Thermal power plant DCS fault prediction and diagnosis method and system

Abstract

The invention discloses a thermal power plant DCS fault prediction and diagnosis method and system, and relates to the technical field of fault prediction and diagnosis, and the method comprises the steps: obtaining equipment information and operation parameter reference values, and generating a to-be-detected equipment list; target equipment is determined based on the importance degree, a communication channel is established to collect real-time data, a parameter fluctuation characteristic matrix is constructed, a reference value is dynamically calibrated, and a calibration parameter set is generated; historical fault feature data are collected, and a fault feature library is established; generating a state vector matrix based on the calibration parameter set and the real-time data, and calculating an early warning index in combination with the fault feature library; calculating a risk coefficient during monitoring, dynamically optimizing an early warning threshold value, and completing early warning configuration; on the basis, diagnosis is executed, a standardized diagnosis process is generated, and a diagnosis knowledge base is formed; and when an exception occurs, triggering hierarchical rollback and updating the knowledge base to realize continuous optimization. The system comprises a master control module, a configuration module, a prediction diagnosis module, a transmission module and a display module.

Description

Technical Field

[0001] This invention relates to the field of fault prediction and diagnosis technology, specifically to a method and system for fault prediction and diagnosis in a thermal power plant DCS. Background Technology

[0002] Distributed Control System (DCS) fault monitoring and diagnosis methods and systems aim to improve the operational safety and intelligent operation and maintenance level of industrial equipment. In practical applications, DCS equipment is widely distributed in key industries such as power, petrochemical, and metallurgy, and its operating status directly affects the stability and safety of production processes. By collecting equipment operating parameters, performing dynamic calibration, condition modeling, and risk assessment, potential anomalies can be detected in a timely manner under complex operating conditions. Standardized diagnostic processes enable rapid and accurate fault identification and handling, thereby reducing equipment downtime and maintenance costs.

[0003] However, most existing DCS fault monitoring technologies rely on static thresholds or single-parameter indicators, making it difficult to effectively utilize the fluctuation characteristics of operating parameters and historical fault feature databases. This results in insufficient early warning sensitivity and a high false alarm rate. Furthermore, the lack of a standardized diagnostic process based on diagnostic model optimization hinders consistency and adaptability in diagnosis, thus limiting the efficiency of fault early warning configuration and diagnosis. Therefore, a method and system for DCS fault prediction and diagnosis in thermal power plants is needed to address these issues. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a method and system for DCS fault prediction and diagnosis in thermal power plants. This solution overcomes the limitations of existing DCS fault monitoring technologies, which largely rely on static thresholds or single-parameter indicators. These technologies struggle to effectively utilize the fluctuation characteristics of operating parameters and historical fault feature databases, resulting in insufficient early warning sensitivity and a high false alarm rate. Furthermore, the lack of a standardized diagnostic process based on diagnostic model optimization hinders consistency and adaptability in diagnosis, thus limiting the efficiency of fault early warning configuration and diagnosis.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for predicting and diagnosing DCS faults in thermal power plants includes: Obtain information and baseline values ​​of operating parameters for the DCS devices to be tested, and generate a list of DCS devices to be tested; Based on the list of DCS devices to be tested, the target DCS devices are identified, a communication channel is established to collect the operating data of the target DCS devices, and a parameter fluctuation characteristic matrix is ​​constructed. Based on the parameter fluctuation characteristic matrix, the dynamic calibration coefficient matrix is ​​obtained, and the reference values ​​of the operating parameters are dynamically calibrated based on the dynamic calibration coefficient matrix to generate a calibration parameter set; Collect historical fault characteristic data of the target DCS equipment and establish a historical fault characteristic database; Acquire real-time operating data of the target DCS device, and generate a real-time state vector matrix based on the calibration parameter set and real-time operating data; A fault early warning index is constructed based on the real-time state vector matrix and the historical fault feature library; Anomaly monitoring of target DCS equipment is performed based on the fault early warning index, and the fault risk coefficient is calculated during the monitoring process. Optimize the threshold of the fault warning index based on the fault risk coefficient, and complete the fault warning configuration; After completing the fault warning configuration, the real-time diagnostic program is invoked to perform fault diagnosis on the target DCS equipment, generating a standardized diagnostic process. The standardized diagnostic process is then optimized using a diagnostic process optimization model to form a standardized diagnostic process template, which is used for fault monitoring and diagnosis of the DCS equipment under test.

[0006] In an optional embodiment, the step of determining the target DCS device based on the list of DCS devices to be detected, establishing a communication channel to collect the operating data of the target DCS device, and constructing a parameter fluctuation feature matrix specifically includes: Acquire information on the DCS equipment to be tested and benchmark values ​​of operating parameters, and establish a database of information on the DCS equipment to be tested and a database of benchmark values ​​of operating parameters. The equipment information database includes information related to equipment number, equipment model, operating location, role in the power generation production chain, redundancy, and safety impact level. Extract the baseline values ​​from the operating parameter baseline value library, and associate them with the equipment information library through the equipment number to obtain the equipment information in the equipment information library; Based on the baseline values ​​of equipment operating parameters and equipment information, the importance of the equipment is calculated according to its role, redundancy and safety impact in the power generation production chain. The equipment information database is sorted based on equipment importance to generate a list of DCS equipment to be tested; Based on the list of DCS devices to be tested, the device ranked first is identified as the target DCS device. Obtain the communication address of the target DCS device and establish a real-time data transmission channel to acquire device operation data; The fluctuation characteristic values ​​of each parameter within the monitoring period are extracted from the equipment operation data, including parameter deviation value, fluctuation standard deviation, maximum fluctuation amplitude and number of consecutive abnormal fluctuations, and a parameter fluctuation characteristic matrix is ​​constructed using the fluctuation characteristic values ​​as elements. The formula for expressing the parameter fluctuation characteristic matrix is ​​as follows: In the formula, Represents the parameter fluctuation characteristic matrix. For parameters Deviation value within the monitoring period, For parameters The standard deviation of fluctuation during the monitoring period, For parameters The maximum fluctuation range during the monitoring period, For parameters Number of consecutive abnormal fluctuations within the monitoring period This represents the total number of running parameters.

[0007] In an optional embodiment, the step of obtaining a dynamic calibration coefficient matrix based on the parameter fluctuation characteristic matrix, and dynamically calibrating the operating parameter reference values ​​based on the dynamic calibration coefficient matrix to generate a calibration parameter set specifically includes: Based on the parameter fluctuation feature matrix, the deviation value, standard deviation and maximum fluctuation amplitude of each operating parameter of the target DCS equipment are obtained; Using the parameter deviation value as the first optimization benchmark value, the deviation calibration coefficient is calculated based on the average deviation of the operating parameters within the monitoring period. The stability calibration coefficient is calculated using the standard deviation of parameter fluctuation as the second tuning benchmark. The maximum fluctuation range of the parameter is used as the third optimization benchmark value to constrain the upper and lower limits of the benchmark value of the operating parameter, so as to ensure that the calibration parameter is within a safe range; Integrate the deviation calibration coefficients and the stability calibration coefficients to obtain the dynamic calibration coefficient matrix; The reference values ​​of the operating parameters are corrected item by item using the dynamic calibration coefficient matrix to obtain preliminary calibration values, which are then trimmed within the upper and lower limits to generate a calibration parameter set.

