A tokamak plasma multi-parameter coordinated shape analysis method

Abstract

The application discloses a kind of tokamak plasma multi-parameter collaborative configuration analysis method, it is related to plasma multi-parameter collaborative configuration analysis technical field, including the following steps: constructing parameter database to store plasma parameter;Plasma parameter is normalized, and the normalized parameter of plasma parameter is obtained;Analysis whether there is relevance between two plasma parameters and obtains independent correlation;Analysis whether plasma parameter is simultaneously influenced by more than two plasma parameters, and analysis their comprehensive correlation;When the configuration of plasma is regulated, based on independent correlation and comprehensive correlation, plasma parameter is cooperatively regulated;The application is used to solve the existing plasma multi-parameter collaborative configuration analysis technique still exists between different plasma parameters The relevance analysis is not comprehensive enough, leading to unable to better cooperatively control and analyze plasma.

Description

Technical Field

[0001] This invention relates to the field of plasma multi-parameter cooperative configuration analysis technology, specifically to a tokamak plasma multi-parameter cooperative configuration analysis method. Background Technology

[0002] Plasma multi-parameter synergistic configuration analysis technology refers to a systematic research methodology and supporting technical system for magnetic confinement fusion research. It regards the macroscopic performance of tokamak plasma (such as stability, confinement, and combustion efficiency) as the "synergistic" result of the combined effects and mutual constraints of numerous internal physical parameters (such as current, pressure, density, and rotation). Through advanced data science and physical modeling methods, it quantitatively reveals the dynamic correlation between these parameters, thereby deeply understanding, predicting, and optimizing the overall "configuration" state for producing high-performance plasma.

[0003] Existing plasma multi-parameter collaborative configuration analysis techniques are typically only applicable to linearly correlated plasma parameters when analyzing the correlation between different plasma parameters. They are weak in analyzing the correlation between nonlinearly correlated plasma parameters, and in cases where plasma parameters interact with each other, a single plasma parameter may be influenced by multiple parameters. Existing plasma multi-parameter collaborative configuration analysis techniques struggle to determine the correlation between multiple parameters and a single parameter. For example, patent application CN119719922A discloses a "configuration prediction system and method for tokamak plasma," but this scheme cannot analyze whether there is a correlation between multiple state parameters acting together and a single state parameter, leading to significant deviations in plasma configuration prediction results. Furthermore, existing plasma multi-parameter collaborative configuration analysis techniques are not comprehensive enough in analyzing the correlation between different plasma parameters, resulting in limitations in better collaborative control and analysis of the plasma. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in the prior art. It involves monitoring and recording plasma parameters in a tokamak, simultaneously constructing a parameter database to store these parameters, normalizing them to obtain normalized parameters, acquiring correlation data based on large datasets, analyzing the correlation threshold of discrete deviations using this data, analyzing the discrete deviations between any two plasma parameters, determining the existence of correlations between the two parameters using the correlation thresholds, obtaining independent correlation relationships, constructing an influence queue for each plasma parameter, and sequentially determining whether a plasma parameter is simultaneously affected by two or more plasma parameters based on the influence queues, analyzing their comprehensive correlation relationships, and finally, when controlling the plasma configuration, coordinating the control of plasma parameters based on independent and comprehensive correlation relationships. This addresses the shortcomings of existing multi-parameter coordinated configuration analysis techniques for plasma, which lack comprehensive analysis of the correlations between different plasma parameters, resulting in inadequate coordinated control and analysis of the plasma.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for multi-parameter cooperative configuration analysis of tokamak plasma, comprising the following steps:

[0006] The plasma parameters in the tokamak are monitored and recorded, and a parameter database is constructed to store the plasma parameters.

[0007] The plasma parameters are normalized to obtain the normalized parameters of the plasma parameters.

[0008] The discrete deviation between any two plasma parameters is analyzed, and correlation data is obtained based on big data. The correlation threshold of the discrete deviation is analyzed through the correlation data, and then the correlation threshold is used to determine whether there is a correlation between the two plasma parameters and obtain the independent correlation relationship.

[0009] Based on discrete bias and correlation threshold analysis, we can determine whether plasma parameters are simultaneously affected by more than two plasma parameters, and analyze the comprehensive correlation between them.

[0010] When controlling the configuration of plasma, plasma parameters are synergistically controlled based on independent correlation and comprehensive correlation.

[0011] Furthermore, the plasma parameters in the tokamak are monitored and recorded, and a parameter database is constructed to store the plasma parameters, including the following sub-steps:

[0012] The plasma in the tokamak is monitored to obtain plasma parameters, which are all observable and recordable data of the plasma.

[0013] A parameter database is constructed to record plasma parameters, and the plasma parameters recorded in the parameter database are named historical parameters.

[0014] Further, the plasma parameters are normalized to obtain the normalized plasma parameters, which includes the following sub-steps:

[0015] The plasma parameters are numbered using the symbol PP. n This indicates that n is a non-zero natural number and n is the index of PP;

[0016] PP recorded in the m-th historical parameter n Let HP(n,m) be the index of HP.

[0017] The normalized value of HP(n,m) is calculated using a normalization algorithm and named the normalization parameter, denoted by NP(n,m).

[0018] Furthermore, the discrete deviations between any two plasma parameters are analyzed, and correlation data is obtained based on big data. The correlation threshold of the discrete deviations is analyzed through the correlation data, and then the correlation threshold is used to determine whether there is a correlation between the two plasma parameters and obtain an independent correlation relationship. This includes the following sub-steps:

[0019] Based on big data, we acquire correlation data and analyze the correlation threshold of discrete deviations using the correlation data.

