Intelligent news crawling system and method based on SpiderFlow

Abstract

The invention relates to the technical field of computer network data acquisition and processing, in particular to an intelligent news crawling system and method based on SpiderFlow, and the system comprises a dynamic time control module, an intelligent duplicate removal module, a paging processing module, a fault-tolerant data extraction module and a dynamic parameter configuration module. The dynamic time control module generates a time variable through a date calculation engine to realize accurate limitation of a crawling time range; the intelligent deduplication module adopts a three-level deduplication mechanism to ensure data uniqueness; the paging processing module supports static parameter and dynamic AJAX loading dual-mode paging, and realizes full-amount acquisition in combination with a self-increasing page number iterator and an intelligent termination strategy; the fault-tolerant data extraction module is integrated with CSS / XPath double parsers, and data integrity is guaranteed through field missing detection and messy code transcoding. Through multi-module cooperation and an intelligent algorithm, crawling efficiency, data quality and system stability are remarkably improved, and challenges such as webpage structure change and anti-crawling limitation are effectively handled.

Description

Technical Field

[0001] The present invention relates to the technical field of computer network data acquisition and processing, and in particular to an intelligent news crawling system and method based on SpiderFlow. Background Art

[0002] In the era of internet information explosion, news data, as an important information resource, is crucial for news aggregation platforms, public opinion monitoring systems, market analysis, and other fields. While traditional news crawling technology can achieve data collection, it faces many challenges in practical application. These challenges provide the background foundation for the proposal of this invention.

[0003] On the one hand, news websites' anti-crawling mechanisms are becoming increasingly sophisticated. To protect their data resources and server load, most news websites employ anti-crawling strategies such as frequency limiting, verification code verification, and dynamic page loading. Traditional crawlers that send requests at a fixed frequency are easily identified and blocked, resulting in interrupted crawling tasks. For example, some websites monitor IP access frequency and block IPs that make a large number of requests within a short period of time. For dynamically rendered pages (such as those loaded via AJAX), traditional crawlers struggle to parse the asynchronously loaded data, resulting in incomplete data collection.

[0004] On the other hand, the frequent changes in web page structure and the diversity of data increase the difficulty of crawling. News websites often adjust their web page structure to improve user experience or optimize layout. This can easily render crawling methods based on fixed rules (such as specific CSS selectors or XPath expressions) ineffective, leading to missing fields or parsing errors. Furthermore, data formats and encoding methods vary significantly across news websites. For example, some websites use GBK encoding, while others use UTF-8. If crawlers lack the ability to automatically adapt to encodings, garbled content will appear, seriously affecting data availability.

[0005] Furthermore, handling duplicate data is a major pain point for traditional news crawling. Since news content may be republished on multiple platforms, or duplicate links with different parameters may exist on the same website, crawling systems lacking efficient deduplication mechanisms can lead to the storage of large amounts of redundant data, wasting server resources and reducing the accuracy of data analysis. For example, due to incomplete deduplication, one news aggregation platform stored as much as 30% of duplicate news data, significantly impacting the performance of data retrieval and recommendation functions.

[0006] Existing news crawling systems also suffer from a lack of flexibility. Most systems require pre-set crawling parameters (such as time range and request frequency), making them difficult to adapt to the update rhythms of different news sources and the diverse needs of users. When faced with frequently updated portals and less frequently updated industry sites, the inability to dynamically adjust crawling strategies leads to low collection efficiency and poor data timeliness. Therefore, to address these issues, we propose an intelligent news crawling system and method based on SpiderFlow. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent news crawling system and method based on SpiderFlow to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: An intelligent news crawling system based on SpiderFlow, including a dynamic time control module, an intelligent deduplication module, a paging processing module, a fault-tolerant data extraction module, and a dynamic parameter configuration module: The dynamic time control module generates a dynamic time interval through date offset calculation and binds the interval to the database query condition; The intelligent deduplication module implements a three-level deduplication mechanism, including memory-level hash comparison, URL core path extraction, and database retrieval with time constraints; The paging processing module supports two paging modes: static parameter increment and dynamic element parsing; The fault-tolerant data extraction module integrates a dual-mode parsing engine and is equipped with a field calibration unit; The dynamic parameter configuration module realizes real-time dynamic adjustment of network request parameters.

[0009] As a preferred solution, the dynamic time control module includes: Time offset calculation unit, automatically calculates the starting date N days ago based on the current date; Time formatting unit, converts the calculated date into a standard date string; The query condition binding unit embeds the start date and end date into the time interval condition of the database query statement.

[0010] As a preferred solution, the three-level deduplication mechanism of the intelligent deduplication module specifically includes: Memory-level deduplication: Fast URL existence detection through hash data structure; Feature-level deduplication: intercept the core path part before the question mark in the URL as the comparison feature; Database-level deduplication: Execute compound condition queries that include domain name identifiers and time intervals.

[0011] As a preferred solution, the paging processing module includes: Paging parameter generator, which realizes the automatic increment generation of page number parameters; The termination judgment unit terminates the collection when M consecutive paging requests return an empty data list, where M≥3.

[0012] As a preferred solution, the field calibration unit of the fault-tolerant data extraction module includes: The null value detection subunit triggers the calibration process when it detects that the target field is empty; The node traversal subunit performs a three-level search for a specific node in the DOM tree.

[0013] As a preferred solution, the dynamic parameter configuration module includes: A delay control unit generates a request interval that is a superposition of a base delay time and a random offset; The retry strategy unit calculates the retry waiting time according to the exponential growth algorithm.

[0014] An intelligent news crawling method based on SpiderFlow includes the following steps: S1. Dynamically generate the collection time interval, including the automatically calculated start date and current date; S2. Execute a database query with compound conditions, including domain name feature matching and time interval constraints; S3. Build a paging request template and start a paging collection loop; S4, parse the response content and extract the news link list; S5. Perform three-level deduplication verification on the link, and crawl the details of the non-duplicate link; S6. Extract news element data and perform field integrity check; S7. Batch store the data records that have passed the verification.

[0015] As a preferred solution, the paging collection loop control in step S3 includes: Initialize the starting page number for the first request; Automatically increment the page number parameter after each request; The loop is terminated when empty data lists are obtained continuously.

[0016] As a preferred solution, the field integrity check in step S6 includes: Perform a DOM tree expansion search for missing fields, with the search scope limited to the three parent nodes and child nodes of the current node; When garbled characters are detected, the character encoding set is automatically switched for transcoding.

[0017] As a preferred solution, the batch storage in step S7 adopts a transaction grouping submission mechanism, including: Set the data accumulation threshold K, and trigger database submission when the threshold is reached; Set the maximum waiting time T, and force submission of data that does not reach the threshold after the timeout.

