Method, device, equipment and medium for early warning of solid-state power source failure of particle accelerator

Abstract

A method, device, equipment and medium for early fault warning of a solid-state power source of a particle accelerator, comprising: obtaining a forward voltage signal of a radio frequency cavity V f and an output signal of a digital low-level system V DAC ; drawing a measured characteristic diagram of the solid-state power source according to the forward voltage signal V f and the output signal V DAC ; fitting the measured characteristic diagram of the solid-state power source to determine an optimal fitting curve; calculating a normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the optimal fitting curve as a key characteristic parameter Y RMSE ; comparing the key characteristic parameter Y RMSE with a set threshold value, and if the key characteristic parameter Y RMSE is greater than the set threshold value, it is determined that the solid-state power source is abnormal. Therefore, the present invention can be widely applied to the field of particle accelerators.

Description

Technical Field

[0001] The present invention relates to a method, a device, a equipment and a medium for early warning of a solid-state power source of a particle accelerator, and relates to the technical field of particle accelerators. Background Art

[0002] The RF power source is the key driving component of the RF superconducting cavity, and its performance directly affects the operating efficiency of the accelerator. SSA (solid-state power source) is gradually replacing traditional vacuum power devices due to its technical advantages such as high reliability, high stability and easy maintenance, and has become the mainstream power source choice for continuous wave RF superconducting accelerators at home and abroad. However, when SSA operates under long-term high-power conditions, it is prone to problems such as hardware aging, insufficient heat dissipation and electrical interference.

[0003] Although existing technologies can quickly locate problems after a fault occurs, they generally lack effective early warning and monitoring methods. Generally speaking, system fluctuations in the early stages of a fault are relatively weak, and macro parameters are usually maintained within the allowable range, which can still meet the power requirements of the accelerator. If these potential risks are not discovered in time, it may cause large-scale fluctuations in the system and trigger the accelerator protection mechanism, thereby reducing the reliability of the machine and the availability of the beam.

[0004] At present, some studies have tried to improve the accuracy and response speed of abnormality detection by adding sensor equipment or improving monitoring methods, but these methods usually require expensive equipment upgrades or complex system configurations, which increases operation and maintenance costs. Therefore, how to develop a set of technologies to monitor the operating status of solid-state power sources in real time and accurately predict abnormal conditions in a low-cost and high-efficiency manner has become an urgent problem to be solved in this field.

[0005] In summary, the existing solid-state power source abnormality detection technology still has shortcomings in prediction accuracy, prediction time and real-time performance, and usage cost. There is an urgent need for a method that can monitor the operating status of the solid-state power source in real time and issue an early warning in the early stage of the abnormal state. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, in view of the above problems, the purpose of the present invention is to provide a method, device, equipment and medium for early warning of solid-state power sources of particle accelerators, which can improve the operating efficiency and reliability of the accelerator and provide reliable judgment criteria for early warning of faults in a timely manner.

[0007] In order to realize the above technical solution, the technical solution adopted by the present invention is:

[0008] In a first aspect, the present invention provides a method for early warning of a particle accelerator solid-state power source failure, comprising:

[0009] Get the forward voltage signal of the RF cavity V f and digital low level system output signals V DAC ;

[0010] According to the forward voltage signal V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources;

[0011] Fitting the measured characteristic diagram of the solid-state power source to determine the optimal fitting curve;

[0012] Calculate the normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the best fitting curve as the key characteristic parameter Y RMSE ;

[0013] The key characteristic parameters Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE If it is greater than the set threshold, the solid-state power source is determined to be abnormal.

[0014] Further, according to the forward voltage signal V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources, including:

[0015] In the data to be measured after each RF superconducting cavity failure, the output signal of the digital low-level system is V DAC The amplitude is the horizontal axis, and the forward voltage signal V f The amplitude of is taken as the ordinate, and a first characteristic diagram of the solid-state power source is drawn, wherein the data to be measured is high-speed fault data;

[0016] Draw a second characteristic diagram of the solid-state power source according to the fitting reference data, wherein the fitting reference data is slow data;

[0017] The first characteristic diagram and the second characteristic diagram are combined to form a measured characteristic diagram of the solid-state power source.

