A highly robust web crawler system

Abstract

This invention discloses a highly robust web crawler system, belonging to the fields of big data acquisition and artificial intelligence technology. The system is a closed-loop intelligent system with multiple modules working collaboratively. It includes a multi-data source database construction and pre-training module, a real-time monitoring module for crawler operation status, a data semantic analysis and association mining module, a data request-matching evaluation and parameter optimization module, and a dataset generation and delivery module. Through tight coupling between modules and effective transmission of data flow and control flow, the system achieves full-process automation and intelligence from data discovery, intelligent crawling, dynamic adaptation to high-quality delivery. The system features high robustness and anti-crawling capabilities, strong data semantic understanding and association accuracy, and low manual costs.

Description

Technical Field

[0001] This invention belongs to the field of big data collection and artificial intelligence technology, and in particular relates to a highly robust web crawler system. Background Technology

[0002] The basic principles and architecture of traditional web crawlers: Traditional web crawlers, also known as web spiders or web robots, work by simulating human user browsing behavior. They use automated programs to send HTTP requests to the target website's server, retrieve webpage content (usually HTML documents), and then parse this content to extract the required data and new links (URLs). This process repeats continuously, starting from one or a set of initial URLs and gradually traversing the entire website or even the entire internet. Their basic architecture typically includes the following core components: a URL scheduler, responsible for managing the queue of URLs to be crawled and the set of already crawled URLs to avoid duplicate crawling; a downloader, responsible for downloading webpage content based on URLs; a parser, responsible for parsing the downloaded webpages, extracting target data and new URLs; and a pipeline, responsible for persistently storing the extracted data. This architecture performs well on websites with simple structures and well-defined rules, and was the primary means of data acquisition on the early internet.

[0003] The limitations of existing web crawlers in the face of anti-crawling measures: With the development of internet technology, websites have deployed various anti-crawling technologies to protect their data resources, reduce server load, or for commercial competition purposes. These technologies greatly limit the effectiveness of traditional web crawlers. Common anti-crawling measures include, but are not limited to: IP address restriction, blocking abnormal IPs by detecting the frequency of requests within a unit of time; User-Agent detection, denying access from non-browser or known crawler programs; CAPTCHA, requiring users to perform human verification, such as the Turing test; dynamic page loading, using JavaScript to dynamically generate page content, making it impossible to obtain complete data by directly downloading HTML; and behavioral analysis, determining whether the user's operation is a real person by analyzing the user's mouse trajectory, click behavior, etc. Traditional web crawlers, due to their fixed request patterns and simple parsing logic, find it difficult to effectively cope with these complex and ever-changing anti-crawling strategies. Once an anti-crawling mechanism is triggered, the crawler often receives error status codes such as HTTP 403 (Forbidden), causing the data collection task to be interrupted, requiring manual intervention to adjust the strategy, which seriously affects the efficiency and stability of data collection.

[0004] Disadvantages and shortcomings of existing technologies: (1) Insufficient robustness: difficult to cope with dynamic anti-crawling strategies A core drawback of existing web crawler technologies is their severe lack of robustness, especially when facing increasingly complex and dynamically changing anti-crawling strategies. Traditional crawlers typically rely on preset, static configuration parameters, such as fixed request headers (User-Agent), fixed request intervals, and fixed IP addresses. This "one-size-fits-all" approach makes the crawler's behavior patterns very easy for websites to identify and predict. Once a website upgrades its anti-crawling strategies—for example, by introducing new CAPTCHA mechanisms, changing dynamic loading logic, or strengthening monitoring of request frequency—traditional crawlers quickly become ineffective. They lack the ability to perceive and adapt dynamically in real time, and cannot intelligently adjust their behavior based on server responses (such as status codes, response headers, and changes in page content). This leads to frequent interruptions in data collection tasks, requiring developers to constantly modify code and configurations manually to cope with new anti-crawling challenges. This not only consumes a lot of manpower but also makes large-scale, continuous data collection extremely difficult.

[0005] (2) Low level of intelligence: Relies on manual configuration and maintenance Most current web crawler systems have a generally low level of intelligence, and their operation and maintenance largely rely on manual intervention. From the initial selection of seed URLs and the formulation of crawling rules (such as regular expressions or XPath expressions), to countering anti-crawling strategies and handling data cleaning and deduplication logic, almost every step requires meticulous manual configuration and debugging. For example, when the target website's page structure changes, developers must manually update the parsing rules; otherwise, the crawler will be unable to extract data correctly. Similarly, when encountering anti-crawling mechanisms, manual analysis of the anti-crawling principles and writing corresponding bypass code are required. This highly manual approach is not only inefficient but also lacks scalability. For large-scale data collection tasks that require monitoring thousands of different websites, the cost of manual maintenance becomes prohibitive. Furthermore, due to a lack of self-learning and optimization capabilities, these crawlers cannot learn from past successes or failures, and their performance does not automatically improve over time.