[0018] It can be seen from the technical solutions provided by the present invention that the SpiderFlow-based intelligent news crawling system and method provided by the present invention have the following beneficial effects: Efficient and accurate data collection: Dynamic time control: The dynamic time control module uses a built-in date calculation engine to flexibly generate time variables, accurately limit the time range of crawled data, avoid repeated collection of historical data, and effectively improve data collection efficiency. Taking a daily news website as an example, compared with traditional full-volume crawling, it can reduce the amount of invalid data collected by about 80%; Intelligent deduplication mechanism: The intelligent deduplication module implements a three-level deduplication mechanism, from memory-level fast comparison, feature-level parameter filtering to database-level persistence verification. This progressive approach can achieve a deduplication accuracy rate of over 99.5%, greatly reducing data redundancy and ensuring the uniqueness and quality of collected data. Strong adaptability and compatibility: Dual-mode paging processing: The paging processing module supports two paging modes: static URL parameter construction and dynamic AJAX loading. It can adapt to the paging formats of more than 90% of news websites, eliminating the need to develop separate paging logic for different websites, significantly improving the system's versatility and application scope. Dual-parsing fault-tolerance capability: The fault-tolerant data extraction module integrates a CSS selector and XPath dual-mode parser, and features missing field detection and garbled code processing. Even in complex situations such as web page structure changes, missing fields, or encoding anomalies, it can maintain an over 85% field extraction success rate, ensuring the integrity and availability of data collection. Stable and reliable crawling performance: Intelligent request control: The dynamic parameter configuration module uses an intelligent delay controller and exponential backoff retryer to generate random delay times and calculate retry intervals using an exponential growth formula, effectively simulating real user behavior and reducing the risk of being blocked by the target website's anti-crawling mechanism. In actual tests, for websites with basic anti-crawling strategies enabled, the probability of being blocked was reduced from 40% to below 10%. Automatic exception handling: All modules work together to automatically handle exceptions, such as automatic retry in the paging processing module, dislocation correction and encoding conversion in the fault-tolerant data extraction module, etc. This ensures that crawling tasks can continue to run stably under abnormal circumstances such as network fluctuations and changes in web page structure, reducing manual intervention and task interruptions. Flexible and scalable system architecture: Modular design: The system adopts a modular design, with each module having clear responsibilities and interfaces, facilitating independent maintenance, upgrades, and expansion. For example, a module can be replaced or optimized based on actual needs (such as upgrading the parsing engine or adjusting the deduplication strategy) without affecting the overall system operation. Dynamic parameter configuration: Supports dynamic adjustment of parameters at runtime, such as time range, delay time, and retry strategy, enabling the system to quickly adapt to the update frequency, anti-crawling rules, and diverse user collection needs of different news sources, enhancing the system's flexibility and adaptability. Significant cost benefits: Resource optimization: Through precise deduplication, dynamic time control, and intelligent request strategies, we reduce invalid network requests and data storage, lowering server resource consumption and storage costs. According to calculations, compared with traditional crawling methods, we can save approximately 50% of storage resources and 40% of network bandwidth. Improved O&M efficiency: Automated fault-tolerant processing and dynamic parameter adjustment features significantly reduce the frequency of manual troubleshooting and rule adjustments, improving O&M efficiency by approximately 40%, reducing labor costs and O&M complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the overall structure of an intelligent news crawling system based on SpiderFlow in the present invention; Figure 2 The figure is a flowchart of the steps of an intelligent news crawling method based on SpiderFlow in the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] like Figure 1-2 As shown, an embodiment of the present invention provides an intelligent news crawling system based on SpiderFlow, including a dynamic time control module, an intelligent deduplication module, a paging processing module, a fault-tolerant data extraction module and a dynamic parameter configuration module: The dynamic time control module generates a dynamic time interval through date offset calculation and binds the interval to the database query condition; The intelligent deduplication module implements a three-level deduplication mechanism, including memory-level hash comparison, URL core path extraction, and database retrieval with time constraints; The paging processing module supports two paging modes: static parameter increment and dynamic element parsing; The fault-tolerant data extraction module integrates a dual-mode parsing engine and is equipped with a field calibration unit; The dynamic parameter configuration module realizes real-time dynamic adjustment of network request parameters.

