Control system function safety quantitative evaluation system based on digital twinning technology

Abstract

The invention relates to the technical field of data analysis, and particularly discloses a control system function safety quantitative evaluation system based on a digital twinborn technology, which comprises an evaluation module, an analysis module and an optimization module, and is characterized in that the evaluation module is used for establishing a digital twinborn model of a control system; extracting operation data of the control system based on the digital twin model, and obtaining a function safety quantitative score of the control system based on the operation data; the analysis module is used for acquiring sub-curves on the fitted curve and screening reference points according to the sub-curves; and the optimization module is used for adjusting the maintenance period according to the distribution condition of the reference points. According to the invention, dynamic monitoring of the safety state of the control system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a control system functional safety quantitative evaluation system based on digital twin technology. Background Art

[0002] Digital twin makes full use of data such as physical models, sensors, and operation history, integrates the simulation processes of multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, and completes the mapping in the virtual space, so as to reflect the entire life cycle process of the corresponding physical equipment.

[0003] In the practice of traditional control system functional safety assessment, single assessments are mostly carried out: through methods such as hazard analysis, fault tree, and quantitative probability calculation, the safety integrity level or probability index is calculated, and then corresponding treatment measures are taken accordingly. However, this method only stays at a certain static time point and lacks the dynamic mining and utilization of the long-term operation history data of the system. Summary of the Invention

[0004] The purpose of the present invention is to provide a control system functional safety quantitative evaluation system based on digital twin technology to solve the following technical problems: In the practice of traditional control system functional safety assessment, single assessments are mostly carried out: through methods such as hazard analysis, fault tree, and quantitative probability calculation, the safety integrity level or probability index is calculated, and then corresponding treatment measures are taken accordingly. However, this method only stays at a certain static time point and lacks the dynamic mining and utilization of the long-term operation history data of the system.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A control system functional safety quantitative evaluation system based on digital twin technology includes an evaluation module, an analysis module, and an optimization module. Specifically: Evaluation module: Establish a digital twin model of the control system, extract the operation data of the control system based on the digital twin model, and obtain the functional safety quantitative score of the control system based on the operation data; Analysis module: Periodically obtain the functional safety quantitative score within a preset monitoring period, generate a fitting curve, obtain the monotonically decreasing part of the fitting curve, denoted as a sub-curve. When the length of the domain of the sub-curve is greater than a preset duration threshold T and the functional safety quantitative score corresponding to the starting point of the sub-curve is less than a preset score threshold, take the starting point of the sub-curve as a reference point; Optimization module: Sort the reference points in chronological order, calculate the time interval corresponding to the reference points at two adjacent sorting positions, denoted as the target interval; Statistically determine the difference between the target interval and a preset time interval, and denote the difference less than the preset difference threshold as an abnormal difference. Statistically determine the proportion C of the abnormal difference in the difference, and calculate a new maintenance cycle JC = (1 - C) / jc, where jc represents the preset maintenance cycle.

[0006] As a further aspect of the present invention: The process of obtaining the functional safety quantification score of the control system based on the operation data includes: Normalize the operation data into positive data, where the positive data is positively correlated with the functional safety of the control system; Determine the weights of the positive data based on Sobol sensitivity analysis, and perform weighted summation on the positive data to obtain the functional safety quantification score of the control system.

[0007] As a further aspect of the present invention: The process of generating a fitting curve includes: Generate coordinate points (t b , P b ), where P b represents the functional safety quantification score obtained at the b-th time, and t b represents the time point when the functional safety quantification score is obtained at the b-th time. Fit the coordinate points to obtain a fitting curve f(t), where t represents time.

[0008] As a further aspect of the present invention: The process of obtaining a reference point further includes: Obtain the time point T1 corresponding to the end point of the sub-curve Z1. When the point A corresponding to the time point T1 - T is on the sub-curve Z1 and the functional safety quantification score corresponding to the point A is less than a preset value, use the point A as the reference point.

[0009] As a further aspect of the present invention: In the optimization module, when the maintenance cycle JC ≤ T2, set the maintenance cycle JC = T2, where T2 represents the shortest length of the preset maintenance cycle.

