A broadband network fault intelligent diagnosis system and method

Abstract

This invention discloses an intelligent broadband network fault diagnosis system and method, belonging to the field of broadband network technology. The system includes a front-end system, a middleware system, and a back-end system. The front-end system integrates multiple diagnostic function modules, such as network diagnosis, network speed testing, and WiFi analysis, used to detect the user's broadband network environment and send the results to the middleware system. The middleware system is responsible for receiving and processing the data sent by the front-end system, forming standardized data for distribution to both the front-end and back-end systems, performing real-time analysis and providing preliminary optimization suggestions, performing aggregate analysis, and generating statistical reports. The back-end system centrally stores data and provides multi-dimensional retrieval and analysis functions. Under the control of the middleware system, the various diagnostic function modules of the front-end system can be sequentially invoked according to a preset process to achieve rapid collection of key network data. Through efficient collaboration and intelligent analysis among the front-end, middleware, and back-end systems, this system improves the efficiency and accuracy of broadband network fault diagnosis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of broadband network, and particularly relates to a broadband network fault intelligent diagnosis system and method. BACKGROUND

[0002] With the popularity and increasing complexity of broadband networks, users have increasingly high requirements for network stability and service quality. However, in actual applications, existing broadband network fault detection tools have single functions and lack comprehensive diagnosis capabilities. In addition, the cooperation between the front-end and back-end is insufficient, resulting in frequent problems such as difficult problem positioning, slow response speed, and untimely optimization suggestions. Specifically, the following problems exist:

[0003] (1) Detection tools are scattered: existing network fault detection tools are mostly independent modules, such as network speed testing, Ping testing, etc., which are difficult to form a complete diagnosis system and cannot comprehensively cover various problems in user broadband network environments.

[0004] (2) Insufficient cooperation between front-end and back-end: the front-end system usually only responsible for data collection, and the back-end system is responsible for data analysis and storage. The data interaction and collaborative work between the two exist delays and inconsistencies, affecting the accuracy and timeliness of fault diagnosis.

[0005] (3) Lack of intelligent analysis: traditional fault diagnosis systems rely on manual analysis and lack intelligent analysis based on big data and machine learning, making it difficult to quickly identify abnormalities and provide effective optimization suggestions.

[0006] (4) Insufficient data management and visualization: existing systems are 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.

[0007] In view of the above problems, it is urgent to build a broadband network fault intelligent diagnosis system that integrates multiple diagnostic function modules, has efficient cooperation between front-end and back-end, and has intelligent analysis capabilities, to improve the efficiency and accuracy of network fault diagnosis. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a broadband network fault intelligent diagnosis system and method to solve the above defects of the prior art.

[0009] To achieve the above purpose, the present application provides a broadband network fault intelligent diagnosis system, which comprises a front-end system, a middle-end system and a back-end system; wherein:

[0010] The front-end system is configured to integrate multiple diagnostic function modules to realize detection on a user broadband network environment, and send the detection result to the middle-end system; the diagnostic function modules include: a network diagnosis module, a network speed measurement module, a rapid collection module, a WiFi analysis module, a Ping test module, an application packet capture module, a route detection module, and a domain name resolution module.

[0011] The middle-end system is configured to:

[0012] (1) receive and process the original 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 the diagnostic suggestions of the back-end system and forward them to the front-end system;

[0013] (2) perform real-time analysis based on the standardized data to quickly identify abnormalities, provide preliminary optimization suggestions and detection suggestions to the front-end system according to preset rules, the preliminary optimization suggestions are obtained by support engineers based on historical data analysis stored in the back-end, and the abnormalities are synchronized to the back-end system;

[0014] (3) perform aggregated analysis based on the standardized data to generate statistical reports of multiple time granularities, and send the statistical reports to the back-end system; the statistical reports include regional diagnosis frequency distribution reports, fault type proportion, and network quality trend reports;

[0015] The middle-end system includes a data receiving and processing module, a data distribution module, a data real-time analysis module, and a data aggregated analysis module.

[0016] The back-end system is configured to store the standardized data transmitted by the middle-end system, and provide multi-dimensional search and analysis functions; the back-end system includes a storage module, a search and visualization module, and a system management and configuration module.

[0017] The rapid collection module is configured to collect key network data when a fault is summarized in one key; the rapid collection module sends a rapid collection request to the middle-end system in response to a trigger request of a maintenance engineer, the middle-end system triggers the corresponding diagnostic function modules of the front-end system to collect key network data in a preset order, and then sends the collection results of each diagnostic function module to the front-end system and the back-end system after summarizing.

[0018] In the broadband network fault intelligent diagnosis system of the application, the front-end system includes a fault diagnosis application installed on a smart mobile terminal of a maintenance engineer, the smart mobile terminal is a mobile phone or a tablet computer equipped with an Android system, and each diagnostic function module realizes data collection by calling system bottom functions or hardware resources provided by the API of the Android system.

[0019] In the intelligent broadband network fault diagnosis system of the present invention, 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 exit using segmented detection technology, and graphically present the status of each node; the network speed test module is configured to detect the uplink and downlink speeds between 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 graded evaluation based on preset thresholds, 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 packets of the application and generate a packet capture file; the route detection module is configured to locate the routing path and bottleneck nodes 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.

[0020] In the intelligent diagnostic system for broadband network faults of the present invention, the diagnostic function module further includes a video testing module, which is configured to specifically detect the playback rate, buffering times, and bitrate adaptation indicators of a specific video platform.

[0021] In the intelligent diagnostic system for broadband network faults of the present invention, the diagnostic function module further includes an IP calculator module, a webpage response speed module, and an auxiliary settings module.

[0022] In the intelligent broadband network fault 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 characteristic information, and carries the diagnostic terminal device information and network characteristic information in the data interaction with the middle platform system; the middle platform system distinguishes different diagnostic terminal devices through the diagnostic terminal device information and constructs user IDs through the network characteristic information to distinguish different user-side environments.

