Network health detection method and system based on network comprehensive monitoring means

Abstract

The invention discloses a network health detection method based on a network comprehensive monitoring means, and the method comprises the steps: collecting first network health data related to a target device and a to-be-detected network based on a ping tool, and enabling the first network health data to be used for detecting the network connectivity and packet loss conditions; collecting second network health data related to the target equipment and the network to be detected based on an mtr tool, wherein the second network health data is used for positioning a bottleneck or a fault point in the network; third network health data related to the target device and the to-be-detected network are collected based on an ns lookup tool and used for detecting whether DNS analysis and domain name access are normal or not; integrating and analyzing the first network health data, the second network health data and the third network health data to obtain equipment risks and network problems related to the target equipment and the to-be-tested network; and carrying out real-time alarm and early warning based on the equipment risks and network problems related to the target equipment and the to-be-tested network. The invention further discloses a network health detection system based on the network comprehensive monitoring means, electronic equipment and a computer readable storage medium.

Description

Technical Field

[0001] The present invention relates to the technical fields of network monitoring and network health detection, and particularly to a network health detection method and system based on network comprehensive monitoring means. Background Art

[0002] With the rapid development of information technology, the network environment has become increasingly complex and changeable. For enterprises, a stable and reliable network connection is the key to ensuring business continuity and data security. However, in actual applications, due to reasons such as network device failures, configuration errors, and network congestion, network problems often occur, seriously affecting the normal operation of enterprises. Therefore, how to effectively monitor and detect the network in real time, and timely discover and solve potential network problems, has become an important issue to be solved currently.

[0003] Traditional network monitoring methods often rely on a single detection tool or technology, and it is difficult to comprehensively reflect the true state of the network. Tools such as ping, mtr (My Traceroute), and ns lookup each have unique advantages and can detect and analyze the network from different perspectives. Therefore, it is of great practical significance and application value to develop a comprehensive network monitoring and detection method by combining the characteristics and functions of these tools. Summary of the Invention

[0004] To solve the problems existing in the prior art, the present invention provides a network health detection method and system based on network comprehensive monitoring means, which comprehensively uses network monitoring tools such as ping, mtr (My Traceroute), and ns lookup for network health detection, so as to ensure that the network can be effectively monitored and detected in real time, and potential network problems can be timely discovered and solved; among them, tools such as ping, mtr, and ns lookup each play a key role. These tools not only help detect network connectivity, but also help locate network problems, provide important information for optimizing network performance, improve the accuracy and efficiency of network monitoring, and provide strong support for network optimization and fault troubleshooting.

[0005] On the one hand, the present invention provides a network health detection method based on network comprehensive monitoring means, including:

[0006] S1. Collect the first network health data related to the target device and the network under test based on the ping tool, collect the second network health data related to the target device and the network under test based on the mtr tool, and collect the third network health data related to the target device and the network under test based on the ns lookup tool. Among them, the first network health data is used to detect network connectivity and packet loss, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal.

[0007] S2. Integrate and analyze the first network health data, the second network health data, and the third network health data to obtain device risks and network problems related to the target device and the network under test.

[0008] S3. Perform real-time alarms and warnings based on the device risks and network problems related to the target device and the network under test.

[0009] Preferably, S1 includes:

[0010] S11. Collect the first network health data based on the ping tool.

[0011] S12. Collect the second network health data based on the mtr tool.

[0012] S13. Collect the third network health data based on the ns lookup tool.

[0013] Preferably, S11 includes:

[0014] (1) Test the connectivity of the network under test based on the ping command, including: regularly send ICMP echo request packets to the target device and wait for a response, record the response time and packet loss situation and receive echo data, and the echo data is used as the first sub-data of the first network health. The first sub-data of the first network health detects the connectivity of the target device by judging whether the host of the target device is reachable and measuring the round-trip time parameter.

[0015] (2) Obtain the response time and packet loss rate of ping by sending the ping command through the network under test as the second sub-data of the first network health, and evaluate the connection stability and latency of the network under test based on the analysis of the second sub-data of the first network health.

[0016] S12 includes:

[0017] (1) Perform network path tracing based on the mtr tool, and obtain packets representing detailed path information from the source address to the target address as the first sub-data of the second network health data.

