Industrial control communication fault visual diagnosis system and method and electronic equipment

Abstract

The invention provides an industrial control communication fault visual diagnosis system and method and electronic equipment. The system comprises a plurality of distributed detection nodes, a central intelligent analysis unit and a visual human-computer interface, wherein the distributed detection nodes are deployed on key equipment and lines and are responsible for collecting original data; the central intelligent analysis unit adopts an industrial-grade embedded system, is equipped with a multi-core processor and a large-capacity memory, and is responsible for data analysis and fault diagnosis; and the visual human-computer interface is used for visualizing fault positioning and generating processing suggestions. According to the invention, the three-layer architecture design of the distributed detection nodes, the central intelligent analysis unit and the visual human-computer interface is adopted, the communication state of the industrial control system can be displayed in real time, and fault equipment or line elements can be accurately positioned. And a fault which needs to be checked for more than half an hour by a qualified engineer by virtue of experience is converted into a task which can be solved within five minutes by a common maintainer under the guidance of a screen, so that the working efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control, and more specifically, to a visual diagnostic system, method, and electronic device for industrial control communication faults. Background Technology

[0002] Industrial control systems play a crucial role in modern manufacturing, and the stability of communication between the host computer and field devices is a key factor in ensuring smooth production processes. Currently, the main technical solutions for diagnosing communication faults in industrial control systems include: Segmented detection method: This method uses physical segmentation, requiring maintenance personnel to inspect each line and component (such as surge protectors, switches, repeaters, etc.) individually; Diagnostic buffer analysis: This method connects to the PLC via software, examines error logs in the CPU's diagnostic buffer, and analyzes the network topology to determine the cause of the fault; Network packet capture analysis: This method uses software to perform port mirroring on the switch, captures and analyzes data packets to determine the cause of communication interruption; Visual diagnostic systems: Some systems display device images on the host computer, label the detected components, and change colors based on whether the detected signal is normal or not. These existing technical solutions have the following significant drawbacks: Inefficient: The segmented testing method requires eliminating fault points one by one. This method is not only inefficient and time-consuming, but also heavily relies on the experience level of maintenance personnel and cannot quickly locate the fault, resulting in prolonged equipment downtime.

[0003] High level of expertise required: Diagnostic buffer analysis and network packet capture analysis require professional personnel to operate, and ordinary maintenance personnel cannot quickly master and use them.

[0004] Insufficient visualization: Existing visualization solutions mainly display the status of equipment, but cannot show the fault propagation path and the scope of impact, lacking a system-level perspective.

[0005] Latent faults are difficult to detect: Traditional methods are not effective in diagnosing and locating latent faults such as electromagnetic interference and intermittent power supply problems.

[0006] Lack of correlation among multi-source data: There is a lack of effective correlation analysis among multi-source information such as equipment data, network data, and cycle time data, making it difficult to locate the root cause of the fault at the system level. Summary of the Invention

[0007] This invention addresses the technical problems existing in the prior art by providing a visual diagnostic system, method, and electronic device for industrial control communication faults. This invention can transform a fault that originally required a senior engineer to troubleshoot for more than half an hour based on experience into a task that an ordinary maintenance worker can solve within five minutes by following the on-screen instructions, thereby improving work efficiency.

[0008] According to a first aspect of the present invention, an industrial control communication fault visualization diagnostic system is provided, comprising: multiple distributed detection nodes, a central intelligent analysis unit, and a visual human-machine interface; wherein, Distributed detection nodes are deployed on key equipment and lines to collect raw data; The central intelligent analysis unit adopts an industrial-grade embedded system, equipped with a multi-core processor and a large-capacity memory, and is responsible for data analysis and fault diagnosis. A visual human-machine interface is used for fault location, displaying a visual interface, and generating handling suggestions.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Optionally, the raw data includes: device status data, network quality data, and signal integrity data; the distributed detection nodes include: The protocol analysis module is used to analyze the line transmission protocol and extract key data from it. Key data includes: station number, data cycle period, response time, and error frame count. Signal quality sensors are used to measure signal strength, waveform distortion, and noise interference levels. The microprocessor is responsible for receiving raw data and adding high-precision timestamps; The communication module sends data to the central intelligent analysis unit, supporting industrial Ethernet, 5G, and Wi-Fi communication methods to ensure reliable data transmission.