[0020] Analyze the discrete deviation between any two plasma parameters, and then determine whether there is a correlation between the two plasma parameters by using the correlation threshold to obtain the independent correlation relationship.

[0021] Furthermore, based on big data, obtaining correlation data and analyzing the correlation threshold of discrete deviations through correlation data includes the following sub-steps:

[0022] The correlation data refers to two sets of data that are recognized or proven to be correlated. The correlation data includes different proven correlation groups. Each proven correlation group includes a first number of first parameters and second parameters. The first parameters and second parameters are correlated, and each first parameter corresponds to one second parameter.

[0023] Calculate the normalized values ​​of the first parameter and the second parameter, and name them the first normalized parameter and the second normalized parameter, respectively.

[0024] A two-dimensional coordinate system is established with the first normalized parameter as the X-axis and the second normalized parameter as the Y-axis, named the verified correlation analysis graph. The second normalized parameter is entered into the verified correlation analysis graph according to the first normalized parameter, and the coordinate points in the verified correlation analysis graph are named verified correlation analysis points.

[0025] Discrete regression analysis was performed on the proven correlation analysis graph. The function obtained from the analysis was named the proven correlation function. The residual between the proven correlation analysis points and the proven correlation function was obtained and named the discrete deviation.

[0026] Analyze the discrete deviation of each proven correlation analysis point in each proven correlation group, statistically analyze the range of discrete deviations to obtain the deviation range, and evenly divide the deviation range into a second number of sub-ranges, named the deviation sub-ranges. Count the number of discrete deviations within each deviation sub-range, named the deviation hit count, and number the deviation sub-ranges in ascending order, using the symbol S. i It means that S i The number of biased hits is marked as HC i , where i is a non-zero natural number and i is the index of S and HC;

[0027] Divide each deviation subrange equally, so that each deviation subrange is split into two deviation subranges, and recount S. i and HC i Repeat this process until any HC exists during the next split. i Until the value is zero, the final S obtained from the split will be... i and HC i They are respectively labeled as FS i and FHC i ;

[0028] Get FS i The median, labeled SM i , with SM i For the X-axis, FHC i Establish a two-dimensional coordinate system for the Y-axis, named the deviation analysis coordinate system, and set FHC... i According to SM i Enter the deviation analysis coordinate system, perform discrete regression analysis on the deviation analysis coordinate system, and name the function obtained from the analysis as the deviation analysis function;

[0029] Substitute the Y-axis value of zero into the deviation analysis function to solve for the X-axis value. Name the solution as the deviation threshold. Name the deviation threshold that is closest to the maximum value of the deviation range and greater than the maximum value of the deviation range as the correlation threshold.

[0030] Furthermore, analyzing the discrete deviation between any two normalized parameters, and then determining whether there is a correlation between the two normalized parameters and obtaining the independent correlation relationship through the correlation threshold, includes the following sub-steps:

[0031] When analyzing any two normalized parameters, label one of them as parameter A and the other as parameter B. Create a correlation analysis graph with parameter A as the X-axis and parameter B as the Y-axis, and name it the real-time analysis graph. Enter the NP(n,m) of parameter A and parameter B into the real-time analysis graph and analyze the discrete deviation. Name the maximum value of the discrete deviation as the maximum deviation.

[0032] Determine whether the maximum deviation is less than or equal to the correlation threshold. If so, mark the correlation between parameter A and parameter B, and name the proven correlation function obtained from the real-time analysis graph as an independent correlation.

[0033] Furthermore, the analysis of whether plasma parameters are simultaneously affected by two or more plasma parameters based on discrete bias and correlation threshold, and the analysis of their comprehensive correlation relationship, includes the following sub-steps:

[0034] An influence queue is constructed for each plasma parameter;

[0035] Based on the influence queue, it is determined whether the plasma parameters are simultaneously affected by two or more plasma parameters, and the comprehensive correlation between them is analyzed.

[0036] Furthermore, constructing an influence queue for each plasma parameter includes the following sub-steps:

[0037] When analyzing a certain plasma parameter, it is named the target analysis parameter, and the other plasma parameters are named secondary analysis parameters.

[0038] Obtain the maximum deviation between the target analysis parameter and the secondary analysis parameter, and name it the deviation to be analyzed;

[0039] The deviations to be analyzed are numbered in ascending order and designated by the symbol T. h This indicates that h is a non-zero natural number and h is the index of T;

[0040] T h The corresponding secondary analysis parameter is labeled TG. h , by TG h The influence queue is formed according to the order of h from smallest to largest.