[0006] (3) Poor data quality: lack of effective semantic understanding and cleaning mechanisms Traditional web crawlers often lack effective mechanisms to ensure the quality of the final output data after data extraction. They typically only perform simple structured extraction and cannot understand the deep semantics of the data. This leads to several serious problems. First, there is data redundancy and noise. Crawlers may collect a large amount of information unrelated to the target topic, such as advertisements, navigation links, and copyright notices. This noisy data can severely affect the accuracy of subsequent analysis. Second, there is data inconsistency. For the same entity, different websites may use different names, formats, or descriptions. Traditional crawlers cannot identify these different expressions referring to the same object, leading to difficulties in data integration. Finally, there is a lack of data correlation. Traditional crawlers treat each data source as an isolated entity and cannot uncover the inherent connections between different data sources regarding the same event, entity, or topic. For example, it may crawl reports and comments about the same product from news websites and social media separately, but it cannot automatically correlate them to form a more comprehensive view. This "blindness" to semantics significantly reduces the value of the collected data.

[0007] (4) Single data source: lack of ability to associate and integrate multi-source data. Existing web crawling technologies are typically designed for specific websites or website types, resulting in relatively limited data source coverage. A crawler designed for news websites is difficult to apply directly to e-commerce websites or social media platforms. This specialization limits the breadth and diversity of data. More importantly, traditional crawlers lack the ability to correlate and integrate information across data sources. They treat each data source as an independent information silo, resulting in fragmented and disjointed data. For example, in market research, data from a single source (such as price information only from an e-commerce platform) may not provide comprehensive market insights. Integrating data from e-commerce platforms with user reviews on social media, industry reports from news websites, and analytical reports from professional data websites can create a comprehensive, multi-dimensional market view. However, traditional crawlers cannot achieve this cross-source data fusion, leaving users with only partial and incomplete information, thus affecting the scientific rigor and accuracy of decision-making. Summary of the Invention

[0008] In view of this, the present invention discloses a highly robust web crawler system.

[0009] The present invention adopts the following technical solution: A highly robust web crawler system, the system being a closed-loop intelligent system with multiple modules working collaboratively, the system comprising: A multi-source database construction and pre-training module is used for constructing multi-source databases and AI pre-training, and for building a data source knowledge graph rich in semantic associations. The real-time monitoring module for the crawler's running status is used to continuously monitor the crawler's behavior and the server's response, ensuring the stability and security of the data collection process. The data semantic analysis and association mining module receives user query requests and filters relevant data sources from the data source knowledge graph. The data request-matching evaluation and parameter optimization module is used to transform intelligent decisions into actual actions and to perform self-optimization based on the action results. A dataset generation and delivery module is used to transform raw data into a standard-compliant dataset that can be directly applied. The AI ​​engine runs throughout the entire system, providing algorithmic support for system modules.

[0010] Furthermore, the data request-matching evaluation and parameter optimization module includes an intelligent data request submodule, a data matching degree evaluation submodule, and a parameter adaptive optimization submodule.

[0011] Furthermore, the intelligent data request submodule initiates an HTTP request based on the relevant data sources selected by the data semantic analysis and association mining module, and dynamically and intelligently adjusts the various parameters of the request.

[0012] Furthermore, the data matching evaluation submodule uses a matching algorithm to compare the acquired new data with existing related old data records in the database; the matching algorithm is used to calculate the similarity between the new and old data in multiple dimensions such as content, structure, and timestamp, and generate a quantified matching score.

[0013] Furthermore, the parameter adaptive optimization submodule performs closed-loop feedback based on the matching degree evaluation results; if the matching degree is higher than the preset threshold, it indicates that the current data acquisition strategy is effective, and the system will record and strengthen these strategies; if the matching degree is lower than the preset threshold, the system will analyze the reasons and automatically adjust the relevant parameters.

[0014] Furthermore, the monitoring metrics include: sent HTTP requests, received page response status codes, page load time, and whether specific abnormal elements appear in the page content; When the real-time monitoring module for the crawler's running status detects an abnormal state, the system triggers an anti-crawling mechanism and automatically activates an emergency strategy. The emergency strategy includes intelligent avoidance, simulated manual operation, and data source switching. The data source switching includes: automatically selecting a backup data source with similar content based on the data source knowledge graph pre-trained by the multi-data source database construction and pre-training module, and seamlessly migrating the crawler task there.

[0015] Furthermore, the construction of the multi-data source database includes: based on preset rules, using automated scripts to extensively and continuously discover and collect various data sources from the Internet, storing the collected data source information in the database to form a dynamically updated, large data source pool; the AI ​​pre-training is used to mine potential relationships between the data source information; the AI ​​pre-training uses deep learning algorithms to perform unsupervised pre-training on the data sources in the database, discovering data sources with similar content, related themes, or similar structures and establishing similarity mapping relationships.

[0016] Furthermore, the core of the data semantic analysis and association mining module is a deep learning model based on the Transformer architecture, supplemented by a self-attention mechanism; the deep learning model is used to deeply analyze and understand the semantic connotation of the data source; the self-attention mechanism is used to automatically and dynamically assign different weights to different words when processing text, accurately capturing the most important information in the sentence and the long-distance dependencies between different words.

[0017] Furthermore, the data semantic analysis and association mining module performs deep semantic encoding on the text content of different data sources and compares it with the semantic encoding of the target information summary. The comparison is based on the similarity calculation of semantic vectors, so as to select one or a group of data sources that are most semantically matched and have the highest correlation with the target data from a massive number of candidate data sources.

