Industrial data signal isolation transmission control method and system

Abstract

The invention relates to the technical field of industrial data signal transmission control, and discloses an industrial data signal isolation transmission control method and system, and the method comprises the steps: collecting the operation parameters of an isolator through an isolation gateway, and carrying out the preprocessing of the operation parameters; evaluating an isolation performance state through an isolation gateway according to the preprocessed operation parameters, and analyzing a change trend of the isolation performance state; predicting the future state of the isolation performance through the isolation gateway according to the isolation performance state and the change trend, and generating isolation performance health information; reporting isolation performance health information to a central control system through an isolation gateway; analyzing whether data exception occurs or not through the central control system, and when the data exception occurs, judging the source of the exception according to the isolation performance health information; the central control system executes early warning and maintenance scheduling or production task adjustment and optimization according to the isolation performance health information, and carries out real-time monitoring, performance evaluation, trend prediction and abnormity diagnosis on the industrial data signal isolation transmission process.

Description

Technical Field

[0001] This application relates to the field of industrial data signal transmission and control technology, and more specifically, to an industrial data signal isolation transmission and control method and system. Background Technology

[0002] In modern industrial production, ensuring stable and reliable data signal transmission is crucial for the precise control and safe operation of automated systems. The electrical environment in industrial settings is complex and harsh, characterized by high voltage, strong current, and various electromagnetic interferences. Therefore, signal isolation technology is an indispensable component. Signal isolation equipment aims to electrically separate different parts of a system to prevent ground loop currents and common-mode interference from affecting sensitive control circuits, thereby protecting equipment and maintaining data integrity. However, with the increasing demand for flexible manufacturing and the introduction of advanced high-power equipment, the challenges facing signal isolation are constantly evolving, and the limitations of traditional static isolation methods are becoming increasingly apparent. In flexible manufacturing systems, the physical connections and layout of production modules may change frequently, leading to complex and unpredictable dynamic changes in the grounding potential difference between modules. Simultaneously, the frequency range and intensity of common-mode noise also change, and the frequency and peak voltage of transient interference events may increase significantly. Furthermore, the introduction of new high-power, high-frequency modules generates electromagnetic interference far exceeding that of traditional modules, including broadband radiated and conducted interference, as well as high-energy transient overvoltages and surge currents. These interference signals have a wide frequency range and high peak voltage, making existing statically selected signal isolators inadequate in terms of anti-interference and transient tolerance when faced with these new modules, and the isolation barrier is frequently pushed to its performance limit.

[0003] Prolonged exposure to high-intensity electromagnetic interference, transient overvoltages, and continuously changing ground potential differences, even without immediate catastrophic failure, will cause cumulative and irreversible damage to the isolator's internal isolation barrier and critical electronic components. For example, the insulating material inside the isolator may experience micro-fatigue under repeated electrical stress, leading to a gradual decrease in dielectric strength and an increase in leakage current. The light output efficiency of the LEDs inside the optocoupler will gradually decline, and the current transfer ratio of the phototransistor may also decrease due to photoaging effects. These micro-level performance degradations will ultimately manifest in macro-level isolation performance indicators, such as isolation withstand voltage, common-mode rejection, and transient immunity, which will slowly decrease. This decline is a gradual process, not a sudden failure, making it difficult for traditional "on / off" self-diagnostic functions based on simple threshold judgments to detect in a timely manner. The isolator's main signal transmission function may still appear to be working normally, but its integrity as an "electrical barrier" has been compromised, and its ability to suppress interference is significantly reduced.

[0004] As isolation performance gradually declines, the central control system begins to receive intermittent, elusive data anomalies from affected modules. These anomalies are not obvious signal interruptions or error codes, but rather more subtle "soft faults." For example, temperature sensor data may occasionally fluctuate by a few degrees, pressure transmitter readings may drift slightly under certain operating conditions, or the positioning feedback signal of a servo motor may occasionally exhibit minute jitters during high-speed operation. These subtle anomalies are often difficult to locate using conventional fault diagnosis procedures because they do not conform to typical sensor failure modes, nor do they resemble communication protocol errors. Upon receiving an alarm, maintenance personnel may conduct a comprehensive inspection of sensors, field wiring, module controllers, and even the central control unit, only to find that all components are "normal" when tested individually. The signal isolator itself may also appear to function perfectly under routine continuity tests or simple signal transmission tests, but its deep isolation integrity has been compromised, allowing external interference to enter the signal path with a lower attenuation rate. This "ghostly" failure mode leads to frequent unexplained production line stoppages, product quality fluctuations, and even malfunctions in certain critical control points. This significantly increases the time and cost of fault diagnosis, severely impacting the overall reliability and data trustworthiness of the system. Operators and engineers become less able to accurately assess the system's status, unable to distinguish whether these intermittent problems stem from occasional faults in field equipment, momentary congestion in communication links, or external interference caused by a decline in isolator isolation capabilities. This uncertainty makes system maintenance extremely difficult and increases potential security risks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides an industrial data signal isolation transmission control method and system.

[0006] In a first aspect, this application provides an industrial data signal isolation transmission control method, comprising: The isolator's operating parameters are collected through the isolation gateway, and these parameters are preprocessed. The isolation gateway evaluates the isolation performance status based on the preprocessed operating parameters and analyzes the changing trend of the isolation performance status. Based on the isolation performance status and trends, the isolation gateway predicts the future state of isolation performance and generates isolation performance health information. The isolation performance health information is reported to the central control system through the isolation gateway; The central control system analyzes whether there are any data anomalies, and when data anomalies are found, the source of the anomaly is determined based on the isolation performance health information. The central control system performs early warning and maintenance scheduling, or adjusts and optimizes production tasks based on the isolation performance health information.

[0007] By real-time monitoring, performance evaluation, trend prediction, and anomaly diagnosis of the industrial data signal isolation transmission process, the challenges faced by traditional isolation methods in complex industrial environments are effectively addressed, thereby improving system reliability and data trustworthiness.

[0008] Furthermore, this application also proposes a step for predicting the future state of isolation performance based on the current state and trends of isolation performance, including: Based on the isolation performance status and changing trend, continuously calculate the instantaneous rate of decrease of the isolation performance parameters; Calculate the rate of change of the instantaneous rate of descent based on the instantaneous rate of descent. Based on the rate of change of the instantaneous rate of decrease, determine whether the decreasing trend of isolation performance meets the preset accelerated decay condition; When the downward trend meets the accelerated decay condition, the prediction mode will be switched from linear decay mode to accelerated decay mode. Based on the isolation performance status, changing trends, and prediction patterns, predict the future state of isolation performance.

[0009] By dynamically identifying the trend of declining isolation performance and intelligently switching prediction modes, the future state of isolation performance can be predicted more accurately, providing a basis for timely maintenance and adjustment.