[0008] In an optional embodiment, the step of monitoring the target DCS equipment for anomalies based on a fault early warning index and calculating a fault risk coefficient during the monitoring process specifically includes: Obtain historical operation data from the target DCS device's historical operation records and fault logs; The acquired historical operating data is preprocessed, and the preprocessed historical operating data is classified according to different fault types to generate a historical fault feature library. Acquire real-time operating data of the target DCS device, and standardize the real-time operating data based on the calibration parameter set to obtain standardized parameter values; The standardized parameter values ​​are arranged in chronological order to form a real-time state vector; The real-time state vectors of continuous time windows are combined to generate a real-time state vector matrix; Based on the real-time state vector matrix and its time series, the deviation, fluctuation amplitude and trend change rate of the operating parameters are extracted and synthesized into a comprehensive early warning score according to the preset weights. The comprehensive early warning score is matched and compared with the historical fault feature database. The comprehensive early warning score is then corrected based on the matching results to obtain the fault early warning index. The formula for calculating the real-time state vector matrix is ​​as follows: In the formula, Indicates the first Each running parameter at a given time point Standardized values, This represents the total number of running parameters. This represents the number of sampling points within the time window. The formula for calculating the fault warning index is as follows: In the formula, This is a fault warning index. To determine the comprehensive early warning score, The similarity factor is the historical fault characteristics. For abnormal fluctuation frequency, , , These are the weighting coefficients.

[0009] In an optional embodiment, optimizing the threshold parameter of the fault warning index based on the fault risk coefficient and completing the fault warning configuration specifically includes: Anomaly monitoring of the target DCS equipment is performed based on the fault early warning index, and monitoring data is obtained by combining the anomaly duration information within the monitoring time window. Based on the monitoring data, obtain the failure risk coefficient of the target DCS equipment in the current time window; The threshold of the fault early warning index is dynamically adjusted based on the fault risk coefficient. The adjusted threshold parameters are applied to the real-time early warning mechanism of the target DCS equipment to complete the fault early warning configuration. The formula for calculating the failure risk coefficient is as follows: In the formula, For a moment The fault warning index, and These are the minimum and maximum values ​​of the warning index, respectively. The rate of change of the early warning index. and These are the weighting coefficients.

[0010] In an optional embodiment, after completing the fault warning configuration, the step of calling a real-time diagnostic program to perform fault diagnosis on the target DCS device, generating a standardized diagnostic process, and optimizing the standardized diagnostic process using a diagnostic process optimization model to form a standardized diagnostic process template for fault monitoring and diagnosis of the DCS device under test, specifically includes: Based on the fault warning configuration, the real-time diagnostic program based on real-time operation data input is invoked to perform fault diagnosis on the operating status of the target DCS equipment and obtain the fault diagnosis results. Based on the fault diagnosis results, a fault diagnosis knowledge base is constructed, and the fault diagnosis results are classified according to the fault type. The results are standardized through format unification to generate a standardized diagnosis process. Collect historical fault cases, operation event records, and power plant standard operation and maintenance procedures, and merge the data with the fault diagnosis knowledge base to form the basic data for the diagnosis process; Data cleaning is performed on the basic data of the diagnostic process to remove redundant records and abnormal data, and data standardization is performed through numerical normalization to extract key diagnostic feature information that represents the correlation between operating status and fault mode. Based on key diagnostic feature information, a diagnostic process optimization model is obtained by training a machine learning model. The standardized diagnostic process is optimized by using a diagnostic process optimization model to generate a standardized diagnostic process template. Based on a standardized diagnostic process template, diagnostic tasks are assigned to the DCS devices to be tested in the list of DCS devices to be tested, and diagnostic execution configurations are generated. Based on the diagnostic execution configuration, the batch monitoring program is launched to perform real-time fault warnings and diagnoses on the DCS equipment under test, and the monitoring feedback is written back to the fault diagnosis knowledge base. When the monitoring feedback results deviate from the standardized diagnostic process template and an anomaly occurs, the anomaly is classified into minor, severe, and fatal levels according to the preset anomaly level classification rules, and a graded rollback mechanism is triggered based on the corresponding level to obtain the rollback result. The rollback results are recorded and updated to the fault diagnosis knowledge base to support the continuous optimization and self-evolution of the diagnosis process.

[0011] Furthermore, a DCS remote automated fault prediction and diagnosis system for thermal power plants, used to implement the prediction and diagnosis methods described above, includes: The main control module is used to receive data transmitted by the configuration module, monitoring module, diagnostic module and display module through the transmission module, process and analyze the received data, and control the operation of the configuration module, monitoring module, diagnostic module and display module according to the processing results; The configuration module is used to configure the initial parameters and establish communication channels for the target devices in the list of DCS devices to be tested, so as to ensure the accurate collection of monitoring data. The prediction and diagnosis module is used to collect real-time operating data of the target DCS equipment, extract parameter fluctuation characteristics, construct state vector matrix, assess and predict fault risks, generate fault warnings, diagnose faults, and perform graded rollback. The transmission module is used for data transmission between the main control module and the configuration module, monitoring module, diagnostic module and display module; The display module is used to display the processing results of the main control module, the configuration process and results of the configuration module, the real-time monitoring results of the monitoring module, and the fault diagnosis results of the diagnostic module.

[0012] In an optional embodiment, the main control module includes: The data receiving unit is used to receive real-time operating data, historical fault characteristic data, parameter fluctuation characteristic matrix, real-time status vector matrix and fault early warning index of the target DCS equipment. The data processing unit is used to establish a target DCS equipment information database and a historical fault feature database, analyze and calculate the fault warning index and state vector matrix, generate a fault risk coefficient, and optimize the fault warning threshold based on the fault risk coefficient. The evaluation unit is used to determine the anomaly level of the target DCS equipment based on the fault risk coefficient and to feed the anomaly information back to the diagnostic module. The control unit is used to control the configuration module, monitoring module, diagnostic module and display module according to the analysis results.

[0013] In an optional embodiment, the configuration module includes: The device configuration unit is used to establish a communication channel based on the target DCS device information, initialize the device operating parameters, and perform dynamic calibration to generate a calibration parameter set. The parameter configuration unit is used to configure the parameters of each device in the DCS device list to be tested based on the dynamic calibration parameter set and device importance, and to ensure that the configuration complies with the safety constraints.

[0014] In an optional embodiment, the prediction and diagnosis module includes: The monitoring unit is used to collect the operating data of the target DCS equipment in real time and construct a parameter fluctuation feature matrix and a real-time state vector matrix; A fault assessment unit is used to calculate a fault risk coefficient during the monitoring process and predict faults based on the fault risk coefficient. The early warning unit is used to adjust the threshold of the fault early warning index according to the risk coefficient, and complete the fault early warning configuration based on the adjusted threshold parameter. The diagnostic unit is used to perform fault diagnosis on the target DCS equipment based on the fault warning configuration, generate a standardized diagnostic process template, and trigger a graded rollback mechanism according to the anomaly level to update the rollback results to the fault diagnosis knowledge base, thereby realizing continuous optimization of the diagnostic process.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This solution proposes a method and system for DCS fault prediction and diagnosis in thermal power plants. By analyzing and dynamically calibrating the real-time operating data of the target DCS equipment, a parameter fluctuation feature matrix is ​​established to comprehensively evaluate the equipment's operating status. This reveals which parameter anomalies or fluctuations may lead to equipment failure, determines whether the operating strategy needs to be adjusted to optimize equipment performance, reduce failures, and ensure the safe and stable operation of the power generation system. At the same time, through in-depth analysis of parameter fluctuations, key equipment can be monitored, improving equipment management efficiency and response speed.

