Performance monitoring and evaluation method for advanced space-based solar observatory hard x-ray imaging instrument

Abstract

The application provides a performance monitoring and evaluation method for an advanced space-based solar observatory hard X-ray imager, comprising the following steps: acquiring temperature and voltage data related to the HXI, analyzing the change trend of the temperature and voltage data with time, and screening effective data in a normal working voltage range; further screening the data in combination with a low count rate area of observation data, eliminating abnormal data, and finally acquiring per-event and energy spectrum data; fitting a characteristic peak of the acquired energy spectrum data and performing energy calibration to obtain performance parameters such as gain, peak area and energy resolution. Finally, abnormal points are determined and evaluated by analyzing the temperature, voltage, gain, peak area and energy resolution. Through the systematic data processing and abnormal detection method, the efficiency and accuracy of the on-orbit monitoring of the HXI are improved; through the comprehensive analysis of multiple parameters, the real-time monitoring capability of the instrument state is enhanced, and the stable operation of the instrument and the acquisition of high-quality data are ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of space astronomical instrument monitoring, and specifically relates to a performance monitoring and evaluation method for an advanced space-based solar observatory hard X-ray imager. BACKGROUND

[0002] Currently, hard X-ray imagers are increasingly used in space exploration, especially in the field of solar physics. Hard X-ray imagers can provide important observation data. However, due to the complexity of the space environment, the performance of hard X-ray imagers may be affected by various factors such as temperature fluctuations, high pressure changes, and radiation environment, which may reduce the accuracy of observation data.

[0003] Existing monitoring methods usually rely on periodic ground calibration and simple data analysis methods, which are difficult to monitor and dynamically evaluate the performance changes of the imager in real time. This approach has the following shortcomings: 1) poor real-time performance: existing methods often cannot acquire and process data in real time, resulting in a lag in responding to performance changes in hard X-ray imagers; 2) incomplete data analysis: existing technologies use single parameters or simple statistical analysis, lacking comprehensive analysis of multiple parameters, making it difficult to fully evaluate the performance changes of the imager; 3) insufficient environmental adaptability: the space environment is complex and variable, and existing technologies are difficult to effectively monitor and adjust the performance of the imager under different environmental conditions; 4) low automation level: many monitoring and evaluation tasks require manual intervention, which is inefficient and prone to human error.

[0004] Therefore, there is an urgent need for a method that can monitor and evaluate the performance of hard X-ray imagers in real time, comprehensively and automatically to ensure the reliability and accuracy of observation data and improve the scientific value of space exploration missions. SUMMARY

[0005] The present application provides a performance monitoring and evaluation method for an advanced space-based solar observatory hard X-ray imager to address the shortcomings of existing technologies. By processing and analyzing multiple data, the method achieves accurate monitoring and evaluation of the performance changes of the imager, thereby improving its reliability and data quality in complex space environments.

[0006] To achieve the above-mentioned purposes, the present application adopts the following technical solutions:

[0007] A performance monitoring method for an advanced space-based solar observatory hard X-ray imager, characterized by comprising:

[0008] Obtaining engineering parameters of the satellite platform, engineering parameters of the HXI, and scientific data of the HXI;

[0009] Obtaining temperature values of each temperature measuring point on the HXI at different time and voltage values of the FEE board at different time from engineering parameters of the satellite platform, and establishing a time series graph of temperature and voltage; obtaining time-varying information of the temperature of the energy quantifier probe and the FEE board from engineering parameters of the HXI, and establishing a time series graph of temperature; selecting observation data from scientific data of the HXI, obtaining the per-event spectrum and the energy spectrum therefrom, and obtaining the total count of each probe of each frame, and establishing a time series graph of count;

[0010] According to the time series graph of voltage and the time series graph of count, and in combination with the set working voltage interval, the count interval and the frame interval, the observation data is screened;

[0011] The per-event spectrum and the energy spectrum of the screened observation data are nonlinearly corrected; the per-event spectrum after the nonlinear correction is used for energy calibration to determine the relationship between the ADC channel number and the energy; according to the relationship between the ADC channel number and the energy, a time series graph of the gain of each probe is obtained, and the characteristic peaks in the energy spectrum are fitted to obtain fitting parameters of the characteristic peaks; according to the fitting parameters of the characteristic peaks, a time series graph of the peak area and the energy resolution of the characteristic peaks is obtained;

[0012] The abnormal data of temperature and voltage is detected through the time series graph of temperature and voltage; the abnormal data of gain, peak area and energy resolution is detected through the time series graphs of gain, peak area and energy resolution.

