Thermal Control System and Method Based on Frequency Modulation Decision Algorithm and Valve Fault Prediction

Abstract

This application discloses a thermal control system and method based on frequency regulation decision algorithm and valve fault prediction, relating to the field of steam turbine optimization operation in thermal power generation. The communication module receives grid frequency regulation commands from the monitoring platform and transmits them to the control module, while simultaneously transmitting multi-dimensional system data back to the monitoring platform, achieving bidirectional interaction between remote commands and data. The data acquisition module collects multi-dimensional operating condition data in real time through a sensor array, providing raw input to the control module. The control module performs valve fault prediction on the operating condition data, generates frequency regulation control commands by combining the grid frequency regulation commands and prediction results, and simultaneously receives execution status data from the execution module, forming a decision-making closed loop. The execution module adjusts the valve opening according to the control commands, synchronously collects execution status data, and transmits it back to the control module, completing execution feedback. This application's end-to-end closed loop effectively adapts to multi-variable dynamic operating conditions, solving the problems of response lag and insufficient regulation accuracy in traditional methods.

Description

Technical Field

[0001] This application relates to the field of steam turbine optimization operation in thermal power generation, and in particular to a thermal control system and method based on frequency regulation decision algorithm and valve fault prediction. Background Technology

[0002] In industrial process control systems such as thermal power, chemical, and metallurgical industries, thermal control systems maintain stable temperature, pressure, and flow rates by adjusting actuators (such as control valves). With increasing load fluctuations and system operational complexity, frequency modulation control has become a crucial means of ensuring stable system operation. However, existing frequency modulation technologies and actuator monitoring methods still have several limitations in dynamic environments, making it difficult to meet the demands of modern industrial production.

[0003] In related technologies, thermal control systems typically employ frequency regulation methods based on fixed-parameter PID controllers or manual experience settings, adjusting actuators (such as valves) to maintain stable temperature, pressure, and flow. These systems generally have the following typical implementation schemes: First, they use post-event diagnostic mechanisms to monitor valve status, intervening through threshold alarms or manual checks when valves exhibit significant jamming, lag, or abnormal execution. Second, they decentralize the control system and fault prediction functions, forming multiple independently operating subsystems. For example, some existing technologies judge the status by collecting single parameters such as valve position deviation, or rely on preset fixed dead time for frequency regulation response. However, the applicant recognizes that existing frequency regulation methods cannot cope with complex, multi-variable dynamic operating conditions, resulting in system response lag and insufficient regulation accuracy under dynamic conditions. Secondly, post-event diagnostic monitoring modes cannot effectively predict potential valve faults; when hidden problems such as mechanical jamming or wear occur, the best handling opportunity is often missed. Moreover, the decentralized system architecture leads to poor data interaction, making it difficult to achieve closed-loop coordination between control strategies and health management, increasing system maintenance costs and significantly raising the risk of unplanned downtime. These defects severely restrict the reliability and safety of industrial thermal control systems in complex operating environments. Summary of the Invention

[0004] In view of this, this application provides a thermal control system based on frequency modulation decision algorithm and valve fault prediction. The main purpose is to solve the problems of existing frequency modulation methods, which are unable to adapt to multi-variable dynamic operating conditions, resulting in slow response and insufficient accuracy. Furthermore, the post-event diagnosis of valve faults is prone to missing the opportunity for processing, and the distributed architecture has poor data interaction, which seriously restricts the reliability and safety of industrial thermal control systems.

[0005] According to the first aspect of this application, a thermal control system based on frequency modulation decision algorithm and valve fault prediction is provided, including a communication module, a data acquisition module, a control module, and an execution module;

[0006] The communication module is used to receive the power grid frequency regulation command signal transmitted by the monitoring platform, transmit the power grid frequency regulation command signal to the control module, and transmit the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency regulation control command generated by the control module, and the execution status data collected by the execution module to the monitoring platform.

[0007] The data acquisition module is used to collect the multi-dimensional working condition data in real time through the sensor group and transmit the multi-dimensional working condition data to the control module.

[0008] The control module is used to perform fault prediction on the multi-dimensional operating condition data, generate the prediction result characterizing the health status of the control valve, generate the frequency regulation control command using the power grid frequency regulation command signal, the multi-dimensional operating condition data and the prediction result, transmit the frequency regulation control command to the execution module, receive the execution status data, and transmit the multi-dimensional operating condition data collected by the data acquisition module, the prediction result generated by the control module, the frequency regulation control command generated by the control module, and the execution status data collected by the execution module to the communication module.

[0009] The execution module is used to perform a gate opening adjustment operation according to the frequency modulation control command, and to collect the execution status data during the execution of the gate opening adjustment operation and transmit the execution status data to the control module.

[0010] According to a second aspect of this application, a thermal control method based on a frequency modulation decision algorithm and valve fault prediction is provided. The method is applied to a thermal control system, which includes a communication module, a data acquisition module, a control module, and an execution module. The thermal control method includes:

[0011] The communication module receives the power grid frequency regulation command signal transmitted by the monitoring platform and transmits the power grid frequency regulation command signal to the control module;

[0012] The data acquisition module collects multi-dimensional operating condition data in real time through the sensor group and transmits the multi-dimensional operating condition data to the control module;

[0013] The control module performs fault prediction on the multi-dimensional operating condition data, generates prediction results characterizing the health status of the regulating valve, uses the power grid frequency regulation command signal, the multi-dimensional operating condition data and the prediction results to generate a frequency regulation control command, and transmits the frequency regulation control command to the execution module.

[0014] The execution module performs a gate opening adjustment operation according to the frequency modulation control command, and collects execution status data during the gate opening adjustment operation, and transmits the execution status data to the control module.

[0015] The control module receives the execution status data and transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control command generated by the control module, and the execution status data collected by the execution module to the communication module.

[0016] The communication module transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the monitoring platform.

[0017] By employing the above technical solutions, the technical solutions provided in the embodiments of this application have at least the following advantages:

[0018] This application provides a thermal control system and method based on frequency regulation decision algorithm and valve fault prediction. The system comprises a communication module, a data acquisition module, a control module, and an execution module, with each module forming a closed loop through data interaction. The communication module receives power grid frequency regulation commands from the monitoring platform and transmits them to the control module, while simultaneously transmitting multi-dimensional system data back to the monitoring platform, achieving bidirectional interaction between remote commands and data. The data acquisition module collects multi-dimensional operating condition data in real time using a sensor array, providing raw input to the control module. The control module performs valve fault prediction based on the operating condition data, generates frequency regulation control commands by combining the power grid frequency regulation commands and prediction results, and simultaneously receives execution status data from the execution module, forming a decision-making closed loop. The execution module adjusts the valve opening according to the control commands, synchronously collects execution status data, and transmits it back to the control module, completing execution feedback. This application's end-to-end closed loop effectively adapts to multi-variable dynamic operating conditions, solving the problems of response lag and insufficient regulation accuracy in traditional methods. Moreover, it breaks down the data barriers of the distributed architecture, with clear data flow and close collaboration among modules, reducing system maintenance costs and achieving deep collaboration between frequency regulation strategy and health management. Frequency regulation decision-making integrates power grid commands, operating condition data and regulator health status, ensuring frequency regulation response performance and dynamically adjusting strategies based on equipment health, thereby improving the overall reliability and security of the system.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0021] Figure 1 This paper illustrates a schematic diagram of a thermal control system based on a frequency modulation decision algorithm and valve fault prediction, according to an embodiment of this application.

[0022] Figure 2 This paper illustrates a schematic diagram of a thermal control system architecture provided in an embodiment of this application.

[0023] Figure 3 This illustration shows a schematic diagram of a thermal control system architecture based on a frequency modulation decision algorithm and valve fault prediction, provided in an embodiment of this application.

[0024] Figure 4 This paper shows a schematic diagram of the main components of a steam turbine control valve system provided in an embodiment of this application;

[0025] Figure 5 This paper presents a schematic flowchart of a method for controlling a steam turbine control valve according to an embodiment of this application.

[0026] Figure 6 A schematic flowchart of another method for controlling a steam turbine control valve provided in an embodiment of this application is shown. Detailed Implementation

[0027] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0030] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0031] Existing frequency modulation technology and actuator monitoring methods still have many limitations in dynamic environments, making it difficult to meet the needs of modern industrial production. The existing technologies have the following defects and shortcomings:

[0032] 1. Limited Frequency Regulation Methods and Inflexible Response: Existing frequency regulation methods primarily rely on manual settings or adjustments based on simple PID controllers. This traditional approach struggles to handle complex, multi-variable dynamic conditions, especially when load fluctuations are severe or factors such as steam flow and boiler load vary significantly. The system response is sluggish, and regulation accuracy is poor. The system cannot comprehensively optimize based on multi-dimensional inputs such as boiler load, steam flow, and frequency deviation, leading to reduced energy efficiency, energy waste, and frequent fluctuations in the control process.

