A method and system for optimizing parameters of a terahertz diagnostic system of a fusion device

Abstract

The application relates to the technical field of fusion device plasma diagnosis, and provides a fusion device terahertz diagnosis system parameter optimization method and system, which comprises the following steps: receiving an original terahertz diagnosis signal of a fusion device, and performing analog-digital conversion to obtain an analog-digital conversion original signal; transmitting the analog-digital conversion original signal to a first signal processing channel, performing smoothing processing, and obtaining smoothing data to realize smoothing parameter optimization; transmitting the analog-digital conversion original signal to a second signal processing channel in parallel, performing high-frequency information analysis, and identifying an instability precursor; generating an early warning signal according to the instability precursor to realize timely early warning and assist parameter optimization. The application has the advantages that the plasma instability precursor and random noise can be effectively distinguished, the plasma state can be timely warned, the dynamic optimization of the diagnosis system parameters can be assisted, and the safety and reliability of the fusion device operation are improved.

Description

Technical Field

[0001] This invention relates to the technical field of plasma diagnostics for fusion devices, and specifically to a method and system for optimizing parameters of a terahertz diagnostic system for fusion devices. Background Technology

[0002] Plasma diagnostics in fusion devices is a key technology for ensuring their stable operation. Terahertz diagnostic systems, as a non-contact measurement method, can obtain important information such as the plasma's internal current density and magnetic field structure by analyzing the changes in terahertz waves after they penetrate the plasma. However, in actual operation, the accuracy and reliability of the diagnostic system's output signals often face severe challenges due to the complexity of plasma behavior, the inherent characteristics of diagnostic hardware, and interference from the external environment. These problems are particularly pronounced when the fusion device operates under extreme conditions, potentially directly affecting the safety and success rate of fusion experiments.

[0003] Existing terahertz diagnostic systems for fusion devices face multiple challenges: first, how to effectively distinguish between the inherent weak high-frequency physical fluctuations and random clutter of plasma as precursors to instability; second, how to compensate for the nonlinear response and saturation effects of core diagnostic hardware (such as detectors) when plasma enters a high-pressure, high-radiation state; and third, how to identify and eliminate signal contamination caused by transient electromagnetic interference generated in the diagnostic environment due to extreme operating conditions. The fixed source power, preset optical path collimation, and detector gain settings of existing systems, among other static parameters, cannot dynamically adapt to the evolution of plasma behavior from stable to violently unstable states, the nonlinear response of diagnostic hardware, the filtering of critical information by data processing logic, and unknown environmental electromagnetic interference under extreme conditions. This results in the system's inability to accurately identify ongoing physical processes, potentially missing crucial early warning opportunities.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing parameters of a terahertz diagnostic system for fusion devices. This method and system can effectively distinguish between plasma instability precursors and random clutter, provide timely early warning of plasma conditions, and assist in the dynamic optimization of diagnostic system parameters, thereby improving the safety and reliability of fusion device operation.

[0006] This application provides a method for optimizing parameters of a terahertz diagnostic system for a fusion device, the technical solution of which is as follows:

[0007] A method for optimizing parameters of a terahertz diagnostic system for a fusion device, comprising:

[0008] The original terahertz diagnostic signal from the fusion device is received and converted from analog to digital to obtain the original analog-to-digital signal.

[0009] The original analog-to-digital conversion signal is transmitted to the first signal processing path for smoothing to obtain smoothed data, thereby optimizing the smoothing parameters.

[0010] The original analog-to-digital conversion signal is transmitted in parallel to the second signal processing path for high-frequency information analysis to identify instability precursors.

[0011] Based on the precursors of instability, early warning signals are generated to provide timely warnings and assist in the parameter optimization process.

[0012] The above solution solves the problems in existing technologies, such as the inability to effectively distinguish between instability precursors and random clutter, the inability to dynamically adapt to plasma behavior evolution, the inability to diagnose hardware nonlinear response, and environmental electromagnetic interference. It can provide timely early warning and assist in parameter optimization.

[0013] Optionally, this application also proposes a step of transmitting the original analog-to-digital converted signal in parallel to a second signal processing path for high-frequency information analysis to identify instability precursors, including:

[0014] Acquire the instantaneous energy of the original signal from the analog-to-digital conversion;

[0015] Obtain the instantaneous energy of the smoothed data in the first signal processing path;

[0016] The energy difference is obtained by calculating the difference between the instantaneous energy of the original signal from the analog-to-digital conversion and the instantaneous energy of the smoothed data.

[0017] Monitor the rate of change of energy difference;

[0018] When the rate of change of the energy difference exceeds a preset threshold, it is recorded as an instability precursor and an early warning signal is generated.

[0019] The above scheme provides a specific method for identifying precursors of instability, and improves the accuracy and timeliness of early warning by monitoring the rate of change of energy difference.

[0020] Optionally, this application also proposes a step of transmitting the original analog-to-digital conversion signal to a first signal processing path for smoothing processing to obtain smoothed data, thereby optimizing the smoothing parameters, including:

[0021] The original analog-to-digital conversion signal is input to the first signal processing path;

[0022] A low-pass filter is used to perform real-time smoothing filtering on the original analog-to-digital conversion signal to filter out frequency components higher than the preset cutoff frequency.

[0023] Based on the filtered and smoothed signal, the plasma current density and magnetic field structure parameters are inverted and used as smoothing data.

[0024] The above scheme clarifies the details of the smoothing process, and ensures the effectiveness of the smoothed data through low-pass filtering and plasma parameter inversion.

[0025] Optionally, this application also proposes that the steps for monitoring the rate of change of the energy difference include:

[0026] Continuously monitor the rate of change of the energy difference;

[0027] When the rate of change of the energy difference exceeds the first preset threshold, the high-frequency components in the original analog-to-digital conversion signal that cause the energy difference to increase are subjected to spectral analysis to obtain the spectral analysis results of the high-frequency components.

[0028] Based on the spectral analysis results of the high-frequency components, it is determined whether the energy of the high-frequency components is concentrated in a specific frequency range, and the first judgment result is obtained.

[0029] Determine whether the increase in the energy difference continues to exceed the second preset threshold, and obtain the second determination result;

[0030] A warning signal is generated when both the first and second judgment results are true.

[0031] The above scheme further refines the judgment logic of instability precursors, and by combining spectrum analysis and continuous growth judgment, it avoids false alarms and missed alarms.

[0032] Optionally, this application also proposes a step of performing spectral analysis on the high-frequency components in the original analog-to-digital conversion signal that cause an increase in energy difference, including:

[0033] Preliminary spectral analysis is performed on the high-frequency components in the original analog-to-digital conversion signal that cause an increase in energy difference, to obtain the preliminary frequency range of high-frequency energy concentration;

[0034] Based on the initial frequency range of the high-frequency energy concentration, adjust the parameters of the spectrum analysis, including frequency resolution and analysis bandwidth;

[0035] Based on the adjusted frequency resolution and analysis bandwidth, a second-order spectral analysis is performed on the high-frequency components to obtain the spectral analysis results of the high-frequency components.

[0036] The above scheme optimizes the accuracy of high-frequency component spectrum analysis and improves the ability to capture key frequency information through preliminary analysis and parameter adjustment.

[0037] Optionally, this application also proposes a step of adjusting the parameters of the spectrum analysis based on the initial frequency range of the high-frequency energy concentration, including:

[0038] Monitor the spectral energy distribution within the initial frequency range;

[0039] Based on the spectral energy distribution, the corresponding instantaneous center frequency and instantaneous bandwidth are calculated in real time;

[0040] Calculate the corresponding drift amount based on the instantaneous center frequency;

[0041] Calculate the corresponding change based on the instantaneous bandwidth;

[0042] The frequency resolution and analysis bandwidth are adjusted in real time based on the instantaneous center frequency drift and the instantaneous bandwidth change.

