State awareness scheduling method based on ECRH system and EPICS integration

Abstract

The invention discloses a state awareness scheduling method based on ECRH system and EPICS integration, belongs to the technical field of control of nuclear fusion devices, and solves the problem that a traditional ECRH system is slow in scheduling response when the equipment state changes. According to the invention, through the IOC module and the Channel Access protocol of the EPICS system, the equipment state data can be collected and transmitted to the central control system in real time, so that the rapid feedback of the equipment state is ensured; by monitoring and analyzing real-time state data, the detection precision of the system on equipment abnormity is improved, and it is ensured that the system can be rapidly adjusted when the state of the equipment changes; by dynamically adjusting the priority of the control signals, resource allocation can be optimized in real time according to changes of equipment states and experiment requirements; the overall efficiency of the system is improved; by dynamically adjusting the transmission frequency of the control signal, adaptive adjustment can be performed according to the actual load condition of the equipment, and resource waste or insufficiency caused by fixed frequency scheduling is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of control of nuclear fusion devices and relates to a state perception scheduling method based on the integration of an ECRH system and EPICS. Background Art

[0002] The electron cyclotron resonance heating (ECRH) system is one of the commonly used heating methods in nuclear fusion reactors. It provides energy to the plasma through the interaction between high-power microwaves and plasma, so that its temperature reaches the conditions required for nuclear fusion reactions. The core equipment of the ECRH system includes gyrotrons, antennas, power sources, etc., which need to achieve high-precision collaborative work and real-time control during operation. However, with the expansion of the scale of nuclear fusion experimental devices, the ECRH system faces a series of problems such as increased control complexity, difficulty in coordination between multiple devices, and data transmission delays.

[0003] At present, the control of ECRH systems usually relies on dedicated controllers and independent data transmission networks, and the coordination and scheduling between devices rely on a fixed-cycle scheduling mechanism. Although this control mode can meet the requirements under simple experimental conditions, as the complexity of the experiment increases, especially under high load and high real-time requirements, the traditional fixed-cycle scheduling method exposes the following defects:

[0004] 1) Slow response to equipment status changes: In the ECRH system, the power fluctuation of the gyrotron, the angle adjustment of the antenna, the voltage output of the power source, etc. may change rapidly according to the experimental conditions. However, the traditional fixed-cycle scheduling mechanism cannot respond to changes in equipment status in a timely manner, resulting in transmission delays in control signals and the inability of equipment to adjust in time, affecting the accuracy and efficiency of the experiment;

[0005] 2) Difficulty in multi-device coordination: Multiple devices in the ECRH system must work closely together. For example, the power output of the gyrotron and the antenna must be precisely synchronized to ensure the plasma heating effect. However, the existing system lacks an efficient multi-device coordinated control mechanism, and there is a delay or packet loss in the signal transmission between devices, which causes the devices to work asynchronously and affects the experimental results.

[0006] 3) Unreasonable resource allocation: The fixed-cycle scheduling method sends control signals to the device according to the preset time interval, and cannot dynamically adjust the scheduling frequency according to the real-time status of the device. This method will lead to a waste of resources, especially when the status of some devices is relatively stable, control signals are still sent frequently, wasting network bandwidth and system resources; under high load conditions, insufficient scheduling frequency causes key devices to be unable to obtain control signals in time, resulting in performance degradation.

[0007] Experimental Physics and Industrial Control System (EPICS) is a mature distributed control system framework that is widely used in the control of large-scale scientific experimental equipment. EPICS has good scalability and flexibility, and can uniformly manage and schedule experimental equipment. However, the existing EPICS applications have not yet introduced an intelligent scheduling mechanism, especially for the complex multi-device collaborative control and data transmission requirements of the ECRH system, there is still a lack of effective solutions. Therefore, how to deeply integrate EPICS with the ECRH system and optimize the control signal transmission and resource allocation between devices in real time has become a technical problem that needs to be solved urgently. Summary of the invention

[0008] The technical solution of the present invention is used to solve the problem of slow scheduling response of the traditional ECRH system when the equipment status changes.