[0011] Optionally, each of the distributed detection nodes is equipped with IN and OUT data ports and a management port. The network cable connecting the device is inserted into the IN port of the detection node, and another network cable is used to connect the OUT port of the detection node to the device. Using a new network cable, the management port of the detection node is connected to an industrial switch that supports PTP. All data between the PLC and the device passes through as is, and the data is copied and monitored for analysis.

[0012] Optionally, the central intelligent analysis unit includes a multi-source data association model and an intelligent fault diagnosis and reasoning engine; The multi-source data association model is based on a dynamic time warping mechanism to perform spatiotemporal correlation matching of equipment data, network data, and cycle time data to generate a fault propagation chain. The intelligent fault diagnosis and inference engine uses intelligent fault diagnosis algorithms and topology discovery algorithms to determine faults. The intelligent fault diagnosis algorithm combines a rule engine and machine learning algorithms to automatically identify and classify fault modes. The topology discovery algorithm is used to automatically identify devices in the network and their connection relationships to build a real-time topology map.

[0013] Optionally, the network ports of the central intelligent analysis unit can be connected to different subnets or VLANs to monitor data streams of different protocols.

[0014] Optionally, the visual human-computer interface includes: Human-machine interface device for displaying a visual interface; real-time topology map for graphically displaying the device connection relationship of the entire industrial control network, with fault points marked by flashing red icons; device status panel for displaying the status information of each device, including communication quality, signal strength, and error count. The visual human-machine interface records the occurrence time, duration, handling process, and root cause of each fault, and provides early warnings of potential faults based on historical data analysis and equipment health status assessment.

[0015] According to a second aspect of the present invention, a method for visually diagnosing industrial control communication faults is provided, comprising: Distributed detection nodes collect equipment status data, network quality data, and signal integrity data in real time, and transmit the data to the central intelligent analysis unit via industrial Ethernet; The central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns. The fault points and their impact range are displayed intuitively in the form of a topology map on a visual human-machine interface, and handling suggestions and preventive measures are provided according to the fault type.

[0016] Optionally, the central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns, including: High-precision timestamps are used to perform spatiotemporal alignment of heterogeneous data. Dynamic time warping algorithm is used to analyze the morphological similarity and temporal sequence of different data sequences, thereby identifying the causal relationship between abnormal events and constructing a fault propagation chain from root cause to manifestation. In the fault judgment stage, rule engine and machine learning model are used to make diagnostic decisions on the feature patterns formed after correlation analysis to determine whether there is a real fault and its type.

[0017] Optionally, the step of visually displaying the fault point and its impact range in the form of a topology map on a human-machine interface, and providing handling suggestions and preventive measures based on the fault type, includes: Once a fault is confirmed, the fault location and visualization mechanism is immediately activated. The root cause device and the path of the fault are highlighted in red with a flashing animation on the system topology map, and a structured diagnostic report is automatically generated, clearly showing the fault location, propagation chain and handling solution.

[0018] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to implement a method for visually diagnosing industrial control communication faults by executing a computer program stored in the memory.

[0019] The technical effects and advantages of this invention are as follows: This invention provides a visual diagnostic system, method, and electronic device for industrial control communication faults. It employs a three-layer architecture design consisting of distributed detection nodes, a central intelligent analysis unit, and a visual human-machine interface. This provides an intuitive fault display interface capable of displaying the real-time communication status of the industrial control system and accurately locating faulty equipment or circuit components. It achieves multi-source data correlation analysis, organically combining equipment data, network data, and controller data, and accurately identifying the root cause of the fault through spatiotemporal correlation matching. This lowers the professional threshold for fault diagnosis, enabling ordinary maintenance personnel to quickly identify and resolve communication faults. It enables fault prediction and prevention by analyzing historical data to predict potential fault points and provide maintenance suggestions. It solves the challenge of diagnosing hidden faults, particularly intermittent problems that are difficult to detect, such as electromagnetic interference and power fluctuations.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of a visual diagnostic system for communication faults in an industrial control system, provided in an embodiment of the present invention. Figure 2 A flowchart illustrating the steps of a visual diagnostic method for communication faults in an industrial control system, as provided in this embodiment of the invention. Figure 3 This is a schematic diagram of system operation and fault analysis records provided in an embodiment of the present invention. Detailed Implementation

[0022] 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, and 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.

