Intelligent power generation control system and method for open development environment

Abstract

This invention discloses an intelligent power generation control system and method with an open development environment, relating to the field of power generation control technology. It includes: an independently controllable DCS basic platform, employing a domestically produced multi-core CPU controller, equipped with high-speed signal acquisition modules, built on a domestic operating system, and featuring a dual-machine hot standby redundancy design; an intelligent monitoring module, integrating AI and mechanistic models, incorporating multiple algorithms and functions, quantifying health status and providing early warnings, and featuring a built-in expert system; a centralized monitoring module, based on industrial Ethernet, using standard communication protocols, and developing a visual master plan; a unit-level one-button start / stop control module, developing dedicated algorithms, and controlling start / stop processes through configuration logic; and an open development environment module, pre-configured with multiple data interfaces, encapsulating an algorithm repository, employing zero-code graphical configuration, and supporting rapid model development and third-party integration. This invention achieves independent controllability of the DCS, intelligent monitoring replaces manual labor to reduce risks, and optimizes the entire process to ensure the safe and efficient operation of the unit.

Description

Technical Field

[0001] This invention relates to the field of power generation control technology, and in particular to an intelligent power generation control system and method with an open development environment. Background Technology

[0002] As the thermal power generation industry transforms towards cleaner, more efficient, flexible, and intelligent systems, the performance requirements of distributed control systems (DCS) for unit operation control are becoming increasingly demanding. Traditional DCS systems suffer from high upgrade costs and limited scalability, making it difficult to adapt to the needs of domestic units for customized services and large-scale monitoring expansion. Furthermore, traditional DCS task scheduling often employs a single-level model, which is prone to computational congestion and response delays when faced with the massive amounts of real-time control, communication, and system monitoring data generated during unit operation. This impacts the real-time performance and stability of unit operation, failing to meet the complex control requirements of large-capacity units of 300MW and above.

[0003] The monitoring and integration capabilities of existing DCS systems have significant shortcomings, hindering the improvement of unit operation and maintenance efficiency. The monitoring process has long relied on manual operation personnel, requiring the simultaneous monitoring of thousands of parameters across multiple screens. This is not only labor-intensive but also prone to human error, leading to delayed warnings of parameter degradation and missed early signs of faults, potentially causing equipment failures to escalate or even resulting in unit outages. While some systems have introduced preliminary intelligent monitoring functions, they mostly employ single AI models or mechanistic models, failing to achieve deep integration of the two types of models. This results in insufficient accuracy in parameter prediction and fault identification, and a lack of linkage mechanisms with video surveillance, making it difficult for operators to intuitively grasp the on-site status of equipment. More importantly, traditional DCS systems are highly closed, with limited data interface types, making it difficult for third-party developers to access and develop models and algorithms. This creates information silos, hindering the effective integration of new intelligent power generation technologies (such as predictive control and neural network algorithms) and restricting the iterative upgrade of system functions.

[0004] The level of intelligence in unit start-up and shutdown and centralized monitoring also needs improvement. Traditional unit-level start-up and shutdown control mostly uses fixed configuration logic, which can only adapt to a single operating condition. When facing multiple operating condition switching such as cold, hot, and extremely hot conditions, it is necessary to rely on the rich experience of operators to manually adjust parameters, which not only results in long start-up and shutdown times, but also easily leads to safety risks due to non-standard operation. In terms of centralized monitoring, existing systems are mostly limited to the independent monitoring of a single unit or a single subsystem (such as peak boilers or the first station of the heating network), and have not achieved integrated monitoring of multiple units in joint circulation. Inspection routes need to be planned manually, the timeliness of equipment hazard detection is poor, and there is a lack of full-process security protection design, making the system susceptible to unauthorized access and data leakage risks. These problems together mean that traditional DCS systems cannot fully meet the needs of the modern power generation industry for safety, reliability, efficiency, intelligence, openness and compatibility. There is an urgent need for a comprehensive solution with an independent and controllable foundation, intelligent monitoring capabilities, an open development environment, and efficient control functions. Summary of the Invention

[0005] This invention proposes an intelligent power generation control system and method with an open development environment to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent power generation control system with an open development environment, comprising:

[0007] The independently controllable DCS basic platform serves as the core support of the system, adopting a controller based on the domestic ARM architecture Phytium FT-2000 / 4 multi-core CPU; it integrates four-level task scheduling technology, grouping and processing tasks according to real-time control level, communication interaction level, system supervision level, and idle time optimization level; it is equipped with high-performance and high-speed signal acquisition modules; the platform is built on the domestic Kylin operating system, adopts a dual-machine hot standby redundancy design, and provides customized API interfaces;

[0008] The intelligent monitoring module deeply integrates AI and mechanistic models to construct a comprehensive evaluation system. The AI ​​model integrates multiple algorithms and has a built-in training library of unit fault samples. The mechanistic model integrates physical property functions, filtering analysis functions, and various logic components. The module collects DCS system tag parameters in real time and generates upper and lower limits of parameters based on the 3σ principle, which can quantify the health status from three dimensions: equipment safety, operating economy, and system reliability. The built-in expert system stores typical fault handling plans and automatically matches early warning outputs operation guidance.

[0009] The centralized monitoring module is built on an industrial Ethernet ring network architecture and adopts the IEC61850 communication protocol; it develops a plant-wide visual monitoring map, integrating equipment labeling, parameter display, and fault highlighting functions, and supports high-definition video linkage; it has a built-in intelligent inspection engine to generate priority inspection routes.

[0010] The unit-level one-button start-stop control module adopts a dual-drive design of "configuration logic + digital twin simulation"; a dedicated algorithm is developed for analog quantity control, and the operating conditions are identified in real time through sensors, the start-stop process nodes are sorted out and the configuration logic program is written.

[0011] The open development environment module is built with a three-layer architecture of "algorithm repository - configuration platform - interface adaptation" and has four pre-built general data interfaces. The algorithm repository encapsulates more than 120 intelligent algorithm modules and marks the applicable scenarios. It adopts a zero-code graphical configuration modeling tool, provides a drag-and-drop component library, supports the whole process of model development and automatic generation of front-end functions, and has a built-in model version management system.

[0012] Furthermore, it also includes a dynamic health assessment unit for intelligent monitoring, with the assessment method being H=αS+βE+γR. Where H is the unit health score; α is the safety weight; β is the economic weight; γ is the reliability weight, calculated based on the number of fault warnings and parameter deviation; E is the economic score, calculated based on the difference between actual and designed coal consumption; and R is the reliability score, calculated based on continuous equipment operating time and fault repair rate. This calculation allows for dynamic adjustment of the assessment focus to match the unit's operational stage.

[0013] Furthermore, it also includes a one-button start / stop multi-condition adaptive unit, with the adaptability evaluation method being A=ΔTk1+ΔPk2+τk3. Where A is the operating condition adaptability index; ΔT is the absolute value of the deviation between the actual cylinder temperature and the standard operating condition cylinder temperature; k1 is the temperature deviation coefficient; ΔP is the absolute value of the deviation between the actual main steam pressure and the standard operating condition pressure; k2 is the pressure deviation coefficient; τ is the system response time; and k3 is the response time coefficient. This evaluation allows for automatic adjustment of configuration logic parameters.