[0009] The present invention solves the above technical problems through the following technical solutions:

[0010] The state-aware scheduling method based on the integration of ECRH system and EPICS includes the following steps:

[0011] Connect the devices in the ECRH system to EPICS through the IOC module, collect the status data of the devices in the ECRH system, and transmit the status data to the EPICS database through the EPICS Channel Access protocol;

[0012] Analyze the status data of the equipment to determine whether an abnormal state has occurred, and thus determine whether the priority and transmission frequency of the control signal need to be adjusted;

[0013] Based on the importance score of the device status and the urgency score of the experimental requirements, the priority of the control signal is dynamically set. According to the importance and current status changes of each device, the control signal of the device is managed according to the priority queue;

[0014] Calculate the load factor, optimize the transmission path and frequency of the control signal, and adjust the control signal priority through load factor feedback to improve the response speed and stability of the system.

[0015] Furthermore, the method for determining whether an abnormal state occurs and whether the priority and transmission frequency of the control signal need to be adjusted is as follows:

[0016] Determine whether an abnormal state occurs according to the following formula:

[0017] StatusChange=DeviceValue>Threshold

[0018] Among them, DeviceValue is the status data of the device, and Threshold is the preset threshold;

[0019] When StatusChange is true, it means that the device status data exceeds the threshold, and the priority and transmission frequency of the control signal need to be adjusted.

[0020] Furthermore, the formula for dynamically setting the priority of the control signal based on the importance score of the device status and the urgency score of the experimental requirement is as follows:

[0021] Priority=w1·StateImportance+w2·UrgencyLevel

[0022] Among them, StateImportance is the importance score of the device state, UrgencyLevel is the urgency score of the experimental requirement, w1 and w2 are weight coefficients, w1=1-w2, which are used to adjust the impact of each factor on the priority, and the range of w1 is set to [0, 1].

[0023] Furthermore, the calculation formula of the load factor is as follows:

[0024]

[0025] Among them, LoadFactor(i) is the load factor of device i, ActualLoad(i) is the actual load of device i, and StandardLoad is the standard load of the device.

[0026] Furthermore, the method for optimizing the transmission path of the control signal is specifically as follows:

[0027] According to the load and device status data, the shortest data transmission path is dynamically selected to avoid network congestion. The calculation method of the shortest data transmission path is as follows:

[0028] Cost(v)=min(Cost(u)+Distance(u,v)), u∈Neighbors(v)

[0029] Among them, Cost(u) is the minimum cumulative cost from the starting node to node u, Cost(v) is the minimum cumulative cost from the starting node to node v, Distance(u, v) is the transmission cost from node u to node v, and Neighbors(v) is the set of adjacent nodes of node v.

[0030] Furthermore, the method for optimizing the frequency of the control signal is specifically as follows:

[0031] Adjust the transmission frequency of the control signal according to the device load factor. The specific formula is as follows:

[0032]

[0033] Among them, BaseFrequency is the basic transmission frequency, and LoadFactor is the device load factor.

[0034] Furthermore, the adjustment formula of the control signal priority is as follows:

[0035] Priority(i)=BasePriority+k·LoadFactor(i)

[0036] Among them, Priority(i) is the control signal priority of device i, BasePriority is the basic priority, and k is the adjustment factor used to adjust the impact of the load factor on the control signal priority.

[0037] Furthermore, the value range of the adjustment factor is [1, 5].

[0038] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned state-aware scheduling method based on the integration of an ECRH system and EPICS, and the processor is configured to execute the program stored in the memory.

[0039] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the state-aware scheduling method based on the integration of an ECRH system and EPICS.