[0043] The above scheme enables real-time adaptive adjustment of spectrum analysis parameters, ensuring accurate capture of high-frequency information in complex dynamic environments.

[0044] Optionally, this application also proposes a step of generating an early warning signal based on instability precursors to achieve timely early warning and assist in the parameter optimization process, including:

[0045] Perform Fast Fourier Transform processing on the signal in the second signal processing path to extract the energy distribution within a preset frequency range;

[0046] Determine whether the signal energy within the preset frequency range exhibits a continuous increase exceeding a predetermined growth rate within a preset time window, and obtain the continuous growth judgment result;

[0047] When the continuous growth judgment result indicates that continuous growth has occurred, it is judged as an instability precursor. The signal waveform is compared with the pre-stored dangerous instability waveform template to obtain the waveform matching result.

[0048] When the waveform matching result is a successful match, a corresponding early warning signal is generated to provide timely warnings and assist in the parameter optimization process.

[0049] The above scheme provides a more specific and reliable mechanism for generating early warning signals, and enhances the accuracy and reliability of early warnings through Fourier transform and waveform matching.

[0050] Optionally, this application also proposes a step of adjusting the frequency resolution and analysis bandwidth in real time based on the drift of the instantaneous center frequency and the change in the instantaneous bandwidth, including:

[0051] Based on the instantaneous center frequency drift and the instantaneous bandwidth change, the adjustment values ​​for frequency resolution and analysis bandwidth are determined using a pre-defined lookup table, and the adjustments are then completed.

[0052] The above scheme provides an automated and efficient way to adjust spectrum analysis parameters, and simplifies the real-time adjustment process by using lookup tables.

[0053] Optionally, this application also proposes that the steps for constructing a pre-defined lookup table include:

[0054] The range of values ​​for the combination of instantaneous center frequency drift and instantaneous bandwidth change is divided into several discrete interval combinations.

[0055] Perform statistical analysis on plasma operation data or simulation results;

[0056] Establish a statistical model relating the instantaneous center frequency drift, the instantaneous bandwidth change, the frequency resolution adjustment, and the analysis bandwidth adjustment.

[0057] Based on the statistical model, the corresponding frequency resolution adjustment value and analysis bandwidth adjustment value are determined for each discrete interval combination, thus obtaining the matching adjustment value information for each discrete interval combination;

[0058] A pre-defined lookup table is constructed based on the mapping relationship between discrete interval combinations and matching adjustment value information.

[0059] The above scheme provides a specific method for constructing lookup tables, ensuring their accuracy and usability, thereby improving the system's adaptive adjustment performance.

[0060] A parameter optimization system for a terahertz diagnostic system of a fusion device, used to perform parameter optimization of the terahertz diagnostic system of a fusion device, comprising:

[0061] The signal analog-to-digital conversion module is used to receive the raw terahertz diagnostic signal from the fusion device and perform analog-to-digital conversion to obtain the original analog-to-digital converted signal.

[0062] The smoothing optimization module is used to transmit the original analog-to-digital conversion signal to the first signal processing path for smoothing processing to obtain smoothed data, thereby optimizing the smoothing parameters.

[0063] The high-frequency information identification module is used to transmit the original analog-to-digital conversion signal in parallel to the second signal processing path for high-frequency information analysis and to identify instability precursors.

[0064] The early warning signal generation module is used to generate early warning signals based on the precursors of instability, so as to achieve timely early warning and assist in the parameter optimization process.

[0065] The above scheme provides a system for implementing the above method, which has a modular structure and is easy to deploy and implement.

[0066] As can be seen from the above, the terahertz diagnostic system parameter optimization method and system for fusion devices provided in this application, through parallel processing of the original signal, performs smoothing processing to obtain conventional diagnostic data on the one hand, and performs high-frequency information analysis to identify instability precursors and generate early warning signals on the other hand. This can effectively distinguish between plasma instability precursors and random clutter, realize timely early warning of plasma status, and assist in the dynamic optimization of diagnostic system parameters, thereby improving the safety and reliability of fusion device operation. Attached Figure Description

[0067] Figure 1 This is a flowchart of a method for optimizing parameters of a terahertz diagnostic system for a fusion device, according to one embodiment of the present invention.

[0068] Figure 2 This is one of the flowcharts of a method for optimizing parameters of a terahertz diagnostic system for a fusion device according to another embodiment of the present invention;

[0069] Figure 3 This is a second flowchart of a method for optimizing parameters of a terahertz diagnostic system for a fusion device, as described in another embodiment of the present invention.

[0070] Figure 4 This is the third flowchart of a method for optimizing parameters of a terahertz diagnostic system for a fusion device, as described in another embodiment of the present invention.

[0071] Figure 5 This is the fourth flowchart of a method for optimizing parameters of a terahertz diagnostic system for a fusion device, as described in another embodiment of the present invention.

[0072] Figure 6 This is a system block diagram of a terahertz diagnostic system parameter optimization system for a fusion device according to another embodiment of the present invention;

[0073] Explanation of reference numerals in the attached figures:

[0074] 1. Terahertz diagnostic system parameter optimization system for fusion device; 11. Signal analog-to-digital conversion module; 12. Smoothing optimization module; 13. High-frequency information identification module; 14. Early warning signal generation module. Detailed Implementation

[0075] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0076] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0077] Traditional terahertz diagnostic systems for fusion devices have limitations in real-time plasma monitoring. They cannot effectively distinguish between weak high-frequency physical fluctuations, which are precursors to instability, and random clutter. Furthermore, the core hardware of the diagnostic system is prone to nonlinear responses and saturation effects when the plasma enters a high-pressure, high-radiation state, leading to signal distortion. In addition, transient electromagnetic interference generated under extreme operating conditions can also contaminate the diagnostic signal. These problems collectively pose a severe challenge to the accuracy and reliability of the diagnostic system's output signal.

[0078] To address this, this application proposes a parameter optimization method for a terahertz diagnostic system of a fusion device, combining... Figure 1 As shown, it includes:

[0079] S1 receives the original terahertz diagnostic signal from the fusion device and performs analog-to-digital conversion to obtain the original analog-to-digital conversion signal;

[0080] S2, transmit the original analog-to-digital conversion signal to the first signal processing path for smoothing processing to obtain smoothed data, thereby optimizing the smoothing parameters;

[0081] S3 transmits the original analog-to-digital conversion signal in parallel to the second signal processing path for high-frequency information analysis to identify instability precursors.

[0082] S4 generates early warning signals based on the precursors of instability, so as to achieve timely early warning and assist in the parameter optimization process.