[0010] Furthermore, this application also proposes a step for determining whether the decreasing trend of isolation performance meets a preset accelerated decay condition based on the rate of change of the instantaneous decreasing rate, including: Based on the isolation performance status, select the currently applicable accelerated decay condition from the preset accelerated decay condition library; The rate of change of the instantaneous rate of decline is compared with the currently applicable accelerated decay conditions to determine whether the declining trend of the isolation performance meets the preset accelerated decay conditions.

[0011] By dynamically selecting the most suitable accelerated decay condition based on the current state of isolation performance, the accuracy and adaptability of predictions are improved.

[0012] Furthermore, this application also proposes that the steps for determining the source of the abnormality based on isolation performance health information include: Receive transient penetration event reports from the isolation gateway, which indicate the transient suppression capability of the isolation barrier; When data anomalies occur, the data anomalies are cross-referenced with transient penetration event reports and combined with isolation performance health information to determine the source of the anomalies.

[0013] Furthermore, this application proposes a method for determining the source of anomalies by cross-referencing data anomalies with transient penetration event reports and combining this with isolation performance health information, including the following steps: If a transient penetration event report indicates that the isolation barrier is inadequate in suppressing transients during the data anomaly, the data anomaly is attributed to the isolation gateway's insufficient transient suppression of impulse interference. If the transient penetration event report indicates that the isolation barrier's transient suppression capability was normal during the data anomaly, the data anomaly is attributed to the impulse interference directly affecting downstream equipment.

[0014] By leveraging detailed information from transient penetration event reports, we can perform refined attribution of data anomalies, guiding more precise troubleshooting and maintenance.

[0015] Furthermore, this application also proposes, and includes, the following steps: Adjust the reliability weights or control parameters of data from the isolation gateway based on the attribution of data anomalies.

[0016] Furthermore, this application also proposes a step of selecting a currently applicable accelerated decay condition from a preset accelerated decay condition library based on the isolation performance status, including: Monitor the electrical parameters of the operating environment; Identify the degradation mode of the isolator based on the rate of change of the instantaneous decrease rate of the electrical parameters of the operating environment and the isolation performance parameters; Based on the isolation performance status and degradation mode, select the currently applicable accelerated degradation condition from the preset accelerated degradation condition library.

[0017] Furthermore, this application also proposes a step for identifying the degradation mode of an isolator based on the electrical parameters of the operating environment and the rate of change of the instantaneous degradation rate of the isolation performance parameters, including: Extract transient pulse characteristics, harmonic component characteristics, and broadband noise energy distribution characteristics from the electrical parameters of the operating environment; The transient pulse characteristics, harmonic component characteristics, broadband noise energy distribution characteristics, and the rate of change of the instantaneous decrease rate of the isolation performance parameters are correlated to obtain the correlation results. Based on the correlation results, the isolator's degradation mode is identified by comparing it with the feature set of the preset interference source type and its corresponding degradation mode.

[0018] By analyzing electrical parameters from multiple dimensions, the degradation patterns of isolators can be accurately identified, providing crucial information for targeted maintenance.

[0019] Furthermore, this application also proposes that the transient penetration event report is generated by the isolation gateway, and the process by which the isolation gateway generates the transient penetration event report includes: Monitor the transient response of the input and output signals of the isolation barrier; The transient suppression capability of the isolation barrier is determined based on the degree of attenuation or distortion of the transient response; Based on the determined transient suppression capability, a transient penetration event report is generated.

[0020] By enabling the isolation gateway to autonomously generate transient penetration event reports, real-time monitoring and reporting of the transient suppression capability of the isolation barrier can be achieved, thereby enhancing the system's self-diagnostic capabilities.

[0021] Secondly, this application also proposes an industrial data signal isolation transmission control system, the system comprising: The isolation gateway is used to collect the operating parameters of the isolator, which reflect the performance degradation of the isolator. It also preprocesses the operating parameters, evaluates the isolation performance status based on the preprocessed operating parameters, analyzes the changing trend of the isolation performance status, predicts the future state of the isolation performance based on the isolation performance status and changing trend, generates isolation performance health information, and reports the isolation performance health information to the central control system. The central control system is used to receive isolation performance health information, analyze whether data anomalies occur, and determine the source of the anomaly based on the isolation performance health information when anomalies occur. It is also used to perform early warning and maintenance scheduling, or adjust and optimize production tasks based on the isolation performance health information.

[0022] In summary, the industrial data signal isolation transmission control method and system provided in this application collects and preprocesses isolator operating parameters in real time through an isolation gateway, thereby assessing the isolation performance status, analyzing changing trends, and predicting the future state of isolation performance to generate isolation performance health information. This health information is reported to the central control system, which uses it to determine the source of data anomalies and executes early warnings, maintenance scheduling, or production task adjustments and optimizations. This effectively solves the problem that traditional static isolation methods are unable to cope with complex and ever-changing industrial environments, leading to a gradual decline in isolator performance, which in turn causes difficult-to-diagnose data anomalies and reduced system reliability. Through dynamic monitoring and prediction of isolation performance, this application can promptly detect potential performance degradation of isolators, avoid the impact of "ghost" soft faults on production, significantly improve the efficiency and accuracy of fault diagnosis, reduce maintenance costs, and enhance the overall reliability and data trustworthiness of the industrial control system. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an industrial data signal isolation transmission control method provided in an embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the structure of an industrial data signal isolation transmission control system provided in an embodiment of this application.

[0025] Labeling explanation: 210, Isolation gateway; 220, Central control system. Detailed Implementation

[0026] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] In modern industrial production, ensuring stable and reliable data signal transmission is crucial for the precise control and safe operation of automated systems. The electrical environment in industrial settings is complex and harsh, characterized by high voltage, strong current, and various electromagnetic interferences. Therefore, signal isolation technology is an indispensable component. Signal isolation equipment aims to electrically separate different parts of a system to prevent ground loop currents and common-mode interference from affecting sensitive control circuits, thereby protecting equipment and maintaining data integrity. However, with the increasing demand for flexible manufacturing and the introduction of advanced high-power equipment, the challenges facing signal isolation are constantly evolving, and the limitations of traditional static isolation methods are becoming increasingly apparent. Long-term exposure to these high-intensity electromagnetic interferences, transient overvoltages, and continuously changing ground potential differences, even if not immediately leading to catastrophic failure of the isolator, will cause cumulative and irreversible damage to its internal isolation barriers and critical electronic components. This "ghost" failure mode leads to frequent unexplained production line stoppages, product quality fluctuations, and even malfunctions in certain critical control stages. This significantly increases the time and cost of fault diagnosis, severely impacting the overall reliability of the system and data trustworthiness.

