Broadband network fault intelligent diagnosis system and method

Abstract

The invention discloses a broadband network fault intelligent diagnosis system and method, and belongs to the technical field of broadband networks. The system comprises a foreground system, a middle platform system and a background system. The foreground system integrates a plurality of diagnosis function modules, such as network diagnosis, network speed measurement, WiFi analysis and the like, and is used for detecting a user broadband network environment and sending a result to the middle station system. And the middle platform system is responsible for receiving and processing data sent by the foreground system, forming standardized data, distributing the standardized data to the foreground system and the background system, performing real-time analysis, providing preliminary optimization suggestions, performing aggregation analysis and generating a statistical report. And the background system stores data in a centralized manner and provides multi-dimensional retrieval and analysis functions. Under the control of the middle platform system, all the diagnosis function modules of the foreground system can be called in sequence according to a preset process, so that quick collection of key network data is realized. According to the system, the efficiency and the accuracy of broadband network fault diagnosis are improved through efficient cooperation and intelligent analysis of the foreground, the middle platform and the background.

Description

Technical Field

[0001] The present invention relates to the technical field of broadband networks, and in particular to a broadband network fault intelligent diagnosis system and method. Background Art

[0002] With the increasing popularity and complexity of broadband networks, users are increasingly demanding network stability and service quality. However, in practice, existing broadband network fault detection tools have limited functionality and lack comprehensive diagnostic capabilities. Furthermore, insufficient front-end and back-end collaboration leads to frequent problems such as difficulty locating problems, slow response times, and untimely optimization recommendations. These problems manifest themselves in the following ways: (1) Detection tools are scattered: Existing network fault detection tools are mostly independent modules, such as network speed test, Ping test, etc., which make it difficult to form a complete diagnostic system and cannot fully cover various problems in the user's broadband network environment.

[0003] (2) Insufficient coordination between front-end and back-end systems: The front-end system is usually only responsible for data collection, while the back-end system is responsible for data analysis and storage. There are delays and inconsistencies in the data interaction and collaborative work between the two, which affects the accuracy and timeliness of fault diagnosis.

[0004] (3) Lack of intelligent analysis: Traditional fault diagnosis systems rely on manual analysis and lack intelligent analysis methods based on big data and machine learning, making it difficult to quickly identify anomalies and provide effective optimization suggestions.

[0005] (4) Insufficient data management and visualization: The existing system is relatively weak in data management and visualization, and cannot provide multi-dimensional data retrieval and statistical reports, making it difficult to support decision-making and long-term trend analysis.

[0006] In view of the above problems, there is an urgent need to build a broadband network fault intelligent diagnosis system that integrates multiple diagnostic function modules, has efficient front-end and back-end collaboration, and intelligent analysis capabilities to improve the efficiency and accuracy of network fault diagnosis. Summary of the Invention

[0007] The technical problem to be solved by the present invention is: to provide a broadband network fault intelligent diagnosis system and method in view of the above-mentioned defects of the prior art.

[0008] To achieve the above objectives, the present invention provides a broadband network fault intelligent diagnosis system, which includes a front-end system, a middle-end system, and a back-end system; wherein: The front-end system is configured to integrate multiple diagnostic function modules to detect the user's broadband network environment and send the detection results to the middle-end system; the diagnostic function modules include: network diagnosis module, network speed measurement module, fast collection module, WiFi analysis module, Ping test module, application packet capture module, routing detection module, and domain name resolution module; The middle platform system is configured as follows: (1) Receive and process the raw diagnostic data collected by the front-end system to form standardized data, and send the standardized data to the front-end system and the back-end system; receive diagnostic suggestions from the back-end system and forward them to the front-end system; (2) Conducting real-time analysis based on the standardized data to quickly identify anomalies and providing preliminary optimization suggestions and detection suggestions to the front-end system based on preset rules. The preliminary optimization suggestions are obtained by support engineers based on the analysis of historical data stored in the back-end, and the anomalies are synchronized to the back-end system; (3) Performing aggregate analysis based on the standardized data to generate statistical reports of multiple time granularities, and sending the statistical reports to the backend system; the statistical reports include a report on the distribution of diagnosis times in each region, a report on the proportion of fault types, and a report on network quality trends; The middle platform system includes a data receiving and processing module, a data distribution module, a data real-time analysis module, and a data aggregation analysis module; The backend system is configured to centrally store the standardized data transmitted by the middle-end system and provide multi-dimensional retrieval and analysis functions; the backend system includes a storage module, a retrieval and visualization module, and a system management and configuration module; The quick collection module is configured to summarize key network data at the time of a fault with one click. The quick collection module responds to the trigger request of the installation and maintenance engineer and sends a quick collection request to the middle-office system. The middle-office system triggers the corresponding diagnostic function modules of the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0009] In the broadband network fault intelligent diagnosis system of the present invention, the front-end system includes a fault diagnosis application installed on the smart mobile terminal of the installation and maintenance engineer. The smart mobile terminal is a mobile phone or tablet computer equipped with an Android system. Each diagnostic function module calls the system's underlying functions or hardware resources through the API provided by the Android system to realize data collection.

[0010] In the broadband network fault intelligent diagnosis system of the present invention, the network diagnosis module is configured to sequentially collect the delay and packet loss rate of each segment of the entire link from the user terminal to the Internet exit through segment detection technology, and graphically present the status of each node; the network speed measurement module is configured to detect the uplink and downlink speeds of the user terminal and the intranet speed measurement server; the WiFi analysis module is configured to detect the WiFi quality of the user environment, perform a graded evaluation based on a preset threshold, and output improvement suggestions; the Ping test module is configured to detect the network connectivity and quality between the terminal and the target node; the application packet capture module is configured to capture the network interaction messages of the application program and generate a packet capture file; the routing detection module is configured to locate the routing path and bottleneck node from the terminal to the target address; and the domain name resolution module is configured to verify the DNS resolution function and compare the resolution results of different servers.

[0011] In the broadband network fault intelligent diagnosis system of the present invention, the diagnosis function module also includes a video test module, and the video test module is configured to specifically detect the playback rate, buffering times, and bit rate adaptation indicators of a specific video platform.

[0012] In the broadband network fault intelligent diagnosis system of the present invention, the diagnosis function module further includes an IP calculator module, a web page response speed module and an auxiliary setting module.