[0083] The analog-to-digital conversion (ADC) raw signal refers to the digital signal formed after the original analog signal received by the terahertz diagnostic system of the fusion device is processed by the ADC. It can be implemented using various sampling rates and quantization accuracies, such as sampling and quantization via a high-speed ADC. Its main purpose is to convert the continuous analog signal into a discrete digital form, facilitating subsequent digital signal processing and analysis. The first signal processing path refers to a signal processing path specifically designed to smooth the ADC raw signal. It can be implemented using hardware circuits, software modules, or a combination of both, such as a data stream processing unit implemented using an FPGA or DSP. Its main purpose is to preprocess the raw signal and extract its main trend information. Smoothing refers to eliminating high-frequency noise and random fluctuations in the ADC raw signal through filtering algorithms or other data processing techniques. It can be implemented using various algorithms such as low-pass filtering, moving average, and Savitzky-Golay filtering. For example, a digital low-pass filter can be used to filter out components above a specific cutoff frequency. Its main purpose is to improve the signal-to-noise ratio and make the overall trend of the signal clearer. The second signal processing path, set up in parallel with the first signal processing path, is dedicated to performing high-frequency information analysis on the raw analog-to-digital conversion signal. It can be implemented using independent computing units or parallel processing architectures, such as multi-core processors or independent signal analysis modules. Its main purpose is to capture key high-frequency features that may be present in the raw signal without affecting smoothing processing. High-frequency information analysis involves in-depth detection and analysis of high-frequency components in the raw analog-to-digital conversion signal to identify subtle fluctuations related to plasma instability. This can be achieved using various methods such as spectral analysis, wavelet transform, and energy difference monitoring. For example, Fourier transform analysis can be used to analyze energy distribution within a specific frequency range. Its main purpose is to extract early features indicating potential instability development from complex signals. Instability precursors are early signals or features identified through high-frequency information analysis that foreshadow potential macroscopic instability in the fusion device plasma. These can manifest as abnormal energy increases at specific frequencies, or matching of signal waveforms with dangerous modes. For example, detecting that the energy of a specific high-frequency component continuously exceeds a preset threshold. Its main purpose is to provide early warning before plasma instability occurs, allowing time for intervention. Warning signals are alarm information generated based on identified signs of instability, used to prompt operators or control systems to take intervention measures. They can be implemented in various forms, such as visual indicators, auditory alarms, and data commands. For example, sending a trigger command to the main control system is mainly to inform potential risks in a timely manner and activate corresponding safety protection or parameter adjustment mechanisms.The auxiliary parameter optimization process refers to the fact that the generation of early warning signals is not only used for timely early warning, but also provides a basis for adjusting the adjustable parameters (such as source power, detector gain, etc.) of the terahertz diagnostic system of the fusion device. It can be achieved by feedback control, adaptive adjustment or manual intervention. For example, according to the type and intensity of instability precursors, the relevant working parameters of the diagnostic system can be automatically or manually adjusted. Its main purpose is to improve the adaptability and diagnostic accuracy of the diagnostic system under different plasma conditions.

[0084] In some preferred embodiments, this application is implemented as follows. First, the raw terahertz diagnostic signal of the fusion device, such as the analog voltage signal from Faraday rotation diagnostics, is sampled and quantized by a high-speed analog-to-digital converter (ADC) to generate a raw analog-to-digital converter (ADC) signal. This ADC can be a 14-bit ADC chip with a sampling rate of 100 MSPS to ensure signal fidelity. Subsequently, this raw ADC signal is copied and simultaneously fed into two independent digital signal processing units. The first digital signal processing unit, acting as the first signal processing path, internally operates a digital low-pass filter, such as a Butterworth filter with a cutoff frequency set at 5 kHz, to perform real-time smoothing of the raw ADC signal. After filtering, smoothed data is obtained, which can be used to invert the plasma's current density and magnetic field structure parameters in real time and input as feedback signals to the main control system of the fusion device to optimize the plasma's operating parameters. The second digital signal processing unit, acting as the second signal processing path, receives the same raw ADC signal in parallel. This unit does not perform smoothing but focuses on high-frequency information analysis. For example, it can calculate the difference between the instantaneous energy of the original analog-to-digital conversion signal and the instantaneous energy of the smoothed data in the first signal processing path in real time, and monitor the rate of change of this energy difference. When the rate of change of the energy difference exceeds a preset threshold, the system identifies it as a precursor to instability, such as indicating the early development of tear mode instability or edge localized modes. Once an instability precursor is identified, the warning signal generation module immediately generates a warning signal. This warning signal can be a high-level pulse that triggers the audible and visual alarm in the fusion device control room and simultaneously sends an interrupt request to the main control system, prompting it to adjust operating parameters such as plasma heating power or gas injection volume, or to adjust the parameters of the terahertz diagnostic system itself, such as increasing the output power of the terahertz source or adjusting the gain of the detector, to improve the signal acquisition capability and accuracy of the diagnostic system in unstable states.

[0085] Optional, combined Figure 2 As shown, S3 transmits the original analog-to-digital converted signal in parallel to the second signal processing path for high-frequency information analysis. The steps for identifying instability precursors include:

[0086] S31, acquire the instantaneous energy of the original signal from the analog-to-digital conversion;

[0087] S32, Obtain the instantaneous energy of the smoothed data in the first signal processing path;

[0088] S33, calculate the difference between the instantaneous energy of the original signal from the analog-to-digital conversion and the instantaneous energy of the smoothed data to obtain the energy difference;

[0089] S34, monitors the rate of change of energy difference;

[0090] S35, when the rate of change of the energy difference exceeds the preset threshold, it is recorded as an instability precursor and an early warning signal is generated.

[0091] The instantaneous energy of the original analog-to-digital conversion (ADC) signal refers to the energy intensity contained in the original signal after ADC at a certain moment or within a very short time window. It can be calculated using the square of the signal amplitude, the square of the signal envelope, or by obtaining the square of the instantaneous amplitude through Hilbert transform. Its purpose is to capture the energy intensity of rapidly changing high-frequency components in the signal. The instantaneous energy of the smoothed data refers to the energy intensity contained in the data after smoothing by the first signal processing path at a certain moment or within a very short time window. It can be obtained using methods similar to those used for calculating the instantaneous energy of the original signal, such as the square of the signal amplitude or the square of the signal envelope. Its purpose is to reflect the low-frequency trend and background energy level of the signal. The energy difference refers to the difference between the instantaneous energy of the original ADC signal and the energy of the smoothed data. The difference between instantaneous energies can be obtained by direct subtraction, aiming to highlight the energy of high-frequency components in the original signal, effectively separate high-frequency components, and reduce interference from low-frequency signals. The rate of change of the energy difference refers to how fast the energy difference changes over time. It can be calculated by methods such as first-order difference, linear regression slope within a sliding window, or Kalman filtering, aiming to reflect the drastic change in the energy of high-frequency components and predict the speed of instability development. The preset threshold is a critical value used to judge whether the rate of change of the energy difference reaches the standard for instability precursors. It can be set based on historical operating data, simulation results, or expert experience, aiming to filter out energy difference fluctuations caused by noise or other non-critical factors and avoid misjudgment.

[0092] In some preferred embodiments, this application is implemented as follows: The system can utilize a digital signal processor (DSP) or a field-programmable gate array (FPGA) to process terahertz diagnostic signals in real time. First, for acquiring the instantaneous energy of the original analog-to-digital conversion signal, a sliding window root mean square (RMS) algorithm can be used. That is, at each sampling point, the square root of the average of the sum of squares of the original signal samples within a fixed-length time window is calculated as the instantaneous energy at that point. Simultaneously, for acquiring the instantaneous energy of the smoothed data in the first signal processing path, the same sliding window RMS algorithm as the instantaneous energy of the original signal can be used, but applied to the smoothed data after low-pass filtering. Next, when calculating the energy difference, the instantaneous energy of the original signal can be directly subtracted from the instantaneous energy of the smoothed data. For example, if the instantaneous energy of the original signal is E_raw and the instantaneous energy of the smoothed data is E_smooth, then the energy difference E_diff = E_raw - E_smooth. Subsequently, the rate of change of the energy difference can be monitored using the difference method, that is, calculating the difference between the energy difference at the current moment and the energy difference at the previous moment and dividing by the time interval, or using a more complex Kalman filter to estimate its trend. Finally, when the calculated rate of change of the energy difference, such as the energy increase per microsecond, exceeds a preset threshold, such as 10 units of energy increase per microsecond, the system will trigger the recording of instability precursors and immediately generate an early warning signal. This early warning signal can be a digital flag or an alarm message containing a timestamp and the rate of change of the energy difference, which is sent to the main control system.

[0093] Optional, combined Figure 3 As shown, S2 transmits the original analog-to-digital conversion signal to the first signal processing path for smoothing processing to obtain smoothed data. The steps for optimizing the smoothing parameters include:

[0094] S21, input the original analog-to-digital conversion signal to the first signal processing path;

[0095] S22 uses a low-pass filter to perform real-time smoothing filtering on the original analog-to-digital conversion signal, filtering out frequency components higher than the preset cutoff frequency;

[0096] S23. Based on the filtered and smoothed signal, the plasma current density and magnetic field structure parameters are inverted and used as smoothed data.