[0023] It is understandable that, based on the deficiencies in the background technology, this invention proposes a visual diagnostic system for communication faults in industrial control systems, specifically as follows: Figure 1As shown, it includes: multiple distributed detection nodes, a central intelligent analysis unit, and a visual human-machine interface; among which, Distributed detection nodes are deployed on key equipment and lines, integrating voltage / current sensors and signal quality detection chips, and are responsible for collecting raw data; The raw data includes: device status data, network quality data, and signal integrity data. This includes electrical characteristic data such as signal strength, noise level, and waveform distortion at the physical layer; network quality data such as communication delay, jitter, throughput, packet loss rate, and CRC error frames at the link layer; service interaction data such as device status words, process data values, and heartbeat packet status at the protocol layer; and operational status data such as device temperature, load rate, and power supply voltage. This multi-dimensional raw data collectively constitutes a complete fault diagnosis evidence chain from the physical line to the upper-layer application, providing comprehensive factual evidence for subsequent intelligent analysis and precise location.

[0024] Specifically, the distributed detection node includes a protocol analysis module, a signal quality sensor, a microprocessor, and a communication module. The protocol analysis module is an embedded board that integrates multiple industrial protocol stacks (PROFINET, EtherNet / IP, Modbus-TCP, etc.). Its main function is not to convert protocols, but to "understand" what protocol is running on the line and extract key data (such as station number, data cycle time, response time, and error frame count).

[0025] Signal quality sensor: A high-precision current transformer and voltage sampling circuit, non-invasively coupled to the network cable, to measure signal strength, waveform distortion and noise interference levels.

[0026] Microprocessor (MCU): Responsible for receiving raw data from the above modules and adding high-precision timestamps.

[0027] Communication module: This module sends data to the central intelligent analysis unit. It supports multiple communication methods including industrial Ethernet, 5G, and Wi-Fi to ensure reliable data transmission. The primary connection method is serial access.

[0028] In this embodiment, each distributed monitoring node has two data ports (IN and OUT) and one management port. The network cable from the original connected device (such as a robot) is unplugged from the PLC port and plugged into the IN port of the monitoring node. A new network cable is used to connect the OUT port of the monitoring node to the device. Another network cable is used to connect the management port of the monitoring node to an industrial switch that supports PTP. All data between the PLC and the device passes through uninterrupted, without affecting production, while a copy of the data is made and monitored for analysis.

[0029] The central intelligent analysis unit employs an industrial-grade embedded system, equipped with a multi-core processor and large-capacity memory, responsible for data analysis and fault diagnosis. The central intelligent analysis unit includes a multi-source data association model and an intelligent fault diagnosis and inference engine. The multi-source data association model, based on a dynamic time warping mechanism, performs spatiotemporal correlation matching of device data, network data, and cycle time data to generate fault propagation chains. The intelligent fault diagnosis and inference engine uses intelligent fault diagnosis algorithms and topology discovery algorithms for fault judgment. The intelligent fault diagnosis algorithm, combined with a rule engine and machine learning algorithms, achieves automatic identification and classification of fault modes. The topology discovery algorithm is used to automatically identify devices in the network and their connections, constructing a real-time topology map.

[0030] The central intelligent analysis unit contains a multi-protocol converter, which functions as a software library. Physically, it is a multi-port industrial gateway / computer. Its main function is to convert data from different protocols into a unified, standard internal data format when multiple protocols coexist in the network (e.g., some devices use PROFINET, while others use EtherNet / IP), before handing it over to the analysis engine for processing.

[0031] Connection method: Its multiple network ports are connected to different subnets or VLANs to monitor data streams of different protocols.

[0032] A visual human-machine interface is used for fault location, displaying a visual interface, and generating handling suggestions.

[0033] The visual human-computer interface includes: Human-computer interaction devices: including industrial touch screens, monitoring screens, etc., used to display visual interfaces.

[0034] Real-time topology map: Displays the device connections of the entire industrial control network graphically, with fault points marked by flashing red icons. Device Status Panel: Displays detailed status information for each device, including communication quality, signal strength, error count, etc.

[0035] Fault history record: Records the occurrence time, duration, handling process, and root cause of each fault.

[0036] Predictive maintenance alerts: Based on historical data analysis and equipment health assessment, provide early warnings of potential failures.