[0016] This solution proposes a DCS fault prediction and diagnosis method and system for thermal power plants. By constructing a real-time state vector matrix and combining it with a historical fault feature library, it generates a fault warning index and a fault risk coefficient, and dynamically adjusts the warning threshold. This enables real-time monitoring of abnormal equipment status, rapid identification of potential faults, and triggering a graded rollback mechanism based on the level of abnormality. This achieves automated and self-evolving optimization of fault diagnosis, thereby improving system stability and operation and maintenance efficiency, and reducing manual intervention and downtime risks. Attached Figure Description

[0017] Figure 1 This is a flowchart of a DCS fault prediction and diagnosis method for thermal power plants proposed in this invention; Figure 2 This is a flowchart of the device parameter acquisition and dynamic calibration process in this invention; Figure 3 This is a flowchart of the fault warning, diagnosis and rollback process in this invention; Figure 4 This is a system framework diagram of a DCS fault prediction and diagnosis system for thermal power plants proposed in this invention. Detailed Implementation

[0018] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0019] Reference Figure 1 - Figure 4 As shown in the figure, a DCS fault prediction and diagnosis method for thermal power plants in an embodiment of the present invention includes: Obtain information and baseline values ​​of operating parameters for the DCS devices to be tested, and generate a list of DCS devices to be tested; Based on the list of DCS devices to be tested, the target DCS devices are identified, a communication channel is established to collect the operating data of the target DCS devices, and a parameter fluctuation characteristic matrix is ​​constructed. Based on the parameter fluctuation characteristic matrix, the dynamic calibration coefficient matrix is ​​obtained, and the reference values ​​of the operating parameters are dynamically calibrated based on the dynamic calibration coefficient matrix to generate a calibration parameter set; Collect historical fault characteristic data of the target DCS equipment and establish a historical fault characteristic database; Acquire real-time operating data of the target DCS device, and generate a real-time state vector matrix based on the calibration parameter set and real-time operating data; A fault early warning index is constructed based on the real-time state vector matrix and the historical fault feature library; Anomaly monitoring of target DCS equipment is performed based on the fault early warning index, and the fault risk coefficient is calculated during the monitoring process. Optimize the threshold of the fault warning index based on the fault risk coefficient, and complete the fault warning configuration; After completing the fault warning configuration, the real-time diagnostic program is invoked to perform fault diagnosis on the target DCS equipment, generating a standardized diagnostic process. The standardized diagnostic process is then optimized using a diagnostic process optimization model to form a standardized diagnostic process template, which is used for fault monitoring and diagnosis of the DCS equipment under test.

[0020] Furthermore, based on the list of DCS devices to be tested, the target DCS devices are identified, a communication channel is established to collect the operating data of the target DCS devices, and a parameter fluctuation characteristic matrix is ​​constructed, specifically including: Obtain information on the DCS equipment to be tested and baseline values ​​of operating parameters, and establish a database of information on the DCS equipment to be tested and a database of baseline values ​​of operating parameters. The equipment information database includes information related to equipment number, equipment model, operating location, role in the power generation production chain, redundancy, and safety impact level. Extract the baseline values ​​from the operating parameter baseline value library, and associate them with the equipment information library through the equipment number to obtain the equipment information in the equipment information library; Based on the baseline values ​​of equipment operating parameters and equipment information, the importance of the equipment is calculated according to its role, redundancy and safety impact in the power generation production chain. The equipment information database is sorted based on equipment importance to generate a list of DCS equipment to be tested; Specifically, the first step is to acquire the basic information and corresponding benchmark values ​​of the DCS equipment under test. The basic information includes the equipment number, model, operating location, role of the equipment in the power generation chain, redundancy, and safety impact level. This information is uniformly stored in the DCS equipment information database under test, forming structured data for subsequent calculations and analysis. Simultaneously, the operating parameters of various equipment under normal operating conditions, such as pressure, temperature, current, and signal response time, are stored in the operating parameter benchmark value database as reference standards for comparison and monitoring.

[0021] Next, the baseline values ​​for the target equipment are extracted from the operating parameter baseline value library and associated with the corresponding entries in the equipment information database through the equipment number. This not only allows us to obtain the equipment's operating parameter baseline values ​​but also, combined with information such as its model, installation location, and role in the production chain, forms a complete set of equipment characteristic data, providing data support for subsequent evaluation.

[0022] Based on the acquired equipment baseline parameters and information, the system further calculates the importance of the equipment. Equipment importance consists of three parts: the weight of the equipment's role in the power generation production chain, the weight of equipment redundancy, and the weight of the equipment's safety impact level. First, the primary importance index is determined based on the equipment's role in the production chain; for example, equipment in core control links is assigned a higher index value. Second, the secondary importance index is determined based on the equipment's redundancy; if multiple redundant units exist, the importance of a single unit decreases. Finally, the tertiary importance index is determined based on the equipment's safety impact level; if equipment failure would significantly impact system safety and production stability, this index value increases. By assigning weight coefficients to these three indicators, a weighted summation method is used to calculate the overall importance value of the equipment. This importance value serves as the basis for subsequent equipment monitoring priorities and diagnostic strategy allocation.

[0023] The system calculates the importance value of each device to be tested and sorts the devices in the equipment database based on this value. Devices with higher importance values ​​are prioritized because they play a critical role in the power generation chain, have lower redundancy, or have a higher safety impact level. Devices with lower importance values ​​are ranked lower. The sorting process uses descending order, from highest to lowest importance, to form a list of DCS devices to be tested. This list can be used for subsequent equipment monitoring and diagnostic processes, ensuring that limited maintenance resources are prioritized for critical equipment, thereby improving the overall system reliability and security.

[0024] Based on the list of DCS devices to be tested, the device ranked first is identified as the target DCS device. Obtain the communication address of the target DCS device and establish a real-time data transmission channel to acquire device operation data; The fluctuation characteristic values ​​of each parameter within the monitoring period are extracted from the equipment operation data, including parameter deviation value, fluctuation standard deviation, maximum fluctuation amplitude and number of consecutive abnormal fluctuations, and a parameter fluctuation characteristic matrix is ​​constructed using the fluctuation characteristic values ​​as elements. The formula for expressing the parameter fluctuation characteristic matrix is ​​as follows: In the formula, Represents the parameter fluctuation characteristic matrix. For parameters Deviation value within the monitoring period, For parameters The standard deviation of fluctuation during the monitoring period, For parameters The maximum fluctuation range during the monitoring period, For parameters Number of consecutive abnormal fluctuations within the monitoring period This represents the total number of operating parameters. Understandably, after identifying the target DCS device, the system first establishes a real-time communication channel with it and collects its operating parameters during the monitoring period. Subsequently, it extracts various feature values ​​from the collected operating data to construct a parameter fluctuation feature matrix. Among these, the parameter deviation value characterizes the difference between the actual value of the operating parameter and the operating baseline value, reflecting the degree of deviation of the device's operating state from its normal state. If the parameter deviation value is large, it indicates that the parameter may have deviated from the set reasonable range and requires further monitoring.

[0025] The standard deviation of fluctuation measures the stability of operating parameters within the monitoring period. A larger standard deviation indicates more drastic fluctuations and greater instability in the parameter's operation; conversely, a smaller standard deviation indicates relatively stable parameter values. Maximum fluctuation amplitude captures extreme changes in operating parameters within the monitoring period, specifically the difference between the parameter's maximum and minimum values. If the maximum fluctuation amplitude of an operating parameter exceeds a preset threshold, it may indicate severe or abnormal fluctuations in the equipment during the monitoring period. The number of consecutive abnormal fluctuations counts the number of times an operating parameter exceeds an abnormal threshold consecutively. This indicator reveals the frequency and persistence of parameter anomalies within a short period, helping to determine if there is a risk of persistent equipment failure.