[0041] Furthermore, based on the influence queue, the process of determining whether a normalized parameter is simultaneously affected by two or more normalized parameters and analyzing the comprehensive correlation between them includes the following sub-steps:

[0042] Set a first auxiliary index and a second auxiliary index, labeled L and K respectively, where K and L are both positive integers, 1≤K≤L, and L is initially set to 2. Label the target analysis parameter NP(n,m) as DR. m , TGK The NP(n,m) is denoted as GP(K,m), where GP(K,m) represents the TG corresponding to the target analysis parameter. K The m-th normalized parameter in the historical parameters, and DR m Belonging to the same historical parameter, the TG K That is, TG of h=K h ;

[0043] Based on target analysis parameters and TG K Real-time analysis graphs obtain target analysis parameters and TG K The correlation monotonicity includes positive and negative correlations, and the correlation monotonicity can be directly obtained through a real-time analysis graph;

[0044] If TG K If it is positively correlated with the target analysis parameter, then GP(K,m) is set to a positive number and labeled as GVP(K,m). If TG K If it is negatively correlated with the target analysis parameter, then GP(K,m) is set to a negative number and labeled as GVP(K,m);

[0045] calculate The calculation result is labeled as E. m , with E m For the X-axis, DR m Establish a two-dimensional coordinate system for the Y-axis, name it the Comprehensive Analysis Chart, and set DR... m According to E m Enter the comprehensive analysis chart;

[0046] The maximum deviation of the comprehensive analysis chart is named the comprehensive deviation. If the comprehensive deviation is less than or equal to the correlation threshold, a comprehensive correlation signal is output; otherwise, a comprehensive correlation signal is output.

[0047] If the output comprehensive correlation signal is valid, then mark the target analysis parameters and TG1 to TG2. L If there is a correlation, the proven correlation function of the comprehensive analysis graph is named the comprehensive effective function. L is incremented and the analysis is repeated. If the comprehensive correlation is output again, the comprehensive effective function is updated and the process is repeated.

[0048] If the output signal indicates that the comprehensive correlation is not valid, the analysis is stopped, and the latest valid comprehensive function is named the comprehensive correlation function, which is the comprehensive correlation relationship.

[0049] Furthermore, in controlling the plasma configuration, the coordinated control of plasma parameters based on independent and comprehensive correlations includes the following sub-steps:

[0050] When controlling any plasma parameter, the plasma parameters are coordinated by referring to the independent correlation and the comprehensive correlation. That is, during the control process, it is necessary to ensure that different plasma parameters satisfy the independent correlation and the comprehensive correlation, and at the same time, the changes of plasma parameters can be predicted based on the independent correlation and the comprehensive correlation.

[0051] The beneficial effects of this invention are as follows: This invention monitors and records the plasma parameters in a tokamak, stores the plasma parameters in a parameter database, normalizes the plasma parameters to obtain normalized parameters, obtains correlation data based on big data, analyzes the correlation threshold of discrete deviations through the correlation data, analyzes the discrete deviations between any two plasma parameters, and then uses the correlation threshold to determine whether there is a correlation between the two plasma parameters and obtain an independent correlation relationship. The advantage is that it can analyze and obtain an independent correlation between two plasma parameters, and the analysis and use of the correlation threshold can be applied to the analysis of nonlinear correlation, rather than being limited to the analysis of linearly correlated plasma parameters, thus improving the comprehensiveness and accuracy of plasma multi-parameter collaborative configuration analysis.

[0052] This invention constructs an influence queue for each plasma parameter, then sequentially determines whether a plasma parameter is simultaneously affected by two or more plasma parameters based on the influence queue, and analyzes the comprehensive correlation between them. Finally, when controlling the plasma configuration, it performs synergistic control of the plasma parameters based on both independent and comprehensive correlations. The advantage lies in analyzing whether a plasma parameter is simultaneously affected by two or more plasma parameters, ensuring that the most realistic correlation between different plasma parameters can be obtained, rather than just analyzing the independent correlation between two plasma parameters. This further improves the comprehensiveness and accuracy of multi-parameter synergistic configuration analysis of plasma. Attached Figure Description

[0053] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0054] Figure 2 This is a schematic diagram of the proven correlation analysis graph of the present invention;

[0055] Figure 3 This is a schematic diagram of the deviation analysis coordinate system of the present invention;

[0056] Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1, please refer to Figure 1 As shown, this application provides a multi-parameter cooperative configuration analysis method for tokamak plasmas, comprising the following steps:

[0059] Step S1 involves monitoring and recording the plasma parameters in the tokamak, and simultaneously constructing a parameter database to store these parameters. Step S1 includes the following sub-steps:

[0060] Step S101: The plasma in the tokamak is monitored to obtain plasma parameters, which are all observable and recordable data of the plasma.

[0061] Step S102: Construct a parameter database, record the plasma parameters, and name the plasma parameters recorded in the parameter database as historical parameters;

[0062] In practical implementation, the tokamak device is a controlled nuclear fusion device, which requires the regulation of plasma to generate nuclear fusion. When regulating the plasma, different plasmas have mutual influence forces, and regulating a certain plasma parameter may produce a chain reaction. Therefore, it is necessary to analyze the correlation between plasma parameters in order to accurately regulate the plasma and make the control of controlled nuclear fusion more precise. Plasma parameters include a variety of physical parameters such as current, pressure, density, and oscillation frequency. This embodiment does not list them all. All monitored plasma parameters are recorded in the parameter database, and each historical parameter contains the values ​​of all plasma parameters.

[0063] Step S2 involves normalizing the plasma parameters to obtain normalized plasma parameters. Step S2 includes the following sub-steps:

[0064] Step S201: Number the plasma parameters using the symbol PP. n This indicates that n is a non-zero natural number and n is the index of PP;

[0065] Step S202, retrieve the PP recorded in the m-th historical parameter. n Let HP(n,m) be the index of HP.

[0066] Step S203: Calculate the normalized value of HP(n,m) using a normalization algorithm, name it the normalization parameter, and use the symbol NP(n,m);

[0067] In specific implementation, the existing normalization algorithm is used to convert plasma parameters of different dimensions into values ​​between 0 and 1 in order to analyze different plasma parameters. This embodiment will not explain the calculation process of the normalization value in detail. Assuming that PP1 is the oscillation frequency, HP(1,m) represents the value of the oscillation frequency in the m-th historical parameter, and NP(1,m) represents the normalized value of HP(1,m).