[0018] Furthermore, fitting the measured characteristic diagram of the solid-state power source to determine the optimal fitting curve includes:

[0019] Defines a reference characteristic curve describing the input-output relationship of a single amplifier module of a solid-state power source F (x ),Right now ,in, V in and V out They are the input and output signals of a single power amplifier module, respectively, and the absolute value represents the amplitude of the complex signal;

[0020] Defining new functions G ( x ), its mathematical meaning is to refer to the characteristic curve F ( x ) scales in the horizontal and vertical directions a and b Times:

[0021] ;

[0022] Defining the measured characteristics of solid-state power sources g ( x ) satisfies the relationship: ;

[0023] definition G ( x )and g ( x ) e ( x ):

[0024] ;

[0025] calculate e ( x ) E :

[0026] ;

[0027] in, x i is the horizontal coordinate of any scattered point. V DAC The amplitude, N is the total number of scattered points;

[0028] Arithmetic progression a =[ a 1, a 2, a m , … a M ] is the horizontal axis, b =[ b 1, b 2, b k , … bK ] is the vertical axis, plot the function z = E ( a , b ) to obtain the global minimum E The corresponding coordinates ( a opt , b opt ) is the optimal scaling parameter, and accordingly, the optimal fitting curve is determined G opt ( x ) = b opt · F ( x / a opt ).

[0029] Furthermore, the normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the optimal fitting curve is calculated as the key characteristic parameter Y RMSE ,include:

[0030] Calculate the best fit curve G opt ( x ) and measured characteristic diagram g ( x ) y :

[0031] ;

[0032] Normalized deviation Δ Y :

[0033] ;

[0034] in, y max The best fitting curve G opt ( x ) in the vertical coordinate value of the saturation point;

[0035] Calculate Δ Y The root mean square value of Y RMSE :

[0036] .

[0037] In a second aspect, the present invention further provides a device for early warning of failure of a solid-state power source of a particle accelerator, comprising:

[0038] A signal acquisition unit configured to acquire a forward voltage signal of the radio frequency cavity V f and digital low level system output signals V DAC ;

[0039] The characteristic map unit is configured to V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources;

[0040] A curve simulation unit is configured to fit a measured characteristic diagram of the solid-state power source and determine an optimal fitting curve;

[0041] A parameter calculation unit is configured to calculate a normalized root mean square deviation between a measured characteristic diagram of the solid-state power source and a best fitting curve as a key characteristic parameter Y RMSE ;

[0042] The fault warning unit is configured to Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE When it is greater than the set threshold, the solid-state power source is determined to be abnormal.

[0043] In a third aspect, the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute any one of the methods described.

[0044] In a fourth aspect, the present invention further provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include computer instructions, and the computer instructions are used to enable a computer to execute any one of the methods described.

[0045] The present invention adopts the above technical solution, and has the following characteristics:

[0046] 1. The present invention adopts a highly robust fitting algorithm, and can fit the nonlinear input-output characteristic curve (referred to as the characteristic curve) of the SSA regardless of whether the solid-state power source is in a normal operating state or an unhealthy working state, and ensure the consistency between the fitting result and the actual measurement result.

[0047] 2. The present invention extracts key characteristic parameters from the fitting characteristic curve. These key characteristic parameters can effectively characterize the inherent characteristics of the characteristic curve. When the solid-state power source is in a healthy state, the key characteristic parameters remain stable; when the solid-state power source enters an unhealthy or abnormal working state, the key characteristic parameters will show obvious fluctuations, thereby providing a reliable criterion for early warning of faults.

[0048] 3. The present invention adopts general equipment to complete signal acquisition and processing, and utilizes the existing digital low-level system to realize the digital acquisition of solid-state power source signals, making full use of existing hardware resources without the need for additional special equipment, thereby significantly reducing the cost of system transformation and simplifying equipment configuration and maintenance.

[0049] In summary, the present invention can be widely applied in the field of particle accelerators. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0051] Figure 1 The present invention is a flowchart of a method for early warning of a solid-state power source failure of a particle accelerator according to an embodiment of the present invention.

[0052] Figure 2 It is a structural diagram of an accelerator power source fault online detection device according to an embodiment of the present invention.