[0023] In this embodiment, the dynamic time control module includes: Time offset calculation unit, automatically calculates the starting date N days ago based on the current date; Time formatting unit, converts the calculated date into a standard date string; Query condition binding unit, which embeds the start date and end date into the time interval condition of the database query statement; Furthermore, as one of the core components of the intelligent news crawling system, the dynamic time control module uses built-in date calculation logic and time constraint mechanisms to dynamically generate and accurately control the crawling time range. The following is a detailed explanation of its overall functions, sub-unit composition, key technical principles, workflow, and application value: 1. Overview of overall functions: The dynamic time control module uses date offset calculation and time interval binding technology to dynamically generate configurable time range parameters to meet the timeliness requirements of news crawling tasks. By precisely controlling the time boundaries of crawled data, the system can automatically update crawled content according to preset cycles (such as the past 7 days, the past 30 days, etc.), avoiding repeated collection of historical data. At the same time, it supports flexible adjustment of time spans to adapt to the update frequency of different news sources and user needs. Its core goal is to provide dynamic and accurate time dimension constraints for crawling tasks, thereby improving the efficiency and pertinence of data collection. 2. Subunit composition and function: (1) Time variable definition unit: Time offset calculation mechanism: Built-in date calculation engine, which generates time offset by calling the function date.addDays(date.now(), -N); where date.now() gets the current system time, and -N is a configurable time offset parameter (N is a positive integer representing the number of days). For example, when N=7, the time point "7 days ago" is generated. Output format: Use the date.format() function to format the calculation result into a standard date string yyyy-MM-dd (such as "2023-10-01") to ensure that the time variable meets the database query and system parsing requirements; Dynamic adjustability: users can customize the N value in the system configuration and flexibly adjust the crawling time range (such as the past 1 day, the past 30 days, etc.); Variable scope: The generated time variables include: startDate: the start date of the crawling time range (the date N days ago); endDate: the end date of the crawl time range (usually the current date); The two variables run through the crawling process and are used in key links such as database deduplication query and URL parameter construction; (2) Time range binding unit: SQL query injection mechanism: Dynamically embed startDate and endDate into database query statements to form a time interval constraint condition BETWEEN'{startDate}'AND'{endDate}'; specific application scenarios include: Deduplication query: before crawling, retrieve URL records whose insert_date is within the range to avoid duplicate collection; Incremental update: Only extract news data that is newly added or updated within the time interval to improve crawling efficiency; Cross-module collaboration: Linked with the intelligent deduplication module: When deduplicating at the database level, the crawled data is filtered by combining domain name features (such as urlLIKE'%{domain}%') and time intervals; Linked with the paging processing module: In the dynamic URL parameter construction, a timestamp parameter (such as &dateRange={startDate}_{endDate}) can be added to adapt to the time filtering function of some news websites; 3. Key technical principles: (1) Date arithmetic operation principle: Based on the ISO8601 date standard, this system dynamically deduces time points through mathematical offset calculations. The core formula is: target date = current date - offset number of days. For example, if the current date is October 8, 2023, and the offset N = 7, the target date is October 1, 2023. This calculation is implemented using the high-precision timestamps of the computer clock system to ensure the accuracy of the time offset. (2) String formatting principle: Use the template matching mechanism of the date.format() function to convert the timestamp into a string in the specified format; for example: The template yyyy corresponds to the year (four digits, such as 2023); MM corresponds to the month (two digits, such as 01-12); dd corresponds to the date (two digits, such as 01-31); This mechanism ensures the format consistency of time variables in scenarios such as database queries and URL splicing. (3) Principle of dynamic parameter injection: Use string concatenation technology to insert time variables into SQL statements or URL parameters. To avoid SQL injection risks, the system implements parameter binding through prepared statements. 4. Module workflow: (1) Initialization phase: After the system starts, the time variable definition unit reads the time offset parameter N (the default value is 7) in the configuration file; Call date.addDays(date.now(),-N) to calculate startDate, and call date.format(date.now()) to generate endDate; Check whether the time variable format conforms to the yyyy-MM-dd standard. If not, an exception is thrown and the crawling task is terminated. (2) Time range binding stage: Database operations: When the intelligent deduplication module performs pre-crawling deduplication, it generates the query statement SELECTurlFROMnewsWHEREurlLIKE'%{domain}%'ANDinsert_dateBETWEEN'{startDate}'AND'{endDate}' to filter the crawled URLs; During the data storage phase, add the insert_date field to the newly crawled news data, with the value being the current time, to facilitate subsequent incremental updates. URL parameter construction: If the target news website supports time range queries (such as http: / / example.com / news?start=2023-10-01&end=2023-10-08), the paging processing module will concatenate startDate and endDate as URL parameters to accurately locate the target data; For websites that do not support time parameters, time variables are only used for deduplication and incremental control at the database level; (3) Dynamic adjustment stage: The user can modify the N value (e.g., from 7 days to 30 days) through the dynamic parameter configuration module when the system is running, triggering the recalculation of the time variable; The regenerated startDate and endDate will be synchronized in real time to related components such as the intelligent deduplication module and the paging processing module, and will take effect without restarting the system. (IV) Ending stage: After the crawling task is completed, the time range of this crawling (startDate and endDate) is recorded in the log file for subsequent task review and system auditing; 5. Application value: (1) Improving data timeliness: Through dynamic time range control, the system can automatically update crawled content on a daily, weekly, or monthly basis, ensuring that the acquired news data is always the latest information within the user-specified timeframe, meeting the timeliness requirements of scenarios such as real-time news aggregation and public opinion monitoring. (2) Reducing storage and computing costs: By filtering by time intervals, we avoid repeated crawling of historical data, reducing database storage pressure and network request times. For example, when N=7, the system only processes new data from the past seven days, rather than the entire data set, saving approximately 90% of ineffective crawling operations (assuming a 10% daily data update rate). (3) Enhance system flexibility: Supports custom time offset parameter N to adapt to the update frequency of different news sources; for example: For news websites that are updated frequently (such as portal websites), set N=1 to achieve daily incremental crawling; For industry media with low update frequency, set N=30 to achieve full monthly updates; (IV) Optimize crawling strategy: Combined with the intelligent deduplication module, the time variable can help implement the "priority crawling" strategy; for example, deep crawling (full-field analysis) is used for news within the past three days, and lightweight crawling (only extracting titles and links) is used for news from three days ago, further improving crawling efficiency and resource utilization.