[0026] Furthermore, the size of the matrix is ​​determined by the total number of operating parameters. Each operating parameter corresponds to one row of data within the monitoring period, and each row contains four elements: parameter deviation value, standard deviation of fluctuation, maximum fluctuation amplitude, and number of consecutive abnormal fluctuations. The resulting parameter fluctuation characteristic matrix can comprehensively reflect the dynamic operating characteristics of the target equipment, providing a quantitative basis for subsequent fault prediction and diagnosis.

[0027] Furthermore, based on the parameter fluctuation characteristic matrix, a dynamic calibration coefficient matrix is ​​obtained, and the operating parameter reference values ​​are dynamically calibrated based on the dynamic calibration coefficient matrix to generate a calibration parameter set, specifically including: Based on the parameter fluctuation feature matrix, the deviation value, standard deviation and maximum fluctuation amplitude of each operating parameter of the target DCS equipment are obtained; Using the parameter deviation value as the first optimization benchmark value, the deviation calibration coefficient is calculated based on the average deviation of the operating parameters within the monitoring period. The formula for calculating the deviation calibration coefficient is as follows: In the formula, For the first The baseline value of each operating parameter, This indicates the deviation value of the parameter; The stability calibration coefficient is calculated using the standard deviation of parameter fluctuation as the second tuning benchmark. The formula for calculating the stability calibration coefficient is as follows: In the formula, This represents the standard deviation of the parameter's fluctuation over the monitoring period; Using the maximum fluctuation range of the parameters as the third tuning benchmark value, the upper and lower limits of the operating parameter benchmark values ​​are constrained to ensure that the calibration parameters are within a safe range. The formula is as follows: In the formula, and These are the minimum and maximum allowable baseline values ​​for the operating parameters, respectively. Integrate the deviation calibration coefficients and the stability calibration coefficients to obtain the dynamic calibration coefficient matrix; The reference values ​​of the operating parameters are corrected item by item using the dynamic calibration coefficient matrix to obtain the preliminary calibration values, and then trimmed within the upper and lower limit constraints to generate a set of calibration parameters. Specifically, firstly, a deviation calibration coefficient is calculated based on the deviation value of each operating parameter, and a stability calibration coefficient is calculated based on the standard deviation of the fluctuation of each operating parameter. The deviation and stability calibration coefficients are arranged in parameter order to form a dynamic calibration coefficient matrix. Each row of the matrix corresponds to an operating parameter, and each row contains both the deviation calibration coefficient and the stability calibration coefficient for that parameter. Next, the reference values ​​of the operating parameters are corrected item by item using the dynamic calibration coefficient matrix. For each operating parameter's reference value, a weighted correction is performed according to the corresponding calibration coefficient in the matrix to obtain a preliminary calibration value. In this way, the preliminary calibration value can reflect the deviation and stability characteristics of the equipment in actual operation. Subsequently, a maximum fluctuation amplitude constraint is applied to the preliminary calibration value to ensure that the calibration value of each parameter does not exceed its allowable deviation range. Simultaneously, the calibration value is further trimmed to the upper and lower limits of each operating parameter to ensure that the calibration results are within a safe and reliable range. After the above steps, the final calibration parameter set considers both parameter deviation and fluctuation stability, and meets safety constraints, and can be used as a standard reference value for subsequent fault prediction and diagnosis.

[0028] Furthermore, anomaly monitoring of the target DCS equipment is performed based on the fault early warning index, and a fault risk coefficient is calculated during the monitoring process, specifically including: Obtain historical operation data from the target DCS device's historical operation records and fault logs; The acquired historical operating data is preprocessed, and the preprocessed historical operating data is classified according to different fault types to generate a historical fault feature library. Acquire real-time operating data of the target DCS device, and standardize the real-time operating data based on the calibration parameter set to obtain standardized parameter values; Specifically, the calibration parameter set provides a reference value and allowable upper and lower limit ranges for each operating parameter. During the standardization process, the system first acquires the real-time value of each operating parameter and compares it with its corresponding calibration reference value. Then, the system compares the parameter value with its calibration upper and lower limit ranges: if the parameter value is higher than the reference value, the system scales it according to the distance from the upper limit; if it is lower than the reference value, it scales it according to the distance from the lower limit. In this way, the original parameter values ​​are mapped to a uniform dimensionless range, such as [-1,1] or [0,1]. This eliminates differences in units and ranges of different parameters while preserving the parameter's offset characteristics relative to the reference value.

[0029] The standardized parameter values ​​are arranged in chronological order to form a real-time state vector; The real-time state vectors of continuous time windows are combined to generate a real-time state vector matrix; Based on the real-time state vector matrix and its time series, the deviation, fluctuation amplitude and trend change rate of the operating parameters are extracted and synthesized into a comprehensive early warning score according to the preset weights. Specifically, the standardized value of each operating parameter at each time point is considered an element in the matrix. Each row of the matrix corresponds to the sequence of values ​​of the same operating parameter at different time points, and each column corresponds to the standardized values ​​of all operating parameters at the same time point. In this way, the real-time state vector matrix simultaneously reflects the dynamic changes of each operating parameter over time and clarifies the correspondence between each element in the matrix and the specific operating parameter and sampling time.

[0030] After constructing the real-time state vector matrix, the system extracts parameter feature information based on the data sequence in the matrix. Specifically, for the numerical sequence of each operating parameter within the monitoring period, the average deviation from the calibration benchmark value is first calculated to obtain the deviation amount; further, the difference between the maximum and minimum values ​​of the parameter within the monitoring period is calculated to obtain the fluctuation amplitude; finally, based on the changing trend of the parameter over time, the slope of the time series is obtained using a linear fitting method to obtain the trend change rate. Through the above methods, the operating status of the target equipment within the monitoring period can be comprehensively characterized.

[0031] Subsequently, the system extracts the deviation, fluctuation amplitude, and trend change rate of each operating parameter from the real-time state vector matrix, and weights them with preset numerical weights. These weights are fixed values ​​predetermined during the system design or experimental phase based on historical equipment operating data and safety management requirements, eliminating the need for manual judgment of equipment importance or characteristic differences. The weighted results of each operating parameter at the same time point are summed to obtain a comprehensive early warning score for that time point. This score quantifies the degree of anomaly of the target equipment at that time point and provides a unified basis for subsequent calculation of the fault early warning index and threshold adjustment.

[0032] The comprehensive early warning score is matched and compared with the historical fault feature database. The comprehensive early warning score is then corrected based on the matching results to obtain the fault early warning index. Understandably, the system first uses the comprehensive early warning score obtained from the aforementioned steps. This score quantifies the degree of anomaly of the target DCS equipment within the current time window. However, the comprehensive early warning score alone cannot accurately determine whether an anomaly is likely to develop into an actual fault. Therefore, the system introduces a historical fault feature database for comparison. This database records the historical fault types of each device, along with corresponding operating parameter deviations, fluctuation amplitudes, and trend changes, providing a reference for comparison.