[0053] Figure 3 This is a waveform diagram of long-term slow-speed data recorded in an embodiment of the present invention, wherein the data sampling rate is 1 Hz, that is, 1 data point is sampled per second.

[0054] Figure 4 This is high-speed fault data recorded when a radio frequency superconducting cavity fails in an embodiment of the present invention, wherein the data sampling rate is set to 10 kHz, that is, one data point is sampled every 100 microseconds.

[0055] Figure 5 3 is a characteristic diagram of SSA actually measured in an embodiment of the present invention, wherein the gray dots are characteristic diagrams drawn based on long-term slow data records, and the green dots are characteristic diagrams drawn based on high-speed cavity fault data records.

[0056] Figure 6Schematic diagram for determining the optimal scaling parameter and the optimal fitting curve in the embodiment of the present invention, a is the reference characteristic curve of a single power amplifier module of the solid-state power source in the embodiment of the present invention, b is the characteristic diagram of the SSA actually measured in the embodiment of the present invention, the red curve in the figure is the curve obtained by scaling the reference characteristic curve according to the given scaling parameter, because the given scaling parameter is not in the best position, the red curve and the measured characteristic diagram cannot be well matched, c is the scanning contour map for determining the optimal scaling parameter in the embodiment of the present invention, wherein the coordinates of the red triangle are the optimal scaling parameters a opt and b opt The position d is the optimal characteristic curve redrawn by using the optimal scaling parameters in the embodiment of the present invention. It can be seen that the optimal characteristic curve is in good agreement with the measured results.

[0057] Figure 7 It is a measured characteristic diagram (scatter diagram) and an optimal characteristic curve (red curve) when the power source is working healthily in an embodiment of the present invention.

[0058] Figure 8 It is a measured characteristic diagram (scatter diagram) and an optimal characteristic curve (red curve) when the power source is working unhealthily in an embodiment of the present invention.

[0059] Fig. 9 For Figure 8 The measured characteristic diagram and the normalized result of the optimal characteristic curve are shown.

[0060] Fig.10 The superconducting cavity CM in the embodiment of the present invention 4-2 The best fitting parameters and normalized RMS deviation of the power source Y RMSE Statistics for the past year.

[0061] Fig.11 The superconducting cavity CM in the embodiment of the present invention 4-3 The best fitting parameters and normalized RMS deviation of the power source Y RMSE Statistics for the past year.

[0062] Fig.12 FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0064] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0065] For ease of description, spatially relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figures, such as "inside", "outside", "inner side", "outside", "below", "above", etc. Such spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0066] Solid-state power sources usually use multiple small power amplifier modules (referred to as power amplifier modules) to achieve power output through power synthesis. The input and output characteristic curves (referred to as reference characteristic curves) of a single power amplifier module are generally provided by the manufacturer, and can also be obtained through digital low-level system measurements, such as Figure 6 When the SSA is operating normally, the shape of its overall input-output characteristic diagram (abbreviated as: characteristic diagram) is basically consistent with the shape of the reference characteristic curve, as shown in Figure 7 When the SSA enters an abnormal operating state, the shape of its characteristic diagram will change significantly and deviate from the reference characteristic curve, as shown in Figure 8As shown. However, directly monitoring the shape changes of the characteristic diagram is often difficult to implement due to the high computational complexity. The present invention fits the characteristic diagram of SSA by scaling the reference characteristic curve, and extracts key characteristic parameters therefrom. These key characteristic parameters remain basically stable when the SSA is operating healthily; when the SSA enters an abnormal working state, the key characteristic parameters will fluctuate significantly. By monitoring the changes in these key characteristic parameters, data support for fault warning can be effectively provided. Compared with directly determining the anomaly of a characteristic diagram containing massive data, monitoring and determining the anomaly of key characteristic parameters has the advantages of simple calculation, fast and efficient, thereby reducing the difficulty of implementation and improving the real-time nature of fault detection. The method, device, equipment and medium for early fault warning of a solid-state power source of a particle accelerator provided by the present invention include: obtaining a forward voltage signal of a radio frequency cavity V f and digital low level system output signals V DAC ; According to the forward voltage signal V f and digital low level system output signals V DAC Draw the measured characteristic diagram of the solid-state power source; fit the measured characteristic diagram of the solid-state power source to determine the optimal fitting curve; calculate the normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the optimal fitting curve as the key characteristic parameter Y RMSE ; The key characteristic parameters Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE When the value is greater than the set threshold, the solid-state power source is determined to be abnormal. Therefore, the present invention can achieve early fault warning of SSA.