[0018] (2) Integrate the functions of traceroute and ping based on the mtr tool to obtain the response time of each node in the network to be tested, the latency of each hop in the network, and the packet loss rate as the second sub-data of the second network health data. The second sub-data of the second network health data is used to identify and quickly locate bottlenecks and fault points in the network to be tested;

[0019] The S13 includes:

[0020] (1) Query DNS information based on the ns lookup tool to obtain the IP address and configuration information corresponding to the domain name as the first sub-data of the third network health data; the first sub-data of the third network health data is used to verify the correctness of DNS resolution, so as to judge whether there is a problem with domain name access;

[0021] (2) Monitor the status performance, stability of the DNS server representing the network to be tested, and diagnose fault packets in the domain name resolution process based on the ns lookup tool as the second sub-data of the third network health data; the second sub-data of the third network health data is used to obtain faults or configuration errors of the DNS server in the network to be tested in real time, and diagnose faults in the domain name resolution process.

[0022] Preferably, the S2 includes:

[0023] S21, integrate the first network health data, the second network health data, and the third network health data to form a full network status view;

[0024] S22, based on machine learning, pattern recognition, and big data analysis methods, deeply mine and analyze the multi-layer and multi-dimensional data in the full network status view to identify potential device risks and network problems related to the target device and the network to be tested.

[0025] Preferably, the S22 includes:

[0026] (1) Based on machine learning, pattern recognition, and big data analysis methods, deeply mine and analyze the multi-layer and multi-dimensional data in the full network status view to obtain the real-time comprehensive health data of the target device and the network to be tested; among them, the real-time comprehensive health data of the target device and the network to be tested includes:

[0027] Analyze the data returned by the ping command, calculate indicators such as the average response time, the maximum response time, and the packet loss rate, and evaluate the connectivity and stability of the network to be tested;

[0028] Analyze the output result of mtr to identify bottlenecks or fault points in the network path of the network to be tested, and the fault points include nodes with high latency and / or high packet loss rate;

[0029] Detect the query results of ns lookup, confirm whether the domain name resolution is normal, and whether there are problems with DNS server failures or configuration errors;

[0030] (2) Based on the comparison between the preset comprehensive health data threshold and the real-time comprehensive health data, obtain the health detection and analysis results of the target device and the network to be tested;

[0031] Among them, the real-time comprehensive health data of the target device and the network to be tested obtained by deeply mining and analyzing the multi-layer and multi-dimensional data in the full network status view based on machine learning, pattern recognition, and big data analysis methods includes:

[0032] A. Data collection in the full network status view, including: using data collection tools to collect data in the full network status view, including collecting network public data through web crawlers and reading business data from databases;

[0033] B. Data preprocessing in the full network status view, including: cleaning the data collected in the full network status view to remove duplicate and incorrect data; performing data conversion, including standardizing and normalizing the data to meet the algorithm input requirements;

[0034] C. Information and feature extraction based on the preprocessed data in the full network status view, including:

[0035] · Statistical analysis: Initially understand the distribution characteristics of the data in the full network status view by calculating mean and variance statistics;

[0036] · Feature engineering: Use the principal component analysis dimensionality reduction algorithm to extract key features, reduce the data dimension, and retain the main information at the same time;

[0037] D. Based on the extracted information and features, use machine learning, pattern recognition, and big data analysis methods to identify potential device risks and network problems related to the target device and the network to be tested, including:

[0038] · Machine learning algorithms: Select algorithms according to the identification tasks corresponding to device risks and network problems; for example, based on decision trees or random forests, classify and predict whether the target device and the network to be tested are abnormal; based on regression algorithms, predict the traffic trend of the network to be tested;

[0039] · Pattern recognition: Build a network behavior pattern library, and use template matching and neural network methods to compare the real-time collected network data with the pattern library to identify network status patterns;

[0040] · Big data analysis technology: Use a distributed computing framework to process the large-scale network data of the network to be tested, and analyze the real-time data of the network to be tested through real-time stream processing technology to promptly detect changes in the state of the network to be tested.