[0013] To optimize the above technical solutions, the specific measures taken further include:

[0014] Further, the engineering parameters of the satellite platform include the temperature of the collimator frame, the pointing mirror, the satellite mounting base plate and the energy quantifier base, and the voltage of the FEE board; the engineering parameters of the HXI include the temperature of the probe and the FEE board; the scientific data of the HXI is read according to the package type identifier on the specific byte position to determine the data format, and the observation data is screened accordingly.

[0015] Further, the working voltage interval is 700 to 900 V, the count interval is 300 to 600 per frame, and the frame interval is not less than 4 seconds.

[0016] Further, the nonlinear correction of the per-event spectrum and the energy spectrum of the screened observation data is specifically: the per-event spectrum and the energy spectrum are re-binned by using the nonlinear calibration data of the ground calibration.

[0017] Further, the energy calibration is performed by using the nonlinear corrected per-event spectrum, and the relationship between the ADC channel number and the energy is determined, specifically: the peak searching and Gaussian fitting are performed on the characteristic peaks of the nonlinear corrected per-event spectrum to obtain the ADC channel number of each energy peak, and the linear fitting is performed on the ADC channel number and the corresponding energy value to determine the relationship between the ADC channel number and the energy.

[0018] Further, the fitting parameters of the characteristic peaks are obtained by fitting the characteristic peaks in the energy spectrum, specifically: the energy spectrum is converted to the energy unit according to the energy calibration, the continuous spectrum of the energy spectrum is fitted by using a quadratic function, the continuous spectrum is deducted from the energy spectrum, and then the double-Gaussian fitting is performed on the interval containing the characteristic peaks to obtain the fitting parameters of the characteristic peaks.

[0019] Further, the time sequence diagram of the peak area and the energy resolution of the characteristic peaks is obtained according to the fitting parameters of the characteristic peaks, specifically: the peak area S and the energy resolution η of the characteristic peaks are calculated according to the Gaussian function area formula and the energy resolution expression respectively, wherein A represents the peak value, E represents the peak position, and σ represents the standard deviation, and the time sequence diagram of the peak area and the energy resolution is drawn according to the calculation results.

[0020] Further, the abnormal data of the temperature and the voltage are detected through the time sequence diagram of the temperature and the voltage, specifically: the standard score of each data point of the temperature and the voltage is calculated by using the z-score method, and the threshold is set to judge the abnormal points; the data points exceeding the threshold are marked, and the time and specific value of the abnormal occurrence are recorded.

[0021] Further, the abnormal data of the gain, the peak area and the energy resolution are detected through the time sequence diagram of the gain, the peak area and the energy resolution, specifically: the relative change rate RCR is used to evaluate the daily change, RCR=|(P n -P n-1 ) / P n-1 |*100%, wherein P n is the performance parameter value of the nth day, and P n-1 is the performance parameter value of the previous day; the RCR threshold is set, and when the RCR of the gain, the peak area and the energy resolution exceeds the RCR threshold, the abnormal change is marked.

[0022] Correspondingly, the application provides a performance evaluation method of an advanced space-based solar observatory hard X-ray imager, and the working state and the performance change of the HXI are evaluated through the results obtained by the performance monitoring method.