[0067] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0068] Embodiment 1: Figure 1 As shown, the method for early warning of failure of a particle accelerator solid-state power source provided in this embodiment includes:

[0069] S1. Baseband signal reading.

[0070] In this embodiment, the original forward voltage of the RF superconducting cavity is obtained. V fand digital low level system output signals V DAC wait.

[0071] like Figure 2 As shown, the accelerator power source fault online detection device provided in this embodiment includes a digital low-level system 1, a solid-state power source 2, a directional coupler 3, an input coupler 4, a radio frequency cavity 5, a signal extraction coupler 6, a down-conversion module 7 and a host computer 8. The output end of the digital low-level system 1 is connected to the input end of the solid-state power source 2, the output of the solid-state power source 2 is connected to the input end of the directional coupler 3, the output end (through port) of the directional coupler 3 is fed into the radio frequency cavity through the input coupler 4, and the directional coupler 3 extracts the cavity incident signal P f and reflected signal P r The signal extraction coupler 6 is used to connect the RF cavity 5 to extract the cavity sampling signal. P t , the above signals ( P f , P r and P t ) is received by the digital low-level system 1 after being down-converted by the down-conversion module 7. The digital low-level system 1 receives the radio frequency signal ( P f , P r and P t ) to demodulate the corresponding digital baseband in-phase and quadrature (I / Q) components, and finally upload them to the host computer 8 through the data bus.

[0072] Furthermore, the digital low-level system 1 is provided with a field programmable gate array (FPGA for short), and the FPGA is provided with a digital signal processing module, which converts the cavity sampling signal of the radio frequency superconducting cavity P t , cavity incident signal P f and reflected signal P r The corresponding three-way original voltage signals are processed and down-converted into intermediate frequency signals. Three groups of original digital baseband signals are obtained by sampling the intermediate frequency signals and performing I / Q demodulation, namely: the cavity pressure of the RF superconducting cavity V c , forward voltage V f and reverse voltage V r In addition, the digital low-level system 1 outputs the processed control signal through the analog-to-digital converter DACV DAC Signal.

[0073] Unless otherwise specified, the parameters involved in this embodiment, such as V c , V f , V r , V DAC , V in and V out are all complex numbers, which can be expressed in the form of amplitude and phase, for example: V =| V | e j∠V , where | V | is the amplitude (or modulus), ∠ V is the phase (or argument).

[0074] S2. Draw a feature map.

[0075] In this embodiment, the signals are calculated respectively V f and signal V DAC The amplitude of the signal V DAC The amplitude of the signal is the horizontal axis, V f The characteristic diagram of SSA is plotted with the amplitude as the vertical axis.

[0076] Furthermore, the data sources used to draw the characteristic diagram of SSA include two categories: one is slow data, and the other is high-speed fault data.

[0077] Slow data refers to the slow speed of SSA's long-term operation. V f Signal and V DAC The above slow signal can be directly obtained through the experimental physics and industrial control system (EPICS system for short) inside the digital low-level system 1, and its data sampling rate is generally 1 Hz. Figure 3 For slow V c , V f and V DAC The waveform diagram of the signal contains data on various situations such as the stable working state of the RF superconducting cavity, the fault state, the cavity closure after the fault, and the loading process.

[0078] High-speed fault data refers to the waveform of the high-speed signal after the RF superconducting cavity fails. Figure 4 As shown in the figure, when a RF superconducting cavity fails, its cavity pressure V c The amplitude will fluctuate abnormally (around 2 seconds). When the abnormal fluctuation lasts for more than a given time (usually 1 second), the fault protection and data access mechanism will be triggered. The digital low-level system 1 will include V c , V f ,and V DAC The high-speed fault data packets of the data are uploaded to the data bus and stored in the host computer 8. The sampling rate of the high-speed data packets can be set by the host computer 8 (usually 10 kHz). V f and signal V DAC The amplitude of will change significantly when the RF superconducting cavity fails, which provides data support for describing the dynamic behavior of SSA. Therefore, the RF superconducting cavity failure data can be selected to draw the characteristic diagram of SSA. In this embodiment, the above high-speed failure data is defined as "data to be measured".