[0041] Preferably, the S3 includes:

[0042] S31, once abnormal situations occur in device risks and network problems related to the target device and the network to be tested, immediately trigger an alarm or early warning mechanism;

[0043] S32, based on the triggered alarm or early warning mechanism, send the alarm information to the administrator or relevant personnel by one or more of email, text message, and system notification, so as to take measures for processing in a timely manner;

[0044] S33, display the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information in the form of charts and / or reports on a display screen, so as to intuitively display to the user and facilitate the user to quickly understand the overall network situation;

[0045] S34, based on the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information, combined with the network topology structure and historical data, locate the abnormal indicators, potential problems, the reasons for generating abnormal indicators and potential problems, and different fault types of the network to be tested. At the same time, provide detailed prompt information and explanations for abnormal indicators or potential problems on a display screen to help the user better understand the problem; provide corresponding solutions and measures for the different fault types, and the corresponding solutions and measures include optimizing the network path and / or adjusting network device parameters.

[0046] The second aspect of the present invention provides a network health detection system based on network comprehensive monitoring means, providing a unified interface and platform for displaying network status, analysis results, and alarm information, supporting users to customize monitoring parameters and rules, and meeting the monitoring requirements of different scenarios, including:

[0047] A data acquisition module (101), configured to collect first network health data related to the target device and the network to be tested based on the ping tool, collect second network health data related to the target device and the network to be tested based on the mtr tool, and collect third network health data related to the target device and the network to be tested based on the nslookup tool; wherein the first network health data is used to detect network connectivity and packet loss situations, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal;

[0048] A data analysis module (102) for integrating and analyzing the first network health data, the second network health data, and the third network health data to obtain device risks and network problems related to the target device and the network under test;

[0049] An alarm and early warning module (103) for performing real-time alarm and early warning based on the device risks and network problems related to the target device and the network under test.

[0050] Preferably, the network health detection system based on network comprehensive monitoring means further includes:

[0051] A configuration and management module for providing functions for users to configure and manage the network health detection system based on network comprehensive monitoring means, and supporting permission management and logging functions to ensure the security and stability of the network health detection system based on network comprehensive monitoring means.

[0052] A third aspect of the present invention provides an electronic device, including a processor and a memory, where the memory stores multiple instructions, and the processor is configured to read the instructions and execute the method as described in the first aspect.

[0053] A fourth aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores multiple instructions, and the multiple instructions can be read and executed by a processor to execute the method as described in the first aspect.

[0054] The method, system, and electronic device provided by the present invention have the following beneficial effects:

[0055] A detection method based on network monitoring is provided. By comprehensively using a variety of network tools and technical means, it realizes comprehensive monitoring, analysis, and troubleshooting of the network status, providing strong support for improving network performance and user experience, including:

[0056] (1) By deeply mining and processing data, potential problems and risks hidden deep in the network can be discovered.

[0057] (2) The result display method is intuitive and easy to understand, facilitating users to quickly understand the network status and take corresponding measures.

[0058] (3) The fault location and solution suggestions are highly targeted, which can improve the efficiency and quality of fault troubleshooting and solution. Description of the Drawings

[0059] Figure 1 It is a flowchart of a network health detection method based on network comprehensive monitoring means according to an embodiment of the present invention;

[0060] Figure 2Flowchart of the method for collecting the first network health data based on the ping tool according to an embodiment of the present invention;

[0061] Figure 3 Flowchart of the method for collecting the first network health data based on the mtr monitoring task according to an embodiment of the present invention;

[0062] Figure 4 Flowchart of the method for collecting the first network health data based on the ns lookup monitoring task tool according to an embodiment of the present invention;

[0063] Figure 5 Architecture diagram of the network health detection system based on network comprehensive monitoring means according to an embodiment of the present invention;

[0064] Figure 6 Comprehensive system architecture diagram including the front-end UI, display layer, business layer, data layer, database, data collection, and operating environment according to an embodiment of the present invention;

[0065] Figure 7 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed implementation manners

[0066] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0067] Embodiment 1

[0068] As Figure 1 shown, this embodiment provides a network health detection method based on network comprehensive monitoring means, including:

[0069] S1. Collect the first network health data related to the target device and the network to be tested based on the ping tool, collect the second network health data related to the target device and the network to be tested based on the mtr tool, and collect the third network health data related to the target device and the network to be tested based on the ns lookup tool; wherein the first network health data is used to detect network connectivity and packet loss situation, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal;

[0070] S2. Integrate and analyze the first network health data, the second network health data, and the third network health data to obtain device risks and network problems related to the target device and the network to be tested;

[0071] S3. Perform real-time alarm and early warning based on the device risks and network problems related to the target device and the network to be tested.

