Power station real-time control and intelligent calculation division-coordination system and method

Abstract

The application discloses a power station real-time control and intelligent calculation division-cooperation system and method, which comprises an intelligent calculation server cluster, an intelligent calculation network connected with the intelligent calculation server cluster and used for realizing internal high-speed calculation communication of the server, realizing data transmission and task allocation, a data scheduling network connected with the intelligent calculation network, intelligent scheduling service and the control cluster respectively, an intelligent scheduling service used for coordinating task allocation and data exchange between the intelligent calculation server cluster and the control cluster, a control cluster connected with the data scheduling network and an input / output unit connected with the control cluster. Through the innovative system architecture and technical means, the deep integration of the power station control and the intelligent calculation is realized, the operation efficiency and the reliability of the power station are significantly improved, the real-time control and the intelligent calculation cooperation problem of the existing industrial control system under the condition of high proportion of new energy access of the new power system is solved, and the application has remarkable practical value and wide application prospect.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new power systems, and specifically relates to a power station real-time control and intelligent calculation division-cooperation system and method. BACKGROUND

[0002] With the promotion of global energy transformation and power market reform, the operation environment of the power system is more complex and variable, and higher requirements are put forward for the flexibility, data processing capability, integration and security of the control system. The intermittency and instability of renewable energy bring great challenges to the stable operation of the power system. The traditional power system relies on fossil fuel power generation, and the operation condition is relatively fixed, which is difficult to adapt to the complex changes of high proportion of renewable energy access. The opening and competition of the power market intensify, and the power price fluctuates frequently, which puts higher requirements on the flexibility and response speed of the power system. The requirements of users on power quality and reliability are also increasing, especially the continuity and stability of power supply for industrial users and data centers. Therefore, the power system needs more intelligent and automated control means to cope with high proportion of renewable energy access, opening of the power market and diversification of user demand.

[0003] The traditional industrial control system of the power industry faces many challenges under the background of new power systems. First, the traditional industrial control system is mainly designed for stable operation under fixed working conditions, and it is difficult to quickly respond to the fluctuations and load changes of the power grid, especially in the case of high proportion of renewable energy access, the operation condition of the power system changes frequently, and the traditional industrial control system is difficult to adapt to this dynamic environment. Second, the data processing capability of the traditional industrial control system is relatively weak, and it is difficult to process large-scale and high-frequency real-time data, and the data analysis capability is insufficient, which cannot fully utilize advanced technologies such as big data and machine learning to realize comprehensive monitoring and optimal control of the system state. In addition, the integration between the subsystems of the traditional industrial control system is low, and the information island phenomenon is serious, the data exchange and collaborative control capability between the subsystems is weak, and it is difficult to realize cross-system collaborative optimization and intelligent control. Finally, the hardware architecture of the traditional industrial control system is relatively fixed, and the expansibility is poor, which is difficult to adapt to the changing needs of the power system, and a large amount of time and cost is needed when adding new functions and modules, which affects the flexibility and adaptability of the system, and the software architecture is rigid, which is difficult to quickly integrate new control algorithms and applications, limiting the innovation and development of the system.

[0004] To address these issues, current efforts, both domestically and internationally, are primarily focused on integrating intelligent control technologies into traditional industrial control systems to achieve local optimization and improve system performance. For example, by combining machine learning with optimization algorithms, traditional industrial control systems' control strategies can be optimized. Predictive control algorithms can predict future system states based on historical and real-time data, allowing control parameters to be adjusted in advance to improve system responsiveness and stability. Without impacting the overall system architecture, key subsystems can be optimized locally. For example, optimizing boiler combustion control systems can improve combustion efficiency and reduce emissions, and optimizing steam turbine control systems can enhance turbine operating efficiency and reliability. While local optimization technologies based on intelligent control within traditional industrial control systems can improve system performance to a certain extent, they also have limitations and drawbacks. First, local optimization can only improve the performance of specific subsystems and cannot fully enhance the flexibility and responsiveness of the entire system. Global optimization requires deeper system transformation and integration. Second, integrating intelligent control algorithms and edge computing devices into traditional industrial control systems requires overcoming compatibility and communication issues between different systems, increasing system complexity and maintenance difficulties. Intelligent control relies on high-quality and complete data, but data acquisition and transmission in traditional industrial control systems can be subject to delays, loss, or errors, compromising the effectiveness of intelligent control. Introducing intelligent control technologies and equipment requires additional investment, including hardware acquisition, software development, and system integration, increasing the initial project costs. Some intelligent control algorithms and technologies are still under development, and their stability and reliability have not been fully verified, potentially posing risks in practical applications. Summary of the Invention