[0018] Furthermore, the process of transforming raw data into a standard-compliant, directly applicable dataset includes data cleaning and integration, and data delivery. Data cleaning and integration includes deduplication, validation, standardization, and correlation fusion. Deduplication removes completely duplicated or highly similar data records. Validation verifies the format, type, and value range of data according to preset business rules or data patterns to identify, mark, or correct erroneous data. Standardization converts heterogeneous data from different data sources according to a unified data model and format. Correlation fusion, based on the semantic analysis capabilities of the data semantic analysis and correlation mining module, extracts data about the same entity or event from different sources and merges them into a more comprehensive and information-rich record.

[0019] The beneficial effects of this invention are: (1) Improved system robustness and anti-crawling capabilities This invention constructs a dynamic anti-crawling response system through real-time monitoring of the crawler's running status and parameter optimization of the data request-matching evaluation and parameter optimization module. Unlike traditional crawlers that passively endure attacks and require manual repair upon failure, this system proactively detects the triggering of anti-crawling measures and automatically adopts various strategies to circumvent them, such as intelligently switching proxy IP pools, dynamically adjusting request frequency, and simulating more realistic human browsing behavior. This adaptive capability allows the system to maintain a high data collection success rate and stability even when facing constantly changing anti-crawling strategies from target websites, greatly improving the system's robustness. Simultaneously, the multi-source database construction and pre-training module provides ample "backup resources" for the system. When the primary data source is blocked, the system can seamlessly switch to a backup data source, ensuring the continuity of data collection tasks and fundamentally solving the vulnerability problem of traditional crawlers being "completely wiped out" due to the failure of a single data source.

[0020] (2) Enhanced the accuracy of data semantic understanding and association. This invention introduces a data semantic analysis and association mining module based on the Transformer model, achieving deep semantic understanding of data requirements and accurate parsing of web page content. Compared to the "mechanical" crawling of traditional web crawlers that relies on keyword matching, this system can understand context like a human and distinguish between multiple meanings of a word, thus greatly improving the accuracy of data acquisition. For example, the system can accurately distinguish between "Apple" company and "apple" fruit, and accordingly filter out truly relevant data sources. Furthermore, the association mining capability of the data semantic analysis and association mining module enables the system to discover inherent connections between different data sources regarding the same entity or event, providing a solid foundation for subsequent data fusion and knowledge graph construction. This deep semantic-based association capability is completely lacking in traditional web crawlers, elevating data collection from simple information copying to the level of knowledge discovery.

[0021] (3) Adaptive parameter optimization was achieved, reducing labor costs. The data request-matching evaluation and parameter optimization module of this invention achieves adaptive optimization of crawler parameters through a closed-loop mechanism of "collection-evaluation-feedback-adjustment". The system no longer relies on engineers' experience to manually set parameters such as request frequency and parsing rules, but can automatically and dynamically adjust based on real-time feedback from data matching evaluation. If the system detects that the current strategy is causing a decline in data quality or triggering anti-crawling measures, it will automatically reduce the request frequency or try new parsing rules. This self-learning and self-optimization capability frees data engineers from tedious maintenance and debugging work, greatly reducing the system's manual operation and maintenance costs. At the same time, this adaptive mechanism also allows the system to adapt to new websites and new anti-crawling strategies more quickly, improving the system's scalability and deployment efficiency.

[0022] (4) Improve the completeness, accuracy and diversity of the dataset. After rigorous quality control through five modules, the dataset generated by this system far surpasses that of traditional web crawlers in quality. First, completeness is guaranteed; intelligent switching and fusion of multi-source data minimizes information incompleteness caused by data source failures or missing data. Second, accuracy is significantly improved; deep semantic analysis and matching evaluation mechanisms effectively filter out noisy data and irrelevant information, ensuring high data relevance. Finally, diversity is greatly enhanced; the system integrates data from multiple channels and perspectives, including news, social media, and professional forums, providing users with a comprehensive and multi-dimensional data view. This high-quality, highly complete dataset can be directly used for high-precision analysis, modeling, and decision support, requiring little or no secondary cleaning, thus significantly shortening the path from data to insight. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the highly robust web crawler system architecture of the present invention. Detailed Implementation

[0025] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Example 1 A highly robust web crawler system, comprising a closed-loop intelligent system with multiple modules working collaboratively, used for data source discovery, data acquisition, data processing, and dataset generation; the system includes: A multi-source database construction and pre-training module is used for constructing multi-source databases and AI pre-training, and for building a data source knowledge graph rich in semantic associations. The real-time monitoring module for the crawler's running status is used to continuously monitor the crawler's behavior and the server's response, ensuring the stability and security of the data collection process. The data semantic analysis and association mining module receives user query requests and filters relevant data sources from the data source knowledge graph. The data request-matching evaluation and parameter optimization module is used to transform intelligent decisions into actual actions and to perform self-optimization based on the action results. The dataset generation and delivery module is used to transform raw data processed by other modules into high-quality datasets that conform to standards and can be directly applied. The AI ​​engine runs throughout the entire system, providing algorithmic support for system modules.

[0028] Furthermore, the data request-matching evaluation and parameter optimization module includes an intelligent data request submodule, a data matching degree evaluation submodule, and a parameter adaptive optimization submodule.

[0029] Furthermore, the intelligent data request submodule initiates an HTTP request based on the relevant data sources selected by the data semantic analysis and association mining module, and dynamically and intelligently adjusts the various parameters of the request.