[0033] 2. Lag in Control Valve Monitoring and Fault Prediction: Existing control valve monitoring methods mostly adopt a post-event diagnostic approach, meaning that problems are detected through manual inspection or single-threshold alarms when obvious jamming, lag, or abnormal operation of the control valve occurs. This approach is lagging and cannot provide early warning before faults occur. Especially for latent faults such as potential mechanical jamming, wear, or actuator malfunctions, it often misses the opportunity for early intervention. Furthermore, most existing monitoring methods rely on only a single parameter (such as valve position deviation), lacking comprehensive utilization of multi-dimensional data such as actuator current, action time, and signal noise, failing to build an effective fault prediction model, and thus unable to achieve early warning.

[0034] 3. Dispersed control and fault prediction, resulting in low system integration: Currently, control systems and fault prediction are mostly implemented in a decentralized manner, lacking a unified device and platform. This decentralized approach not only increases the cost of hardware deployment and system maintenance but also leads to poor data exchange between different systems, making it difficult to achieve integrated closed-loop control and health management. Furthermore, the lack of modular design makes later upgrades and expansions complex and difficult, failing to adapt to rapidly changing production needs.

[0035] 4. Insufficient frequency regulation accuracy and reliability, making unplanned downtime difficult to avoid: Under long-term operation and complex load changes, the regulation performance of existing systems is prone to decline during continuous operation. Especially under long-term high loads or complex environmental conditions, the accuracy and response speed of frequency regulation decrease, leading to reduced energy efficiency and decreased system safety. Due to the lack of advanced fault prediction and health monitoring mechanisms, regulator failures often trigger unplanned downtime, severely impacting production reliability and stability.

[0036] To address the aforementioned issues, this application proposes a thermal control system based on frequency modulation decision-making algorithms and valve fault prediction. A closed-loop architecture deeply integrating frequency modulation decision-making and valve fault prediction is constructed. A data acquisition module acquires multi-dimensional operating condition data in real time, while the control module simultaneously performs valve fault prediction and dynamic frequency modulation decisions. Efficient data interaction is achieved between modules and the monitoring platform. This not only solves the problems of lag and insufficient accuracy in response to multi-variable dynamic operating conditions using traditional frequency modulation methods, but also upgrades valve fault management from post-event diagnosis to pre-event prediction. Furthermore, it breaks down data barriers in distributed architectures, achieving synergy between control strategies and health management, significantly improving the system's reliability and intelligence under complex operating conditions. The thermal control system based on frequency modulation decision-making algorithms and valve fault prediction relies on the computing power of servers to provide services to users. These servers can be independent servers or servers providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing platforms such as big data and artificial intelligence platforms.

[0037] This application provides a thermal control system based on a frequency modulation decision algorithm and valve fault prediction, such as... Figure 1 As shown, the system includes a communication module 1, a data acquisition module 2, a control module 3, and an execution module 4.

[0038] Communication module 1 is used to receive power grid frequency regulation command signals transmitted from the monitoring platform and transmit them to control module 3. The power grid frequency regulation command signals originate from the power grid dispatch center, such as AGC automatic generation control commands, primary frequency regulation / secondary frequency regulation trigger signals, etc. It also transmits multi-dimensional operating condition data collected by data acquisition module 2, prediction results generated by control module 3, frequency regulation control commands generated by control module 3, and execution status data collected by execution module 4 to the monitoring platform to ensure remote monitoring and control of the system and provide functions such as data uploading and fault alarm.

[0039] Data acquisition module 2 is used to collect multi-dimensional operating condition data in real time through the sensor group and transmit the multi-dimensional operating condition data to control module 3. The multi-dimensional operating condition data includes temperature, pressure, steam flow, valve opening degree and actuator current, etc.

[0040] The control module 3 is used to perform fault prediction on multi-dimensional operating condition data, generate prediction results that characterize the health status of the control valve, generate frequency regulation control commands using the power grid frequency regulation command signal, multi-dimensional operating condition data and prediction results, transmit the frequency regulation control commands to the execution module 4, and receive execution status data. It transmits the multi-dimensional operating condition data collected by the data acquisition module 2, the prediction results generated by the control module 3, the frequency regulation control commands generated by the control module 3, and the execution status data collected by the execution module 4 to the communication module 1.

[0041] The execution module 4 is used to perform gate opening adjustment operations according to frequency modulation control commands, ensuring stable system operation. During the gate opening adjustment operation, it collects execution status data and transmits the data to the control module 3. The adjustment process of the execution module 4 is monitored in real time by the control module 3 to ensure that each adjustment conforms to the optimal control strategy.

[0042] Specifically, the data acquisition module 2 includes a data acquisition unit 201 and a data aggregation and transmission unit 202.

[0043] The data acquisition unit 201 is used to acquire initial multi-dimensional operating condition data in real time through the sensor group and transmit the initial multi-dimensional operating condition data to the data aggregation and transmission unit 202. The sensor group includes a temperature sensor, a pressure transmitter, a flow meter, a position encoder and a current sensor. The initial multi-dimensional operating condition data includes temperature data, pressure data, steam flow data, valve position opening data and actuator current data.

[0044] The data aggregation and transmission unit 202 is used to filter the initial multi-dimensional working condition data to obtain multi-dimensional working condition data, and transmit the multi-dimensional working condition data to the control module 3.

[0045] Specifically, the control module 3 includes a data processing unit 301, a frequency modulation decision unit 302, and a modulation gate fault prediction unit 303.

[0046] The valve fault prediction unit 303 receives multi-dimensional operating condition data, inputs this data into the valve fault prediction model for fault prediction, obtains the prediction results, and transmits the prediction results to the data processing unit 301. The prediction results include the valve health index (H: 0-1, 1 being fully healthy), fault risk level (R: low risk R1 / medium risk R2 / high risk R3), predicted fault type (e.g., jamming, wear, leakage, if present), and fault warning threshold (H_min, triggering constraints if below the threshold). The thermal control system incorporates valve fault prediction models such as LSTM / CNN-based remaining life prediction models and fault risk assessment models. The valve fault prediction unit 303 combines historical and real-time data, using trend analysis and anomaly detection models to monitor the actuator's health and provide early warnings of potential faults.

[0047] The data processing unit 301 is used to receive multi-dimensional operating condition data and power grid frequency regulation command signals, extract target operating condition parameters from the multi-dimensional operating condition data, extract target command data from the power grid frequency regulation command signals, standardize and verify the prediction results, target operating condition parameters, and target command data to ensure data reliability, obtain standardized processed data, and transmit the standardized processed data to the frequency regulation decision unit 302. The target operating condition parameters include the current actual power generation of the unit, main steam pressure, reheat steam temperature, actual opening of the regulating valve, and regulating valve action rate. The target command data includes the frequency regulation target load deviation, frequency regulation response dead time, and frequency regulation duration.

[0048] The frequency regulation decision unit 302 is used to determine the valve status label (i.e., valve health status judgment) using the valve health index, fault risk level, and fault warning threshold in the standardized data. It uses the sum of the normalized frequency regulation target load deviation and the normalized actual power generation of the unit in the standardized data as the theoretical frequency regulation load target (i.e., frequency regulation demand analysis), and corrects the theoretical frequency regulation load target to obtain the frequency regulation load target and frequency regulation response priority. Specifically, the frequency regulation demand analysis can include using the sum of the normalized frequency regulation target load deviation and the normalized actual power generation of the unit as the theoretical frequency regulation load target P_target; determining whether P_target is within the safe operating range of the unit (e.g., maximum boiler output P_max, minimum stable output P_min); if it exceeds this range, it is corrected to P_target_mod = min(P_max, max(P_min, P_target)); the output is the frequency regulation load target P_target_mod and the frequency regulation response priority (e.g., primary frequency regulation priority > secondary frequency regulation). In the differentiated frequency modulation control strategy, the target frequency modulation control strategy corresponding to the regulating valve status label is determined. Frequency modulation control commands are generated using the target frequency modulation control strategy, the frequency modulation load target, and the frequency modulation response priority, and then transmitted to the execution module 4. The frequency modulation decision unit 302 makes real-time decisions on regulating valve adjustments based on collected multi-dimensional operating condition data and an optimization algorithm.

[0049] Specifically, the data processing unit 301 is used to perform filtering and outlier removal processing on the target operating condition parameters and target command data to obtain processed data. The filtering and outlier removal processing includes high-frequency noise processing based on moving average filtering and... The criteria for sensor fault outlier removal processing (outliers such as sudden increases / decreases in power and valve opening data) include clean frequency regulation target load deviation, clean frequency regulation response dead time, clean unit actual power generation, and clean valve actual opening.