[0037] In summary, the industrial control system communication fault visualization diagnostic system described in this embodiment of the invention, combined with the overall distribution of the industrial control system and following the "critical path" principle, includes communication link devices such as Ethernet switches, serial communication gateways, and wireless access points, as well as PLCs, remote I / O stations, servo / frequency converter drives, robot controllers, intelligent instruments for key process sections, HMIs, and power supply / UPS terminal equipment. This system completely changes the outdated model that relied on manual segmented troubleshooting. Through multi-dimensional data fusion analysis, it can shorten the average fault location time from several hours to minutes, greatly improving production continuity. Its core value is reflected in three levels: at the diagnostic level, the system automatically constructs fault propagation chains through intelligent algorithms, accurately identifying the root cause of faults rather than surface phenomena, enabling ordinary technicians to quickly solve complex problems; at the operation and maintenance level, predictive maintenance capabilities based on historical data can detect potential fault hazards in advance, achieving a fundamental shift from passive maintenance to proactive prevention; at the management level, the transparent and visualized display of the entire system's health status provides data support for decision-making, significantly reducing reliance on external experts and the impact on production. This system ultimately constructs a comprehensive operation and maintenance solution that integrates accurate diagnosis, intelligent early warning, and decision support, truly realizing the digital and intelligent transformation of industrial control system maintenance.

[0038] This invention also provides a method for visually diagnosing communication faults in industrial control systems, such as... Figure 2 As shown, it includes the following steps: Distributed detection nodes collect equipment status data, network quality data, and signal integrity data in real time, and transmit the data to the central intelligent analysis unit via industrial Ethernet; The central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns. The fault points and their impact range are displayed intuitively in the form of a topology map on a visual human-machine interface, and handling suggestions and preventive measures are provided according to the fault type.

[0039] The workflow of this method begins with the data acquisition phase, which comprehensively collects multi-dimensional data such as physical signals, network communication, protocol interaction, and device status through distributed detection nodes.

[0040] Subsequently, the central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns, including: High-precision timestamps are used to perform spatiotemporal alignment of heterogeneous data. Dynamic time warping algorithm is used to analyze the morphological similarity and temporal sequence of different data sequences, thereby identifying the causal relationship between abnormal events and constructing a fault propagation chain from root cause to manifestation. In the fault judgment stage, rule engine and machine learning model are used to make diagnostic decisions on the feature patterns formed after correlation analysis to determine whether there is a real fault and its type.

[0041] Finally, the fault points and affected areas are visually displayed in the form of a topology map on the human-machine interface, and handling suggestions and preventive measures are provided according to the fault type, including: Once a fault is confirmed, the fault location and visualization mechanism is immediately activated. The root cause device and the path of the fault are highlighted in red with an animated flashing pattern on the system topology map, and a structured diagnostic report is automatically generated, clearly showing the fault location, propagation chain, and handling plan. This allows maintenance personnel to intuitively grasp the full picture of the fault and quickly execute precise repairs. The entire system continuously optimizes its diagnostic capabilities through learning from historical data, forming a self-improving intelligent closed loop.

[0042] Appendix Figure 3 This section details the four core operational stages of the industrial control system communication fault visualization diagnostic system, demonstrating the complete closed-loop process from data acquisition to problem resolution. Specifically, it includes: Data Acquisition and Reporting: Distributed detection nodes continuously monitor network traffic and signal quality, perform precise time synchronization via the PTP protocol, and upload the raw data with timestamps to the central server for storage in real time.

[0043] Anomaly Triggering and Correlation Analysis: The analysis engine continuously monitors data metrics. When a parameter exceeds a threshold, it immediately initiates multi-source correlation analysis and uses algorithms such as dynamic time warping to construct a fault propagation chain to accurately locate the root cause of the problem.

[0044] Visualized location and alarm: The system pushes the analysis results to the visualization interface, which intuitively displays the fault location and handling suggestions through topology map color changes (red flashing) and diagnostic report generation.

[0045] Personnel intervention and feedback: Maintenance engineers handle issues on-site according to system guidance, and confirm completion in the system after resolving the problem, forming a feedback loop and continuously optimizing the diagnostic model.

[0046] This system enables intelligent management of the entire process from fault occurrence to resolution, and significantly improves operation and maintenance efficiency through data-driven approaches.

[0047] It should be noted that the workflow of the central intelligent analysis unit is as follows: collecting clues (data -> aligning the timeline (spatiotemporal alignment) -> finding patterns (correlation matching) -> making judgments based on experience (intelligent diagnosis). Spatiotemporal correlation matching and abnormal pattern recognition are used to find which events in the entire system are related and their order in time when a fault occurs.