[0023] In the intelligent fault diagnosis system for broadband networks of the present invention, the data receiving and processing module receives structured and unstructured data uploaded by the front-end system in real time via the TCP protocol, checks the data integrity, and then cleans and classifies the data. If data is missing, the middleware system sends feedback to the front-end system, prompting the installation and maintenance engineer to fill in the missing data.

[0024] This invention also provides a method for intelligent diagnosis of broadband network faults, implemented using the broadband network fault intelligent diagnosis system described above. The method includes the following steps:

[0025] Step S1: The installation and maintenance engineer initiates a diagnostic request through the fault diagnosis application of the front-end system;

[0026] Step S2: Collect broadband network environment data according to the diagnostic request, form raw diagnostic data, and upload it to the middleware system;

[0027] Step S3: The middle platform system cleans and processes the raw diagnostic data to form standardized data, and then distributes the standardized data to the front-end system and the back-end system.

[0028] Step S4: The middle platform system analyzes the standardized data in real time, identifies anomalies, synchronizes the anomaly information to the backend system, and provides preliminary optimization and detection suggestions to the frontend system based on the identified anomalies; the middle platform system uses big data analysis technology to aggregate and analyze the standardized data, generates statistical reports, and sends them to the backend system.

[0029] Step S5: The backend system stores standardized data and statistical reports, enabling 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 platform system.

[0030] Step S6: The installation and maintenance engineer repairs the network fault based on the solution and optimization suggestions, and reports the repair status to the back-end system;

[0031] Step S7: The background system archives the entire fault handling data.

[0032] If the diagnostic request in step S1 is a rapid collection request, the middle platform system triggers the corresponding diagnostic function modules of the front-end system to collect key network data in a preset order, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0033] The intelligent fault diagnosis system for broadband networks provided by this invention has the following beneficial effects:

[0034] (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 service.

[0035] (2) Efficient data processing and distribution: The middle platform system is responsible for receiving, processing and forwarding the original diagnostic data of the front-end system, forming standardized data, and distributing it to the front-end system and the back-end system to ensure data consistency and availability.

[0036] (3) Real-time and aggregated analysis: The middle platform system can quickly identify anomalies and provide preliminary optimization suggestions through real-time analysis and aggregated analysis. At the same time, it generates statistical reports with multiple time granularities to help support engineers to conduct in-depth analysis and decision-making.

[0037] (4) Seamless collaboration between front-end and back-end: An efficient data flow and information sharing mechanism is formed between the front-end system, the middle platform system and the back-end system, ensuring the timeliness and accuracy of fault diagnosis.

[0038] (5) One-click quick collection: The quick collection module can summarize key network data with one click, simplifying the troubleshooting process, reducing the operational complexity of installation and maintenance engineers, and improving work efficiency. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0040] Figure 1 This is a schematic diagram of the architecture of a broadband network fault intelligent diagnosis system provided in an embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of a broadband network fault intelligent diagnosis system provided in an embodiment of the present invention.

[0042] Figures 3-4 This is a schematic diagram of the network diagnosis module user interface of the fault diagnosis application of the front-end system of the intelligent broadband network fault diagnosis system provided in the embodiments of the present invention.

[0043] Figure 5 This is a schematic diagram of the auxiliary settings module of the fault diagnosis application of the front-end system of the intelligent fault diagnosis system for broadband networks provided in this embodiment of the invention.

[0044] Figure 6 This is a schematic diagram of the main user interface of the fault diagnosis application of the front-end system of the intelligent fault diagnosis system for broadband networks provided in this embodiment of the invention.

[0045] Figure 7 This is a schematic diagram illustrating the steps of the intelligent fault diagnosis method for broadband networks provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0048] This invention is applicable to intelligent diagnosis of broadband network faults in home WiFi environments, providing installation and maintenance engineers and support engineers with a unified and quantifiable testing basis, and solving the problems of scattered traditional diagnostic tools, inconsistent standards, and difficulties in front-end and back-end collaboration.

[0049] like Figure 1 As shown, this embodiment of the invention provides an intelligent fault diagnosis system for broadband networks, the system comprising a front-end system, a middleware system, and a back-end system; wherein:

[0050] 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 diagnostic module, network speed test module, fast collection module, WiFi analysis module, Ping test module, application packet capture module, route detection module, and domain name resolution module;

[0051] The middleware system is configured as follows:

[0052] (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;

[0053] (2) Real-time analysis is performed based on the standardized data to quickly identify anomalies. Preliminary optimization suggestions and detection suggestions are provided to the front-end system according to preset rules. The preliminary optimization suggestions are obtained by the support engineer based on the historical data stored in the back-end, and the anomalies are synchronized to the back-end system.

[0054] (3) Based on the standardized data, perform aggregation analysis to generate statistical reports with multiple time granularities, and send the statistical reports to the backend system; the statistical reports include a report on the distribution of diagnostic times in each region, a report on the proportion of fault types, and a report on network quality trends;

[0055] The middleware system includes a data receiving and processing module, a data distribution module, a real-time data analysis module, and a data aggregation and analysis module.

[0056] The backend system is configured to centrally store the standardized data transmitted by the middle platform 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;

[0057] The rapid collection module is configured to summarize key network data during a fault with one click. In response to a trigger request from an installation and maintenance engineer, the rapid collection module sends a rapid collection request to the middleware system. The middleware system triggers the corresponding diagnostic function modules of the front-end system to collect key network data in a preset order, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0058] In this embodiment of the invention, a collaborative architecture of front-end, middle-end, and back-end is used to integrate and optimize network fault diagnosis functions. Through the coordinated process of "front-end data collection - middle-end processing - back-end management," efficient execution of terminal diagnostic functions, real-time data exchange, and unified management of system configurations are ensured. This forms a closed-loop system of "tool integration - data exchange - collaborative operation and maintenance," effectively solving the problems of insufficient detection tools and inadequate front-end and back-end collaboration in existing technologies, thereby improving fault location efficiency and network service quality.