[0050] The data acquisition link for data processing is obtained, and the communication link integrity is checked (such as whether sensor communication is interrupted or whether a transmitter failure occurs). The valid data markers corresponding to each node in the data acquisition link are obtained. The valid data markers are any one of available, backup alternative, and emergency alarm.

[0051] The processed data is normalized to unify parameters of different dimensions into the [0,1] interval (the valve health index H directly adopts the [0,1] interval) to facilitate subsequent decision-making logic calculations. Combined with the prediction results, standardized processed data is obtained and transmitted to the frequency regulation decision unit 302. The standardized processed data includes the normalized frequency regulation target load deviation, the normalized actual power generation of the unit, the valve health index, the fault risk level, and the fault warning threshold.

[0052] Specifically, the data processing unit 301 is also used to, for each node in the data acquisition link, when a communication interruption is detected at the node, use an emergency alarm as a valid data marker corresponding to the node.

[0053] When it is detected that there is no communication interruption at the node and the node's data is valid, it can be used as a valid data marker for the node.

[0054] When it is detected that there is no communication interruption at the node and the node's data is invalid, an estimated value is obtained through a backup data switching mechanism. The backup replacement is used as the valid data marker for the corresponding node. The backup data switching mechanism can be to use historical data from the same period or to use model estimates for replacement.

[0055] Specifically, the frequency modulation decision unit 302 is also used for judging the health status of the modulation gate:

[0056] When the valve health index is greater than or equal to the fault warning threshold and the fault risk level is low risk R1, the valve is determined to be "healthy and can participate in frequency modulation normally", and a valve status label indicating normal operation is generated.

[0057] When the valve health index is less than the fault warning threshold, the valve health index is greater than or equal to the preset fault value, and the fault risk level is medium risk R2, the valve is determined to be "sub-healthy, and the frequency modulation load / action rate needs to be limited", and a valve status label indicating sub-health constraints is generated.

[0058] When the health index of the control valve is less than the preset fault value and the fault risk level is high risk R3, it is determined that "the control valve is nearing a fault and needs to reduce / prohibit participation in frequency regulation, triggering an operation and maintenance warning". A control valve status label indicating high risk restriction is generated. The fault warning threshold is 0.6 and the preset fault value is 0.4.

[0059] Specifically, the frequency regulation decision unit 302 is also used to generate differentiated frequency regulation control strategies based on the regulation gate status label and the frequency regulation load target P_target_mod, wherein the regulation gate command is the main one, and other frequency regulation methods are linked when necessary:

[0060] When the valve status label is normal (R1), the first control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. This first control strategy includes a first valve command, but no additional linkage commands (only the valve is executed). The first valve command includes the first valve target opening and the first valve action rate. The first valve action rate, v_target_1, is the rated rate, such as 5% / s. The first valve target opening is calculated from the frequency regulation load target using the thermal control system characteristic curve, i.e., the first valve target opening α_target_1 = f(P_target_mod, P_steam), where P_target_mod is the frequency regulation load target, P_steam is the main steam pressure, and f() represents the thermal control system characteristic curve. The core decision logic prioritizes using the valve to achieve rapid frequency regulation (fastest response speed), calculating the valve target opening based on P_target_mod obtained from the frequency regulation demand analysis.

[0061] When the valve status label is sub-health constraint R2, the second control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. The second control strategy includes a second valve command and a bypass valve opening command β_target (to supplement the remaining 20% ​​load deviation). The second valve command includes a second valve target opening and a second valve action rate. The second valve target opening is obtained by multiplying the first valve target opening by 80%, i.e., second valve target opening α_target_2 = 0.8 × f(P_target_mod, P_steam) (reducing load contribution by 20%). The second valve action rate is obtained by dividing the rated rate by 2, i.e., second valve action rate v_target_2 = 0.5 × rated rate (slowing down the action and reducing wear). The core decision logic is to limit the valve frequency regulation load / action rate to avoid aggravating valve losses, and, if necessary, link the bypass valve to assist in frequency regulation.

[0062] When the valve status label is high-risk limit R3, the third control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. This third control strategy includes a third valve command, a backup frequency regulation method command, and an alarm command. The third valve command includes the target valve opening, which is the actual valve opening (the valve is locked, allowing only minor adjustments). The backup frequency regulation method command is either a steam-driven feedwater pump speed regulation control command or a boiler combustion rate regulation control command. The alarm command sends a "valve high risk, shutdown for maintenance" signal to the operation and maintenance system. The core decision logic is to prohibit the valve from undertaking the primary frequency regulation task, switch to the "backup frequency regulation method," and simultaneously trigger an operation and maintenance alarm.

[0063] Specifically, the execution module 4 includes a regulating gate and a drive motor, which adjusts the opening degree of the regulating gate after receiving a signal from the control module. For example... Figure 2As shown, components such as the control valve, actuator, sensor, and control module are interconnected with the sensor system via pipelines, forming a complete thermal control system. Under the control of the frequency modulation decision unit, the actuator precisely adjusts the valve opening, thereby ensuring stable system operation. Through intelligent frequency modulation control and control valve fault prediction, the system's operational reliability, accuracy, and safety are enhanced, while reducing maintenance and operating costs.

[0064] When the regulating gate status label corresponding to the frequency modulation control command is normal, the first regulating gate command is sent to the regulating gate servo system to drive the regulating gate servo system to perform regulating gate opening adjustment operation.

[0065] When the regulating valve status label corresponding to the frequency modulation control command is sub-health constraint, the second regulating valve command is sent to the regulating valve servo system to drive the regulating valve servo system to perform regulating valve opening adjustment operation, and the bypass valve opening command is sent to the bypass valve controller to drive the bypass valve controller to perform regulating valve opening adjustment operation.

[0066] When the regulating gate status label corresponding to the frequency modulation control command is high-risk restriction, the third regulating gate command is sent to the regulating gate servo system to drive the regulating gate servo system to perform regulating gate opening adjustment operation, the backup frequency modulation means command is sent to the backup system to drive the backup system to perform regulating gate opening adjustment operation, and the alarm command is sent to the communication module so that the communication module sends the alarm command to the monitoring platform.

[0067] It should be noted that while sending the control valve command, bypass valve command, and backup means command to the corresponding actuator (control valve servo system, bypass valve controller, etc.), the decision results (such as control valve status, frequency regulation load allocation, etc.) are also sent to the thermal control system monitoring interface for operators to view.

[0068] Specifically, the execution module 4 also includes an execution status feedback unit 401.

[0069] The execution status feedback unit 401 is used to collect execution status data, which includes the valve feedback opening, actual action rate, and actual power change of the unit.

[0070] The execution status data is transmitted to the valve fault prediction unit 303 of the control module 3, so that the valve fault prediction unit 303 inputs the execution status data into the valve fault prediction model for fault prediction and updating, and obtains the frequency modulation prediction result. Its function is to update the input characteristics of the fault prediction model and optimize the calculation accuracy of health H and risk level R, such as correcting the remaining life prediction result based on the actual wear and tear of the valve.

[0071] The difference between the target opening of the regulating valve and the feedback opening of the regulating valve is taken as the regulating valve opening deviation, and the difference between the frequency regulation load target and the actual power change of the unit is taken as the unit power deviation. Specifically, when the regulating valve status label corresponding to the frequency regulation control command is normal, the regulating valve target opening is the first regulating valve target opening; when the regulating valve status label corresponding to the frequency regulation control command is sub-health constraint, the regulating valve target opening is the second regulating valve target opening; and when the regulating valve status label corresponding to the frequency regulation control command is high-risk restriction, the regulating valve target opening is the third regulating valve target opening.

[0072] The valve opening deviation threshold and the unit power deviation threshold are obtained. If the valve opening deviation is greater than the valve opening deviation threshold or the unit power deviation is greater than the unit power deviation threshold, a correction command is generated and transmitted to the frequency regulation decision unit 302 of the control module 3 so that the frequency regulation decision unit 302 can regenerate the frequency regulation control command.

[0073] Specifically, the control module 3 also includes an auxiliary unit 304.