[0005] The purpose of the present invention is to provide a power plant real-time control and intelligent computing divide-and-conquer collaborative system and method, so as to overcome the problem that existing industrial control systems are difficult to solve in the new power system with a high proportion of new energy access conditions and efficient collaboration of intelligent computing and real-time control, and to improve the system's flexibility, scalability, compatibility and global optimization capabilities.

[0006] In order to solve the above problems, the present invention adopts the following technical solutions:

[0007] A power plant real-time control and intelligent computing divide-and-conquer collaborative system, comprising an intelligent computing server cluster and an intelligent dispatching service, wherein the intelligent computing server cluster and the intelligent dispatching service are connected and communicated with each other via an intelligent computing network and a data dispatching network in turn;

[0008] The data scheduling network is further connected to at least one control cluster, and each control cluster is connected to an input / output unit;

[0009] The intelligent computing server cluster is used to perform intelligent computing tasks;

[0010] The intelligent computing network is used for connecting the intelligent computing server cluster, realizing high-speed computing communication within the server, and providing a communication interface for interaction with the data scheduling network to realize data transmission and task allocation.

[0011] The data scheduling network is used for connecting the intelligent scheduling service and a plurality of control clusters, and is used for real-time data transmission and scheduling.

[0012] The intelligent scheduling service is used for coordinating task allocation and data exchange between the intelligent computing server cluster and the control cluster.

[0013] The control cluster includes a plurality of controllers for real-time control of power plant equipment.

[0014] The input / output unit is used for collecting field data and sending control instructions.

[0015] Further, the intelligent scheduling service adopts a master-backup redundancy design and a heartbeat monitoring mechanism, and has the functions of multi-objective optimization scheduling, active and dynamic scheduling combination, and distributed collaborative scheduling.

[0016] Further, each control cluster includes a plurality of control units, and the control unit includes a controller adopting a redundancy design, and the controller is built-in with an adaptive control algorithm, and supports remote configuration and update.

[0017] The control cluster adopts an adaptive resource pooling technology to form a flexible control resource pool with minimum control units, and supports dynamic resource scheduling and elastic load.

[0018] Further, the intelligent computing network adopts a distributed cache technology and a traffic shaping algorithm, the intelligent computing server cluster is built-in with an adaptive resource scheduling algorithm and a task priority management system, and each server in the intelligent computing server cluster is equipped with a dual-redundant trusted encryption network communication module.

[0019] Further, the data scheduling network supports multi-protocol transmission, adaptive traffic slicing, integrated encryption technology, and data integrity verification mechanism, and is built-in with an intelligent monitoring module.

[0020] The input / output unit integrates AI, AO, DI, DO, and Modbus soft and hard integrated devices, unifies hardware interfaces, defines software input / output modes, and forms an adaptive module supporting conversion of multiple input / output modes.

[0021] In a second aspect, a power station real-time control and intelligent computing division-coordination method is provided, including the following steps:

[0022] The input / output unit collects real-time field data and transmits the data to the intelligent scheduling service.

[0023] The intelligent scheduling service generates task allocation instructions according to real-time data and historical data analysis results;

[0024] The intelligent computing server cluster receives the task allocation instructions through the intelligent computing network and the data scheduling network, and acquires real-time data on site.

[0025] The intelligent computing server cluster executes intelligent computing tasks and feeds back the computing results to the intelligent scheduling service.

[0026] The intelligent scheduling service generates control instructions according to the computing results and sends them to the corresponding control cluster through the data scheduling network.