[0068] Step S3 involves analyzing the discrete deviation between any two plasma parameters, acquiring correlation data based on large datasets, analyzing the correlation threshold of the discrete deviation using the correlation data, and then using the correlation threshold to determine whether there is a correlation between the two plasma parameters and obtain an independent correlation relationship. Step S3 includes the following sub-steps:

[0069] Step S301: Obtain correlation data based on big data, and analyze the correlation threshold of discrete deviation through correlation data;

[0070] Step S301 includes the following sub-steps:

[0071] Step S301.1: The correlation data consists of two sets of data that are recognized or proven to be correlated. The correlation data includes different proven correlation groups. Each proven correlation group includes a first number of first parameters and second parameters. The first parameters and second parameters are correlated, and each first parameter corresponds to one second parameter.

[0072] Step S301.2: Calculate the normalized values ​​of the first parameter and the second parameter, and name them as the first normalized parameter and the second normalized parameter, respectively.

[0073] In practice, correlated data actually refers to two types of data that are generally accepted or have been proven to be correlated. For example, it is generally accepted in real life that higher temperatures lead to more water consumption by residents; there is a recognized correlation between them, and this is also supported by certain data. In this case, temperature and water consumption constitute a proven correlation group. Historical data on temperature and water consumption are obtained from big data, totaling a first number of records. The first number is not a fixed value but depends on the specific number of records obtained. Then, the normalized value of each record is calculated to obtain the first normalization parameter and the second normalization parameter. The first normalization parameter and the second normalization parameter represent the temperature, water consumption, and water consumption, respectively. Normalized values ​​and normalized values ​​of residential water consumption, temperature and residential water consumption are only one proven correlation group. In actual analysis, there are many more proven correlation groups involving different data from different fields. The purpose is to analyze the correlation threshold through them, so that the correlation threshold has a certain basis and reliability. Existing correlation analysis algorithms do not have an accurate correlation threshold. They are usually judged by humans without specific numerical values ​​to judge the correlation. Therefore, this embodiment analyzes various common-sense or proven correlated data to calculate a specific correlation threshold, thereby judging the correlation between different data.

[0074] Please see Figure 2 As shown, in step S301.3, a two-dimensional coordinate system is established with the first normalized parameter as the X-axis and the second normalized parameter as the Y-axis, named the verified correlation analysis diagram. The second normalized parameter is entered into the verified correlation analysis diagram according to the first normalized parameter, and the coordinate points in the verified correlation analysis diagram are named verified correlation analysis points.

[0075] Step S301.4: Perform discrete regression analysis on the proven correlation analysis graph, name the function obtained from the analysis as the proven correlation function, and obtain the residual between the proven correlation analysis points and the proven correlation function, which is named the discrete deviation.

[0076] In specific implementation, taking temperature and residential water consumption as an example, the relationship between temperature and residential water consumption is obtained from big data, including but not limited to online searches and data from water supply bureau databases. Due to the large amount of data, this embodiment only shows a portion of the data to simply reveal the relationship between temperature and residential water consumption. The partial relationship between temperature and residential water consumption in a certain region is shown in Table 1 below:

[0077] Table 1. Partial Relationship between Temperature and Residential Water Consumption

[0078]

[0079] Table 1 clearly shows that residential water consumption increases with rising temperature. The established correlation analysis diagram is shown below. Figure 2 As shown, Figure 2The X-axis represents the normalized parameter corresponding to air temperature, and the Y-axis represents the normalized parameter corresponding to residential water consumption. A proven correlation function is obtained through discrete regression analysis, and the discrete deviation is calculated based on this function. Since existing discrete regression analyses can usually directly extract residuals, the proven correlation function in step S301.4 is not important; it is only used to calculate residuals, which can be directly extracted. Therefore, this embodiment will not provide a specific explanation or example of the proven correlation function. The discrete deviation is essentially the deviation between the observed and theoretical values. Figure 2 The discrete deviation is 0.11.

[0080] Step S301.5: Analyze the discrete deviation of each proven correlation analysis point in each proven correlation group, statistically analyze the range of discrete deviations to obtain the deviation range, and evenly divide the deviation range into a second number of sub-ranges, named the deviation sub-ranges. Count the number of discrete deviations within each deviation sub-range, named the deviation hit count, and number the deviation sub-ranges in ascending order, using the symbol S. i It means that S i The number of biased hits is marked as HC i , where i is a non-zero natural number and i is the index of S and HC;

[0081] In specific implementation, the discrete deviation of each proven correlation analysis point in each proven correlation group is analyzed, and the deviation range is statistically obtained as [0, 0.12]. This means that in the generally recognized correlated data, their observed values ​​and theoretical values ​​do not exceed 0.12. However, the deviation range is not complete. This embodiment cannot analyze all data. The deviation range calculated here can only represent the data analyzed in this embodiment. However, there are still some data that are not analyzed in this embodiment but are correlated with each other. That is, the deviation range still has a certain error and cannot summarize all cases. It also has a certain redundancy. This embodiment needs to calculate the redundancy of the deviation range based on the existing deviation range. The final result is the correlation threshold. Since the deviation sub-range will gradually split, the second quantity can be set to 2. The division results S1 to S2 are [0, 0.06] and [0.06, 0.12], respectively. At this time, 1≤i≤2. The statistical results show that HC1 is 125823 and HC2 is 87469.