[0013] In the broadband network fault intelligent diagnosis system of the present invention, the front-end system automatically obtains its diagnostic terminal device information, connects to the user's home WiFi, automatically collects network feature information, and carries the diagnostic terminal device information and network feature information in the data interaction with the middle-end system; the middle-end system distinguishes different diagnostic terminal devices through the diagnostic terminal device information, and constructs a user ID through the network feature information to distinguish different user-side environments.

[0014] In the broadband network fault intelligent diagnosis system of the present invention, the data receiving and processing module receives the structured data and unstructured data uploaded by the front-end system in real time through the TCP protocol, checks the data integrity, and then cleans and classifies the above data; if there is missing data, the middle-end system sends feedback to the front-end system to prompt the installation and maintenance engineer to fill in the missing data.

[0015] The present invention also provides a method for intelligent diagnosis of broadband network faults, which is implemented using the broadband network fault intelligent diagnosis system described above. The method comprises the following steps: Step S1: The installation and maintenance engineer initiates a diagnostic request through the fault diagnosis application of the front-end system; Step S2: collecting broadband network environment data according to the diagnostic request, generating raw diagnostic data and uploading it to the middle platform system; Step S3: The middle-end system cleans and processes the original diagnostic data to form standardized data, and then distributes the standardized data to the front-end system and the back-end system; Step S4: The middle-office system analyzes the standardized data in real time, identifies anomalies, synchronizes the anomaly information to the back-end system, and feeds back preliminary optimization suggestions and detection suggestions to the front-end system based on the identified anomalies; the middle-office system uses big data analysis technology to aggregate and analyze the standardized data, and generates statistical reports that are sent to the back-end system; Step S5: The backend system stores standardized data and statistical reports, supporting engineers to analyze and locate faults through the backend system, generate solutions and optimization suggestions, and push the solutions and optimization suggestions to the frontend system through the middle-end system; Step S6: The installation and maintenance engineer repairs the network failure according to the solution and optimization suggestions, and reports the repair status to the backend system; Step S7: The backend system archives the entire link data of the fault processing; If the diagnostic request in step S1 is a quick collection request, the middle-office system triggers the diagnostic function modules corresponding to the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0016] The broadband network fault intelligent diagnosis system provided by the present invention has the following beneficial effects: (1) Multifunctional integrated diagnosis: The front-end system integrates multiple diagnostic function modules, such as network diagnosis, network speed test, WiFi analysis, Ping test, etc., which can fully cover the user's broadband network environment and provide one-stop fault detection services.

[0017] (2) Efficient data processing and distribution: The middle-office system is responsible for receiving, processing and forwarding the original diagnostic data from the front-end system, forming standardized data, and distributing it to the front-end system and back-end system to ensure data consistency and availability.

[0018] (3) Real-time and aggregated analysis: The middle-office system uses real-time and aggregated analysis to quickly identify anomalies and provide preliminary optimization suggestions. It also generates statistical reports at multiple time granularities to help support engineers conduct in-depth analysis and decision-making.

[0019] (4) Seamless collaboration between front-end and back-end systems: An efficient data flow and information sharing mechanism is formed between the front-end system, middle-end system and back-end system, ensuring the timeliness and accuracy of fault diagnosis.

[0020] (5) One-click quick collection: The quick collection module can summarize key network data with one click, simplifying the troubleshooting process, reducing the operation complexity of installation and maintenance engineers, and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a schematic diagram of the architecture of a broadband network fault intelligent diagnosis system provided by an embodiment of the present invention.

[0022] Figure 2 A schematic diagram of a scenario of a broadband network fault intelligent diagnosis system provided by an embodiment of the present invention.

[0023] Figure 3-Figure 4 This is a schematic diagram of the user interface of a network diagnosis module of a fault diagnosis application program of a front-end system of a broadband network fault intelligent diagnosis system provided by an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the user interface of the auxiliary setting module of the fault diagnosis application of the front-end system of the broadband network fault intelligent diagnosis system provided by an embodiment of the present invention.

[0025] Figure 6 This is a schematic diagram of the main user interface of a fault diagnosis application program of a front-end system of a broadband network fault intelligent diagnosis system provided by an embodiment of the present invention.

[0026] Figure 7 A schematic diagram of the steps of a broadband network fault intelligent diagnosis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] The embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0029] The present invention is suitable for intelligent diagnosis of broadband network faults in home WiFi environments, providing installation and maintenance engineers and support engineers with a unified and quantifiable detection basis, solving the problems of scattered traditional diagnostic tools, inconsistent standards, and difficulty in front-end and back-end collaboration.

[0030] like Figure 1As shown, an embodiment of the present invention provides a broadband network fault intelligent diagnosis system, which includes a front-end system, a middle-end system, and a back-end system; wherein: The front-end system is configured to integrate multiple diagnostic function modules to detect the user's broadband network environment and send the detection results to the middle-end system; the diagnostic function modules include: network diagnosis module, network speed measurement module, fast collection module, WiFi analysis module, Ping test module, application packet capture module, routing detection module, and domain name resolution module; The middle platform system is configured as follows: (1) Receive and process the raw diagnostic data collected by the front-end system to form standardized data, and send the standardized data to the front-end system and the back-end system; receive diagnostic suggestions from the back-end system and forward them to the front-end system; (2) Conducting real-time analysis based on the standardized data to quickly identify anomalies and providing preliminary optimization suggestions and detection suggestions to the front-end system based on preset rules. The preliminary optimization suggestions are obtained by support engineers based on the analysis of historical data stored in the back-end, and the anomalies are synchronized to the back-end system; (3) Performing aggregate analysis based on the standardized data to generate statistical reports of multiple time granularities, and sending the statistical reports to the backend system; the statistical reports include a report on the distribution of diagnosis times in each region, a report on the proportion of fault types, and a report on network quality trends; The middle platform system includes a data receiving and processing module, a data distribution module, a data real-time analysis module, and a data aggregation analysis module; The backend system is configured to centrally store the standardized data transmitted by the middle-end system and provide multi-dimensional retrieval and analysis functions; the backend system includes a storage module, a retrieval and visualization module, and a system management and configuration module; The quick collection module is configured to summarize key network data at the time of a fault with one click. The quick collection module responds to the trigger request of the installation and maintenance engineer and sends a quick collection request to the middle-office system. The middle-office system triggers the corresponding diagnostic function modules of the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0031] In this embodiment, a collaborative architecture across the front-end, middle-end, and back-end platforms integrates and optimizes network fault diagnosis capabilities. This coordinated process of "front-end data collection - middle-end processing - back-end management" ensures efficient terminal diagnostic functionality, real-time data interoperability, and unified control of system configurations. This creates a closed-loop system of "tool integration - data interoperability - collaborative operations and maintenance," effectively addressing the existing challenges of a lack of detection tools and insufficient front-end and back-end collaboration, improving fault location efficiency and network service quality.