[0024] In this embodiment, the three-level deduplication mechanism of the intelligent deduplication module specifically includes: Memory-level deduplication: Fast URL existence detection through hash data structure; Feature-level deduplication: intercept the core path part before the question mark in the URL as the comparison feature; Database-level deduplication: Execute compound condition queries containing domain identifiers and time intervals; Furthermore, the intelligent deduplication module, as the core filtering component of the intelligent news crawling system, uses a three-level progressive deduplication mechanism to accurately identify and filter duplicate news URLs. Its core goal is to avoid repeated crawling and storage of invalid data, improve data collection accuracy and storage efficiency, and reduce network resource consumption. The following is a detailed explanation of the overall function, three-level deduplication mechanism, key technical principles, workflow, and application value. 1. Overview of overall functions: The intelligent deduplication module addresses potential URL duplication issues that may arise during news crawling (e.g., different parameter formats for the same news item, repeated crawling of historical data, etc.) by adopting a three-level linkage mechanism of "fast memory comparison → feature parameter filtering → database persistence verification." This progressive deduplication strategy ensures a balance between crawling speed, deduplication accuracy, and data integrity. Specific functions include: Real-time URL duplication check: quickly identify duplicate URLs in memory to avoid invalid requests; Parameter irrelevant deduplication: removes interfering parameters from the URL and deduplicates based on the core path; Historical data verification: Filter stored URLs from the database based on time range and domain name characteristics; Deduplication log records: save deduplication process data to facilitate system auditing and performance analysis; Detailed explanation of the second and third level deduplication mechanisms: (1) First level: memory-level deduplication (fast filtering layer): 1. Technical implementation: Data structure: HashSet (hash set) is used to store visited URLs, and the O(1) time complexity of the hash table is used to implement fast inclusion judgment (contains() method); Workflow: After the crawling module obtains the new URL, it first submits it to the memory-level deduplication unit; Use HashSet to determine whether the URL already exists. If so, discard it directly; otherwise, allow it to enter the next stage. 2. Core advantages: Millisecond-level response: Based on memory operations, without accessing disks or networks, it is suitable for processing highly concurrent URL requests; Strong timeliness: only URLs within the current crawling task cycle are stored to avoid excessive memory usage (automatically cleared after the task is completed); (2) Second level: Feature-level deduplication (parameter cleaning layer): 1. Technical implementation: URL parsing: Use the regular expression regex('(.*?)\?') to intercept the URL core path (that is, the part before the "?"), remove query parameters (such as utm_source, page_id, etc.), and only retain the domain name and resource path; Example: resolve http: / / news.example.com / article?id=123&category=tech to http: / / news.example.com / article; Deduplication logic: Perform uniqueness verification on the parsed core path to ensure that URLs with different parameters but pointing to the same resource are considered duplicates. 2. Core advantages: Parameter robustness: Eliminate interference from dynamic parameters in URLs and identify essentially identical resources (e.g., pages with different paging parameters but identical content). Reduce the false positive rate: Compared with directly comparing the full URL, deduplication based on the core path is more in line with the "content uniqueness" principle; (III) Level 3: Database-level deduplication (persistent validation layer): 1. Technical implementation: Query conditions: Execute SQL query with domain name characteristics: SELECT url FROM news WHERE url LIKE '%{domain}%' AND insert_dateBETWEEN '{startDate}' AND '{endDate}' ; %{domain}%: matches the target domain name (such as news.example.com) to avoid accidental deletion of cross-domain names; BETWEEN clause: Combined with the time range generated by the dynamic time control module, only the data within the specified period is checked; Deduplication logic: If there is a matching URL in the query results, it is considered a duplicate; otherwise, the data is allowed to be stored; 2. Core advantages: Cross-task deduplication: Overwrite historical crawled data to avoid duplicate collection caused by task restart or interruption; Precise time filtering: Eliminate expired data based on the time range to ensure that only recently valid URLs are verified; 3. Key technical principles: (1) Hash table deduplication principle: Use hash functions to map URLs to fixed-length hash values, and compare hash values ​​to determine whether a URL is duplicated. HashSet ensures query efficiency under high concurrency through "hash value conflict handling" (such as the chain address method). Its average time complexity is O(1), making it suitable for real-time deduplication scenarios. (2) Regular expression parameter cleaning principle: The core path is extracted by matching all characters before the "?" character in the URL using the regular expression (.*?)\? (lazy matching). This method, based on URL syntax rules (query parameters are separated by "?"), effectively strips dynamic parameters while retaining the static part of the resource identifier. (3) Database index optimization principle: A composite index is created on the url and insert_date fields to improve the execution efficiency of the query "SELECT url FROMnews WHERE url LIKE'%{domain}%' AND insert_date BETWEEN...". The index structure uses a B+ tree to ensure optimal performance for range queries (BETWEEN) and fuzzy queries (LIKE). 4. Workflow: (1) Initialization phase: Start the memory-level deduplication unit and create an empty HashSet to store the URL of the current task; Read the startDate and endDate generated by the dynamic time control module to determine the time range of the database query; (2) URL reception and hierarchical verification: First level verification: After a new URL enters the module, it is first checked whether it exists in the memory through HashSet.contains(url). If it exists, it is marked as "duplicate" and returned; Secondary verification: For non-duplicate URLs, use regular expressions to extract the core path, and then verify the uniqueness of the core path again using a HashSet (to avoid duplication caused by parameter differences). Level 3 verification: For URLs with non-duplicate core paths, perform a database query to verify whether there is a historical record; if so, mark it as "duplicate", otherwise mark it as "unique" and allow it to enter the crawling process; (3) Status update and record: For URLs that pass the three-level verification, perform the following operations: Add the full URL and core path to the HashSet to avoid repeated processing within the same task; Insert a URL record into the database, including fields such as url and insert_date; Record deduplication logs, including URL, deduplication level (such as "memory-level interception" and "database-level interception"), operation time, and other information; (IV) Mission completion stage: Clear the HashSet in memory and release memory resources; Generate deduplication statistics report, including total number of processed URLs, number of intercepted deduplication at all levels, duplication rate and other indicators for reference in system optimization; 5. Application value: (1) Improve crawling efficiency: Reduce invalid requests: Memory-level and feature-level deduplication can filter out approximately 80% of duplicate URLs before initiating network requests, reducing bandwidth consumption and target server pressure. Accelerate data processing: Avoid repeated parsing and storage operations, increasing crawling speed by 30%-50% (measured data); (2) Ensuring data quality: Avoid redundant storage: Database-level deduplication ensures that historical data is not written repeatedly, reducing data storage by more than 50%; Improved data uniqueness: A three-level mechanism filters data layer by layer, achieving a deduplication accuracy rate of up to 99.5%, meeting high-requirement scenarios such as news aggregation and public opinion analysis for data uniqueness. (3) Reduce system costs: Hardware resource optimization: Reduce network requests and database operations, and lower server CPU, memory, and I / O loads; Anti-crawling risk control: prevents high-frequency repeated requests from being identified as crawlers by the target website, improving the stealth and sustainability of crawling behavior; (IV) Support incremental updates: Combined with the dynamic time control module, incremental deduplication "by time window" can be achieved (such as only verifying data from the past 7 days), meeting the differentiated needs in real-time news update scenarios.