[0029] Regarding this, firstly, see... Figure 1 This application proposes an industrial data signal isolation transmission control method, comprising: The isolator's operating parameters are collected through the isolation gateway 210, and the operating parameters are preprocessed. The isolation gateway 210 evaluates the isolation performance status based on the preprocessed operating parameters and analyzes the changing trend of the isolation performance status. Based on the isolation performance status and changing trends, the isolation gateway 210 predicts the future state of isolation performance and generates isolation performance health information. The isolation performance health information is reported to the central control system 220 through the isolation gateway 210; The central control system 220 analyzes whether there are any data anomalies, and when data anomalies are found, the source of the anomaly is determined based on the isolation performance health information. Based on the isolation performance health information, the central control system 220 performs early warning and maintenance scheduling, or adjusts and optimizes production tasks.

[0030] This application achieves dynamic and intelligent control of industrial data signal isolation transmission through real-time monitoring, evaluation, prediction, and anomaly diagnosis of isolator operating parameters. It effectively solves the problems of performance degradation and difficulty in fault diagnosis in complex industrial environments using traditional static isolation methods, significantly improving the reliability and maintenance efficiency of industrial systems. This application aims to use intelligent means to monitor, evaluate, and predict the performance status of industrial isolators in real time, and on this basis, trace and process data anomalies, ultimately achieving optimized control of industrial production processes. The isolation gateway 210 is one of the core devices for implementing this method. Its main function is to act as a bridge between the field isolators and the central control system 220, responsible for data acquisition, preliminary processing, performance evaluation, and information reporting. Isolator operating parameters refer to key indicators reflecting the isolator's working status and performance, such as isolation resistance, leakage current, common-mode rejection ratio, and transient withstand voltage. Changes in these parameters can directly or indirectly indicate the degree of degradation in the isolator's isolation performance. Isolation performance health information is a comprehensive report generated by the isolation gateway 210 based on the analysis and prediction results of the operating parameters, containing information such as the isolator's current health status, future performance trends, and potential risks. The central control system 220 is the core of the entire industrial control system. It is responsible for receiving health information reported by the isolation gateway 210, performing data anomaly analysis, and making decisions based on the analysis results, such as issuing early warnings, scheduling maintenance resources, or adjusting production tasks.

[0031] Specifically, the isolator's operating parameters are first collected via the isolation gateway 210, and these parameters are then preprocessed. The isolation gateway 210 can be configured with various sensor interfaces to connect to sensors on the isolator, such as those for voltage, current, temperature, and humidity, to acquire real-time operating data. For example, it can collect common-mode voltage, differential-mode voltage, insulation resistance, and internal temperature from the isolator's input and output terminals. The preprocessing process may include data filtering, noise removal, and dimensional normalization to ensure the accuracy of subsequent evaluation and analysis. For instance, moving average filtering or Kalman filtering can be used to remove random noise from the sensor data, or parameters with different dimensions can be uniformly converted into dimensionless indicators for comprehensive evaluation.

[0032] Subsequently, the isolation gateway 210 evaluates the isolation performance status based on the preprocessed operating parameters and analyzes the changing trend of the isolation performance status. The evaluation of isolation performance status can be achieved by establishing a multi-parameter comprehensive evaluation model. For example, algorithms such as fuzzy logic reasoning, neural networks, or support vector machines can be used, taking multiple preprocessed operating parameters as input and outputting a quantitative isolation performance health index. This health index can be a value between 0 and 100, with higher values ​​indicating better isolation performance. Trend analysis can be achieved through time series analysis methods, such as linear regression, exponential smoothing, or ARIMA models, to model historical health index data and predict the trend of the health index over a future period. For example, if the health index continues to decline, it indicates a deteriorating trend in isolation performance.

[0033] Furthermore, the isolation gateway 210 predicts the future state of isolation performance based on the current isolation performance status and trends, and generates isolation performance health information. Predicting the future state can employ various prediction algorithms, such as machine learning models based on historical data or state-space prediction based on physical models. For example, if the evaluation results show an isolation performance health index of 70, and the trend analysis indicates that the index is decreasing at a rate of 2 points per month, then it can be predicted that the health index will decrease to 64 within the next 3 months. The isolation performance health information can include the current health index, the predicted future health index, the predicted rate of performance decline, and a risk level generated based on this information (e.g., "Normal," "Watchout," "Warning," "Danger").

[0034] Next, the isolation gateway 210 reports the generated isolation performance health information to the central control system 220. The reporting method can employ standard industrial communication protocols, such as Modbus TCP, EtherNet / IP, or OPC UA. The isolation gateway 210 can encapsulate the health information into data packets and send them to the central control system 220 via wired or wireless network. After receiving this information, the central control system 220 can store it in a database and display it visually on a human-machine interface, allowing operators to monitor the isolator's health status in real time.

[0035] After receiving isolation performance health information, the central control system 220 analyzes whether any data anomalies have occurred. This analysis can be achieved through setting thresholds, statistical methods, or machine learning algorithms. For example, it can monitor whether data from field devices exceeds a preset normal range or exhibits fluctuations that do not conform to historical patterns. When data anomalies are detected, the central control system 220 determines the source of the anomaly based on the isolation performance health information. For instance, if the data from a temperature sensor suddenly fluctuates significantly, and the isolation performance health information shows that the corresponding isolator health index is low and exhibits a rapid downward trend, the central control system 220 can preliminarily determine that the data anomaly may be related to a decline in isolator performance.

[0036] Finally, the central control system 220 executes early warning and maintenance scheduling, or adjusts and optimizes production tasks based on the isolation performance health information. If the isolation performance health information indicates a severe decline in isolator performance or that it is about to reach a dangerous threshold, the central control system 220 can automatically trigger an early warning mechanism, send an alarm to maintenance personnel, automatically generate a maintenance work order, and schedule maintenance resources for inspection or replacement. Simultaneously, if data anomalies are determined to be caused by a decline in isolator performance, the central control system 220 can also automatically adjust production tasks based on the severity of the anomaly and its impact on production. For example, it can reduce the operating speed of the affected production line, switch to standby equipment, or adjust process parameters to avoid product quality degradation or equipment damage due to isolation performance issues.