[0059] like Figure 2 The diagram illustrates a scenario of the intelligent broadband network fault diagnosis system according to an embodiment of the present invention. Broadband users access the internet through the cable network metropolitan area network and access network to browse web pages or watch videos. When a broadband user reports a fault, installation and maintenance engineers provide on-site service, and technical experts provide remote support through a page provided by the backend system. The speed test server is an internal speed test server of the broadband operator. The front-end system includes a fault diagnosis application installed on the installation and maintenance engineer's smart mobile terminal. The smart mobile terminal is a mobile phone or tablet running the Android system. Each diagnostic function module uses the API (Application Programming Interface) provided by the Android system to call the underlying system functions or hardware resources to achieve data collection, such as... Figure 6 The diagram shows the user interface of a fault diagnosis application on a smart mobile terminal used by an installation and maintenance engineer. The engineer uses this application's user interface to access various diagnostic modules, collect raw diagnostic data, and send it to the backend system via a middleware system. Based on their experience, feedback from the middleware system, or diagnostic suggestions from support engineers, the engineer then repairs network faults. The middleware system receives and processes the raw diagnostic data to create standardized data, which is then synchronized to both the frontend and backend systems. Real-time analysis and aggregation of this standardized data are performed to provide decision support for the support engineers. The backend system provides a management page through which support engineers can search and analyze data, remotely providing diagnostic suggestions to the installation and maintenance engineer.

[0060] In this embodiment of the invention, the network diagnostic module is configured to sequentially collect latency and packet loss rate data for each segment of the entire link from the user terminal to the internet egress using segmented detection technology, and graphically present the status of each node to quickly locate faulty nodes. Specifically, segmented detection technology is used to sequentially collect indicators such as terminal operating data (e.g., IP, gateway, DNS), WiFi environment data (e.g., signal strength, interference), home gateway (ONU / CM), metropolitan area network equipment, and internet egress latency and packet loss rate, graphically present the status of each node, and package and transmit 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 network status APIs (e.g., the ConnectivityManager class).

[0061] In some embodiments of this invention, the entire link from the user terminal to the Internet egress includes: a user terminal layer, a home LAN layer, an access network layer, a metropolitan area network layer, and an Internet egress layer. Each layer is connected in series through network devices, and any abnormality in any link may affect network quality. Specifically, the user terminal layer includes the Android smart terminal of the installation and maintenance engineer and user-side devices such as mobile phones and computers; the home LAN layer includes: a home WiFi network (including routers and signal coverage areas) and a home gateway (ONU optical modem or CM cable modem); the access network layer includes: access layer switches and optical distribution network equipment from the operator; the metropolitan area network layer includes: aggregation switches, core routers, and other backbone equipment of the metropolitan area network; and the Internet egress layer includes: the egress router connecting the operator to the public network, and the network 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, Ping testing (ICMP protocol) is used to send Echo request messages to the target node, recording the time difference from sending to receiving the Echo response, and taking the average, maximum, and minimum delays after multiple tests. For TCP protocol application scenarios, the Tcping test is used to send TCP SYN packets to a specific port (default 80) of the target node, and the time difference for establishing the connection is recorded as latency. During the TCP and Ping tests, the total number of packets sent and the number of response packets received are recorded, and the packet loss rate is calculated as (1 - number of responses / number of packets sent) × 100%. The network diagnostic module integrates the test results from each stage and displays the latency and packet loss rate from the terminal to the home gateway, metropolitan area network, and Internet exit through a graphical interface such as a node link diagram, intuitively marking abnormal nodes.

[0062] Figure 3 This is a schematic diagram of basic WiFi data in the user interface of the network diagnostics module. Figure 4This diagram illustrates the evaluation of WiFi data and segment quality in the network diagnostic module's user interface. The evaluation and scoring are determined based on a pre-set, unified evaluation standard, which is used across the front-end, middleware, and back-end. Additionally, the network diagnostic module's user interface includes basic network information and network segment detection results. The network segment detection results are graphically displayed, showing the segment detection latency, packet loss rate, and evaluation.

[0063] In this embodiment of the invention, the network speed test module is configured to detect the uplink and downlink speeds between the user terminal and the internal network speed test server, verifying whether the network meets the promised bandwidth. Specifically, by sending a large file to the speed test server, monitoring the speed changes during download and upload, recording and storing multiple test results, and generating an uplink and downlink speed report. The speed test server is an internal speed test server of the 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 speeds, and implement the network speed test function. The user interface diagram of the network speed test module visually displays the downlink and uplink bandwidth using graphs and shows the speed test results.

[0064] The rapid data collection module is configured to summarize key network data during faults with a single click, assisting in the analysis of complex faults. Responding to a trigger request from an installation and maintenance engineer, the rapid data collection module sends a rapid data collection request to the middleware system. The middleware system then triggers the corresponding collection modules of the front-end system to collect key network data in a preset order, and finally summarizes the collection results from each module and sends them to both the front-end and back-end systems. The rapid data collection module is primarily used in scenarios where installation and maintenance engineers are troubleshooting complex network faults on-site and need to efficiently summarize key data and collaborate with back-end support engineers for analysis. Specifically, it includes:

[0065] (1) When it is difficult to locate the fault in the field in a timely manner: When the installation and maintenance engineer encounters a complex fault in the user's home (such as intermittent network interruption, specific application lag, WiFi signal is sometimes good and sometimes bad, etc.) and cannot directly locate the cause through basic diagnosis (such as speed test, Ping test), the basic information such as terminal IP, gateway, DNS, etc., as well as test data such as Ping gateway, route detection, DNS resolution, etc. can be summarized with one click through the quick collection function. If necessary, packet capture files of the problematic application can be attached to provide the back-end support engineer with a complete on-site network snapshot.

[0066] (2) When remote assistance from back-end support engineers is required: For technical problems that installation and maintenance engineers cannot solve on-site (such as metropolitan area network link abnormalities, cross-node packet loss, etc., which involve the backbone layer of the operator's network), standardized data (diagnostic reports and packet capture files in a unified format) are quickly collected and sent back to the back-end in real time. Support engineers can directly call the data for in-depth analysis through the back-end management system without having to repeatedly communicate to supplement information, thus shortening the response time of remote collaboration.