[0014] Furthermore, it also includes a third-party module compatibility verification unit, with the compatibility verification method being C=λN / μM. Where C is the compatibility index; λ is the third-party module adaptation success rate; N is the total number of connected third-party modules; μ is the system's inherent module adaptation coefficient; and M is the total number of system's inherent modules. This evaluation is accompanied by an automated testing process that records module response time and resource utilization.

[0015] Furthermore, it also includes a unit energy consumption intelligent optimization module that communicates with the DCS basic platform; the module integrates boiler combustion optimization algorithms and turbine sliding pressure operation curves, and collects energy consumption-related parameters in real time; the module supports historical energy consumption data retrospective analysis, generates daily, monthly, and quarterly energy consumption trend reports, and marks energy consumption anomalies and optimization measures.

[0016] Furthermore, it also includes a data intelligence analysis and traceability module, which adopts a distributed database architecture to support the classified storage of DCS real-time data, historical operation data, model training data, and fault record data; integrates anomaly data root cause localization algorithms, and uses cause-effect graph analysis to trace the source of anomalies; and develops data visualization analysis tools to support multi-dimensional data filtering and chart display, allowing maintenance personnel to generate customized analysis reports through drag-and-drop operations.

[0017] Furthermore, it also includes a full-layer security protection module, constructing a four-dimensional protection system of "hardware-software-data-access"; at the hardware level, a secure encryption chip is used to protect CPU running data; at the software level, an AI intrusion detection model is integrated, trained based on 100,000+ attack samples; at the data level, the AES-256 encryption algorithm is used to ensure transmission security; at the access level, four-level permission management is implemented, using "account + password + biometrics" triple authentication.

[0018] Furthermore, this includes the following steps:

[0019] Step 1: Build an independent and controllable DCS basic platform, select Phytium FT-2000 / 4 multi-core CPU controller, deploy Kylin operating system, and configure dual-machine hot standby redundancy module; develop a four-level task scheduling program and test the response time of tasks of each priority; install high-speed signal acquisition module and perform opto-isolation and electromagnetic shielding calibration; configure monitoring points based on unit scale and develop customized API interface documentation for subsequent function expansion.

[0020] Step 2: Construct an intelligent monitoring module, collect 3 years of DCS historical operation data of the target unit and 800+ fault handling records, and perform data preprocessing; use the random forest classification algorithm to train the fault identification model, the LSTM regression algorithm to train the parameter prediction model, integrate the IF97 water vapor function and vibration FFT analysis function to construct the mechanism model, and fuse the two types of models through a weighted fusion algorithm;

[0021] Step 3: Deploy the centralized monitoring module, build an industrial Ethernet ring network, configure the IEC61850 communication gateway, and connect the peak furnace, the first station of the heating network, and the generator control system; develop a plant-wide visual monitoring master map, set alarm trigger video switching rules; develop an intelligent inspection engine, import equipment ledgers, set inspection cycles, and develop a mobile inspection APP.

[0022] Step 4: Configure the unit-level one-click start / stop logic. Based on the digital twin model, build a simulation environment for the start / stop process to simulate the process under four operating conditions: cold, steady, hot, and extremely hot. Develop a multi-modal PID algorithm and fuzzy adaptive bypass control logic. Test the start / stop process under each operating condition in the simulation environment.

[0023] Step 5: Configure an open development environment and deploy Ethernet, MODBUSRTU / TCP, OPCUA, and MQTT data interfaces; build an algorithm repository, encapsulate 120+ intelligent algorithm modules, and write algorithm documentation; develop a "zero-code, graphical" configuration tool, provide a drag-and-drop component library, test the business expert modeling process, and automatically generate the front-end interface after model release; deploy a version management system and set model version number rules.

[0024] Step Six: System integration and optimization, integrating the functions of each module, accessing actual unit operation data, testing the accuracy of intelligent monitoring panel health assessment, one-click start / stop adaptability to multiple operating conditions, and compatibility with third-party modules; continuously running for 72 hours to monitor system resource utilization, and finally forming a system operation manual and maintenance specifications.

[0025] Furthermore, it also includes a dynamic optimization method for the intelligent monitoring model, the steps of which are as follows: setting the model update cycle, collecting real-time operating data of the unit within 24 hours before each update, performing data preprocessing and supplementing it to the training set; using the sliding window algorithm to extract new features and supplementing them to the model input layer; using the gradient descent algorithm to optimize the AI ​​model weights, and correcting the function coefficients in the mechanism model in combination with actual fault handling results; re-integrating the two types of models; and pushing the model update package online through an open environment, using a breakpoint resume method so that the monitoring function is not interrupted during the update process.

[0026] Furthermore, it includes a full-process integration method for third-party modules, with the following steps: Develop an open development guide, clarifying data interface protocols, model configuration specifications, and compatibility testing standards. The third party develops a dedicated module based on the guide, connects to the system via the OPCUA interface, and submits basic module information; Automated compatibility testing is initiated, the compatibility index C is calculated, and the third party is guided to modify and retest; After successful deployment, operations personnel use a graphical configuration tool to associate the third-party module with the existing system and test module interoperability; After going live, the module's operational status is monitored in real time.

[0027] Compared with existing technologies, the beneficial effects of this invention are:

[0028] Regarding independent controllability and system stability, this invention builds a DCS basic platform based on a domestically produced multi-core CPU and the Kylin operating system. Both core hardware and software are domestically produced, completely eliminating reliance on imported components. Simultaneously, a four-level task scheduling technology is used to hierarchically process tasks such as real-time control and communication interaction, significantly improving system response speed and concurrent processing capabilities. The dual-machine hot standby redundancy design and the integration of anti-interference signal acquisition modules further enhance the reliability of system operation, effectively reducing operational risks caused by hardware failures or signal interference, and providing a solid foundation for the continuous and stable operation of the unit.

[0029] The intelligent monitoring and maintenance efficiency improvement are remarkable. This invention achieves accurate early warning of parameter degradation and early fault identification through the deep integration of AI and mechanistic models, combined with massive fault sample training and multi-dimensional health assessment. This fundamentally replaces manual monitoring and significantly reduces the workload of operators. The addition of video linkage and intelligent inspection engine enables fault warnings to be quickly linked to on-site equipment video and optimized inspection routes, significantly improving the timeliness of potential problems detection and handling efficiency, avoiding unit downtime losses caused by fault escalation. At the same time, the built-in expert system can automatically output operation guidance, reducing reliance on the experience of maintenance personnel.

[0030] The construction of an open development environment breaks down the closed barriers of traditional systems. By pre-installing various general data interfaces and "zero-code, graphical" configuration tools, it greatly reduces the integration threshold for third-party models and algorithms, enabling rapid development and deployment with "modeling as you go." The encapsulation and version management functions of the algorithm repository not only facilitate customized development by business experts and third-party developers, but also promote the rapid implementation and iteration of new smart power generation technologies, effectively avoiding the problem of information silos and providing the possibility for continuous upgrades of system functions.

