A Distributed Control and Dynamic Scheduling Method and System for ECRH Systems

Abstract

This invention discloses a distributed control and dynamic scheduling method and system for an ECRH system. The method includes: a distributed control architecture based on EPICS, dividing the ECRH device into multiple independently operating IOC nodes, each IOC node responsible for local data acquisition and control; each IOC node preprocesses the acquired data, calculates the priority score of each data based on access frequency and real-time requirements, and marks the data with priority levels; the CA protocol is used to publish and share the data marked with priority levels; each IOC node subscribes to the shared data according to its own relevance and importance to the data; based on the shared data and the data subscribed by each IOC node, combined with the given task, dynamic adjustment control is performed to realize intelligent scheduling and allocation of tasks. This invention can achieve rapid transmission and real-time processing of high-frequency critical data, ensuring that the system can complete the response within microseconds.

Description

Technical Field

[0001] This invention relates to the field of electronic cyclotron resonance heating system technology, specifically to a distributed control and dynamic scheduling method and system for an ECRH system. Background Technology

[0002] The Electron Cyclotron Resonant Heating (ECRH) system is a crucial subsystem in nuclear fusion devices, primarily used for plasma heating and current driving. Its performance directly impacts the success or failure of fusion experiments. Under the demands of high-parameter, long-pulse experiments, the ECRH system requires real-time monitoring and control of key parameters such as RF source output power, transmission line standing wave ratio (VSWR), and reflected power. These parameters typically exhibit high-frequency variations, placing extremely high demands on the real-time performance and response speed of the control system. However, traditional centralized control architectures, limited by data transmission delays and processing bottlenecks, struggle to meet microsecond-level response requirements. Furthermore, as experimental scale expands, the number of devices and data sampling rates within the ECRH system continuously increase, making the computational load and communication bandwidth of the centralized control center a bottleneck for system performance, further restricting the system's scalability.

[0003] Traditional centralized architectures also exhibit low efficiency in multi-node collaboration. Centralized architectures rely on a single control center to handle all data acquisition, analysis, and command generation. Because all nodes must transmit data to the control center for centralized processing before distributing the results to individual devices, overall data transmission and processing latency is high, failing to meet the microsecond-level dynamic response requirements of the ECRH system. In centralized architectures, the collaboration between subsystems (such as the RF source, cooling system, and transmission antenna) is weak. Data transmission and control commands between subsystems often need to be relayed through the centralized control center, creating communication bottlenecks and affecting the efficiency of real-time collaboration between nodes. For example, subsystems such as the RF source, cooling system, and transmission antenna in the ECRH system require close collaboration to maintain stable system operation. When the output power of the RF source becomes abnormal, the cooling system must adjust the cooling flow in real time to ensure equipment safety. However, due to fundamental limitations in data sharing and collaboration mechanisms, traditional control systems struggle to achieve efficient task allocation and dynamic collaboration between nodes, impacting overall operational efficiency.

[0004] Experimental Physics and Industrial Control Systems (EPICS) have been widely used in the control of nuclear fusion devices in recent years due to their open and distributed architecture. EPICS achieves real-time control and status monitoring of equipment through distributed Input / Output Controllers (IOCs). Each IOC node operates independently and shares data through the Channel Access (CA) protocol, and its distributed architecture offers high flexibility and scalability. However, the traditional EPICS framework still has shortcomings in the high-dynamic experimental scenarios of the ECRH system: on the one hand, the CA protocol has a certain delay in high-frequency data sharing, which cannot fully meet the requirements of microsecond-level data acquisition and control; on the other hand, the coordination mechanism between IOC nodes is not optimized for complex task allocation and dynamic load balancing, resulting in low multi-node coordination efficiency. Summary of the Invention

[0005] The technical problem to be solved by this invention is that the traditional EPICS framework cannot fully meet the needs of microsecond-level data acquisition and control.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A distributed control and dynamic scheduling method for an ECRH system includes:

[0008] Based on the distributed control architecture of EPICS, the ECRH device is divided into multiple independently running IOC nodes, each of which is responsible for local data acquisition and control.