[0072] As a preferred embodiment, S1 includes:

[0073] As Figure 2 shown, S11, collecting first network health data based on the ping tool, including:

[0074] (1) Testing the connectivity of the network to be measured based on the ping command, including: regularly sending ICMP (Internet Control Message Protocol) echo request packets to the target device and waiting for a response, recording the response time and packet loss situation, and receiving echo data, where the echo data is used as the first sub-data of the first network health; the first sub-data of the first network health detects the connectivity of the target device by determining whether the host of the target device is reachable and measuring the round-trip time parameter;

[0075] (2) Obtaining the response time and packet loss rate of ping by sending the ping command through the network to be measured as the second sub-data of the first network health, and evaluating the connection stability and latency of the network to be measured based on the analysis of the second sub-data of the first network health.

[0076] As Figure 3 shown, S12, collecting second network health data based on the mtr tool, including:

[0077] (1) Conducting network path tracing based on the mtr tool to obtain packets representing detailed path information from the source address to the target address as the first sub-data of the second network health data;

[0078] (2) Based on the mtr tool, after integrating the functions of traceroute and ping, obtaining the response time of each node in the network to be measured, the latency situation of each hop in the network, and the packet loss rate as the second sub-data of the second network health data, where the second sub-data of the second network health data is used to identify and quickly locate bottlenecks and fault points in the network to be measured.

[0079] In this embodiment, mtr is a network connectivity judgment tool that combines the characteristics of ping, traceroute, and ns lookup. Through the monitoring of mtr, network latency and packet loss problems can be discovered and solved in a timely manner, improving the availability and performance of network services.

[0080] As Figure 4 shown, S13, collecting third network health data based on the ns lookup tool, including:

[0081] (1) querying DNS (Domain Name System) information based on the ns lookup tool to obtain the IP address and configuration information corresponding to the domain name as the first sub-data of the third network health data; the first sub-data of the third network health data is used to verify the correctness of the DNS resolution, so as to determine whether there is a problem with the domain name access;

[0082] (2) Based on the ns lookup tool monitoring, the data packet used to characterize the status performance, stability of the DNS server of the network under test and diagnose the fault in the domain name resolution process is used as the second sub-data of the third network health data; the second sub-data of the third network health data is used to obtain the fault or configuration error of the DNS server of the network under test in real time, and diagnose the fault in the domain name resolution process.

[0083] As a preferred embodiment, S2 includes:

[0084] S21, integrating the first network health data, the second network health data and the third network health data to form a full network status view.

[0085] S22, based on machine learning, pattern recognition and big data analysis methods, deeply mine and analyze the multi-layer and multi-dimensional data in the full network status view to identify potential device risks and network problems related to the target device and the network to be tested.

[0086] As a preferred implementation, the S22 includes:

[0087] (1) Based on machine learning, pattern recognition and big data analysis methods, the multi-layer and multi-dimensional data in the full network status view are deeply mined and analyzed to obtain real-time comprehensive health data of the target device and the network to be tested; wherein the real-time comprehensive health data of the target device and the network to be tested includes:

[0088] Analyze the data returned by the ping command, calculate indicators such as average response time, maximum response time, and packet loss rate, and evaluate the connectivity and stability of the network under test;

[0089] Analyze the output results of mtr to identify bottlenecks or failure points in the network path of the network to be tested, wherein the failure points include nodes with high latency and / or high packet loss rate;

[0090] Check the query results of ns lookup to confirm whether the domain name resolution is normal and whether there is a DNS server failure or configuration error.