[0030] Furthermore, the data matching evaluation submodule uses a specially designed matching algorithm to compare the acquired new data with existing related old data records in the database. This matching algorithm is also semantically based, used to calculate the similarity between the new and old data across multiple dimensions, including content, structure, and timestamps, and to generate a quantified matching score. This matching score reflects the accuracy, completeness, and novelty of the new data.

[0031] Furthermore, the parameter adaptive optimization submodule performs closed-loop feedback based on the matching degree evaluation results; if the matching degree is higher than the preset threshold, it indicates that the current data acquisition strategy is effective, and the system will record and strengthen these strategies; if the matching degree is lower than the preset threshold, the system will analyze the reasons and automatically adjust the relevant parameters.

[0032] Furthermore, the monitored metrics include: sent HTTP requests, received page response status codes (such as 200, 301, 403, 404, 503, etc.), page load time, and whether specific abnormal elements appear in the page content (such as CAPTCHA images, human verification pop-ups, error messages, etc.).

[0033] When the real-time monitoring module for the crawler's running status detects an abnormal state, such as receiving multiple consecutive 403 (Forbidden) status codes or the appearance of a specific CAPTCHA identifier in the page's DOM structure, the system triggers the anti-crawling mechanism and automatically activates emergency strategies. These emergency strategies include intelligent avoidance, simulated manual operation, and data source switching. The data source switching involves automatically selecting a backup data source with similar content based on the data source knowledge graph pre-trained by the multi-data source database construction and pre-training module, and seamlessly migrating the crawler task there. This complete real-time monitoring and emergency response mechanism ensures that the system can quickly perceive and effectively respond to anti-crawling challenges, minimizing data collection interruption time and guaranteeing task continuity and success rate.

[0034] Furthermore, the construction of the multi-data source database includes: based on preset rules, using automated scripts to extensively and continuously discover and collect various data sources from the Internet. These data sources are categorized, covering different types such as news and information platforms, professional data websites, social media, e-commerce websites, and government open data platforms, ensuring the breadth of industry coverage and the diversity of content. The collected data source information (such as domain names, URL patterns, page structure features, etc.) is stored in the database, forming a dynamically updated, large data source pool; the AI ​​pre-training is used to mine potential correlations between the data source information; the AI ​​pre-training includes: using deep learning algorithms, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to perform unsupervised pre-training on the data sources in the database. CNNs are good at extracting local spatial features such as page layout and images, while RNNs can effectively process text sequence information. Through learning from a large amount of historical data, the model can automatically discover data sources with similar content, related themes, or homogeneous structures and establish similarity mapping relationships between them. For example, the model can learn that multiple news websites with different domain names have highly similar page structures and keyword distributions when reporting the same event. This pre-training process provides powerful prior knowledge for subsequent data source selection and replacement. When a primary data source becomes unavailable, the system can quickly find one or more alternative data sources with similar content from the pool of candidates, thereby greatly enhancing the robustness of the system.

[0035] Furthermore, the core of the data semantic analysis and association mining module is a deep learning model based on the Transformer architecture, supplemented by a self-attention mechanism. The deep learning model is used to deeply analyze and understand the semantic connotation of the data source. The self-attention mechanism is used to automatically and dynamically assign different weights to different words when processing a text, thereby accurately capturing the most important information in the sentence and the long-distance dependencies between different words.

[0036] Furthermore, the data semantic analysis and association mining module performs deep semantic encoding on the text content of different data sources and compares it with the semantic encoding of the target information summary. The comparison is based on the similarity calculation of semantic vectors, so as to accurately select one or a group of data sources that are most semantically matched and have the highest correlation with the target data from a massive number of candidate data sources.

[0037] Furthermore, the process of transforming raw data into a standard-compliant, directly applicable dataset includes data cleaning and integration, and data delivery. Data cleaning and integration includes deduplication, verification, standardization, and correlation fusion. Deduplication involves using hash algorithms or similarity calculations to remove completely duplicate or highly similar data records. Verification refers to checking the format, type, and value range of data according to preset business rules or data patterns to identify, mark, or correct erroneous data. Standardization involves converting heterogeneous data from different data sources according to a unified data model and format. Correlation fusion, based on the semantic analysis capabilities of the data semantic analysis and correlation mining module, extracts data about the same entity or event from different sources and merges them into a more comprehensive and information-rich record.

[0038] Example 2 1. Overall Technical Architecture The highly robust web crawler system proposed in this invention is a closed-loop intelligent system with multiple functional modules working collaboratively. Driven by data flow and with an AI model as the core decision engine, this architecture achieves end-to-end intelligent operation from data source discovery, data acquisition, data processing to system optimization. The entire architecture can be abstracted into five core layers: a data input and preprocessing layer (multi-data source database construction and pre-training module), an intelligent decision-making and execution layer (data semantic analysis and association mining module & data request-matching evaluation and parameter optimization module), a status monitoring and feedback layer (real-time monitoring module of crawler operation status), a data output and delivery layer (dataset generation and delivery module), and an AI engine running throughout. The data input and preprocessing layer is responsible for building and maintaining a large and diverse data source database and mining the inherent relationships between data sources through pre-trained models. The intelligent decision-making and execution layer is the "brain" of the system; it selects the optimal data source based on semantic analysis results, initiates data requests, and performs matching and evaluation on the acquired data. The status monitoring and feedback layer acts like a "nervous system," monitoring the crawler's operational status in real time. Upon detecting an anomaly, it immediately triggers emergency procedures and feeds the status information back to the decision-making layer. The data output and delivery layer is responsible for cleaning, integrating, and formatting the final data to generate a high-quality dataset. The AI ​​engine provides algorithmic support for each module, including CNN / RNN models for pre-training and Transformer models for semantic analysis.