[0037] This application achieves dynamic and intelligent control of isolated transmission of industrial data signals through real-time monitoring, evaluation, prediction, and anomaly diagnosis of isolator operating parameters. Compared with traditional static isolation methods, this application can detect potential performance degradation of isolators earlier, fundamentally solving the problems of difficult fault diagnosis, delayed maintenance, and reduced production efficiency in traditional methods. The collection and preprocessing of isolator operating parameters by the isolation gateway 210 lays the data foundation for subsequent performance evaluation. The assessment of isolation performance status and analysis of change trends by the isolation gateway 210 can grasp the health status of the isolator from a macroscopic and dynamic perspective. Predicting the future state of isolation performance and generating health information transforms maintenance from passive response to proactive prevention, greatly improving maintenance efficiency and system reliability. The intelligent judgment and source tracing of data anomalies by the central control system 220 avoids the problems of difficult fault location and time-consuming processes in traditional methods. Finally, through early warning and optimization of maintenance scheduling or production tasks, this application can effectively avoid production interruptions and economic losses caused by isolation performance problems, significantly improving the overall reliability and operating efficiency of the industrial system.

[0038] Furthermore, based on the current state and trend of isolation performance, the steps for predicting the future state of isolation performance include: Based on the isolation performance status and changing trend, continuously calculate the instantaneous rate of decrease of the isolation performance parameters; Calculate the rate of change of the instantaneous rate of descent based on the instantaneous rate of descent. Based on the rate of change of the instantaneous rate of decrease, determine whether the decreasing trend of isolation performance meets the preset accelerated decay condition; When the downward trend meets the accelerated decay condition, the prediction mode will be switched from linear decay mode to accelerated decay mode. Based on the isolation performance status, changing trends, and prediction patterns, predict the future state of isolation performance.

[0039] Specifically, the instantaneous rate of decrease of isolation performance parameters refers to the rate of change of isolation performance parameters relative to the previous moment or over a period of time at a specific instant. This rate can be calculated by performing differencing, regression analysis, or Kalman filtering on historical isolation performance data to capture the rate of degradation of isolation performance in real time and dynamically. Furthermore, the rate of change of the instantaneous rate of decrease reflects the speed of degradation of isolation performance, i.e., whether the degradation process is accelerating or decelerating. This rate of change can be obtained by further differencing, curve fitting, or trend analysis on the calculated instantaneous rate of decrease to identify potential signs of accelerated decay in the trend of declining isolation performance.

[0040] The preset accelerated degradation conditions are a set of pre-defined thresholds, rules, or models used to define when the trend of isolation performance decline changes from normal or linear degradation to accelerated degradation. These conditions can be set based on a large amount of historical operating data, expert experience, degradation curves of equipment manufacturers, or specific industry standards. For example, when the rate of change of the instantaneous degradation rate exceeds a certain threshold for multiple consecutive monitoring periods, or when its growth trend conforms to a certain nonlinear model, it is judged that the accelerated degradation condition is met. When the decline trend is judged to meet the preset accelerated degradation condition, the prediction mode will be switched from linear degradation mode to accelerated degradation mode. The linear degradation mode usually uses simple linear regression, least squares method, or trend extrapolation method for prediction, which is suitable for the case of stable and slow degradation of isolation performance. The accelerated degradation mode may use exponential decay model, Weibull distribution model, nonlinear regression model, or other more complex nonlinear prediction algorithm to more accurately simulate and predict the rapid degradation process. Therefore, the future state of isolation performance will be predicted comprehensively based on the current isolation performance state, its trend, and the selected prediction mode. For example, in accelerated decay mode, the prediction results will reflect a faster rate of performance degradation and a shorter remaining lifespan, thus providing a more accurate basis for subsequent early warning and maintenance scheduling.

[0041] This application dynamically captures subtle changes in isolator performance degradation by introducing continuous monitoring and calculation of the instantaneous rate of decrease and the rate of change of isolation performance parameters. It is precisely this accurate identification of the degradation rate and its accelerating trend that allows for timely determination of whether isolation performance has entered an accelerated degradation phase. Furthermore, by switching the prediction mode from the traditional linear degradation mode to a more realistic accelerated degradation mode, the system can employ more targeted prediction algorithms, thus avoiding prediction lag or inaccuracy caused by using a single linear model in accelerated degradation scenarios. This mechanism of dynamically adjusting the prediction mode ensures that the prediction of the future state of isolation performance more accurately reflects the actual operating condition of the isolator, providing more reliable data support for subsequent decision-making.

[0042] This application significantly improves the accuracy of predicting the future performance status of isolators in industrial data signal isolation transmission, especially under complex operating conditions where isolation performance degrades rapidly. Compared to traditional methods that rely solely on a single prediction model, this application effectively avoids prediction bias caused by changes in performance degradation modes by dynamically identifying and switching prediction modes. This allows the central control system 220 to obtain health information on isolation performance earlier and more accurately. Consequently, it provides a more timely and reliable basis for early warning and maintenance scheduling, effectively reducing the risk of system failures caused by sudden declines in isolator performance, and ensuring the continuity of industrial production and the reliability of data transmission.

[0043] Furthermore, the step of determining whether the decreasing trend of isolation performance meets the preset accelerated decay condition based on the rate of change of the instantaneous descent rate includes: Based on the isolation performance status, select the currently applicable accelerated decay condition from the preset accelerated decay condition library; The rate of change of the instantaneous rate of decline is compared with the currently applicable accelerated decay conditions to determine whether the declining trend of the isolation performance meets the preset accelerated decay conditions.

[0044] Specifically, the isolation performance status refers to the current operational health of the isolator, which can be obtained by preprocessing and evaluating operating parameters (such as insulation resistance, leakage current, transmission delay, etc.) collected by the isolation gateway 210. The preset accelerated degradation condition library is a collection of various accelerated degradation conditions, pre-established based on different isolator types, operating environments, historical degradation data, and expert experience. Each condition corresponds to a specific isolation performance status or degradation mode. Selecting the currently applicable accelerated degradation condition means that the system dynamically matches the most suitable degradation condition from the condition library based on the isolator's current isolation performance status. For example, when the isolation performance status is in the initial, slight degradation stage, a more lenient accelerated degradation condition might be selected; while when the isolation performance status has significantly decreased, a more stringent condition will be selected. In practical applications, comparing the rate of change of the instantaneous degradation rate with the currently applicable accelerated degradation condition involves comparing the calculated rate of change of the instantaneous degradation rate with a threshold or functional relationship defined in the selected condition. If the rate of change exceeds the threshold or satisfies a specific functional relationship, the degradation trend of the isolation performance is considered to meet the accelerated degradation condition.

[0045] This application overcomes the limitations of traditional fixed-condition judgment by dynamically selecting applicable accelerated decay conditions based on the isolation performance status. Because the isolation performance status reflects the isolator's current actual health condition, the selected accelerated decay conditions can more accurately match the characteristics of the current degradation stage. By comparing the rate of change of the instantaneous decay rate with this dynamically selected condition, it is possible to more sensitively and accurately identify whether the isolation performance has entered the accelerated decay stage, thus avoiding misjudgments or omissions due to condition mismatches and providing a reliable basis for subsequent prediction mode switching. Compared to using a single or fixed condition for judgment, this application can dynamically adjust the judgment criteria according to the actual operating state of the isolator, enabling the prediction model to capture signs of accelerated decay in isolation performance earlier and more accurately. This helps the central control system 220 to take timely measures such as early warning, maintenance scheduling, or production task adjustment, effectively avoiding industrial data transmission interruptions or data anomalies caused by sudden deterioration of isolation performance, thereby improving the reliability and security of the entire industrial data signal isolation transmission control system.

