Method for detecting quality of network agent

Abstract

The invention provides a network agent quality detection method, and relates to the technical field of network agent quality detection. According to the method, a dynamic evaluation system based on real environment sample comparison is established, the affinity of the proxy IP and the target website can be accurately quantified, key indexes such as the anti-crawling trigger rate and the DOM matching degree can be included, and a scientific basis is provided for selection and use of the proxy IP; according to dynamic weight distribution of different task types (such as data crawling and account registration), the method can intelligently recommend adaptive proxy IPs, such as automatic distribution of residence IPs and high-frequency collection of preferable machine room IPs in a strong anti-crawling scene, thereby improving the success rate and efficiency of data crawling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network proxy quality detection, in particular to a network proxy quality detection method. BACKGROUND

[0002] The existing proxy IP quality detection scheme has significant defects: the traditional method only relies on laboratory environment test of basic network indicators (such as delay, bandwidth), resulting in a serious disconnection between the evaluation results and the actual business scenario - high-quality proxies still trigger anti-crawling mechanisms frequently on specific target websites, and high-cost resources (such as residential IPs) are inefficiently mismatched due to lack of task scenario adaptability analysis; at the same time, single supplier IP pool is easily identified as robot traffic by the risk control system due to the convergence of behavior patterns; these defects cause the crawler system to face three technical bottlenecks of high blocking rate (> 40%), resource waste (invalid cost > 30%) and dynamic confrontation failure. The present application fundamentally solves the core problem of disconnection between proxy evaluation and business demand by building a real environment sample comparison system and a double-blind test mechanism, and first creates a proxy affinity quantization model based on the actual response of the target website. SUMMARY

[0003] The present application provides a network proxy quality detection method, comprising the following steps: Real environment sample collection: access the target website through the direct connection without proxy, record the benchmark sample as the comparison benchmark; simulate real user operation, collect dynamic loading data, so that the benchmark sample covers the complete business scenario; Proxy IP test execution: adopt double-blind test, which includes task group test and control group test; the task group test uses proxy IP to access the target website, records the returned page content and anti-crawling triggering condition; the control group test uses the same proxy IP to access the low-risk neutral website, detects its basic performance; compares the test results of the task group and the control group, excludes the quality problem of the proxy itself, and identifies the influence of the anti-crawling strategy specific to the target website.

[0004] As a preferred technical solution of the present application, it further comprises key indicator calculation, which includes anti-crawling compatibility indicator and stability indicator.

[0005] As a preferred technical solution of the present application, the anti-crawling compatibility indicator includes: DOM matching rate: compare the similarity of the HTML structure of the page returned by the proxy and the benchmark sample; Anti-crawling triggering rate: count the frequency of triggering blocking or verification code when the proxy accesses the target website.

[0006] As a preferred technical solution of the present application, the stability indicator includes: Continuous available duration: the longest time that the proxy IP maintains stable connection on the target website; Pressure test fault tolerance rate: gradient increase concurrent request, observe error rate change.

[0007] As the preferred technical solution of the present application, it further includes a proxy IP recommendation and optimization process, which comprises: Task adaptability scoring model, the task adaptability scoring model is used for dynamically allocating index weight according to different task types.

[0008] As the preferred technical solution of the present application, the proxy IP recommendation and optimization process further comprises an intelligent recommendation engine, which comprises: Real-time scoring and ranking: calculate the comprehensive score of each proxy IP according to the current task demand, and preferentially allocate high-score proxies; Automatic switching strategy: When the proxy triggers the ban, automatically switch to the standby IP of the same supplier or switch the supplier; When the proxy cost exceeds the income threshold, trigger a low-cost alternative.

[0009] As the preferred technical solution of the present application, the proxy IP recommendation and optimization process further comprises supplier dynamic optimization, which comprises: Multi-supplier horizontal comparison: regularly test the proxy pool of different suppliers and record their performance in various tasks; Elimination and introduction mechanism, including: Blacklist: the proxy IP with a ban rate higher than 10% is isolated for 12 hours; New supplier injection: introduce 10% new proxy resources every month to prevent IP pool aging.

[0010] As the preferred technical solution of the present application, the low-cost alternative comprises: Same supplier low-cost IP switching: preferentially select low-cost proxy IPs that are not used or have low load from the proxy pool of the current supplier to replace, avoiding the connection interruption risk caused by directly switching suppliers; Cross-supplier low-cost resource introduction: if there is no suitable replacement IP in the same supplier, call the low-cost solution from the pre-connected multi-supplier proxy pool to ensure task continuity.