[0074] The auxiliary unit 304 receives the valid data markers corresponding to each node in the data acquisition link transmitted by the data processing unit 301, and the frequency modulation prediction results transmitted by the regulating valve fault prediction unit 303. It uses the frequency modulation prediction results and the valid data markers corresponding to each node to generate early warning information, which is then transmitted to the communication module 1, enabling the communication module 1 to transmit the early warning information to the monitoring platform. For example, the auxiliary unit 304 performs multi-dimensional fusion analysis on the valid data markers and the frequency modulation prediction results. If the valid data marker of a data acquisition node is an emergency alarm, and the frequency modulation prediction results show that the regulating valve fault risk level is high-risk R3, then a composite early warning information of data node communication interruption + regulating valve high-risk fault is generated. If the valid data markers of multiple nodes are backup alternatives, and the regulating valve health index is lower than the early warning threshold, then an early warning information of insufficient data link reliability + regulating valve sub-health is generated. The auxiliary unit 304 transmits the generated early warning information to the communication module 1. The communication module transmits the early warning information to the monitoring platform through the fault alarm interface in accordance with the remote communication protocol. Finally, it presents the information to the operation and maintenance personnel in the form of visual alarms (such as pop-ups and audio-visual prompts) so that they can identify and handle system anomalies in a timely manner.

[0075] like Figure 3As shown, the data acquisition module consists of a data acquisition unit (including sensors for temperature, pressure, steam flow, valve opening, actuator current, etc.) and a data aggregation and transmission interface (responsible for signal filtering and format standardization). It collects multi-dimensional operating condition data in real time and transmits it to the data processing subunit of the control module. The control module, as the core of the system, includes a data processing subunit (responsible for data distribution and aggregation), a frequency regulation decision unit (integrating an optimization algorithm module and a valve adjustment decision-maker), and a valve fault prediction unit (equipped with a historical database, trend analysis model, anomaly detection model, and fault early warning analyzer). The data processing subunit performs preprocessing such as filtering and format standardization on the collected data before distributing it to the frequency regulation decision unit and the valve fault prediction unit. The valve fault prediction unit performs fault analysis based on historical data and current operating conditions, outputting prediction results characterizing the valve's health status to assist in decision-making. The frequency regulation decision unit integrates the power grid frequency regulation command, operating condition data, and fault prediction results to generate valve control commands and transmit them to the execution module. The execution module consists of a drive motor, a gate body, and an execution status feedback subunit. After receiving control commands, it performs gate opening adjustment operations. At the same time, the execution status feedback subunit collects execution status data such as the actual gate opening and action rate in real time and sends them back to the control module, forming a closed-loop control of decision-making-execution-feedback.

[0076] The communication module includes a data upload interface, a command receiving interface, a fault alarm interface, and a remote communication protocol module, enabling bidirectional data and command interaction with the host computer / monitoring platform. The control module transmits system operating data (multi-dimensional operating condition data, frequency regulation decision results, and valve fault prediction information, etc.) to the host computer via the communication module's data upload interface, supporting remote real-time monitoring. Remote control commands (such as parameter configuration and strategy adjustment commands) issued by the host computer are received by the communication module's command receiving interface and forwarded to the control module for remote intervention. Simultaneously, fault warning information generated by the control module is sent to the host computer via the communication module's fault alarm interface, completing remote fault alarming. Through layered collaboration and data interaction among the modules, the system constructs a closed-loop architecture encompassing acquisition, processing, decision-making, execution, and monitoring, ensuring both the real-time and accurate frequency regulation response and enabling early prediction and remote operation and maintenance management of valve faults.

[0077] This application introduces a frequency regulation decision unit based on multi-parameter optimization and fuzzy control into the control module, enabling real-time dynamic optimization under load fluctuations. Unlike traditional PID controllers, this decision unit not only responds quickly to load changes but also performs comprehensive optimization based on multi-dimensional data such as boiler load, steam flow, and system frequency deviation, thereby improving the flexibility and stability of system regulation and ensuring efficient system operation in complex environments. By comprehensively utilizing multi-dimensional characteristic data (such as valve position deviation, actuator current fluctuation, response time, etc.) and employing advanced anomaly detection and trend analysis models, this application can predict potential valve faults (such as jamming, wear, etc.) in advance. This technology significantly improves the accuracy of fault warnings, allowing for effective measures to be taken before faults affect system operation, avoiding reactive post-event handling in traditional methods, and improving system reliability and operating efficiency. This application integrates frequency regulation control and fault prediction functions into a single device, eliminating the coordination problems between multiple independent modules in existing technologies. The control module can not only perform real-time system regulation but also monitor the health status of actuators in real time and issue fault warnings based on real-time data. This integrated design reduces hardware deployment and system maintenance costs, simplifies system structure, and enhances system synergy and stability. Through the synergistic function of the frequency regulation decision unit and the fault prediction unit, this application not only improves system energy efficiency and response speed but also effectively reduces the risk of sudden shutdowns. For example, when a potential fault occurs in the regulating valve, the system can promptly predict the fault and take corresponding emergency measures to avoid unplanned shutdowns caused by equipment failure. This significantly improves the operational reliability and safety of industrial thermal control systems, making them particularly suitable for complex operating conditions and long-term operation environments. The design of this application has broad application prospects and can adapt to various industrial scenarios, especially in places requiring precise regulation and actuator health management, such as thermal power units, chemical reaction plants, and metallurgical heating furnaces. With its excellent regulation capabilities, fault prediction function, and high reliability, it can be widely used in multiple industries and has strong market promotion value.

[0078] This application can be implemented in a steam turbine control valve system, combining frequency regulation decision-making and valve fault prediction functions to optimize the real-time dynamic adjustment capability of the thermal control system and the health management of the actuators, thereby ensuring the stability and safety of the industrial thermal control system. Figure 4 As shown, the turbine control valve system includes the following main components:

[0079] 1. Valve Actuator ①: The actuator is one of the core components of the system, responsible for receiving instructions from the control module and driving the valve to perform corresponding opening adjustments. The actuator converts control signals into mechanical actions through electrical or pneumatic means, adjusting the valve opening and achieving real-time system regulation.

[0080] 2. Valve Body ②: The valve body is a key component in the system for controlling the fluid flow. It connects to the actuator and adjusts the opening degree through the actuator's drive. The function of the valve is to precisely control the fluid flow rate according to actual needs, ensuring the normal operation of the thermal control system.

[0081] 3. Sensors and Monitoring Devices ③: Sensors in the system are responsible for collecting various operating parameters in real time, such as temperature, pressure, and flow rate, and transmitting the data to the control module. The monitoring device is used to collect the real-time status of the valves, provide feedback on the actuator's operating status and valve opening, and ensure the system is in optimal control condition.

[0082] 4. Auxiliary Modules and Feedback Mechanisms④: The auxiliary modules mainly include a fault detection unit and an anomaly early warning system. This module analyzes valve operating data, combines historical data with sensor inputs, and predicts potential equipment failures or performance degradation in advance, providing data support for subsequent maintenance decisions.

[0083] The working principle of the steam turbine control valve system is explained in detail below:

[0084] Data Acquisition and Preprocessing Stage: In this stage, various high-precision sensors configured within the system are responsible for continuous real-time monitoring and data acquisition of key operating parameters during turbine operation, such as flow rate, pressure, and temperature. The acquired raw data is then rapidly transmitted to the data processing unit. To ensure data accuracy and reliability and avoid the influence of noise interference, the data processing unit performs a series of preprocessing operations on the data, including but not limited to filtering and normalization. Through these processing steps, the system can effectively eliminate abnormal data, improve data quality, and provide a solid data foundation for subsequent control decisions.

[0085] Frequency regulation decision and valve adjustment stage: In this core stage, the frequency regulation decision unit within the control module comprehensively considers various influencing factors, such as current load fluctuations and the degree of system frequency deviation, to perform intelligent optimization and adjustment. Based on the analysis results of these parameters, the frequency regulation decision unit calculates the optimal valve opening value in real time and instructs the actuator to adjust the valve accordingly. This dynamic adjustment mechanism not only ensures that the turbine maintains a highly efficient and stable operating state under different operating conditions, but also significantly improves the system's response speed, effectively copes with various emergencies, and ensures the operational safety and stability of the entire system.

[0086] Fault Prediction and Early Warning Phase: To further enhance the system's reliability and preventative maintenance capabilities, this control system also integrates advanced fault prediction and early warning functions. The system utilizes multi-dimensional data input, including historical operating data, real-time monitoring data, and various operating parameters, employing big data analytics and machine learning algorithms to identify and assess potential fault risks in the control valves at an early stage. Once abnormal signs or potential fault risks are detected, the system immediately triggers an early warning mechanism, promptly notifying maintenance personnel through various means such as audible and visual alarms and SMS notifications. This allows for appropriate preventative measures to be taken, effectively avoiding unplanned downtime due to equipment failure and maximizing the continuous and stable operation of the steam turbine.