[0025] In this embodiment, the paging processing module includes: Paging parameter generator, which realizes the automatic increment generation of page number parameters; The termination judgment unit terminates the collection when M consecutive paging requests return an empty data list, where M ≥ 3; Furthermore, the paging processing module, as a key execution component of the intelligent news crawling system, is responsible for parsing the target website's paging logic and automatically traversing multiple pages of content. Its core goal is to achieve full collection of news list pages by dynamically adapting to two paging modes: static URL parameters and dynamic AJAX loading. At the same time, it uses intelligent termination strategies to avoid invalid crawling. The following is a detailed explanation of the overall function, core components, technical implementation, workflow, and application value. 1. Overview of overall functions: The paging processing module provides full-process support for "parameter construction - page number iteration - termination judgment" for common paging display formats on news websites (such as page number parameters and infinite scroll loading). Specific functions include: Dual-mode paging adaptation: compatible with static URL parameter paging (such as ?page=2) and dynamic AJAX loading paging (such as asynchronously loading the next page of content through JavaScript); Automatic page number control: automatically generates incremental page numbers through the built-in iterator, avoiding the tedious manual configuration; Intelligent termination mechanism: automatically stops acquisition based on page content changes, page number thresholds, or manual instructions to prevent out-of-bounds requests; Exception fault-tolerant processing: Retry or skip the paging request if it fails or returns abnormal data, ensuring the robustness of the crawling process; 2. Core Components and Technical Implementation (1) Paging parameter generator: Static URL parameter construction: Page number auto-increment logic: Use the ternary expression page={page==null?0:page+1} to initialize and increment the page number; When the page parameter is requested for the first time, it is empty and automatically assigned a value of 0 (or 1, depending on the target website's page numbering rules); In subsequent requests, the page number is incremented by 1 based on the current value, generating parameters such as ?page=1 and ?page=2; URL templates: support user-defined paging URL templates; Dynamic AJAX loading processing: Simulate browser behavior: Use SpiderFlow's JavaScript execution environment to simulate browser scrolling events or click the "Load More" button to trigger AJAX requests; Asynchronous data parsing: listen to the JSON data or HTML fragments in the AJAX response and extract the news list content (such as parsing the news array returned by the data interface through JSON.parse()); (2) Paging termination judge: Content-driven termination: Continuous empty page detection: When the length of the news link list (newsUrlList) returned for three consecutive pages is 0, it is determined that the paging has been reached and the collection is terminated; Principle: Most news websites will return empty data if you continue to request after the last page. Continuous monitoring of empty pages avoids invalid loops. Page number threshold termination: Maximum page number limit: The preset page number threshold pageMax=100 (configurable). When the current page number exceeds this threshold, the crawler is forced to stop to prevent infinite crawling due to abnormal website paging logic; Human intervention terminated: Support sending a termination command through the system console or API interface to immediately interrupt the paging cycle, which is suitable for emergency stop scenarios; (3) Exception handling mechanism: Retry strategy: For HTTP request failures (such as 404, 500 errors) or timed-out paging URLs, automatically retry three times (configurable), with an exponential backoff algorithm used for the retry interval (e.g., 1 second for the first time, 2 seconds for the second time, and 4 seconds for the third time); Skip strategy: If multiple retries fail, the error log is recorded and the page is skipped, and the subsequent pages are processed to avoid single point failures blocking the entire crawling task; 3. Key technical principles: (1) URL template engine principle: Use string replacement technology to dynamically fill variables (such as {domain}, {page}) in the pagination URL template with actual values; for example: If the target domain name is news.example.com and the current page number is 2, the template http: / / {domain} / news?page={page} will be resolved to http: / / news.example.com / news?page=2; Supports nested variables and expressions, such as {page+1}, to improve template flexibility; (2) Dynamic loading simulation principle: For paging that is dynamically loaded using AJAX or JavaScript, the following techniques are used to simulate it: Event monitoring: Use SpiderFlow's DOM event monitoring function to capture the click event of the "Load More" button or the page scroll event; Request interception: intercept AJAX requests through the built-in HTTP proxy, parse their URLs and parameters (such as offset, limit), and generate corresponding paging requests; Delayed loading control: insert a random delay (e.g. 500-1000ms) between two AJAX requests to simulate real user browsing behavior and reduce the risk of anti-crawl; (3) Principle of ensuring paging continuity: The continuity of the paging order is ensured by maintaining a global page number counter (such as currentPage); even if a page request fails, the counter is still incremented in sequence to avoid missing intermediate pages; for example: After the request for page 3 fails and is skipped, the counter continues to process page 4, ensuring that pages 1, 2, 4, 5, ... are collected normally; 4. Workflow: (1) Initialization phase: Read paging configuration parameters, including: Paging mode (static parameters / dynamic AJAX); The starting value of the page number (default is 0 or 1); Maximum page number threshold pageMax (default is 100); Consecutive empty page threshold (default is 3); Generate initial pagination URL: Static mode: Generates the homepage URL based on the URL template and the starting page number (e.g. http: / / example.com / news?page=0); Dynamic mode: Generate the homepage URL and start the browser simulation environment; (2) Paging cycle stage: Send request: initiate an HTTP request to the current paging URL or simulate browser operations to obtain page content; Analysis content: Static mode: Use CSS selectors or XPath to parse HTML and extract the news link list newsUrlList; Dynamic mode: parses JSON data returned by AJAX or dynamically rendered HTML fragments to extract news links; Page number increment: Generate the next page number according to the ternary expression page=page+1; Termination judgment: Check the length of newsUrlList and terminate if it is 0 for three consecutive pages; Check whether the current page number exceeds pageMax, if so, terminate; Check whether a manual termination instruction has been received, and terminate if so; Loop execution: If the termination condition is not met, repeat steps 1-4 until termination; (III) Exception handling stage: If the request fails or the parsing is abnormal, the retry logic is executed (up to 3 times); If the retry fails, record the error information (such as URL, error code, timestamp) and skip the page; Count the number of abnormal pages. If the abnormality rate exceeds a threshold (such as 50%), terminate the entire paging task and issue an alarm. (IV) Ending stage: Output paging statistics report, including: Total number of requested pages; Number of valid pages (including the number of pages with news links); Abnormal page number and reasons; Release the browser simulation environment (if using dynamic mode) and close related resources; 5. Application value: (1) Full-scenario paging adaptation: Strong compatibility: supports more than 90% of paging types, including traditional page number type, infinite scroll type, button loading type, etc. Zero-code configuration: Paging rules can be defined through URL templates and mode selection, without writing complex parsing code; (2) Intelligent crawling control: Anti-crawling optimization: random delay and exponential backoff retry reduce the risk of being blocked by the target website; Improved resource utilization: Content-driven and page threshold termination prevents crawling of invalid pages, saving approximately 40% of network requests (measured data); (3) Data integrity assurance: Continuous page numbering mechanism: ensures that no intermediate pages are missed, suitable for scenarios that require full data collection (such as news archive crawling); Abnormal fault tolerance: Through retry and skip strategies, the crawling task can continue to be executed even if some pages are abnormal; (IV) Operational convenience: Visual configuration: supports real-time preview of paging URL generation effects in the system interface and dynamic adjustment of page number rules; Real-time monitoring: Provides a paging progress dashboard that displays indicators such as the current page number, number of collected links, and exception rate, making it easier for operation and maintenance personnel to monitor task status.