[0009] Each IOC node preprocesses the collected data and calculates the priority score for each data based on access frequency and real-time requirements, and then marks the data with priority levels.

[0010] The CA protocol is used to publish and share data marked with priority levels; each IOC node subscribes to the shared data based on its own relevance and importance to the data.

[0011] Based on the shared data and the data subscribed by each IOC node, combined with the given task, dynamic adjustments and controls are made to achieve intelligent scheduling and allocation of tasks.

[0012] In one embodiment of the present invention, the IOC node includes a radio frequency source IOC node, a cooling system IOC node, and a transmission antenna IOC node.

[0013] In one embodiment of the present invention, the priority of each data item is calculated using the following formula:

[0014] P = w t ·F t +w I ·It ;

[0015] In the formula, P is the priority score of the data, and F t For real-time requirements, I t For access frequency, w t w is a weighted parameter for real-time requirements. I For access frequency weighting parameters;

[0016] When the priority score is greater than or equal to the high priority threshold, the data is marked as high-frequency key data and placed into the real-time processing queue.

[0017] When the priority score is less than the high priority threshold, the data is marked as low-frequency secondary data and placed in the low priority queue.

[0018] In addition, data characteristics are periodically reassessed and priority assignments are updated.

[0019] In one embodiment of the present invention, the real-time requirement weight parameter w t Access frequency weight parameter w I The value should be set according to the application scenario; w is recommended for high dynamic scenarios. t >w I Analysis task recommendation w t <w I .

[0020] In one embodiment of the present invention, the correlation between the shared data and each IOC node is obtained using the following formula:

[0021]

[0022] The correlation between shared data and each IOC node is obtained using the following formula:

[0023]

[0024] In one embodiment of the present invention, during subscription, a score is also calculated based on the relevance and importance of the data to the IOC node to filter subscription priorities; wherein, the relevance and importance score of the data to the IOC node is calculated using the following formula:

[0025] S = γ·R + δ·I;

[0026] In the formula, S is the priority subscription score of the data. The higher the score, the higher the subscription priority. γ is the data relevance weight, δ is the data importance weight, and γ+δ=1.

[0027] In one embodiment of the present invention, intelligent scheduling and allocation of tasks includes:

[0028] By combining the data published by each IOC node and the data subscribed to by each IOC node, the load status of each IOC node is calculated;

[0029] Based on the task set V and the correlation weights E between tasks, construct the task dependency graph G(V,E);

[0030] Perform node mapping on the task dependency graph G(V,E), establish the objective function, and assign tasks to the IOC node with the lowest load;

[0031] IOC nodes monitor their own load status in real time and publish and share the load status along with the collected data. In one embodiment of the present invention, the load status of each IOC node is determined by the following formula:

[0032] L i =α·C i +β·N i ;

[0033] In the formula, L i Let C be the load state of the i-th IOC node. i Let N be the CPU utilization of the i-th IOC node. i Let α and β be the network traffic of i IOC nodes, and their weight parameters be in the range of [0,1].

[0034] Furthermore, the objective function for assigning tasks to the node with the lowest IOC load is as follows:

[0035]

[0036] In the formula, F is the objective function and N is the total number of nodes.

[0037] In one embodiment of the present invention, the constraints for allocating tasks are: each task must be assigned to an IOC node; the load of the IOC node must not exceed a preset load threshold.

[0038] The present invention also provides a system applying the above-described ECRH system distributed control and dynamic scheduling method, comprising:

[0039] The distributed architecture module is used for the distributed control architecture based on EPICS. It divides the ECRH device into multiple independently running IOC nodes. Each IOC node is responsible for local data acquisition and control. After preprocessing the acquired data, each IOC node calculates the priority score of each data according to the access frequency and real-time requirements, and marks the data with priority level.