[0032] like Figure 2 The figure shows a scenario diagram of the broadband network fault intelligent diagnosis system according to an embodiment of the present invention. Broadband users access the Internet through the metropolitan area network and access network of the broadcasting and television network to browse web pages or watch videos. When a broadband user reports a fault, the installation and maintenance engineer will provide on-site service, and the technical expert will provide remote support through the page provided by the background system. The speed test server is the internal speed test server of the broadband operator. The foreground system includes a fault diagnosis application installed on the smart mobile terminal of the installation and maintenance engineer. The smart mobile terminal is a mobile phone or tablet computer equipped with an Android system. Each diagnostic function module calls the underlying system functions or hardware resources through the API (Application Programming Interface) provided by the Android system to realize data collection, such as Figure 6 The figure shows a user interface diagram of a fault diagnosis application on a smart mobile terminal of an installation and maintenance engineer. The installation and maintenance engineer calls each diagnostic function module through the user interface provided by the fault diagnosis application to collect raw diagnostic data and send the raw diagnostic data to the back-end system through the middle-office system, and then repairs the network fault based on his own experience, feedback from the middle-office system, or diagnostic suggestions from support engineers. The middle-office system receives and processes the raw diagnostic data to form standardized data, and synchronizes the standardized data to the front-end system and the back-end system, and performs real-time analysis and aggregate analysis based on the standardized data to provide decision support for the support engineer. The back-end system provides a management page, and support engineers search and analyze through the management page to provide diagnostic suggestions to the installation and maintenance engineers remotely.

[0033] In an embodiment of the present invention, the network diagnostic module is configured to sequentially collect the latency and packet loss rate of each segment of the entire link from the user terminal to the Internet egress using segmented detection technology, graphically presenting the status of each node to quickly locate the faulty node. Specifically, this technology is used to sequentially collect terminal operational data (such as IP, gateway, DNS), WiFi environment data (such as signal strength and interference), home gateway (ONU / CM), metropolitan area network equipment, and Internet egress latency and packet loss rate, graphically presenting the status of each node and packaging it back to the middleware system. The network diagnostic module obtains basic network information such as the terminal's current network type (WiFi / mobile data), IP address, gateway, and DNS server by calling the network status API (such as the ConnectivityManager class).

[0034] In some embodiments of the present invention, the entire link from user terminals to the internet egress includes: the user terminal layer, the home local area network layer, the access network layer, the metropolitan area network layer, and the internet egress layer. Each layer is connected in series through network equipment, and any failure in any link may affect network quality. The user terminal layer includes the installation and maintenance engineer's Android smart terminal and user-side devices such as mobile phones and computers; the home local area network layer includes the home WiFi network (including routers and signal coverage areas) and the home gateway (ONU optical modem or CM cable modem); the access network layer includes the operator's access layer switches, optical distribution network, and other equipment; the metropolitan area network layer includes aggregation switches, core routers, and other metropolitan area network backbone equipment; and the internet egress layer includes the operator's egress router connecting to the public network, as well as connected egress nodes of operators such as China Telecom, China Unicom, and China Mobile. For nodes such as the home gateway and metropolitan area network equipment, a ping test (ICMP protocol) is performed to send an echo request message to the target node. The time difference between sending and receiving the echo response is recorded. After multiple tests, the average, maximum, and minimum delays are calculated. For TCP protocol applications, the TCPping test is used to send TCP SYN packets to a specific port (default 80) on the target node, recording the time difference in establishing a connection as latency. During the TCP Ping test, the total number of packets sent and the number of response packets received are recorded. The packet loss rate = (1 - number of responses / number of packets sent) × 100%. The network diagnostic module integrates the test results of each link and displays the latency and packet loss rate from the terminal to the home gateway, metropolitan area network, and Internet egress through a graphical interface such as a node-link diagram, visually identifying abnormal nodes.

[0035] Figure 3 This is a diagram of basic WiFi data in the network diagnostic module user interface. Figure 4 This diagram shows the evaluation of Wi-Fi data and segment quality in the Network Diagnostics user interface. Evaluation and scoring are based on pre-set, unified evaluation criteria, shared across the front-end, middle-end, and back-end. The Network Diagnostics user interface also includes basic network information and segment detection results. The segment detection results graphically display segment detection latency, packet loss rate, and evaluation.

[0036] In an embodiment of the present invention, the network speed test module is configured to detect the uplink and downlink rates between the user terminal and the intranet speed test server to verify whether the network meets the promised bandwidth. Specifically, by sending large files to the speed test server, monitoring rate changes during download and upload, recording and storing multiple test results, and generating uplink and downlink rate reports. The speed test server is an internal speed test server of a broadband network operator. The network speed test module calls Socket-related APIs (such as java.net.Socket) to establish a TCP connection with the speed test server, send and receive test files, calculate uplink and downlink rates, and implement the network speed test function. The user interface diagram of the network speed test module intuitively displays the downlink and uplink bandwidths through curve graphs and displays the speed test results.

[0037] The quick collection module is configured to summarize key network data at the time of failure with one click to assist in the analysis of difficult faults. The quick collection module responds to the trigger request of the installation and maintenance engineer and sends a quick collection request to the middle-office system. The middle-office system triggers the corresponding collection modules of the front-end system in sequence according to the preset order to collect key network data, and then summarizes the collection results of each collection module and sends them to the front-end system and the back-end system. The quick collection module is mainly used for installation and maintenance engineers to troubleshoot difficult network faults on site, and it is necessary to efficiently summarize key data and collaborate with back-end support engineers for analysis, specifically including: (1) When on-site faults are difficult to locate in a timely manner: When installation and maintenance engineers encounter complex faults at the user's home (such as intermittent network interruptions, specific application freezes, WiFi signals that are sometimes good and sometimes bad, etc.), and are unable to directly locate the cause through basic diagnosis (such as speed test, Ping test), they can use the quick collection function to summarize basic information such as terminal IP, gateway, DNS, and test data such as Ping gateway, route detection, and DNS resolution in one click. If necessary, they can attach the packet capture file of the problem application to provide the back-end support engineer with a complete on-site network snapshot.