[0033] During the comparison process, the system calculates a similarity factor between the current operating state and historical fault modes to quantify the degree of similarity between the current anomaly and known faults. Specifically, it compares the real-time state vector within the current time window with the historical fault feature vectors, and obtains a similarity index by calculating the degree of matching in deviation, fluctuation amplitude, and trend changes. The higher the value, the more similar the current anomaly is to the historical fault.

[0034] Meanwhile, the system calculates the frequency of abnormal fluctuations based on the abnormal fluctuations of each operating parameter within a continuous time window in the real-time state vector matrix. This frequency reflects whether abnormalities have occurred consecutively recently. The method for calculating the frequency of abnormal fluctuations is to count the number of abnormal parameter points exceeding a preset threshold and normalize them into a proportion. A higher value indicates that abnormal events are more frequent.

[0035] Based on the comprehensive early warning score, historical fault mode similarity factor, and abnormal fluctuation frequency, the system corrects the comprehensive early warning score to obtain the final fault early warning index. In this way, the system can simultaneously consider the current abnormal magnitude, historical fault similarity, and abnormal continuity, making the fault early warning index more accurately reflect the potential fault risk of the target equipment and providing a basis for subsequent dynamic threshold adjustment and fault early warning.

[0036] The formula for expressing the real-time state vector matrix is ​​as follows: In the formula, Indicates the first Each running parameter at a given time point Standardized values, This represents the total number of running parameters. This represents the number of sampling points within the time window. The formula for calculating the fault warning index is: In the formula, This is a fault warning index. To determine the comprehensive early warning score, The similarity factor is the historical fault characteristics. For abnormal fluctuation frequency, , , These are the weighting coefficients.

[0037] Furthermore, the threshold parameters of the fault warning index are optimized based on the fault risk coefficient, and the fault warning configuration is completed, specifically including: Anomaly monitoring of the target DCS equipment is performed based on the fault early warning index, and monitoring data is obtained by combining the anomaly duration information within the monitoring time window. Based on the monitoring data, obtain the failure risk coefficient of the target DCS equipment in the current time window; Based on the failure risk coefficient and with reference to preset thresholds, the failure trend of the target DCS equipment that may occur within a certain time window in the future is predicted. Based on the fault risk coefficient, the threshold of the fault warning index is dynamically adjusted, including raising and lowering the upper and lower limits of the warning for each fault type. The adjusted threshold parameters are applied to the real-time early warning mechanism of the target DCS equipment to complete the fault early warning configuration. Specifically, the system monitors target DCS equipment for anomalies based on a generated fault warning index. During anomaly monitoring, the system collects standardized data of equipment operating parameters in real time, calculates the corresponding comprehensive warning score, and corrects the comprehensive warning score by combining it with a historical fault feature database to form a fault warning index. When the fault warning index exceeds a preset threshold, the system determines that the equipment has a potential anomaly and records the time and duration of the anomaly event.

[0038] To more accurately assess equipment risk, the system incorporates the duration of anomalies within the monitoring time window into its analysis. Specifically, the longer the duration of consecutive anomalies, the higher the likelihood of equipment failure. The system combines these anomaly durations with indicators such as anomaly amplitude and fluctuation frequency to generate monitoring data. This monitoring data includes information such as the number of anomalies, duration, intensity, and degree of matching with historical fault characteristics for each fault type within the current time window, which is used for subsequent fault risk assessment.

[0039] Based on monitoring data, the system calculates the fault risk coefficient of the target DCS equipment within the current time window. The fault risk coefficient comprehensively reflects the probability and potential harm of equipment failure; a higher value indicates a greater risk. Based on the fault risk coefficient, the system dynamically adjusts the upper and lower limits of the fault warning index in conjunction with preset thresholds. When the fault risk coefficient exceeds a certain preset level, the system raises the lower limit of the warning value for each fault type, making the equipment more sensitive and triggering warnings earlier. When the fault risk coefficient falls below the preset level, the system lowers the upper limit of the warning value for each fault type to avoid excessive warnings.

[0040] In addition, a fault early warning mechanism has been introduced. This mechanism continuously monitors the real-time operating data of the equipment by setting dynamically adjustable thresholds. When the fault early warning index exceeds the threshold, the system automatically triggers an alarm or records the anomaly. With this mechanism, the sensitivity of the early warning can be flexibly adjusted according to different operating conditions, thereby ensuring that critical equipment receives timely warnings, while preventing frequent false alarms from non-critical equipment due to slight fluctuations.

[0041] The system can use this method to achieve real-time anomaly monitoring of target DCS equipment, dynamically adjust thresholds based on risk conditions, and complete intelligent fault early warning configuration, thereby improving the safety and reliability of equipment operation.

[0042] The formula for calculating the failure risk coefficient is as follows: In the formula, For a moment The fault warning index, and These are the minimum and maximum values ​​of the warning index, respectively. The rate of change of the early warning index. and These are the weighting coefficients.

[0043] Furthermore, after completing the fault warning configuration, the real-time diagnostic program is invoked to perform fault diagnosis on the target DCS equipment, generating a standardized diagnostic process. This standardized diagnostic process is then optimized using a diagnostic process optimization model to form a standardized diagnostic process template, which is used for fault monitoring and diagnosis of the DCS equipment under test. Specifically, this includes: Based on the fault warning configuration, the real-time diagnostic program based on real-time operation data input is invoked to perform fault diagnosis on the operating status of the target DCS equipment and obtain the fault diagnosis results. Specifically, a real-time diagnostic program refers to a software module that can automatically analyze the operating status of a target DCS device and generate fault diagnosis results based on its real-time operating data. This program receives standardized operating parameter data from the target device and automatically judges and identifies equipment faults by comparing the operating status with a historical fault feature database and a standardized diagnostic process template. The system inputs the operating parameter data collected by the target device during the real-time monitoring period into the real-time diagnostic program. This data includes, but is not limited to, key operating parameters such as pressure, temperature, flow rate, current, and control signals, and has undergone calibration and standardization to provide reliable basic data for fault diagnosis. The program compares the real-time operating data with various fault modes stored in the historical fault feature database, and analyzes the operating status of each parameter in conjunction with the standardized diagnostic process template. When certain parameters deviate from the normal range or exhibit abnormal fluctuations, the program identifies them as potential fault states. For potential fault states, the real-time diagnostic program further analyzes their characteristics to determine the possible fault type. For example, it can identify faults such as sensor failure, actuator malfunction, control loop malfunction, or system configuration error, and mark the corresponding abnormal parameters and their occurrence time. The program outputs fault diagnosis results for each target device, including fault type, abnormal parameters, abnormal time, and abnormal severity. The generated fault diagnosis results provide foundational data for subsequently building a fault diagnosis knowledge base and optimizing standardized diagnostic processes, and are used to support fault monitoring and diagnosis of other devices under test.

[0044] Based on the fault diagnosis results, a fault diagnosis knowledge base is constructed, and the fault diagnosis results are classified according to the fault type. The results are standardized through format unification to generate a standardized diagnosis process. Understandably, the system manages equipment fault information systematically based on fault diagnosis results generated by real-time diagnostic programs. First, the system categorizes fault diagnosis results according to fault type, such as sensor faults, actuator faults, control loop anomalies, and communication faults, with each type forming an independent category. This categorization facilitates rapid problem identification and supports subsequent statistical analysis and trend prediction.

[0045] Subsequently, the categorized fault diagnosis results are standardized. Standardization includes unifying the record format, parameter names, and units. For example, all fault results are stored in the order of "fault type - abnormal parameter - abnormal time - diagnosis conclusion" to ensure that diagnosis results from different devices and at different times can be managed and analyzed uniformly.