[0040] The advantages of the present invention are:

[0041] The present invention uses the IOC module and Channel Access protocol of the EPICS system, so that the device status data can be collected and transmitted to the central control system in real time; this real-time monitoring ensures rapid feedback of the device status and reduces the control response problem caused by delay; the monitoring and analysis of real-time status data improves the system's detection accuracy of device anomalies and ensures that the system can make rapid adjustments when the device status changes; by dynamically adjusting the priority of the control signal, the resource allocation can be optimized in real time according to the changes in the device status and experimental requirements; the dynamic adjustment of the priority ensures that the key equipment can obtain the control signal in priority in an emergency; the inflexibility of resource allocation in traditional fixed-cycle scheduling is avoided, so that the system can automatically adjust the resource allocation when the device status changes, thereby improving the overall efficiency of the system; by dynamically adjusting the transmission frequency of the control signal, it can be adaptively adjusted according to the actual load condition of the device, thereby avoiding the waste or shortage of resources caused by fixed-frequency scheduling; load balancing ensures that the device load in the system is evenly distributed, thereby avoiding the problem of device overload and improving the overall stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a state-aware scheduling flow chart of a state-aware scheduling method based on integration of ECRH system and EPICS according to the first embodiment of the present invention;

[0043] Figure 2 It is a flow chart of a state-aware algorithm of a state-aware scheduling method based on integration of an ECRH system and EPICS according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0046] Embodiment 1

[0047] like Figure 1 and Figure 2 As shown, in the integration of the ECRH system and EPICS, the method of the present invention monitors the device status in real time, dynamically adjusts the priority, transmission path and frequency of the control signal, and ensures the normal operation of the device under efficient resource allocation and optimized scheduling, specifically including:

[0048] 1. Determine the change of equipment status

[0049] During the operation of the equipment in the ECRH system, its status (such as power, temperature, load, etc.) will fluctuate with the experimental progress and changes in external conditions. The present invention monitors the status data of each device in real time to quickly determine whether the device needs to adjust the transmission frequency and priority of the control signal.

[0050] 1.1. Equipment status data collection in ECRH system

[0051] Each device in the ECRH system (such as gyrotron, antenna, power source, etc.) is connected to EPICS through its IOC (Input / Output Controller) module. The status data of each device in the ECRH system, including power output, temperature, load, etc., are collected in real time through the IOC module in the EPICS system. The status data is transmitted to the EPICS database through the EPICS Channel Access (CA) protocol for storage, and is managed by the EPICS Record and Field mechanisms for subsequent analysis and processing.

[0052] 1.2. Abnormal state detection

[0053] Analyze the status data of the device to determine whether an abnormal state occurs and whether the priority and transmission frequency of the control signal need to be adjusted.

[0054] StatusChange=DeviceValue>Threshold

[0055] Among them, DeviceValue is the status data of the device (such as power, temperature, etc.), and Threshold is the preset threshold value (such as the maximum allowable power value, the upper limit of the temperature safety range, etc.).

[0056] When the device status (DeviceValue) exceeds the preset threshold (Threshold), that is, when StatusChange is true, it means that the device status exceeds the threshold, and the system determines that the device status has changed, and needs to adjust the priority and transmission frequency of the control signal. For example, when the power output of the gyrotron exceeds the preset threshold, the system will determine that the gyrotron has an overheating risk and needs to quickly adjust the control signal of the cooling system. When the power source load is too high, the system will prioritize the load adjustment of the power source to prevent damage caused by overload.

[0057] 1.3 Dynamic response

[0058] Once a change in equipment status is detected, the system will immediately initiate scheduling optimization to ensure that the equipment operates in the best condition.

[0059] 2. Adjust the priority of control signals

[0060] In traditional fixed-cycle scheduling, the control signals of all devices are transmitted at a fixed frequency, which cannot cope with sudden changes in device status. The scheduling algorithm of the present invention dynamically adjusts the priority of control signals through device status perception, ensuring that key devices can obtain control signals first when needed.

[0061] 2.1. Status priority setting

[0062] In the EPICS system, the priority of the control signal is dynamically set based on the importance score of the equipment status and the urgency score of the experimental requirements, as follows:

[0063] Priority=w1·StateImportance+w2·UrgencyLevel

[0064] Among them, StateImportance is the importance score of the device state (such as the degree of abnormality of the device state), UrgencyLevel is the urgency score of the experimental requirements, w1 and w2 are weight coefficients, w1=1-w2, which are used to adjust the impact of each factor on the priority, and the range of w1 is set to [0, 1].