[0046] In some preferred embodiments, it is assumed that the insulation resistance parameter of an industrial isolator continuously decreases during long-term operation. The isolation gateway 210 continuously collects and preprocesses these parameters and assesses the current isolation performance status. For example, when the isolation performance status is assessed as "mild aging," the system selects an accelerated degradation condition suitable for the "mild aging" state from a preset accelerated degradation condition library. This condition may be defined as the rate of change of the instantaneous rate of decay exceeding a certain small threshold. If the rate of change of the instantaneous rate of decay is subsequently detected to exceed this threshold, it is determined that the accelerated degradation condition is met. Further, if the isolation performance status deteriorates further to "moderate aging," the system dynamically switches to an accelerated degradation condition suitable for the "moderate aging" state. This condition may be defined as the rate of change of the instantaneous rate of decay exceeding a medium threshold. This dynamic selection mechanism ensures the flexibility and accuracy of the judgment criteria. For example, when the isolator operates in harsh environments such as high temperature and high humidity, its degradation rate may accelerate. In this case, the system will select a more stringent accelerated degradation condition based on environmental parameters and the isolation performance status to identify potential accelerated degradation risks earlier. In this way, the system can intelligently adjust the judgment logic according to the actual operating conditions and degree of degradation of the isolator, thereby achieving more refined isolation performance prediction and management.

[0047] Furthermore, based on the isolation performance health information, the steps to determine the source of the above abnormalities include: Receive transient penetration event reports from the aforementioned isolation gateway 210, the aforementioned transient penetration event reports indicating the transient suppression capability of the aforementioned isolation barrier; When data anomalies occur, the aforementioned data anomalies are cross-compared with the aforementioned transient penetration event reports, and combined with the aforementioned isolation performance health information, the source of the anomalies is determined.

[0048] Specifically, a transient penetration event report can be understood as a record of the isolation gateway 210's suppression effect on transient interference (such as pulses, voltage spikes, etc.) within a specific time period. Transient suppression capability refers to the isolation barrier's ability to effectively prevent transient high-voltage or high-frequency interference from penetrating and affecting downstream equipment. In practical applications, cross-comparison refers to comparing and analyzing information such as the time, type, and intensity of data anomalies with the changes or events in the isolation barrier's transient suppression capability recorded in the transient penetration event report. Furthermore, combining the aforementioned isolation performance health information means that, based on cross-comparison, it is also necessary to comprehensively consider the overall health status of the isolation performance (such as long-term degradation trends, current performance levels, etc.) for a more comprehensive judgment.

[0049] This application introduces a transient penetration event report, enabling the central control system 220 to obtain detailed information about the transient suppression capability of the isolation barrier when determining the source of data anomalies. When a data anomaly occurs, the anomaly event can be correlated temporally and compared with the transient penetration event report. For example, if the data anomaly occurs simultaneously with or is causally related to an event indicating insufficient transient suppression capability of the isolation barrier as indicated in the report, it can be preliminarily determined that the anomaly originates from the transient suppression failure of the isolation gateway 210. Conversely, if the transient suppression capability of the isolation barrier is normal during the anomaly, it can be inferred that the anomaly may be caused by other factors, such as external pulse interference directly affecting downstream devices. This mechanism effectively supplements the shortcomings of relying solely on long-term isolation performance health information for judgment, providing a more refined basis for anomaly attribution. By introducing a monitoring and reporting mechanism for the transient suppression capability of the isolation barrier, the central control system 220 no longer relies solely on macroscopic isolation performance health information, but can deeply analyze the impact of transient events on data transmission. This enables the system to accurately distinguish between anomalies caused by insufficient transient suppression capability of the isolation gateway 210 and anomalies caused by external interference directly affecting downstream equipment, thereby avoiding misjudgment and providing a more reliable decision-making basis for subsequent early warning, maintenance scheduling or production task adjustment and optimization, effectively improving the stability and reliability of the industrial control system.

[0050] Furthermore, the steps to determine the source of the anomaly include cross-referencing data anomalies with transient penetration event reports and combining this with isolation performance health information: If the transient penetration event report indicates that the isolation barrier is insufficient in transient suppression during the data anomaly, the data anomaly is attributed to the insufficient transient suppression of impulse interference by the isolation gateway 210. If the transient penetration event report indicates that the isolation barrier's transient suppression capability was normal during the data anomaly, the data anomaly is attributed to the impulse interference directly affecting downstream equipment.

[0051] Specifically, when the central control system 220 receives a transient penetration event report from the isolation gateway 210, and the report explicitly states that the isolation barrier failed to effectively suppress transient pulse interference when data anomalies occurred (i.e., its transient suppression capability was insufficient), the root cause of the data anomaly is considered to be that the isolation gateway 210 failed to fully perform its isolation function, causing pulse interference to penetrate the isolation barrier and affect the integrity of data transmission. If the transient penetration event report indicates that the isolation barrier's transient suppression capability was at a normal level during the data anomaly, meaning the isolation gateway 210 effectively suppressed transient interference as designed, but the data anomaly still occurs, it is determined that the data anomaly was not caused by a problem with the isolation performance of the isolation gateway 210, but rather by pulse interference directly affecting downstream devices, or by the interference source bypassing the isolation gateway 210 and directly impacting downstream devices.

[0052] This application, by introducing transient penetration event reports and making conditional judgments based on the transient suppression capability of the isolation barrier, can accurately distinguish whether data anomalies are caused by insufficient transient suppression capability of the isolation gateway 210 against pulse interference, or by external pulse interference directly affecting downstream equipment. This attribution logic based on transient suppression capability status makes the judgment of the source of anomalies more detailed and accurate. By evaluating the transient suppression capability of the isolation gateway 210 and combining it with the transient penetration event reports when data anomalies occur, it is possible to clearly distinguish whether the problem lies with the isolation gateway 210 itself (insufficient transient suppression) or with external environmental interference directly affecting downstream equipment. This accurate attribution capability helps the central control system 220 to take more targeted early warning, maintenance scheduling, or production task adjustment and optimization measures, avoiding misjudgments and resource waste, thereby improving the reliability and operating efficiency of the entire industrial control system.

[0053] Furthermore, it also includes: The steps for adjusting the reliability weights or control parameters of data from isolation gateway 210 based on the attribution of data anomalies.