[0048] Furthermore, spatiotemporal correlation matching of the data is performed to identify anomalous patterns, including: Data alignment (unifying the spatiotemporal coordinate system) is divided into: Time alignment (using PTP): All data is stamped with high-precision (microsecond-level) PTP timestamps. The system aligns all data streams using a unified timeline as the standard. This forms the basis for all subsequent analysis.

[0049] Spatial alignment (topology mapping): The system memory maintains a real-time updated network topology map. It knows the connection relationships between devices, the central processing unit, and other auxiliary components. This gives data a "spatial" attribute.

[0050] Anomaly pattern recognition (finding "strange" behavior): The system uses the following techniques in parallel to detect anomalies: Threshold-based detection: such as basic parameter statistics like latency rate and error count. The simplest and most direct example is: an anomaly is triggered when the error count of a switch port exceeds 100.

[0051] Statistical model-based detection: The system learns the baseline behavior of each device under normal conditions (such as the mean and variance of network latency). When real-time data deviates significantly from this baseline, an anomaly is triggered.

[0052] Machine learning-based detection: Using unsupervised learning algorithms, it automatically filters out isolated outlier data points. These models do not need to know in advance what the "fault" is; they can automatically discover "unusual" patterns in the data.

[0053] Spatiotemporal correlation matching: After multiple anomalies are detected, the correlation analysis engine begins its work, the core of which is to construct a fault propagation chain. Utilizing the Dynamic Time Warping (DTW) algorithm, time series can be flexibly stretched or compressed to find the most similar morphological matching path between two signals. For example, the waveforms of event A and event B are highly similar in shape, differing only in time. This strongly suggests a possible causal relationship between A and B. On one hand, the system has built-in correlation rules such as "if the CRC error on a certain port of a switch surges, then the communication latency of all devices passing through that port will subsequently increase." By analyzing historical data, the system can also automatically discover new and unknown correlation rules. On the other hand, it fully utilizes graph computation and propagation reasoning. The system treats the entire network as a graph, with devices as nodes and communication links as edges. When a node is marked as an anomaly, the system searches along the edges of the graph to its neighboring nodes (upstream and downstream devices), observing how the anomaly propagates like ripples. The node where the anomaly first appears is the root cause of the fault.

[0054] Additionally, the intelligent diagnostic engine aims to find the root cause of a fault, determining the fault type and location based on abnormal patterns, including: 1. Knowledge Base Behind the diagnostic engine is a structured fault knowledge base containing fault feature vectors: each known fault type is described by a feature vector.

[0055] 2. Pattern Matching and Inference Similarity Calculation: The diagnostic engine matches the real-time fault propagation chain and features output by the correlation analysis engine with the fault feature vectors in the knowledge base. An algorithm is used to calculate the degree of matching between the real-time features and each fault feature in the knowledge base.

[0056] Confidence assessment: The fault type with the highest matching degree will be selected as the diagnostic result, and a confidence level (e.g., 95%) will be given. This solves the "like what" problem.

[0057] Rule-based reasoning: For complex compound faults composed of multiple basic faults, the engine will invoke a series of "IF-THEN" rules to perform logical reasoning and gradually approach the most likely root cause.

[0058] 3. Positioning and Output Location: The location of the fault is determined when the propagation chain is built. The diagnostic engine simply confirms this location and associates it with the fault type.

[0059] Output: The engine generates a final structured diagnostic report, including: fault symptoms, occurrence time, root cause location, fault type, confidence level, impact range, and recommended solutions. This report is the final content presented on the visualization interface. An example is shown below: Data: Detection node report [Time T: CRC error on switch Port7 spikes from 0 to 1000 / second]. 1 second later, [Time T+1: Robot A's communication delay becomes infinite].

[0060] Correlation matching: The DTW algorithm found a high correlation between the waveform of a surge in CRC errors and the waveform of increased robot latency, with CRC errors preceding the waveform. The topology diagram shows that robot A is the only device under switch Port7.

[0061] Hypothesis propagation chain: Physical failure of switch Port7 -> Communication interruption of robot A.

[0062] Intelligent Diagnosis: The engine matches features [CRC error surge, signal strength decline, single device impact] with the knowledge base. A match is found for fault F_NET_001 (physical link failure), with a 98% confidence level. The anomaly is located at the network interface of switch Port7 <-> robot A.

[0063] Output: The link is highlighted in red on the visualization interface, and a diagnostic report is provided: "Root cause: Physical link failure, it is recommended to check the network cable." The fault point and its affected area are displayed visually as a topology map on the human-machine interface.