[0039] 2. Core Modules and Implementation Steps 2.1 Multi-datasource database construction and pre-training module This module is the foundation of the system, responsible for providing abundant "raw materials" for all subsequent operations. Its workflow consists of two main steps. The first step is the construction of a multi-data source database. Based on preset rules, the system uses automated scripts to extensively and continuously discover and collect various information sources from the internet. These sources are carefully categorized, covering different types such as news platforms, professional data websites, social media, e-commerce websites, and government open data platforms, ensuring broad industry coverage and content diversity. All collected data source information (such as domain names, URL patterns, page structure features, etc.) is stored in a specially designed database, forming a dynamically updated, massive data source pool. The second step is AI pre-training. To uncover the potential connections between these massive data sources, the system uses deep learning algorithms, especially Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for unsupervised pre-training on the data sources in the database. CNNs excel at extracting local spatial features such as page layout and images, while RNNs can effectively process text sequence information. Through learning from a large amount of historical data, the model can automatically discover data sources with similar content, related themes, or similar structures, and establish similarity mapping relationships between them. For example, the model can learn that news websites with different domains have highly similar page structures and keyword distributions when reporting on the same event. This pre-training process provides strong prior knowledge for subsequent data source selection and replacement. When a primary data source becomes unavailable, the system can quickly find one or more alternative data sources with similar content from the pool of candidates, thereby greatly enhancing the system's robustness.

[0040] 2.2 Real-time monitoring module for crawler running status This module acts as the system's "sentinel," responsible for ensuring the stability and security of the data collection process. Throughout the entire lifecycle of the crawler, it continuously monitors the crawler's behavior and server responses through a highly efficient real-time monitoring module. Key monitoring metrics include: sent HTTP requests, received page response status codes (such as 200, 301, 403, 404, 503, etc.), page load time, and the presence of specific abnormal elements in the page content (such as CAPTCHA images, human verification pop-ups, error messages, etc.). Once the monitoring module detects any abnormal state, such as receiving multiple consecutive 403 (Forbidden) status codes, or the appearance of specific CAPTCHA identifiers in the page DOM structure, the system immediately determines that the anti-crawling mechanism has been triggered. At this point, the emergency response process is automatically initiated. The emergency strategy is multi-layered, first attempting intelligent avoidance, such as automatically changing proxy IPs, randomizing request headers (User-Agent, Accept-Language, etc.), and adjusting request time intervals to simulate more realistic human browsing behavior. If intelligent avoidance fails, the system will attempt to simulate manual operation, such as calling a CAPTCHA recognition service (OCR or a third-party CAPTCHA solving platform) to process simple CAPTCHAs. If the problem persists, the system will execute a data source switching strategy. Based on the association graph obtained from the multi-data source database construction and pre-training module, it will automatically select a backup data source with similar content and seamlessly migrate the crawling task there. This entire real-time monitoring and emergency response mechanism ensures that the system can quickly detect and effectively respond to anti-crawling challenges, minimizing data collection interruption time and guaranteeing task continuity and success rate.

[0041] 2.3 Data Semantic Analysis and Association Mining Module This module is the "intelligent core" of the system, endowing the crawler with deep semantic understanding capabilities that go beyond simple text matching. When the system needs to obtain specific information, it first inputs a pre-requested information summary (e.g., a keyword, a topic description, or an entity name) into this module. At the core of the module is a deep learning model based on the Transformer architecture, supplemented by a self-attention mechanism. The Transformer model has achieved revolutionary success in the field of natural language processing due to its powerful parallel computing capabilities and excellent language representation abilities. In this invention, it is used to deeply analyze and understand the semantic connotations of the data source. The self-attention mechanism allows the model to automatically and dynamically assign different weights to different words when processing a text, thereby accurately capturing the most important information in the sentence and the long-distance dependencies between different words. Through the synergistic effect of these two technologies, the system can perform deep semantic encoding on text content from different data sources and compare it with the semantic encoding of the target information summary. This comparison is no longer a simple keyword matching, but rather a similarity calculation based on semantic vectors. In this way, the system can accurately filter out the one or a group of data sources that are semantically most matched and have the highest relevance to the target data from a massive pool of candidate data sources. For example, when the target is "Apple's latest mobile phone," the system can not only find pages containing these keywords, but also understand the high relevance of words such as "iPhone 15 Pro," "A17 chip," and "titanium body" to the target, thereby locating the most core and relevant data source from numerous technology news and review articles.