[0067] (3) During fault review and case study: For typical and difficult faults that have been resolved, the data packets collected quickly can be archived as case study 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, common problems (such as excessive load on a specific DNS server) can be found by summarizing historical data quickly, providing a basis for network optimization.

[0068] The quick data collection module user interface includes basic network information, PING test, route detection results, and DNS resolution results. The PING test displays the latency, packet loss rate, and evaluation for different target addresses. The route detection results show the hop count for different target addresses and DNS, and clicking the menu will show details of each hop.

[0069] In this embodiment of the invention, the WiFi analysis module is configured to detect the WiFi quality of the user's environment, perform a graded evaluation based on a preset threshold, and output improvement suggestions. Specifically, it detects the current WiFi signal strength, speed, channel distribution, interference, and number of online terminals, performs a graded evaluation based on a preset threshold (e.g., signal strength ≥ -64dBm is "good"), 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 the signal strength (dBm) of the currently connected WiFi, channel information, interference from surrounding WiFi hotspots, and the number of connected terminals. For example, it obtains the signal strength using the getSignalStrength() method and scans the surrounding WiFi channel distribution using the getScanResults() method, supporting WiFi quality assessment and optimization suggestion output.

[0070] The WiFi analysis module user interface includes basic network information, signal strength curves, WiFi speed, channel analysis, WiFi interference results, and a list of online terminals.

[0071] In this embodiment of the invention, the Ping test module is configured to detect the network connectivity and quality between the terminal and the target node. This is achieved by sending ICMP Echo requests or TCP packets, calculating the maximum, minimum, and average latency and packet loss rate, and supporting simultaneous testing of multiple targets with results displayed on the same screen. Specifically, the Ping test module sends ICMP Echo request packets by calling relevant ICMP protocol APIs and records the round-trip time; for TCP ping tests, it calls TCP connection APIs (such as Socket.connect()) to calculate the connection establishment latency. The user interface diagram of the PING test module shows latency fluctuations as a graph for each test target, and also calculates the packet loss rate, minimum latency, average latency, and maximum latency.

[0072] In this embodiment of the invention, the application packet capture module is configured to capture network interaction packets of the application and generate a packet capture file. This is achieved by having the user select a target application such as a video or browser, capturing its communication packets with the server in real time, generating a packet capture file, and sending it to the middleware system. The application packet capture module is implemented through its own integrated packet capture logic, rather than integrating external tools, ensuring lightweight functionality and seamless integration with the overall diagnostic process. Specifically, the application packet capture module requests network access permissions and data packet capture permissions from the Android system, calls the system's underlying network data packet capture API (such as through the libpcap library's encapsulated interface or Android's VpnService class) to listen to the network interaction between the terminal and the target application, captures TCP / UDP packets, and generates a .pcap format packet capture file.

[0073] In this embodiment of the invention, the route detection module is configured to locate the routing path and bottleneck nodes from the terminal to the target address. This is achieved based on the ICMP protocol, sending packets to the target and recording the IP address, latency, and packet loss rate of each hop router to form a routing trajectory map and identify packet loss nodes in the routing path. Specifically, the route detection module implements route tracing functionality by calling relevant interfaces of the network protocol stack, obtaining the IP address of each hop node, and supporting the identification of the routing path and packet loss nodes. In the user interface diagram of the route detection module, users can manually input DNS addresses or quickly select target domain names using icons in the quick domain name area. The detection results display the IP address, packet loss rate, and latency of each detected target.

[0074] In this embodiment of the invention, the domain name resolution module is configured to verify the DNS resolution function and compare the resolution results of different servers. This is achieved by sending resolution requests to multiple preset or custom DNS servers, recording the resolution IP address and duration of the target domain name, and supporting historical result comparison. In the user interface diagram of the domain name resolution module, 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.

[0075] In this embodiment of the invention, the diagnostic function module further includes a video testing module. In home broadband networks, video streaming applications are high-frequency applications for users, and video stuttering and slow loading are typical user complaint scenarios. The video testing module is configured to specifically test indicators such as playback rate, buffering times, and bitrate compatibility for specific video platforms (such as iQiyi and Tencent Video), more accurately locating video service-specific faults, such as CDN node access problems and video protocol compatibility issues, effectively improving user broadband service satisfaction. The video testing module's user interface displays statistical values ​​such as video frame rate, average frame rate, buffering time, average speed, number of stutters, average latency, and network packet loss, as well as playback indicator curves, network indicator curves, and buffering indicator curves.

[0076] In this embodiment of the invention, the diagnostic function module further includes an IP calculator module. The IP calculator module is used for network address calculations, such as subnet masks, gateways, and broadcast addresses. It serves as an auxiliary tool for installation and maintenance engineers. The IP calculator module includes multiple calculation modules such as IP segment -> mask, mask -> IP segment, code point -> mask, and mask -> code point.

[0077] In this embodiment of the invention, the diagnostic function module further includes a webpage response speed module. Web browsing is a fundamental application for users, and response speed, such as DNS resolution latency, TCP connection establishment time, and page loading time, directly affects user experience. Combining the webpage response speed module with the domain name resolution module and the Ping speed test module to perform full-process speed testing can more intuitively reveal the reasons for slow webpages, and can play a certain auxiliary role in improving user experience.

[0078] In this embodiment of the invention, the front-end system further includes an auxiliary settings module, which is configured to allow users to customize tool parameters to adapt to different scenarios. This is achieved by providing a graphical interface that supports modifying default configurations such as quick diagnostic targets, Ping packet count, and route test nodes, and supports one-click factory reset. Figure 5 The user interface diagram for the auxiliary settings module includes areas for modifying network diagnostic parameters, PING test parameters, quick parameter collection, and history records.

[0079] Installation and maintenance engineers throughFigure 6 The menu provided on the main user interface of the application shown collects on-site broadband network environment data. Figure 6 In the application, all menus are divided into three main areas: "Quick Diagnosis," "Detailed Diagnosis," and "Application Settings." Different menus are typically used in different scenarios, as detailed below:

[0080] (1) New broadband network installation scenario

[0081] Core objective: For newly installed scenarios, no in-depth investigation is required; focus on verifying basic indicators, verifying network activation quality, and quickly outputting basic indicators.