[0031] In terms of unit control and safety protection, the multi-condition adaptive one-button start-stop control, through the combination of configuration logic and simulation technology, adapts to start-stop requirements under different cylinder temperature conditions, shortens start-stop time, and standardizes operating procedures. The centralized monitoring module realizes integrated monitoring of multiple units in joint cycles, improving overall operation and maintenance coordination. The full-level security protection system builds barriers from four dimensions: hardware, software, data, and access, effectively preventing unauthorized operations and data leakage risks, and ensuring system operation security. In addition, the integration of energy consumption optimization and data traceability modules can also help improve the economic efficiency of unit operation and accurately trace faults, further expanding the practical value of the system. Attached Figure Description

[0032] Figure 1 This is a schematic block diagram of an intelligent power generation control system with an open development environment proposed in this invention.

[0033] Figure 2 This is a schematic block diagram of an intelligent power generation control system method with an open development environment proposed in this invention.

[0034] Figure 3 A comparison chart of one-key start / stop performance under different operating conditions;

[0035] Figure 4 This is a diagram showing the results of a third-party module compatibility assessment.

[0036] Figure 5 This is a comparison chart of response times for four levels of task scheduling. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, 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," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention 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 invention.

[0039] 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 indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0040] Reference Figures 1 to 5 An intelligent power generation control system with an open development environment, comprising:

[0041] The independently controllable DCS platform serves as the core support of the system. It employs a controller based on a domestically produced ARM architecture multi-core CPU, specifically the Phytium FT-2000 / 4, with a power consumption of 45W and a continuous fault-free operating time of ≥15,000 hours. It supports low-cost pin-compatible upgrades. The platform integrates a four-level task scheduling technology, grouping tasks into real-time control, communication interaction, system supervision, and idle-time optimization levels. Real-time control level task response time is ≤20ms, and communication interaction level data transmission latency is ≤50ms. Equipped with a high-performance, high-speed signal acquisition module, it uses a 16-bit AD converter with an adjustable sampling frequency of 100-500kHz. It integrates opto-isolation and electromagnetic shielding design, achieving anti-interference capabilities up to the IEC61000-4-2 standard. It supports 4-20mA analog signals, DI / DO digital signals, and 100Hz-10kHz pulse signals, with a single module capable of connecting 32 signals. The platform is built on the domestic Kylin operating system, adopts a dual-machine hot standby redundancy design, has a switching time of ≤30ms, provides customized API interfaces, supports elastic expansion of 1000-5000 monitoring points, and is suitable for the needs of 300MW-1000MW thermal power units.

[0042] The intelligent monitoring module deeply integrates AI and mechanistic models to construct a comprehensive evaluation system. The AI ​​model integrates random forest classification, K-means clustering, LSTM regression, and CNN deep learning algorithms, with a built-in training library of over 100,000 unit fault samples, achieving parameter prediction accuracy ≥95%. The mechanistic model integrates IF97 steam property functions, sliding window mean filtering, equipment vibration FFT analysis functions, and R / S trend analysis, M / N voting, and AND / OR / NOT logic components, covering the core systems of boilers, turbines, and generators. The module collects over 2000 labeled parameters from the DCS system in real time, dynamically generating upper and lower limits based on the 3σ principle. An alarm is triggered when the deviation between the predicted and real-time values ​​exceeds 8%. The module quantifies health status from three dimensions: equipment safety (number of fault warnings, risk of protection actions), operational economy (coal consumption, plant power consumption rate), and system reliability (equipment availability coefficient, fault repair time), with an alarm response time ≤1 second. The built-in expert system stores over 500 typical fault handling plans and automatically matches warning information to output operational guidance.

[0043] The centralized monitoring module, built on an industrial Ethernet ring network architecture, uses the IEC61850 communication protocol to connect the control systems of the peak boiler, the first heating network station, and the steam turbine generator set, enabling centralized monitoring of five or more units in a coordinated cycle. A plant-wide visual monitoring map has been developed, integrating equipment location marking, operating parameter display, and fault status highlighting functions. It supports 1080P high-definition video linkage; when the intelligent monitoring panel triggers an alarm, it automatically retrieves the camera footage from the corresponding equipment, with a screen switching delay of ≤800ms. A built-in intelligent inspection engine generates priority inspection routes based on equipment runtime and historical fault frequency, supporting mobile task reception and real-time data upload by inspection personnel. This improves inspection efficiency by ≥40% and the timeliness of equipment hazard detection by ≥60%.

[0044] The unit-level one-button start / stop control module adopts a dual-drive design of "configuration logic + digital twin simulation," adapting to four operating conditions: cold (cylinder temperature <150℃), steady-state (cylinder temperature 150-300℃), hot (cylinder temperature 300-450℃), and extremely hot (cylinder temperature >450℃). A multi-modal PID algorithm was developed for analog closed-loop control, and the bypass regulation system integrates fuzzy adaptive control logic. Operating conditions are identified in real time through a three-in-one sensor (temperature, pressure, and vibration), with a condition switching response time ≤1 second. Over 120 process nodes in the start / stop process were analyzed, and configuration logic programs were written to set parameter thresholds and operation sequences for each node. Cold start time was reduced by ≥25%, hot start time by ≥40%, and the operational standardization of the start / stop process reached 99%, reducing operator steps by ≥70%.

[0045] The open development environment module constructs a three-layer open architecture of "algorithm repository - configuration platform - interface adaptation," pre-configured with four common data interfaces: Ethernet, MODBUSRTU / TCP, OPCUA, and MQTT. Third-party data exchange rates are ≥100MB / s, and it supports SQL, JSON, and XML data format parsing. The algorithm repository encapsulates over 120 intelligent algorithm modules, including predictive control, fuzzy algorithms, and neural networks, annotating algorithm input / output parameters and applicable scenarios. It employs a "zero-code, graphical" configuration modeling tool, providing a drag-and-drop component library. Business experts can complete the entire process of model building, offline testing, and online deployment. After model deployment, the front-end interface automatically generates data display, alarm configuration, and trend analysis functions, achieving "modeling and use immediately." A built-in model version management system supports version rollback and difference comparison, and can record third-party developer operation logs for easy permission tracking.

[0046] This invention also includes an intelligent monitoring and dynamic health assessment unit for accurately quantifying the unit's operating status. The assessment method is H=αS+βE+γR. Where H is the unit health score, ranging from 0 to 100; α is the safety weight, with a value of 0.5 during start-up and shutdown and 0.4 during stable operation; β is the economic weight, with a value of 0.2 during start-up and shutdown and 0.4 during stable operation; γ is the reliability weight, with a constant value of 0.3; S is the safety score, calculated based on the number of fault warnings (5 points deducted per warning) and parameter deviation (2 points deducted for every 10% deviation); E is the economic score, calculated based on the difference between actual coal consumption and designed coal consumption (3 points deducted for every 5g / kWh higher); and R is the reliability score, calculated based on the continuous operating time of the equipment (2 points added for every 1000 hours exceeding the limit) and fault repair rate (4 points deducted for every 90% or lower). This calculation allows for dynamic matching of the evaluation focus during the unit's operational phase. When H < 75 points, it automatically pushes a list of deterioration parameters and expert recommendations to help maintenance personnel intervene in advance and reduce the risk of unit outages.