[0038] (2) When remote assistance from back-end support engineers is required: For technical problems that cannot be solved on-site by installation and maintenance engineers (such as abnormal metropolitan area network links, cross-node packet loss, and other problems involving the backbone layer of the operator's network), standardized data (diagnostic reports and packet capture files in a unified format) can be quickly collected and sent back to the back-end in real time. Support engineers can directly call up data through the back-end management system for in-depth analysis without the need for repeated communication to supplement information, thus shortening the response time of remote collaboration.

[0039] (3) Fault review and case accumulation: For typical and difficult faults that have been resolved, the quickly collected data packets can be archived as case materials in the backend system for reference in subsequent troubleshooting of similar faults. For example, if DNS resolution timeouts occur repeatedly in a certain area, by summarizing historical and quickly collected data, common problems (such as excessive load on a specific DNS server) can be discovered, providing a basis for network optimization.

[0040] The quick collection module user interface includes basic network information, PING test, routing detection results and DNS resolution results; the PING test shows the latency, packet loss rate and evaluation corresponding to different target addresses, and the routing detection results show the number of hops for different target addresses and DNS. Clicking the menu can also view the details of each hop.

[0041] In this embodiment of the present invention, the WiFi analysis module is configured to detect the WiFi quality of the user's environment, perform a graded evaluation based on preset thresholds, and output improvement suggestions. Specifically, it detects the current WiFi signal strength, rate, channel distribution, interference, and number of online terminals. Based on preset thresholds (e.g., signal strength ≥ -64dBm is considered "good"), the module performs a graded evaluation and outputs suggestions such as channel switching and interference reduction. The WiFi analysis module calls WiFi-related system APIs (such as the WifiManager class) to obtain information such as the current WiFi signal strength (dBm), channel information, interference conditions of surrounding WiFi hotspots, and the number of connected terminals. For example, the getSignalStrength() method is used to obtain signal strength, and the getScanResults() method is used to scan the distribution of surrounding WiFi channels, supporting WiFi quality assessment and outputting optimization suggestions.

[0042] The WiFi analysis module user interface includes basic network information, signal strength curve, WiFi rate, channel analysis, WiFi interference results and online terminal list.

[0043] In an embodiment of the present invention, the Ping test module is configured to detect the network connectivity and quality between the terminal and the target node. The implementation method is to send ICMP Echo requests or TCP messages, count the maximum, minimum, average delays and packet loss rates, support simultaneous testing of multiple targets and display the results on the same screen. Specifically, the Ping test module sends ICMP Echo request messages by calling ICMP protocol-related APIs and records the round-trip time; for Tcping tests, the TCP connection API (such as Socket.connect()) is called to count the connection establishment delay. In the user interface diagram of the PING test module, for each test target, the delay fluctuation is displayed through a curve chart, and the packet loss rate, minimum delay, average delay, and maximum delay are counted.

[0044] In an embodiment of the present invention, the application packet capture module is configured to capture the network interaction messages of the application and generate a packet capture file. The implementation method is that the user selects the target application such as video and browser, captures the communication messages between it and the server in real time, generates a packet capture file and sends it to the middle-office system. The application packet capture module is implemented through its own integrated packet capture logic rather than integrating external tools, ensuring lightweight functions and seamless connection with the overall diagnostic process. Specifically, the application packet capture module applies for the network access rights and data packet capture rights of the Android system, calls the network packet capture API at the bottom layer of the system (such as through the encapsulation interface of the libpcap library or the Android VpnService class) to monitor the network interaction between the terminal and the target application, captures TCP / UDP packets, and generates a packet capture file in the .pcap format.

[0045] In an embodiment of the present invention, the routing detection module is configured to locate the routing path and bottleneck nodes from the terminal to the target address. This is achieved by sending a message to the target based on the ICMP protocol and recording the IP address, latency, and packet loss rate of each hop router to form a routing trajectory diagram and identify the packet loss nodes in the routing path. Specifically, the routing detection module implements the routing tracking function by calling the relevant interface of the network protocol stack, obtains the IP address of each hop node, and supports the identification of the routing path and packet loss nodes. In the user interface diagram of the routing detection module, the DNS address can be manually entered, or the target domain name can be quickly selected using the icon in the quick domain name area. The detection results display the IP address, packet loss rate, and latency of each detection target.

[0046] In this embodiment of the present invention, the domain name resolution module is configured to verify DNS resolution functionality and compare resolution results from different servers. This is accomplished by sending resolution requests to multiple pre-set or custom DNS servers, recording the target domain name's resolution IP address and duration, and supporting historical result comparison. In the diagram of the domain name resolution module user interface, the DNS address can be manually entered, or the target domain name can be quickly selected using the icons in the quick domain name area.

[0047] In an embodiment of the present invention, the diagnostic function module also includes a video testing module. In home broadband networks, video streaming applications are frequently used by users, and video freezes and slow loading times are typical user complaints. The video testing module is configured to specifically detect metrics such as playback rate, buffering times, and bitrate adaptation for specific video platforms (such as iQiyi and Tencent Video). This allows for more accurate identification of video service-specific faults, such as CDN node access issues and video protocol adaptation issues, effectively improving user satisfaction with broadband services. The video testing module user interface displays statistics such as video image quality, average frame rate, buffering time, average rate, freeze times, average latency, and network packet loss, as well as playback indicator curves, network indicator curves, and buffering indicator curves.

[0048] In an embodiment of the present invention, the diagnostic function module also includes an IP calculator module. The IP calculator module is used to calculate network addresses, such as subnet mask, gateway, and broadcast address. It serves as an auxiliary tool for installation and maintenance engineers. The IP calculator module includes multiple calculation modules, including IP segment -> mask, mask -> IP segment, code bit -> mask, and mask -> code bit.

[0049] In this embodiment of the present invention, the diagnostic function module also includes a webpage response speed module. Web browsing is a fundamental user experience, and response speed, such as DNS resolution latency, TCP connection establishment time, and page load time, directly impacts the user experience. Combining the webpage response speed module with the domain name resolution module and the ping speed measurement module for full-process speed measurement can more intuitively reveal the causes of slow webpage performance, contributing to a certain improvement in user experience.

[0050] In this embodiment of the present invention, the front-end system also includes an auxiliary settings module, which is configured to allow users to customize tool parameters to adapt to different scenarios. This module is implemented by providing a graphical interface that supports modifying default settings such as the quick diagnosis target, ping packet count, and routing test nodes, and supports a one-click restore to factory settings. Figure 5 This is a diagram of the auxiliary setting module user interface, including the network diagnosis parameter modification area, the PING test parameter modification area, the quick collection parameter modification area, and the history record area.