[0079] It should be pointed out that in this embodiment, the superconducting cavity failure usually refers to the situation where the RF superconducting cavity field fluctuates abnormally. In most cases, the superconducting cavity failure is not directly related to the SSA. Even if the RF superconducting cavity fails, the SSA may still maintain normal operation and be in a healthy state. Conversely, even if the SSA is in an unhealthy state for a short time, it does not mean that the RF superconducting cavity will inevitably fail. However, if the SSA is in an unhealthy state for a long time, it may eventually trigger the machine protection mechanism, causing the RF superconducting cavity to frequently shut down and eventually cause beam interruption.

[0080] Furthermore, in the data to be measured after each superconducting cavity failure, V DAC The amplitude of the signal is the horizontal axis, V f The amplitude of the signal is the ordinate, and the Figure 5 The characteristic diagram of SSA shown in the green scatter plot in the figure. The time when the superconducting cavity failure occurs is the current time (time 0), and the slow data of the past 10 days are extracted. For example, assuming that the superconducting cavity failure occurs on June 10, the slow data from June 1 to June 10 (including that day) are extracted from the EPICS system of the digital low-level system 1 ( Figure 3 ). In the embodiment of the present invention, the above 10-day slow data is defined as "fitting reference data". The characteristic diagram drawn based on the fitting reference data is as follows: Figure 5The measured data and the fitted reference data are combined into the measured characteristic diagram of SSA (at the current moment).

[0081] S3. Determine the optimal fitting curve.

[0082] In this embodiment, the horizontal coordinate and the vertical coordinate of the reference characteristic curve are scaled respectively to fit the measured characteristic graph, and the optimal scaling parameter and the optimal fitting curve are determined.

[0083] Defining reference characteristic curves F ( x ), which is used to describe the input-output relationship of a single power amplifier module, namely: ,in, V in and V out They are the input and output signals of a single power amplifier module respectively, and the absolute value represents the amplitude of the complex signal.

[0084] Defining new functions G ( x ), its mathematical meaning is: the curve F ( x ) scales in the horizontal and vertical directions a and b Times:

[0085] .

[0086] Defining the measured characteristic diagram of SSA g ( x ) satisfies the relationship: ,in, V DAC and V f They are the input and output signals of SSA respectively, and the absolute value represents the amplitude of the complex signal.

[0087] When SSA is working normally, the actual measured characteristic diagram g ( x ) and reference characteristic curve F The shapes are basically the same, but because the input and output signal levels of the two are different, g ( x )and F ( x ) has different input and output gains. As mentioned above, G ( x )Depend on F ( x ) is scaled, so when a and b In the optimal position, G (x ) curve and g ( x ) has the smallest deviation, among which x represents the abscissa of the measured characteristic diagram, i.e. | V DAC |. Through the two-dimensional scanning parameters a and b , that is, the optimal scaling parameter can be determined a opt and b opt .

[0088] Define Curve G ( x )and g ( x ) e ( x ) satisfies the relationship:

[0089] .

[0090] calculate e ( x ) E , the formula is as follows:

[0091] .

[0092] in, x i for Figure 6 The horizontal coordinate of any scattered point in b (i.e. V DAC of magnitude), N is the total number of scattered points.

[0093] Assumptions a ∈[ a 1, a 2, a m , … a M ], b ∈[ b 1, b 2, b k , … b K ], given any pair ( a m , b k ), according to formula (1), we can get a unique function G ( x ), find the curve G ( x ) and measured characteristic diagramg ( x ) can also get a unique average value E ( a m , b k ). Therefore, in an arithmetic progression a =[ a 1, a 2, a m , … a M ] is the horizontal axis, arithmetic progression b =[ b 1, b 2, b k , … b K ] is the vertical axis, plot the function z = E ( a , b ) is a contour map of Figure 6 As shown in c in Figure 1. Among them, light yellow represents the E ( x , y ), dark red represents the E ( a , b ). The red triangle represents the global minimum E , and its corresponding coordinates ( a opt , b opt ) is the optimal scaling parameter, and accordingly, the optimal fitting curve can be determined G opt ( x ) = b opt · F ( x / a opt ).