[0010] As a further aspect of the present invention: In the analysis module, the following steps are further included: When the number of reference points is less than a preset number threshold, do not adjust the maintenance cycle.

[0011] Advantages of the present invention: Compared with the prior art: 1) Real-time dynamic evaluation based on the digital twin model can continuously collect and quantify functional safety indicators throughout the entire system operation process, breaking through the limitations of traditional single static evaluation and realizing refined and real-time monitoring of the safety status of the control system. This invention can timely reflect the potential risks of the system through in-depth mining of operating data, shorten the response time of safety blind spots, improve the accuracy of fault identification, provide a scientific basis for operation and maintenance decisions, and significantly enhance the system's early warning capabilities for sudden anomalies, thereby effectively reducing the probability of safety accidents and improving overall safety and reliability; 2) Use the analysis module to generate a fitting curve and extract a monotonically decreasing sub-curve as a reference point. By presetting the duration threshold and the quantitative score judgment, the starting point of functional safety decline can be accurately located to achieve early capture of the downward trend of system safety performance. This method can periodically track potential fault hazards, avoid the subjectivity of manual experience judgment, and improve prediction accuracy; at the same time, by monitoring the evolution of historical curves, it can guide operation and maintenance personnel to optimize maintenance strategies in a targeted manner, reduce human intervention and blind maintenance, and not only improve maintenance efficiency, but also effectively control maintenance costs; 3) The optimization module automatically calculates the new maintenance cycle JC based on the difference between the reference point time interval and the preset maintenance cycle, and realizes dynamic adaptive adjustment of the maintenance strategy. This mechanism can automatically correct the maintenance frequency according to the actual operation performance of the system, so that the maintenance cycle is neither too frequent to waste resources nor too long to accumulate safety risks; in addition, the maintenance frequency is adjusted by statistically analyzing the abnormal difference ratio C, taking into account both system availability and operational safety, minimizing downtime losses, and continuously improving the maintenance plan during long-term operation, avoiding sudden downtime caused by "over-guarantee operation" and preventing "excessive maintenance" from wasting resources, achieving simultaneous optimization of maintenance costs and safety and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings.

[0013] Figure 1 It is a structural schematic diagram of a control system functional safety quantitative evaluation system based on digital twin technology of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] See also Figure 1As shown in the figure, the present invention is a control system functional safety quantification evaluation system based on digital twin technology, including an evaluation module, an analysis module, and an optimization module. Specifically: Evaluation module: Establish a digital twin model of the control system, extract the operation data of the control system based on the digital twin model, and obtain the functional safety quantification score of the control system based on the operation data; In a preferred embodiment of the present invention, the process of obtaining the functional safety quantification score of the control system based on the operation data includes: Normalize the operation data into positive data, and the positive data is positively correlated with the functional safety of the control system; Determine the weight of the positive data based on Sobol sensitivity analysis, and perform weighted summation on the positive data to obtain the functional safety quantification score of the control system; It should be noted that the entire digital twin construction process takes an object-oriented modeling language (such as Modelica) as the carrier and performs virtual mapping according to the three-level structure of the device layer, the sensing layer, and the control logic layer. First, call standardized components such as gates, pumping stations, liquid level-flow sensors, and PID regulators in the Modelica environment, and complete the model framework construction based on physical equations and parametric interfaces to achieve the organic coupling between subsystems. Secondly, segment the measured data of the past 90 days (including upstream liquid level, downstream flow, valve opening change curve, etc.) and input it into the model, and optimize and calibrate the key parameters through parameter identification algorithms (such as the least squares method or adaptive gradient descent). The identification process relies on automated script-driven iterative solution. After each simulation, calculate the mean square error between the simulation output and the measured sequence, and feedback it to the optimization module to adjust the parameter search step size and direction until the global error converges within 2%, ensuring that the model can accurately reproduce the real system dynamic response under different working conditions; In the model verification stage, use the hold-out method to divide the historical data into a training set and a verification set. While the error on the verification set is maintained within 2%, it is also necessary to meet the stability requirements under fault injection and extreme working conditions. By comparing the simulation results with the measured fluctuation curve, the ability of the model to capture sudden changes (such as high-flow impact or rapid liquid level change) can be further evaluated. When the model shows a high degree of consistency under normal and abnormal working conditions, its high-fidelity characteristics can be confirmed, providing a reliable basis for subsequent functional safety quantification evaluation. This high-fidelity digital twin model can not only output real-time safety indicators, but also be used to carry out "what-if" scenario simulations to simulate the impact of different maintenance strategies or working condition changes on system safety, thereby supporting decision-making optimization; Subsequently, n (the specific value can be freely set) key process variables (such as upstream water level, gate opening, pump outlet pressure, etc.) are captured from the 1Hz running data stream of the twin for real-time playback. Each dimension is normalized to 0-1 within the interval [lower limit, upper limit], and the direction is unified: if the increase of the variable represents an improvement in safety, it remains positive; otherwise, its opposite value is taken (it can also be other methods, which are not restricted here), so that all data is monotonically positively correlated with functional safety. Immediately afterwards, the Sobol global sensitivity analysis is performed on the normalized matrix, and the first-order and total effect indices are extracted and normalized into a weight vector. For example, the upstream water level W1 = 0.21, the gate opening W2 = 0.15, the pump outlet pressure W3 = 0.09, etc., and the sum of all weights is 1. Finally, the weighted sum of the positive data in the same monitoring period is calculated according to the corresponding weights and mapped to the 0-100 interval to obtain the current functional safety quantification score, such as 83.6 points. The lower the value, the smaller the safety margin, thus providing a single, comparable, and traceable measurement benchmark for subsequent trend analysis and maintenance optimization.