[0091] As a preferred embodiment, the real-time comprehensive health data of the target device and the network to be tested obtained by deeply mining and analyzing the multi-layer and multi-dimensional data in the full network state view based on machine learning, pattern recognition, and big data analysis methods includes:

[0092] A. Data collection in the full network state view, including: using appropriate data collection tools to collect data in the full network state view, including collecting network public data through web crawlers and reading business data from databases;

[0093] B. Data preprocessing in the full network state view: cleaning the collected data in the full network state view to remove duplicate and error data; performing data transformation, including standardizing and normalizing the data to meet the algorithm input requirements;

[0094] C. Information and feature extraction based on the preprocessed data in the full network state view, including:

[0095] · Statistical analysis: Initially understand the distribution characteristics of the data in the full network state view by calculating mean and variance statistics;

[0096] · Feature engineering: Use dimensionality reduction algorithms such as principal component analysis (PCA) to extract key features, reduce the data dimension, and retain the main information at the same time;

[0097] D. Based on the extracted information and features, use machine learning, pattern recognition, and big data analysis methods to identify potential device risks and network problems related to the target device and the network to be tested, including:

[0098] · Machine learning algorithms: Select algorithms according to the identification tasks corresponding to device risks and network problems; for example, based on decision trees or random forests for classification prediction of whether the target device and the network to be tested are abnormal; based on regression algorithms to predict the traffic trend of the network to be tested; in this embodiment, the selected algorithms correspond to the training of specific models. When training the model, it is necessary to divide the training set and the test set, and optimize the model parameters of the specific model through cross-validation.

[0099] · Pattern recognition: Build a network behavior pattern library, and use methods such as template matching and neural networks to compare the real-time collected network data with the pattern library to identify network state patterns.

[0100] · Big data analysis technology: Use distributed computing frameworks (such as Hadoop, Spark) to process the large-scale network data of the network to be tested, and analyze the real-time data of the network to be tested through real-time stream processing technology (such as Flink) to timely discover the state changes of the network to be tested.

[0101] (2) Obtain the health detection and analysis results of the target device and the network to be tested based on the comparison between the preset comprehensive health data threshold and the real-time comprehensive health data.

[0102] As a preferred implementation manner, the S3 includes:

[0103] S31, once abnormal situations occur in the device risks and network problems related to the target device and the network to be tested, immediately trigger the alarm or early warning mechanism.

[0104] S32, based on the triggered alarm or early warning mechanism, send the alarm information to the administrator or relevant personnel by one or more of email, text message, and system notification, so as to take measures for handling in a timely manner;

[0105] S33, display the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information in the form of charts and / or reports on the display screen, so as to be intuitively presented to the user and facilitate the user to quickly understand the overall network situation;

[0106] S34, based on the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information, combined with the network topology structure and historical data, locate the abnormal indicators, potential problems, the reasons for generating the abnormal indicators and potential problems, and different fault types of the network to be tested. At the same time, provide detailed prompt information and explanations for the abnormal indicators or potential problems on the display screen to help the user better understand the problem; provide corresponding solution suggestions and measures for the different fault types, and the corresponding solution suggestions and measures include optimizing the network path and / or adjusting the network device parameters.

[0107] Embodiment 2

[0108] As Figures 5-6 shown, this embodiment provides a network health detection system based on network comprehensive monitoring means, which provides a unified interface and platform for displaying network status, analysis results, and alarm information, supports users to customize monitoring parameters and rules, and meets the monitoring requirements of different scenarios, including:

[0109] The data collection module 101 is used to collect the first network health data related to the target device and the network to be tested based on the ping tool, collect the second network health data related to the target device and the network to be tested based on the mtr tool, and collect the third network health data related to the target device and the network to be tested based on the nslookup tool; wherein the first network health data is used to detect network connectivity and packet loss, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal;

[0110] In this embodiment, the data collection module is used to call tools such as ping, mtr, and ns lookup as needed to collect network status data; it supports multi-threaded or asynchronous methods to improve the efficiency and real-time performance of data collection.

[0111] The data analysis module 102 is used to integrate and analyze the first network health data, the second network health data, and the third network health data, so as to obtain device risks and network problems related to the target device and the network to be tested;

[0112] In this embodiment, the data analysis module is used to process and analyze the collected data, extract useful information and features; apply machine learning algorithms, pattern recognition, and big data analysis technologies to predict and evaluate the network status.

[0113] The alarm and early warning module 103 is used to perform real-time alarm and early warning based on the device risks and network problems related to the target device and the network to be tested.

[0114] In this embodiment, the alarm and early warning module triggers the alarm or early warning mechanism according to the analysis results and preset rules, and supports multiple alarm methods to ensure that the administrator can receive the alarm information in time.