[0082] Step S301.6: Divide each deviation sub-range equally, so that the deviation sub-range is split into two deviation sub-ranges, and recalculate S. i and HC i Repeat this process until any HC exists during the next split. i Until the value is zero, the final S obtained from the split will be... i and HC i They are respectively labeled as FS iand FHC i ;

[0083] In practice, each deviation subrange is divided equally to obtain a new deviation subrange. At this point, S1 to S4 are [0, 0.03], [0.03, 0.06], [0.06, 0.09], and [0.09, 0.12], respectively. Since 1 ≤ i ≤ 2, HC is recalculated. i At this time, HC i Since none of them are 0, the process is repeated, resulting in 32 biased sub-ranges. Due to the large amount of data, this embodiment does not demonstrate this in detail. If the process is repeated at this point, HC will occur. i The case where the value equals 0 indicates that the splitting process stops, resulting in FS. i and FHC i At this point, 1≤i≤32, if HC appears i A value of 0 indicates an unreasonable division of the range, which will result in blank values ​​affecting the analysis results in subsequent analyses.

[0084] Please see Figure 3 As shown, in step S301.7, obtain FS. i The median, labeled SM i , with SM i For the X-axis, FHC i Establish a two-dimensional coordinate system for the Y-axis, named the deviation analysis coordinate system, and set FHC... i According to SM i Enter the deviation analysis coordinate system, perform discrete regression analysis on the deviation analysis coordinate system, and name the function obtained from the analysis as the deviation analysis function;

[0085] Step S301.8: Substitute the value of the Y-axis equal to zero into the deviation analysis function to solve for the value of the X-axis. Name the solution as the deviation threshold. Name the deviation threshold that is closest to the maximum value of the deviation range and greater than the maximum value of the deviation range as the correlation threshold.

[0086] In practice, since recognized or proven correlated data are selected to calculate the discrete deviation, these data typically exhibit strong correlation, meaning their discrete deviations are small. Therefore, a deviation analysis coordinate system is constructed as follows: Figure 3 As shown, by Figure 3 It is easy to see that as the discrete deviation increases, the number of deviation hits gradually decreases. When i reaches its maximum, the number of deviation hits is not zero. This is because there is still some redundancy in the discrete deviation. Therefore, the discrete deviation at which the number of deviation hits equals zero is calculated according to the deviation analysis function, which is the correlation threshold including the redundant part. Due to the large amount of data, this embodiment will not list them all. Figure 3 Different SMs can be observed i and FHCi Discrete regression analysis yielded the deviation analysis function as Y = 237563 × X. 2 -86861×X+7520, where Y is FHC i X is SM i Substituting Y=0 into the deviation analysis function, we can solve for X, which is either 0.14 or 0.22. The result is rounded to two decimal places, and the final correlation threshold is 0.14.

[0087] Step S302: Analyze the discrete deviation between any two plasma parameters, and then determine whether there is a correlation between the two plasma parameters by using the correlation threshold and obtain the independent correlation relationship;

[0088] Step S302 includes the following sub-steps:

[0089] Step S302.1: When analyzing any two normalized parameters, label one of the normalized parameters as parameter A and the other as parameter B. Establish an association analysis graph with parameter A as the X-axis and parameter B as the Y-axis, and name it the real-time analysis graph. Enter the NP(n,m) of parameter A and parameter B into the real-time analysis graph and analyze the discrete deviation. Name the maximum value of the discrete deviation as the maximum deviation.

[0090] Step S302.2: Determine whether the maximum deviation is less than or equal to the correlation threshold. If so, mark that there is a correlation between parameter A and parameter B, and name the proven correlation function obtained from the real-time analysis graph as an independent correlation relationship.

[0091] In specific implementation, the process of constructing and analyzing the real-time analysis graph is the same as the process and principle in step S301. Assuming parameter A is current and parameter B is pressure, the analysis is to determine whether there is an independent correlation between current and pressure. An independent correlation is a correlation that is not affected by other plasma parameters. The analysis process is exactly the same as the process of extracting the maximum deviation in step S301, so it will not be described in detail in this embodiment. Extract the maximum deviation. If the maximum deviation is less than or equal to the correlation threshold, it means that there is a correlation between the two normalized parameters being analyzed. In this case, the proven correlation function between them is used as the independent correlation relationship.

[0092] Step S4 involves analyzing whether plasma parameters are simultaneously affected by two or more plasma parameters based on discrete bias and correlation thresholds, and analyzing the comprehensive correlation between them. Step S4 includes the following sub-steps:

[0093] Step S401: Construct an influence queue for each plasma parameter;

[0094] Step S401 includes the following sub-steps:

[0095] Step S401.1: When analyzing a certain plasma parameter, name it as the target analysis parameter and name the other plasma parameters as secondary analysis parameters.

[0096] Step S401.2: Obtain the maximum deviation between the target analysis parameter and the secondary analysis parameter, and name it the deviation to be analyzed;

[0097] Step S401.3: Number the deviations to be analyzed in ascending order, using the symbol T. h This indicates that h is a non-zero natural number and h is the index of T;

[0098] Step S401.4, T h The corresponding secondary analysis parameter is labeled TG. h , by TG h The influence queue is formed according to the order of h from smallest to largest;

[0099] In practice, secondary analytical parameters that are independently correlated with the target analytical parameter are not included in the analysis. For example, if there are 8 plasma parameters, and during analysis there is one target analytical parameter and 7 secondary analytical parameters, and one of these secondary analytical parameters is independently correlated with the target analytical parameter, then there are only 6 actual secondary analytical parameters. This leads to the calculation of T. h 1≤h≤6, TG1 to TG6 are the influence queues. For example, when the target analysis parameter is the oscillation frequency, the secondary analysis parameters include pressure, temperature, electron density, current, magnetization and Debye length.