[0011] As the preferred technical solution of the present application, the real environment sample collection further comprises: Browser fingerprint consistency collection: record the browser fingerprint characteristics of the benchmark sample through direct connection without proxy, including but not limited to User-Agent, screen resolution, font list, time zone information; Dynamic behavior feature collection: simulate the browsing track of real users to generate a behavior heat map as a benchmark behavior pattern.

[0012] As a preferred technical solution of the present application, the key indicator calculation further includes a response integrity indicator, the response integrity indicator comprising: Comparing the hash values of the resource files contained in the proxy return page with the corresponding resource files in the benchmark sample, the proportion of missing or tampered resource files is counted; For specific data fields returned by the target website, the completeness and accuracy of these fields in the proxy return data are checked, and the number of missing or incorrect data fields is calculated by comparing with the corresponding fields in the benchmark sample.

[0013] Compared with the prior art, the present application has the following advantages: In the scheme of the present application: 1. Precise quantification of the affinity of proxy IP and target website: by establishing a dynamic evaluation system based on real environment sample comparison, the present application can precisely quantify the affinity of proxy IP and target website, including key indicators such as anti-crawl trigger rate and DOM matching degree, providing a scientific basis for the selection and use of proxy IP; 2. Intelligent recommendation of task-adapted proxy: according to the dynamic weight distribution of different task types (such as data crawling and account registration), the present application can intelligently recommend the adapted proxy IP, such as automatically allocating residential IP in strong anti-crawl scenarios and selecting machine room IP for high-frequency collection, thereby improving the success rate and efficiency of data crawling; 3. Dynamic optimization of proxy resource pool: the present application realizes the dynamic optimization of proxy resource pool by eliminating inefficient IP in real time and introducing multiple supplier redundancy mechanisms, effectively reducing the ban rate and invalid cost, and improving resource utilization. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The flowchart of the network proxy quality detection method provided by the present application is shown. DETAILED DESCRIPTION

[0015] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0016] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict.

[0017] It should be noted that like reference numerals and characters refer to like items throughout the drawings, and once an item is defined in one drawing, further definition and explanation of the item in subsequent drawings is not required.

[0018] Embodiment 1, please refer to Figure 1 A method for detecting network proxy quality, comprising the following steps: Real environment sample collection: access the target website through the direct connection without proxy, record the benchmark sample as the comparison benchmark; the benchmark sample includes HTML structure, HTTP response header, loading time; simulate real user operation (such as clicking, scrolling), collect dynamic loading data (such as AJAX request), so that the benchmark sample covers the complete business scenario; the benchmark sample obtained through direct connection without proxy can truly reflect the state of the target website under normal access, providing accurate and comprehensive comparison basis for subsequent proxy IP testing; covering HTML structure, HTTP response header, loading time and other information, as well as simulating real user operation to collect dynamic data, which can ensure that the benchmark sample covers the complete business scenario, avoid the misjudgment of proxy quality caused by incomplete benchmark information, and improve the accuracy and comprehensiveness of the detection; Proxy IP test execution: double-blind test is adopted, which includes task group test and control group test; the task group test uses the proxy IP to access the target website, records the returned page content and anti-crawl triggering condition (such as blocking, verification code); the control group test uses the same proxy IP to access the low-risk neutral website, detects the basic performance (delay, availability); compare the test results of the task group and the control group, exclude the quality problem of the proxy itself (such as network fluctuation), and identify the influence of the anti-crawl strategy of the target website; the setting of the task group and the control group in the double-blind test can effectively distinguish the influence of the anti-crawl strategy of the target website and the quality problem of the proxy itself. By comparing the results of the two, the network fluctuation and other factors of the proxy itself can be accurately excluded, the identification of the anti-crawl strategy specific to the target website is focused on, the evaluation of the proxy performance on the target website is more accurate, and reliable basis is provided for subsequent proxy selection and optimization.

[0019] Further, it further includes key indicator calculation, which includes anti-crawl compatibility indicator and stability indicator; The anti-crawl compatibility indicator includes: DOM matching rate: compare the similarity of the page returned by the proxy and the HTML structure of the benchmark sample (based on XPath / text similarity algorithm); Anti-crawl triggering rate: statistics the frequency of triggering blocking or verification code when the proxy accesses the target website.