[0097] A low-pass filter is an electronic filter that allows signals below a preset cutoff frequency to pass through while attenuating or blocking signals above that cutoff frequency. It can be implemented using analog circuits, such as RC or LC filters, or using digital signal processing algorithms, such as finite impulse response (FIR) or infinite impulse response (IIR) filters. Its purpose is to remove high-frequency noise and clutter from the signal while retaining valuable low-frequency components. Real-time smoothing filtering refers to the continuous or quasi-continuous processing of the original analog-to-digital conversion signal to suppress instantaneous changes and obtain a smooth data stream. This can be achieved using algorithms such as moving average, exponential smoothing, or Kalman filtering. Its purpose is to ensure timely data processing and provide immediate and stable input for subsequent parameter optimization. The preset cutoff frequency is a pre-set frequency value in low-pass filtering; signal components above this frequency will be filtered out. This can be determined through experimental data analysis, theoretical model calculations, or empirical setting based on practical application requirements. Its purpose is to accurately control the degree of filtering, removing noise while avoiding the filtering out of low-frequency physical information valuable for plasma parameter inversion. Inversion refers to the process of inferring indirect measurable physical parameters from observed signal data through mathematical models and algorithms. Specifically, it can be achieved through methods such as least squares, Bayesian inference, or neural network models. Its purpose is to accurately extract the plasma current density and magnetic field structure parameters from the filtered and smoothed signal, providing a reliable basis for the evaluation of the fusion device's operating status and parameter optimization.

[0098] In one specific implementation, the raw terahertz diagnostic signal from the fusion device, after analog-to-digital conversion (ADC), yields a series of digital ADC-converted raw signals. These digital signals are input to a specific first signal processing path, which can be implemented by a powerful digital signal processor (DSP) or a field-programmable gate array (FPGA). Specifically, in this first signal processing path, a digital low-pass filter can be used to perform real-time smoothing filtering on the ADC-converted raw signals. For example, a fourth-order Butterworth low-pass filter with a preset cutoff frequency of 10 kHz can be used. This filter, through real-time calculations on continuously input signal samples, can filter out all components with frequencies higher than 10 kHz, thereby removing high-frequency noise and clutter while retaining physical information such as plasma current density and magnetic field structure. The filtering algorithm can be programmed into the DSP or FPGA to ensure real-time processing. Furthermore, based on the smoothed signal after low-pass filtering, the plasma current density and magnetic field structure parameters can be accurately inverted using established physical models and inversion algorithms. For example, a physical model based on the Faraday rotation effect, combined with a least-squares fitting algorithm, can be used to extract the instantaneous current density distribution and magnetic field topology of the plasma from the smoothed signal. These inverted parameters, such as the plasma center current density and edge safety factor, are output as smoothed data for parameter optimization and feedback control of the fusion device's main control system.

[0099] Optional, combined Figure 4 As shown, the steps for S34 to monitor the rate of change of the energy difference include:

[0100] S341, continuously monitors the rate of change of energy difference;

[0101] S342, when the rate of change of the energy difference exceeds the first preset threshold, perform spectral analysis on the high-frequency components in the original analog-to-digital conversion signal that cause the energy difference to increase, and obtain the spectral analysis results of the high-frequency components;

[0102] S343, Based on the spectral analysis results of the high-frequency components, determine whether the energy of the high-frequency components is concentrated in a specific frequency range, and obtain the first judgment result;

[0103] S344, determine whether the increase in energy difference continues to exceed the second preset threshold, and obtain the second judgment result;

[0104] S345, when the first judgment result is yes and the second judgment result is yes, a warning signal is generated.

[0105] High-frequency components refer to the signal components in the original analog-to-digital conversion signal whose frequencies are higher than the cutoff frequency set by the smoothing process. Specifically, they can be separated from the original signal through bandpass filtering or high-pass filtering. The purpose is to capture weak, rapidly changing physical disturbances that may exist within the plasma, which are often early signs of instability development. Spectrum analysis refers to the process of converting a time-domain signal into a frequency-domain signal through mathematical transformations, specifically using methods such as Fast Fourier Transform or Wavelet Transform. Its purpose is to reveal the energy distribution of different frequency components in the signal, thereby identifying potential instability modes. A specific frequency range refers to one or more frequency intervals that are pre-set or dynamically determined. The body can be determined based on the characteristic frequencies of known plasma instability modes. The purpose is to distinguish random noise from physically meaningful instability signals, since instability usually manifests as energy concentration at specific frequencies. The first preset threshold is a numerical limit used for preliminary screening of the rate of change of energy difference. It can be set based on historical system data or experience. Its purpose is to trigger subsequent refined analysis and avoid complex processing of all signals. The second preset threshold is a numerical limit used to judge the persistence of energy difference growth. It can be set based on the typical time scale of instability development. Its purpose is to eliminate transient interference and ensure that the identified instability precursors are persistent.

[0106] In some preferred embodiments, this application is implemented as follows: The system can be configured with a high-speed data acquisition unit for continuously acquiring and processing the rate of change data of energy difference. This unit can use a real-time processor or an FPGA (Field-Programmable Gate Array) to continuously calculate and monitor the rate of change of energy difference. When the rate of change of energy difference is detected to exceed a first preset threshold, for example, this threshold can be set according to the upper limit of the normal fluctuation range in historical operating data, the system will immediately start a spectrum analysis module. This spectrum analysis module can be a dedicated digital signal processor that performs a fast Fourier transform operation on the high-frequency part of the original analog-to-digital conversion signal that is related to the increase in energy difference to obtain its spectrum analysis results. For example, a frequency window can be set, and fast Fourier transform can only be performed on the signal within the window to reduce the amount of computation. Then, a pattern recognition algorithm can be applied to the spectrum analysis results to determine whether the energy of the high-frequency components is concentrated in a specific frequency range. For example, the algorithm can identify whether there is an obvious energy peak in the spectrum graph, and whether the peak is located in a predefined frequency range related to a known instability mode, thereby obtaining a first judgment result. Simultaneously, a time series analysis module can continuously track the growth trend of the energy difference and determine whether its growth continues to exceed a second preset threshold. For example, by calculating the average growth rate of the energy difference over several past sampling periods and comparing it with the second preset threshold, a second judgment result is obtained. Only when the pattern recognition algorithm confirms that the energy is concentrated in a specific frequency range, and the time series analysis module confirms that the energy difference continues to grow, will the system trigger the generation of an early warning signal and send the signal to the main control system or operator interface.

[0107] Optionally, the step of performing spectral analysis on the high-frequency components in the original analog-to-digital conversion signal that cause an increase in energy difference includes:

[0108] Preliminary spectral analysis is performed on the high-frequency components in the original analog-to-digital conversion signal that cause an increase in energy difference, to obtain the preliminary frequency range of high-frequency energy concentration;

[0109] Based on the initial frequency range of the high-frequency energy concentration, adjust the parameters of the spectrum analysis, including frequency resolution and analysis bandwidth;

[0110] Based on the adjusted frequency resolution and analysis bandwidth, a second-order spectral analysis is performed on the high-frequency components to obtain the spectral analysis results of the high-frequency components.