[0082] The required menus are: 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. Specifically, the "Network Diagnosis" menu corresponds to the network diagnosis module of the front-end system, used to test end-to-end 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 uplink and downlink bandwidth meet standards; the "Video Test" menu corresponds to the video test module of the front-end system, used to verify video playback experience; and the "IP Calculator" menu corresponds to the IP calculator module of the front-end system, used for network address calculation.

[0083] (2) Typical network fault handling scenarios

[0084] Core objective: To address user-reported issues such as inability to access the internet or slow internet speeds, and to use detailed diagnostic tools to break down the causes of the faults layer by layer, providing targeted on-site repairs.

[0085] The required menus are: the "Network Diagnosis" menu in the quick diagnosis area and the "WIFI Analysis," "PING Test," "Domain Name Resolution," and "Route Detection" menus in the detailed diagnosis area. Specifically, the "Network Diagnosis" menu corresponds to the network diagnosis module of the front-end system, used to quickly locate faulty nodes; the "WIFI Analysis" menu corresponds to the WiFi analysis module of the front-end system, used to detect WiFi signal strength, interference, and channel distribution, resolving WiFi lag; the "PING Test" corresponds to the Ping test module of the front-end system, used to verify connectivity from the terminal to the gateway and server; the "Domain Name Resolution" menu corresponds to the domain name resolution module of the front-end system, used to troubleshoot problems caused by DNS anomalies that prevent web pages from opening; and the "Route Detection" menu corresponds to the route detection module of the front-end system, used to locate routing nodes in the link that are experiencing packet loss.

[0086] (3) Troubleshooting scenarios

[0087] Core objective: For complex faults, such as intermittent network outages or the inability to use specific apps, installation and maintenance engineers need to quickly collect complete data on-site for analysis by back-end support engineers.

[0088] The required menus are: 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 Quick Collection module in the front-end system, used to trigger a standardized process of IP collection → Ping test → Route probe → DNS resolution → Application Packet Capture with one click, automatically packaging the data. The "Application Packet Capture" menu corresponds to the Application Packet Capture module in the front-end system, used to capture packets for a specific application individually. The "Webpage Response Speed" menu corresponds to the Webpage Response Speed ​​module in the front-end system, used to record the loading time of the target webpage to assist in the analysis of application-layer issues.

[0089] (4) Application scenarios in non-standard network environments

[0090] Core objective: To adapt to special environments such as non-standard network environments, such as the special gateways of government and enterprise users, by customizing tool parameters.

[0091] The required menu is the "Accessibility Settings" menu in the application settings area. The "Accessibility Settings" menu corresponds to the accessibility settings module of the front-end system, which is used to modify the detection target of network diagnostics, the packet size of Ping tests, the default DNS server address for domain name resolution, etc.

[0092] Data collected from the "Quick Diagnosis" and "Detailed Diagnosis" menus can be shared via third-party tools such as WeChat or email.

[0093] In this embodiment of the invention, the front-end system further includes a data feedback and interaction module. This module establishes a data connection between the front-end system and the middleware system, and performs bidirectional data transmission based on this connection. Specifically, the data feedback and interaction module calls HTTP / HTTPS related APIs (such as the OkHttp library or the HttpURLConnection class) to establish a network connection with the middleware system, encrypts the collected diagnostic data (such as speed test results and packet capture files), and uploads it to the middleware system in real time. It also receives optimization suggestions and configuration updates returned by the middleware system, thus achieving direct data communication between the front-end and middleware systems, as well as indirect data communication between the front-end and back-end systems.

[0094] The front-end system automatically acquires its diagnostic terminal device information and connects to the user's home WiFi, automatically collecting network characteristic information. This diagnostic terminal device information and network characteristic information are carried during data interaction with the middle-end system. The diagnostic terminal device information refers to the installation engineer's smart mobile terminal, including IMEI, device MAC address, and a unique device code generated during the installation of the fault diagnosis application. The network characteristic information includes the home gateway's MAC address, gateway device number, user terminal's IP address, WiFi BSSID, and GPS location information. Furthermore, the installation engineer can also input the user's name, mobile phone number, and home address through the fault diagnosis application as supplementary network characteristic information. The middle-end system distinguishes different diagnostic terminal devices using the diagnostic terminal device information and constructs user IDs using the network characteristic information to differentiate different user-side environments. It accurately correlates the raw diagnostic data uploaded by the front-end system with the data analysis results of the middle-end system and the fault files of the back-end system, forming a complete user-side fault handling chain.

[0095] In this embodiment of the invention, the middleware system is built based on C, Java, and big data technologies. It serves as the core hub connecting the front-end and back-end systems, providing business data stream cleaning, processing, business orchestration, and in-depth analysis. The middleware system includes a data receiving and processing module, a data distribution module, a real-time data analysis module, a data aggregation and analysis module, a modeling engine, a message in-depth analysis component, and a workflow engine.

[0096] The data receiving and processing module receives structured data (such as latency and packet loss rate) and unstructured data (such as packet capture files) uploaded by the front-end system in real time via the TCP protocol. It checks the data integrity and then cleans the data. Checking data integrity specifically includes verifying whether structured data has missing values ​​and whether unstructured data is missing key fields. If missing data is found, the middleware system sends feedback to the front-end system, prompting the installation and maintenance engineer to fill in the missing data. Data cleaning specifically includes standardizing the format and units of structured data and filtering outliers; unstructured data is parsed into searchable message logs using a deep message analysis component (extracting fields such as source and destination IPs, protocol type, and interaction time), and categorized and labeled according to application type (such as video and webpage). The purpose of categorization and labeling is to provide a foundation for engineers to quickly match corresponding solutions. In video application packet capture data, frequent TCP retransmissions and bandwidth fluctuations typically indicate CDN node anomalies or insufficient home bandwidth. Solutions could involve switching video nodes or upgrading bandwidth plans. In web application message logs, DNS resolution timeouts and first-packet response delays may be related to DNS server configuration and web server load. Solutions could include changing DNS addresses and optimizing webpage caching. After the middleware system categorizes and tags diagnostic data, the backend system can query historical data by application type and fault type. This allows support engineers to directly reuse mature solutions by filtering historical cases of similar applications, reducing redundant analysis costs and improving the efficiency of handling complex faults.