[0087] This application provides a thermal control system based on a frequency regulation decision algorithm and valve fault prediction. Compared with existing technologies, this application consists of a communication module, a data acquisition module, a control module, and an execution module, with each module forming a closed loop through data interaction. The communication module receives power grid frequency regulation commands from the monitoring platform and transmits them to the control module, while simultaneously transmitting multi-dimensional system data back to the monitoring platform, achieving bidirectional interaction between remote commands and data. The data acquisition module collects multi-dimensional operating condition data in real time through a sensor array, providing raw input to the control module. The control module performs valve fault prediction on the operating condition data, generates frequency regulation control commands by combining the power grid frequency regulation commands and prediction results, and simultaneously receives execution status data from the execution module, forming a decision-making closed loop. The execution module adjusts the valve opening according to the control commands, synchronously collects execution status data, and transmits it back to the control module, completing execution feedback. This application's end-to-end closed loop effectively adapts to multi-variable dynamic operating conditions, solving the problems of response lag and insufficient regulation accuracy in traditional methods. Moreover, it breaks down the data barriers of the distributed architecture, with clear data flow and close collaboration among modules, reducing system maintenance costs and achieving deep collaboration between frequency regulation strategy and health management. Frequency regulation decision-making integrates power grid commands, operating condition data and regulator health status, ensuring frequency regulation response performance and dynamically adjusting strategies based on equipment health, thereby improving the overall reliability and security of the system.

[0088] Furthermore, as Figure 1 In a specific implementation of the method, this application provides a thermal control method based on a frequency modulation decision algorithm and valve fault prediction, such as... Figure 5 As shown, the method includes:

[0089] S101 The communication module receives the power grid frequency regulation command signal transmitted by the monitoring platform and transmits the power grid frequency regulation command signal to the control module.

[0090] In this embodiment, the communication module acts as the interaction hub between the system and the monitoring platform. It receives power grid frequency regulation command signals (such as AGC signals and primary / secondary frequency regulation trigger signals) transmitted from the monitoring platform in real time and accurately transmits them to the control module, providing instruction basis for frequency regulation decisions. Simultaneously, it enables real-time issuance of power grid frequency regulation requirements, ensuring the system's rapid response to power grid frequency regulation and improving the timeliness of frequency regulation.

[0091] S102 The data acquisition module collects multi-dimensional operating condition data in real time through the sensor group and transmits the multi-dimensional operating condition data to the control module.

[0092] In this embodiment, the data acquisition module uses a group of sensors for temperature, pressure, steam flow, valve opening, actuator current, etc., to collect multi-dimensional operating condition data from all angles. After signal normalization, the data is transmitted to the control module, providing raw data support for fault prediction and frequency adjustment decisions. This ensures the comprehensiveness and real-time nature of the operating condition data, enabling subsequent fault prediction and frequency adjustment decisions to be based on real dynamic operating conditions, thus enhancing the system's adaptability to complex operating conditions.

[0093] S103 The control module performs fault prediction on multi-dimensional operating condition data, generates prediction results characterizing the health status of the regulating valve, and generates frequency regulation control commands using the power grid frequency regulation command signal, multi-dimensional operating condition data and prediction results, and transmits the frequency regulation control commands to the execution module.

[0094] In this embodiment, the control module first performs fault prediction on multi-dimensional operating condition data, and generates valve health status prediction results (such as health level, fault risk level, and potential fault type) through trend analysis, anomaly detection, and other models. Then, it integrates the power grid frequency regulation command signal and operating condition data, generates valve control commands (such as target opening degree and action rate) through a frequency regulation decision algorithm, and transmits them to the execution module. This enables intelligent coordination between valve fault prediction and frequency regulation decision-making, which not only avoids valve fault risks in advance but also allows the frequency regulation strategy to adapt to the equipment health status, significantly reducing the risk of unplanned downtime while improving frequency regulation accuracy.

[0095] S104. The execution module performs the gate opening adjustment operation according to the frequency modulation control command, and collects execution status data during the execution of the gate opening adjustment operation, and transmits the execution status data to the control module.

[0096] In this embodiment, the execution module drives the regulating valve to perform opening adjustment operations according to the frequency modulation control command. Simultaneously, the execution status feedback unit collects execution status data in real time, including the actual opening degree of the regulating valve, the action rate, and changes in unit power, and transmits this data back to the control module. This constructs a closed-loop control link of decision-making, execution, and feedback, enabling the control module to verify the execution effect in real time. If deviations occur, they can be corrected promptly, ensuring the accuracy and stability of the frequency modulation operation.

[0097] S105. The control module receives the execution status data and transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the communication module.

[0098] In this embodiment, the control module receives the execution status data from the execution module, summarizes multi-dimensional operating condition data, valve fault prediction results, frequency modulation control commands, and execution status data, and then transmits them to the communication module. This provides complete data support for uploading data to the monitoring platform, ensuring the integrity of the data uploaded to the monitoring platform. This enables remote monitoring to cover the entire process of data collection, prediction, decision-making, execution, and feedback, improving the comprehensiveness and accuracy of operation and maintenance.

[0099] S106. The communication module transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the monitoring platform.

[0100] In this embodiment, the communication module aggregates multi-dimensional operating condition data, valve fault prediction results, frequency modulation control commands, and execution status data, and transmits them to the monitoring platform via a remote communication protocol. This enables remote visual monitoring of the entire system process, allowing maintenance personnel to grasp the overall system operation in real time, facilitating remote management, fault tracing, and strategy optimization.

[0101] This application provides a thermal control method based on frequency regulation decision-making algorithms and valve fault prediction. Compared with existing technologies, this application achieves real-time and efficient interaction between power grid frequency regulation commands and multi-dimensional operating condition data through a closed-loop process encompassing command reception, data acquisition, fault prediction decision-making, execution feedback, data uploading, and data aggregation. During this process, advanced control modules accurately predict and intelligently decide on valve faults, ensuring that the frequency regulation strategy can flexibly adapt to the real-time health status of the equipment. Simultaneously, the timely feedback mechanism of the execution module and the remote monitoring function of the communication module work together to form a deep collaborative system from decision-making to execution and operation and maintenance management. This not only significantly improves the timeliness and operational accuracy of frequency regulation response but also provides early warnings before faults occur, effectively avoiding potential risks from valve faults and ensuring the stability and reliability of the system under complex and variable operating conditions. Furthermore, this closed-loop management provides comprehensive and detailed data support for remote operation and maintenance, significantly reducing system maintenance costs, decreasing the probability of unplanned downtime events, and further improving overall operation and maintenance efficiency and system sustainability.

[0102] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, this application provides another thermal control method based on frequency modulation decision algorithm and valve fault prediction, such as... Figure 6 As shown, the method includes:

[0103] S201 The communication module receives the power grid frequency regulation command signal transmitted by the monitoring platform and transmits the power grid frequency regulation command signal to the control module.

[0104] In this embodiment, the communication module is responsible for receiving power grid frequency regulation command signals from the monitoring platform and accurately transmitting these command signals to the control module. This process not only ensures the real-time and efficient issuance of power grid frequency regulation demands but also provides timely and accurate command basis for the control module's subsequent intelligent frequency regulation decisions based on regulator fault prediction. This approach ensures the timeliness and accuracy of the system's response to power grid frequency regulation from the source, avoiding inaccurate frequency regulation caused by command transmission delays or errors.

[0105] S202 The data acquisition module collects multi-dimensional operating condition data in real time through the sensor group and transmits the multi-dimensional operating condition data to the control module.

[0106] In this embodiment, the data acquisition unit of the data acquisition module collects initial multi-dimensional operating condition data in real time through a sensor group and transmits the initial multi-dimensional operating condition data to the data aggregation and transmission unit. The sensor group includes a temperature sensor, a pressure transmitter, a flow meter, a position encoder, and a current sensor. The initial multi-dimensional operating condition data includes temperature data, pressure data, steam flow data, valve opening data, and actuator current data. The diverse sensor group enables comprehensive acquisition of operating condition data and ensures data integrity.

[0107] Then, the data acquisition module's data aggregation and transmission unit filters the initial multi-dimensional operating condition data to improve the reliability and accuracy of the data, obtains multi-dimensional operating condition data, and transmits the multi-dimensional operating condition data to the control module.

[0108] S203. The valve fault prediction unit of the control module receives multi-dimensional operating condition data, inputs the multi-dimensional operating condition data into the valve fault prediction model to perform fault prediction, obtains the prediction result, and transmits the prediction result to the data processing unit of the control module.

[0109] In this embodiment, the control module's valve fault prediction unit receives multi-dimensional operating condition data, inputs this data into the valve fault prediction model for fault prediction, obtains the prediction results, and transmits the prediction results to the data processing unit. The prediction results include the valve health index, fault risk level, predicted fault type, and fault warning threshold. This enables proactive valve fault prediction, upgrading the traditional post-event diagnosis model to early identification of potential faults (such as mechanical jamming, wear, etc.), avoiding missing the optimal handling opportunity. Simultaneously, the multi-dimensional prediction results provide the frequency regulation decision unit with precise equipment health constraints and provide maintenance personnel with comprehensive evidence for fault location and risk assessment. This significantly reduces the risk of unplanned downtime while ensuring the adaptability of the frequency regulation strategy, thereby improving the overall reliability and safety of the system.