[0111] Preliminary spectrum analysis refers to the initial, usually rapid or coarse, frequency decomposition of the signal to identify the main trends or approximate regions of energy distribution. This can be achieved using methods such as Fast Fourier Transform (FFT) or Short-Time Fourier Transform (STFT), with the aim of quickly locating concentrated areas of high-frequency energy. The preliminary frequency range of high-frequency energy concentration refers to one or more frequency intervals where the high-frequency energy of the signal is significantly concentrated, as determined by the preliminary spectrum analysis. This can be a continuous frequency band or a set of multiple discrete frequency points, aiming to provide a focused range for subsequent refined analysis. Adjusting the spectrum... The parameters of the spectrum analysis refer to the settings used for subsequent spectrum analysis that are dynamically changed based on the results of the preliminary analysis to optimize the accuracy and efficiency of the analysis. They can be automatically adjusted according to preset rules, algorithms, or lookup tables, with the aim of making the spectrum analysis more adaptable to the actual characteristics of the signal. Frequency resolution refers to the smallest frequency interval that can be distinguished in the spectrum analysis. It can be determined by the length of the analysis window or the number of sampling points, and its purpose is to affect the ability to identify signal details. Analysis bandwidth refers to the frequency range covered by the spectrum analysis. It can be dynamically adjusted according to the actual frequency distribution of the signal, with the aim of ensuring that the analysis can fully cover the target frequency components.

[0112] In some preferred embodiments, when the system determines that spectral analysis is needed on the high-frequency components in the original analog-to-digital converted signal that cause an increase in energy difference, it can be implemented as follows: First, a preliminary Fast Fourier Transform (FFT) is performed on the high-frequency components. For example, a short time window and a low sampling rate can be used to quickly obtain a rough spectrum. From this preliminary spectrum, one or more preliminary frequency ranges where high-frequency energy is significantly concentrated can be identified. For example, if the energy is found to be mainly concentrated between 50kHz and 100kHz, this range is determined as the preliminary frequency range. Subsequently, based on the characteristics of this preliminary frequency range, the system can dynamically adjust the spectral parameters used for subsequent fine analysis. For example, if the preliminary frequency range is narrow, such as only 10kHz, the system can increase the frequency resolution, for example, by increasing the number of sampling points in the Fourier transform or extending the analysis time window, so that the small frequency differences within this 10kHz range can be more clearly distinguished in subsequent analysis. Conversely, if the preliminary frequency range is wide, such as covering 200kHz, the system can appropriately increase the analysis bandwidth to ensure that this wider frequency range can be fully covered in the secondary analysis. This adjustment can be based on preset logical rules or by consulting a pre-built parameter adjustment table. Finally, based on these adjusted frequency resolutions and analysis bandwidths, the system performs a secondary spectral analysis on the high-frequency components. For example, if the preliminary analysis determines that the high-frequency energy is concentrated around 80kHz, and the system adjusts the frequency resolution to 100Hz and the analysis bandwidth to 20kHz (i.e., 70kHz to 90kHz), then the secondary spectral analysis will precisely focus on this 70kHz to 90kHz frequency band and perform a detailed analysis at a granularity of 100Hz, thereby obtaining the spectral analysis results of the high-frequency components. Through this step-by-step, adaptive analysis process, high-precision spectral information can be obtained when identifying precursors to instability.

[0113] Optionally, the steps for adjusting the parameters of the spectrum analysis based on the initial frequency range of the high-frequency energy concentration include:

[0114] Monitor the spectral energy distribution within the initial frequency range;

[0115] Based on the spectral energy distribution, the corresponding instantaneous center frequency and instantaneous bandwidth are calculated in real time;

[0116] Calculate the corresponding drift amount based on the instantaneous center frequency;

[0117] Calculate the corresponding change based on the instantaneous bandwidth;

[0118] The frequency resolution and analysis bandwidth are adjusted in real time based on the instantaneous center frequency drift and the instantaneous bandwidth change.

[0119] The spectral energy distribution within the initial frequency range refers to the distribution of signal energy at different frequency points within the high-frequency energy concentration area determined by preliminary spectral analysis. This can be obtained by performing a detailed Fourier transform or wavelet transform on the signal within this frequency range to acquire detailed information about the energy distribution. The instantaneous center frequency refers to the weighted average frequency of the signal's spectral energy distribution at a given moment. This can be obtained by calculating the centroid frequency of the spectral energy distribution, reflecting the real-time changes in the main frequency components of the signal. The instantaneous bandwidth refers to the effective frequency width of the signal's spectral energy distribution at a given moment. This can be obtained by calculating the root mean square bandwidth or 3dB bandwidth of the spectral energy distribution, reflecting the real-time changes in the dispersion of the signal's frequency components. The drift amount refers to the instantaneous center frequency... The frequency deviation relative to a reference frequency or its historical average can be calculated using the difference between the instantaneous center frequency and the reference frequency. Its purpose is to quantify the deviation trend of the main frequency components of the signal. The change refers to the degree of change of the instantaneous bandwidth relative to a reference bandwidth or its historical average. It can be calculated using the difference or ratio between the instantaneous bandwidth and the reference bandwidth. Its purpose is to quantify the evolution of the frequency dispersion of the signal. Frequency resolution refers to the smallest frequency interval that can be distinguished in spectrum analysis. It can be achieved by adjusting the window function length or the number of sampling points of the Fourier transform. Its purpose is to determine the level of detail in spectrum analysis. Analysis bandwidth refers to the frequency range covered by spectrum analysis. It can be achieved by adjusting the sampling frequency of the Fourier transform or the cutoff frequency of the filter. Its purpose is to determine the frequency range of spectrum analysis.

[0120] In some preferred embodiments, this application is implemented as follows: First, to monitor the spectral energy distribution within a preliminary frequency range, a real-time spectrum analysis module can be used. This module performs continuous short-time Fourier transform (STFT) processing on the original analog-to-digital converted signal within the preliminary frequency range, displaying a time-frequency graph within that frequency range through sliding time and frequency windows, thereby showing the energy distribution at different frequencies and time points. Next, based on the acquired spectral energy distribution, the instantaneous center frequency and instantaneous bandwidth are calculated in real time. For example, a weighted average method of the spectrum can be used to calculate the instantaneous center frequency, i.e., the spectral energy within each time window is weighted and summed, with the weight being the corresponding frequency value, and then divided by the total energy. Simultaneously, the instantaneous bandwidth can be determined by calculating the root mean square bandwidth of the spectrum or the energy concentration, for example, calculating the width of the frequency range containing 90% of the total energy. Subsequently, based on the real-time calculated instantaneous center frequency, its drift is calculated. This can be obtained by comparing the current instantaneous center frequency with the average instantaneous center frequency of the previous moment or a past period, for example, calculating the difference between the two. Similarly, the change in instantaneous bandwidth is calculated based on the real-time calculation, which can also be obtained by comparing it with the average instantaneous bandwidth of the previous moment or a period of time. Finally, the frequency resolution and analysis bandwidth are adjusted in real time based on the drift of the instantaneous center frequency and the change in instantaneous bandwidth. For example, if the drift of the instantaneous center frequency exceeds a preset threshold, it indicates that the signal center frequency is drifting. In this case, the analysis bandwidth can be appropriately increased to ensure that the subsequent secondary spectrum analysis can fully cover the actual frequency range of the signal. If the change in instantaneous bandwidth is large, it indicates that the frequency components of the signal are spreading or contracting. In this case, the frequency resolution can be appropriately adjusted. For example, when the bandwidth increases, the resolution can be slightly reduced to ensure the stability of the analysis, while when the bandwidth contracts, the resolution can be increased to capture detailed structures. These adjustments can be achieved by dynamically changing the number of sampling points or the window function type of the Fourier transform, so that the spectrum analysis parameters can match the real-time characteristics of the signal.

[0121] Optional, combined Figure 5 As shown, the steps of S4 in generating an early warning signal based on instability precursors to achieve timely early warning and assist in the parameter optimization process include:

[0122] S41, Perform fast Fourier transform processing on the signal in the second signal processing path to extract the energy distribution within the preset frequency range;

[0123] S42, determine whether the signal energy within the preset frequency range has a continuous increase exceeding the predetermined growth rate within the preset time window, and obtain the continuous growth judgment result;

[0124] S43, when the continuous growth judgment result indicates that continuous growth has occurred, it is judged as an instability precursor. The signal waveform is compared with the pre-stored dangerous instability waveform template to obtain the waveform matching result.