[0094] Furthermore, the arithmetic progression a and b The selection rule of the value of is as follows: Figure 6As shown in a, the coordinates of the reference characteristic curve at the solid-state power saturation point are (20000, 10000). The solid-state power source saturation point refers to the maximum limit of the solid-state power source output power. When the solid-state power source reaches this point, it can no longer increase the output power, and any additional input signal cannot lead to a further increase in power output. In the digital low-level system, when the solid-state power source is running stably, the maximum value range of the horizontal coordinate of its saturation point is: (20000, 40000), and the maximum value range of the vertical coordinate is: (10000, 20000). Therefore, a and b The value range of is (1,2). You can choose a and b is an arithmetic progression with a minimum value of 1 and a maximum value of 2, and a tolerance of 0.01; that is a = b = [1,1.01,1.02,1.03,…1.99,2]. In practice, it can be appropriately adjusted according to the value range of the saturation point of the solid-state power source corresponding to the specific superconducting cavity.

[0095] Arithmetic progression a =[ a 1, a 2, a m , … a M ] is the horizontal axis, arithmetic progression b =[ b 1, b 2, b k , … b K ] is the vertical coordinate, we can get the following two-dimensional graph, function z = E ( a , b ) is the mean error value.

[0096] ;

[0097] Among them, drawing z = E ( a , b ) The contour map can call the contour function of Matlab. The result of the contour map is as follows Figure 6 As shown in c.

[0098] S4. Calculate the root mean square deviation.

[0099] In this embodiment, the measured characteristic diagram of SSA is calculated g ( x) and the best fitting curve G opt ( x ) Y RMSE , the specific process is:

[0100] S41. Calculate the best fitting curve G opt ( x ) and measured characteristic diagram g ( x ) y :

[0101] .

[0102] S42. Usually, different cavities consume different RF powers. At the same time, the line loss and attenuation of different RF loops are also different. Therefore, it is necessary to y Normalize. Figure 8 and Fig. 9 The characteristic diagrams before and after normalization are shown. It can be seen that Fig. 9 Will Figure 8 The saturation point coordinates of the best fitting curve in ( x max , y max ) is normalized to the coordinate (1,1), and the coordinates in the measured characteristic diagram are also normalized synchronously (| V DAC |Divide by x max , will| V f |Divide by y max ).

[0103] In this embodiment, Fig. 9 As shown, the normalized deviation Δ Y It can be expressed as:

[0104] .

[0105] in, y max The best fitting curve G opt ( x ) in the vertical coordinate value of the saturation point, such as Figure 8 shown y max =14000.

[0106] S43, calculate Δ Y The root mean square value of YRMSE As key characteristic parameters:

[0107] .

[0108] in, N for Figure 8 or Fig. 9 is the total number of scattered points.

[0109] S5. Early failure warning of solid-state power sources.

[0110] In this embodiment, the key characteristic parameters Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE When it is greater than the set threshold, the solid-state power source is determined to be abnormal.

[0111] The application of the method for early fault warning of a particle accelerator solid-state power source of this embodiment is described in detail below through specific embodiments.

[0112] The solid-state power source 2 used in this embodiment is manufactured by Chengdu Kaiteng Company. Each solid-state power source 2 includes 6 inserts to provide RF power for 4-6 RF superconducting cavities 5, and the saturated output power of a single insert is about 1 kilowatt. The RF superconducting cavity 5 adopts a half-wavelength RF superconducting cavity, the resonant frequency of the cavity is 162.5 MHz, and the half bandwidth is about 120 Hz. The above-mentioned solid-state power source 2 and RF superconducting cavity 5 are both installed on the superconducting linear accelerator CAFE2 device.

[0113] 1. Fluctuation detection of optimal scaling parameters.

[0114] To monitor the stability of SSA in real time, the optimal scaling parameters should be tracked regularly. a opt or b opt As time goes by, when the SSA is working properly, its characteristic curve should be approximately stable. a opt or b opt will remain approximately stable; on the contrary, when SSA is working in an unhealthy job, a opt or b opt Abnormal fluctuations may occur. For example, Fig.11 As shown, the radio frequency superconducting cavity CM 4-3 Solid-state power sources were observed in mid-June and late October. a opt or b optAfter investigation, it was found that the above two abnormal fluctuations were caused by the damage of some power amplifier modules.