[0047] This invention also includes a one-button start / stop multi-condition adaptive unit for optimizing control logic under different operating conditions. The adaptability evaluation method is A = ΔTk1 + ΔPk2 + τk3. Where A is the operating condition adaptability index, with a value ≥ 0.85 indicating qualified adaptability; ΔT is the absolute value of the deviation between the actual cylinder temperature and the standard operating condition cylinder temperature, in °C; k1 is the temperature deviation coefficient, with a value of 0.35; ΔP is the absolute value of the deviation between the actual main steam pressure and the standard operating condition pressure, in MPa; k2 is the pressure deviation coefficient, with a value of 0.45; τ is the system response time, in seconds; and k3 is the response time coefficient, with a value of 0.2. Through this evaluation, configuration logic parameters can be automatically adjusted. Under cold conditions, the boiler preheating time is extended to 60 minutes, and the coal feed rate is increased to 2 t / min; under extremely hot conditions, the turbine start-up time is shortened to 15 minutes, and the bypass opening adjustment rate is reduced to 5% / min, ensuring that the pressure and temperature fluctuations during the start-up and shutdown processes under each operating condition are ≤ 10%.

[0048] This invention also includes a third-party module compatibility verification unit to ensure system stability in an open environment. The compatibility verification method is C=λN / μM. Here, C is the compatibility index, with a value ≥0.92 indicating successful compatibility; λ is the third-party module adaptation success rate, i.e., the ratio of the number of successfully running third-party modules to the total number of connected modules, ranging from 0 to 1; N is the total number of connected third-party modules; μ is the system's inherent module adaptation coefficient, with a value of 0.98; and M is the total number of inherent system modules. This evaluation is accompanied by an automated testing process. After connecting a third-party module, it automatically performs 1000 read / write operations and 50 fault simulations, recording module response time and resource utilization. When C < 0.92, the system automatically outputs an interface adaptation report, prompting adjustments to the data interaction frequency (recommended ≤10Hz) or optimization of module memory usage (recommended ≤512MB) to ensure that the connection of the third-party module does not affect the original system's response speed (fluctuation ≤5%).

[0049] This invention also includes a unit energy consumption intelligent optimization module, which interconnects with the DCS basic platform and constructs an energy consumption prediction model based on real-time operating data. The module integrates boiler combustion optimization algorithms and turbine sliding pressure operation curves, and collects 120 energy consumption-related parameters in real time, including coal feed rate, air volume, feedwater temperature, and main steam pressure. It calculates the coal consumption per unit of power generation every 5 minutes and generates optimization instructions by comparing the difference with the design coal consumption. When the coal consumption deviation is >10g / kWh, it automatically adjusts the boiler secondary air ratio (adjustment step size 2%) and the turbine valve opening (adjustment step size 1%), and pushes optimization suggestions to the operator interface. The module supports historical energy consumption data retrospective analysis, generates daily, monthly, and quarterly energy consumption trend reports, marks energy consumption anomalies and optimization measures, and improves unit operating economy by ≥3%.

[0050] This invention also includes a data intelligent analysis and traceability module, employing a distributed database architecture (Hadoop + Spark) with a storage capacity of 20TB. It supports the categorized storage of DCS real-time data (sampling interval 100ms), historical operational data (stored for 3 years), model training data (stored for 5 years), and fault record data (permanently stored). An integrated anomaly root cause localization algorithm automatically correlates upstream and downstream equipment data when parameter anomalies are detected, using causal graph analysis to trace the source of the anomaly with a localization accuracy of ≥90%. A data visualization analysis tool is developed, supporting multi-dimensional data filtering (time, equipment, parameter type) and chart display (line chart, bar chart, heatmap). Maintenance personnel can generate customized analysis reports through drag-and-drop operations. Data query response time is ≤2s, meeting the needs of fault tracing and maintenance optimization.

[0051] This invention also includes a multi-layered security protection module, constructing a four-dimensional protection system encompassing hardware, software, data, and access. At the hardware level, a secure encryption chip (using the national cryptographic SM4 algorithm) protects CPU operating data. At the software level, an AI intrusion detection model is integrated, trained on over 100,000 attack samples, achieving an accuracy rate of ≥99.8% in identifying abnormal access behavior. At the data level, AES-256 encryption ensures secure transmission, and data storage employs a fragmented encryption method. At the access level, four-level permission management is implemented: administrator (full permissions), operations engineer (control and monitoring permissions), third-party developer (modeling and data query permissions), and visitors (read-only permissions). Triple authentication using "account + password + biometrics (fingerprint / face)" is employed, automatically locking the account after three failed authentication attempts to prevent unauthorized operations.

[0052] This invention includes the following steps:

[0053] Step 1: Build an independent and controllable DCS basic platform, selecting the Phytium FT-2000 / 4 multi-core CPU controller, deploying the Kylin operating system, configuring a dual-machine hot standby redundancy module, and debugging the master-slave switchover time to ≤30ms. Develop a four-level task scheduling program, setting real-time control tasks (such as boiler water level regulation) as the highest priority, communication interaction tasks as the second level, system monitoring tasks as the third level, and idle-time optimization tasks (such as data statistics) as the fourth level. Test the response time of each priority task to ensure that the real-time control level is ≤20ms. Install a high-speed signal acquisition module, perform opto-isolation and electromagnetic shielding calibration, connect to a standard signal source (4-20mA, 0-10V) to test sampling accuracy, and control the error within ±0.1%. Configure monitoring points based on the unit scale, reserving 50% for expansion points, and develop customized API interface documentation for future functional expansion.

[0054] Step Two: Construct an intelligent monitoring module, collecting 3 years of historical DCS operation data for the target unit and over 800 fault handling records. Data preprocessing (missing value imputation, outlier removal, and normalization) is performed, filtering for 200 key parameters such as main steam temperature, drum pressure, and turbine vibration, along with their corresponding 100 feature parameters. A random forest classification algorithm is used to train the fault identification model, and an LSTM regression algorithm is used to train the parameter prediction model. An IF97 steam function and a vibration FFT analysis function are integrated to construct a mechanistic model. The two models are then fused using a weighted fusion algorithm (AI model weight 0.6, mechanistic model weight 0.4). Model performance is debugged, ensuring parameter prediction deviation ≤5% and fault warning lead time ≥15 minutes. Over 500 fault handling plans are entered into the expert system, establishing a mapping between warning information and plans.

[0055] Step 3: Deploy a centralized monitoring module, build an industrial Ethernet ring network, configure an IEC61850 communication gateway, connect the peak boiler, the first heating network station, and the generator control system, and test communication stability (no packet loss for 24 consecutive hours). Develop a plant-wide visual monitoring map, marking the locations and operating parameter display areas of 2000+ devices, integrating 1080P camera signals, and setting alarm trigger video switching rules (e.g., retrieving the furnace camera when the boiler pressure is abnormal). Develop an intelligent inspection engine, import equipment records (including location, model, and historical faults), set inspection cycles (24 hours / time for routine equipment, 8 hours / time for critical equipment), and develop a mobile inspection APP to achieve task assignment, data upload, and hazard reporting functions. Test that the optimized inspection route improves efficiency by ≥40%.

[0056] Step 4: Configure the unit-level one-click start / stop logic. Based on a digital twin model, build a simulation environment for the start / stop process, simulating the process under four operating conditions: cold, steady, hot, and extremely hot. Analyze the operation timing and parameter thresholds of 120 process nodes. Develop a multimodal PID algorithm and fuzzy adaptive bypass control logic, and write the configuration logic program. Set the boiler preheating time to 60 minutes and the turbine start-up time to 30 minutes under cold conditions, and the boiler preheating time to 20 minutes and the turbine start-up time to 15 minutes under extremely hot conditions. Test the start / stop process for each operating condition in the simulation environment, recording the start / stop time and pressure / temperature fluctuations. Adjust the logic parameters until the fluctuation range is ≤10%, and the start / stop time is reduced by ≥25% compared to the traditional method.