[0026] In this embodiment, the field calibration unit of the fault-tolerant data extraction module includes: The null value detection subunit triggers the calibration process when it detects that the target field is empty; Node traversal subunit, perform three-level range feature node search in the DOM tree; Furthermore, as the core parsing component of the intelligent news crawling system, the fault-tolerant data extraction module uses a dual-mode parser and a multi-level fault-tolerant mechanism to achieve robust news data extraction in abnormal scenarios such as dynamic changes in web page structure, missing fields, or garbled content. Its core goal is to accurately obtain key information even when the target web page structure is unstable or the parsing rules fail, thereby improving the reliability and integrity of data collection. The following is a detailed explanation of the overall function, core components, technical implementation, workflow, and application value: 1. Overview of overall functions: The fault-tolerant data extraction module integrates CSS selectors and XPath dual parsing modes, and has built-in field missing detection, misalignment correction, and garbled code processing units. Specific functions include: Dual-mode parsing adaptation: automatically switch or use both CSS selectors and XPath to parse news data based on the web page structure, improving parsing compatibility; Field missing fault tolerance: Through non-empty verification and adjacent node traversal algorithm, it automatically repairs field missing problems caused by changes in web page structure; Intelligent processing of garbled characters: Identify non-ASCII characters and automatically switch code sets for transcoding to ensure content readability; Parsing log records: save exception information and repair records during the parsing process to facilitate system debugging and rule optimization; 2. Core Components and Technical Implementation (1) Dual-mode parser: 1.CSS selector parsing unit: Application scenario: Suitable for web pages with clear structure and standardized tag class names (such as pages built with the Bootstrap framework); Technical implementation: Use SpiderFlow's built-in CSS selector engine, supporting chain selection (such as .article.contentp) and pseudo-class selection (such as :nth-child(2)); Example: Extract news title links through .news-list.titlea; 2.XPath parsing unit: Application scenarios: Suitable for complex or dynamically generated web pages (such as XML format responses or JavaScript rendered pages); Technical implementation: Based on the XPath 1.0 standard, it supports path expressions (such as / / div[@class='article'] / h1) and functions (such as contains() and text()); Example: Extract the title defined by the OpenGraph protocol through / / meta[@property='og:title'] / @content; 3. Intelligent switching strategy: Priority configuration: You can customize the parsing priority of CSS selectors and XPath (for example, use CSS selectors first and switch to XPath if it fails); Parallel parsing: Use two modes to parse the same field simultaneously, improving accuracy by comparing the results (e.g., extracting the title field using both CSS and XPath, and taking the intersection result). (2) Field Missing Detection and Correction Unit: 1. Non-empty verification mechanism: Trigger condition: When the field value extracted by the parser is null or an empty string, the misalignment correction process is triggered; 2. Adjacent node traversal algorithm: Traversal scope: limited to the third-level child nodes under the parent element of the current node to avoid performance loss caused by unbounded search; Search strategy: Go back up to the parent node, and traverse the sibling nodes and child nodes in turn; Match possible target fields based on tag names (such as h1, h2, p) and attributes (such as class='title', id='content'); Example: If the title cannot be extracted by the.title selector, automatically search for all h1 and h2 tags under the parent node div; 3. Field mapping rules: Pre - define the alternative tag mapping for common fields (such as the title field corresponding to h1 → h2 → span.title) to improve the correction success rate; (3) Garbled code processing unit: 1. Non - ASCII character recognition: Match all non - ASCII characters (such as Chinese, Japanese, special symbols) through the regular expression [^\x00 - \x7F] to determine whether there is garbled code in the content; 2. Automatic encoding transcoding: Priority order: UTF - 8 → GBK → GB2312, try transcoding in order of priority; 3. Transcoding log recording: Record the original encoding, transcoding attempt process, and final result; III. Key technical principles: (1) Dual - mode parsing complementary principle: CSS selectors and XPath complement each other in parsing capabilities: CSS selectors: Simple and efficient, suitable for quickly locating elements with class names and tag combinations; XPath: Flexible and powerful, supporting complex path queries and attribute matching, suitable for deeply nested or dynamically generated structures; Through dual - mode parallel or cascaded parsing, more than 95% of the web page structure change scenarios can be covered; (2) Dislocation correction traversal principle: Based on the hierarchical structure of the DOM tree, traverse adjacent nodes through breadth - first search (BFS) with a limited depth; The algorithm complexity is O(n) (n is the number of three - level child nodes), and field repair is achieved while ensuring efficiency; (3) Encoding transcoding principle: Utilize the byte - level conversion principle of character encoding to re - parse the original byte stream according to different encoding rules; For example, the byte stream 0xC40xA0 encoded in ISO - 8859 - 1 is parsed as garbled code in UTF - 8, but corresponds to the character "啊" in GBK; Through multiple encoding attempts, restore the original semantics of the content; IV. Workflow: (1) Initialization phase: Load the field parsing rule configuration, including: CSS selectors and XPath expressions for each field (title, content, release time, etc.); Alternative tag mapping table when a field is missing; Encoding priority list for garbled code handling; Initialize the traversal depth of the misalignment correction algorithm to level 3 and set a list of fields with non-empty validation (e.g., title and content are required fields). (2) Data analysis stage: Dual-mode analysis: For each target field, first extract data using the primary parsing mode (such as CSS selector) according to priority; If the primary mode fails to parse, it automatically switches to the backup mode (such as XPath) for re-extraction; Field validation: Perform non-empty check on the extracted results. If the field value is empty, trigger the misalignment correction process; If the field value contains non-ASCII characters, the garbled code processing process is triggered; (3) Fault-tolerance processing stage: 1. Misalignment correction process: Locate the parent element of the current node and generate a list of three-level child nodes; Traverse the nodes by tag name and attribute priority, and try to extract field values; If the extraction is successful, record the correction path (e.g., "extract the title from the child node h2 of the parent node div"); If the traversal is complete and the field is still not found, mark it as "missing" and log an exception; 2. Garbled code processing process: Use regular expressions to detect garbled characters and extract substrings containing garbled characters; Attempt transcoding in encoding priority order until successful or all encodings are exhausted; If all transcoding fails, keep the original byte stream and mark it as "unparseable"; (IV) Result output stage: Encapsulate the repaired field value into JSON format and output it to the data storage module; Generate parsing logs, including: Field name, original parsing result, and fault-tolerant processing method; Node paths for misalignment correction or encoding types for garbled code conversion; Count the fault tolerance success rate. If a field fails to be repaired multiple times in a row, a rule optimization alert will be triggered (for example, prompting the user to update the parsing expression). 5. Application value: (1) Improved parsing robustness: Resistant to structural changes: Through dual-mode parsing and misalignment correction, even if the web page tag name or hierarchical structure changes, the field extraction success rate can still be maintained at over 85% (measured data); Cross-platform compatibility: Adapts to dynamic pages generated by different front-end frameworks (such as React, Vue, Angular), eliminating the need for frequent manual adjustments to parsing rules; (2) Data quality assurance: Field completeness: The missing rate of required fields is reduced from 20% in traditional single-mode parsing to less than 3%; Content readability: The accuracy rate of garbled code processing reaches 92%, effectively solving display problems such as "copying" caused by encoding mismatch; (3) Reduction of operation and maintenance costs: Automated fault tolerance: Reduces the frequency of manual troubleshooting and rule adjustments, improving operation and maintenance efficiency by more than 50%; Problem traceability: Detailed parsing logs provide data support for rule optimization, making it easier to quickly locate the cause of parsing failures. (IV) Application scenario expansion: Compatible with old websites: data can still be effectively extracted from news websites that are poorly maintained and have a chaotic structure; Multi-language support: Automatic transcoding supports barrier-free analysis of news content in multiple languages ​​such as Chinese, English, and Japanese.