[0054] Specifically, attributing data anomalies refers to using the aforementioned technical solutions to clearly trace the root cause of abnormal phenomena in industrial data signal transmission. For example, attributing it to insufficient transient suppression of pulse interference by the isolation gateway 210, or to the pulse interference directly affecting downstream equipment. Adjustment refers to dynamically modifying or optimizing relevant parameters in the system based on the identified source and nature of the anomaly. Reliability weight can be understood as a numerical index assigned to the data from the isolation gateway 210, reflecting the current reliability of the data in system decision-making or control. When data anomalies are attributed to problems with the isolation gateway 210 itself, its reliability weight may be reduced to minimize its negative impact on overall system judgment. Control parameters refer to various setpoints or algorithm parameters used in industrial control systems to adjust the operating state or process behavior of equipment. For example, when an anomaly is attributed to external interference directly affecting downstream equipment, the filtering parameters, response thresholds, or control algorithms of the relevant equipment can be adjusted to enhance its anti-interference capability or optimize its response mechanism to anomalies.

[0055] This application achieves dynamic adaptive optimization of the industrial data signal isolation transmission control system by immediately adjusting the reliability weight or control parameters of the data from the isolation gateway 210 after clearly identifying the cause of the data anomaly. When the anomaly is attributed to a problem with the isolation gateway 210 itself, reducing its data reliability weight can effectively avoid misjudgments or erroneous control commands caused by unreliable data, ensuring that the central control system 220 makes decisions based on more accurate information. Conversely, if the anomaly is attributed to external interference directly affecting downstream equipment, adjusting the control parameters can enhance the robustness of downstream equipment to interference or optimize its response strategy to anomalies. This allows for effective management and suppression of anomalies at the source or receiving end, enabling not only problem identification but also targeted, real-time countermeasures based on the nature of the problem, significantly improving the system's resilience and stability. After a data anomaly occurs and is accurately attributed, the system no longer passively records or issues warnings but actively adjusts the data processing strategy or control logic. This not only effectively reduces the negative impact of abnormal data on system decision-making and improves the accuracy and reliability of the control system, but also enhances the adaptability and anti-interference ability of the entire industrial control system to complex environmental changes through the optimization of control parameters, thereby ensuring the continuity, stability and safety of industrial production processes and avoiding potential production interruptions or equipment damage risks.

[0056] Furthermore, the step of selecting the currently applicable accelerated decay condition from the preset accelerated decay condition library based on the isolation performance status includes: Monitor the electrical parameters of the operating environment; Identify the degradation mode of the isolator based on the rate of change of the instantaneous decrease rate of the electrical parameters of the operating environment and the isolation performance parameters; Based on the isolation performance status and degradation mode, select the currently applicable accelerated degradation condition from the preset accelerated degradation condition library.

[0057] Specifically, monitoring the electrical parameters of the operating environment refers to the real-time or periodic collection and analysis of key electrical indicators in the working environment of the isolator. These electrical parameters may include, but are not limited to, ambient temperature, humidity, fluctuations in supply voltage, electromagnetic interference intensity, transient pulse voltage, and harmonic content. Monitoring these parameters aims to obtain detailed information about the actual operating environment of the isolator, which is crucial for understanding the external causes of isolator performance degradation. Identifying the isolator's degradation mode involves analyzing the correlation between the monitored electrical parameters of the operating environment and the rate of change of the instantaneous degradation rate of the isolation performance parameters to determine the specific failure or aging mode the isolator may currently be in. For example, degradation modes may include dielectric breakdown, material aging, loose connections, thermal stress fatigue, and electrochemical corrosion. Identifying degradation modes helps to more accurately pinpoint the root cause of isolation performance degradation, providing a more targeted basis for subsequent prediction and maintenance. In practical applications, when selecting the currently applicable accelerated degradation conditions, the isolation performance status and the identified degradation modes will be comprehensively considered. For example, if the isolation performance status shows moderate degradation and the degradation mode is identified as dielectric aging caused by high-frequency harmonic interference, an accelerated degradation condition specifically for this type of degradation mode can be selected from the accelerated degradation condition library to improve the accuracy of the prediction.

[0058] This application addresses the limitations of selecting accelerated degradation conditions solely based on isolation performance status by introducing monitoring of the electrical parameters of the operating environment and identification of isolator degradation modes. Specifically, the electrical parameters of the operating environment provide real-time information about the external environment in which the isolator operates, and this information is often a direct or indirect factor leading to a decline in isolation performance. By correlating these electrical parameters with the rate of change of the instantaneous decline rate of isolation performance parameters, a deeper understanding of the intrinsic mechanism of isolator performance degradation can be achieved, thereby identifying specific degradation modes. It is precisely because of the identification of degradation modes that the selection of accelerated degradation conditions can be combined with the isolation performance status and the specific causes of its decline, thus avoiding generalized selections and ensuring that the selected conditions more closely reflect the actual degradation pattern of the isolator. By considering the electrical parameters of the operating environment, external factors affecting isolator performance can be captured more comprehensively; by identifying the isolator's degradation modes, the intrinsic mechanism of performance degradation can be understood more deeply. Therefore, the selected accelerated degradation conditions can more accurately reflect the actual degradation pattern of the isolator, providing a more reliable basis for early warning and maintenance scheduling, effectively reducing false alarm rates, and extending the service life of the isolator.

[0059] Furthermore, based on the electrical parameters of the operating environment and the rate of change of the instantaneous rate of decrease of the isolation performance parameters, the steps for identifying the degradation mode of the isolator include: Extract transient pulse characteristics, harmonic component characteristics, and broadband noise energy distribution characteristics from the electrical parameters of the operating environment; The transient pulse characteristics, harmonic component characteristics, broadband noise energy distribution characteristics, and the rate of change of the instantaneous decrease rate of the isolation performance parameters are correlated to obtain the correlation results. Based on the correlation results, the isolator's degradation mode is identified by comparing it with the feature set of the preset interference source type and its corresponding degradation mode.

[0060] Among them, the electrical parameters of the operating environment refer to the electrical signal characteristics generated by various electromagnetic interferences or power quality problems encountered by the isolator in the actual working environment. Specifically, transient pulse characteristics can be understood as short-term high-amplitude voltage or current surges, such as spike signals caused by switching operations, lightning strikes, or electrostatic discharges; harmonic component characteristics refer to integer multiples of the frequency components in the power supply voltage or current waveform other than the fundamental frequency, which are usually caused by nonlinear loads; broadband noise energy distribution characteristics represent low-energy signals randomly distributed over a wide frequency range, such as noise generated by arc discharges or high-frequency switching power supplies. The extraction of these characteristics aims to comprehensively capture external electrical stresses that may lead to the degradation of isolator performance.