[0065] For example, when the gyrotron is overheated, the system will increase the control signal priority of the cooling system to ensure that it obtains more scheduling resources to quickly reduce the gyrotron temperature. In the stable operation stage, the control signal priority of the power source may be reduced to save system resources.

[0066] 2.2 Priority Queue Management

[0067] According to the importance of each device and the current status change, the control signal of the device is managed according to the priority queue. Through the Process Variables (PV) and Records of EPICS, the system dynamically updates the priority setting of the device. The control signals of key devices and emergency devices will be processed first.

[0068] 2.3. Priority Adjustment

[0069] When the status of a device is critical to the experimental process (such as the gyrotron needs to maintain stable power output during the critical stage of the experiment), the control signal of the device will be transmitted first to ensure its stable operation.

[0070] 3. Scheduling Optimization

[0071] In order to avoid network congestion and device control delay, the present invention not only adjusts the priority of the control signal, but also optimizes the transmission path and frequency of the signal, thereby improving the response speed and stability of the system.

[0072] 3.1 Transmission Path Optimization

[0073] According to the load and device status data, the shortest data transmission path is dynamically selected to avoid network congestion. The calculation method of the shortest data transmission path is as follows:

[0074] Cost(v)=min(Cost(u)+Distance(u,v)), u∈Neighbors(v)

[0075] Among them, Cost(u) is the minimum cumulative cost from the starting node to node u, Cost(v) is the minimum cumulative cost from the starting node to node v, Distance(u, v) is the transmission cost from node u to node v, and Neighbors(v) is the set of neighboring nodes of node v. Through multi-path transmission, the system can avoid congestion of a single path and reduce data transmission delay. When the current network channel is detected to be congested, the system will automatically select an alternative transmission path to transmit the control signal to the device to ensure the real-time performance of the system.

[0076] 3.2 Transmission frequency optimization

[0077] Adjust the transmission frequency of the control signal according to the equipment load factor. The specific formula is as follows:

[0078]

[0079] Among them, BaseFrequency is the basic transmission frequency, and LoadFactor is the device load factor (relative value of device load), which is a key parameter for dynamically adjusting device load and resource allocation. The higher the device load, the larger the LoadFactor, and the transmission frequency will increase accordingly. It represents the ratio of the current device load to the system load standard, and is usually used to determine the transmission frequency and priority of control signals.

[0080] By optimizing the transmission path and transmission frequency, the real-time and efficiency of data transmission can be improved, and delays and network congestion can be reduced.

[0081] 4. Load balancing and resource scheduling

[0082] (1) Calculate the actual load of the equipment

[0083] ActualLoad(i)=Power(i)+Temperature(i)+Load(i)

[0084] Among them, ActualLoad(i) is the actual load of device i, Power(i) is the power output of the device, Temperature(i) is the temperature of the device, and Load(i) is the load index of the device.

[0085] (2) Calculate the standard load of the system

[0086] StandardLoad=MaximumCapacity or NominalLoad

[0087] Among them, StandardLoad is the standard load of the device, MaximumCapacity is the maximum load capacity of the device, and NominalLoad is the preset normal working load.

[0088] (3) Calculate the load factor

[0089] The actual load of the equipment is monitored in real time and compared with the standard load, and the load factor is calculated as follows:

[0090]

[0091] Where LoadFactor(i) is the load factor of device i.

[0092] (4) Feedback adjustment control signal priority

[0093] The control signal priority is adjusted by load factor feedback, and the adjustment formula of the control signal priority is as follows:

[0094] Priority(i)=BasePriority+k·LoadFactor(i)

[0095] Among them, Priority(i) is the control signal priority of device i, BasePriority is the basic priority, and k is the adjustment factor range that can be controlled to [1, 5], which is used to adjust the impact of the load factor on the control signal priority. The larger the load factor, the higher the control signal priority will be.