[0040] The sharing and collaboration module is used to publish and share data marked with priority levels using the CA protocol; each IOC node subscribes to the shared data based on its own relevance and importance to the data.

[0041] The response and task optimization module is used to dynamically adjust and control the task based on shared data and data subscribed by each IOC node, combined with the given task, to achieve intelligent scheduling and allocation of tasks.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] The current centralized architecture relies on a single control center to handle all data acquisition, analysis, and command generation. Because all nodes must transmit data to the control center for centralized processing before distributing the results to each device, the overall data transmission and processing latency is high, which cannot meet the microsecond-level dynamic response requirements of the ECRH system.

[0044] This invention employs a distributed control architecture based on EPICS, dividing the ECRH system's devices into multiple independently operating IOC nodes, each performing local control and data acquisition. Simultaneously, cross-node data sharing and collaboration are achieved through the EPICS CA protocol, significantly reducing data transmission latency. The parallel processing capability of the distributed architecture significantly improves control response speed, enabling the system to meet microsecond-level dynamic control requirements.

[0045] In centralized architectures, the coordination capabilities between subsystems (such as RF sources, cooling systems, and transmission antennas) are relatively weak. Data transmission and control commands between subsystems often need to be relayed through a centralized control center, creating communication bottlenecks and affecting the efficiency of real-time collaboration between nodes.

[0046] This invention enables data sharing and real-time collaboration among nodes through the CA protocol of EPICS. Each IOC node can directly publish or subscribe to key parameters such as reflected power and cooling flow rate without relying on a control center for relay, thereby achieving efficient data synchronization and collaborative operation. Through this mechanism, each node can quickly adjust its control strategy, improving overall operational efficiency.

[0047] The scalability of centralized control architectures is limited. When new devices are added to the system or the amount of data increases significantly, the computing and communication load on the centralized control center will increase significantly, leading to performance degradation or even system instability.

[0048] This invention employs a distributed architecture design, where each IOC node operates independently and handles specific device tasks. When the system expands, only the corresponding IOC nodes need to be added, avoiding the performance bottleneck of a centralized control center. Simultaneously, the independence between nodes ensures that the system maintains efficient and stable operation even during expansion.

[0049] Centralized control architectures lack dynamic scheduling capabilities for data processing and cannot prioritize data based on real-time requirements. Low-priority data may consume valuable transmission and processing resources, impacting the response time of critical data.

[0050] This invention introduces dynamic priority scheduling, which categorizes data according to real-time requirements and access frequency. High-priority critical data, such as reflection power, is transmitted and processed first, while low-priority data, such as environmental monitoring parameters, is processed with a delay, thus optimizing the utilization of system resources.

[0051] This invention has the following characteristics:

[0052] 1. Advantages of a Single Module: In the RF source IOC node, the high-speed data acquisition board PXI-6356 and localized processing significantly reduce data transmission latency, improving response speed from milliseconds to microseconds compared to traditional architectures. Simultaneously, a dynamic priority scheduling algorithm processes key data such as reflected power and VSWR in real time, ensuring the stability and accuracy of RF power output. In the cooling system IOC node, real-time monitoring of cooling water flow and temperature, through intelligent adjustment, ensures thermal balance and stable operation of the equipment. Meanwhile, a delay processing mechanism for low-frequency secondary data effectively reduces resource consumption. In the transmission antenna IOC node, real-time acquisition of power feedback data and dynamic adjustment of antenna direction significantly improve energy transmission efficiency. When faced with excessive node load or failure, transmission tasks can be migrated to backup nodes, further ensuring experimental continuity.

[0053] 2. Synergistic Advantages of Module Combination: The collaborative effect of multiple IOC nodes significantly improves the overall system performance. Through EPICS' CA protocol and optimized publish-subscribe model, the data sharing mechanism reduces the transmission of irrelevant data and improves data sharing efficiency. The multi-node collaboration mechanism ensures rapid response from each node in critical scenarios; for example, in the event of abnormal RF source power, the cooling system and transmission antenna nodes can adjust in real time to ensure equipment safety. Based on the task dependency graph and node load-based task allocation algorithm, the system's computational and communication loads are balanced, avoiding single-point bottlenecks. Simultaneously, the dynamic task adjustment mechanism significantly improves the stability and efficiency of task execution.