[0110] S204. The data processing unit of the control module receives multi-dimensional operating condition data and power grid frequency regulation command signal, extracts target operating condition parameters from the multi-dimensional operating condition data, extracts target command data from the power grid frequency regulation command signal, performs standardization processing and verification on the prediction results, target operating condition parameters, and target command data to obtain standardized processed data, and transmits the standardized processed data to the frequency regulation decision unit of the control module.

[0111] In this embodiment, the data processing unit of the control module receives multi-dimensional operating condition data and grid frequency regulation command signals. It extracts target operating condition parameters from the multi-dimensional operating condition data and target command data from the grid frequency regulation command signals. The target operating condition parameters include the current actual power generation of the unit, main steam pressure, reheat steam temperature, actual opening of the regulating valve, and regulating valve action rate. The target command data includes frequency regulation target load deviation, frequency regulation response dead time, and frequency regulation duration.

[0112] Next, the data processing unit of the control module standardizes and verifies the prediction results, target operating parameters, and target command data to obtain standardized data. The specific process is as follows:

[0113] The data processing unit performs filtering and outlier removal on the target operating condition parameters and target command data to obtain processed data. The filtering and outlier removal processes include high-frequency noise processing based on moving average filtering and... The standard sensor fault outlier removal process effectively eliminates high-frequency noise and sensor fault outliers, ensuring the cleanliness and reliability of input data. Processed data includes clean frequency regulation target load deviations, clean frequency regulation response dead time, clean actual unit power generation, and clean actual valve opening.

[0114] Next, the data processing unit acquires the data acquisition link for processing the data and performs communication link integrity checks on the data acquisition link to obtain a valid data marker for each node in the data acquisition link. The valid data marker can be any one of the following: available, backup alternative, or emergency alarm, ensuring the robustness of the data link and avoiding data failure due to single point of failure. Specifically, for each node in the data acquisition link, when a communication interruption is detected, the data processing unit uses an emergency alarm as the valid data marker for the node; when no communication interruption is detected and the node's data is valid, it uses available as the valid data marker for the node; when no communication interruption is detected and the node's data is invalid, it obtains an estimated value through a backup data switching mechanism and uses a backup alternative as the valid data marker for the node. The backup data switching mechanism either uses historical data from the same period or a model estimate for replacement.

[0115] Subsequently, the data processing unit normalizes the processed data and combines it with the prediction results to obtain standardized processed data. This standardized processed data is then transmitted to the frequency regulation decision unit. The standardized processed data includes the normalized frequency regulation target load deviation, the normalized actual generating power of the unit, the regulator health index, the fault risk level, and the fault warning threshold. This achieves the coordinated unification of multi-dimensional and different-scale data, providing the frequency regulation decision unit with standardized inputs that conform to physical meaning while taking into account equipment health constraints. Ultimately, this improves the accuracy of frequency regulation decisions and the reliability of system operation, and reduces the risk of unplanned downtime caused by data quality issues or equipment failures.

[0116] S205. The frequency modulation decision unit of the control module uses the valve health index, fault risk level, and fault warning threshold in the standardized processed data to determine the valve status label.

[0117] In this embodiment, the specific process by which the frequency modulation decision unit of the control module determines the modulation status label is as follows:

[0118] When the valve health index is greater than or equal to the fault warning threshold and the fault risk level is low risk R1, the valve is determined to be "healthy and can participate in frequency modulation normally", and a valve status label indicating normal operation is generated.

[0119] When the valve health index is less than the fault warning threshold, the valve health index is greater than or equal to the preset fault value, and the fault risk level is medium risk R2, the valve is determined to be "sub-healthy, and the frequency modulation load / action rate needs to be limited", and a valve status label indicating sub-health constraints is generated.

[0120] When the health index of the control valve is less than the preset fault value and the fault risk level is high risk R3, it is determined that "the control valve is nearing a fault and needs to reduce / prohibit participation in frequency regulation, triggering an operation and maintenance warning". A control valve status label indicating high risk restriction is generated. The fault warning threshold is 0.6 and the preset fault value is 0.4.

[0121] S206. The frequency regulation decision unit of the control module takes the sum of the normalized frequency regulation target load deviation and the normalized actual power generation of the unit in the standardized processing data as the theoretical frequency regulation load target, corrects the theoretical frequency regulation load target, and obtains the frequency regulation load target and frequency regulation response priority.

[0122] In this embodiment, the frequency regulation decision unit of the control module uses the sum of the normalized frequency regulation target load deviation and the normalized actual power generation of the unit in the standardized data as the theoretical frequency regulation load target. It then corrects the theoretical frequency regulation load target to obtain the frequency regulation load target and the frequency regulation response priority. For example, it determines whether the theoretical frequency regulation load target P_target is within the safe operating range of the unit (e.g., maximum boiler output P_max, minimum stable output P_min). If it exceeds this range, it corrects it to P_target_mod = min(P_max, max(P_min, P_target)). The output is the frequency regulation load target P_target_mod and the frequency regulation response priority (e.g., primary frequency regulation priority > secondary frequency regulation priority).

[0123] S207. The frequency modulation decision unit of the control module determines the target frequency modulation control strategy corresponding to the modulation gate status label in the differentiated frequency modulation control strategy, generates frequency modulation control instructions using the target frequency modulation control strategy, frequency modulation load target and frequency modulation response priority, and transmits the frequency modulation control instructions to the execution module.

[0124] In this embodiment, the specific process by which the frequency modulation decision unit of the control module determines the target frequency modulation control strategy is as follows:

[0125] When the valve status label is normal (R1), the first control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. This first control strategy includes a first valve command, but no additional linkage commands (only the valve is executed). The first valve command includes the first valve target opening and the first valve action rate. The first valve action rate, v_target_1, is the rated rate, such as 5% / s. The first valve target opening is calculated from the frequency regulation load target using the thermal control system characteristic curve, i.e., the first valve target opening α_target_1 = f(P_target_mod, P_steam), where P_target_mod is the frequency regulation load target, P_steam is the main steam pressure, and f() represents the thermal control system characteristic curve. The core decision logic prioritizes using the valve to achieve rapid frequency regulation (fastest response speed), calculating the valve target opening based on P_target_mod obtained from the frequency regulation demand analysis.

[0126] When the valve status label is sub-health constraint R2, the second control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. The second control strategy includes a second valve command and a bypass valve opening command β_target (to supplement the remaining 20% ​​load deviation). The second valve command includes a second valve target opening and a second valve action rate. The second valve target opening is obtained by multiplying the first valve target opening by 80%, i.e., second valve target opening α_target_2 = 0.8 × f(P_target_mod, P_steam) (reducing load contribution by 20%). The second valve action rate is obtained by dividing the rated rate by 2, i.e., second valve action rate v_target_2 = 0.5 × rated rate (slowing down the action and reducing wear). The core decision logic is to limit the valve frequency regulation load / action rate to avoid aggravating valve losses, and, if necessary, link the bypass valve to assist in frequency regulation.

[0127] When the valve status label is high-risk limit R3, the third control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. This third control strategy includes a third valve command, a backup frequency regulation method command, and an alarm command. The third valve command includes the target valve opening, which is the actual valve opening (the valve is locked, allowing only minor adjustments). The backup frequency regulation method command is either a steam-driven feedwater pump speed regulation control command or a boiler combustion rate regulation control command. The alarm command sends a "valve high risk, shutdown for maintenance" signal to the operation and maintenance system. The core decision logic is to prohibit the valve from undertaking the primary frequency regulation task, switch to the "backup frequency regulation method," and simultaneously trigger an operation and maintenance alarm.

[0128] S208. The execution module performs the gate opening adjustment operation according to the frequency modulation control command.

[0129] In this embodiment, the control valve servo system is directly driven under normal conditions to ensure timely frequency regulation response. Under sub-health constraints, the bypass valve is activated to maintain frequency regulation capability while limiting valve movement, achieving a balance between equipment health protection and frequency regulation needs. Under high-risk constraints, backup frequency regulation methods are activated and alarm commands are sent, ensuring uninterrupted frequency regulation and timely intervention to prevent fault escalation. This hierarchical control mechanism achieves precise coordination between valve health status and frequency regulation execution, significantly improving the reliability and safety of frequency regulation under complex operating conditions, significantly reducing the risk of unplanned downtime, and maximizing the completion rate of power grid frequency regulation tasks.

[0130] S209. The execution status feedback unit of the execution module collects execution status data during the execution of the gate opening adjustment operation and transmits the execution status data to the control module.