[0125] S44: When the waveform matching result is a successful match, a corresponding warning signal is generated to provide timely warning and assist in the parameter optimization process.

[0126] Fast Fourier Transform (FFT) processing is a mathematical method that converts time-domain signals into frequency-domain signals. It can be implemented using a fast algorithm based on Discrete Fourier Transform (DFT). Its purpose is to reveal the energy distribution of different frequency components in the signal, thereby identifying frequency characteristics related to plasma instability. A preset frequency range refers to a specific frequency range pre-determined and focused on after FFT processing. This range can be set based on historical data analysis, physical model predictions, or expert experience. Its purpose is to specifically monitor high-frequency energy changes related to instability modes, improving the targeting and efficiency of detection. A preset time window refers to the time period used to judge continuous growth of signal energy. This can be determined based on the typical evolution timescale of instability precursors. Its purpose is to capture the dynamic characteristics of energy changes, distinguish between instantaneous fluctuations and continuous growth, thereby improving the accuracy of judgment. A predetermined growth rate refers to the threshold for judging whether continuous growth of signal energy has occurred. This threshold can be set based on the operating characteristics of the fusion device, noise levels, and requirements for early warning sensitivity. Its purpose is to flexibly adjust the system's response sensitivity, avoiding excessively high or low sensitivity. Dangerous instability waveform templates refer to pre-stored typical signal waveform feature data representing known dangerous instability events. These can be derived from historical experimental data, numerical simulation results, or theoretical calculations. The purpose is to further verify the authenticity of instability precursors and reduce the false judgment rate by comparing them with real-time signal waveforms.

[0127] In some preferred embodiments, this application is implemented as follows: During the operation of the fusion device, the second signal processing path continuously receives the analog-to-digital converted raw signals. To identify instability precursors, these signals can be processed in real time. Specifically, a digital signal processor or field-programmable gate array (FPGA) can be used to perform Fast Fourier Transform (FFT) processing on the signals in the second signal processing path. For example, an FFT module can be configured to transform signal data blocks at regular intervals to extract the energy distribution of the signal in the frequency domain. The preset frequency range can be set, for example, from 10 kHz to 500 kHz, because characteristic frequencies associated with magnetohydrodynamic instability are typically observed within this range. Subsequently, the energy distribution within the extracted preset frequency range can be monitored. For example, the sum of the energy of all frequency components within the frequency range can be calculated and used as the energy index at the current time point. To determine continuous growth, a sliding window can be maintained, for example, containing the energy indexes of the most recent 100 time points. The system can calculate the linear regression slope of the energy index within the window, or compare the difference between the current energy index and the energy index at the beginning of the window, and compare it with a predetermined growth rate. The predetermined growth rate can be set, for example, to increase energy by 5% per microsecond. If the energy growth rate exceeds the predetermined growth rate at multiple consecutive time points, a continuous growth judgment result is obtained, indicating continuous growth has occurred. When the continuous growth judgment result indicates continuous growth, the system will determine it as a precursor to instability. At this time, to further confirm, a segment of signal waveform data synchronized with the energy growth event can be extracted from the second signal processing path, for example, the waveform from 10 microseconds before the energy begins to increase to the point where the growth ends. Simultaneously, the system can pre-store various dangerous instability waveform templates, such as typical waveform characteristics of tearing modes, neoclassical tearing modes, or edge localization modes. The extracted real-time signal waveform can be compared with these pre-stored dangerous instability waveform templates using correlation coefficient methods, Euclidean distance methods, or machine learning-based pattern recognition algorithms to obtain waveform matching results. Finally, when the waveform matching result is successful, for example, if the correlation coefficient exceeds a preset threshold, the system can immediately generate a corresponding warning signal. The warning signal can be a digital signal transmitted to the main control system of the fusion device via Ethernet or fiber optic cable, triggering an audible and visual alarm, or automatically adjusting operating parameters such as plasma heating power and fuel injection amount, so as to achieve timely warning and assist in the parameter optimization process, thereby avoiding or mitigating the impact of instability on the operation of the fusion device.

[0128] Optionally, the steps of adjusting the frequency resolution and analysis bandwidth in real time based on the drift of the instantaneous center frequency and the change in the instantaneous bandwidth include:

[0129] Based on the instantaneous center frequency drift and the instantaneous bandwidth change, the adjustment values ​​for frequency resolution and analysis bandwidth are determined using a pre-defined lookup table, and the adjustments are then completed.

[0130] The pre-defined lookup table refers to a pre-built data structure that takes the instantaneous center frequency drift and the instantaneous bandwidth change as input and directly maps them to the corresponding frequency resolution adjustment value and analysis bandwidth adjustment value. It can be implemented using a two-dimensional array, hash table, or database, and its purpose is to provide a fast and direct basis for parameter adjustment. The frequency resolution adjustment value refers to the quantized value used to change the frequency resolution in spectrum analysis. It can be an increment, a decrement, or a target value, and its purpose is to enable spectrum analysis to distinguish frequency components more finely or more broadly. The analysis bandwidth adjustment value refers to the quantized value used to change the analysis bandwidth range in spectrum analysis. It can be an increment, a decrement, or a target value, and its purpose is to enable spectrum analysis to cover a narrower or wider frequency range.

[0131] In some preferred embodiments, real-time adjustment of frequency resolution and analysis bandwidth can be implemented as follows: The system continuously receives the drift of the instantaneous center frequency and the change in instantaneous bandwidth calculated by the upstream steps. These drifts and changes can be quantified into a series of discrete values ​​or intervals. For example, the drift of the instantaneous center frequency can be divided into intervals such as "small drift," "medium drift," and "large drift," and the change in instantaneous bandwidth can be divided into intervals such as "bandwidth contraction," "bandwidth stabilization," and "bandwidth expansion." Then, the system uses the currently calculated drift and change as indexes to query a preset lookup table. This lookup table can be a two-dimensional array, where the row index corresponds to the drift interval of the instantaneous center frequency, the column index corresponds to the change interval of the instantaneous bandwidth, and each cell stores a predetermined adjustment value for the frequency resolution and the analysis bandwidth. For example, if the query result shows that the current drift is "medium drift" and the change is "bandwidth expansion," the lookup table will return a set of preset adjustment values, such as "increase frequency resolution by 10%" and "increase analysis bandwidth by 20%." Upon receiving these adjustment values, the system immediately applies them to the current spectrum analysis module, updating its frequency resolution and analysis bandwidth parameters to complete the adjustment. This approach ensures that the spectrum analysis parameters can respond rapidly in a predefined and optimized manner when the plasma state evolves quickly, avoiding complex online calculations and improving the system's response speed and reliability.

[0132] Optionally, the steps for constructing a predefined lookup table include:

[0133] The range of values ​​for the combination of instantaneous center frequency drift and instantaneous bandwidth change is divided into several discrete interval combinations.

[0134] Perform statistical analysis on plasma operation data or simulation results;

[0135] Establish a statistical model relating the instantaneous center frequency drift, the instantaneous bandwidth change, the frequency resolution adjustment, and the analysis bandwidth adjustment.

[0136] Based on the statistical model, the corresponding frequency resolution adjustment value and analysis bandwidth adjustment value are determined for each discrete interval combination, thus obtaining the matching adjustment value information for each discrete interval combination;

[0137] A pre-defined lookup table is constructed based on the mapping relationship between discrete interval combinations and matching adjustment value information.