[0057] Step 5: Configure an open development environment, deploy Ethernet, MODBUSRTU / TCP, OPCUA, and MQTT data interfaces, test third-party data read / write speeds ≥100MB / s, and support SQL, JSON, and XML format parsing. Build an algorithm repository, encapsulating 120+ intelligent algorithm modules, and write algorithm documentation (input / output, parameter range, applicable scenarios). Develop a "zero-code, graphical" configuration tool, providing a drag-and-drop component library (data acquisition, algorithm calling, logic judgment, interface display), test the business expert modeling process, ensuring that from setup to release it takes ≤30 minutes, and the front-end interface is automatically generated after model release, achieving "modeling and use immediately". Deploy a version management system, set model version number rules (year + month + serial number), and support version rollback and difference comparison.

[0058] Step Six: System Integration and Optimization. Integrate the functions of each module, access actual unit operation data, and test the accuracy of the intelligent monitoring panel's health assessment (consistency with maintenance expert judgment ≥90%), the adaptability of one-click start / stop under multiple operating conditions (A≥0.85), and the compatibility with third-party modules (C≥0.92). Simulate 10 fault scenarios, including boiler tube leakage and excessive turbine vibration, to verify that the intelligent monitoring panel's early warning recall rate is ≥95% and the expert suggestion matching degree is ≥90%. Simulate cold and extremely hot start / stop conditions to test the compliance of start / stop time and parameter fluctuations. Continuously run for 72 hours to monitor system resource utilization (CPU≤60%, memory≤70%), record abnormal situations and optimize accordingly, ultimately forming a system operation manual and maintenance specifications.

[0059] This invention also includes a dynamic optimization method for the intelligent monitoring model, comprising the following steps: setting a model update cycle (incremental update every 24 hours, full retraining every 30 days); collecting real-time unit operation data (including parameter changes, fault records, and operational adjustments) within 24 hours before each update, performing data preprocessing, and then supplementing the training set; using a sliding window algorithm to extract new features (such as parameter mutation rate and cross-device parameter correlation) and supplementing them to the model input layer; using a gradient descent algorithm to optimize the AI ​​model weights, setting the learning rate to 0.001, and iterating 500 times; and correcting the function coefficients in the mechanism model (such as vibration analysis thresholds) based on actual fault handling results; re-integrating the two types of models; and testing to ensure that the updated model's prediction accuracy improves by ≥3% and the early warning recall rate improves by ≥2%; and pushing the model update package online through an open environment, using a breakpoint resume method to ensure that the monitoring function is not interrupted during the update process (service pause time ≤10s), ensuring that the model continuously adapts to changes in the unit's operating status.

[0060] This invention also includes a method for the full-process integration of third-party modules, comprising the following steps: Developing an open development guide, clarifying the data interface protocol (including communication rate, data format, and error code definitions), model configuration specifications (including input / output parameter naming, data types, and threshold ranges), and compatibility testing standards. The third party develops a dedicated module (such as a desulfurization efficiency optimization model) based on the guide, accesses the system through the OPCUA interface, and submits basic module information (functional description, resource requirements, and applicable scenarios). Automated compatibility testing is initiated, executing 1000 data read / write operations and 50 fault simulations, calculating the compatibility index C. If C ≥ 0.92, the deployment phase begins; if C < 0.92, the system outputs interface optimization suggestions (such as reducing the data interaction frequency to 8Hz and optimizing module memory usage to 400MB), guiding the third party to modify and retest. After successful deployment, maintenance personnel use a graphical configuration tool to associate the third-party module with the existing system (such as associating desulfurization tower operating parameters) and test module synergy (startup response time ≤ 1s, resource utilization ≤ 15%). After going live, the module's operating status is monitored in real time, and a performance report is generated every 24 hours. When a module fails, it is automatically isolated without affecting other functions of the system, ensuring the security and reliability of third-party integration.

[0061] The following two examples further illustrate the specific implementation of this system:

[0062] Example 1: 300MW thermal power unit DCS intelligent transformation project (Tianjin Huadian Fuyuan Thermal Power Application Scenario)

[0063] This embodiment addresses the upgrade requirements of the DCS system for the 300MW coal-fired power unit of Tianjin Huadian Fuyuan Thermal Power Plant. The original system was an old imported DCS, which had problems such as response delay, strong isolation, and reliance on manual monitoring. The present invention is used to achieve intelligent transformation, with a focus on adapting to the centralized monitoring of peak boilers and the first station of the heating network. The specific implementation process is as follows.

[0064] 1. Construction of an independent and controllable DCS basic platform

[0065] The Phytium FT-2000 / 4 multi-core CPU controller (45W power consumption, 15,000 hours of continuous operation without failure) was selected, and the Kylin V10 operating system was deployed. A dual-machine hot standby redundancy module was configured, with the primary and backup machines synchronizing data via fiber optic cable. The switching time was 28ms (≤30ms requirement). A four-level task scheduling program was developed: real-time control tasks such as boiler water level regulation and turbine speed control were set as Level 1, with a response time of 18ms; plant communication and grid data interaction were set as Level 2, with a delay of 42ms; system self-check and module status monitoring were set as Level 3; and nighttime data statistics and energy consumption analysis were set as Level 4, running automatically during idle periods.

[0066] High-speed signal acquisition modules (16-bit AD converter, sampling frequency 200kHz) were installed, and the modules underwent opto-isolation calibration (isolation voltage 2500V) and electromagnetic shielding treatment (compliant with IEC61000-4-2 standard). 4-20mA analog signals (e.g., main steam pressure), DI digital signals (e.g., valve on / off status), and 1kHz pulse signals (e.g., coal feeder speed) were input. Each module could input 32 signals, with a total of 8 modules covering 256 signals. Based on a 300MW unit scale, 3000 monitoring points were deployed, with 1500 points reserved for future expansion. A customized API interface (including parameter read / write and status query functions) was developed, and interface documentation was written.

[0067] 2. Construction of Intelligent Monitoring Module

[0068] Historical DCS data (approximately 500GB) and 720 fault records (including boiler tube leaks and excessive turbine vibration) from 2021 to 2023 were collected for this unit. Data preprocessing employed linear interpolation to fill missing values, outliers were removed using the 3σ principle, and data was normalized to the [0,1] interval. 180 key parameters, including main steam temperature, drum pressure, and turbine shaft vibration, and their corresponding 90 characteristic parameters (such as temperature change rate and pressure fluctuation amplitude) were selected.

[0069] AI Model Training: A random forest classification algorithm (100 decision trees) was used to train the fault identification model, with 800 input fault samples and a test set accuracy of 96%. An LSTM regression algorithm (64 neurons in the hidden layer) was used to train the parameter prediction model, predicting parameter values ​​for the next 10 minutes with a bias of 4.2%. The mechanism model integrates IF97 water vapor property function (to calculate vapor enthalpy), sliding window (5s window size) mean filtering (to smooth vibration signals), FFT vibration analysis function (to identify vibration frequencies from 10-1000Hz), R / S trend analysis, and M / N voting logic (M=2, N=3, two-out-of-two voting). A comprehensive model was obtained through weighted fusion (AI weight 0.6, mechanism weight 0.4), with an alarm response time of 0.8s.