[0097] In this embodiment of the invention, after the data receiving and processing module cleans the structured 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. Then, it adds metadata such as timestamps and area tags to lay the foundation for storage and analysis. The timestamps are typically in the second range. The area tags combine the maintenance engineer's responsibility area and the on-site GPS location area. After adding the timestamps and area tags, the data distribution module synchronizes the standardized data to the back-end and front-end systems as needed, ensuring data consistency across the front-end, middle-end, and back-end systems. Furthermore, the data distribution module also forwards diagnostic reports and operational suggestions from the back-end system to the front-end system in real time.

[0098] In this embodiment of the invention, the real-time data analysis module performs instant analysis on the diagnostic data uploaded to the front end, quickly identifies anomalies, and synchronizes them to the back-end system, enabling support engineers to promptly perceive anomalies and respond accordingly. The anomaly types identified by the middle platform system focus on three main categories: network link anomalies, device and environment anomalies, and application and service anomalies, covering common fault handling scenarios for engineers. Among them, network link anomalies include home gateway anomalies, access network and metropolitan area network anomalies, and egress link anomalies. Specifically, the judgment criteria for home gateway anomalies include packet loss rate > 5% and latency > 100ms. The middle platform system processes the diagnostic data uploaded to the front end in real time through Spark Streaming. When five consecutive records in a certain area show a home gateway packet loss rate > 5%, the modeling engine triggers the anomaly warning model, generates warning information, and pushes it to the back-end system. The judgment criteria for access network and metropolitan area network anomalies include > 30% of the diagnostic records in a certain area showing a packet loss rate > 3% and latency fluctuation > 50ms to the core network equipment. The judgment criteria for egress link anomalies include a packet loss rate to the Internet egress > 2%, or a difference in egress rate between different operators > 30%. Equipment and environmental anomalies include WiFi environment anomalies and home gateway / optical modem anomalies. Specifically, the criteria for judging WiFi environment anomalies include signal strength < -75dBm, more than 5 interference sources on the same channel, and more than 15 online terminals causing a sudden drop in speed; the criteria for judging home gateway / optical modem anomalies include frequent offline events and abnormal indicator light status. Application and service anomalies include speed test anomalies, video / webpage anomalies, and batch anomalies. The criteria for judging speed test anomalies include uplink and downlink speeds < 80% of the contracted bandwidth and fluctuations > 20% in multiple tests; the criteria for judging video / webpage anomalies include video stuttering more than 3 times / 5 minutes, webpage first screen loading time > 3 seconds, and DNS resolution timeout > 500ms; the criteria for judging batch anomalies include more than 20% of engineers in a certain area reporting the same fault within 10 minutes, which is judged as a regional fault and triggers priority response. After the real-time data analysis module identifies anomalies, it provides preliminary optimization suggestions and detection suggestions to the front-end system according to preset rules. The preliminary optimization suggestions are obtained by support engineers based on historical data stored in the back-end.

[0099] The data aggregation and analysis module aggregates and analyzes the full dataset on a daily / weekly / monthly basis. It constructs statistical models using a modeling engine to generate statistical reports such as the distribution of diagnostic frequency in each region, the proportion of fault types, and network quality trends. The modeling engine's role is to build and run analysis models based on the data, transforming raw data into structured results that can be directly used for business decisions. In this embodiment, the backend system is the core of centralized data storage, storing all standardized data. The middle platform system, as the core link in data processing and analysis, only briefly stores data for real-time processing and short-term analysis, avoiding storage redundancy. During data aggregation and analysis, especially when analyzing on a monthly basis, the middle platform system obtains the full dataset from the backend system. The statistical reports generated by the data aggregation and analysis module need to be synchronized to the backend system for support engineers to view, export, or use for management decisions. When the middle platform system detects a high frequency of a certain type of fault, it pushes a corresponding detection prompt to the frontend system to help installation and maintenance engineers find the cause of the fault more quickly. For example, when WiFi channel interference occurs frequently, a detection prompt prioritizing channel distribution analysis is pushed to the frontend system. Meanwhile, the middleware system synchronizes the statistical results to the backend system to facilitate engineers in developing optimization plans, such as adjusting the recommended channel for the area in response to frequent WiFi channel interference.

[0100] When the front-end system initiates a rapid data collection request, the middle platform system, through its workflow engine, pre-defines standardized business processes, such as IP collection → Ping test → Route detection → DNS resolution → Packet capture (optional). It then calls the corresponding diagnostic function modules of the front-end system to collect data step-by-step in sequence and integrates the results into a unified report, returning it to both the front-end and back-end systems. During rapid data collection, the middle platform system, through its workflow engine, plays a crucial role in standardized process orchestration, cross-module collaborative scheduling, result integration, and anomaly handling. The front-end system, however, is only responsible for executing specific functions and cannot independently complete the end-to-end process loop. Rapid data collection must be executed according to fixed steps. The order, dependencies, and execution time limits of these steps need to be pre-defined as standardized processes by the middle platform system's workflow engine. By using predefined processes, the middle platform system forces the front-end system to execute according to specifications, ensuring that the rapid data collection results from all installation and maintenance engineers are in consistent format and complete, meeting the standardized requirements for analysis by the back-end system. The front-end system returns the execution result of only a single step, while the middle-platform system is responsible for aggregating all step data and generating reports in a unified format, avoiding fragmented results caused by limited storage or computing power on the front-end. The middle-platform 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 frequent manual checks of module results. If a rapidly collected step fails, the middle-platform system determines whether to retry, skip, or terminate the process. For example, the middle-platform system can preset rules: if a ping speed test fails, it will retry twice; if it still fails, the exception will be recorded and the route detection step will continue.