[0100] Step S402: Based on the influence queue, determine whether the plasma parameters are simultaneously affected by two or more plasma parameters, and analyze the comprehensive correlation between them.

[0101] Step S402 includes the following sub-steps:

[0102] Step S402.1: Set the first auxiliary sequence number and the second auxiliary sequence number, labeled L and K respectively, where 1≤K≤L, and L is initially set to 2. Label the target analysis parameter NP(n,m) as DR. m , TG K The NP(n,m) is labeled GP(K,m), where GP(K,m) represents the TG corresponding to the target analysis parameter. K The m-th normalized parameter in the historical parameters, and DR m Belonging to the same historical parameter, TG K That is, TG of h=K h ;

[0103] Step S402.2, based on the target analysis parameters and TG KReal-time analysis graphs obtain target analysis parameters and TG K The correlation monotonicity includes both positive and negative correlations, and can be directly obtained through real-time analysis plots.

[0104] Step S402.3, if TG K If it is positively correlated with the target analysis parameter, then GP(K,m) is set to a positive number and labeled as GVP(K,m). If TG K If it is negatively correlated with the target analysis parameter, then GP(K,m) is set to a negative number and labeled as GVP(K,m);

[0105] In practice, the first and second auxiliary serial numbers are used to analyze the values ​​of n in the serial number (n, m) from smallest to largest. For example, when the target analysis parameter is PP1, i.e., the oscillation frequency, DR... m That is, NP(1,m). When L=2, TG1 and TG2 are analyzed, where TG1 and TG2 are the electron density and pressure, respectively. n If PP3 and PP6 are respectively, then GP(1,m) is actually NP(3,m), and GP(2,m) is actually NP(6,m). The association monotonicity can be easily obtained through linear regression, and the principle is relatively simple. This embodiment will not explain it in detail. If the association monotonicity of TG1 and TG2 is positive and negative respectively, then GVP(1,m) is GP(1,m), and GVP(2,m) is -GP(2,m).

[0106] Step S402.4, calculate The calculation result is labeled as E. m , with E m For the X-axis, DR m Establish a two-dimensional coordinate system for the Y-axis, name it the Comprehensive Analysis Chart, and set DR... m According to E m Enter the comprehensive analysis chart;

[0107] Step S402.5: Analyze the maximum deviation of the comprehensive analysis chart and name it as comprehensive deviation. If the comprehensive deviation is less than or equal to the correlation threshold, output a comprehensive correlation established signal; otherwise, output a comprehensive correlation not established signal.

[0108] Step S402.6: If the output comprehensive correlation signal is established, then mark the target analysis parameters and TG1 to TG2. L If there is a correlation, the proven correlation function of the comprehensive analysis graph is named the comprehensive effective function. L is incremented and the analysis is repeated. If the comprehensive correlation is output again, the comprehensive effective function is updated and the process is repeated.

[0109] Step S402.7: If the output signal indicates that the comprehensive correlation is not valid, then stop the analysis and name the latest effective comprehensive function as the comprehensive correlation function.

[0110] In practical implementation, for example, GVP(1,12) is 0.6 and GVP(2,12) is -0.3, E is calculated. 12 The threshold is 0.3. A comprehensive analysis chart is then constructed and analyzed. Since the construction and analysis of the comprehensive analysis chart are the same as those of the proven correlation analysis chart, this embodiment will not provide further explanation. The final analysis yields a comprehensive deviation, representing the deviation between the theoretical and observed values ​​of the influence of the two plasma parameters, PP3 and PP6, on the target analysis parameter. If the comprehensive deviation is less than or equal to the correlation threshold, it indicates a correlation exists, and a comprehensive correlation establishment signal is output, resulting in a comprehensive effective function. L is incremented and re-analyzed. This involves analyzing whether the three secondary analysis parameters, TG1 to TG3, are correlated with the target analysis parameter under their combined action. This process is repeated. When a comprehensive correlation failure signal is output, it indicates that the influence of multiple secondary analysis parameters on the target analysis parameter does not meet the conditions for correlation. Furthermore, the subsequent secondary analysis data shows a larger deviation from the target analysis parameter, making correlation even less likely. Therefore, the analysis is stopped, and the comprehensive correlation function is finally obtained, which represents the comprehensive correlation relationship.

[0111] Step S5 involves coordinating the control of plasma parameters based on independent and comprehensive correlations when regulating the plasma configuration. Step S5 includes the following sub-steps:

[0112] Step S501: When adjusting any plasma parameter of the plasma, the plasma parameters are coordinated and adjusted by referring to the independent correlation relationship and the comprehensive correlation relationship. That is, during the adjustment process, it is necessary to ensure that different plasma parameters meet the independent correlation relationship and the comprehensive correlation relationship, and at the same time, the changes of plasma parameters can be predicted based on the independent correlation relationship and the comprehensive correlation relationship.

[0113] In practice, the independent and comprehensive correlations reveal the relationships between different plasma parameters, providing a reliable reference for subsequent regulation and prediction. This embodiment focuses on analyzing the relationships between different plasma parameters. Subsequent use and analysis can be based on existing technologies, and will not be specifically described in this embodiment.