[0070] 500 fault contingency plans were entered into the expert system, establishing a correlation mapping such as "excessive vibration → check bearing temperature → adjust lubricating oil quantity". The health assessment formula H=αS+βE+γR was used for calculation: α=0.5, β=0.2, γ=0.3 for the start-up / shutdown phase; α=0.4, β=0.4, γ=0.3 for the stable operation phase. During a certain steady-state operation period, S=90 points (1 warning, deduct 5 points), E=85 points (coal consumption 5g / kWh high, deduct 3 points), R=92 points (1200 hours of operation, add 2 points), H=0.4×90+0.4×85+0.3×92=88.6 points, indicating a normal state.

[0071] 3. Centralized monitoring and one-click start / stop configuration

[0072] An industrial Ethernet ring network (1000Mbps bandwidth) was built, configured with an IEC61850 communication gateway, and connected to the control systems of three peak boilers, one heating network primary station, and generator sets. A 24-hour communication packet loss rate of 0% was achieved. A comprehensive plant-wide visual monitoring map was developed, marking the locations of 2000 devices including boilers, turbines, and peak boilers. Parameters such as main steam pressure and heating network water supply temperature were displayed in real time. 48 1080P cameras were integrated; when the steam drum pressure exceeds the limit, the furnace camera feed is automatically retrieved with a switching delay of 750ms.

[0073] The intelligent inspection engine imports equipment ledgers (including 300 equipment models and historical faults), sets daily equipment to be inspected once every 24 hours, and critical equipment (such as water pumps) to be inspected once every 8 hours, generates priority routes (prioritizing equipment with high failure rates), and maintenance personnel receive tasks through a mobile APP and upload inspection data in real time, improving inspection efficiency by 45%.

[0074] The one-button start / stop configuration is based on digital twin simulation, simulating cold (cylinder temperature 120℃), steady state (250℃), hot state (380℃), and extremely hot state (480℃) operating conditions. The operating condition adaptation formula is A=ΔTk1+ΔPk2+τk3, where k1=0.35, k2=0.45, and k3=0.2. Under cold conditions, ΔT=30℃, ΔP=1.2MPa, τ=0.8s, A=30×0.35+1.2×0.45+0.8×0.2=10.5+0.54+0.16=11.2 (normalized 0.89≥0.85, suitable), adjust the preheating time to 60 minutes, and increase the coal feed rate by 2t / min; under extremely hot conditions, ΔT=20℃, ΔP=0.8MPa, τ=0.5s, A=20×0.35+0.8×0.45+0.5×0.2=7+0.36+0.1=7.46 (normalized 0.91), preheating time is 20 minutes, and start-up time is 15 minutes.

[0075] 4. Open development environment and system integration

[0076] The system deploys Ethernet, MODBUSTCP, OPCUA, and MQTT interfaces, achieving a third-party data exchange rate of 120MB / s, and supports SQL and JSON parsing. An algorithm repository encapsulates 120 algorithms (such as predictive control and neural networks), with accompanying documentation annotating input and output parameters. A "zero-code configuration tool" provides a drag-and-drop component library, enabling heating network engineers to build "heating network water supply temperature optimization models" within 25 minutes of setup and deployment. The front-end automatically generates trend charts and alarm interfaces, achieving "modeling and use immediately."

[0077] During the commissioning phase, a third-party desulfurization efficiency optimization module was integrated, using the compatibility formula C=λN / μM, where λ=0.95 (19 modules ran successfully), N=20, μ=0.98, M=50, and C=0.95×20 / (0.98×50)=19 / 49≈0.387 (normalized 0.94≥0.92, compatible). After 72 hours of continuous operation, CPU utilization was 55% and memory utilization was 68%, meeting the requirements.

[0078] 5. Performance Data Representation

[0079] Table 1: Performance Comparison of Intelligent Monitoring and Manual Monitoring

[0080]

[0081] Explanation: The data in Table 1 comes from the operational statistics one month after the upgrade. Manual monitoring relies on shift work, which is prone to delays and misjudgments when dealing with thousands of parameters, and cannot provide early warnings of faults. Intelligent monitoring achieves a rapid response of 0.8 seconds through model fusion, provides early warnings within 18 minutes (e.g., early intervention to avoid shutdown in case of excessive turbine vibration), and achieves zero misjudgments within 8 hours. The labor intensity score has decreased from 75 to 30 points, completely eliminating the limitations of manual monitoring. This confirms the innovative aspect of this invention, "transforming manual monitoring into machine monitoring," significantly improving unit operational safety and maintenance efficiency, and solving the pain points of high labor intensity and delayed early warnings in traditional monitoring.

[0082] Example 2: New 1000MW Ultra-Supercritical Unit Project (Application Scenario of a Coastal Power Generation Base)

[0083] This example is for a new 1000MW ultra-supercritical thermal power unit project. It is necessary to build an independent, controllable, open and compatible intelligent power generation control system to adapt to the large-capacity and high-parameter operation requirements of the unit. The key is to integrate third-party energy consumption optimization modules and intelligent inspection systems. The following is the specific implementation process.

[0084] 1. Construction of a high-specification DCS basic platform

[0085] The Phytium FT-2000 / 4 enhanced CPU controller (48W power consumption, 18,000 hours of continuous operation without failure) was selected, and the Kylin Advanced Server Operating System was deployed, with a dual-machine hot standby switching time of 25ms. Four-level task scheduling optimization: Level 1 task (such as main steam temperature closed-loop control) response time 15ms; Level 2 task (communication with the power grid AGC system) latency 38ms; Level 3 task (equipment status diagnosis); Level 4 task (monthly energy consumption analysis).

[0086] The high-speed signal acquisition module has been upgraded to a 500kHz sampling frequency, with 12 modules covering 384 signals. It integrates dual electromagnetic shielding (metal shell + shielded cable), and the sampling error under pulse interference is 0.08% in the anti-interference test. It is configured with 5000 monitoring points, with 2500 points reserved for expansion. The API interface supports secondary development (such as connecting to a future carbon emission monitoring module).

[0087] 2. Construction of a high-precision intelligent monitoring module

[0088] Five years of historical data (1.2TB) and 1200 fault records from a 1000MW unit of the same type were collected. Preprocessing employed the K-nearest neighbor algorithm to fill missing values ​​and wavelet transform to remove high-frequency noise. 220 key parameters (such as reheat steam temperature and high-pressure cylinder exhaust pressure) and 110 feature parameters were selected. The AI ​​model used an improved random forest (200 decision trees) and a bidirectional LSTM (128 hidden neurons). The fault identification accuracy was 98%, and the parameter prediction bias was 3.8%.

[0089] The mechanistic model integrates a high-precision IF97 water vapor function (calculation accuracy 0.01kJ / kg), a 10s sliding window filter, and logic components that support custom M / N voting (M=3, N=5). The fused model has an alarm response time of 0.6s. The health assessment H is calculated during the start-up and shutdown phases: S=88 points (2 warnings, deduct 10 points), E=82 points (high coal consumption 8g / kWh, deduct 6 points), R=95 points (1500 hours of operation, add 3 points), H=0.5×88+0.2×82+0.3×95=44+16.4+28.5=88.9 points, triggering a mild warning and pushing a parameter list.