[0027] The controller in the control cluster adjusts the operation parameters of the power plant equipment in real time according to the received control instructions and issues the control instructions to the input / output unit.

[0028] Further, the intelligent scheduling service automatically optimizes the task allocation and resource scheduling strategy according to real-time data and historical data analysis results, continuously optimizes the scheduling strategy using reinforcement learning algorithm, predicts future task load based on historical data, and pre-allocates resources.

[0029] Further, the intelligent scheduling service supports multi-objective optimization.

[0030] Further, the input / output unit has self-diagnosis function, fault log recording and remote backtracking function.

[0031] Compared with the prior art, the present application has the following beneficial technical effects:

[0032] The present application provides a power station real-time control and intelligent computing division-cooperation system, which comprises an intelligent computing server cluster, an intelligent computing network connected with the intelligent computing server cluster for realizing internal high-speed computing communication of the server and data transmission and task allocation, a data scheduling network connected with the intelligent computing network, the intelligent scheduling service and the control cluster respectively for coordinating task allocation and data exchange between the intelligent computing server cluster and the control cluster, a control cluster connected with the data scheduling network, and an input / output unit connected with the control cluster.

[0033] Intelligent scheduling service module: adopts main and standby redundant design and heartbeat monitoring mechanism to ensure high availability and reliability of the system. Through multi-objective optimization scheduling, active and dynamic scheduling combination and distributed collaborative scheduling, the intelligent scheduling service module can automatically optimize task allocation and resource scheduling strategy according to real-time data and historical data analysis results, predict future task load in advance and perform resource pre-allocation, ensure stable operation of the system under high proportion of new energy access, and support multi-objective optimization, taking into account performance, energy consumption and cost, etc. multiple dimensions to achieve more efficient resource utilization. Through reinforcement learning algorithm to continuously optimize scheduling strategy, based on historical data to predict future task load, to perform resource pre-allocation in advance to ensure efficient operation of the system under high proportion of new energy access.

[0034] Control cluster: integrates multiple minimum control units into a control cluster, adopts adaptive resource pooling technology to form a flexible control resource pool, realizes dynamic allocation and elastic scaling of resources. The control cluster supports dynamic resource scheduling, automatically adjusts resource allocation according to real-time task demand and system load, ensures that resources can be quickly expanded under high load and released under low load, and improves resource utilization.

[0035] Improve the flexibility and scalability of the system:

[0036] Intelligent computing server cluster: built-in adaptive resource scheduling algorithm and task priority management system, each server is equipped with dual-redundant trusted encryption network communication module to ensure the security and reliability of data transmission. The intelligent computing server cluster can dynamically adjust computing resources according to real-time data and task allocation instructions to ensure efficient operation of the system under different loads, greatly improving the flexibility and response speed of the system.

[0037] Input / output unit: integrates traditional independent AI, AO, DI, DO and Modbus soft and hard integrated devices, unifies hardware interfaces, defines software input / output mode, forms an adaptive module that supports multiple input / output mode conversion. Through software-defined functions, users can flexibly configure the functions of each interface according to actual needs, realize the conversion of multiple input / output modes, improve the flexibility and scalability of the system, and have self-diagnosis function, fault log recording and remote backtracking function, which can monitor the state in real time and immediately issue an alarm when abnormal, helping technicians quickly locate the problem source and shorten the maintenance time.

[0038] Enhance the compatibility and global optimization capability of the system:

[0039] Intelligent computing network: adopts distributed cache technology and traffic shaping algorithm, effectively reduces network congestion, and ensures the priority transmission of key tasks. Intelligent computing network can efficiently process large-scale and high-frequency real-time data, support advanced technologies such as big data and machine learning, and realize comprehensive monitoring and optimal control of system state.

[0040] Data scheduling network: supports multi-protocol transmission, integrates encryption technology and data integrity verification mechanism, and has an intelligent monitoring module built-in. Data scheduling network adopts adaptive traffic slicing technology to divide the physical network into multiple logical network slices, each slice is customized according to specific task requirements, ensuring that task scheduling, data transmission and control instructions are not affected and highly reliable. By using communication protection mechanism, it effectively ensures the bypass transmission of business after the failure of network lines or equipment.