[0016] Analysis module: During the preset monitoring period, the functional safety quantification score is periodically obtained, a fitting curve is generated, and the monotonically decreasing part of the fitting curve is obtained and denoted as a sub-curve. When the length of the domain of the sub-curve is greater than the preset time threshold T and the functional safety quantification score corresponding to the starting point of the sub-curve is less than the preset score threshold, the starting point of the sub-curve is used as a reference point. Another preferred embodiment of the present invention, the process of generating the fitting curve includes: Generating coordinate points (t b , P b ), where P b represents the functional safety quantification score obtained in the b-th acquisition, and t b represents the time point of the b-th acquisition of the functional safety quantification score. The coordinate points are fitted to obtain a fitting curve f(t), where t represents time. Another preferred embodiment of the present invention, the process of obtaining the reference point further includes: Obtaining the time point T1 corresponding to the end point of the sub-curve Z1. When the point A corresponding to the time point T1 - T is on the sub-curve Z1 and the functional safety quantification score corresponding to the point A is less than the preset value, the point A is used as a reference point. It is worth noting that first, a monitoring period of, for example, 24h and a sampling period of 60s are set. The functional safety quantification score is read from the evaluation module at regular intervals to form a discrete point sequence (tb, Pb). Subsequently, cubic splines are used to perform least-squares fitting on all coordinate points to generate a continuous function f(t), and the sub-curve and the reference point are extracted. Exemplarily, a period of pump station operation data shows a continuous decline during the period from 10:15 to 11:05. Δt = 50 min > T, and the score at t1 = 10:15 is 72.3 < 75. Then 10:15 is recorded as a reference point; if it still falls within the monotonically decreasing interval when tracing back T minutes from the end point T1 of the same sub-curve, and the score at this point is also lower than 75, then this point is also supplemented as a reference point. Through this process of "sampling - fitting - segmentation - threshold filtering", the system can, without manual intervention, capture in real time the sections where functional safety deteriorates continuously and has a significant time span, output accurate time coordinates, and provide a reliable basis for optimizing the subsequent maintenance cycle.

[0017] Optimization module: Sort the reference points in the order of the time axis, calculate the time interval corresponding to the reference points at two adjacent sorted positions, and denote it as the target interval; Statistically analyze the difference between the target interval and the preset time interval, denote the difference less than the preset difference threshold as the abnormal difference, statistically analyze the proportion C of the abnormal difference in the difference, and calculate the new maintenance cycle JC = (1 - C) / jc, where jc represents the preset maintenance cycle; In a preferred embodiment of the present invention, in the optimization module, when the maintenance cycle JC ≤ T2, set the maintenance cycle JC = T2, where T2 represents the shortest length of the preset maintenance cycle; It should be noted that in the analysis module, the following steps are further included: When the number of the reference points is less than the preset number threshold, do not adjust the maintenance cycle.