[0054] 3. Overall Technical Advantages: Compared to traditional centralized architectures, this architecture achieves higher real-time response and load balancing capabilities through distributed design and dynamic task optimization. Compared to the basic EPICS framework, the introduced priority scheduling and filtering algorithms significantly optimize data sharing efficiency and inter-node collaboration. Furthermore, this architecture and algorithm are not only applicable to the ECRH system but can also be extended to other industrial control scenarios requiring real-time response and efficient collaboration, such as intelligent manufacturing, nuclear fusion devices, and energy distribution networks, demonstrating broad technological competitiveness and application value. Attached Figure Description

[0055] Figure 1 This is a flowchart of a distributed control and dynamic scheduling method for an ECRH system according to an embodiment of the present invention.

[0056] Figure 2 This is a block diagram of a distributed control and dynamic scheduling system for an ECRH system according to an embodiment of the present invention.

[0057] Figure 3 This is a block diagram of the distributed architecture module in an embodiment of the present invention.

[0058] Figure 4 This is a block diagram of the sharing and collaboration module in an embodiment of the present invention.

[0059] Figure 5 This is a block diagram of the response and task optimization module in an embodiment of the present invention. Detailed Implementation

[0060] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0061] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0062] Please see Figure 1 As shown, this invention provides a distributed control and dynamic scheduling method for an ECRH system, comprising:

[0063] S10, based on the distributed control architecture of EPICS, divides the ECRH device into multiple independently operating IOC nodes, each of which is responsible for local data acquisition and control.

[0064] In one embodiment of the present invention, the distributed control architecture based on EPICS divides the physical devices of the ECRH system into multiple independently operating IOC nodes, each node being responsible for localized data acquisition and real-time control, thereby significantly improving the control response speed and meeting the needs of high-dynamic experiments in the ECRH system.

[0065] In one embodiment of the present invention, the plurality of independently operating IOC nodes include an RF source IOC node, a cooling system IOC node, and a transmission antenna IOC node.

[0066] Among them, the radio frequency source IOC node collects high-speed feedback signals, such as reflected power and standing wave ratio, and controls the power output of the radio frequency source.

[0067] The cooling system IOC node collects cooling water flow and temperature signals and is responsible for dynamically adjusting the flow output of the cooling system.

[0068] The transmission antenna IOC node collects antenna direction information and adjusts the antenna direction to optimize energy transmission.

[0069] S20: After preprocessing the collected data, each IOC node calculates the priority score of each data based on the access frequency and real-time requirements, and marks the data with priority level.

[0070] In one embodiment of the present invention, the IOC node signal acquisition and preprocessing are performed by PXI series boards. For example, the PXI-6259 is responsible for slow signal acquisition, such as temperature and flow rate of the cooling system, with a sampling rate of approximately 1 kHz. The PXI-6356 is responsible for high-speed signal acquisition, such as power feedback and VSWR of the RF source, with a sampling rate as high as 100 kHz. The signals acquired by the boards are transmitted to the Linux driver layer through a high-speed data channel. The Linux driver performs preliminary formatting on the acquired raw data, such as removing invalid data and normalizing the signal amplitude, to ensure that the data can be directly processed by the IOC node.

[0071] In one embodiment of the invention, the collected data is categorized according to access frequency and real-time requirements: high-frequency critical data, such as reflected power and VSWR, are crucial for real-time system control, have high evaluation values, are marked as high priority, and are transmitted to the real-time processing queue for direct processing by the corresponding IOC node. Low-frequency secondary data, such as cooling water temperature, have a lower access frequency and short time delays have little impact on control, are marked as low priority, allocated to a low-priority queue, and processed when the system is idle. This dynamic priority allocation enables rapid response to high-frequency data while reducing unnecessary processing delays.