[0041] Adaptive control algorithm built-in controller: The controller can dynamically adjust the control parameters according to real-time feedback to ensure that the equipment always runs in the best state. At the same time, it supports remote configuration and update, which is convenient for maintenance personnel to carry out centralized management, and improves the maintenance efficiency and reliability of the system.

[0042] The present application provides a kind of power station real-time control and intelligent computing division-cooperation method, and intelligent computing server cluster is dynamically adjusted according to real-time data and the task allocation instruction generated by intelligent scheduling service Calculation resource, execute intelligent computing task, and feedback calculation result to intelligent scheduling service, and intelligent scheduling service generates control instruction and issues to control cluster and input / output unit, this kind of division-cooperation mode not only optimizes resource utilization, but also improves the real-time performance and flexibility of system, further enhances the adaptability of power station to complex working condition, especially in high proportion new energy access new type power system, can effectively guarantee the stable operation of power station, reduce operation and maintenance cost, improve economic benefit, provide strong technical support for the development of new type power system. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 For the system structure diagram of a kind of power station real-time control and intelligent computing division-cooperation in the embodiment of the present application;

[0044] Figure 2 For the flow chart of a kind of power station real-time control and intelligent computing division-cooperation method in the embodiment of the present application.

[0045] In the figure, 1, intelligent computing server cluster;2, intelligent computing network;3, data scheduling network;4, intelligent scheduling service;5, control cluster;6, input / output unit. DETAILED DESCRIPTION

[0046] In order to make the technical problems solved by the present application, technical solutions and beneficial effects clearer, the following specific embodiments are used to further explain the present application. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0047] In order to make the technical problems solved by the present application, technical solutions and beneficial effects clearer, the following specific embodiments are used to further explain the present application. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0048] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents the selected embodiments of the application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0049] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0050] In the description of the embodiments of the present application, it should also be noted that, unless otherwise explicitly specified and limited, if the terms "arrange", "mount", "connect", "connect" appear, they should be understood in a broad sense, for example, can be fixedly connected, can also be detachably connected, or integrally connected; can be mechanically connected, can also be electrically connected; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0051] A power station real-time control and intelligent computing division-cooperation system, as shown in Figure 1 The system includes an intelligent computing server cluster 1, an intelligent computing network 2, a data scheduling network 3, an intelligent scheduling service 4, a control cluster 5 and an input / output unit 6, wherein the intelligent computing server cluster 1, the intelligent computing network 2, the data scheduling network 3 and the intelligent scheduling service 4 are connected in sequence, the input end of the control cluster 5 is connected with the data scheduling network 3, and the output end of the control cluster 5 is connected with the input / output unit 6.

[0052] In this embodiment, the intelligent scheduling service 4 and the control cluster 5 are both designed redundantly to improve reliability. Specifically, the intelligent scheduling service 4 has two, which are used as the main and standby respectively; the data scheduling network 3 is connected to multiple control clusters 5, each control cluster 5 includes multiple minimum control units, and each minimum control unit includes two intelligent controllers. Similarly, the two intelligent controllers are designed redundantly as the main and standby.

[0053] The controller has a built-in adaptive control algorithm, which can dynamically adjust the control parameters according to real-time feedback to ensure that the device always operates in the best state. At the same time, it supports remote configuration and update, which is convenient for maintenance personnel to manage centrally.

[0054] The input / output unit 6 has multiple, each of which contains multiple input / output modules for collecting field data and sending control instructions.

[0055] In an optional embodiment, a power station real-time control and intelligent computing divide-and-conquer-collaboration system is provided, which includes an intelligent computing server cluster 1, an intelligent computing network 2, a data scheduling network 3, an intelligent scheduling service 4, a plurality of control clusters 5, and a plurality of input / output units 6. The intelligent computing server cluster 1 is used to perform complex intelligent computing tasks. The intelligent computing network 2 is used to connect the intelligent computing server cluster 1, realize internal high-speed computing communication of the server, and provide a communication interface to interact with the data scheduling network to realize data transmission and task allocation. The data scheduling network 3 connects the intelligent scheduling service 4 and the plurality of control clusters 5, and is used for real-time data transmission and scheduling. The intelligent scheduling service 4 is used to coordinate the task allocation and data exchange between the intelligent computing server cluster 1 and the control cluster 5. Each control cluster in the plurality of control clusters 5 contains multiple controllers for real-time control of power plant equipment. Each input / output unit 6 in the plurality of input / output units 6 contains multiple input / output modules for collecting field data and sending control instructions.