[0090] 3. Centralized monitoring and start / stop optimization of multiple systems

[0091] The industrial Ethernet ring network bandwidth has been upgraded to 2000Mbps, connecting 4 boilers, 2 steam turbines, 6 peak boilers, and the first station of the heating network. The communication gateway supports 5000 points of concurrent data transmission with a 24-hour packet loss rate of 0%. The overall monitoring system integrates GIS map positioning, marking the geographical coordinates of the equipment. The cameras have been upgraded to 4K resolution, and the alarm screen switching latency is 680ms.

[0092] The intelligent inspection engine connects to 500 devices and uses a genetic algorithm to optimize inspection routes, reducing the inspection cycle of key equipment to 4 hours. The mobile app supports AR scanning to check equipment information, improving inspection efficiency by 50%. One-click start / stop is optimized for the high-parameter characteristics of ultra-supercritical units: cold state (cylinder temperature 100℃) A=0.92, preheating for 70 minutes; extremely hot state (cylinder temperature 500℃) A=0.93, start-up for 12 minutes, with pressure fluctuation of 8% and temperature fluctuation of 7% during start / stop.

[0093] 4. Open environment and third-party integration

[0094] The data interface test rate is 150MB / s, compatible with the OPCUA over TSN real-time communication protocol. The algorithm repository has added dedicated carbon emission calculation algorithms (such as the IPCC emission factor method), and the configuration tool supports batch model import (10 models can be imported at once). Three third-party energy consumption optimization modules and two equipment diagnostic modules have been integrated. Compatibility testing showed C=λN / μM, λ=0.98 (49 successful), N=50, μ=0.98, M=80, C=0.98×50 / (0.98×80)=50 / 80=0.625 (normalized 0.96≥0.92). The system automatically outputs an interface adaptation report, and the module resource utilization rate is 12%.

[0095] 5. Security Protection and Joint Debugging Optimization

[0096] Full-layer security protection deployment: The hardware encryption chip adopts the national cryptographic SM4 algorithm, the AI ​​intrusion detection model has an accuracy rate of 99.9% in identifying abnormal access, the data transmission is encrypted with AES-256, the access permissions are divided into four levels (administrator, operation and maintenance, developer, visitor), and a triple authentication of "account + password + face" is adopted. The account is locked after 3 failed authentications.

[0097] The system simulated 15 fault scenarios (such as feedwater pump tripping and boiler fire extinguishing), achieving a 96% recall rate for intelligent monitoring and early warning, and a 92% matching rate for expert suggestions. After 168 hours of continuous operation, the CPU utilization was 58%, memory utilization was 65%, and the energy consumption optimization module reduced coal consumption by 3g / kWh.

[0098] 6. Performance Data Representation

[0099] Table 2: Comparison of one-key start / stop performance under different operating conditions

[0100]

[0101] Explanation: The data in Table 2 comes from tests conducted under four typical operating conditions during the unit commissioning phase. Traditional manual start-up and shutdown rely on personnel following procedures, which are cumbersome and have low parameter control accuracy. Cold start-up and shutdown takes 4.5 hours, with pressure and temperature fluctuations exceeding 10%. This invention significantly shortens start-up and shutdown time (29% reduction in cold conditions and 40% reduction in extremely hot conditions) through multi-condition adaptive logic and configuration control, with fluctuations controlled within 8%, and the number of operating steps reduced by more than 70%. This solves the problems of traditional start-up and shutdown being "highly dependent on experience, time-consuming, and highly volatile," enabling safe, rapid, and standardized start-up and shutdown under different operating conditions, reducing operational risks and personnel workload, and adapting to the high-parameter operation requirements of ultra-supercritical units.

[0102] Reference Figure 3This diagram clearly demonstrates the technical advantages of the "multi-condition adaptive start-stop" system of this invention. Traditional start-stop control often uses fixed logic, which can only adapt to a single operating condition. When facing the transition from a cold to an extremely hot operating condition, manual adjustments are required, resulting in lengthy start-stop times and drastic parameter fluctuations. This invention, through automatic operating condition identification and dynamic adjustment of configuration logic, reduces the start-stop time from 3.2 hours to 0.6 hours as the cylinder temperature rises (from a cold to an extremely hot state), and reduces pressure and temperature fluctuations to 6% and 5%, respectively. This reflects the precision of the "configuration logic + operating condition adaptation" design, solving the problems of "strong reliance on experience and unstable process" in traditional start-stop systems, and achieving safe, rapid, and standardized start-stop of the unit under different operating conditions.

[0103] Reference Figure 4 This diagram visually demonstrates the core advantage of the "open development environment" of this invention. Traditional DCS systems are highly closed, with single data interfaces, making it difficult for third-party dedicated modules to connect, easily forming information silos. This invention, through pre-built Ethernet, OPCUA and other common interfaces, coupled with standardized configuration specifications, ensures that the compatibility index of five types of third-party modules, including energy consumption optimization and carbon emission monitoring, is ≥0.92, with the highest reaching 0.96. This means that third-party developers can quickly integrate and deploy dedicated functional modules without large-scale modifications to the original system, effectively breaking down the closed barriers of traditional systems and providing flexible expansion capabilities for continuous iterative upgrades of unit functions.

[0104] Reference Figure 5 This diagram clearly demonstrates the technical effectiveness of the "multi-core CPU hierarchical scheduling" method of this invention. Traditional DCS task scheduling is mostly in a single-level mode, which easily leads to competition for computing power between real-time control tasks and idle processing tasks, causing core control response delays. This invention divides tasks into four levels according to priority. Core real-time control tasks (such as boiler water level regulation) have a response time of only 18ms, communication interaction and system monitoring tasks are delayed sequentially, and idle optimization tasks (such as data statistics) are delayed by 150ms, achieving balanced execution of various tasks. This solves the problem of limited single-core computing power of domestic controllers, significantly improves the system's real-time performance and concurrent processing capabilities, and provides solid computing power support for the complex control requirements of the unit.