[0046] After classification and standardization, the system integrates this data to form a standardized diagnostic process. A standardized diagnostic process refers to a fault diagnosis operation procedure organized according to a unified format and logical sequence, which can be represented by tables, data structures, or flowcharts. For example, each fault type corresponds to a set of processing steps, including data acquisition, anomaly identification, diagnostic methods, and handling measures. Through the standardized diagnostic process, the system can reuse unified diagnostic methods on different devices and facilitate subsequent process optimization and batch monitoring tasks.

[0047] Collect historical fault cases, operation event records, and power plant standard operation and maintenance procedures, and merge the data with the fault diagnosis knowledge base to form the basic data for the diagnosis process; Data cleaning is performed on the basic data of the diagnostic process to remove redundant records and abnormal data, and data standardization is performed through numerical normalization to extract key diagnostic feature information that represents the correlation between operating status and fault mode. Understandably, the system first performs data cleaning on the basic data of the diagnostic process to remove redundant and abnormal records. Redundant records refer to duplicate or invalid fault records or operational event data, while abnormal data refers to erroneous or missing data that deviates significantly from the normal range. Through cleaning, the data quality of subsequent analysis can be ensured, improving the accuracy of diagnosis and modeling.

[0048] Subsequently, the system performs numerical normalization on the cleaned data to achieve data standardization. Normalization refers to converting operating parameters with different dimensions or numerical ranges to the same numerical interval (e.g., 0 to 1), so that different parameters can be compared and analyzed in the same computational model, avoiding the impact of numerical scale differences on feature extraction or machine learning models.

[0049] After data cleaning and normalization, the system further extracts key diagnostic features from the basic data. Key diagnostic features refer to data characteristics that characterize the relationship between equipment operating status and fault modes, such as abnormal fluctuation patterns of a parameter, trend change rates, abrupt change points, or joint abnormal patterns between parameters. Extracting these features provides input for subsequent machine learning-based diagnostic process optimization and fault mode identification, thereby improving the accuracy and efficiency of fault diagnosis.

[0050] Based on key diagnostic feature information, a diagnostic process optimization model is obtained by training a machine learning model. The standardized diagnostic process is optimized by using a diagnostic process optimization model to generate a standardized diagnostic process template. Specifically, the system optimizes the diagnostic process using a machine learning model based on extracted key diagnostic feature information. Key diagnostic features include the correlation between various operating parameters and fault modes, parameter fluctuation trends, abrupt change points, and joint anomaly patterns between parameters. These features are used as input to the machine learning model, training it to learn and identify the patterns and regularities between equipment operating states and fault modes. The trained model is the diagnostic process optimization model, which can predict potential fault paths, optimize the fault identification sequence, and provide suggestions on the priority of each step in the diagnostic process.

[0051] Using a trained diagnostic process optimization model, the system optimizes existing standardized diagnostic processes. Optimization includes adjusting the order of diagnostic steps for each fault type, prioritizing the analysis of key feature parameters, and simplifying repetitive or redundant diagnostic operations, thereby improving fault diagnosis efficiency and accuracy. After optimization, the system generates a standardized diagnostic process template. This template is a machine learning-optimized diagnostic operation specification, which can be presented as a table, flowchart, or data structure, clearly defining the diagnostic steps, required parameters, analysis methods, and handling measures for each fault type. Through this standardized diagnostic process template, the system can reuse the same optimized process when diagnosing other DCS devices under test, achieving standardization and scalability in fault diagnosis.

[0052] Based on a standardized diagnostic process template, diagnostic tasks are assigned to the DCS devices to be tested in the list of DCS devices to be tested, and diagnostic execution configurations are generated. Based on the diagnostic execution configuration, the batch monitoring program is launched to perform real-time fault warnings and diagnoses on the DCS equipment under test, and the monitoring feedback is written back to the fault diagnosis knowledge base. When the monitoring feedback results deviate from the standardized diagnostic process template and an anomaly occurs, the anomaly is classified into minor, severe, and fatal levels according to the preset anomaly level classification rules, and a graded rollback mechanism is triggered based on the corresponding level to obtain the rollback result. The rollback results are recorded and updated to the fault diagnosis knowledge base to support the continuous optimization and self-evolution of the diagnosis process. Specifically, when the system performs real-time monitoring and fault diagnosis on the target DCS equipment, it compares the actual operating status of the monitored equipment with the expected results preset in the standardized diagnostic process template. Here, mismatch refers to a difference between the actual monitoring data and the expected state defined in the template; for example, the change of a certain operating parameter exceeds the expected fluctuation range, or a fault phenomenon not included in the template occurs.

[0053] When such differences reach a certain level, they are judged as abnormal. An abnormality does not mean that all deviations are considered problems; rather, it refers to deviations that may affect equipment operation or pose potential risks. When judging an anomalies, the system comprehensively considers the magnitude of the deviation, its duration, the frequency of fluctuations, and the importance of the parameter in the entire system, thereby identifying the truly problematic anomalies that require action.

[0054] For identified anomalies, the system categorizes them according to preset anomaly classification rules. Anomaly levels typically include minor, severe, and critical. The classification criteria may include: the degree of impact of the anomaly on the production system, the potential risk to equipment safety, the duration of the anomaly, and the importance of key parameters. For example, a minor anomaly may be a short-term fluctuation of some non-critical parameters; a severe anomaly may involve deviations from key operating parameters and last for a long time; a critical anomaly may involve core control equipment or a failure that could lead to system downtime.

[0055] The system further triggers a tiered rollback mechanism based on the anomaly level, executing corresponding processing operations. Minor anomalies may only require parameter correction or partial rollback, severe anomalies may trigger subsystem rollback or issue warnings, and fatal anomalies may execute a full system rollback or emergency protection measures. Rollback results are recorded and updated to the fault diagnosis knowledge base to support continuous optimization and self-evolution of the diagnostic process, achieving automation and intelligence in equipment monitoring and fault handling.

[0056] Furthermore, a DCS remote automated fault prediction and diagnosis system for thermal power plants, used to implement the prediction and diagnosis methods described above, includes: The main control module receives data transmitted by the configuration module, monitoring module, diagnostic module, and display module through the transmission module, processes and analyzes the received data, and controls the operation of the configuration module, monitoring module, diagnostic module, and display module based on the processing results. The configuration module is used to configure the initial parameters and establish communication channels for the target devices in the list of DCS devices to be tested, so as to ensure the accurate acquisition of monitoring data. The prediction and diagnosis module is used to collect real-time operating data of the target DCS equipment, extract parameter fluctuation characteristics, construct state vector matrix, assess and predict fault risks, generate fault warnings, diagnose faults and perform graded rollback. The transmission module is used for data transmission between the main control module and the configuration module, monitoring module, diagnostic module, and display module. The display module is used to display the processing results of the main control module, the configuration process and results of the configuration module, the real-time monitoring results of the monitoring module, and the fault diagnosis results of the diagnostic module.

[0057] Furthermore, the main control module includes: The data receiving unit is used to receive real-time operating data, historical fault characteristic data, parameter fluctuation characteristic matrix, real-time status vector matrix and fault early warning index of the target DCS equipment. The data processing unit is used to establish a target DCS equipment information database and a historical fault feature database, analyze and calculate the fault warning index and state vector matrix, generate fault risk coefficients, and optimize the fault warning thresholds based on the fault risk coefficients. The assessment unit is used to determine the anomaly level of the target DCS equipment based on the fault risk coefficient and to feed the anomaly information back to the diagnostic module. The control unit is used to control the configuration module, monitoring module, diagnostic module and display module based on the analysis results.

