security information. The execution Workers perform data security processing on the tracking data according to the data processing flow, and the network Worker uploads the processed tracking data to the server to obtain the upload result and reports the upload result to the main thread. This application achieves adaptive scheduling, security enhancement processing, and reliable reporting of end-to-end front-end tracking tasks in the domestic IT innovation environment.

[0346] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment, characterized in that, include: In the main thread, perform deep perception of the information technology innovation environment to obtain multi-dimensional features, and construct an environment profile based on the multi-dimensional features; Based on the environmental profile, an intelligent worker pool is created. In the main thread, main thread tracking tasks and resource-intensive tasks are obtained, and the main thread tracking tasks and resource-intensive tasks are dynamically routed to the intelligent worker pool using a preset intelligent dispatcher. Based on the intelligent Worker pool, the resource-intensive tasks are processed to obtain the corresponding resource tracking tasks, and the main thread tracking tasks and resource tracking tasks are integrated into a full-link tracking task. Based on the security information in the environmental profile, a data processing flow is constructed. In the intelligent worker pool, data security processing operations are performed on the data of the full-link data tracking task to obtain the processed data. The network status of the information technology innovation environment is detected, and the processed data is uploaded or stored and waited for based on the detection results.

2. The front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment as described in claim 1, characterized in that, The process of performing deep perception of the domestic IT innovation environment and obtaining multi-dimensional features in the main thread includes: Multi-dimensional micro-benchmark tests are performed on the main thread to obtain hardware and software information; Execute a feature detection script on the main thread to obtain security information; The multi-dimensional features are constructed by combining the hardware information, software information, and security information.

3. The front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment as described in claim 1, characterized in that, The process of creating a smart worker pool based on the environmental profile includes: Analyze the software and hardware information in the environmental profile to determine the allocation mode of the intelligent worker pool; Based on the allocation mode, different types and quantities of Workers are generated to form an intelligent Worker pool.

4. The front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment as described in claim 1, characterized in that, The intelligent worker pool includes: I / O worker, compute worker, network worker, and storage worker.

5. The front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment as described in claim 4, characterized in that, In the intelligent worker pool, data security processing is performed on the data collection data of the end-to-end data collection task to obtain processed data collection data. The network status of the domestic IT innovation environment is then detected. Based on the detection results, the processed data collection data is either uploaded or stored and awaiting processing. The process includes: The I / O Worker and the Computation Worker sequentially perform data security processing operations on the data points of the end-to-end data point tracking task to obtain the processed data points. The network worker detects the network status of the domestic IT innovation environment. When the detection result is that the network is connected, the network worker uploads the processed data to the server; when the detection result is that the network is not connected, the processed data is sent to the storage worker. The storage worker receives and processes the embedded data and stores it in the local database.

6. The front-end end-to-end data tracking method based on Worker pool intelligent scheduling for the information technology innovation environment as described in claim 5, is characterized in that, When the detection result indicates that the network is not connected, the network worker continues to detect the network status until the network is connected. Then, the storage worker reads the processed data from the local database in sequence and uploads it to the server through the network worker.

7. A front-end end-to-end data tracking system based on Worker pool intelligent scheduling for the information technology innovation environment, characterized in that, The system includes: The environment perception module is used to perform deep perception of the domestic information technology innovation environment in the main thread, obtain multi-dimensional features, and construct an environment profile based on the multi-dimensional features. The Worker pool creation module is used to create an intelligent Worker pool based on the environment profile, obtain main thread tracking tasks and resource-intensive tasks from the main thread or the intelligent Worker pool, and dynamically route the main thread tracking tasks and resource-intensive tasks to the intelligent Worker pool using a preset intelligent dispatcher. The event tracking acquisition module processes the resource-intensive tasks based on the intelligent Worker pool to obtain the corresponding resource event tracking tasks, and integrates the main thread event tracking tasks and resource event tracking tasks into a full-link event tracking task. The upload processing module is used to construct a data processing stream based on the security information in the environment profile, perform data security processing operations on the data of the full-link data tracking task in the intelligent worker pool to obtain the processed data, detect the network status of the information technology innovation environment, and perform upload processing operations or storage waiting operations on the processed data according to the detection results.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, enables the computer to implement the front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the front-end full-link data embedding method based on Worker pool intelligent scheduling for the information technology innovation environment as described in any one of claims 1 to 6.

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