[0020] The stability indicator includes: Continuous available time length: the longest time that the proxy IP keeps stable connection on the target website; Pressure test fault tolerance: Gradient increase concurrent requests (1→50 threads), observe error rate changes; Key indicators calculate the quality of the proxy from two important dimensions: anti-crawl compatibility and stability; DOM matching rate and anti-crawl trigger rate can directly reflect the ability of the proxy to respond to the anti-crawl strategy of the target website; Continuous available time and pressure test fault tolerance reflect the stability of the proxy in different scenarios; These quantitative indicators make the evaluation of proxy quality more objective and operable, making it easier to compare and select different proxies.

[0021] Further, it also includes a proxy IP recommendation and optimization process, which includes: Task adaptability scoring model, the task adaptability scoring model is used to dynamically allocate index weights according to different task types (such as data crawling, account registration); Different task types have different requirements for proxies, for example, data crawling may pay more attention to the accuracy of page content (DOM matching rate), and account registration may pay more attention to the anti-crawl trigger rate; The task adaptability scoring model dynamically allocates index weights to make the proxy scoring more in line with the specific task requirements, improving the relevance and applicability of proxy recommendation, thereby improving task execution efficiency.

[0022] Further, the proxy IP recommendation and optimization process also includes an intelligent recommendation engine, which includes: Real-time scoring and ranking: Calculate the comprehensive score of each proxy IP according to the current task requirements, and preferentially allocate high-score proxies; Automatic switching strategy: Switch to backup IP from the same supplier or switch suppliers when the proxy triggers a ban; Trigger a low-cost alternative when the proxy cost exceeds the revenue threshold (such as single-task cost > revenue 20%); Real-time scoring and ranking can ensure that proxies with better overall performance are used first, improving the efficiency and success rate of initial task execution; The automatic switching strategy can respond in a timely manner when the proxy has problems, switching when the proxy triggers a ban can avoid task interruption and ensure task continuity; Switching when the cost exceeds the threshold can effectively control costs and improve the economic efficiency of the task, achieving a balance between efficiency and economy in the use of proxies.

[0023] Further, the proxy IP recommendation and optimization process also includes supplier dynamic optimization, which includes: Multi-supplier horizontal comparison: Regularly test proxy pools from different suppliers and record their performance in various tasks; Elimination and introduction mechanism, including: Blacklist: Proxy IPs with a ban rate consistently higher than 10% are isolated for 12 hours; New supplier injection: Introduce 10% new agent resources every month to prevent IP pool aging; Multi-supplier horizontal comparison can help users clearly understand the advantages and disadvantages of different supplier agent pools, providing a basis for selecting high-quality suppliers. The elimination mechanism isolates high-ban rate agents, reducing task failures caused by poor agents. The regular injection of new suppliers can continuously inject fresh blood into the agent pool, effectively preventing IP pool aging and ensuring the vitality and quality of agent resources, improving the reliability of overall agent services.

[0024] Further, it also includes data feedback to the detection module: Collect and integrate data generated in each stage, including agent IP performance indicators, task execution results, cost-benefit analysis, and other information; Establish an efficient data feedback channel to feed back the integrated data to the detection module in real time or periodically. The detection module continuously monitors and evaluates the quality of agent IPs based on these feedback data, and promptly discovers potential problems and optimization points; The detection module analyzes the feedback data to uncover the underlying patterns and trends, providing data support and decision-making basis for subsequent agent IP selection, task allocation, and optimization strategies. For example, by analyzing agent IP performance under different time periods and task types, the weight distribution of the intelligent recommendation engine and the strategy of dynamic optimization execution can be adjusted to further improve the accuracy and efficiency of network proxy quality detection.

[0025] In embodiment 2, the method for detecting the quality of network proxies provided in embodiment 1 is further optimized. Specifically, the low-cost alternative solution includes: Same-supplier low-cost IP switching: Preferentially select low-cost agent IPs (such as shared IPs, dynamic residential IPs) that are not used or have low load from the current supplier's agent pool for replacement, avoiding the connection interruption risk caused by directly switching suppliers; Cross-supplier low-cost resource introduction: If there is no suitable replacement IP from the same supplier, call low-cost solutions (such as third-party machine room IPs, rotating IP services) from the pre-accessed multi-supplier agent pool to ensure task continuity; In the low-cost alternative solution, the same-supplier low-cost IP switching is preferred, which can minimize the connection interruption risk caused by switching suppliers and ensure smooth task performance. When there is no suitable IP from the same supplier, cross-supplier low-cost resources are introduced to ensure that the task continues under controllable cost, controlling cost and ensuring task continuity, further improving the economy and reliability of agent use.