[0018] It can be understood that by arranging the reference points in sequence, calculating the adjacent time intervals and comparing them with the expected intervals, the system can gain real-time insight into the speed change of functional safety deterioration; if the proportion of short intervals increases, it indicates that the risk accumulation accelerates, and the algorithm will automatically compress the new maintenance cycle, otherwise it will maintain or moderately extend it. At the same time, the shortest cycle constraint and the reference point quantity threshold jointly suppress the misjudgment caused by occasional noise, prevent the waste of resources due to overly frequent maintenance, and avoid sudden shutdowns caused by maintenance lags. This mechanism enables the maintenance decision to dynamically expand and contract with the health status, establishing an adaptive balance between reliability and economy, and ultimately significantly improving the safety margin and full-life efficiency of the control system.

[0019] The above formulas are all calculated by taking the numerical values after dimensionless, and the formulas are obtained by software simulation of collecting a large amount of data to approximate the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0020] The above has described in detail an embodiment of the present invention, but the above content is only a preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A control system functional safety quantitative evaluation system based on digital twin technology, characterized in that: It includes evaluation module, analysis module and optimization module, specifically: Evaluation module: establishing a digital twin model of the control system, extracting operating data of the control system based on the digital twin model, and obtaining a functional safety quantitative score of the control system based on the operating data; Analysis module: within a preset monitoring period, periodically obtain the functional safety quantitative score, generate a fitting curve, obtain the monotonically decreasing part of the fitting curve, record it as a sub-curve, and when the length of the definition domain of the sub-curve is greater than the preset time threshold T and the functional safety quantitative score corresponding to the starting point of the sub-curve is less than the preset score threshold, take the starting point of the sub-curve as a reference point; Optimization module: sorting the reference points according to the time axis order, calculating the time interval corresponding to the reference points at two adjacent sorting positions, and recording it as the target interval; The difference between the target interval and the preset time interval is counted, and the difference less than the preset difference threshold is recorded as an abnormal difference. The proportion C of the abnormal difference to the difference is counted, and a new maintenance cycle JC=(1-C) / jc is calculated, where jc represents the preset maintenance cycle.

2. According to claim 1, a control system functional safety quantitative evaluation system based on digital twin technology is characterized in that: The process of obtaining a quantitative score of functional safety of the control system based on the operating data includes: normalizing the operating data into positive data, wherein the positive data is positively correlated with the functional safety of the control system; The weight of the positive data is determined based on Sobol sensitivity analysis, and the functional safety quantitative score of the control system is obtained by weighted summing of the positive data.

3. According to claim 1, a control system functional safety quantitative evaluation system based on digital twin technology is characterized in that: The process of generating a fitted curve includes: Generate coordinate points (t b , P b ), P b represents the functional safety quantitative score obtained for the bth time, t b represents the time point at which the functional safety quantitative score is obtained for the bth time, and the coordinate point is fitted to obtain a fitting curve f(t), where t represents time.

4. According to claim 1, a control system functional safety quantitative evaluation system based on digital twin technology is characterized in that: The process of obtaining reference points also includes: The time point T1 corresponding to the end point of the sub-curve Z1 is obtained. When the point A corresponding to the time point T1-T is on the sub-curve Z1 and the functional safety quantization score corresponding to the point A is less than a preset value, the point A is used as a reference point.

5. According to claim 1, a control system functional safety quantitative evaluation system based on digital twin technology is characterized in that: In the optimization module, when the maintenance cycle JC≤T2, the maintenance cycle JC=T2, where T2 represents the preset shortest length of the maintenance cycle.

6. According to claim 1, a control system functional safety quantitative evaluation system based on digital twin technology is characterized in that: The analysis module further includes the following steps: When the number of the reference points is less than a preset number threshold, the maintenance cycle is not adjusted.

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