[0051] Installation and maintenance engineers pass Figure 6 The menus provided on the main user interface of the application shown collect on-site broadband network environment data. Figure 6 In the , all menus are divided into three major areas: "Quick Diagnosis", "Detailed Diagnosis", and "Application Settings". Different menus are usually used in different scenarios, as follows: (1) Broadband network new installation scenario Core goal: No in-depth investigation is required for new installation scenarios. Focus on basic indicator verification, verify network commissioning quality, and quickly output basic indicators.

[0052] Required menus: The "Network Diagnosis" and "Network Speed ​​Test" menus in the Quick Diagnosis area, and the "Video Test" and "IP Calculator" menus in the Detailed Diagnosis area. The "Network Diagnosis" menu corresponds to the network diagnosis module of the front-end system, used to test full-link connectivity and generate segmented quality reports. The "Network Speed ​​Test" menu corresponds to the network speed test module of the front-end system, used to verify whether the upstream and downstream bandwidths meet the standards. The "Video Test" menu corresponds to the video test module of the front-end system, used to verify the video playback experience. The "IP Calculator" menu corresponds to the IP calculator module of the front-end system, used to calculate network addresses.

[0053] (2) Common network troubleshooting scenarios Core goal: For user reports of problems, such as inability to access the Internet or slow Internet speeds, we use detailed diagnostic tools to analyze the causes of the problems layer by layer and provide targeted repairs on-site.

[0054] Required menus: The "Network Diagnosis" menu in the Quick Diagnosis area and the "Wi-Fi Analysis," "Ping Test," "Domain Name Resolution," and "Route Detection" menus in the Detailed Diagnosis area. The "Network Diagnosis" menu corresponds to the network diagnosis module of the front-end system and is used to quickly locate faulty nodes. The "Wi-Fi Analysis" menu corresponds to the Wi-Fi analysis module of the front-end system and is used to detect Wi-Fi signal strength, interference, and channel distribution to resolve Wi-Fi lags. The "Ping Test" corresponds to the Ping test module of the front-end system and is used to verify connectivity between the terminal and the gateway and server. The "Domain Name Resolution" menu corresponds to the domain name resolution module of the front-end system and is used to troubleshoot webpage issues caused by DNS anomalies. The "Route Detection" menu corresponds to the route detection module of the front-end system and is used to locate routing nodes in the link that are causing packet loss.

[0055] (3) Troubleshooting scenarios Core goal: For difficult faults, such as intermittent network disconnection and unavailability of specific apps, installation and maintenance engineers are required to quickly collect complete data on site for analysis by backend support engineers.

[0056] Required menus: the "Quick Collection" menu in the Quick Diagnosis area, and the "Application Packet Capture" and "Webpage Response Speed" menus in the Detailed Diagnosis area. The "Quick Collection" menu corresponds to the front-end system's Quick Collection module, triggering a standardized process of IP acquisition → Ping test → Route detection → DNS resolution → Application packet capture with a single click, automatically packaging data. The "Application Packet Capture" menu corresponds to the front-end system's Application Packet Capture module, capturing packets for specific applications. The "Webpage Response Speed" menu corresponds to the front-end system's Webpage Response Speed ​​module, recording the time it takes to load a target webpage to aid in analyzing application-layer issues.

[0057] (4) Application scenarios in non-standard network environments Core goal: For non-standard network environments, such as special gateways for government and enterprise users, customize tool parameters to adapt to special environments.

[0058] Menu to be used: "Auxiliary Settings" menu in the application settings area. The "Auxiliary Settings" menu corresponds to the auxiliary settings module of the front-end system, which is used to modify the detection target of network diagnosis, the packet size of Ping test, the default DNS server address of domain name resolution, etc.

[0059] The collected data in the "Quick Diagnosis" and "Detailed Diagnosis" area menus can be shared through third-party tools such as WeChat or email.

[0060] In an embodiment of the present invention, the front-end system also includes a data feedback and interaction module, which is used to establish a data connection between the front-end system and the middle-end system, and perform two-way data transmission based on the connection. Specifically, the data feedback and interaction module calls HTTP / HTTPS related APIs (such as OkHttp library or HttpURLConnection class) to establish a network connection with the middle-end system, encrypts the collected diagnostic data (such as speed test results, packet capture files) and uploads it to the middle-end system in real time, and receives optimization suggestions and configuration updates and other processing results returned by the middle-end system, thereby realizing direct data intercommunication between "front-end and middle-end" and indirect data intercommunication between "front-end and back-end".

[0061] The front-end system automatically obtains its diagnostic terminal device information, connects to the user's home WiFi, automatically collects network feature information, and carries the diagnostic terminal device information and network feature information in the data interaction with the middle-end system. Among them, the diagnostic terminal device information is the device information of the installation and maintenance engineer's smart mobile terminal, including IMEI, device MAC address, and the unique device code generated when the fault diagnosis application is installed; the network feature information includes the MAC address of the home gateway, the gateway device number, the IP address of the user terminal, the BSSID of the WiFi, and the GPS positioning information. In addition, the installation and maintenance engineer can also enter the user's name, mobile phone number, and home address through the fault diagnosis application as a supplement to the network feature information. The middle-end system distinguishes different diagnostic terminal devices through the diagnostic terminal device information, and constructs a user ID through the network feature information to distinguish different user-side environments. The original diagnostic data uploaded by the front-end system is accurately associated with the data analysis results of the middle-end system and the fault file of the back-end system to form a complete user-side fault processing link.

[0062] In this embodiment of the present invention, the mid-stage system is built based on C, Java, and big data technologies. It is the core hub connecting the front-end system and the back-end system, providing business data flow cleaning, processing, business orchestration, and in-depth analysis. The mid-stage system includes a data reception and processing module, a data distribution module, a real-time data analysis module, a data aggregation analysis module, a modeling engine, a message in-depth analysis component, and a workflow engine.