[0115] As a preferred implementation manner, the network health detection system based on network comprehensive monitoring means further includes:

[0116] The configuration and management module is used to provide the functions for users to configure and manage the network health detection system based on network comprehensive monitoring means, and support the functions of permission management and log recording to ensure the security and stability of the network health detection system based on network comprehensive monitoring means.

[0117] The present invention also provides a memory storing multiple instructions for implementing the method as in Embodiment 1.

[0118] Such as Figure 7As shown in the figure, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores multiple instructions, which can be loaded and executed by the processor, so that the processor can execute the method as in Embodiment 1.

[0119] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A network health detection method based on network comprehensive monitoring means, characterized in that: include: S1, collecting first network health data related to the target device and the network to be tested based on the ping tool, collecting second network health data related to the target device and the network to be tested based on the mtr tool, and collecting third network health data related to the target device and the network to be tested based on the nslookup tool; wherein the first network health data is used to detect network connectivity and packet loss, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal; S2, integrating and analyzing the first network health data, the second network health data, and the third network health data, so as to obtain device risks and network problems related to the target device and the network to be tested; S3, provides real-time alarms and warnings based on device risks and network issues related to the target device and the network under test.

2. A network health detection method based on network comprehensive monitoring means according to claim 1, characterized in that: The S1 includes: S11, collecting first network health data based on a ping tool; S12, collecting the second network health data based on the mtr tool; S13, collecting third network health data based on the nslookup tool.

3. A network health detection method based on network comprehensive monitoring means according to claim 2, characterized in that: The S11 includes: (1) Testing the connectivity of the network to be tested based on the ping command, including: regularly sending ICMP echo request data packets to the target device and waiting for a response, recording the response time and packet loss situation and receiving echo data, wherein the echo data is used as the first network health first sub-data; the first network health first sub-data detects the connectivity of the target device by determining whether the host of the target device is reachable and measuring a round-trip time parameter; (2) sending a ping command through the network to be tested to obtain a ping response time and a packet loss rate as the second sub-data of the health of the first network, and evaluating the connection stability and delay of the network to be tested based on the analysis of the second sub-data of the health of the first network; The S12 includes: (1) Perform network path tracing based on the mtr tool to obtain a data packet representing detailed path information from a source address to a target address as the first sub-data of the second network health data; (2) Based on the mtr tool, the functions of traceroute and ping are integrated to obtain the response time of each node in the network to be tested, the delay of each hop in the network, and the packet loss rate as the second sub-data of the second network health data, and the second sub-data of the second network health data is used to identify and quickly locate the bottlenecks and fault points in the network to be tested; The S13 includes: (1) querying DNS information based on the nslookup tool to obtain the IP address and configuration information corresponding to the domain name as the first sub-data of the third network health data; the first sub-data of the third network health data is used to verify the correctness of the DNS resolution, so as to determine whether there is a problem with the domain name access; (2) Based on the nslookup tool monitoring, the data packets used to characterize the status performance, stability of the DNS server of the network to be tested, and to diagnose faults in the domain name resolution process are used as the second sub-data of the third network health data; the second sub-data of the third network health data is used to obtain the faults or configuration errors of the DNS server of the network to be tested in real time, and to diagnose faults in the domain name resolution process.

4. A network health detection method based on network comprehensive monitoring means according to claim 3, characterized in that: The S2 includes: S21, integrating the first network health data, the second network health data and the third network health data to form a full network status view; S22, based on machine learning, pattern recognition and big data analysis methods, deeply mine and analyze the multi-layer and multi-dimensional data in the full network status view to identify potential device risks and network problems related to the target device and the network to be tested.