[0023] The beneficial effects of the present application are: the present application realizes the performance monitoring and evaluation of the hard X-ray imager in orbit operation through systematic data processing, comprehensive data analysis, real-time anomaly detection and high-precision fitting. Specifically, through systematic processing of temperature and voltage data, detailed change images are generated; combined with temperature, voltage, event-by-event data and energy spectrum data, multi-parameter comprehensive analysis is carried out to provide comprehensive performance monitoring and evaluation; through screening of normal working voltage and low background particle flow time period data, real-time detection and elimination of abnormal data are realized to ensure the accuracy and reliability of the data; using various fitting methods to analyze the characteristic peaks, the gain, peak area and energy resolution are determined to provide high-precision performance evaluation; through monitoring data triggering script running, automatic and real-time performance detection and analysis are realized. Through these innovations, the present application improves the accuracy of data analysis, enhances the real-time monitoring capability, provides reliable performance evaluation, optimizes the data processing process, and significantly improves the performance monitoring and evaluation level of the hard X-ray imager in orbit operation, providing reliable data support for solar physics research. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The performance monitoring and evaluation flowchart of the advanced space-based solar observatory hard X-ray imager of the present application.

[0025] Figure 2 The comparison chart of event-by-event spectrum and energy spectrum of the same probe.

[0026] Figure 3a And Figure 3b The time series charts of the temperature of the temperature measuring points before and after the collimator and the voltage of FEE 0, respectively.

[0027] Figure 4a And Figure 4b The time series charts of the average temperature of the temperature measuring points before and after the collimator and the average voltage of FEE 0, respectively.

[0028] Figure 5 The time series chart of the average temperature of the HXI partial probe.

[0029] Figure 6 The time series chart of the count of all HXI probes in one day.

[0030] Figure 7 The comparison chart of energy spectrum before and after non-linear correction.

[0031] Figure 8 The fitting result chart of different characteristic peaks of event-by-event spectrum.

[0032] Figure 9 The time series chart of the gain of the HXI partial probe.

[0033] Figure 10 Time series plot of the 59.5 keV characteristic peak area for the HXI part probe.

[0034] Figure 11 Time series plot of the 59.5 keV characteristic peak energy resolution for the HXI part probe. DETAILED DESCRIPTION

[0035] The application will be further described in detail in conjunction with the accompanying drawings.

[0036] The application proposes a performance monitoring and evaluation method for an advanced space-based solar observatory hard X-ray imager, as shown in the figure, the technical scheme is to obtain various performance parameters from in-orbit data, including satellite data acquisition, screening, anomaly detection, statistical analysis and calibration fitting and other operations. Figure 1

[0037] Considering that the application is implemented based on a hard X-ray imager (HXI), the structure and working principle of HXI will be briefly introduced as follows. The main body of HXI is composed of three parts: collimator, energy measurer and electric control box. The collimator and energy measurer are installed on the satellite mounting base (also called base). The collimator is composed of a support structure, front and rear grating arrays and a pointing mirror. The support structure is a titanium alloy frame, and the two base plates in front and rear of the frame are used to install the grating arrays. The middle of the rear base plate is installed with the pointing mirror system. The energy measurer opposite to the rear of the collimator is used to measure the X-ray flux and count through the collimator, which is composed of a carbon fiber support structure (i.e. energy measurer base), an array of 99 LaBr3 scintillator probes, front-end electronics systems (FEEs) installed behind the detection unit and a high-voltage fan-out board. The signal measured by the probe is amplified by a photomultiplier tube (PMT) to generate a voltage pulse. The pulse signal is transmitted to the FEEs through the electronic basic circuit, and the FEEs amplify the pulse signal and then digitize it to convert it into ADC channel number and record it. The above is the general structure and basic working principle of HXI in the field, which may differ in size, number, shape and detector type.

[0038] Regarding the performance monitoring and evaluation method of the advanced space-based solar observatory hard X-ray imager, the first thing to do is to acquire data, which covers engineering parameters and observation data. The engineering parameters include temperature and voltage data, and the observation data include energy spectrum data and per-event data. To obtain them, the following steps are taken:

[0039] Step 1.1: Obtain the engineering parameters of the satellite platform, including the key temperature and voltage information of HXI, such as the temperature data of the collimator frame, pointing mirror, base and energy measurer base, and the voltage data of the 8 FEE boards.

[0040] ​Step 1.2: Obtain the engineering parameters of HXI, including the temperature data of the calorimeter, covering the temperature measurement points on 24 probes and 8 FEE boards.