[0063] The data receiving and processing module receives structured data (such as latency, packet loss rate) and unstructured data (such as packet capture files) uploaded by the front-end system in real time through the TCP protocol, checks the data integrity, and then cleans the above data. Checking data integrity specifically includes: verifying whether there are missing values ​​in the structured data, and verifying whether the unstructured data is missing key fields. If there are missing values, the middle-office system sends feedback to the front-end system to prompt the installation and maintenance engineer to fill in the missing data. Data cleaning specifically includes: unifying the format and unit of structured data, filtering outliers; parsing unstructured data into retrievable message logs through the message depth analysis component (extracting fields such as source IP, protocol type, interaction time, etc.), and classifying and marking them by application type (such as video, web page). The purpose of classification and marking is to provide a basis for supporting engineers to quickly match corresponding solutions. Frequent TCP retransmissions and bandwidth fluctuations in video application packet capture data often indicate CDN node anomalies or insufficient home bandwidth. Solutions can include switching video nodes or upgrading bandwidth plans. DNS resolution timeouts and first-packet response delays in web application message logs may be related to DNS server configuration and web server load. Solutions may include changing DNS addresses and optimizing web caches. The middleware system categorizes and labels diagnostic data, allowing the backend system to query historical data by application type and fault type. This allows support engineers to filter historical cases for similar applications and directly reuse proven solutions, reducing the cost of repeated analysis and improving the efficiency of troubleshooting.

[0064] In an embodiment of the present invention, after the data receiving and processing module cleans the structured data and unstructured data uploaded by the front-end system, it converts the cleaned data into a format recognizable by the back-end system to form standardized data, such as JSON format, and then adds metadata such as timestamps and area tags to lay the foundation for storage and analysis. Among them, the timestamp is usually in seconds. The area tag combines two dimensions: the area of ​​responsibility of the installation and maintenance engineer and the on-site GPS positioning area. After adding metadata such as timestamps and area tags, the data distribution module synchronizes the standardized data to the back-end system and the front-end system as needed to ensure data consistency among the front-end, middle-end, and back-end. In addition, the data distribution module also forwards the diagnostic reports and operation suggestions sent by the back-end system to the front-end system in real time.

[0065] In this embodiment of the present invention, the real-time data analysis module instantly analyzes diagnostic data uploaded by the frontend, quickly identifies anomalies, and synchronizes them to the backend system, enabling support engineers to promptly detect and respond to anomalies. The middle-office system identifies anomalies in three categories: network link anomalies, device and environment anomalies, and application and service anomalies, covering common troubleshooting scenarios for engineers. Network link anomalies include home gateway anomalies, access network and metropolitan area network anomalies, and egress link anomalies. Specifically, home gateway anomaly criteria include packet loss rate >5% and latency >100ms. The middle-office system processes the diagnostic data uploaded in real time by the frontend using Spark Streaming. When five consecutive records in a certain area show a home gateway packet loss rate >5%, the modeling engine triggers an anomaly warning model, generates an alert, and pushes it to the backend system. Access network and metropolitan area network anomaly criteria include >30% of diagnostic records within a certain area showing a packet loss rate >3% to core network equipment and latency fluctuation >50ms. Egress link anomaly criteria include >2% packet loss to the internet or >30% difference in egress speed between different carriers. Device and environment anomalies include WiFi environment anomalies and home gateway / optical modem anomalies. Specifically, WiFi environment anomaly criteria include signal strength <-75dBm, >5 interference sources on the same channel, and >15 online terminals leading to a sudden drop in speed. Home gateway / optical modem anomalies include frequent offline status and abnormal indicator light status. Application and service anomalies include speed measurement anomalies, video / web page anomalies, and batch anomalies. Speed ​​measurement anomaly criteria include uplink and downlink speeds <80% of the contracted bandwidth and fluctuations >20% across multiple tests. Video / web page anomalies include video freezes >3 times / 5 minutes, web page first screen load >3 seconds, and DNS resolution timeouts >500ms. Batch anomalies are identified when >20% of engineers in a region report the same fault within 10 minutes, resulting in a regional failure and triggering a priority response. After identifying an anomaly, the real-time data analysis module provides preliminary optimization and detection recommendations to the front-end system based on pre-set rules. These preliminary optimization recommendations are generated by support engineers based on analysis of historical data stored in the back-end.

[0066] The data aggregation and analysis module aggregates and analyzes the full data set on a daily, weekly, and monthly basis. Using a modeling engine, it constructs statistical models to generate statistical reports such as the distribution of diagnostic times per region, the percentage of fault types, and network quality trends. The modeling engine builds and runs analytical models based on the data, transforming raw data into structured results that can be directly used for business decision-making. In this embodiment of the present invention, the backend system is the core of centralized data storage, storing the full set of standardized data. The middle-office system, as the core link for data processing and analysis, only temporarily stores data for real-time processing and short-term analysis to avoid storage redundancy. When performing data aggregation and analysis, particularly monthly analysis, the middle-office system retrieves the full set of data from the backend system. The statistical reports generated by the data aggregation and analysis module must be synchronized to the backend system for unified review and export by support engineers, or for use in management decision-making. When the middle-office system detects a high frequency of a certain type of fault, it pushes a corresponding detection prompt to the front-end system to help installation and maintenance engineers more quickly identify the cause. For example, if WiFi channel interference is frequent, a detection prompt is pushed to the front-end system prioritizing channel distribution analysis. At the same time, the middle-office system synchronizes the statistical results to the back-end system to facilitate support engineers in formulating optimization plans, such as adjusting the regional recommended channels to address the frequent problem of WiFi channel interference.

[0067] When the front-end system initiates a rapid collection request, the middle-office system presets a standardized business process through the workflow engine, such as IP collection → Ping test → route detection → DNS resolution → packet capture (optional), calls the corresponding diagnostic function module of the front-end system to implement sequential step-by-step collection, and integrates the results into a unified report and returns it to the front-end system and the back-end system. During the rapid collection process, the middle-office system assumes the key role of standardized process orchestration, cross-module collaborative scheduling, result integration and exception handling through the workflow engine, while the front-end system is only responsible for the execution of specific functions and cannot independently complete the end-to-end process closed loop. Rapid collection needs to be performed according to fixed steps. The order and dependencies of these steps, execution time limits, etc. need to be preset as standardized processes through the workflow engine of the middle-office system. The middle-office system uses predefined processes to force the front-end system to execute according to specifications, ensuring that the rapid collection results of all installation and maintenance engineers are consistent in format and data complete, meeting the standardized requirements of back-end system analysis. The front-end system only returns the execution results of a single step. The middle-end system is responsible for summarizing the data of all steps and generating reports in a unified format to avoid fragmentation of results due to limited storage or computing power at the front-end. The middle-end system synchronizes the process progress to the front-end system in real time, allowing installation and maintenance engineers to intuitively understand the current status without having to frequently manually check the results of each module. If a step in the quick collection fails, the middle-end system determines whether to retry, skip, or terminate the process. For example, the middle-end system can preset rules: if the Ping speed test fails, retry twice. If it still fails, record the exception and continue to execute the route detection step.