[0042] 2.4 Data Request - Matching Evaluation and Parameter Optimization Module This module serves as the system's "execution and feedback hub," responsible for translating intelligent decisions into concrete actions and self-optimizing based on the results. It comprises three tightly coupled sub-functions: intelligent data request, data matching evaluation, and adaptive parameter optimization. First, in the intelligent data request phase, the crawler initiates HTTP requests based on the optimal data source selected by the data semantic analysis and association mining module. During this process, the system dynamically and intelligently adjusts various request parameters. For example, it adaptively adjusts the request time interval (from seconds to minutes) based on the target website's anti-crawling strength, randomly switches different User-Agents and proxy IPs, and even simulates browser JavaScript loading behavior to maximize request success rates and avoid triggering anti-crawling mechanisms. After acquiring the data, the system enters the data matching evaluation phase. It uses a specially designed matching algorithm to meticulously compare the newly acquired data with existing related data records in the database. This matching algorithm is also semantically based, calculating the similarity between new and old data across multiple dimensions such as content, structure, and timestamps, and generating a quantified matching score. This score reflects the accuracy, completeness, and novelty of the new data. Finally, in the parameter adaptive optimization phase, the system provides closed-loop feedback based on the matching degree evaluation results. If the matching degree is higher than a preset threshold, it indicates that the current data acquisition strategy is effective, and the system will record and strengthen these strategies. If the matching degree is low, the system will conduct in-depth analysis of the reasons and automatically adjust relevant parameters. For example, if data is found to be missing, the parsing rules may be adjusted; if the data duplication rate is high, the deduplication algorithm may be optimized; if anti-scraping is frequently triggered, the request frequency will be further reduced or the proxy strategy will be changed. This feedback-based parameter optimization mechanism based on evaluation results enables the entire system to form a self-learning and self-improving closed loop, and its data acquisition accuracy and efficiency will continue to improve with the increase of running time.

[0043] 2.5 Dataset Generation and Delivery Module This module serves as the system's "final product workshop," responsible for transforming the raw data processed in previous stages into high-standard, directly applicable, high-quality datasets. This process primarily includes two steps: data cleaning and integration, and data delivery. During the data cleaning and integration phase, the system performs a series of rigorous processes on the collected data. First, deduplication is performed using efficient hash algorithms or more complex similarity calculations to remove completely duplicate or highly similar data records. Second, validation is performed, where the system validates the data's format, type, and value range according to preset business rules or data patterns, identifying, marking, or correcting erroneous data. For example, for date fields, the system ensures consistent formatting; for numeric fields, it checks whether they are within a reasonable range. Third, standardization is performed, transforming heterogeneous data from different data sources according to a unified data model and format. For example, different names for the same company on different websites (such as "Tencent," "Tencent Company") are uniformly mapped to the same standard entity ID. Finally, there's the association and fusion process. Leveraging the semantic analysis capabilities of the data semantic analysis and association mining modules, data about the same entity or event is extracted from different sources and merged into a richer, more complete record. After this series of processes, the system ultimately generates a structured, clean, consistent, and information-rich high-quality dataset. During the data delivery phase, the system will export the final dataset in various formats (such as CSV, JSON, XML) or directly write it to a specified database (such as MySQL, MongoDB, Elasticsearch) according to user needs, ensuring that users can easily and quickly access and use these valuable data resources.

[0044] Example 3 Overall workflow of the system of this invention The overall workflow of this system is a highly integrated, interconnected, intelligent closed-loop process designed to efficiently transform raw web data into high-quality structured datasets. The entire process begins with the multi-data source database construction and pre-training module. In this stage, the system constructs a database containing various types of information sources through extensive web crawling and pre-trains these data sources using deep learning models such as CNN and RNN to mine potential relationships between them, forming an intelligent data source knowledge graph. When the system receives a specific data collection task, the process enters the data semantic analysis and association mining module. Task requirements (such as keywords or topic descriptions) are input into the Transformer-based semantic analysis module, which leverages its powerful language understanding capabilities to accurately identify the list of data sources that semantically best match the task requirements from the pre-trained data source knowledge graph. Subsequently, the data request-matching evaluation and parameter optimization module is launched. The crawler begins executing the data scraping task based on the selected data source list. During this process, the real-time monitoring module for the crawler's running status remains online, closely monitoring the response to each request. If everything is normal, the data is successfully acquired. If anti-crawling measures are encountered (such as 403 errors or CAPTCHAs), the real-time monitoring module immediately triggers an emergency procedure and notifies the data request-matching evaluation and parameter optimization module. After acquiring the data, the matching algorithm in the data request-matching evaluation and parameter optimization module evaluates the data quality. If the data quality is high, it proceeds to the dataset generation and delivery module; if the data quality is low or acquisition fails, the data request-matching evaluation and parameter optimization module analyzes the reasons and dynamically adjusts the crawler parameters (such as changing the IP address, modifying the request frequency, adjusting the parsing rules, etc.), and then retryes the request. All high-quality data that passes the evaluation is sent to the dataset generation and delivery module: the high-quality dataset generation and delivery module performs final deduplication, integration, and structuring processing to generate the final dataset and deliver it to the user. Throughout the process, the data source knowledge graph of the multi-data source database construction and pre-training module and the parameter optimization strategy of the data request-matching evaluation and parameter optimization module are continuously updated and learned based on the new crawling results, so that the system performance continues to improve over time.

[0045] The collaborative mechanism between modules of the system of this invention The modules work closely together through efficient data and control flows, forming an organic whole: The collaboration between the multi-source database construction and pre-training module and the data semantic analysis and association mining module: The multi-source database construction and pre-training module provides a rich pool of semantically related data source candidates for the data semantic analysis and association mining module. The precise selection capability of the data semantic analysis and association mining module, in turn, verifies the effectiveness of the pre-training of the multi-source database construction and pre-training module and provides new related data samples for continuous optimization of the data source knowledge graph.

