Quantum-deep learning cooperation-based nuclear fusion plasma multi-scale dynamic optimization method and system
Through the quantum-deep learning collaboration method, the magnetic field distribution of nuclear fusion plasma and real-time prediction of fracture risk are solved, and the problems of control error, calculation delay and high energy consumption in the existing technology are solved, and high precision and efficient plasma control are achieved.
Patent Information
- Application Number
- CN202510426005.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-07
- Publication Date
- 2025-07-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The plasma control technology of existing nuclear fusion devices has problems such as high error rate, insufficient energy constraint time, large calculation delay and multi-physics coupling, which cannot meet the needs of high precision and real-time control.
Using a method based on quantum-deep learning collaboration, the magnetic field distribution is optimized through quantum annealing algorithm, combined with spatiotemporal convolutional neural network to predict plasma fracture risk, and dynamically allocate computing resources through reinforcement learning, a dual redundancy verification mechanism is built to achieve multi-physics coordinated optimization.
It significantly improves the control accuracy and calculation efficiency of nuclear fusion plasma, reduces calculation delay and energy consumption, improves the stability and data integrity of the system, and meets the real-time control needs of the tokamak device.
Smart Images
Figure CN120278290A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and specifically to a multi-scale dynamic optimization method and system for fusion plasma based on the collaboration of quantum and deep learning. Background Technique
[0002] The plasma control technology of existing fusion devices mainly relies on traditional PID control and pure quantum optimization schemes, but there are the following problems:
[0003] The error rate of traditional PID control in plasma position control is relatively high (±2.1 cm), which cannot meet the high-precision control requirements; the pure quantum optimization scheme fails to effectively solve the problem of multi-physical field coupling, resulting in an energy confinement time of less than 30 seconds.
[0004] The existing prediction models have a large delay (>10 ms), cannot suppress the high-energy electron mode (TEM) in real time, and the magnetic surface optimization requires solving a 20-million-order partial differential equation system, and the traditional calculation takes more than 10 seconds, far exceeding the millisecond-level control period of the tokamak device.
[0005] These problems limit the stable operation of the fusion device and the maintenance of the high confinement mode, and there is an urgent need for an efficient multi-scale dynamic optimization method. Summary of the Invention
[0006] Technical Problems to be Solved
[0007] In view of the deficiencies of the prior art, the present invention provides a multi-scale dynamic optimization method and system for fusion plasma based on the collaboration of quantum and deep learning.
[0008] Technical Solution
[0009] To achieve the above object, the present invention provides the following technical solution:
[0010] A multi-scale dynamic optimization method for fusion plasma based on the collaboration of quantum and deep learning, characterized by comprising the following steps:
[0011] Discretize the plasma cross-section into grids, construct an Ising model containing quantum bits, and optimize the magnetic field distribution through a quantum annealing algorithm;
[0012] Adopt a spatio-temporal convolutional neural network (ST-CNN) to predict the risk of plasma rupture, and the input layer includes multi-channel magnetic probe time-series data;
[0013] Dynamically allocate quantum computing and classical computing resources through reinforcement learning to achieve collaborative optimization of multi-physical fields.
[0014] As a further aspect of the present invention, the quantum annealing algorithm uses a quantum bit parameterized circuit to optimize the magnetic field distribution through the interaction between a quantum processor and a GPU cluster.
[0015] As a further aspect of the present invention, the spatio-temporal convolutional neural network includes a residual connection module for improving the prediction accuracy and reducing overfitting.
[0016] As a further aspect of the present invention, the reinforcement learning dynamically adjusts the allocation ratio of quantum and classical computing resources through a reward mechanism to minimize the computing latency.
[0017] As a further aspect of the present invention, the system is provided with a dual redundancy check mechanism, including quantum error correction coding in the quantum layer and data integrity check in the classical layer.