[0101] In this embodiment of the invention, the backend system is based on the FastAdmin framework with a B / S architecture, enabling the storage, analysis, and visualization of diagnostic data. It supports multi-dimensional searching, sorting, and data export by time, region, fault type, and other dimensions, facilitating engineers' fault analysis and enabling collaboration with installation and maintenance engineers to resolve user-reported issues. The backend system is deployed on a private cloud, ensuring high availability through redundant hardware resource backups.

[0102] In this embodiment of the 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. Front-end diagnostic data and mid-platform analysis results are linked and stored using key fields such as user ID and timestamps to form a complete fault handling archive.

[0103] In this embodiment of the invention, the retrieval and visualization module provides multi-dimensional retrieval and visualization functions. Specifically, it supports data retrieval based on conditions such as time, region, and fault type, and implements full-text search through Elasticsearch. It generates bar charts, pie charts, etc., using FastAdmin's front-end components to intuitively present the data analysis results.

[0104] In this embodiment of the invention, the system management and configuration module includes permission management and parameter configuration functions. It controls access permissions 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 (such as speed test server IP, Ping packet count) of the front-end system in the back-end system. The configuration information is synchronized to the front-end system through the middle platform system.

[0105] Different broadband network operators differ in network layer structure, network equipment, and protocols. This embodiment of the invention supports the adjustment of some parameters in the auxiliary settings module of the front-end system. It is understood that the differences between different broadband network operators can also be made into adjustable configuration items or configuration files in the middle-end and back-end systems, allowing this solution to adapt to different broadband network architectures.

[0106] The broadband network fault intelligent diagnosis system of this invention can be used in conjunction with a 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, complementing the external work order system. The external work order system is responsible for the dispatch, circulation, and progress tracking of fault work orders, while this system focuses on providing accurate data support for work order processing. Diagnostic reports and packet capture files generated by this system can be uploaded as attachments to work orders, providing objective evidence for work order processing. After the installation and maintenance engineer completes the fault repair through this system, the repaired detection results can be synchronized to the work order system as evidence for work order archiving, ensuring that the fault handling is supported by data.

[0107] like Figure 7 As shown in the figure, this embodiment of the invention also provides a method for intelligent diagnosis of broadband network faults. This method relies on the above-mentioned intelligent diagnosis system for broadband network faults to form a standardized diagnosis process, the steps of which are as follows:

[0108] Step S1: The installation and maintenance engineer initiates a diagnostic request through the fault diagnosis application of the front-end system;

[0109] Step S2: Collect broadband network environment data according to the diagnostic request, form raw diagnostic data, and upload it to the middleware system;

[0110] Step S3: The middle platform system cleans and processes the raw diagnostic data to form standardized data, and then distributes the standardized data to the front-end system and the back-end system.

[0111] Step S4: The middle platform system analyzes the standardized data in real time, identifies anomalies, synchronizes the anomaly information to the backend system, and provides preliminary optimization and detection suggestions to the frontend system based on the identified anomalies; the middle platform system uses big data analysis technology to aggregate and analyze the standardized data, generates statistical reports, and sends them to the backend system.

[0112] Step S5: The backend system stores standardized data and statistical reports, enabling 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 platform system.

[0113] Step S6: The installation and maintenance engineer repairs the network fault based on the solution and optimization suggestions, and reports the repair status to the back-end system;

[0114] In step S7, the backend system archives the entire fault handling data.

[0115] If the diagnostic request in step S1 is a rapid collection request, the middle platform system triggers the corresponding diagnostic function modules of the front-end system to collect key network data in a preset order, and then summarizes the collection results of each diagnostic function module and sends them to the front-end system and the back-end system.

[0116] Understandably, the above method is particularly suitable for situations requiring support engineers to assist in analysis. For simple faults, installation and maintenance engineers can independently repair them using the application's detection results or by combining the detection results with suggestions provided by the platform system. For more complex faults, installation and maintenance engineers typically need to conduct multiple rounds of supplementary testing and on-site debugging, with the guidance of support engineers, to ultimately resolve the fault.

[0117] The broadband network fault intelligent diagnosis system and method of this invention solves the problem of insufficient front-end and back-end collaboration by constructing a collaborative architecture of "front-end APP - middle platform - back-end management system" from the aspects of data interoperability, process optimization, tool integration, and standardization. The specific manifestations are as follows:

[0118] (1) The front-end system application can package user-side network data and send it back to the middle platform system in real time through functions such as network diagnostics, rapid collection, and application packet capture. The middle platform system then forms standardized data and sends it to the back-end system. Back-end support engineers can directly view the standardized data through the system without waiting for installation and maintenance engineers to manually organize and send emails, avoiding information errors or delays, and achieving seamless connection between "terminal detection data and back-end analysis data".

[0119] (2) By using 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 clearly defined: the installation and maintenance engineer completes basic testing and data collection through the application and marks the fault phenomenon; the back-end support engineer retrieves data in the management system by time, region and other dimensions, quickly locates the fault node, and provides the solution or supplementary testing suggestions to the front end in real time; the two parties communicate based on the same set of testing indicators (such as latency threshold, packet loss rate classification) to reduce repeated communication caused by inconsistent standards.

[0120] (3) The application of the front-end system integrates functions such as route detection, domain name resolution, and video testing, which can collect in-depth data (such as route hop count and DNS resolution differences) required by the back-end engineers, avoiding incomplete data due to lack of tools or unfamiliarity with operation by the installation and maintenance engineers; the back-end system supports data export and multi-dimensional search (such as by fault type and user region), enabling engineers to quickly filter similar fault cases, extract common solutions and synchronize them to the front-end, thereby improving the overall fault handling efficiency.