[0072] In this embodiment, a priority score is calculated for each data point, and data characteristics are periodically reassessed to update the priority allocation. Here, data characteristics refer to the book data based on access frequency and real-time requirements. The priority score for each data point is obtained using the following formula:

[0073] P = w t ·F t +w I ·I t ;

[0074] In the formula, P is the priority score of the data, and F t For real-time requirements, I t For access frequency, w t w is a weighted parameter for real-time requirements. I This is the access frequency weighting parameter.

[0075] In this embodiment, real-time requirements Where T is the maximum allowed response time. Access frequency

[0076] When the priority score is greater than or equal to the high priority threshold, the data is marked as high-frequency key data, which is given high priority and is placed in the real-time processing queue.

[0077] When the priority score is less than the high priority threshold, the data is marked as low-frequency secondary data, which is of low priority and is placed in the low priority queue.

[0078] In this embodiment, w t and w I It is the normalization parameter, w t The parameters reflect the real-time requirements and are directly related to the safety or stability of the equipment. I This reflects the frequency of parameter access and is related to data processing efficiency. t +w i =1, 0≤w t w i <<1. w t =1, w i =0 indicates that the system is entirely focused on real-time performance, and the priority is determined solely by the real-time requirement F. t Decision made. This applies to high-dynamic experimental scenarios, such as RF source power control. t =0, w i =1 indicates that the system focuses entirely on access frequency, and the priority is determined entirely by the data access frequency I. t Decision. This applies to analytical tasks such as long-term trend analysis or retrieval of archived data.

[0079] In this embodiment, w is recommended for high dynamic scenes. t >wi Analysis task recommendation w t <w i For initial value settings, in scenarios with high dynamic requirements, w t =0.9, w i =0.1. Low dynamic demand scenario w t =0.4, w i =0.6. The specific value can be fine-tuned according to the actual situation.

[0080] S30 uses the CA protocol to publish and share data marked with priority levels; each IOC node subscribes to the shared data based on its own relevance and importance to the data.

[0081] In one embodiment of the present invention, data sharing and synchronization are crucial aspects of achieving node collaboration in a distributed control architecture. IOC nodes share device operating status and key parameters through the EPICS Channel Access (CA) protocol, and dynamically adjust load allocation based on task optimization, thereby significantly improving the efficiency of data sharing and the stability of system operation.

[0082] In this embodiment, data publishing is implemented through a publish-subscribe model in the EPICS-based distributed control architecture. Each IOC node publishes its locally collected key equipment operating parameters to the shared network in real time. Specifically, the RF source IOC node is responsible for collecting high-dynamic data such as reflected power and VSWR, the cooling system IOC node collects environmental parameters such as cooling water flow rate and temperature, and the transmission antenna IOC node is responsible for collecting information such as antenna direction and power feedback. This data is broadcast via the EPICS Channel Access (CA) protocol, allowing other IOC nodes to subscribe as needed.

[0083] In this embodiment, data priority sorting is implemented: To improve the system's processing speed for important data, a priority queue concept is introduced into the data sharing mechanism. Based on the data tags from the previous step, high-priority data, such as reflected power and VSWR data, are marked as high priority due to their high frequency of change and critical impact on system operation. During data release, they are ensured to be pushed in real-time and processed with priority. Environmental monitoring data collected by the cooling system, on the other hand, is classified as low priority due to its lower update frequency and lower real-time requirements, and is only transmitted and processed when the system is idle. This priority sorting mechanism optimizes resource allocation while ensuring rapid response to high-frequency critical data and overall system operating efficiency.