[0115] 2. Key characteristic parameters Y RMSE Real-time detection.

[0116] Generally, when a solid-state power source is not working properly, the key characteristic parameters are Y RMSE Optimal scaling parameters a opt or b opt More sensitive. Fig.10 As shown, in mid-June, the radio frequency superconducting cavity CM 4-2 Some power amplifier modules of the solid-state power source are abnormal, resulting in key characteristic parameters Y RMSE produces spikes, but the optimal scaling parameters a opt or b opt No significant changes were observed.

[0117] 3. Determination of abnormal status of solid-state power source.

[0118] Since the key characteristic parameters Y RMSE It has a stronger correlation with the working status of SSA and is more suitable as a basis for judging abnormalities of solid-state power sources.

[0119] In this embodiment, when the key characteristic parameters for 3 consecutive days are Y RMSE If all of them are greater than 0.1, it can be determined that SSA is abnormal. According to the above criteria, CM 4-2 The solid-state power source can be diagnosed as abnormal on June 10. In fact, frequent beam interruptions caused by the solid-state power source failure occurred around July 10. Similarly, on June 26, CM 4-3 The solid-state power source showed its first abnormality, with frequent beam interruptions occurring around July 10. Around October 20, CM 4-3 The solid-state power source had a second abnormality, and frequent beam interruption occurred on December 2. It can be seen that the method of the present invention can achieve abnormality warning at least half a month in advance.

[0120] Embodiment 2: Embodiment 1 above provides a method for early warning of a solid-state power source of a particle accelerator. Correspondingly, this embodiment provides a device for early warning of a solid-state power source of a particle accelerator. The device provided in this embodiment can implement the method for early warning of a solid-state power source of a particle accelerator of Embodiment 1, and the device can be implemented by software, hardware, or a combination of software and hardware. For the convenience of description, this embodiment is described by functions divided into various units and described separately. Of course, the functions of each unit can be implemented in the same or one or more software and / or hardware during implementation. For example, the device may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of Embodiment 1. Since the device of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple, and the relevant parts can refer to the partial description of Embodiment 1. The embodiment of the device for early warning of a solid-state power source of a particle accelerator provided by the present invention is only illustrative.

[0121] Specifically, the device for early warning of failure of a solid-state power source of a particle accelerator provided in this embodiment includes:

[0122] A signal acquisition unit configured to acquire a forward voltage signal of the radio frequency cavity V f and digital low level system output signals V DAC ;

[0123] The characteristic map unit is configured to V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources;

[0124] A curve simulation unit is configured to fit a measured characteristic diagram of the solid-state power source and determine an optimal fitting curve;

[0125] A parameter calculation unit is configured to calculate a normalized root mean square deviation between a measured characteristic diagram of the solid-state power source and a best fitting curve as a key characteristic parameter Y RMSE ;

[0126] The fault warning unit is configured to Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE When it is greater than the set threshold, the solid-state power source is determined to be abnormal.

[0127] Embodiment 3: This embodiment provides an electronic device corresponding to the method for early warning of particle accelerator solid-state power source failure provided in this embodiment 1. The electronic device may be an electronic device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0128] like Fig.12 As shown, the electronic device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can be run on the processor. When the processor runs the computer program, it executes the method of Embodiment 1. The implementation principle and technical effect are similar to those of Embodiment 1 and will not be repeated here. Those skilled in the art can understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computing device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0129] In a preferred embodiment, the logic instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), optical disk and other media that can store program codes.

[0130] In a preferred embodiment, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited herein.

[0131] Embodiment 4: This embodiment provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include computer instructions. When the computer instructions are executed by a computer, the computer executes the method provided in the above-mentioned embodiment 1.

[0132] Embodiment 5: This embodiment provides a computer program product. The computer program product may include a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method provided in the above-mentioned embodiment 1. Its implementation principle and technical effects are similar to those of embodiment 1 and will not be repeated here.

[0133] In a preferred embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device, such as but not limited to an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause the computer to execute the method provided in the first embodiment.