[0068] In this embodiment of the present invention, the backend system, based on the FastAdmin framework in a B / S architecture, implements the storage, analysis, and visualization of diagnostic data. It supports multi-dimensional retrieval, sorting, and data export by time, region, and fault type, facilitating fault analysis by support engineers and enabling them to collaborate with installation and maintenance engineers to resolve user-reported issues. The backend system is deployed in a private cloud, ensuring high system availability through redundant hardware resource backups.

[0069] In this embodiment of the present invention, the storage module uses a MySQL database to store structured data (such as diagnostic records and user information) and a distributed file system (such as HDFS) to store unstructured data (such as packet capture files and PDF reports), ensuring data security and scalability. Key fields such as user ID and timestamp are used to associate front-end diagnostic data with mid-end analysis results, creating a complete troubleshooting archive.

[0070] In this embodiment of the present invention, the retrieval and visualization module provides multi-dimensional retrieval and visualization capabilities. Specifically, it supports data retrieval by time, region, fault type, and other criteria, enabling full-text search through Elasticsearch. FastAdmin's front-end component generates bar charts, pie charts, and other visualizations to intuitively present data analysis results.

[0071] In an embodiment of the present invention, the system management and configuration module includes permission management and parameter configuration functions, which controls access rights to data through role assignment (such as super administrator, regional engineer) to ensure data security; at the same time, it supports administrators to configure the default test targets of the foreground system (such as speed test server IP, number of Ping packets) in the background system, and the configuration information is synchronized to the front-end system through the middle-end system.

[0072] Different broadband network operators have differences in network hierarchical structures, network equipment, and protocols. The embodiments of the present invention support the adjustment of some parameters in the auxiliary setting module of the front-end system. It is understandable that in the middle-end and back-end systems, the differentiated parts of different broadband network operators can also be made into adjustable configuration items or configuration files, making this solution adaptable to different broadband network architectures.

[0073] The broadband network fault intelligent diagnosis system of the embodiment of the present invention can be used in conjunction with the work order system. This system focuses on solving the problems of insufficient network detection capabilities and inefficient front-end and back-end collaboration through tool support, and complements the external work order system. The external work order system is responsible for the distribution, circulation, and progress tracking of fault work orders, while this system focuses on providing accurate data support for work order processing. The diagnostic reports, packet capture files, etc. generated by this system can be uploaded as work order attachments to provide an objective basis for work order processing. After the installation and maintenance engineer completes the fault repair through this system, the repaired test results can be synchronized to the work order system as a voucher for work order archiving to ensure that the fault handling is supported by data.

[0074] like Figure 7 As shown, an embodiment of the present invention further provides a method for intelligent diagnosis of broadband network faults. The method relies on the above-mentioned intelligent diagnosis system for broadband network faults to form a standardized diagnosis process, and the steps are as follows: Step S1: The installation and maintenance engineer initiates a diagnostic request through the fault diagnosis application of the front-end system; Step S2: collecting broadband network environment data according to the diagnostic request, generating raw diagnostic data and uploading it to the middle platform system; Step S3: The middle-end system cleans and processes the original diagnostic data to form standardized data, and then distributes the standardized data to the front-end system and the back-end system; Step S4: The middle-office system analyzes the standardized data in real time, identifies anomalies, synchronizes the anomaly information to the back-end system, and feeds back preliminary optimization suggestions and detection suggestions to the front-end system based on the identified anomalies; the middle-office system uses big data analysis technology to aggregate and analyze the standardized data, and generates statistical reports that are sent to the back-end system; Step S5: The backend system stores standardized data and statistical reports, supporting engineers to analyze and locate faults through the backend system, generate solutions and optimization suggestions, and push the solutions and optimization suggestions to the frontend system through the middle-end system; Step S6: The installation and maintenance engineer repairs the network failure according to the solution and optimization suggestions, and reports the repair status to the backend system; In step S7, the background system archives the entire link data of the fault processing.

[0075] If the diagnostic request in step S1 is a quick collection request, the middle-office system triggers the diagnostic function modules corresponding to the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0076] It's understandable that the above method is particularly suitable for situations where support engineers are needed for analysis. For simple faults, maintenance engineers can independently fix them based on the application's test results or by combining these results with the mid-tier system's recommendations. More complex faults often require maintenance engineers, guided by support engineers' advice, to conduct multiple rounds of supplementary testing and on-site debugging before they can ultimately resolve the issue.

[0077] The broadband network fault intelligent diagnosis system and method of the embodiments of the present invention solves the problem of insufficient front-end and back-end collaboration by building a collaborative architecture of "front-end app - middle-end - back-end management system" from the aspects of data interoperability, process optimization, tool integration, and standardization. The specific implementation is as follows: (1) The front-end system's application can package user-side network data and transmit it back to the middle-end system in real time through network diagnosis, rapid collection, and application packet capture functions. The middle-end system then forms standardized data and transmits it to the back-end system. Back-end support engineers can directly view standardized data through the system without having to wait for installation and maintenance engineers to manually organize and send emails, thus avoiding information errors or delays and achieving a seamless connection between "terminal detection data and back-end analysis data."

[0078] (2) Through a unified fault handling process (such as "front-end collection → middle-end processing → back-end analysis → feedback solution"), the division of labor between the front and back ends is clarified: installation and maintenance engineers complete basic testing and data collection through the application program and mark the fault phenomenon; back-end support engineers retrieve data in the management system according to dimensions such as time and region, quickly locate the fault node, and provide real-time feedback on solutions or supplementary detection suggestions to the front end; both parties communicate based on the same set of detection indicators (such as delay threshold and packet loss rate classification) to reduce repeated communication caused by inconsistent standards.

[0079] (3) The front-end system's application integrates functions such as routing detection, domain name resolution, and video testing, which can collect in-depth data (such as routing hops and DNS resolution differences) required by back-end engineers, avoiding incomplete data due to lack of tools or unskilled operation by installation and maintenance engineers; the back-end system supports data export and multi-dimensional search (such as by fault type and user area), allowing engineers to quickly screen similar fault cases, extract common solutions and synchronize them to the front-end, thereby improving overall fault handling efficiency.