[0084] In this embodiment, data subscription involves each IOC node selectively subscribing to highly relevant data published by other nodes based on the task requirements of its own functional modules. For example, the cooling system IOC node subscribes to the reflected power data published by the RF source IOC node, adjusting the cooling flow in real time to ensure the thermal balance and stable operation of the equipment. Simultaneously, the transmission antenna IOC node subscribes to the power feedback data from the RF source IOC node, optimizing the antenna orientation based on real-time data to improve energy transmission efficiency.

[0085] In this embodiment, the relevance R between data and node tasks is typically determined by the weights in the task dependency graph, ranging from 0 to 1. Furthermore, the relevance between shared data and each IOC node is obtained using the following formula:

[0086]

[0087] The importance of data (I) reflects the degree of impact of the data on the overall system, and is determined by historical statistics and domain knowledge, ranging from 0 to 1. Furthermore, the relevance of shared data to each IOC node is obtained using the following formula:

[0088]

[0089] In this embodiment, after subscribing to data relevant to itself, these subscribed data are also prioritized for subscription filtering. Specifically, a priority subscription score is calculated based on the relevance and importance of the data. The relevance and importance scores of the data with the IOC node are determined using the following formula:

[0090] S = γ·R + δ·I;

[0091] In the formula, S is the priority subscription score of the data. The higher the score, the higher the subscription priority. γ is the data relevance weight, δ is the data importance weight, and γ+δ=1.

[0092] Cooling system nodes prioritize subscribing to highly relevant reflection power data to reduce the bandwidth and resource consumption of irrelevant data.

[0093] Applications of subscription priority include, for example, cooling system nodes prioritizing subscription to highly relevant reflection power data to reduce the consumption of bandwidth and resources by irrelevant data.

[0094] S40 dynamically adjusts and controls the allocation of tasks based on shared data and data subscribed by each IOC node, combined with the given task, to achieve intelligent scheduling and allocation of tasks.

[0095] In one embodiment of the present invention, data acquisition and broadcasting: In the distributed control architecture, the realization of cooperative control relies on data acquisition, sharing, and dynamic adjustment strategies among the IOC nodes to ensure efficient system operation and real-time response capabilities. First, each IOC node periodically acquires the operating status of its local devices and broadcasts it to the shared network via the CA protocol. The RF source IOC node acquires key data such as reflected power and VSWR at high speed and broadcasts it to the shared network in real time. The cooling system IOC node acquires environmental parameters such as cooling water flow rate and temperature at a lower frequency and also publishes them via the CA protocol. The transmission antenna IOC node acquires antenna direction information and shares its status data with the network, providing real-time reference for other nodes.

[0096] In one embodiment of the present invention, the control strategy is dynamically adjusted: during actual operation, each IOC node adjusts its control strategy in real time based on the received data. For example, when the RF source power fluctuation exceeds a set threshold, the cooling system IOC node will respond quickly and dynamically adjust the flow output to prevent the equipment from overheating. If the antenna direction deviates from the target, the transmission antenna IOC node will optimize the antenna parameters in real time based on feedback data to ensure the accuracy of beam transmission and energy utilization efficiency.

[0097] In one embodiment of the present invention, priority scheduling is implemented: to prioritize the processing of critical data, a priority scheduling mechanism is introduced. Based on data tags, high-priority critical data, such as reflection power, is directly sent to the real-time processing queue to ensure rapid response, while low-priority data, such as cooling system temperature, enters the delay queue to be processed when the system is idle. This priority ranking greatly optimizes the utilization of system resources and the responsiveness to high-frequency critical tasks.

[0098] In one embodiment of the present invention, to achieve efficient data sharing and load balancing, the present invention employs task optimization based on distributed collaboration, which can intelligently allocate and dynamically adjust tasks according to node load and task dependencies. Specifically, the intelligent scheduling and allocation of tasks includes:

[0099] S41, combining the data published by each IOC node and the data subscribed to by each IOC node, calculate the load status of each IOC node.