[0018] As a further aspect of the present invention, the system architecture includes a quantum computing unit, a deep learning unit, a hybrid optimization engine, and an actuator, and realizes high-speed data interaction through a PCIe 4.0x16 bus.
[0019] As a further aspect of the present invention, the data preprocessing uses wavelet bases to eliminate magnetic probe noise and improve the quality of input data.
[0020] As a further aspect of the present invention, the system is verified in a tokamak device, and its technical effects are proved through comparative analysis of experimental data.
[0021] Advantageous Effects
[0022] Compared with the prior art, the present invention provides a multi-scale dynamic optimization method and system for fusion plasma based on quantum-deep learning collaboration, and has the following advantageous effects:
[0023] Through the quantum-deep learning collaboration technology, the control accuracy and computing efficiency of the fusion plasma are significantly improved. The quantum annealing algorithm optimizes the magnetic field distribution and solves the computing bottleneck of traditional methods in multi-physical field coupling problems; the spatio-temporal convolutional neural network predicts the plasma disruption risk in real time, reduces the control latency from 12.3 ms to 0.78 ms, and improves the position deviation from ±2.1 cm to ±0.7 cm; the reinforcement learning dynamically allocates computing resources, reduces the computing energy consumption from 3.2 kW to 180 W, and the reduction ratio reaches 94.4%. In addition, the dual redundancy check mechanism ensures the stability and data integrity of the system. The present invention can be widely applied to the high confinement mode operation and tearing mode suppression of tokamak devices, and provides technical support for international fusion projects such as ITER. Brief Description of the Drawings
[0024] Figure 1System architecture diagram of a multi-scale dynamic optimization method and system for fusion plasma based on the collaboration of quantum and deep learning proposed by the present invention;
[0025] Figure 2 Quantum circuit diagram of a multi-scale dynamic optimization method and system for fusion plasma based on the collaboration of quantum and deep learning proposed by the present invention;
[0026] Figure 3 GRU network training curve of a multi-scale dynamic optimization method and system for fusion plasma based on the collaboration of quantum and deep learning proposed by the present invention. Detailed implementation manners
[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0028] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meanings. The terms "connection" and "coupling" used in the present invention, unless otherwise clearly defined and limited, both include direct and indirect connection (coupling). In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention.
[0029] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.
[0030] A multi-scale dynamic optimization method for fusion plasma based on the collaboration of quantum and deep learning, characterized by comprising the following steps:
[0031] Discretize the plasma cross-section into a grid, construct an Ising model containing qubits, and optimize the magnetic field distribution through a quantum annealing algorithm;
[0032] Use a spatio-temporal convolutional neural network (ST-CNN) to predict the risk of plasma disruption. The input layer contains multi-channel magnetic probe time series data;
[0033] Dynamically allocate quantum computing and classical computing resources through reinforcement learning to achieve collaborative optimization of multiple physical fields.
[0034] Specifically, the quantum annealing algorithm uses a qubit parameterized circuit to optimize the magnetic field distribution through the interaction between a quantum processor and a GPU cluster.
[0035] Specifically, the spatio-temporal convolutional neural network includes a residual connection module to improve the prediction accuracy and reduce overfitting.
[0036] Specifically, the reinforcement learning dynamically adjusts the allocation ratio of quantum and classical computing resources through a reward mechanism to minimize the computing latency.
[0037] Specifically, the system sets up a dual redundancy check mechanism, including quantum error correction coding in the quantum layer and data integrity check in the classical layer.
[0038] Specifically, the system architecture includes a quantum computing unit, a deep learning unit, a hybrid optimization engine, and an actuator, and realizes high-speed data interaction through a PCIe 4.0x16 bus.
[0039] Specifically, the data preprocessing uses wavelet bases to eliminate magnetic probe noise and improve the quality of the input data.
[0040] Specifically, the system is verified in a tokamak device, and its technical effects are proved through comparative analysis of experimental data.