[0061] Furthermore, the transient pulse characteristics, harmonic component characteristics, broadband noise energy distribution characteristics, and the rate of change of the instantaneous degradation rate of isolation performance parameters are correlated to establish a quantitative relationship between the external electrical environment and the internal degradation process of the isolator. This correlation allows analysis of how specific types of electrical interference affect the degradation rate of isolation performance and its accelerating trend. For example, high-frequency transient pulses may cause dielectric breakdown or coupling capacitance degradation, while continuous harmonic components may cause thermal stress or material fatigue. Therefore, based on the correlation results, a comparison is made with a preset set of features for interference source types and their corresponding degradation modes to accurately identify the current degradation mode of the isolator. The preset set of features for interference source types and their corresponding degradation modes can be a database or rule set containing mapping relationships between different types of electrical interference (such as transient overvoltage, continuous overcurrent, high-frequency noise, etc.) and specific degradation mechanisms of the isolator (such as dielectric aging, insulation breakdown, coupling failure, etc.). By comparing the actual monitored correlation results with these preset modes, it is possible to determine which primary external stress is causing the isolator's degradation, thus providing an accurate basis for selecting subsequent accelerated degradation conditions.

[0062] This application employs a refined analysis of the electrical parameters of the operating environment to extract transient pulse characteristics, harmonic component characteristics, and broadband noise energy distribution characteristics. These characteristics are then correlated with the rate of change of the instantaneous degradation rate of isolation performance parameters, enabling a deeper understanding of the root causes of isolator performance degradation. Traditional degradation mode identification may rely solely on macroscopic changes in isolation performance parameters, neglecting the external factors leading to these changes. Because different types of electrical interference affect isolators through different mechanisms, their resulting degradation modes also differ. By identifying these specific electrical characteristics and combining them with performance degradation trends, a causal chain between external stress and internal degradation can be established. This allows for accurate identification of the current degradation mode experienced by the isolator from multiple potential degradation paths, such as insulation fatigue caused by frequent transient impacts or thermal aging caused by continuous harmonic stress.

[0063] Compared to judging degradation modes solely based on isolation performance status or changes in a single parameter, this application incorporates electrical parameters of the operating environment. This allows for a more precise identification of degradation modes, moving beyond a rough classification to pinpoint the specific internal degradation mechanism caused by external disturbances. This ability to accurately identify degradation modes directly enhances the accuracy of selecting applicable accelerated degradation conditions from a pre-defined library of conditions. Once a degradation mode is accurately identified, the most suitable accelerated degradation condition can be selected, significantly improving the accuracy of predicting future isolation performance. This provides a more reliable and refined decision-making basis for the central control system 220 to perform early warning, maintenance scheduling, or production task adjustment and optimization, effectively avoiding resource waste or potential production risks caused by misjudgments of degradation modes.

[0064] Furthermore, the transient penetration event report is generated by the isolation gateway 210, and the process by which the isolation gateway 210 generates the transient penetration event report includes: Monitor the transient response of the input and output signals of the isolation barrier; The transient suppression capability of the isolation barrier is determined based on the degree of attenuation or distortion of the transient response; Based on the determined transient suppression capability, a transient penetration event report is generated.

[0065] Specifically, monitoring the transient response of the input and output signals of the isolation barrier refers to the continuous real-time monitoring of signals on both sides of the isolation barrier by the isolation gateway 210, paying particular attention to rapid changes in the signal within a short period of time, such as voltage spikes and current pulses, to capture signal characteristics that may indicate a decline in isolation performance or the occurrence of transient interference events. Determining the transient suppression capability of the isolation barrier based on the attenuation or distortion of the transient response can be understood as the isolation gateway 210 quantifying the barrier's suppression effect on these transient interferences by analyzing the differences between the monitored input and output signals, such as amplitude attenuation, waveform distortion, and delay of the transient signal. For example, when a high-amplitude narrow pulse appears at the input, the output of the isolation barrier should exhibit a significantly attenuated pulse; the degree of attenuation reflects the transient suppression capability, objectively evaluating the isolation barrier's performance in responding to rapidly changing electrical interference.

[0066] In practical applications, based on the determined transient suppression capability, generating a transient penetration event report specifically involves the isolation gateway 210 encapsulating the transient suppression capability data obtained from the aforementioned assessment, combined with information such as timestamps, event types, and relevant parameters, into a structured report. For example, when the transient suppression capability is lower than a preset threshold, a report containing detailed transient response data and suppression capability assessment results can be triggered, providing the central control system 220 with direct evidence regarding the transient performance of the isolation barrier, enabling accurate attribution analysis when data anomalies occur.

[0067] This application actively monitors the transient response of the isolation barrier through the isolation gateway 210 and determines its transient suppression capability based on this, thereby generating a transient penetration event report. This allows the central control system 220 to obtain direct and real-time information about the transient performance of the isolation barrier when it receives data anomalies. By cross-referencing the data anomalies with this report, it can accurately determine whether the anomaly is caused by insufficient transient suppression capability of the isolation barrier, rather than relying solely on a macroscopic assessment of isolation performance health information. This improves the accuracy and reliability of anomaly source identification. The isolation gateway 210's ability to actively and in real-time assess and report the transient suppression capability of the isolation barrier provides the central control system 220 with more refined diagnostic criteria. This enables a more accurate identification of whether data anomalies during industrial data transmission originate from insufficient transient suppression of pulse interference by the isolation gateway 210 or from pulse interference directly affecting downstream equipment. This precise anomaly attribution capability significantly improves the efficiency and accuracy of system fault diagnosis, helping the central control system 220 to take timely and targeted early warning, maintenance scheduling, or production task adjustment and optimization measures, thereby ensuring the stable operation of the industrial control system and the reliability of data transmission.

[0068] Secondly, see Figure 2This application also discloses an industrial data signal isolation transmission control system, which includes: Isolation gateway 210 is used to collect isolator operating parameters, which reflect the performance degradation of the isolator, and to preprocess the operating parameters. It is also used to evaluate the isolation performance status based on the preprocessed operating parameters, analyze the changing trend of the isolation performance status, predict the future state of the isolation performance based on the isolation performance status and changing trend, generate isolation performance health information, and report the isolation performance health information to the central control system 220. The central control system 220 is used to receive isolation performance health information, analyze whether data anomalies occur, and determine the source of the anomaly based on the isolation performance health information when data anomalies occur. It is also used to perform early warning and maintenance scheduling or adjust and optimize production tasks based on the isolation performance health information.