[0100] In this embodiment, the load status of each IOC node is determined by the following formula:

[0101] L i =α·C i +β·N i ;

[0102] In the formula, L i Let C be the load state of the i-th IOC node. iLet N be the CPU utilization of the i-th IOC node. i For the network traffic of i IOC nodes, the weight parameters α and β range from [0,1], determining the degree of influence of CPU and network traffic.

[0103] S42. Based on the task set V and the correlation weights E between tasks, construct the task dependency graph G(V,E).

[0104] In this embodiment, the task dependency graph describes the correlation between tasks, such as the coupling degree between the cooling task and the reflected power monitoring task. The task dependency graph G(V,E) represents the task set V, such as reflected power monitoring and cooling water flow adjustment, and the correlation weight E between tasks. For example, the correlation between the cooling task and the reflected power monitoring task is 0.8.

[0105] S43, perform node mapping on the task dependency graph G(V,E), establish the objective function, and assign tasks to the IOC node with the lowest load.

[0106] In this embodiment, the objective function for assigning tasks to the node with the lowest IOC load is as follows:

[0107]

[0108] In the formula, F is the objective function and N is the total number of nodes.

[0109] In this embodiment, the constraints for task allocation are: each task must be assigned to an IOC node; the load of an IOC node must not exceed a preset load threshold. Based on the current load allocation scheme, the node allocation of some tasks is adjusted to ensure load balance: if a node is overloaded, some tasks are migrated to a backup node. If network traffic is too high, task priorities are adjusted, and the frequency of non-critical tasks is reduced. If task dependencies change, such as a sudden increase in the load on the cooling system, tasks are reassigned.

[0110] S44, the IOC node monitors its own load status in real time and publishes the load status along with the collected key data for sharing.

[0111] In this embodiment, each IOC node monitors its own load status in real time, such as CPU utilization and network traffic, and publishes this load information along with the collected key parameters to the shared network. For example, when the RF source IOC node publishes reflection power data, it also includes information on the utilization of computing resources for other nodes to reference. After receiving this load information, the cooling system IOC node dynamically adjusts its subscription and task strategies. For highly correlated critical tasks, such as cooling tasks related to reflection power, the node will prioritize acquisition and immediate processing. When a node's load is too high, such as when the RF source IOC node experiences computing resource constraints due to processing high-frequency power data, the cooling system IOC node can proactively share some data processing tasks or transfer non-critical tasks to backup nodes. This dynamic adjustment of task allocation effectively avoids load conflicts between nodes and improves the overall efficiency of task execution.

[0112] Please refer to 1 to Figure 5 As shown, the present invention also provides a distributed control and dynamic scheduling system for an ECRH system, which applies the above-described distributed control and dynamic scheduling method for an ECRH system, including:

[0113] The distributed architecture module is used for the distributed control architecture based on EPICS. It divides the ECRH device into multiple independently running IOC nodes. Each IOC node is responsible for local data acquisition and control. After preprocessing the acquired data, each IOC node calculates the priority score of each data according to the access frequency and real-time requirements, and marks the data with priority level.

[0114] The sharing and collaboration module is used to publish and share data marked with priority levels using the CA protocol; each IOC node subscribes to the shared data based on its own relevance and importance to the data.

[0115] The response and task optimization module is used to dynamically adjust and control the task based on shared data and data subscribed by each IOC node, combined with the given task, to achieve intelligent scheduling and allocation of tasks.

[0116] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0117] The above embodiments are merely examples of implementation methods of the invention. The scope of protection of the present invention is not limited to the above embodiments. For those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A method for distributed control and dynamic scheduling of an ECRH system, characterized in that, include: Based on the distributed control architecture of EPICS, the ECRH device is divided into multiple independently running IOC nodes, each of which is responsible for local data acquisition and control. Each IOC node preprocesses the collected data and calculates a priority score for each data point based on access frequency and real-time requirements, thus assigning a priority level to the data. The CA protocol is used to publish and share data marked with priority levels; each IOC node subscribes to the shared data based on its own relevance and importance to the data. Based on the shared data and the data subscribed by each IOC node, combined with the given task, dynamic adjustments and controls are made to achieve intelligent scheduling and allocation of tasks.