[0131] In this embodiment of the application, the execution status feedback unit of the execution module collects execution status data, which includes the valve feedback opening, actual action rate, and actual power change of the unit.

[0132] Next, the execution status feedback unit transmits the execution status data to the valve fault prediction unit of the control module, so that the valve fault prediction unit inputs the execution status data into the valve fault prediction model for fault prediction and updating, and obtains the frequency modulation prediction result, making the valve fault prediction more dynamic and accurate, and continuously optimizing the fault identification capability.

[0133] Subsequently, the status feedback unit uses the difference between the target valve opening and the feedback valve opening as the valve opening deviation, and the difference between the frequency regulation load target and the actual power change of the unit as the unit power deviation. Specifically, when the valve status label corresponding to the frequency regulation control command is normal, the valve target opening is the first valve target opening; when the valve status label corresponding to the frequency regulation control command is sub-health constraint, the valve target opening is the second valve target opening; and when the valve status label corresponding to the frequency regulation control command is high-risk restriction, the valve target opening is the third valve target opening.

[0134] Finally, the status feedback unit obtains the valve opening deviation threshold and the unit power deviation threshold. If the valve opening deviation is greater than the threshold or the unit power deviation is greater than the threshold, a correction command is generated and transmitted to the frequency regulation decision unit of the control module, so that the frequency regulation decision unit can regenerate the frequency regulation control command. By calculating the valve opening deviation and the unit power deviation, and combining the target opening corresponding to different valve status labels, the correction command mechanism triggered by the deviation threshold drives the frequency regulation decision unit to regenerate the control command, realizing dynamic calibration of frequency regulation control, significantly improving frequency regulation accuracy and response speed, and allowing the correction strategy to adapt to the valve health status. Under the premise of ensuring the completion of the power grid frequency regulation task, it effectively protects the valve equipment, reduces the risk of unplanned downtime, and improves the overall reliability and economy of the system.

[0135] S210. The control module receives the execution status data and transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the communication module.

[0136] In this embodiment, the control module comprehensively transmits multi-dimensional operating condition data, valve fault prediction results, frequency modulation control commands, and execution status data to the communication module. This enables centralized aggregation and uploading of data throughout the entire system operation process, allowing the monitoring platform to monitor the complete system status from data acquisition to execution feedback in real time, providing comprehensive data support for remote operation and maintenance and strategy optimization. On the other hand, the auxiliary unit of the control module receives valid data markers corresponding to each node in the data acquisition link transmitted by the data processing unit, as well as frequency modulation prediction results transmitted by the valve fault prediction unit. It then uses the frequency modulation prediction results and valid data markers corresponding to each node to generate early warning information, which is transmitted to the communication module. This allows the communication module to transmit the early warning information to the monitoring platform, accurately integrating two types of anomalies: data link reliability and valve health risk. This dual early warning system transmits the information to the monitoring platform, enabling maintenance personnel to promptly identify data anomalies and potential equipment faults, intervene early, and significantly reduce the risk of fault escalation and unplanned downtime. This improves system reliability and operation and maintenance efficiency from both data management and equipment health perspectives.

[0137] S211 The communication module transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the monitoring platform.

[0138] In this embodiment, the communication module transmits multi-dimensional operating condition data, prediction results, frequency modulation control commands, and execution status data to the monitoring platform, realizing remote visualization of the entire process of data collection, prediction, decision-making, and execution. This enables maintenance personnel to grasp global information such as the health status of the control valve, the execution effect of the frequency modulation command, and dynamic changes in operating conditions in real time. This facilitates remote fault tracing, strategy optimization, and operation and maintenance management, while also improving system synergy and reducing maintenance costs through a closed-loop data process. At the same time, it allows for the timely detection of potential anomalies to ensure the reliability and safety of the system under complex operating conditions.

[0139] This application provides a thermal control method based on frequency regulation decision-making algorithms and valve fault prediction. Compared with existing technologies, this application achieves real-time and efficient interaction between power grid frequency regulation commands and multi-dimensional operating condition data through a closed-loop process encompassing command reception, data acquisition, fault prediction decision-making, execution feedback, data uploading, and data aggregation. During this process, advanced control modules accurately predict and intelligently decide on valve faults, ensuring that the frequency regulation strategy can flexibly adapt to the real-time health status of the equipment. Simultaneously, the timely feedback mechanism of the execution module and the remote monitoring function of the communication module work together to form a deep collaborative system from decision-making to execution and operation and maintenance management. This not only significantly improves the timeliness and operational accuracy of frequency regulation response but also provides early warnings before faults occur, effectively avoiding potential risks from valve faults and ensuring the stability and reliability of the system under complex and variable operating conditions. Furthermore, this closed-loop management provides comprehensive and detailed data support for remote operation and maintenance, significantly reducing system maintenance costs, decreasing the probability of unplanned downtime events, and further improving overall operation and maintenance efficiency and system sustainability.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0143] In an exemplary embodiment, a computer device is also provided, which includes a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the thermal control method based on frequency modulation decision algorithm and valve fault prediction described in the above embodiments.

[0144] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the thermal control method based on frequency modulation decision algorithm and valve fault prediction.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0146] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0147] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0148] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0149] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A thermal control system based on frequency modulation decision algorithm and valve fault prediction, characterized in that, It includes a communication module, a data acquisition module, a control module, and an execution module; The communication module is used to receive the power grid frequency regulation command signal transmitted by the monitoring platform, transmit the power grid frequency regulation command signal to the control module, and transmit the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency regulation control command generated by the control module, and the execution status data collected by the execution module to the monitoring platform. The data acquisition module is used to collect the multi-dimensional working condition data in real time through the sensor group and transmit the multi-dimensional working condition data to the control module. The control module is used to perform fault prediction on the multi-dimensional operating condition data, generate the prediction result characterizing the health status of the regulating valve, generate the frequency regulation control command using the power grid frequency regulation command signal, the multi-dimensional operating condition data, and the prediction result, transmit the frequency regulation control command to the execution module, and receive the execution status data. It also transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction result generated by the control module, the frequency regulation control command generated by the control module, and the execution status data collected by the execution module to the communication module. The control module includes a data processing unit, a frequency regulation decision unit, and a regulating valve fault prediction unit. The regulating valve fault prediction unit receives the multi-dimensional operating condition data, inputs the multi-dimensional operating condition data into the regulating valve fault prediction model for fault prediction, obtains the prediction result, and transmits the prediction result to the data processing unit. The data processing unit receives the multi-dimensional operating condition data and the power grid frequency regulation command signal, extracts target operating condition parameters from the multi-dimensional operating condition data, extracts target command data from the power grid frequency regulation command signal, and processes the prediction result, target operating condition parameters, and the prediction result into a control module. The target instruction data is standardized and verified to obtain standardized data, which is then transmitted to the frequency regulation decision unit. The frequency regulation decision unit uses the valve health index, fault risk level, and fault warning threshold from the standardized data to determine the valve status label. It uses the sum of the normalized frequency regulation target load deviation and the normalized actual generating power of the unit from the standardized data as the theoretical frequency regulation load target, and corrects the theoretical frequency regulation load target to obtain the frequency regulation load target and frequency regulation response priority. This is then used in the differentiated frequency regulation control strategy. The target frequency regulation control strategy corresponding to the valve status label is determined. The frequency regulation control command is generated using the target frequency regulation control strategy, the frequency regulation load target, and the frequency regulation response priority. The frequency regulation control command is transmitted to the execution module. The prediction results include the valve health index, fault risk level, predicted fault type, and fault warning threshold. The target operating condition parameters include the current actual power generation of the unit, main steam pressure, reheat steam temperature, actual valve opening, and valve action rate. The target command data includes the frequency regulation target load deviation, frequency regulation response dead time, and frequency regulation duration. The execution module is used to perform a gate opening adjustment operation according to the frequency modulation control command, and to collect the execution status data during the execution of the gate opening adjustment operation and transmit the execution status data to the control module.

2. The thermal control system according to claim 1, characterized in that, The data acquisition module includes a data acquisition unit and a data aggregation and transmission unit; The data acquisition unit is used to acquire initial multi-dimensional operating condition data in real time through the sensor group and transmit the initial multi-dimensional operating condition data to the data aggregation and transmission unit. The sensor group includes a temperature sensor, a pressure transmitter, a flow meter, a position encoder, and a current sensor. The initial multi-dimensional operating condition data includes temperature data, pressure data, steam flow data, valve position opening data, and actuator current data. The data aggregation and transmission unit is used to filter the initial multi-dimensional operating condition data to obtain the multi-dimensional operating condition data, and transmit the multi-dimensional operating condition data to the control module.