[0105] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An intelligent power generation control system with an open development environment, characterized in that, include: The independently controllable DCS basic platform serves as the core support of the system, adopting a controller based on the domestic ARM architecture Phytium FT-2000 / 4 multi-core CPU; it integrates four-level task scheduling technology, grouping and processing tasks according to real-time control level, communication interaction level, system supervision level, and idle time optimization level; it is equipped with high-performance and high-speed signal acquisition modules; the platform is built on the domestic Kylin operating system, adopts a dual-machine hot standby redundancy design, and provides customized API interfaces; The intelligent monitoring module deeply integrates AI and mechanistic models to construct a comprehensive evaluation system. The AI ​​model integrates multiple algorithms and has a built-in training library of unit fault samples. The mechanistic model integrates physical property functions, filtering analysis functions, and various logic components. The module collects DCS system tag parameters in real time and generates upper and lower limits of parameters based on the 3σ principle, which can quantify the health status from three dimensions: equipment safety, operating economy, and system reliability. The built-in expert system stores typical fault handling plans and automatically matches early warning outputs operation guidance. The centralized monitoring module is built on an industrial Ethernet ring network architecture and adopts the IEC61850 communication protocol. Develop a comprehensive visual monitoring map for the entire plant, integrating equipment labeling, parameter display, and fault highlighting functions, and supporting high-definition video linkage; Built-in intelligent inspection engine generates priority inspection routes; The unit-level one-button start-stop control module adopts a dual-drive design of "configuration logic + digital twin simulation"; a dedicated algorithm is developed for analog quantity control, and the operating conditions are identified in real time through sensors, the start-stop process nodes are sorted out and the configuration logic program is written. The open development environment module is built with a three-layer architecture of "algorithm repository - configuration platform - interface adaptation" and has four pre-built general data interfaces; the algorithm repository encapsulates a variety of intelligent algorithm modules and marks applicable scenarios; it adopts a zero-code graphical configuration modeling tool, provides a drag-and-drop component library, supports the entire model development process and automatic generation of front-end functions; and has a built-in model version management system. The intelligent monitoring and dynamic health assessment unit uses the assessment method H=αS+βE+γR; where H is the unit health score; α is the safety weight; β is the economic weight; γ is the reliability weight, calculated based on the number of fault warnings and parameter deviation; and E is the economic score, calculated based on the difference between actual coal consumption and design coal consumption. R represents the reliability score, calculated based on the equipment's continuous operating time and fault repair rate; the evaluation focus can be dynamically adjusted to match the unit's operating stage.

2. The intelligent power generation control system with an open development environment according to claim 1, characterized in that, It also includes a one-button start / stop multi-condition adaptive unit, with the adaptability evaluation method being A=ΔTk1+ΔPk2+τk3; where A is the operating condition adaptability index; ΔT is the absolute value of the deviation between the actual cylinder temperature and the standard operating condition cylinder temperature; k1 is the temperature deviation coefficient; ΔP is the absolute value of the deviation between the actual main steam pressure and the standard operating condition pressure; k2 is the pressure deviation coefficient; τ is the system response time; and k3 is the response time coefficient; the configuration logic parameters can be automatically adjusted.

3. The intelligent power generation control system with an open development environment according to claim 1, characterized in that, It also includes a third-party module compatibility verification unit, with the compatibility verification method being C=λN / μM; where C is the compatibility index; λ is the third-party module adaptation success rate; N is the total number of third-party modules connected; μ is the system's inherent module adaptation coefficient; and M is the total number of system's inherent modules; and it is accompanied by an automated testing process to record module response time and resource utilization.

4. The intelligent power generation control system with an open development environment according to claim 1, characterized in that, It also includes a unit energy consumption intelligent optimization module that communicates with the DCS basic platform; the module integrates boiler combustion optimization algorithms and turbine sliding pressure operation curves, and collects energy consumption-related parameters in real time; the module supports historical energy consumption data retrospective analysis, generates daily, monthly, and quarterly energy consumption trend reports, and marks energy consumption anomalies and optimization measures.

5. The intelligent power generation control system with an open development environment according to claim 1, characterized in that, It also includes a data intelligence analysis and traceability module, which adopts a distributed database architecture and supports the classified storage of DCS real-time data, historical operation data, model training data, and fault record data; it integrates anomaly data root cause localization algorithms and uses cause-effect graph analysis to trace the source of anomalies; it develops data visualization analysis tools that support multi-dimensional data filtering and chart display, and operation and maintenance personnel can generate customized analysis reports through drag-and-drop operations.

6. The intelligent power generation control system with an open development environment according to claim 1, characterized in that, It also includes a full-layer security protection module, constructing a four-dimensional protection system of "hardware-software-data-access"; at the hardware level, a secure encryption chip is used to protect CPU running data; at the software level, an AI intrusion detection model is integrated, trained based on multiple attack samples; at the data level, the AES-256 encryption algorithm is used to ensure transmission security; at the access level, four-level permission management is implemented, using "account + password + biometrics" triple authentication.

7. A method for an intelligent power generation control system using an open development environment as described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: Build an independent and controllable DCS basic platform, select Phytium FT-2000 / 4 multi-core CPU controller, deploy Kylin operating system, and configure dual-machine hot standby redundancy module; Develop a four-level task scheduler and test the response time of tasks of different priorities; Install high-speed signal acquisition modules and perform opto-isolation and electromagnetic shielding calibration; configure monitoring points based on unit scale and develop customized API interface documentation for future function expansion; Step 2: Construct an intelligent monitoring module, collect 3 years of DCS historical operation data of the target unit and multiple fault handling records, and perform data preprocessing; use the random forest classification algorithm to train the fault identification model, the LSTM regression algorithm to train the parameter prediction model, integrate the IF97 water vapor function and the vibration FFT analysis function to construct the mechanism model, and fuse the two models through a weighted fusion algorithm; Step 3: Deploy the centralized monitoring module, build an industrial Ethernet ring network, configure the IEC61850 communication gateway, and connect the peak furnace, the first station of the heating network, and the generator control system; develop a plant-wide visual monitoring master map, set alarm trigger video switching rules; develop an intelligent inspection engine, import equipment ledgers, set inspection cycles, and develop a mobile inspection APP. Step 4: Configure the unit-level one-click start / stop logic. Build a start / stop process simulation environment based on a digital twin model to simulate the process under four operating conditions: cold, steady, hot, and extremely hot. Develop a multi-modal PID algorithm and fuzzy adaptive bypass control logic. Test the start / stop process under each operating condition in the simulation environment. Step 5: Configure an open development environment and deploy Ethernet, MODBUSRTU / TCP, OPCUA, and MQTT data interfaces; build an algorithm repository, encapsulate various intelligent algorithm modules, and write algorithm documentation; develop a "zero-code, graphical" configuration tool, provide a drag-and-drop component library, test the business expert modeling process, and automatically generate the front-end interface after the model is released; deploy a version management system and set model version number rules. Step Six: System integration and optimization, integrating the functions of each module, accessing actual unit operation data, testing the accuracy of intelligent monitoring health assessment, one-click start / stop multi-condition adaptability, and third-party module compatibility; The system resource utilization was monitored for 72 consecutive hours, and the final system operation manual and maintenance specifications were developed.

8. The method for an intelligent power generation control system in an open development environment according to claim 7, characterized in that, It also includes a dynamic optimization method for the intelligent monitoring model, the steps of which are as follows: set the model update cycle, collect real-time operating data of the unit within 24 hours before each update, perform data preprocessing and supplement it to the training set; use the sliding window algorithm to extract new features and supplement them to the model input layer; use the gradient descent algorithm to optimize the AI ​​model weights, and combine the actual fault handling results to correct the function coefficients in the mechanism model; re-integrate the two types of models; push the model update package online through an open environment, and use the breakpoint resume method so that the monitoring function is not interrupted during the update process.

9. The method for an intelligent power generation control system in an open development environment according to claim 7, characterized in that, It also includes a full-process integration method for third-party modules, with the following steps: compiling an open development guide, clarifying data interface protocols, model configuration specifications, and compatibility testing standards; third parties developing dedicated modules based on the guide, accessing the system through the OPCUA interface, and submitting basic module information; Initiate automated compatibility testing, calculate the compatibility index C, and guide third parties to retest after making modifications; After successful deployment, operations and maintenance personnel use graphical configuration tools to associate third-party modules with the existing system and test module interoperability; after going live, they monitor the module's operating status in real time.

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