5. A network health detection method based on network comprehensive monitoring means according to claim 4, characterized in that: The S22 includes: (1) Based on machine learning, pattern recognition and big data analysis methods, the multi-layer and multi-dimensional data in the full network status view are deeply mined and analyzed to obtain real-time comprehensive health data of the target device and the network to be tested; wherein the real-time comprehensive health data of the target device and the network to be tested includes: Analyze the data returned by the ping command, calculate indicators such as average response time, maximum response time, and packet loss rate, and evaluate the connectivity and stability of the network under test; Analyze the output results of mtr to identify bottlenecks or failure points in the network path of the network to be tested, wherein the failure points include nodes with high latency and / or high packet loss rate; Check the query results of nslookup to confirm whether the domain name resolution is normal and whether there is a DNS server failure or configuration error; (2) Obtain health detection and analysis results of the target device and the network to be tested based on the comparison between the preset comprehensive health data threshold and the real-time comprehensive health data; The method of deeply mining and analyzing the multi-layer and multi-dimensional data in the full network status view based on machine learning, pattern recognition and big data analysis to obtain real-time comprehensive health data of the target device and the network to be tested includes: A. Data collection in the full network status view, including: using data collection tools to collect data in the full network status view, including collecting network public data through web crawlers and reading business data from databases; B. Data preprocessing in the full network status view, including: cleaning the collected data in the full network status view to remove duplicate and erroneous data; performing data conversion, including standardizing and normalizing the data to make it meet the algorithm input requirements; C. Extract information and features based on the preprocessed data in the full network status view, including: Statistical analysis: By calculating the mean and variance statistics, we can get a preliminary understanding of the distribution characteristics of the data in the full network status view; Feature Engineering: Use principal component analysis to reduce the dimension of data while retaining the main information. D. Based on the extracted information and features, use machine learning, pattern recognition, and big data analysis methods to identify potential device risks and network issues related to the target device and the network under test, including: Machine learning algorithm: select algorithms based on the identification tasks corresponding to device risks and network problems; for example, classify and predict whether the target device and the network under test are abnormal based on decision trees or random forests; predict the traffic trend of the network under test based on regression algorithms; Pattern recognition: Build a network behavior pattern library, use template matching and neural network methods to compare the real-time collected network data with the pattern library, and identify network status patterns; Big data analysis technology: Use a distributed computing framework to process the large-scale network data of the network under test, and use real-time stream processing technology to analyze the real-time data of the network under test to timely discover the state changes of the network under test.

6. A network health detection method based on network comprehensive monitoring means according to claim 5, characterized in that: The S3 includes: S31, once the device risks and network problems related to the target device and the network under test are abnormal, an alarm or early warning mechanism is immediately triggered; S32, based on the triggered alarm or early warning mechanism, sending the alarm information to the administrator or relevant personnel through one or more of email, SMS and system notification, so that timely measures can be taken to handle it; S33, displaying the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information in the form of charts and / or reports on a display screen as a carrier, so as to intuitively display them to the user, so that the user can quickly understand the overall status of the network; S34, based on the device status and network status of the target device and the network to be tested, the analysis results of the device status and network status, and the alarm information, combined with the network topology and historical data, locate the abnormal indicators, potential problems, causes of the abnormal indicators and potential problems, and different fault types of the network to be tested, and use the display screen as a carrier to provide detailed prompt information and explanations for the abnormal indicators or potential problems to help users better understand the problem; provide corresponding solution suggestions and measures for the different fault types, and the corresponding solution suggestions and measures include optimizing network paths and / or adjusting network device parameters.

7. A network health detection system based on network comprehensive monitoring means, used to implement any of the methods of claims 1-6, characterized in that: include: A data collection module (101) is used to collect first network health data related to a target device and a network to be tested based on a ping tool, collect second network health data related to a target device and a network to be tested based on an mtr tool, and collect third network health data related to a target device and a network to be tested based on an nslookup tool; wherein the first network health data is used to detect network connectivity and packet loss, the second network health data is used to locate bottlenecks or fault points in the network, and the third network health data is used to detect whether DNS resolution and domain name access are normal; A data analysis module (102), configured to integrate and analyze the first network health data, the second network health data, and the third network health data, so as to obtain device risks and network problems related to the target device and the network to be tested; The alarm and warning module (103) is used to issue real-time alarms and warnings based on device risks and network problems related to the target device and the network to be tested.

8. A network health detection system based on network comprehensive monitoring means according to claim 7, characterized in that: Also includes: The configuration and management module is used to provide users with the functions of configuring and managing the network health detection system based on network comprehensive monitoring means, and supports permission management and log recording functions to ensure the security and stability of the network health detection system based on network comprehensive monitoring means.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is used to read the instructions and execute the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method according to any one of claims 1 to 6.

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