3. The thermal control system according to claim 1, characterized in that, The data processing unit is used to filter and remove outliers from the target operating condition parameters and the target command data to obtain processed data. The filtering and outlier removal processes include high-frequency noise processing based on moving average filtering and... The criteria for sensor fault outlier removal processing include clean frequency regulation target load deviation, clean frequency regulation response dead time, clean unit actual power generation, and clean valve actual opening. The data acquisition link of the processed data is obtained, and the communication link integrity is checked on the data acquisition link to obtain the valid data tag corresponding to each node in the data acquisition link. The valid data tag is any one of available, backup alternative, and emergency alarm. The processed data is normalized and combined with the prediction results to obtain the standardized processed data. The standardized processed data is then transmitted to the frequency regulation decision unit. The standardized processed data includes the normalized frequency regulation target load deviation, the normalized actual generating power of the unit, the regulator health index, the fault risk level, and the fault warning threshold.

4. The thermal control system according to claim 3, characterized in that, The data processing unit is also used to, for each node in the data acquisition link, when a communication interruption is detected at the node, use an emergency alarm as a valid data marker corresponding to the node; When it is detected that the node has not experienced a communication interruption and the node's data is valid, it can be used as a valid data marker for the node. When it is detected that the node has not experienced a communication interruption and the node's data is invalid, an estimated value is obtained through a backup data switching mechanism, and the backup replacement is used as the valid data marker corresponding to the node. The backup data switching mechanism is to use historical data from the same period or to use model estimates for replacement.

5. The thermal control system according to claim 1, characterized in that, The frequency modulation decision unit is further configured to generate a normal modulation status label when the modulation gate health index is greater than or equal to the fault warning threshold and the fault risk level is low risk; generate a sub-healthy status label when the modulation gate health index is less than the fault warning threshold, the modulation gate health index is greater than or equal to a preset fault value and the fault risk level is medium risk; and generate a high-risk restriction label when the modulation gate health index is less than the preset fault value and the fault risk level is high risk.

6. The thermal control system according to claim 1, characterized in that, The frequency modulation decision unit is further configured to, when the valve status label is normal, use the first control strategy in the differentiated frequency modulation control strategy as the target frequency modulation control strategy, wherein the first control strategy includes a first valve command, the first valve command includes a first valve target opening degree and a first valve action rate, the first valve target opening degree is calculated from the frequency modulation load target through the thermal control system characteristic curve, and the first valve action rate is the rated rate; and when the valve status label is sub-healthy constraint, use the second control strategy in the differentiated frequency modulation control strategy as the target frequency modulation control strategy, wherein the second control strategy includes a second valve command and a bypass valve opening degree command. The valve control command includes a second valve target opening degree and a second valve action rate. The second valve target opening degree is the value obtained by multiplying the first valve target opening degree by 80%, and the second valve action rate is the value obtained by dividing the rated rate by 2. When the valve status label is high-risk restriction, the third control strategy in the differentiated frequency regulation control strategy is used as the target frequency regulation control strategy. The third control strategy includes a third valve control command, a backup frequency regulation means command, and an alarm command. The third valve control command includes a third valve target opening degree, which is the actual valve opening degree. The backup frequency regulation means command is a steam-driven feedwater pump speed regulation control command or a boiler combustion rate regulation control command.

7. The thermal control system according to claim 6, characterized in that, The execution module is configured to: when the regulating valve status label corresponding to the frequency modulation control command is normal, send the first regulating valve command to the regulating valve servo system to drive the regulating valve servo system to perform regulating valve opening adjustment operation; when the regulating valve status label corresponding to the frequency modulation control command is sub-health constraint, send the second regulating valve command to the regulating valve servo system to drive the regulating valve servo system to perform regulating valve opening adjustment operation, and send the bypass valve opening command to the bypass valve controller to drive the bypass valve controller to perform regulating valve opening adjustment operation; when the regulating valve status label corresponding to the frequency modulation control command is high-risk restriction, send the third regulating valve command to the regulating valve servo system to drive the regulating valve servo system to perform regulating valve opening adjustment operation, send the backup frequency modulation means command to the backup system to drive the backup system to perform regulating valve opening adjustment operation, and send the alarm command to the communication module so that the communication module sends the alarm command to the monitoring platform.

8. The thermal control system according to claim 7, characterized in that, The execution module further includes an execution status feedback unit; The execution status feedback unit is used to collect the execution status data, which includes the valve feedback opening, actual action rate, and actual power change of the unit. The execution status data is transmitted to the valve fault prediction unit of the control module, so that the valve fault prediction unit inputs the execution status data into the valve fault prediction model for fault prediction and updating, obtaining the frequency regulation prediction result. The difference between the target valve opening and the valve feedback opening is taken as the valve opening deviation, and the difference between the frequency regulation load target and the actual power change of the unit is taken as the unit power deviation. When the valve status label corresponding to the frequency regulation control command is normal, the frequency regulation... The target opening degree of the control valve is the first target opening degree of the control valve; when the control valve status label corresponding to the frequency modulation control command is sub-health constraint, the target opening degree of the control valve is the second target opening degree of the control valve; when the control valve status label corresponding to the frequency modulation control command is high-risk restriction, the target opening degree of the control valve is the third target opening degree of the control valve; obtain the control valve opening degree deviation threshold and the unit power deviation threshold; if the control valve opening degree deviation is greater than the control valve opening degree deviation threshold or the unit power deviation is greater than the unit power deviation threshold, generate a correction command and transmit the correction command to the frequency modulation decision unit of the control module so that the frequency modulation decision unit regenerates the frequency modulation control command; Accordingly, the control module further includes an auxiliary unit; the auxiliary unit is used to receive the valid data markers corresponding to each node in the data acquisition link transmitted by the data processing unit, and the frequency modulation prediction results transmitted by the valve fault prediction unit, generate early warning information using the frequency modulation prediction results and the valid data markers corresponding to each node, and transmit the early warning information to the communication module so that the communication module transmits the early warning information to the monitoring platform.

9. A thermal control method based on frequency modulation decision algorithm and valve fault prediction, characterized in that, The method is applied to a thermal control system, which includes a communication module, a data acquisition module, a control module, and an execution module. The method includes: The communication module receives the power grid frequency regulation command signal transmitted by the monitoring platform and transmits the power grid frequency regulation command signal to the control module; The data acquisition module collects multi-dimensional operating condition data in real time through the sensor group and transmits the multi-dimensional operating condition data to the control module; The control module performs fault prediction on the multi-dimensional operating condition data, generates prediction results characterizing the health status of the regulating valve, and generates a frequency regulation control command using the power grid frequency regulation command signal, the multi-dimensional operating condition data, and the prediction results. The frequency regulation control command is then transmitted to the execution module. The control module includes a data processing unit, a frequency regulation decision unit, and a regulating valve fault prediction unit. The regulating valve fault prediction unit receives the multi-dimensional operating condition data, inputs the multi-dimensional operating condition data into a regulating valve fault prediction model for fault prediction, obtains the prediction result, and transmits the prediction result to the data processing unit. The data processing unit receives the multi-dimensional operating condition data and the power grid frequency regulation command signal, extracts target operating condition parameters from the multi-dimensional operating condition data, extracts target command data from the power grid frequency regulation command signal, standardizes and verifies the prediction result, the target operating condition parameters, and the target command data to obtain standardized data, and transmits the standardized data to the frequency regulation decision unit. The frequency regulation decision unit is used to determine the valve status label using the valve health index, fault risk level, and fault warning threshold in the standardized processed data; to use the sum of the normalized frequency regulation target load deviation and the normalized actual power generation of the unit in the standardized processed data as the theoretical frequency regulation load target; to correct the theoretical frequency regulation load target to obtain the frequency regulation load target and frequency regulation response priority; to determine the target frequency regulation control strategy corresponding to the valve status label in the differentiated frequency regulation control strategy; to generate the frequency regulation control command using the target frequency regulation control strategy, the frequency regulation load target, and the frequency regulation response priority; and to transmit the frequency regulation control command to the execution module. The prediction results include the valve health index, fault risk level, predicted fault type, and fault warning threshold. The target operating condition parameters include the current actual power generation of the unit, main steam pressure, reheat steam temperature, actual valve opening, and valve action rate. The target command data includes the frequency regulation target load deviation, frequency regulation response dead time, and frequency regulation duration. The execution module performs a gate opening adjustment operation according to the frequency modulation control command, and collects execution status data during the gate opening adjustment operation, and transmits the execution status data to the control module. The control module receives the execution status data and transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control command generated by the control module, and the execution status data collected by the execution module to the communication module. The communication module transmits the multi-dimensional operating condition data collected by the data acquisition module, the prediction results generated by the control module, the frequency modulation control commands generated by the control module, and the execution status data collected by the execution module to the monitoring platform.

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