[0121] (4) By setting unified thresholds and calculation logic for core indicators such as network speed testing, Ping testing, and WiFi analysis, the detection results under 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), seamless data transmission and storage between the front and back ends are achieved, avoiding information errors and omissions caused by format differences, thus achieving standardization of data formats. The usage steps of installation and maintenance engineers and support engineers are unified, such as the fixed process of "quick diagnosis - data collection - fault reporting", as well as the standardized triggering methods of functions such as packet capture and sharing, reducing the communication costs of cross-position collaboration and achieving standardization of operation processes. For the quality status of the entire network link, a unified graded evaluation system is established (such as "excellent / good / medium / poor" corresponding to clear latency and packet loss rate ranges), so that the judgment basis of the front and back ends on network quality is consistent, reducing subjective bias and achieving standardization of result evaluation.

[0122] Through the above mechanism, a closed-loop collaboration was achieved, enabling real-time data upload from the installation and maintenance site, remote analysis by back-end experts, and rapid feedback of solutions. This standardized testing indicators, data formats, operating procedures, and result evaluation, reducing the rate of secondary on-site visits for faults, shortening MTTR (Mean Time To Repair), and ultimately improving network service quality.

[0123] The above are merely specific embodiments of the present invention and should not be construed as limiting the scope of the present invention. Equivalent variations made by those skilled in the art based on this invention, as well as changes well-known to those skilled in the art, should still fall within the scope of the present invention.

Claims

1. A broadband network fault intelligent diagnosis system, characterized in that, The system comprises a foreground system, a middle system and a background system; wherein: The foreground system is configured to integrate multiple diagnostic function modules to realize detection of a user broadband network environment, and send detection results to the middle system; the diagnostic function modules comprise a network diagnosis module, a network speed measurement module, a rapid collection module, a WiFi analysis module, a Ping test module, an application packet capture module, a route detection module and a domain name resolution module; The middle system is configured to: (1) receive and process original diagnostic data collected by the foreground system to form standardized data, and send the standardized data to the foreground system and the background system; receive diagnostic suggestions from the background system and forward them to the foreground system; (2) perform real-time analysis based on the standardized data to quickly identify abnormalities, provide preliminary optimization suggestions and detection suggestions to the foreground system according to preset rules, the preliminary optimization suggestions are obtained by support engineers based on historical data analysis stored in the background, and the abnormalities are synchronized to the background system; (3) perform aggregated analysis based on the standardized data to generate multiple time-granularity statistical reports, and send the statistical reports to the background system; the statistical reports comprise regional diagnosis frequency distribution reports, fault type proportion, network quality trend reports; The middle system comprises a data receiving and processing module, a data distribution module, a data real-time analysis module and a data aggregated analysis module; The background system is configured to centrally store standardized data transmitted by the middle system, and provide multi-dimensional search and analysis functions; the background system comprises a storage module, a search and visualization module and a system management and configuration module; The rapid collection module is configured to collect key network data when a fault occurs; the rapid collection module sends a rapid collection request to the middle system in response to a trigger request from a maintenance engineer; the middle system triggers the corresponding diagnostic function modules of the foreground system in a preset order to collect key network data, and then sends the collection results of each diagnostic function module to the foreground system and the background system after aggregation; The network diagnosis module is configured to collect the time delay and packet loss rate of each segment of the full link from the user terminal to the Internet exit by segment detection technology, and present the status of each node in a graphical manner; the network speed measurement module is configured to detect the uplink and downlink rates of the user terminal and the internal network speed measurement server; the WiFi analysis module is configured to detect the WiFi quality of the user environment, perform hierarchical 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 route detection module is configured to locate the route 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. The diagnostic function module further comprises a video test module configured to detect the playing rate, the buffering times, and the code rate adaptation index of a specific video platform.

2. The intelligent broadband network fault diagnosis system of claim 1, wherein, The front-end system comprises a fault diagnosis application installed on a smart mobile terminal of the installation and maintenance engineer, the smart mobile terminal being a mobile phone or a tablet computer running an Android system, and each diagnostic function module realizes data acquisition by calling the system bottom function or hardware resource provided by the API of the Android system.

3. The intelligent broadband network fault diagnosis system of claim 1, wherein, The diagnostic function module further comprises an IP calculator module, a webpage response speed module, and an auxiliary setting module.

4. The intelligent broadband network fault diagnosis system of claim 1, wherein, The front-end system automatically acquires the information of the diagnostic terminal device thereof, connects a user's home WiFi, automatically acquires network characteristic information, carries the diagnostic terminal device information and the network characteristic information in data interaction with the middle platform system, the middle platform system distinguishes different diagnostic terminal devices through the diagnostic terminal device information and constructs a user ID through the network characteristic information to distinguish different user side environments.

5. The intelligent broadband network fault diagnosis system of claim 1, wherein, The data receiving and processing module receives the structured data and the 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 data; if there is a missing data, the middle platform system sends feedback to the front-end system to prompt the installation and maintenance engineer to fill in the missing data.

6. A method for intelligent diagnosis of broadband network faults, implemented by using the intelligent diagnosis system of broadband network faults according to any one of claims 1-5, characterized in that, The method comprises the following steps: Step S1, the installation and maintenance engineer initiates a diagnosis request through the fault diagnosis application of the front-end system; Step S2, broadband network environment data is collected according to the diagnosis request to form original diagnosis data uploaded to the middle platform system; Step S3, the middle platform system cleans and processes the original diagnosis 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 platform system analyzes the standardized data in real time, identifies abnormalities, synchronizes the abnormal information to the back-end system, and feeds back preliminary optimization suggestions and detection suggestions to the front-end system according to the identified abnormalities; the middle platform system aggregates and analyzes the standardized data by using big data analysis technology to form statistical reports sent to the back-end system; Step S5, the back-end system stores the standardized data and the statistical reports, supports the engineers to analyze and locate faults through the back-end system, generates solutions and optimization suggestions, and pushes the solutions and optimization suggestions to the front-end system through the middle platform system; Step S6, the installation and maintenance engineer repairs the network fault according to the solutions and optimization suggestions, and feeds back the repair situation to the back-end system; Step S7, the back-end system archives the fault processing whole link data; If the diagnosis request in step S1 is a quick collection request, the middle platform system triggers the diagnostic function modules of the front-end system in a preset order to collect key network data, and then sends the collection results of the diagnostic function modules to the front-end system and the back-end system after summarizing.

Images