[0064] Specific examples are as follows: First, data acquisition and anomaly detection are performed: After the industrial control system is built, the system is run under normal production conditions for a period of time. The system will automatically learn and record the normal behavior patterns of each link, such as the normal network latency range (e.g., 1ms ~ 5ms), the normal signal strength range, the normal data flow cycle, and the normal cycle time of the equipment. A normal industrial control system database is built as a benchmark for judging anomalies in the later stage.

[0065] Secondly, multi-source data correlation analysis: it does not look at an alarm in isolation, but asks: "What happened to B and C at the same time or before and after the anomaly occurred in A?" Spatiotemporal alignment: using high-precision timestamps, it perfectly aligns time-series data from different nodes and devices on the same timeline.

[0066] Correlation analysis: Case: The system detected an alarm for "Robot A response timed out".

[0067] Analysis: The system automatically backtracks the timeline to check the moment just before the timeout: At the network level: Are there a large number of CRC errors on the switch port connecting to robot A? (Spatial correlation) Power supply level: Is there a momentary voltage drop in the power supply line of robot A? (Object-related) Other devices: Did other devices on the same switch also experience brief delays? (Scope of impact) Cycle time: Did the PLC's logic scan cycle lengthen at that moment? (Time correlation) Constructing a fault propagation chain: Based on the above correlation, the system infers the fault propagation path: [voltage drop] -> [robot A controller restart] -> [network communication timeout] -> [PLC error].

[0068] Root cause identification identifies the earliest point in the fault propagation chain where an anomaly occurs as the root cause. The system will pinpoint "voltage drop" as the root cause of the fault, rather than the apparent "network timeout."

[0069] Knowledge Base Matching & Decision: The system matches the characteristics of the current fault (root cause, propagation path, device type) with the historical fault database and expert rule database.

[0070] Match successful: Directly output diagnostic conclusions and verified handling suggestions, such as: "Root cause: Power grid fluctuations caused robot A to lose power momentarily. Recommendation: Check whether the power connection terminals of robot A are loose and consider installing a voltage regulator." Unknown Faults: For new faults, the system will record all characteristics and provide the most likely inferences for engineers to refer to and learn from. At the same time, it will provide early warnings of potential faults to avoid bigger problems.

[0071] A simplified example of a device status panel table is shown in Table 1 below:

[0072] Warnings are marked in yellow, and faults in red for quick identification. The device status panel provides a clear view of fault locations and allows for appropriate repairs based on system suggestions. It also reminds maintenance personnel to increase patrols of areas requiring enhanced monitoring, providing early warnings of potential faults and preventing more serious problems.

[0073] The following are examples of failure scenarios: A robot in the production area suddenly stopped, and the PLC reported a "robot communication timeout". Step 1: Overall Overview - Detecting Anomalies Immediately When maintenance personnel open the interface, the first thing they see is the industrial control system topology map.

[0074] Performance: On the layout diagram of the entire industrial control system, device icons are usually displayed in green.

[0075] Suddenly, an icon in the production area started flashing red, accompanied by a faint alarm sound.

[0076] The blinking icon has a prominent exclamation mark. When you hover the mouse over it, the device name and a brief warning message (such as "Robot_02: Communication Timeout") will be displayed.

[0077] Step 2: Precise Analysis - Click to Locate the Root Cause When maintenance personnel click the flashing robot icon, a detailed diagnostic panel for the device will pop up. This panel contains the core information.

[0078] The panel contains multiple tabs: Tab 1: Real-time Status Card content: Device Name: Robot_02 Main status: FAULT Communication delay: Displays the current value (--- ms) and historical maximum / minimum values. The pointer will show "Out of Range" at this time.

[0079] Signal strength: The pointer is in the "weak" red area.

[0080] Last normal time: 2023-10-27 14:02:15 Tab 2: Diagnosis Report, as shown in Table 2 below:

[0081] The aforementioned fault diagnosis report is the output of the system's intelligent analysis, presented in report form, and represents the core value of the system.

[0082] Tab 3: Historical Trend Chart (Trend Analysis) Content: Multi-parameter trend charts with customizable time ranges.

[0083] Preset fault time view: The system automatically locks the timeline to 15 minutes before and after the fault occurs.

[0084] Curve 1 (red): CRC error count. The graph shows that around 14:05:20, the curve rises vertically from 0.