[0046] The collaboration between the real-time crawler operation status monitoring module and the data request-matching evaluation and parameter optimization module: The real-time crawler operation status monitoring module acts as the "eyes" and "trigger" for the data request-matching evaluation and parameter optimization module. The real-time monitoring results from the real-time crawler operation status monitoring module provide direct feedback signals for parameter optimization in the data request-matching evaluation and parameter optimization module. The optimization decisions of the data request-matching evaluation and parameter optimization module (such as changing IP addresses) are then validated through monitoring by the real-time crawler operation status monitoring module. Together, they constitute the core of the system's anti-crawling response.

[0047] The collaboration between the data semantic analysis and association mining module and the data request-matching evaluation and parameter optimization module: The data semantic analysis and association mining module determines "where to crawl," while the data request-matching evaluation and parameter optimization module determines "how to crawl" and "whether the crawl is correct." The precise positioning of the data semantic analysis and association mining module reduces the trial-and-error cost of the data request-matching evaluation and parameter optimization module, while the feedback from the data request-matching evaluation and parameter optimization module (such as an overly strict anti-crawling strategy for a certain data source) can also provide a basis for the data semantic analysis and association mining module to adjust the priority of data sources.

[0048] The collaboration between the Data Request-Matching Evaluation and Parameter Optimization module and the Dataset Generation and Delivery module: The Data Request-Matching Evaluation and Parameter Optimization module acts as the "quality inspector" for the Dataset Generation and Delivery module. Only data that passes the rigorous evaluation of this module is eligible to enter the Dataset Generation and Delivery module for final integration and delivery. The quality metrics (such as accuracy and completeness) of the final dataset generated by the Dataset Generation and Delivery module can also serve as an important basis for evaluating the performance of the matching algorithm in the Data Request-Matching Evaluation and Parameter Optimization module.

[0049] Example 4 System architecture diagram of this invention like Figure 1As shown in the diagram, this system architecture diagram clearly illustrates how the five core modules—multi-source database construction and pre-training module, crawler running status real-time monitoring module, data semantic analysis and association mining module, data request-matching evaluation and parameter optimization module, and dataset generation and delivery module—work together to form an intelligent data acquisition closed loop.

[0050] Multi-data source database construction and pre-training module: As the starting point of the entire process, the multi-data source database construction and pre-training module uses AI models such as CNN / RNN to pre-train a wide range of collected multi-source data sources, constructing a "data source knowledge graph" rich in semantic associations, providing a foundation for subsequent intelligent decision-making.

[0051] Data Semantic Analysis and Association Mining Module: This module receives user query requests, uses the Transformer model to deeply understand their semantics, and selects the most relevant data sources from the knowledge graph generated by the multi-data source database construction and pre-training module.

[0052] Data Request - Matching Evaluation and Parameter Optimization Module: This module is the core of the system's execution and optimization. It initiates data requests based on the decisions made by the data semantic analysis and association mining module, and evaluates the quality of the acquired data. The evaluation results are fed back to the module to dynamically adjust the crawler parameters, achieving self-optimization.

[0053] The real-time monitoring module for the crawler's running status acts as a "sentinel" throughout the entire execution process, monitoring the crawler's status in real time. Once anti-crawling measures are detected, it immediately sends feedback to the data request-matching evaluation and parameter optimization module, triggering an emergency handling process to ensure the continuity of data collection.

[0054] Dataset Generation and Delivery Module: The dataset generation and delivery module is the final output of the system. It is responsible for integrating, cleaning, and formatting the high-quality data that has undergone rigorous screening and processing through multiple stages to generate the final high-quality dataset.

[0055] AI Engine: The AI ​​engine provides core algorithm support for key modules such as multi-data source database construction and pre-training, data semantic analysis and association mining, and data request-matching evaluation and parameter optimization, and is the key to realizing system intelligence.

[0056] The entire architecture achieves full-process automation and intelligence from data discovery, intelligent capture, dynamic adaptation to high-quality delivery through tight coupling between modules and effective transmission of data flow and control flow.

[0057] The core objective of this invention is to systematically address the four major problems exposed by existing web crawler technologies when facing the complex environment of the modern internet. First, addressing the issue of insufficient robustness, this invention aims to introduce real-time monitoring and intelligent emergency response mechanisms, enabling the crawler system to dynamically perceive and effectively respond to various anti-crawling measures (such as CAPTCHAs, IP blocking, and behavior detection), thereby ensuring the continuity and stability of data collection tasks. Second, to address the pain point of low intelligence, this invention proposes a parameter adaptive optimization mechanism based on matching evaluation, enabling the system to automatically adjust key parameters such as request strategies and parsing rules based on feedback from data acquisition, minimizing manual intervention and achieving self-evolution. Third, addressing the deficiency of low data quality, this invention utilizes advanced natural language processing technology, particularly a Transformer-based semantic analysis model, to deeply understand and clean the collected data, effectively removing noise, unifying the format, and associating entities, thereby generating high-quality structured datasets. Finally, to overcome the limitations of a single data source, this invention constructs a multi-source heterogeneous database and uses an AI pre-trained model to mine potential relationships between different data sources, achieving cross-platform and cross-domain data integration and complementarity, and providing users with a comprehensive and multi-dimensional information view.