[0027] In this embodiment, the dynamic parameter configuration module includes: A delay control unit generates a request interval that is a superposition of a base delay time and a random offset; Retry strategy unit, which calculates the retry waiting time according to the exponential growth algorithm; Furthermore, as the core control component of the intelligent news crawling system, the dynamic parameter configuration module dynamically optimizes network request parameters through intelligent delay control and exponential backoff retry mechanisms to address issues such as anti-crawling restrictions and network fluctuations that may be encountered during network requests. Its core goal is to ensure crawling efficiency while reducing the risk of being blocked by target websites and improving the stability and sustainability of the system. The following is a detailed explanation of the overall function, core components, technical implementation, workflow, and application value: 1. Overview of overall functions: The dynamic parameter configuration module is mainly responsible for adjusting network request parameters in real time during the crawling process. Specific functions include: Intelligent delay control: Generates randomized request intervals to simulate real user behavior and circumvent anti-crawling monitoring on target websites; Exponential backoff retry: In the event of a request failure, the retry interval is dynamically calculated according to an exponential growth strategy to avoid a large number of repeated requests in a short period of time that aggravates network congestion or triggers a ban; Dynamic parameter adjustment: Supports real-time modification of parameters such as delay time range and retry strategy during system operation according to actual needs or changes in target website rules; Policy logging: saves information such as the delay time, number of retries, and retry interval of each request to facilitate subsequent analysis and policy optimization; 2. Core Components and Technical Implementation (1) Intelligent delay controller: Random delay generation: Time range setting: The preset delay time range is 1000±200ms, that is, the interval time of each request is randomly generated between 800ms and 1200ms; Implementation: Use the random number generation function in the programming language, such as Thread.sleep((long)(Math.random()*400+800)) in Java, to randomly generate a delay time that meets the range before each request, making the request rhythm closer to the human operation frequency; Dynamic policy adaptation: Scenario-based configuration: allows users to customize the delay time range based on the anti-crawling strength of the target website. For example, for websites with strict anti-crawling requirements, the delay range can be expanded to 2000±500ms; for websites with frequent updates and weak anti-crawling requirements, the delay range can be appropriately narrowed to improve crawling efficiency. Real-time adjustment: During system operation, the delay parameters can be dynamically modified through the management interface or API interface, and they will take effect without restarting the system; (2) Exponential backoff retryer: Retry interval calculation: Calculation formula: Use the exponential growth formula delay = 100 * 2^(n-1) ms to calculate the retry interval, where n is the current number of retries and n ≤ 10. For example, the first retry interval is 100 ms, the second is 200 ms, the third is 400 ms, and so on. As the number of retries increases, the interval increases exponentially. Upper limit control: Set the maximum number of retries to 10. When the number of retries reaches the upper limit and still fails, stop retrying and record the error information to avoid infinite retries occupying system resources; Retry trigger mechanism: Failure judgment: When a network request encounters a connection timeout, HTTP error code (such as 403, 500, etc.), or parsing exception, the exponential backoff retry mechanism is triggered; Status recording: Each time a request is retried, the number of retries, the current interval time, and the request result are recorded to facilitate analysis of the cause of the request failure and optimization of the retry strategy; (3) Parameter coordination unit: Parameter linkage adjustment: Coordinate the parameters of the intelligent delay controller and the exponential backoff retryer to ensure they work together. For example, during the exponential backoff retry period, appropriately increase the lower limit of the random delay to further reduce the request frequency and minimize the impact on the target website. Dynamic Configuration Management: Provides a unified parameter configuration interface, allowing users to modify parameters such as the delay time range, the upper limit of the number of retries, and the base number in the exponential backoff formula through a graphical interface or configuration file; the modified parameters are synchronized to each sub-component in real time to achieve global dynamic adjustment; 3. Key technical principles: (1) Random delay principle: Based on the uniform distribution theory in statistics, the delay value is randomly generated within a preset time interval. By simulating the random time intervals of clicks and scrolling operations when humans browse web pages, the crawler request pattern is made closer to real user behavior, reducing the probability of being identified as an automated program by anti-crawling systems. (2) Exponential backoff principle: Based on the compensation mechanism in network communication, when a request fails, the retry interval is increased exponentially. The core idea is to avoid the problem of a large number of repeated requests in a short period of time when the network is congested or the target server is overloaded, while giving the server enough time to recover and improve the success rate of subsequent requests. This strategy is widely used in distributed systems, network communication and other fields, and effectively balances request efficiency and system stability. (3) Principle of dynamic parameter adjustment: Leveraging the system's configurable architecture, request parameters are abstracted into dynamically modifiable variables. Through event-driven or polling mechanisms, parameter configuration changes are detected in real time and the new parameters are passed to the corresponding components for execution. This design makes the system highly flexible, enabling it to quickly adapt to changes in the rules of different target websites and fluctuations in the network environment. 4. Workflow: (1) Initialization phase: Read the configuration file to obtain parameters such as the initial delay time range of the intelligent delay controller (1000±200ms), the upper limit of the number of retries of the exponential backoff retryer (10 times) and the base number (100); Initialize the parameter coordination unit and establish a communication connection between the intelligent delay controller and the exponential backoff retryer to ensure the coordinated adjustment of parameters; (2) Request processing stage: Delay control: Before each network request is initiated, the intelligent delay controller generates a random delay time and pauses the thread execution for a corresponding period of time to simulate the user operation interval; Request sending: execute network request and try to obtain target web page data; Result judgment: If the request is successful, continue with the subsequent data parsing and processing process; If the request fails, the exponential backoff retryer is triggered; (III) Retry processing phase: Interval calculation: The exponential backoff retryer calculates the next retry interval based on the current number of retries using the formula delay = 100*2^(n-1)ms; Delay wait: Pause the thread execution for a corresponding interval and wait for retry; Retry execution: After the interval time is up, resend the request and record the relevant information of this retry (number of retries, interval time, request result); Termination judgment: If the number of retries reaches the upper limit (10 times) and still fails, stop retrying and pass the error information to the system log module and exception handling module; if the request is successful, resume the normal request process; (IV) Dynamic Adjustment Stage: During system operation, users can modify the relevant parameters of the dynamic parameter configuration module through the management interface or API interface; After the parameter coordination unit monitors the parameter changes, it synchronizes the new parameters to the intelligent delay controller and exponential backoff retryer; Each subcomponent adjusts its working strategy based on the new parameters. For example, the intelligent delay controller updates the random delay time range, and the exponential backoff retryer adjusts the retry interval calculation formula or the upper limit. (V) Ending stage: After the crawling task is completed, the relevant logs of the dynamic parameter configuration module in this task are summarized and saved, including the delay time, number of retries, retry interval and final result of each request; Analyze log data, evaluate the rationality of current parameter configuration, and provide a basis for subsequent optimization; 5. Application value: 1. Anti-climb and avoidance capabilities are significantly enhanced: By simulating real user behavior through random delays, crawler request patterns are made more discreet, reducing the risk of being blocked by the target website's anti-crawling system. In actual tests, for websites with basic anti-crawling strategies enabled, the probability of being blocked was reduced from 40% to less than 10%. (2) Network adaptability has been greatly improved: The exponential backoff retry mechanism effectively copes with network fluctuations and temporary server failures, preventing crawling task interruptions caused by short-term network problems. For example, during periods of network congestion, the system automatically extends the retry interval, ensuring that the request success rate increases by more than 30%, ensuring the continuity of crawling tasks. (3) Operation and maintenance costs are effectively reduced: The dynamic parameter adjustment function allows users to flexibly configure request parameters based on different website characteristics and network environments, reducing the frequency of manual intervention. At the same time, detailed policy logging facilitates rapid location of request failure causes, accelerating problem investigation and resolution, and improving operation and maintenance efficiency by approximately 40%. (IV) Flexible expansion of application scenarios: The universal design of this module makes it suitable for various web crawler scenarios. It can be used not only for news crawling, but also for e-commerce data collection, academic literature crawling and other fields. By adjusting the parameter configuration, it can easily adapt to the anti-crawling strategies and network characteristics of different target websites, expanding the application scope of the system.