[0085] Curve 2 (blue): Communication delay. After the CRC error rises, the delay curve becomes a straight line (indicating timeout, no data).

[0086] Curve 3 (green): Signal strength. There is a noticeable "plunge" drop near the fault point.

[0087] Interaction: Moving the mouse over the curve allows you to view the precise values ​​at any given time point.

[0088] Step 3: Topology Tracing - Identifying Connections On the main topology map, the system will automatically highlight the entire network path related to the faulty device.

[0089] Performance: Robot_02 → Red flashing The network cable connected to Robot_02 → turns into a thick red line. The switch port (Port 7) to which the network cable is connected turns red and displays the port error count.

[0090] The path from the switch to the PLC remains green (indicating the problem is not in the backbone network).

[0091] This view Figure 1 The problem was immediately apparent to the maintenance personnel: "The issue lies in the line between Robot_02 and port 7 of the switch!" Step 4: Action and Feedback - Closed-Loop Management Based on the precise location (Robot_02's XJ12 interface) and troubleshooting suggestions provided by the visual interface, maintenance personnel discovered on-site that the network cable's RJ12 connector clip was broken, causing poor contact. After replacement, the problem was resolved. A "Troubleshooting" button is designed at the bottom of the diagnostic report. Clicking it requires filling in: The root cause: Physical damage to the RJ45 connector on the network cable. Measures taken: Replace the network cable Person handling this: Zhang San After clicking "Confirm," the system will automatically archive all data, analysis process, and final solution related to this fault into the historical case library. The alarm on the interface will be cleared, and the device status will return to green. The system learns from this feedback, and its diagnostic confidence will be higher when encountering similar patterns of CRC error spikes and signal strength drops in the future. The essence of this visualization design lies in: From global to local: Quickly locate the faulty device from the overall industrial control diagram.

[0092] From phenomenon to root cause: not only tell users "what is wrong" (communication timeout), but more importantly, tell users "why it is wrong" (CRC error → network cable problem) through multi-dimensional data correlation.

[0093] From diagnosis to action: It not only identifies the problem, but also provides specific and actionable repair suggestions, guiding maintenance personnel on what to do and where to do it.

[0094] Data to Insight: Transform raw, dry data (CRC counters) into easily understandable insights through trend charts, dashboards, diagnostic reports, and other formats.

[0095] Ultimately, the method described in this embodiment of the invention can transform a fault that originally required a senior engineer to troubleshoot for more than half an hour based on experience into a task that an ordinary maintenance worker can solve within five minutes by following the on-screen instructions, thus improving efficiency, which is its greatest value.

[0096] Compared with the prior art, the present invention has the following significant advantages: Accurate fault location: Through multi-source data correlation analysis, faulty equipment or circuit components can be accurately located, reducing the average fault repair time by more than 70%.

[0097] Visualized diagnostic process: The fault location and scope of impact are displayed in an intuitive graphical interface, enabling ordinary maintenance personnel to quickly understand and handle the fault.

[0098] High system integration: Supports multiple industrial protocols, adapts to equipment from different manufacturers and heterogeneous network environments, and has good versatility and scalability.

[0099] Predictive maintenance capability: By learning from and analyzing historical data, it can predict potential failures and provide preventative maintenance recommendations to prevent problems before they occur.

[0100] Reduced reliance on professional personnel: Intelligent diagnostic systems can automatically analyze the causes of faults and provide troubleshooting suggestions, reducing the need for specialized skills among maintenance personnel.

[0101] Multidimensional data correlation analysis: By using spatiotemporal correlation matching technology, the specific time point and duration of the fault, which PLC and which sensor it is, and whether it is a network problem (surge in switch port errors), an equipment problem (abnormal increase in motor current), or a signal problem (interference with sensor signals) are comprehensively analyzed. This can uncover complex systemic problems that cannot be identified by a single data source, thus providing more accurate fault analysis data.

[0102] Additionally, embodiments of the present invention also provide an electronic device comprising: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute implementation steps of a method for visually diagnosing communication faults in an industrial control system.

[0103] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0105] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0106] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0107] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visual diagnostic system for industrial control communication faults, characterized in that, include: Multiple distributed detection nodes, a central intelligent analysis unit, and a visual human-machine interface; among them, Distributed detection nodes are deployed on key equipment and lines to collect raw data; The central intelligent analysis unit adopts an industrial-grade embedded system, equipped with a multi-core processor and a large-capacity memory, and is responsible for data analysis and fault diagnosis. A visual human-machine interface is used for fault location, displaying a visual interface, and generating handling suggestions.