[0058] By addressing the aforementioned technical problems, this invention aims to achieve the following significant technical effects: First, it achieves highly robust data acquisition: the system can maintain stable operation even when facing complex and ever-changing anti-scraping strategies, significantly improving the data acquisition success rate and greatly reducing the task interruption rate. Second, it achieves highly intelligent autonomous operation and maintenance: the system possesses self-diagnosis and self-adjustment capabilities, and can adaptively optimize based on the operating environment and data feedback, minimizing manual maintenance costs and achieving an efficient "unattended" or "minimally attended" operation and maintenance mode. Third, it produces high-quality data products: the datasets ultimately delivered by the system have high accuracy, completeness, consistency, and timeliness, and can be directly applied to high-precision data analysis, business intelligence, and artificial intelligence model training, requiring little or no secondary processing. Fourth, it provides panoramic data insight capabilities: by integrating and correlating massive amounts of data from different industries and types of websites, the system can provide users with panoramic data insights that go beyond a single data source, offering depth and breadth, and empowering more scientific decision-making.

[0059] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A highly robust web crawler system, characterized in that, The system is a closed-loop intelligent system with multiple modules working collaboratively. The system includes: A multi-source database construction and pre-training module is used for constructing multi-source databases and AI pre-training, and for building a data source knowledge graph rich in semantic associations. The real-time monitoring module for the crawler's running status is used to continuously monitor the crawler's behavior and the server's response, ensuring the stability and security of the data collection process. The data semantic analysis and association mining module receives user query requests and filters relevant data sources from the data source knowledge graph. The data request-matching evaluation and parameter optimization module is used to transform intelligent decisions into actual actions and to perform self-optimization based on the action results. A dataset generation and delivery module is used to transform raw data into a standard-compliant dataset that can be directly applied. The AI ​​engine runs throughout the entire system, providing algorithmic support for system modules.

2. The crawler system according to claim 1, characterized in that, The data request-matching evaluation and parameter optimization module includes an intelligent data request submodule, a data matching degree evaluation submodule, and a parameter adaptive optimization submodule.

3. The crawler system according to claim 2, characterized in that, The intelligent data request submodule initiates an HTTP request based on the relevant data sources selected by the data semantic analysis and association mining module, and dynamically and intelligently adjusts the various parameters of the request.

4. The crawler system according to claim 2, characterized in that, The data matching evaluation submodule uses a matching algorithm to compare the acquired new data with existing related old data records in the database. The matching algorithm is used to calculate the similarity between the new and old data in multiple dimensions such as content, structure, and timestamp, and to generate a quantified matching score.

5. The crawler system according to claim 2, characterized in that, The parameter adaptive optimization submodule performs closed-loop feedback based on the matching degree evaluation results; if the matching degree is higher than the preset threshold, it indicates that the current data acquisition strategy is effective, and the system will record and strengthen these strategies. If the matching degree is lower than the preset threshold, the system will analyze the reasons and adjust the relevant parameters.

6. The crawler system according to claim 1, characterized in that, The metrics for continuous monitoring include: sent HTTP requests, received page response status codes, page load time, and whether specific abnormal elements appear in the page content; When the real-time monitoring module for the crawler's running status detects an abnormal state, the system triggers an anti-crawling mechanism and automatically activates an emergency strategy. The emergency strategy includes intelligent avoidance, simulated manual operation, and data source switching. The data source switching includes: automatically selecting a backup data source with similar content based on the data source knowledge graph pre-trained by the multi-data source database construction and pre-training module, and seamlessly migrating the crawler task there.

7. The crawler system according to claim 1, characterized in that, The construction of the multi-data source database includes: based on preset rules, using automated scripts to widely and continuously discover and collect various data sources from the Internet, storing the collected data source information in the database to form a dynamically updated, large data source pool; the AI ​​pre-training is used to mine potential relationships between the data source information; the AI ​​pre-training uses deep learning algorithms to perform unsupervised pre-training on the data sources in the database, discovering data sources with similar content, related themes, or similar structures and establishing similarity mapping relationships.

8. The crawler system according to claim 1, characterized in that, The core of the data semantic analysis and association mining module is a deep learning model based on the Transformer architecture, supplemented by a self-attention mechanism. The deep learning model is used to deeply analyze and understand the semantic connotation of the data source. The self-attention mechanism is used to automatically and dynamically assign different weights to different words when processing text, accurately capturing the most important information in the sentence and the long-distance dependencies between different words.

9. The crawler system according to claim 8, characterized in that, The data semantic analysis and association mining module performs deep semantic encoding on the text content of different data sources and compares it with the semantic encoding of the target information summary. The comparison is based on the similarity calculation of semantic vectors, so as to select one or a group of data sources that are most semantically matched and have the highest correlation with the target data from a massive number of candidate data sources.

10. The crawler system according to claim 1, characterized in that, The process of transforming raw data into a standard-compliant, directly applicable dataset includes data cleaning and integration, and data delivery. The data cleaning and integration includes deduplication, verification, standardization, and correlation fusion. Deduplication removes completely duplicated or highly similar data records. Verification refers to verifying the format, type, or value range of data according to preset business rules or data patterns to identify, mark, or correct erroneous data. The standardization refers to converting heterogeneous data from different data sources according to a unified data model and format; The association fusion is based on the semantic analysis capabilities of the data semantic analysis and association mining module, which extracts data about the same entity or the same event from different sources and merges them into a more complete record with richer information.

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