[0096] The method of the present invention can effectively and dynamically adjust the priority, transmission path and frequency of the control signal. Through real-time status monitoring and optimization calculation, the system can maintain efficient and stable operation in a complex experimental environment, ensuring that the equipment can operate in the best state.

[0097] Embodiment 2

[0098] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the state-aware scheduling method based on the integration of an ECRH system and EPICS in Example 1, and the processor is configured to execute the program stored in the memory.

[0099] Embodiment 3

[0100] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the state-aware scheduling method based on the integration of an ECRH system and EPICS in Embodiment 1.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A state-aware scheduling method based on the integration of ECRH system and EPICS, characterized in that: The following steps are involved: Connect the devices in the ECRH system to EPICS through the IOC module, collect the status data of the devices in the ECRH system, and transmit the status data to the EPICS database through the EPICS Channel Access protocol; Analyze the status data of the equipment to determine whether an abnormal state has occurred, and thus determine whether the priority and transmission frequency of the control signal need to be adjusted; Based on the importance score of the device status and the urgency score of the experimental requirements, the priority of the control signal is dynamically set. According to the importance and current status changes of each device, the control signal of the device is managed according to the priority queue; Calculate the load factor, optimize the transmission path and frequency of the control signal, and adjust the control signal priority through load factor feedback to improve the response speed and stability of the system.

2. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The method for determining whether an abnormal state occurs and whether the priority and transmission frequency of the control signal need to be adjusted is as follows: Determine whether an abnormal state occurs according to the following formula: StatusChange=DeviceValue>Threshold Among them, DeviceValue is the status data of the device, and Threshold is the preset threshold; When StatusChange is true, it means that the device status data exceeds the threshold, and the priority and transmission frequency of the control signal need to be adjusted.

3. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The formula for dynamically setting the priority of the control signal based on the importance score of the equipment status and the urgency score of the experimental requirements is as follows: Priority=w1·StateImportance+w2·UrgencyLevel Among them, StateImportance is the importance score of the device state, UrgencyLevel is the urgency score of the experimental requirement, w1 and w2 are weight coefficients, w1=1-w2, which are used to adjust the impact of each factor on the priority, and the range of w1 is set to [0,1].

4. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The calculation formula of the load factor is as follows: Among them, LoadFactor(i) is the load factor of device i, ActualLoad(i) is the actual load of device i, and StandardLoad is the standard load of the device.

5. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The method for optimizing the transmission path of the control signal is as follows: According to the load and device status data, the shortest data transmission path is dynamically selected to avoid network congestion. The calculation method of the shortest data transmission path is as follows: Cost(v)=min(Cost(u)+Distance(u,v)),u∈Neighbors(v) Among them, Cost(u) is the minimum cumulative cost from the starting node to node u, Cost(v) is the minimum cumulative cost from the starting node to node v, Distance(u,v) is the transmission cost from node u to node v, and Neighbors(v) is the set of adjacent nodes of node v.

6. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The method for optimizing the frequency of the control signal is as follows: Adjust the transmission frequency of the control signal according to the device load factor. The specific formula is as follows: Among them, BaseFrequency is the basic transmission frequency, and LoadFactor is the device load factor.

7. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 1 is characterized in that: The adjustment formula of the control signal priority is as follows: Priority(i)=BasePriority+k·LoadFactor(i) Among them, Priority(i) is the control signal priority of device i, BasePriority is the basic priority, and k is the adjustment factor used to adjust the impact of the load factor on the control signal priority.

8. The state-aware scheduling method based on the integration of ECRH system and EPICS according to claim 7 is characterized in that: The value range of the adjustment factor is [1,5].

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the state-aware scheduling method based on the integration of the ECRH system and EPICS as described in any one of claims 1 to 7, and the processor is configured to execute the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the state-aware scheduling method based on the integration of the ECRH system and EPICS as claimed in any one of claims 1 to 7 are executed.

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