[0114] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps similar to those in a tokamak plasma multi-parameter coordinated configuration analysis method to achieve the following functions: constructing a parameter database to store plasma parameters; normalizing the plasma parameters to obtain normalized parameters; analyzing whether there is a correlation between two plasma parameters and obtaining independent correlations; analyzing whether a plasma parameter is simultaneously affected by two or more plasma parameters and analyzing their comprehensive correlations; and coordinating the control of plasma configuration based on independent and comprehensive correlations.

[0115] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by the computer, the computer can execute a tokamak plasma multi-parameter coordinated configuration analysis method provided by the above methods. The method includes: constructing a parameter database to store plasma parameters; normalizing the plasma parameters to obtain normalized parameters; analyzing whether there is a correlation between two plasma parameters and obtaining independent correlation relationships; analyzing whether the plasma parameters are simultaneously affected by two or more plasma parameters and analyzing the comprehensive correlation relationships between them; and when controlling the configuration of the plasma, coordinating the control of the plasma parameters based on the independent correlation relationships and the comprehensive correlation relationships.

[0117] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps described above in the tokamak plasma multi-parameter cooperative configuration analysis method to achieve the following functions: constructing a parameter database to store plasma parameters; normalizing the plasma parameters to obtain normalized parameters; analyzing whether there is a correlation between two plasma parameters and obtaining independent correlation relationships; analyzing whether plasma parameters are simultaneously affected by two or more plasma parameters and analyzing their comprehensive correlation relationships; and when controlling the plasma configuration, coordinating the control of plasma parameters based on independent and comprehensive correlation relationships.

[0118] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0119] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for multi-parameter cooperative configuration analysis of tokamak plasma, characterized in that, Includes the following steps: The plasma parameters in the tokamak are monitored and recorded, and a parameter database is constructed to store the plasma parameters. The plasma parameters are normalized to obtain the normalized parameters of the plasma parameters. The discrete deviation between any two plasma parameters is analyzed, and correlation data is obtained based on big data. The correlation threshold of the discrete deviation is analyzed through the correlation data, and then the correlation threshold is used to determine whether there is a correlation between the two plasma parameters and obtain the independent correlation relationship. Based on discrete bias and correlation threshold analysis, we can determine whether plasma parameters are simultaneously affected by more than two plasma parameters, and analyze the comprehensive correlation between them. When controlling the configuration of plasma, plasma parameters are synergistically controlled based on independent correlation and comprehensive correlation. Based on big data, correlation data is obtained. The correlation threshold for discrete deviation is analyzed using correlation data, which includes the following sub-steps: The correlation data refers to two sets of data that are recognized or proven to be correlated. The correlation data includes different proven correlation groups. Each proven correlation group includes a first number of first parameters and second parameters. The first parameters and second parameters are correlated, and each first parameter corresponds to one second parameter. Calculate the normalized values ​​of the first parameter and the second parameter, and name them the first normalized parameter and the second normalized parameter, respectively. A two-dimensional coordinate system is established with the first normalized parameter as the X-axis and the second normalized parameter as the Y-axis, named the verified correlation analysis graph. The second normalized parameter is entered into the verified correlation analysis graph according to the first normalized parameter, and the coordinate points in the verified correlation analysis graph are named verified correlation analysis points. Discrete regression analysis was performed on the proven correlation analysis graph. The function obtained from the analysis was named the proven correlation function. The residual between the proven correlation analysis points and the proven correlation function was obtained and named the discrete deviation. Analyze the discrete deviation of each proven correlation analysis point in each proven correlation group, statistically analyze the range of discrete deviations to obtain the deviation range, and evenly divide the deviation range into a second number of sub-ranges, named the deviation sub-ranges. Count the number of discrete deviations within each deviation sub-range, named the deviation hit count, and number the deviation sub-ranges in ascending order, using the symbol S. i It means that S i The number of biased hits is marked as HC i , where i is a non-zero natural number and i is the index of S and HC; Divide each deviation subrange equally, so that each deviation subrange is split into two deviation subranges, and recount S. i and HC i Repeat this process until any HC exists during the next split. i Until the value is zero, the final S obtained from the split will be... i and HC i They are respectively labeled as FS i and FHC i ; Get FS i The median, labeled SM i , with SM i For the X-axis, FHC i Establish a two-dimensional coordinate system for the Y-axis, named the deviation analysis coordinate system, and set FHC... i According to SM i Enter the deviation analysis coordinate system, perform discrete regression analysis on the deviation analysis coordinate system, and name the function obtained from the analysis as the deviation analysis function; Substitute the Y-axis value of zero into the deviation analysis function to solve for the X-axis value. Name the solution as the deviation threshold. Name the deviation threshold that is closest to the maximum value of the deviation range and greater than the maximum value of the deviation range as the correlation threshold.

2. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 1, characterized in that, Monitoring and recording the plasma parameters in the tokamak, and simultaneously constructing a parameter database to store these parameters, includes the following sub-steps: The plasma in the tokamak is monitored to obtain plasma parameters, which are all observable and recordable data of the plasma. A parameter database is constructed to record plasma parameters, and the plasma parameters recorded in the parameter database are named historical parameters.

3. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 2, characterized in that, Normalizing the plasma parameters to obtain the normalized plasma parameters includes the following sub-steps: The plasma parameters are numbered using the symbol PP. n This indicates that n is a non-zero natural number and n is the index of PP; PP recorded in the m-th historical parameter n Let HP(n,m) be the index of HP. The normalized value of HP(n,m) is calculated using a normalization algorithm and named the normalization parameter, denoted by NP(n,m).

4. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 3, characterized in that, The process involves analyzing the discrete deviations between any two plasma parameters, acquiring correlation data based on large datasets, analyzing the correlation threshold of the discrete deviations using the correlation data, and then using the correlation threshold to determine whether there is a correlation between the two plasma parameters and to obtain an independent correlation relationship. This includes the following sub-steps: Based on big data, we acquire correlation data and analyze the correlation threshold of discrete deviations using the correlation data. Analyze the discrete deviation between any two plasma parameters, and then determine whether there is a correlation between the two plasma parameters by using the correlation threshold to obtain the independent correlation relationship.

5. The tokamak plasma multi-parameter cooperative configuration analysis method according to claim 4, characterized in that, Analyzing the discrete deviation between any two normalized parameters, and then determining whether there is a correlation between the two normalized parameters and obtaining the independent correlation relationship by using a correlation threshold, includes the following sub-steps: When analyzing any two normalized parameters, label one of them as parameter A and the other as parameter B. Create a correlation analysis graph with parameter A as the X-axis and parameter B as the Y-axis, and name it the real-time analysis graph. Enter the NP(n,m) of parameter A and parameter B into the real-time analysis graph and analyze the discrete deviation. Name the maximum value of the discrete deviation as the maximum deviation. Determine whether the maximum deviation is less than or equal to the correlation threshold. If so, mark the correlation between parameter A and parameter B, and name the proven correlation function obtained from the real-time analysis graph as an independent correlation.

6. The tokamak plasma multi-parameter cooperative configuration analysis method according to claim 5, characterized in that, The analysis of whether plasma parameters are simultaneously affected by two or more plasma parameters based on discrete bias and correlation threshold, and the analysis of their comprehensive correlation, includes the following sub-steps: An influence queue is constructed for each plasma parameter; Based on the influence queue, it is determined whether the plasma parameters are simultaneously affected by two or more plasma parameters, and the comprehensive correlation between them is analyzed.

7. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 6, characterized in that, Constructing an influence queue for each plasma parameter includes the following sub-steps: When analyzing a certain plasma parameter, it is named the target analysis parameter, and the other plasma parameters are named secondary analysis parameters. Obtain the maximum deviation between the target analysis parameter and the secondary analysis parameter, and name it the deviation to be analyzed; The deviations to be analyzed are numbered in ascending order and designated by the symbol T. h This indicates that h is a non-zero natural number and h is the index of T; T h The corresponding secondary analysis parameter is labeled TG. h , by TG h The influence queue is formed according to the order of h from smallest to largest.

8. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 7, characterized in that, Based on the influence queue, determine whether the normalized parameter is simultaneously affected by two or more normalized parameters, and analyze the comprehensive correlation between them, including the following sub-steps: Set a first auxiliary index and a second auxiliary index, labeled L and K respectively, where K and L are both positive integers, 1≤K≤L, and L is initially set to 2. Label the target analysis parameter NP(n,m) as DR. m , TG K The NP(n,m) is denoted as GP(K,m), where GP(K,m) represents the TG corresponding to the target analysis parameter. K The m-th normalized parameter in the historical parameters, and DR m Belonging to the same historical parameter, the TG K That is, TG of h=K h ; Based on target analysis parameters and TG K Real-time analysis graphs obtain target analysis parameters and TG K The correlation monotonicity includes positive and negative correlations, and the correlation monotonicity can be directly obtained through a real-time analysis graph; If TG K If it is positively correlated with the target analysis parameter, then GP(K,m) is set to a positive number and labeled as GVP(K,m). If TG K If it is negatively correlated with the target analysis parameter, then GP(K,m) is set to a negative number and labeled as GVP(K,m); calculate The calculation result is labeled as E. m , with E m For the X-axis, DR m Establish a two-dimensional coordinate system for the Y-axis, name it the Comprehensive Analysis Chart, and set DR... m According to E m Enter the comprehensive analysis chart; The maximum deviation of the comprehensive analysis chart is named the comprehensive deviation. If the comprehensive deviation is less than or equal to the correlation threshold, a comprehensive correlation signal is output; otherwise, a comprehensive correlation signal is output. If the output comprehensive correlation signal is valid, then mark the target analysis parameters and TG1 to TG2. L If there is a correlation, the proven correlation function of the comprehensive analysis graph is named the comprehensive effective function. L is incremented and the analysis is repeated. If the comprehensive correlation is output again, the comprehensive effective function is updated and the process is repeated. If the output signal indicates that the comprehensive correlation is not valid, the analysis is stopped, and the latest valid comprehensive function is named the comprehensive correlation function, which is the comprehensive correlation relationship.

9. The method for multi-parameter cooperative configuration analysis of tokamak plasma according to claim 8, characterized in that, When controlling the configuration of plasma, the coordinated control of plasma parameters based on independent correlation and comprehensive correlation includes the following sub-steps: When controlling any plasma parameter, the plasma parameters are coordinated by referring to the independent correlation and the comprehensive correlation. That is, during the control process, it is necessary to ensure that different plasma parameters satisfy the independent correlation and the comprehensive correlation, and at the same time, the changes of plasma parameters can be predicted based on the independent correlation and the comprehensive correlation.

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