[0056] In this embodiment, the intelligent computing server cluster 1 can dynamically adjust CPU, GPU and other hardware resources according to the task allocation of the intelligent scheduling service 4, real-time monitor the task queue, and ensure efficient operation under different loads, greatly improving the flexibility and response speed of the system. The intelligent computing server cluster 1 can dynamically configure and deploy 3 to 128 servers according to the actual needs of the power station, each server is equipped with a dual-redundant trusted encryption network communication module, which transmits data to the server storage module and the execution module internally, and transmits data to other servers or the data scheduling network externally. The dual-redundant communication modules work in parallel as the main and standby, and when one of them fails, the other module automatically takes over the work of the failed communication module.

[0057] The intelligent computing network 2 has high bandwidth and low delay characteristics, ensuring efficient communication between the intelligent computing server cluster 1 and the intelligent scheduling service 4. The intelligent computing network 2 uses distributed caching technology and traffic shaping algorithms to effectively reduce network congestion and ensure priority transmission of critical tasks; supports automatic fault switching and rapid recovery, improving the fault tolerance of the system.

[0058] The data scheduling network 3 supports multi-protocol transmission, ensuring the security and integrity of real-time data. The data scheduling network not only supports multiple transmission protocols, but also integrates advanced encryption technology and data integrity verification mechanisms to ensure the security and consistency of data during transmission; at the same time, an intelligent monitoring module is built in to detect and report any potential security threats in real time.

[0059] The intelligent scheduling network 3 supports adaptive traffic slicing technology, which can automatically slice traffic according to task conditions to ensure that task scheduling, data transmission, and control instructions are mutually independent and highly reliable. The specific implementation is as follows:

[0060] Adaptive traffic slicing: The intelligent scheduling network 3 uses adaptive traffic slicing technology to divide the physical network into multiple logical network slices, each of which is customized according to specific task requirements. Through machine learning and stochastic game tools, the network slices are adaptively and dynamically adjusted to optimize long-term resource utilization and ensure that task scheduling, data transmission, and control instructions are mutually independent and highly reliable.

[0061] High reliability design: The reliability of the intelligent scheduling network 3 as a whole needs to meet the requirements of network topology reliability, device reliability, low network delay, low network routing convergence time, network management stability and reliability, etc. By using communication protection mechanisms, the network can effectively ensure the bypass transmission of services after line or device failure.

[0062] The intelligent scheduling service 4 can automatically optimize task allocation and resource scheduling strategies based on real-time data and historical data analysis results. The intelligent scheduling service uses reinforcement learning algorithms to continuously optimize scheduling strategies, predicts future task loads based on historical data, and pre-allocates resources in advance; at the same time, it supports multi-objective optimization, taking into account performance, energy consumption, and cost in multiple dimensions, achieving more efficient resource utilization.

[0063] The intelligent scheduling service 4 has the functions of multi-objective optimization scheduling, active and dynamic scheduling combination, and distributed collaborative scheduling:

[0064] Multi-objective optimization scheduling: supports multi-objective optimization, taking into account performance, energy consumption, and cost in multiple dimensions, achieving more efficient resource utilization. The intelligent scheduling service module can automatically optimize task allocation and resource scheduling strategies based on real-time data and historical data analysis results, predict future task loads based on historical data, and pre-allocate resources in advance.

[0065] Active and dynamic scheduling combination: combining active scheduling and dynamic scheduling strategies, the intelligent scheduling service module can predict the faults that will occur in the system in advance by comparing and analyzing real-time data and historical data before the device appears abnormally, and on this basis, re-schedule the remaining normal devices. At the same time, for the sudden conditions that appear in production sequencing, such as emergency behavior, the intelligent scheduling service module can adjust the scheduling plan in real time to ensure the efficiency and stability of the production process.