[0041] Furthermore, the present invention optimizes the magnetic field distribution through a quantum annealing algorithm, predicts the plasma disruption risk by combining a spatio-temporal convolutional neural network (ST-CNN), and dynamically allocates quantum and classical computing resources using reinforcement learning, solving the problems of low control accuracy, high computational latency, and high energy consumption in the prior art. The quantum annealing algorithm can quickly solve complex partial differential equations, shortening the magnetic field distribution optimization time from over 10 seconds in traditional methods to the millisecond level, meeting the real-time control requirements of tokamak devices. Through the input of multi-channel magnetic probe time-series data, ST-CNN can predict the plasma disruption risk in real time, detect unstable modes in advance, and thus effectively avoid device damage caused by plasma disruptions. The introduction of reinforcement learning realizes the dynamic allocation of quantum and classical computing resources, automatically adjusting the computing resources according to the task complexity and real-time requirements, and significantly reducing the computational energy consumption of the system (from 3.2 kW to 180 W). In addition, this method improves the plasma position control accuracy (from ±2.1 cm to ±0.7 cm) through multi-physical field collaborative optimization, providing reliable technical support for the high confinement mode operation of fusion devices. Experimental verification shows that this method exhibits excellent stability and adaptability in tokamak devices, can significantly extend the energy confinement time of the plasma, and provides important technical reserves for international fusion projects such as ITER.
[0042] The quantum annealing algorithm uses a quantum bit parameterized circuit to optimize the magnetic field distribution through the interaction between a quantum processor and a GPU cluster, significantly improving the computational efficiency and optimization accuracy. The quantum bit parameterized circuit can effectively handle high-dimensional optimization problems, avoiding the computational bottleneck of traditional optimization methods in multi-physical field coupling problems. The interaction design between the quantum processor and the GPU cluster realizes the seamless connection between quantum computing and classical computing, giving full play to the parallel advantages of quantum computing and the stability of classical computing. This design shortens the computational time for magnetic field distribution optimization from over 10 seconds in traditional methods to the millisecond level, meeting the millisecond-level control cycle requirements of tokamak devices. In addition, by adjusting the annealing parameters (such as γ and β), the quantum bit parameterized circuit can dynamically adapt to different plasma states, further improving the flexibility of optimization.
Claims
1. A multi-scale dynamic optimization method for fusion plasma based on the collaboration of quantum and deep learning, characterized in that It includes the following steps: Discretize the plasma cross-section into a grid, construct an Ising model containing qubits, and optimize the magnetic field distribution through a quantum annealing algorithm; Use a spatio-temporal convolutional neural network (ST-CNN) to predict the plasma disruption risk, and the input layer includes multi-channel magnetic probe time-series data; Dynamically allocate quantum computing and classical computing resources through reinforcement learning to achieve collaborative optimization of multiple physical fields.
2. The method according to claim 1, wherein The quantum annealing algorithm uses a qubit parameterized circuit to optimize the magnetic field distribution through the interaction between a quantum processor and a GPU cluster.
3. The method according to claim 1, wherein The spatio-temporal convolutional neural network includes a residual connection module for improving the prediction accuracy and reducing overfitting.
4. The method according to claim 1, wherein The reinforcement learning dynamically adjusts the allocation ratio of quantum and classical computing resources through a reward mechanism to minimize the computing latency.
5. The method according to claim 1, wherein The system sets up a dual redundant check mechanism, including quantum error correction coding in the quantum layer and data integrity check in the classical layer.
6. The method according to claim 1, characterized in that The system architecture includes a quantum computing unit, a deep learning unit, a hybrid optimization engine, and an actuator, and realizes high-speed data interaction through a PCIe 4.0x16 bus.
7. The method according to claim 1, characterized in that, The data preprocessing uses wavelet bases to eliminate magnetic probe noise and improve the quality of the input data.
8. The method according to claim 1, wherein The system is verified in a tokamak device, and its technical effects are proven through comparative analysis of experimental data.