[0069] This application constructs an intelligent architecture where an isolation gateway 210 and a central control system 220 work collaboratively to achieve dynamic and intelligent control of isolated transmission of industrial data signals. It can monitor, evaluate, and predict the performance status of industrial isolators in real time, and trace and handle data anomalies accordingly, ultimately achieving optimized control of the industrial production process. Through the isolation gateway 210, isolators' operating parameters are collected, preprocessed, their performance evaluated, trend analyzed, and future status predicted at the field layer, generating isolation performance health information. This provides the central control system 220 with a comprehensive and real-time view of the isolators' health status. The central control system 220 then utilizes this health information to not only promptly detect and determine the source of data anomalies but also proactively execute early warnings, maintenance scheduling, or production task adjustments and optimizations. This effectively solves the problems of performance degradation and difficulty in fault diagnosis in complex industrial environments using traditional static isolation methods, significantly improving the reliability and maintenance efficiency of industrial systems.

[0070] Traditional methods often rely on the isolator's "on / off" self-diagnostic function, which struggles to detect gradual degradation in isolation performance, leading to delayed and costly fault diagnosis. This application, through the isolation gateway 210's real-time, multi-dimensional acquisition and intelligent analysis of isolator operating parameters, can identify early micro-level trends in isolation performance decline and predict its future state, thus transforming traditional passive maintenance into proactive prevention. Furthermore, the central control system 220, combined with isolation performance health information, intelligently traces data anomalies, effectively solving the problem of locating "ghost" faults in traditional methods.

[0071] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for isolating and controlling industrial data signal transmission, characterized in that, include: The isolator's operating parameters are collected through the isolation gateway, and the operating parameters are preprocessed. The isolation gateway evaluates the isolation performance status based on the preprocessed operating parameters and analyzes the changing trend of the isolation performance status. The isolation gateway predicts the future state of the isolation performance based on the isolation performance status and the trend of change, and generates isolation performance health information. The isolation performance health information is reported to the central control system through the isolation gateway; The central control system analyzes whether data anomalies occur, and when data anomalies occur, it determines the source of the anomaly based on the isolation performance health information. The central control system performs early warning and maintenance scheduling, or adjusts and optimizes production tasks based on the isolation performance health information.

2. The industrial data signal isolation transmission control method according to claim 1, characterized in that, The step of predicting the future state of the isolation performance based on the current isolation performance state and the trend of change includes: Based on the isolation performance status and the changing trend, continuously calculate the instantaneous rate of decrease of the isolation performance parameters; Calculate the rate of change of the instantaneous rate of descent based on the instantaneous rate of descent; Based on the rate of change of the instantaneous rate of decrease, determine whether the decreasing trend of the isolation performance meets the preset accelerated decay condition; When the downward trend meets the accelerated decay condition, the prediction mode will be switched from linear decay mode to accelerated decay mode. Based on the isolation performance status, the trend of change, and the prediction pattern, predict the future state of the isolation performance.

3. The industrial data signal isolation transmission control method according to claim 2, characterized in that, The step of determining whether the decreasing trend of the isolation performance meets the preset accelerated decay condition based on the rate of change of the instantaneous decreasing rate includes: Based on the isolation performance status, select the currently applicable accelerated decay condition from the preset accelerated decay condition library; The rate of change of the instantaneous rate of decline is compared with the currently applicable accelerated decay condition to determine whether the declining trend of the isolation performance meets the preset accelerated decay condition.

4. The industrial data signal isolation transmission control method according to claim 1, characterized in that, The step of determining the source of the anomaly based on the isolation performance health information includes: Receive transient penetration event reports from the isolation gateway, the transient penetration event reports indicating the transient suppression capability of the isolation barrier; When data anomalies occur, the data anomalies are cross-compared with the transient penetration event reports, and the source of the anomalies is determined by combining the isolation performance health information.

5. The industrial data signal isolation transmission control method according to claim 4, characterized in that, The step of cross-referencing the data anomaly with the transient penetration event report and combining it with the isolation performance health information to determine the source of the anomaly includes: If the transient penetration event report indicates that the isolation barrier has insufficient transient suppression capability during the occurrence of the data anomaly, the data anomaly is attributed to the isolation gateway's insufficient transient suppression of impulse interference; If the transient penetration event report indicates that the isolation barrier has normal transient suppression capability during the data anomaly, the data anomaly is attributed to impulse interference acting directly on downstream devices.

6. The industrial data signal isolation transmission control method according to claim 5, characterized in that, The method further includes the following steps: Based on the attribution of data anomalies, adjust the reliability weights or control parameters of the data from the isolation gateway.

7. The industrial data signal isolation transmission control method according to claim 3, characterized in that, The step of selecting the currently applicable accelerated decay condition from a preset accelerated decay condition library based on the isolation performance status includes: Monitor the electrical parameters of the operating environment; The degradation mode of the isolator is identified based on the electrical parameters of the operating environment and the rate of change of the instantaneous decrease rate of the isolation performance parameters. Based on the isolation performance status and the degradation mode, select the currently applicable accelerated degradation condition from the preset accelerated degradation condition library.

8. The industrial data signal isolation transmission control method according to claim 7, characterized in that, The step of identifying the degradation mode of the isolator based on the electrical parameters of the operating environment and the rate of change of the instantaneous decrease rate of the isolation performance parameters includes: Transient pulse characteristics, harmonic component characteristics, and broadband noise energy distribution characteristics are extracted from the electrical parameters of the operating environment. The transient pulse characteristics, harmonic component characteristics, broadband noise energy distribution characteristics, and the rate of change of the instantaneous decrease rate of the isolation performance parameter are correlated to obtain the correlation results. Based on the correlation results, the degradation mode of the isolator is identified by comparing it with the feature set of the preset interference source type and its corresponding degradation mode.

9. The industrial data signal isolation transmission control method according to claim 4, characterized in that, The transient penetration event report is generated by the isolation gateway, and the process by which the isolation gateway generates the transient penetration event report includes: Monitor the transient response of the input and output signals of the isolation barrier; The transient suppression capability of the isolation barrier is determined based on the degree of attenuation or distortion of the transient response. Based on the determined transient suppression capability, a transient penetration event report is generated.

10. An industrial data signal isolation transmission control system, characterized in that, The system includes: An isolation gateway is used to collect isolator operating parameters, which reflect the performance degradation of the isolator, and to preprocess the operating parameters. It is also used to evaluate the isolation performance status based on the preprocessed operating parameters, analyze the changing trend of the isolation performance status, predict the future status of the isolation performance based on the isolation performance status and the changing trend, generate isolation performance health information, and report the isolation performance health information to the central control system. The central control system is used to receive the isolation performance health information, analyze whether data anomalies occur, and determine the source of the anomaly based on the isolation performance health information when data anomalies occur. It is also used to perform early warning and maintenance scheduling, or adjust and optimize production tasks based on the isolation performance health information.

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