[0066] Distributed collaborative scheduling: support intelligent interconnection with multiple distributed scheduling centers, through computing intelligence and feature analysis technology, group intelligence multi-search operation and knowledge search are performed on the distributed scheduling center, and finally the obtained information is integrated to perform scheduling using knowledge-driven collaborative intelligent algorithm.

[0067] The controllers in the control cluster 5 can adjust the control parameters in real time according to the instructions of the intelligent scheduling service 4 to ensure that the power plant equipment operates in the optimal state. The adaptive control algorithm is built-in the controller, which can dynamically adjust the control parameters according to real-time feedback to ensure that the equipment always operates in the best state; at the same time, it supports remote configuration and update, which is convenient for maintenance personnel to carry out centralized management.

[0068] The control cluster 5 uses adaptive resource pooling technology to form a flexible control resource pool with the smallest control unit, supports dynamic resource scheduling and elastic load, and the specific implementation is as follows:

[0069] Conversion of the smallest control unit to the control cluster: multiple smallest control units are integrated into a control cluster, adaptive resource pooling technology is used to form a flexible control resource pool, dynamic allocation and elastic scaling of resources are realized, and management and scheduling can be performed according to actual control business needs. At the same time, in order to ensure safety, when the resource pool fails, the container independent orchestration technology is used, and each smallest control unit can run as an independent container.

[0070] Dynamic scheduling and elastic load: the control cluster supports dynamic resource scheduling, automatically adjusts resource allocation according to real-time task demand and system load condition. Through the built-in resource scheduling algorithm, combined with the adaptive load mechanism, the resource elastic scaling is realized to ensure that the resources can be quickly expanded in high load and released in low load, improving the resource utilization rate.

[0071] The input / output unit 6 has a self-diagnosis function and can issue an alarm and record fault information in abnormal conditions. The input / output unit uses an embedded intelligent diagnosis chip that can monitor its own state in real time and issue an alarm immediately when an abnormality occurs; at the same time, it has fault log recording and remote backtracking functions to help technical personnel quickly locate the problem source and shorten the maintenance time.

[0072] The input / output unit 6 integrates traditional independent AI, AO, DI, DO, Modbus software and hardware devices, unifies hardware interfaces, defines software input / output modes, and forms an adaptive module supporting conversion of multiple input / output modes. The specific implementation is as follows:

[0073] AI, AO, DI, DO, Modbus integration: The input / output unit 6 adopts a unified hardware interface design and supports input and output of multiple signal types (such as AI, AO, DI, DO, and Modbus). Through software-defined input / output modes, users can flexibly configure the functions of each interface according to actual needs, realizing conversion of multiple input / output modes. For example, through configuration files or graphical interfaces, users can set one interface to AI mode for temperature sensor signal input, or set another interface to DO mode for controlling a relay.

[0074] Unified hardware interface: The hardware interface of the input / output unit 6 adopts standardized design, supports access of multiple signal types, reduces the types and quantity of hardware interfaces, and improves the integration and reliability of the system. At the same time, through unified interface standards, the maintenance and expansion of the system are simplified.

[0075] Software-defined input / output mode: The input / output unit 6 supports software-defined functions, and users can flexibly define the input / output mode of each interface through software configuration files or graphical configuration tools. This software-defined approach enables the input / output unit to adapt to different application scenarios and requirements, improving the flexibility and scalability of the system.

[0076] The application also provides a power station real-time control and intelligent computing division-cooperation method, as shown in the figure, comprising the following steps: Figure 2

[0077] The input / output unit 6 collects real-time data on site and transmits the data to the intelligent scheduling service 4;

[0078] The intelligent scheduling service 4 generates task allocation instructions based on real-time data and historical data analysis results;

[0079] The intelligent computing server cluster 1 receives the task allocation instructions through the intelligent computing network 2 and the data scheduling network 3, and obtains real-time data on site. The intelligent computing server cluster 1 dynamically adjusts computing resources according to real-time data and task allocation instructions;

[0080] The intelligent computing server cluster 1 executes intelligent computing tasks and feeds back the computing results to the intelligent scheduling service 4;

[0081] ​The intelligent scheduling service 4 generates control instructions according to the calculation results and sends them to the corresponding control cluster 5 through the data scheduling network 3;

[0082] The controllers in the control cluster 5 adjust the operating parameters of the power plant equipment in real time according to the received control instructions and issue the control instructions to the input / output units 6.