[0058] Furthermore, the configuration module includes: The device configuration unit is used to establish a communication channel based on the target DCS device information, initialize the device operating parameters, and perform dynamic calibration to generate a calibration parameter set. The parameter configuration unit is used to configure the parameters of each device in the DCS device list to be tested based on the dynamic calibration parameter set and device importance, and to ensure that the configuration complies with the safety constraints.

[0059] Furthermore, the prediction and diagnosis module includes: The monitoring unit is used to collect real-time operating data of the target DCS equipment and construct a parameter fluctuation feature matrix and a real-time status vector matrix. The fault assessment unit is used to calculate the fault risk coefficient during the monitoring process and predict the fault based on the fault risk coefficient. The early warning unit is used to adjust the threshold of the fault early warning index according to the risk coefficient, and complete the fault early warning configuration based on the adjusted threshold parameters. The diagnostic unit is used to perform fault diagnosis on the target DCS equipment based on the fault warning configuration, generate a standardized diagnostic process template, and trigger a graded rollback mechanism according to the anomaly level to update the rollback results to the fault diagnosis knowledge base, thereby achieving continuous optimization of the diagnostic process.

[0060] In summary, the advantages of this invention are as follows: By constructing a parameter fluctuation feature matrix and dynamically calibrating the operating parameters of the target equipment, the fluctuation patterns of the operating parameters under different operating conditions can be revealed, thereby determining whether parameter deviations are abnormal and avoiding the failure of reference values ​​due to differences in equipment operating states. Based on the comprehensive analysis of the parameter fluctuation feature matrix and historical operating data, accurate benchmarking and trend prediction of operating parameters can be achieved, which helps to identify potential hazards in a timely manner, reduce false alarms and missed alarms, and improve the accuracy and reliability of equipment operating status determination.

[0061] By introducing standardized diagnostic processes and a tiered rollback mechanism, real-time monitoring and dynamic handling of equipment failures are achieved. When an anomaly is detected, the corresponding rollback strategy can be automatically triggered based on the anomaly level, and continuous optimization is performed in conjunction with a knowledge base to ensure the stability and adaptability of diagnostic results. The system can quickly roll back to a historical stable state when performance or configuration anomalies occur, reducing the impact on business operations and downtime. Simultaneously, through continuous updates to the rollback knowledge base, a self-evolving diagnostic and handling capability is formed, thereby improving the overall security, stability, and intelligence level of the system.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for power plant DCS fault prediction and diagnosis, characterized in that, The application comprises the following steps: Obtain the DCS device information and the operating parameter reference value, and generate a list of DCS devices to be detected; Based on the list of DCS devices to be detected, determine the target DCS device, establish a communication channel to collect the operating data of the target DCS device, and construct a parameter fluctuation feature matrix; According to the parameter fluctuation feature matrix, obtain a dynamic calibration coefficient matrix, and based on the dynamic calibration coefficient matrix, dynamically calibrate the operating parameter reference value to generate a calibrated parameter set; Collect the historical fault feature data of the target DCS device, and establish a historical fault feature library; Obtain the real-time operating data of the target DCS device, and based on the calibrated parameter set and the real-time operating data, generate a real-time state vector matrix; Based on the real-time state vector matrix and the historical fault feature library, construct a fault early warning index; Based on the fault early warning index, monitor the target DCS device for abnormalities, and calculate the fault risk coefficient during the monitoring process; Based on the fault risk coefficient, optimize the threshold of the fault early warning index, and complete the fault early warning configuration; After completing the fault early warning configuration, call the real-time diagnosis program to diagnose the target DCS device for faults, generate a standardized diagnosis process, and optimize the standardized diagnosis process using a diagnosis process optimization model to form a standardized diagnosis process template for fault monitoring and diagnosis of the DCS devices to be detected.

2. The method for power plant DCS fault prediction and diagnosis according to claim 1, characterized in that, The application comprises the following steps: Obtain the DCS device information and the operating parameter reference value, and generate a list of DCS devices to be detected; Based on the list of DCS devices to be detected, determine the target DCS device, establish a communication channel to collect the operating data of the target DCS device, and construct a parameter fluctuation feature matrix; According to the parameter fluctuation feature matrix, obtain a dynamic calibration coefficient matrix, and based on the dynamic calibration coefficient matrix, dynamically calibrate the operating parameter reference value to generate a calibrated parameter set; Collect the historical fault feature data of the target DCS device, and establish a historical fault feature library; Obtain the real-time operating data of the target DCS device, and based on the calibrated parameter set and the real-time operating data, generate a real-time state vector matrix; Based on the real-time state vector matrix and the historical fault feature library, construct a fault early warning index; Based on the fault early warning index, monitor the target DCS device for abnormalities, and calculate the fault risk coefficient during the monitoring process; Based on the fault risk coefficient, optimize the threshold of the fault early warning index, and complete the fault early warning configuration; wherein, denotes a parameter fluctuation characteristic matrix, is a parameter a deviation value within a monitoring period, is a parameter a fluctuation standard deviation within a monitoring period, is a parameter a maximum fluctuation amplitude within a monitoring period, is a parameter a number of continuous abnormal fluctuations within a monitoring period, is the total number of operating parameters.

3. The method of claim 2, wherein, After completing the fault early warning configuration, call the real-time diagnosis program to diagnose the target DCS device for faults, generate a standardized diagnosis process, and optimize the standardized diagnosis process using a diagnosis process optimization model to form a standardized diagnosis process template for fault monitoring and diagnosis of the DCS devices to be detected. The application comprises the following steps: Obtain the DCS device information and the operating parameter reference value, and generate a list of DCS devices to be detected; Based on the list of DCS devices to be detected, determine the target DCS device, establish a communication channel to collect the operating data of the target DCS device, and construct a parameter fluctuation feature matrix; According to the parameter fluctuation feature matrix, obtain a dynamic calibration coefficient matrix, and based on the dynamic calibration coefficient matrix, dynamically calibrate the operating parameter reference value to generate a calibrated parameter set; Collect the historical fault feature data of the target DCS device, and establish a historical fault feature library; Obtain the real-time operating data of the target DCS device, and based on the calibrated parameter set and the real-time operating data, generate a real-time state vector matrix; Based on the real-time state vector matrix and the historical fault feature library, construct a fault early warning index; Based on the fault early warning index, monitor the target DCS device for abnormalities, and calculate the fault risk coefficient during the monitoring process; Based on the fault risk coefficient, optimize the threshold of the fault early warning index, and complete the fault early warning configuration; After completing the fault early warning configuration, call the real-time diagnosis program to diagnose the target DCS device for faults, generate a standardized diagnosis process, and optimize the standardized diagnosis process using a diagnosis process optimization model to form a standardized diagnosis process template for fault monitoring and diagnosis of the DCS devices to be detected. The parameter deviation value is taken as the first tuning reference value, and the deviation calibration coefficient is calculated according to the average deviation of the operation parameter in the monitoring period; The parameter fluctuation standard deviation is taken as the second tuning reference value, and the stability calibration coefficient is calculated; The maximum fluctuation amplitude of the parameter is taken as the third tuning reference value, and the upper and lower limits of the operation parameter reference value are constrained to ensure that the calibration parameter is within the safe range; The deviation calibration coefficient and the stability calibration coefficient are integrated to obtain a dynamic calibration coefficient matrix; The dynamic calibration coefficient matrix is used to modify the operation parameter reference value item by item to obtain a preliminary calibration value, and the preliminary calibration value is clipped within the upper and lower limits to generate a calibration parameter set.