[0138] Discrete interval combination refers to dividing the value space of two continuous variables—the drift of instantaneous center frequency and the change of instantaneous bandwidth—into a finite number of non-overlapping sub-regions with specific boundaries according to preset rules or algorithms. This can be achieved through equal-interval partitioning, clustering based on data distribution, or non-uniform partitioning based on physical characteristics. The purpose is to discretize the continuous parameter space to facilitate the construction and lookup of tables. Statistical models, on the other hand, are mathematical or algorithmic expressions used to describe the intrinsic relationship between the drift of instantaneous center frequency, the change of instantaneous bandwidth, and the adjustment values ​​of frequency resolution and analysis bandwidth. This can be achieved through regression analysis models, machine learning models (such as neural networks and support vector machines), or probability distribution-based models. The purpose is to learn from historical data and predict the optimal adjustment values ​​to be used under different input conditions.

[0139] In some preferred embodiments, constructing a pre-defined lookup table can be implemented as follows: First, the combined range of instantaneous center frequency drift and instantaneous bandwidth change is divided into several discrete interval combinations. For example, the range of instantaneous center frequency drift (e.g., -50kHz to +50kHz) and the range of instantaneous bandwidth change (e.g., -20kHz to +20kHz) can be equally divided to form a two-dimensional grid, with each grid cell representing a discrete interval combination. For example, the drift can be divided into 10 intervals, and the change into 10 intervals, resulting in 100 discrete interval combinations. Next, a large amount of plasma operation data or simulation results are statistically analyzed. This data can come from the historical operation records of actual fusion devices (e.g., tokamak, stellarator) or from the output of high-fidelity plasma physics simulation code (e.g., MHD simulation, particle simulation). By extracting and labeling features from this data, the optimal frequency resolution and analysis bandwidth adjustment values ​​can be obtained for different instantaneous center frequency drift and instantaneous bandwidth change. Subsequently, a statistical model is established relating the instantaneous center frequency drift, the instantaneous bandwidth change, and the frequency resolution and analysis bandwidth adjustments. For example, a multiple linear regression model, a support vector regression model, or a simple multilayer perceptron neural network can be used, taking the drift and change as input and the frequency resolution and analysis bandwidth adjustments as output. The model is then trained and optimized using training data. Afterward, based on the trained statistical model, the corresponding frequency resolution and analysis bandwidth adjustments are determined for each discrete interval combination, yielding the matching adjustment information for each discrete interval combination. Specifically, for the center point or representative value of each discrete interval combination, it is input into the statistical model, which outputs the corresponding frequency resolution and analysis bandwidth adjustments. These adjustments, together with the corresponding discrete interval combination, constitute the matching adjustment information. Finally, a pre-defined lookup table is constructed based on the mapping relationship between discrete interval combinations and the matching adjustment information. This lookup table can be stored as a two-dimensional array, a hash table, or a database, where each entry contains an identifier for a discrete interval combination and its corresponding frequency resolution and analysis bandwidth adjustments. In practical applications, when the instantaneous center frequency drift and the instantaneous bandwidth change are monitored in real time, the system can quickly query the lookup table to obtain the corresponding adjustment value, thereby achieving dynamic optimization of the spectrum analysis parameters.

[0140] A parameter optimization system for a terahertz diagnostic system of a fusion device, used to perform parameter optimization of the terahertz diagnostic system of a fusion device, combined with... Figure 6 As shown, the parameter optimization system 1 of the terahertz diagnostic system for fusion devices includes:

[0141] The signal analog-to-digital conversion module 11 is used to receive the original terahertz diagnostic signal from the fusion device and perform analog-to-digital conversion to obtain the original analog-to-digital converted signal.

[0142] The smoothing optimization module 12 is used to transmit the original analog-to-digital conversion signal to the first signal processing path for smoothing processing to obtain smoothed data, so as to achieve smoothing parameter optimization.

[0143] The high-frequency information identification module 13 is used to transmit the original analog-to-digital conversion signal in parallel to the second signal processing path, perform high-frequency information analysis, and identify instability precursors.

[0144] The early warning signal generation module 14 is used to generate early warning signals based on the precursors of instability, so as to achieve timely early warning and assist in the parameter optimization process.

[0145] Among them, the signal analog-to-digital conversion module refers to the hardware or software unit responsible for converting the raw terahertz diagnostic signal generated by the fusion device from analog to digital form. Its purpose is to provide basic data for subsequent digital signal processing.

[0146] The smoothing optimization module refers to the unit that performs low-pass filtering or averaging on the digitized raw signal. It can be implemented using digital filters, moving average algorithms, or wavelet denoising techniques. Its purpose is to filter out random noise and high-frequency interference in the signal and extract smooth data that reflects the overall trend of the plasma, thereby providing a stable basis for the optimization of system parameters.

[0147] The high-frequency information identification module refers to the unit that performs high-frequency component analysis on the digitized raw signal. It can be implemented using spectral analysis techniques such as fast Fourier transform, wavelet analysis, or empirical mode decomposition. Its purpose is to capture the precursor signals of instability that may exist in the plasma.

[0148] The early warning signal generation module refers to the unit that generates and sends alarms or control commands based on the instability precursor information output by the high-frequency information identification module. It can be implemented using logic judgment circuits, programmable controllers, or dedicated software algorithms. Its purpose is to promptly notify operators or automatic control systems to take intervention measures to avoid plasma rupture events and to assist in the diagnosis of dynamic adjustments to system parameters.

[0149] This application's solution organically combines a signal analog-to-digital conversion module, a smoothing optimization module, a high-frequency information identification module, and a warning signal generation module into a complete system. This effectively addresses hardware-level issues encountered during terahertz diagnostics of fusion devices, such as detector saturation and raw signal distortion caused by environmental electromagnetic interference. Specifically, the signal analog-to-digital conversion module first receives the raw terahertz diagnostic signal from the fusion device and converts it into digital form. This step ensures that all subsequent processing is performed in the digital domain, improving the signal processing's anti-interference capability and accuracy. The converted digital signal is then transmitted in parallel to two independent signal processing paths. The smoothing optimization module in the first signal processing path smooths the signal, aiming to filter out noise and extract the macroscopic, low-frequency variation trends of the plasma, providing stable data for optimizing diagnostic system parameters. Simultaneously, the high-frequency information identification module in the second signal processing path focuses on high-frequency information analysis of the same raw digital signal, aiming to capture high-frequency signals that can predict plasma instability. This parallel processing mechanism ensures that the system can obtain smoothed macroscopic trend data while simultaneously monitoring instability precursor information, avoiding the shortcomings of traditional single-filter processing that can filter out warning information. When the high-frequency information identification module successfully identifies an instability precursor, the warning signal generation module immediately generates a warning signal based on this precursor information. These warning signals can be used to promptly remind operators to take intervention measures or to assist the diagnostic system in dynamically adjusting parameters, thereby ensuring the safe and stable operation of the fusion device. Through this systematic integration, this solution can comprehensively improve the accuracy and reliability of terahertz diagnostic signals from source to result, effectively compensating for the shortcomings of simple software processing in dealing with hardware distortion and complex interference, enabling the diagnostic system to provide reliable judgment criteria even under extreme operating conditions.

[0150] In some preferred embodiments, this application is implemented as follows:

[0151] The signal analog-to-digital conversion module can be composed of a high sampling rate analog-to-digital converter, such as an ADC chip with 14-bit or 16-bit resolution and a sampling rate of several GHz, which is directly connected to the analog output of the terahertz detector to convert the continuously changing analog voltage signal into a discrete digital sequence, ensuring the signal fidelity of the original signal.

[0152] The smoothing optimization module can be implemented by a field-programmable gate array (FPGA) or a digital signal processor (DSP), which integrates a digital low-pass filter, such as a Butterworth or Chebyshev filter, to set a cutoff frequency to filter out frequency components higher than the macroscopic variation characteristics of the plasma. This module can process the raw analog-to-digital conversion signal in real time and output smoothed data. This data can be used to invert the plasma's current density and magnetic field structure parameters, and serve as a basis for optimizing the smoothing parameters of the terahertz diagnostic system.