[0083] In detail, the specific steps of the above control method are as follows:

[0084] Step S1: Collecting field data through the input / output unit 6 and transmitting the data to the intelligent scheduling service 4; the input / output unit performs preliminary processing on the collected data, such as filtering and compression, to reduce data transmission volume and improve data quality; at the same time, an intelligent filter is built in to only transmit useful data, further optimizing bandwidth usage;

[0085] Step S2: The intelligent scheduling service 4 generates task allocation instructions based on real-time data and historical data analysis results; the intelligent scheduling service uses machine learning algorithms to automatically optimize task allocation and resource scheduling strategies; at the same time, a prediction model is built based on historical data to plan future tasks in advance, ensuring that the system is always in an optimal operating state;

[0086] Step S3: The intelligent computing server cluster 1 receives the task allocation instructions and obtains the required data through the intelligent computing network 2; the intelligent computing server cluster dynamically adjusts computing resources according to task requirements, improving resource utilization and response speed; at the same time, the intelligent computing network uses distributed caching technology to ensure the efficiency and stability of data transmission;

[0087] Step S4: The intelligent computing server cluster 1 executes intelligent computing tasks and feeds back the results to the intelligent scheduling service 4; the intelligent computing server cluster realizes real-time data transmission through the high-bandwidth, low-latency intelligent computing network; at the same time, a task priority management system is built in to ensure that critical tasks are processed first, improving overall efficiency;

[0088] Step S5: The intelligent scheduling service 4 generates control instructions according to the calculation results and sends them to the corresponding control cluster 5 through the data scheduling network 3; the intelligent scheduling service dynamically adjusts the control instructions according to real-time feedback data to ensure the stability and response speed of the system; at the same time, the data scheduling network supports intelligent routing selection, optimizing the transmission path and improving efficiency;

[0089] Step S6: The controllers in the control cluster 5 adjust the operating parameters of the power plant equipment in real time according to the received control instructions; the control cluster uses edge computing technology to process part of the data locally, reducing dependence on the central server and enhancing real-time response capability; at the same time, an adaptive control algorithm is built in to ensure that the equipment is always running in the best state.

[0090] In the context of new power systems, traditional industrial control systems face many challenges, including insufficient flexibility, limited data processing capabilities, low integration, network security risks, and poor scalability. To address these challenges, more advanced control technologies and platforms, such as edge computing, cloud computing, big data analysis, and artificial intelligence, need to be introduced to improve the flexibility, data processing capabilities, integration, and security of the system, and to achieve intelligent and efficient power production and dispatch. Ultimately, to achieve efficient and reliable operation of new power systems, the real-time control and intelligent computing divide-and-conquer-collaboration system and method in the present application are constructed. The present application combines real-time control and intelligent computing, processes local optimization problems through divide-and-conquer strategies, and realizes global optimization through collaborative mechanisms to ensure the overall performance and stability of the system. Specifically, real-time control ensures real-time response and control of key subsystems, improving the flexibility and reliability of the system; intelligent computing uses big data analysis, machine learning, and other technologies to achieve global optimization and intelligent decision-making, improving the overall performance of the system; divide-and-conquer-collaboration processes local optimization problems through divide-and-conquer strategies and realizes global optimization through collaborative mechanisms to ensure the overall performance and stability of the system. This divide-and-conquer-collaboration system and method can effectively solve the limitations of traditional industrial control systems and provide strong support for the construction of new power systems, promoting the digital transformation of the power industry.

[0091] The above detailed the preferred embodiments of the present application, but the present application is not limited thereto. Within the technical concept of the present application, various simple modifications can be made to the technical solutions of the present application, including the combination of various technical features in any other suitable manner. These simple modifications and combinations should also be considered as disclosed by the present application and fall within the protection scope of the present application.