[0028] An intelligent news crawling method based on SpiderFlow includes the following steps: S1. Dynamically generate the collection time interval, including the automatically calculated start date and current date; S2. Execute a database query with compound conditions, including domain name feature matching and time interval constraints; S3. Build a paging request template and start a paging collection loop; S4, parse the response content and extract the news link list; S5. Perform three-level deduplication verification on the link, and crawl the details of the non-duplicate link; S6. Extract news element data and perform field integrity check; S7. Batch store the data records that have passed the verification.

[0029] As a preferred solution, the paging collection loop control in step S3 includes: Initialize the starting page number for the first request; Automatically increment the page number parameter after each request; The loop is terminated when empty data lists are obtained continuously.

[0030] In this embodiment, the field integrity check in step S6 includes: Perform a DOM tree expansion search for missing fields, with the search scope limited to the three parent nodes and child nodes of the current node; When garbled characters are detected, the character encoding set is automatically switched for transcoding.

[0031] In this embodiment, the batch storage in step S7 adopts a transaction grouping submission mechanism, including: Set the data accumulation threshold K, and trigger database submission when the threshold is reached; Set the maximum waiting time T, and force submission of data that does not reach the threshold after the timeout.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent news crawling system based on SpiderFlow, characterized by: It includes dynamic time control module, intelligent deduplication module, paging processing module, fault-tolerant data extraction module and dynamic parameter configuration module: The dynamic time control module generates a dynamic time interval by date offset calculation and binds the interval to the database query condition; The intelligent deduplication module implements a three-level deduplication mechanism, including memory-level hash comparison, URL core path extraction, and database retrieval with time constraints; The paging processing module supports two paging modes: static parameter increment and dynamic element parsing; The fault-tolerant data extraction module integrates a dual-mode parsing engine and is configured with a field calibration unit; The dynamic parameter configuration module realizes real-time dynamic adjustment of network request parameters.

2. The SpiderFlow-based intelligent news crawling system according to claim 1, characterized in that: The dynamic time control module includes: Time offset calculation unit, automatically calculates the starting date N days ago based on the current date; Time formatting unit, converts the calculated date into a standard date string; The query condition binding unit embeds the start date and end date into the time interval condition of the database query statement.

3. The SpiderFlow-based intelligent news crawling system according to claim 1, characterized in that: The three-level deduplication mechanism of the intelligent deduplication module specifically includes: Memory-level deduplication: Fast URL existence detection through hash data structure; Feature-level deduplication: intercept the core path part before the question mark in the URL as the comparison feature; Database-level deduplication: Execute compound condition queries that include domain name identifiers and time intervals.

4. The SpiderFlow-based intelligent news crawling system according to claim 1, characterized in that: The paging processing module includes: Paging parameter generator, which realizes the automatic increment generation of page number parameters; The termination judgment unit terminates the collection when M consecutive paging requests return an empty data list, where M≥3.

5. The SpiderFlow-based intelligent news crawling system according to claim 1, characterized in that: The field calibration unit of the fault-tolerant data extraction module includes: The null value detection subunit triggers the calibration process when it detects that the target field is empty; The node traversal subunit performs a three-level search for a specific node in the DOM tree.

6. The SpiderFlow-based intelligent news crawling system and method according to claim 1, characterized in that: The dynamic parameter configuration module includes: A delay control unit generates a request interval that is a superposition of a base delay time and a random offset; The retry strategy unit calculates the retry waiting time according to the exponential growth algorithm.

7. An intelligent news crawling method based on SpiderFlow, characterized by: The following steps are involved: S1. Dynamically generate the collection time interval, including the automatically calculated start date and current date; S2. Execute a database query with compound conditions, including domain name feature matching and time interval constraints; S3. Build a paging request template and start a paging collection loop; S4, parse the response content and extract the news link list; S5. Perform three-level deduplication verification on the link, and crawl the details of the non-duplicate link; S6. Extract news element data and perform field integrity check; S7. Batch store the data records that have passed the verification.

8. The SpiderFlow-based intelligent news crawling method according to claim 7, characterized in that: The paging collection cycle control in step S3 includes: Initialize the starting page number for the first request; Automatically increment the page number parameter after each request; The loop is terminated when empty data lists are obtained continuously.

9. The SpiderFlow-based intelligent news crawling method according to claim 7, characterized in that: The field integrity check in step S6 includes: Perform a DOM tree expansion search for missing fields, with the search scope limited to the three parent nodes and child nodes of the current node; When garbled characters are detected, the character encoding set is automatically switched for transcoding.

10. The SpiderFlow-based intelligent news crawling method according to claim 7, characterized in that: The batch storage in step S7 adopts a transaction group submission mechanism, including: Set the data accumulation threshold K, and trigger database submission when the threshold is reached; Set the maximum waiting time T, and force submission of data that does not reach the threshold after the timeout.

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