[0026] Further, the real environment sample collection also includes: Browser fingerprint consistency collection: Record the browser fingerprint characteristics of the reference sample through the proxy-free direct connection method, including but not limited to User-Agent, screen resolution, font list, time zone information; Dynamic behavior feature collection: Simulate the browsing track of real users (such as dwell time distribution, page jump path), and generate a behavior heat map as a reference behavior pattern; The addition of browser fingerprint consistency collection and dynamic behavior feature collection makes the reference sample more comprehensive and detailed, and can more realistically simulate the access situation of real users; This helps to more accurately detect whether the proxy can imitate the browser fingerprint and behavior pattern of real users in subsequent proxy testing, so as to better cope with the anti-crawling strategies of the target website based on these characteristics, and improve the anti-crawling compatibility of the proxy.

[0027] Further, the key indicator calculation also includes a response integrity indicator, which includes: Compare the resource files (such as images, CSS files, JavaScript files) contained in the proxy returned page with the hash values of the corresponding resource files in the reference sample, and calculate the proportion of missing or tampered resource files; For specific data fields returned by the target website (such as product prices, user information, and other key data), check the integrity and accuracy of these fields in the proxy returned data, and calculate the number of missing or incorrect data fields by comparing with the corresponding fields in the reference sample; The addition of the response integrity indicator evaluates the completeness and accuracy of the proxy returned content from two aspects of resource files and specific data fields; By comparing the hash values of the resource files, it can accurately detect whether the resource files are missing or tampered; The check of specific data fields ensures the reliability of key data; This makes the evaluation of the proxy more comprehensive, and can effectively avoid the impact of incomplete or inaccurate proxy returned content on task effectiveness, especially for tasks with high data quality requirements.

[0028] Embodiment 3, the specific implementation of the present application is explained in detail by the following steps, combined with the hardware and software architecture and typical application scenarios, to ensure the realizability and repeatability of the technical solution.

[0029] I. System hardware and operating environment Server configuration: Master server (1): CPU≥8 cores, memory≥32GB, used for running proxy detection core algorithm and recommendation engine; Test node (multiple distributed): Each node is configured with CPU≥4 cores, memory≥16GB, and is deployed in different geographic regions (such as Beijing, Shanghai, Guangzhou) to simulate real network environment; Network requirements: The control server and test nodes are connected via a dedicated line or VPN to ensure communication delay < 50ms; the export bandwidth is ≥ 100Mbps, supporting high-concurrency proxy testing (such as 100+ requests per second).

[0030] 2. Software module implementation details, including: (1) Real environment sample collection module Benchmark sample acquisition: import requests def get_baseline(url): response=requests.get(url,headers={"User-Agent":"Mozilla / 5.0"}) with open("baseline.html","w",encoding="utf-8")as f: f.write(response.text)#Store the baseline DOM return response.headers,response.elapsed.total_seconds()#Return response headers and time Dynamic behavior simulation: Use Selenium automation tools to simulate user scrolling and clicking operations and record AJAX request traces (using the browser developer tool API).

[0031] (2) Proxy IP double-blind test module Task group / control group design: Example: Proxy test execution def test_proxy(proxy_ip,target_url,neutral_url="http: / / httpbin.org / ip"): / *Task group: test target website* / target_result=requests.get(target_url,proxies={"http":proxy_ip},timeout=10) #Control group: test neutral website neutral_result=requests.get(neutral_url,proxies={"http":proxy_ip},timeout=5) return { "target_status": target_result.status_code, "neutral_speed": neutral_result.elapsed.total_seconds(), "dom_diff": compare_dom(target_result.text, "baseline.html") # DOM difference calculation (3) Dynamic scoring and recommendation module Weight adaptive algorithm: Example: Scoring calculation in e-commerce crawler scenario def calculate_score(proxy_data, task_type="ecommerce"): weights={"anti_ban_rate":0.4,"dom_match":0.3,"cost":0.2} if task_type=="ecommerce"\ else{"speed":0.5,"uptime":0.3,"cost":0.2} score=sum(proxy_data[k]*weights[k]fork in weights) return score Real-time recommendation logic: Use priority queue (such as Redis Sorted Set) to store proxy IP scores, and pop Top3 candidate IPs when task is requested.