[0134] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for early warning of failure of a solid-state power source of a particle accelerator, characterized in that: include: Get the forward voltage signal of the RF cavity V f and digital low level system output signals V DAC ; According to the forward voltage signal V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources, including: In the data to be measured after each RF superconducting cavity failure, the output signal of the digital low-level system is V DAC The amplitude is the horizontal axis, and the forward voltage signal V f The amplitude of is taken as the ordinate, and a first characteristic diagram of the solid-state power source is drawn, wherein the data to be measured is high-speed fault data; Draw a second characteristic diagram of the solid-state power source according to the fitting reference data, wherein the fitting reference data is slow data; combining the first characteristic graph and the second characteristic graph to form a measured characteristic graph of the solid-state power source; Fitting the measured characteristic diagram of the solid-state power source to determine the optimal fitting curve; Calculate the normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the best fitting curve as the key characteristic parameter Y RMSE ; The key characteristic parameters Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE If it is greater than the set threshold, the solid-state power source is determined to be abnormal.

2. The method for early warning of failure of a solid-state power source of a particle accelerator according to claim 1, characterized in that: Fit the measured characteristic diagram of the solid-state power source to determine the optimal fitting curve, including: Defines a reference characteristic curve describing the input-output relationship of a single amplifier module of a solid-state power source F ( x ),Right now ,in, V in and V out They are the input and output signals of a single power amplifier module, respectively, and the absolute value represents the amplitude of the complex signal; Defining new functions G ( x ), its mathematical meaning is to refer to the characteristic curve F ( x ) scales in the horizontal and vertical directions a and b Times: ； Defining the measured characteristics of solid-state power sources g ( x ) satisfies the relationship: ; definition G ( x )and g ( x ) e ( x ): ； calculate e ( x ) E : ； in, x i is the horizontal coordinate of any scattered point. V DAC The amplitude, N is the total number of scattered points; Arithmetic progression a =[ a 1, a 2, a m , … a M ] is the horizontal axis, b =[ b 1, b 2, b k , … b K ] is the vertical axis, plot the function z = E ( a , b ) to obtain the global minimum E The corresponding coordinates ( a opt , b opt ) is the optimal scaling parameter, and accordingly, the optimal fitting curve is determined G opt ( x ) = b opt · F ( x / a opt ).

3. The method for early warning of failure of a solid-state power source of a particle accelerator according to claim 2, characterized in that: Calculate the normalized root mean square deviation between the measured characteristic diagram of the solid-state power source and the best fitting curve as the key characteristic parameter Y RMSE ,include: Calculate the best fit curve G opt ( x ) and measured characteristic diagram g ( x ) y : ； Normalized deviation Δ Y : ； in, y max The best fitting curve G opt ( x ) in the vertical coordinate value of the saturation point; Calculate Δ Y The root mean square value of Y RMSE : 。 4. A device for early warning of failure of a solid-state power source of a particle accelerator, characterized in that: include: A signal acquisition unit configured to acquire a forward voltage signal of the radio frequency cavity V f and digital low level system output signals V DAC ; The characteristic map unit is configured to V f and digital low level system output signals V DAC Plot measured characteristics of solid-state power sources, including: In the data to be measured after each RF superconducting cavity failure, the output signal of the digital low-level system is V DAC The amplitude is the horizontal axis, and the forward voltage signal V f The amplitude of is taken as the ordinate, and a first characteristic diagram of the solid-state power source is drawn, wherein the data to be measured is high-speed fault data; Draw a second characteristic diagram of the solid-state power source according to the fitting reference data, wherein the fitting reference data is slow data; combining the first characteristic graph and the second characteristic graph to form a measured characteristic graph of the solid-state power source; A curve simulation unit is configured to fit a measured characteristic diagram of the solid-state power source and determine an optimal fitting curve; A parameter calculation unit is configured to calculate a normalized root mean square deviation between a measured characteristic diagram of the solid-state power source and a best fitting curve as a key characteristic parameter Y RMSE ; The fault warning unit is configured to Y RMSE Compared with the set threshold, if the key feature parameter Y RMSE When it is greater than the set threshold, the solid-state power source is determined to be abnormal.

5. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the method according to any one of claims 1-3.

6. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include computer instructions for causing a computer to execute the method according to any one of claims 1-3.

Images