4. The method of claim 3, wherein, The target DCS device is abnormally monitored based on the fault early warning index, and a fault risk coefficient is calculated during the monitoring process, specifically including: Historical operation data is obtained from the historical operation records and fault logs of the target DCS device; The obtained historical operation data is preprocessed, the preprocessed historical operation data is classified according to different fault types, and a historical fault feature library is generated; Real-time operation data of the target DCS device is obtained, and the real-time operation data is standardized based on the calibration parameter set to obtain standardized parameter values; The standardized parameter values are arranged in time sequence to form a real-time state vector; The real-time state vectors of the continuous time window are combined to generate a real-time state vector matrix; The deviation amount, fluctuation amplitude and trend change rate of the operation parameter are extracted based on the real-time state vector matrix and its time sequence, and are combined into a comprehensive early warning score according to a preset weight; The comprehensive early warning score is matched and compared based on the historical fault feature library, and the comprehensive early warning score is modified according to the matching result to obtain a fault early warning index; The calculation formula of the real-time state vector matrix is: wherein denotes the standardized value of the nth operating parameter at the time point t, is the total number of operating parameters, is the number of sampling points within the time window; The calculation formula of the fault early warning index is: In the formula, is a fault early warning index, is a comprehensive early warning score, is a historical fault feature similarity factor, is an abnormal fluctuation frequency, 、 、 is a weight coefficient.

5. The method for power plant DCS fault prediction and diagnosis according to claim 4, characterized in that, The threshold parameter of the fault early warning index is optimized based on the fault risk coefficient, and the fault early warning configuration is completed, specifically including: The target DCS device is abnormally monitored based on the fault early warning index, and the abnormal duration information in the monitoring time window is combined to obtain monitoring data; The fault risk coefficient of the target DCS device in the current time window is obtained according to the monitoring data; The threshold of the fault early warning index is dynamically adjusted according to the fault risk coefficient; The adjusted threshold parameter is applied to the real-time early warning mechanism of the target DCS device to complete the fault early warning configuration; The calculation formula of the fault risk coefficient is: In the formula, For a moment The fault warning index, and These are the minimum and maximum values ​​of the warning index, respectively. The rate of change of the early warning index. and These are the weighting coefficients.

6. The method for power plant DCS fault prediction and diagnosis according to claim 5, characterized in that, After the fault early warning configuration is completed, a real-time diagnosis program is called to diagnose the fault of the target DCS device, a standardized diagnosis process is generated, and the standardized diagnosis process is optimized by a diagnosis process optimization model to form a standardized diagnosis process template, which is used for fault monitoring and diagnosis of the DCS device to be detected, specifically including: Based on the fault early warning configuration, a real-time diagnosis program based on real-time operation data input is called to diagnose the running state of the target DCS device to obtain a fault diagnosis result; Based on the fault diagnosis result, a fault diagnosis knowledge base is constructed, the fault diagnosis result is classified according to fault types, the result is standardized by format unification, and a standardized diagnosis process is generated. Collect historical fault cases, operation event records and power plant standard operation and maintenance procedures, and merge with the fault diagnosis knowledge base to form the diagnosis process basic data; Data cleaning is performed on the diagnosis process basic data to remove redundant records and abnormal data, and data standardization is performed through numerical normalization processing to extract key diagnostic feature information representing the association between operating state and fault mode; Based on the key diagnostic feature information, a diagnosis process optimization model is trained using a machine learning model; The standardized diagnosis process template is generated by optimizing the standardized diagnosis process through the diagnosis process optimization model; Based on the standardized diagnosis process template, the diagnosis tasks are assigned to the DCS devices to be detected in the list of DCS devices to be detected and the diagnosis execution configuration is generated; Based on the diagnosis execution configuration, the batch monitoring program is started to perform real-time fault warning and diagnosis on the DCS devices to be detected, and the monitoring feedback is written back to the fault diagnosis knowledge base; When the monitoring feedback result deviates from the standardized diagnosis process template and an exception occurs, the exception is classified as minor, serious and fatal according to the preset exception level classification rule, and the hierarchical rollback mechanism is triggered based on the corresponding level to obtain the rollback result; The rollback result is recorded and updated to the fault diagnosis knowledge base to support continuous optimization and self-evolution of the diagnosis process.

7. A DCS remote automation fault prediction and diagnosis system for power plants for implementing the method according to any one of claims 1 to 6, characterized in that, Specifically, it comprises: A main control module for receiving data transmitted by a configuration module, a monitoring module, a diagnosis module and a display module through a transmission module, processing and analyzing the received data, and controlling the operation of the configuration module, the monitoring module, the diagnosis module and the display module according to the processing result; A configuration module for initial parameter configuration and communication channel establishment of target devices in the list of DCS devices to be detected to ensure accurate collection of monitoring data; A prediction and diagnosis module for real-time operation data collection, parameter fluctuation feature extraction, state vector matrix construction, fault risk assessment and prediction, fault warning generation, fault diagnosis and hierarchical rollback of target DCS devices; A transmission module for data transmission between the main control module and the configuration module, the monitoring module, the diagnosis module and the display module; A display module for displaying the processing result of the main control module, the configuration process and result of the configuration module, the real-time monitoring result of the monitoring module and the fault diagnosis result of the diagnosis module.

8. The DCS remote automation fault prediction and diagnostics system for a thermal power plant of claim 7, wherein, The main control module comprises: A data receiving unit for receiving real-time operation data, historical fault feature data, parameter fluctuation feature matrix, real-time state vector matrix and fault warning index of target DCS devices; A data processing unit for establishing a target DCS device information library and a historical fault feature library, analyzing and calculating the fault warning index and the state vector matrix, generating a fault risk coefficient, and optimizing the fault warning threshold based on the fault risk coefficient; An evaluation unit for judging the abnormal level of the target DCS device based on the fault risk coefficient and feeding back the abnormal information to the diagnosis module; A control unit is configured to control the configuration module, the monitoring module, the diagnosis module and the display module according to the analysis result.

9. The DCS remote automation fault prediction and diagnostics system for a thermal power plant of claim 7, wherein, The configuration module comprises: A device configuration unit is configured to establish a communication channel according to target DCS device information, initialize device operation parameters, and perform dynamic calibration to generate a calibration parameter set; A parameter configuration unit is configured to perform parameter configuration on devices in the to-be-detected DCS device list one by one based on the dynamic calibration parameter set and the device importance, and ensure that the configuration conforms to the safety constraint range.

10. The DCS remote automation fault prediction and diagnostics system for a thermal power plant of claim 7, wherein, The prediction and diagnosis module comprises: A monitoring unit is configured to collect target DCS device operation data in real time, construct a parameter fluctuation feature matrix and a real-time state vector matrix; A fault assessment unit is configured to calculate a fault risk coefficient during monitoring, and predict a fault according to the fault risk coefficient; An early warning unit is configured to adjust a threshold value of a fault early warning index according to the risk coefficient, complete fault early warning configuration based on the adjusted threshold parameter; A diagnosis unit is configured to perform fault diagnosis on the target DCS device based on the fault early warning configuration, generate a standardized diagnosis process template, and trigger a hierarchical rollback mechanism according to an abnormality level, update a rollback result to a fault diagnosis knowledge base, and realize continuous optimization of the diagnosis process.

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