[0032] III. Implementation case Scenario: Cross-border e-commerce price monitoring crawler 1. Sample collection: Use enterprise direct line to connect to target e-commerce website (such as amazon.com), store homepage HTML and product detail page AJAX data; 2. Proxy testing: Select residential IP pool of supplier A (100 IPs), and execute the following for each IP: Visit amazon.com / product / 123 (task group) to detect whether the verification code is returned; Visit httpbin.org / ip (control group) to detect basic delay; 3. Scoring and recommendation: IPs with anti-crawl trigger rate < 5% and DOM matching rate > 90% are marked as "high affinity", and are preferentially assigned to crawler tasks. IPs triggering ban are automatically isolated and switched to the backup IP pool of supplier B.

[0033] In the present application, unless otherwise clearly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can also be electrical connection or communication with each other; can be directly connected, can also be indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements, unless otherwise clearly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0034] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the content of the present application specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A method for detecting the quality of a network proxy, characterized in that: The following steps are involved: Real-world sample collection: Access the target website through a proxy-free direct connection and record benchmark samples for comparison. Simulate real user operations and collect dynamically loaded data to ensure that the benchmark samples cover the entire business scenario. Proxy IP test execution: A double-blind test is used, which includes a task group test and a control group test. The task group test uses the proxy IP to access the target website, and records the page content and anti-crawl triggering conditions returned. The control group test uses the same proxy IP to access a low-risk neutral website to test its basic performance. The test results of the task group and control groups are compared to eliminate quality issues of the proxy itself and identify the impact of the target website's unique anti-crawl strategy.

2. The method for detecting the quality of a network agent according to claim 1, wherein: It also includes key indicator calculations, including anti-climb compatibility indicators and stability indicators.

3. The method for detecting the quality of a network agent according to claim 2, wherein: Anti-climb compatibility indicators include: DOM matching rate: compares the HTML structure similarity between the page returned by the proxy and the benchmark sample; Anti-crawl trigger rate: Counts the frequency of blocking or verification codes being triggered when the proxy accesses the target website.

4. The method for detecting the quality of a network agent according to claim 2, wherein: Stability indicators include: Continuous availability time: the maximum time the proxy IP maintains a stable connection to the target website; Stress test fault tolerance: Gradually increase concurrent requests and observe changes in error rate.

5. The method for detecting the quality of a network agent according to claim 1, wherein: It also includes a proxy IP recommendation and optimization process, which includes: Task adaptability scoring model,The task adaptability scoring model is used to dynamically assign indicator weights,according to different task types.

6. The method for detecting the quality of a network agent according to claim 5, wherein: The proxy IP recommendation and optimization process also includes an intelligent recommendation engine, which includes: Real-time scoring and sorting: Calculate the comprehensive score of each proxy IP according to the current task requirements, and give priority to high-scoring proxies; Automatic switching strategy: When the proxy triggers a ban, it automatically switches to the backup IP of the same provider or switches providers; When the agency cost exceeds the benefit threshold, the low-cost alternative is triggered.

7. The method for detecting the quality of a network agent according to claim 6, wherein: The proxy IP recommendation and optimization process also includes supplier dynamic optimization, which includes: Multi-vendor comparison: Regularly test proxy pools from different vendors and record their performance across various tasks. Elimination and introduction mechanisms, including: Blacklist: Proxy IPs with a blocking rate consistently higher than 10% will be isolated for 12 hours; New supplier injection: 10% new proxy resources are introduced every month to prevent IP pool aging.

8. The method for detecting the quality of a network agent according to claim 6, wherein: Lower-cost alternatives include: Switching to low-cost IPs from the same provider: Prioritize unused or low-loaded low-cost proxy IPs from the current provider's proxy pool for replacement, avoiding the risk of connection interruption caused by switching providers directly. Introduction of cross-supplier low-cost resources: If there is no suitable alternative IP from the same supplier, a low-cost solution will be called from the pre-connected multi-supplier proxy pool to ensure task continuity.

9. The method for detecting the quality of a network agent according to claim 1, wherein: The real environment sample collection also includes: Browser fingerprint consistency collection: Record the browser fingerprint characteristics of the benchmark sample through a proxy-free direct connection, including but not limited to User-Agent, screen resolution, font list, and time zone information; Dynamic behavior feature collection: simulates the browsing trajectory of real users and generates behavior heat maps as baseline behavior patterns.

10. The method for detecting the quality of a network agent according to claim 2, wherein: The key indicator calculation also includes a response integrity indicator, which includes: Compare the hash values ​​of the resource files contained in the proxy return page with those of the corresponding resource files in the benchmark sample, and calculate the proportion of missing or tampered resource files; For specific data fields returned by the target website, check the completeness and accuracy of these fields in the agent's returned data, and calculate the proportion of missing or incorrect data fields by comparing them with the corresponding fields in the benchmark sample.

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