[0080] (4) By setting unified thresholds and calculation logic for core indicators such as network speed measurement, Ping test, and WiFi analysis, we ensure that the detection results in different scenarios can be directly compared, thus achieving standardization of detection indicators. By unifying data such as packet capture files, diagnostic reports, and fault information into standardized formats (such as PDF, Excel, and specific message formats), we achieve seamless transmission and storage of front-end and back-end data, avoid information errors and omissions caused by format differences, and achieve data format standardization. Unifying the usage steps of installation and maintenance engineers and support engineers, such as the fixed process of "quick diagnosis-data collection-fault reporting", and standardized triggering methods for functions such as packet capture and sharing, reduces the communication cost of cross-position collaboration and achieves standardization of operation procedures. For the quality status of the entire network link, we establish a unified grading evaluation system (such as "excellent / good / medium / poor" corresponding to clear delay and packet loss rate ranges), so that the front-end and back-end have consistent judgment criteria for network quality, reduce subjective bias, and achieve standardization of result evaluation.

[0081] Through the above mechanism, a closed-loop collaboration is achieved, with instant upload of on-site installation and maintenance data, remote analysis by back-end experts, and rapid feedback on solutions. This standardization of detection indicators, data formats, operating procedures, and result evaluation is achieved, reducing the rate of secondary visits for faults, shortening MTTR (mean time to repair), and ultimately improving network service quality.

[0082] The above is only a specific embodiment of the present invention and cannot be used to limit the scope of the present invention. Equal changes made by ordinary technicians in this technical field based on this creation, as well as changes well known to technicians in this field, should still fall within the scope of the present invention.

Claims

1. A broadband network fault intelligent diagnosis system, characterized in that: The system includes a front-end system, a middle-end system, and a back-end system; wherein: The front-end system is configured to integrate multiple diagnostic function modules to detect the user's broadband network environment and send the detection results to the middle-end system; the diagnostic function modules include: network diagnosis module, network speed measurement module, fast collection module, WiFi analysis module, Ping test module, application packet capture module, routing detection module, and domain name resolution module; The middle platform system is configured as follows: (1) Receive and process the raw diagnostic data collected by the front-end system to form standardized data, and send the standardized data to the front-end system and the back-end system; receive diagnostic suggestions from the back-end system and forward them to the front-end system; (2) Conducting real-time analysis based on the standardized data to quickly identify anomalies and providing preliminary optimization suggestions and detection suggestions to the front-end system based on preset rules. The preliminary optimization suggestions are obtained by support engineers based on the analysis of historical data stored in the back-end, and the anomalies are synchronized to the back-end system; (3) Performing aggregate analysis based on the standardized data to generate statistical reports of multiple time granularities, and sending the statistical reports to the backend system; the statistical reports include a report on the distribution of diagnosis times in each region, a report on the proportion of fault types, and a report on network quality trends; The middle platform system includes a data receiving and processing module, a data distribution module, a data real-time analysis module, and a data aggregation analysis module; The backend system is configured to centrally store the standardized data transmitted by the middle-end system and provide multi-dimensional retrieval and analysis functions; the backend system includes a storage module, a retrieval and visualization module, and a system management and configuration module; The quick collection module is configured to summarize key network data at the time of a fault with one click. The quick collection module responds to the trigger request of the installation and maintenance engineer and sends a quick collection request to the middle-office system. The middle-office system triggers the corresponding diagnostic function modules of the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

2. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The front-end system includes a fault diagnosis application installed on the maintenance engineer's smart mobile terminal. The smart mobile terminal is a mobile phone or tablet computer equipped with an Android system. Each diagnostic function module calls the system's underlying functions or hardware resources through the API provided by the Android system to realize data collection.

3. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The network diagnosis module is configured to sequentially collect the latency and packet loss rate of each segment of the entire link from the user terminal to the Internet egress through segment detection technology, and graphically present the status of each node; The network speed test module is configured to detect the uplink and downlink speeds of the user terminal and the intranet speed test server; The WiFi analysis module is configured to detect the WiFi quality of the user environment, perform a graded evaluation based on a preset threshold, and output improvement suggestions; The Ping test module is configured to detect the network connectivity and quality between the terminal and the target node; The application packet capture module is configured to capture network interaction messages of the application program and generate a packet capture file; The routing detection module is configured to locate the routing path and bottleneck nodes from the terminal to the target address; the domain name resolution module is configured to verify the DNS resolution function and compare the resolution results of different servers.

4. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The diagnostic function module also includes a video test module, which is configured to specifically detect the playback rate, buffering times, and bit rate adaptation indicators of a specific video platform.

5. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The diagnostic function module also includes an IP calculator module, a web page response speed module and an auxiliary setting module.

6. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The front-end system automatically obtains its diagnostic terminal device information, connects to the user's home WiFi, automatically collects network feature information, and carries the diagnostic terminal device information and network feature information in the data interaction with the middle-end system; the middle-end system distinguishes different diagnostic terminal devices through the diagnostic terminal device information, and constructs a user ID through the network feature information to distinguish different user-side environments.

7. The broadband network fault intelligent diagnosis system according to claim 1, characterized in that: The data receiving and processing module receives the structured data and unstructured data uploaded by the front-end system in real time through the TCP protocol, checks the data integrity, and then cleans and classifies the above data; if there is missing data, the middle-end system sends feedback to the front-end system, prompting the installation and maintenance engineer to fill in the missing data.

8. A method for intelligent diagnosis of broadband network faults, implemented using the broadband network fault intelligent diagnosis system according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: Step S1: The installation and maintenance engineer initiates a diagnostic request through the fault diagnosis application of the front-end system; Step S2: collecting broadband network environment data according to the diagnostic request, generating raw diagnostic data and uploading it to the middle platform system; Step S3: The middle-end system cleans and processes the original diagnostic data to form standardized data, and then distributes the standardized data to the front-end system and the back-end system; Step S4: The middle-office system analyzes the standardized data in real time, identifies anomalies, synchronizes the anomaly information to the back-end system, and feeds back preliminary optimization suggestions and detection suggestions to the front-end system based on the identified anomalies; the middle-office system uses big data analysis technology to aggregate and analyze the standardized data, and generates statistical reports that are sent to the back-end system; Step S5: The backend system stores standardized data and statistical reports, supporting engineers to analyze and locate faults through the backend system, generate solutions and optimization suggestions, and push the solutions and optimization suggestions to the frontend system through the middle-end system; Step S6: The installation and maintenance engineer repairs the network failure according to the solution and optimization suggestions, and reports the repair status to the backend system; Step S7: The backend system archives the entire link data of the fault processing; If the diagnostic request in step S1 is a quick collection request, the middle-office system triggers the diagnostic function modules corresponding to the front-end system in sequence according to a preset order to collect key network data, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

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