2. The industrial control communication fault visualization diagnostic system according to claim 1, characterized in that, The raw data includes: device status data, network quality data, and signal integrity data; the distributed detection nodes include: The protocol analysis module is used to analyze the line transmission protocol and extract key data from it. Key data includes: station number, data cycle period, response time and error frame count. Signal quality sensors are used to measure signal strength, waveform distortion, and noise interference levels. The microprocessor is responsible for receiving raw data and adding high-precision timestamps; The communication module sends data to the central intelligent analysis unit, supporting industrial Ethernet, 5G, and Wi-Fi communication methods to ensure reliable data transmission.

3. The industrial control communication fault visualization diagnostic system according to claim 1, characterized in that, Each of the distributed detection nodes is equipped with IN and OUT data ports and a management port. Insert the network cable connecting the device into the IN port of the detection node, and use another network cable to connect the OUT port of the detection node to the device. Using a new network cable, connect the management port of the detection node to an industrial switch that supports PTP. All data between the PLC and the device passes through as is, while copying the data and performing monitoring and analysis.

4. The industrial control communication fault visualization diagnostic system according to claim 1, characterized in that, The central intelligent analysis unit includes a multi-source data association model and an intelligent fault diagnosis and reasoning engine. The multi-source data association model is based on a dynamic time warping mechanism to perform spatiotemporal correlation matching of equipment data, network data, and cycle time data to generate a fault propagation chain. The intelligent fault diagnosis and inference engine uses intelligent fault diagnosis algorithms and topology discovery algorithms to determine faults. The intelligent fault diagnosis algorithm combines a rule engine and machine learning algorithms to automatically identify and classify fault modes. The topology discovery algorithm is used to automatically identify devices in the network and their connection relationships to build a real-time topology map.

5. The industrial control communication fault visualization diagnostic system according to claim 1, characterized in that, The network ports of the central intelligent analysis unit are connected to different subnets or VLANs to monitor data streams of different protocols.

6. The industrial control communication fault visualization diagnostic system according to claim 1, characterized in that, The visual human-computer interface includes: Human-machine interface device for displaying a visual interface; real-time topology map for graphically displaying the device connection relationship of the entire industrial control network, with fault points marked by flashing red icons; device status panel for displaying the status information of each device, including communication quality, signal strength, and error count. The visual human-machine interface records the occurrence time, duration, handling process, and root cause of each fault, and provides early warnings of potential faults based on historical data analysis and equipment health status assessment.

7. A method for visually diagnosing industrial control communication faults, used in the industrial control communication fault visualization diagnosis system according to any one of claims 1 to 6, characterized in that, The method includes the following steps: Distributed detection nodes collect equipment status data, network quality data, and signal integrity data in real time, and transmit the data to the central intelligent analysis unit via industrial Ethernet; The central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns. The fault points and their impact range are displayed intuitively in the form of a topology map on a visual human-machine interface, and handling suggestions and preventive measures are provided according to the fault type.

8. The method for visual diagnosis of industrial control communication faults according to claim 7, characterized in that, The central intelligent analysis unit performs spatiotemporal correlation matching on the data, identifies abnormal patterns, and determines the fault type and location based on the abnormal patterns, including: Based on high-precision timestamps, heterogeneous data is spatiotemporally aligned. Dynamic time warping algorithm is used to analyze the morphological similarity and temporal sequence of different data sequences, identify the causal relationship between abnormal events, and construct a fault propagation chain from root cause to manifestation. By using a rule engine and machine learning model, diagnostic decisions are made on the feature patterns formed after correlation analysis to determine whether there are real faults and their types.

9. The method for visual diagnosis of industrial control communication faults according to claim 7, characterized in that, The method of visually displaying fault points and their impact range in the form of a topology map on a human-machine interface, and providing handling suggestions and preventive measures based on the fault type, includes: Once a fault is confirmed, the fault location and visualization mechanism is immediately activated. The root cause device and the path of the fault are highlighted in red with a flashing animation on the system topology map, and a structured diagnostic report is automatically generated, clearly showing the fault location, propagation chain and handling solution.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the processor is used to execute a computer program stored in the memory to implement the industrial control communication fault visualization diagnosis method as described in any one of claims 7 to 9.

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