[0153] The high-frequency information identification module can work in parallel with the smoothing and optimization module, and is also implemented using a field-programmable gate array (FPGA) or digital signal processor (DSP), integrating a fast Fourier transform unit (FFT) or wavelet analysis algorithm. This module can perform real-time spectral analysis on the original analog-to-digital converted signal, continuously monitoring the energy distribution and rate of change within a specific high-frequency range. For example, the monitoring frequency range can be set from 10kHz to 500kHz; when the signal energy within this range shows an abnormal increase within a short period, it is identified as a precursor to instability.

[0154] The early warning signal generation module, implemented using a microcontroller or industrial PC, receives instability precursor signals from the high-frequency information recognition module. Upon receiving an instability precursor, this module can immediately trigger a preset early warning mechanism, such as sending an alarm command to the main control system via an Ethernet interface or driving an audible and visual alarm via a digital output port. Simultaneously, based on the type and intensity of the instability precursor, this module can assist in adjusting relevant parameters of the terahertz diagnostic system, such as suggesting adjustments to the source power or detector gain, to optimize the subsequent diagnostic process.

[0155] Through the above technical solution, this system effectively addresses the challenge of hardware-level issues in terahertz diagnostics of fusion devices, where software processing alone cannot handle problems such as detector saturation and environmental electromagnetic interference causing distortion of the original signal, thus affecting the accuracy of subsequent signal processing. The system ensures the fidelity of the original signal through hardware-level analog-to-digital conversion, laying the foundation for subsequent processing. Through parallel processing pathways, the system can simultaneously handle signal smoothing to obtain macroscopic trends and high-frequency information identification to capture instability precursors, avoiding the risk of filtering out warning information. When instability precursors are identified, the system can promptly generate warning signals, providing immediate feedback to operators and assisting in the dynamic optimization of diagnostic system parameters. This improves the accuracy, reliability, and response speed of the terahertz diagnostic system for fusion devices, ensuring the safe and stable operation of the fusion device.

[0156] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing parameters of a terahertz diagnostic system of a fusion device, characterized in that, The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method.

2. The method of claim 1, wherein the method further comprises: The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. 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The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz diagnostic signal receiving and analog-digital conversion method. The application relates to a fusion device original terahertz The low-pass filter is used to perform real-time smoothing filtering on the analog-to-digital conversion original signal, and filter out frequency components higher than a preset cutoff frequency; According to the filtered smoothed signal, the current density and magnetic field structure parameters of the plasma are inverted as smoothed data.

3. The method of claim 1, wherein the method further comprises: The step of generating a warning signal according to the instability precursor to achieve timely warning and assist in the parameter optimization process includes: Performing fast Fourier transform processing on the signal in the second signal processing path to extract the energy distribution in a preset frequency interval; Determine whether the signal energy in the preset frequency interval has a continuous increase exceeding a predetermined growth rate within a preset time window to obtain a continuous increase judgment result; When the continuous increase judgment result indicates that the continuous increase occurs, it is determined as an instability precursor, and a waveform matching result is obtained by comparing the signal waveform with a pre-stored dangerous instability waveform template; When the waveform matching result is a matching success, a corresponding warning signal is generated to achieve timely warning and assist in the parameter optimization process.

4. The method of claim 1, wherein, The step of adjusting the frequency resolution and the analysis bandwidth in real time according to the drift amount of the instantaneous center frequency and the change amount of the instantaneous bandwidth includes: According to the drift amount of the instantaneous center frequency and the change amount of the instantaneous bandwidth, the adjustment value of the frequency resolution and the adjustment value of the analysis bandwidth are determined through a preset lookup table, and the adjustment is completed.

5. The method of claim 4, wherein the method further comprises: The step of constructing the preset lookup table includes: Divide the combination value range of the drift amount of the instantaneous center frequency and the change amount of the instantaneous bandwidth into several discrete interval combinations; Statistical analysis is performed on the plasma operation data or simulation results; Establish a statistical model between the drift amount of the instantaneous center frequency, the change amount of the instantaneous bandwidth, and the adjustment value of the frequency resolution and the adjustment value of the analysis bandwidth; According to the statistical model, determine the corresponding adjustment value of the frequency resolution and the adjustment value of the analysis bandwidth for each discrete interval combination to obtain matching adjustment value information for each discrete interval combination; According to the mapping relationship between the discrete interval combination and the matching adjustment value information, the preset lookup table is constructed.

6. A parameter optimization system for a terahertz diagnostic system of a fusion device, used to perform parameter optimization of a terahertz diagnostic system of a fusion device, characterized in that, It includes: A signal analog-to-digital conversion module is configured to receive an original terahertz diagnostic signal of a fusion device and perform analog-to-digital conversion to obtain an analog-to-digital conversion original signal; A smoothing processing optimization module is configured to transmit the analog-to-digital conversion original signal to a first signal processing path, perform smoothing processing, and obtain smoothed data to achieve smoothing parameter optimization; A high-frequency information identification module is configured to transmit the analog-to-digital conversion original signal to a second signal processing path in parallel, perform high-frequency information analysis, and identify an instability precursor; It is also used to obtain the instantaneous energy of the analog-to-digital conversion original signal; Obtain the instantaneous energy of the smoothed data in the first signal processing path; Calculate the difference between the instantaneous energy of the analog-to-digital conversion original signal and the instantaneous energy of the smoothed data to obtain an energy difference value; Monitor the change rate of the energy difference value; When the change rate of the energy difference value exceeds a preset threshold, record it as an instability precursor and generate a warning signal; It is also used to obtain the instantaneous energy of the analog-to-digital conversion original signal; Obtain the instantaneous energy of the smoothed data in the first signal processing path; calculating a difference between the instantaneous energy of the analog-to-digital conversion original signal and the instantaneous energy of the smoothed data, to obtain an energy difference value; monitoring the rate of change of the energy difference value; when the rate of change of the energy difference value exceeds a preset threshold, recording as a precursor of instability and generating a warning signal; also used for continuously monitoring the rate of change of the energy difference value; when the rate of change of the energy difference value exceeds a first preset threshold, performing spectral analysis on the high-frequency component in the analog-to-digital conversion original signal that causes the energy difference value to increase, to obtain a spectral analysis result of the high-frequency component; according to the spectral analysis result of the high-frequency component, judging whether the energy of the high-frequency component is concentrated in a specific frequency range, to obtain a first judgment result; judging whether the increase of the energy difference value continues to exceed a second preset threshold, to obtain a second judgment result; when the first judgment result is yes and the second judgment result is yes, generating the warning signal; also used for performing preliminary spectral analysis on the high-frequency component in the analog-to-digital conversion original signal that causes the energy difference value to increase, to obtain a preliminary frequency range of high-frequency energy concentration; according to the preliminary frequency range of high-frequency energy concentration, adjusting the parameters of spectral analysis, including frequency resolution and analysis bandwidth; based on the adjusted frequency resolution and analysis bandwidth, performing secondary spectral analysis on the high-frequency component, to obtain a spectral analysis result of the high-frequency component; also used for monitoring the spectral energy distribution in the preliminary frequency range; according to the spectral energy distribution, calculating the corresponding instantaneous center frequency and instantaneous bandwidth in real time; according to the instantaneous center frequency, calculating the corresponding drift amount; according to the instantaneous bandwidth, calculating the corresponding change amount; according to the drift amount of the instantaneous center frequency and the change amount of the instantaneous bandwidth, adjusting the frequency resolution and analysis bandwidth in real time; the warning signal generation module is used for generating a warning signal according to the precursor of instability, to realize timely warning and assist the parameter optimization process.

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