2. The distributed control of ECRH system and its dynamic scheduling method according to claim 1, characterized in that, IOC nodes include radio frequency source IOC nodes, cooling system IOC nodes, and transmission antenna IOC nodes.

3. The distributed control of ECRH system and its dynamic scheduling method according to claim 1, wherein, The priority of each data point is calculated using the following formula: P = w t • F t + w I • I t ; where P is a priority score of the data, F t is a real-time requirement, I t is a frequency of access, w t is a real-time requirement weight parameter, w I is a frequency of access weight parameter; When the priority score is greater than or equal to the high priority threshold, the data is marked as high-frequency key data and placed in the real-time processing queue. When the priority score is less than the high priority threshold, the data is marked as low-frequency secondary data and placed in the low priority queue. In addition, data characteristics are periodically reassessed and priority assignments are updated.

4. The distributed control of ECRH system and its dynamic scheduling method according to claim 3, characterized in that, a real-time requirement weight parameter w t , an access frequency weight parameter w I The value of the real-time requirement weight parameter w is set according to an application scenario. High dynamic scene recommendation w t > I , analysis task recommendation w t <w I .

5. The distributed control of ECRH system and its dynamic scheduling method according to claim 1, wherein, The correlation between shared data and each IOC node is obtained using the following formula: The importance of shared data and each IOC node can be determined using the following formula:

6. The distributed control of ECRH system and its dynamic scheduling method according to claim 5, wherein, During subscription, a score is also calculated based on the relevance and importance of the data to the IOC nodes to prioritize subscriptions. The relevance and importance score is determined using the following formula: S = γ·R + δ·I; In the formula, S is the priority subscription score of the data. The higher the score, the higher the subscription priority. γ is the data relevance weight, δ is the data importance weight, and γ+δ=1.

7. The distributed control of ECRH system and its dynamic scheduling method according to claim 1, wherein, Intelligent task scheduling and allocation includes: By combining the data published by each IOC node and the data subscribed to by each IOC node, the load status of each IOC node is calculated; Based on the task set V and the correlation weights E between tasks, construct the task dependency graph G(V,E); Perform node mapping on the task dependency graph G(V,E), establish the objective function, and assign tasks to the IOC node with the lowest load; IOC nodes monitor their own load status in real time and publish and share the load status along with the collected data.

8. The distributed control of ECRH system and its dynamic scheduling method according to claim 7, characterized in that, The load status of each IOC node is determined by the following formula: L i = a · C i + β · N i ; In the formula, L i is the load state of the ith IOC node, C i is the CPU utilization of the ith IOC node, N i is the network traffic of the ith IOC node, and the weight parameters of α and β range from [0, 1]. Furthermore, the objective function for assigning tasks to the node with the lowest IOC load is as follows: In the formula, F is the objective function and N is the total number of nodes.

9. The distributed control of ECRH system and its dynamic scheduling method according to claim 7, characterized in that, The constraints for task allocation are: each task must be assigned to an IOC node; the load on an IOC node must not exceed a preset load threshold.

10. A system for applying the distributed control of the ECRH system and the dynamic scheduling method according to any one of claims 1-9, characterized in that, include: The distributed architecture module is used for the distributed control architecture based on EPICS. It divides the ECRH device into multiple independently running IOC nodes. Each IOC node is responsible for local data acquisition and control. After preprocessing the acquired data, each IOC node calculates the priority score of each data according to the access frequency and real-time requirements, and marks the data with priority level. A sharing and cooperation module is configured to publish and share the data marked with the priority level by using the CA protocol; and each IOC node subscribes to the shared data according to the relevance and importance of the IOC node to the data; A response and task optimization module is configured to dynamically adjust and control according to the shared data and the data subscribed by each IOC node in combination with a given task, so as to realize intelligent scheduling and distribution of the task.

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