Claims

1. A power plant real-time control and intelligent computing divide-and-conquer collaborative system, characterized by: It comprises an intelligent computing server cluster (1) and an intelligent scheduling service (4), wherein the intelligent computing server cluster (1) and the intelligent scheduling service (4) are connected and communicated with each other via an intelligent computing network (2) and a data scheduling network (3) in sequence; The data scheduling network (3) is further connected to at least one control cluster (5), and each control cluster (5) is connected to an input / output unit (6); The intelligent computing server cluster (1) is used to execute intelligent computing tasks; The intelligent computing network (2) is used to connect to the intelligent computing server cluster (1), realize high-speed computing communication within the server, and provide a communication interface to interact with the data scheduling network (3) to realize data transmission and task allocation; The intelligent computing network (2) adopts distributed caching technology and traffic shaping algorithm, the intelligent computing server cluster (1) has a built-in adaptive resource scheduling algorithm and task priority management system, and each server in the intelligent computing server cluster (1) is equipped with a dual-redundant trusted encryption network communication module; The data scheduling network (3) is used to connect the intelligent scheduling service (4) and a plurality of control clusters (5) for real-time data transmission and scheduling; The intelligent scheduling service (4) is used to coordinate task allocation and data exchange between the intelligent computing server cluster (1) and the control cluster (5); The control cluster (5) includes a plurality of controllers for performing real-time control of power plant equipment; The input / output unit (6) is used to collect field data and send control instructions; The method of divide-and-conquer and collaborate between power plant real-time control and intelligent computing is as follows: The input / output unit (6) collects real-time data on site and transmits the data to the intelligent scheduling service (4); Intelligent scheduling service (4) generates task allocation instructions based on real-time data and historical data analysis results; The intelligent computing server cluster (1) receives task allocation instructions through the intelligent computing network (2) and the data scheduling network (3) and obtains on-site real-time data. The intelligent computing server cluster (1) dynamically adjusts computing resources according to the real-time data and the task allocation instructions; The intelligent computing server cluster (1) executes intelligent computing tasks and feeds back the computing results to the intelligent scheduling service (4); The intelligent scheduling service (4) generates control instructions based on the calculation results and sends them to the corresponding control cluster (5) through the data scheduling network (3); The controller in the control cluster (5) sends the received control instructions to the input / output unit (6), and adjusts the operating parameters of the power plant equipment in real time through the input / output unit (6).

2. A power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: The intelligent scheduling service (4) adopts a master-slave redundancy design and a heartbeat monitoring mechanism, and has the functions of multi-objective optimization scheduling, combination of active and dynamic scheduling, and distributed collaborative scheduling.

3. The power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: Each of the control clusters (5) includes a plurality of control units, each of which includes a controller with a redundant design, a built-in adaptive control algorithm, and supports remote configuration and updating; The control cluster (5) uses adaptive resource pooling technology to form a flexible control resource pool with minimum control units, supporting dynamic resource scheduling and elastic load.

4. The power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: The data scheduling network (3) supports multi-protocol transmission, adaptive traffic slicing, integrated encryption technology and data integrity verification mechanism, and has a built-in intelligent monitoring module.

5. The power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: The input / output unit (6) integrates AI, AO, DI, DO, and Modbus hardware and software devices, unifies the hardware interface, and defines the input / output mode through software, thereby forming an adaptive module that supports conversion of multiple input / output modes.

6. The power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: The intelligent scheduling service (4) automatically optimizes task allocation and resource scheduling strategies based on real-time data and historical data analysis results, continuously optimizes scheduling strategies using reinforcement learning algorithms, predicts future task loads based on historical data, and pre-allocates resources in advance.

7. A power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 6, characterized in that: The intelligent scheduling service (4) supports multi-objective optimization.

8. The power plant real-time control and intelligent computing divide-and-conquer collaborative system according to claim 1, characterized in that: The input / output unit (6) has a self-diagnosis function, a fault log recording function, and a remote backtracking function.

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