[0041] Step 1.3: Obtain the scientific data of HXI, which has four formats. When reading, determine the data format according to the packet type identifier at a specific byte position, and filter out the observation data accordingly. The observation data includes energy spectrum data and per-event data, and is decoded according to the specified format of the observation data.

[0042] After obtaining the required data, the data is then subjected to preliminary processing and analysis, which involves the establishment of time series images and data filtering. The specific steps are as follows:

[0043] Step 2.1: Obtain the temperature and voltage values of each temperature measurement point and FEE board at different times from the satellite engineering parameters, establish temperature and voltage time series images, and determine the characteristics and abnormal changes of temperature and voltage over time.

[0044] Step 2.2: Obtain the temperature time-varying information of the probes and FEE boards from the engineering parameters of HXI, establish temperature time series images, analyze the temperature variation trend and spatial distribution, and understand the relationship between temperature differences and spatial distribution.

[0045] Step 2.3: Filter out the observation data from the scientific data of HXI, obtain per-event data and energy spectrum data. Per-event data is high-frequency collected data, and the record mode occupies a large storage space, so no more than 100 events per frame are recorded; energy spectrum data has a lower recording frequency, and is characterized by the number of particles at a corresponding energy through the number of ADC channels, which can record the energy spectrum of each frame. Accumulate the total count of each frame to establish a time series image of the count per frame per day.

[0046] Step 2.4: According to the voltage range (700 to 900 V) and the count range (300 to 600 per frame), filter out the time period data of normal working voltage and low environmental particle flux. Specifically, according to the orbital particle environment of HXI, low environmental particle flux generally refers to the particle flux in low geomagnetic latitude regions. According to the change of HXI's per-frame count with the orbit, for all probes of HXI, a per-frame count of 600 is a suitable upper limit of the flux, so whether it is a low environmental particle flux is determined by whether the per-frame count is within the range of 300 to 600. In addition, data during solar flares must also be excluded. During solar flares, HXI enters the flare mode, and the frame interval changes from 4 seconds to 0.125 seconds. By excluding frames with a time interval less than 4 seconds, solar flare data can be excluded. Finally, the observation data under normal working voltage, normal mode, and low background flux is obtained.

[0047] After filtering and obtaining the event-by-event spectrum and energy spectrum of daily observation data, further processing and analysis are then carried out. The specific steps are as follows:

[0048] Step 3.1: Correcting the nonlinearity of the event-by-event spectrum and energy spectrum. Ideally, the width of each ADC channel should be uniform, but in reality, this is not the case, which introduces nonlinearity in the observed spectrum. First, calibrate the electronics nonlinearity on the ground to obtain nonlinearity calibration data for each probe. Based on this calibration data, all ADC channels are re-binned with equal spacing, and counts in the old bins are redistributed to the new bins based on the overlap ratio. This results in the nonlinearity-corrected event-by-event spectrum and energy spectrum.

[0049] Step 3.2: Use the nonlinearly corrected event-by-event spectrum to perform energy calibration and determine the gain. Although the event-by-event spectrum has fewer counts and larger statistical fluctuations, Figure 2 As shown in the figure, its characteristic peaks are still obvious and cover a wider energy range than the energy spectrum, including the 356keV characteristic peak of Ba133, which is the only energy peak in the high-energy range that can be used for calibration. On-orbit calibration was performed using a mixed source of Am241 and Ba133. Gaussian fitting of the characteristic peaks (26.4keV, 59.5keV, 37.4keV, 81keV, and 356keV) was used to obtain the ADC channel number for each energy peak. A linear fit was performed on the corresponding energy values ​​to determine the relationship between ADC channel and energy: Ch = Gain * En + Bias, where Ch is the number of ADC channels, En is the energy, Gain represents the gain of the detector, and Bias is the intercept of the linear fit.

[0050] Step 3.3: After having the linear relationship between the number of ADC channels and energy, convert the energy spectrum into energy units. Perform a comprehensive fit on the strongest 59.5keV peak in the energy spectrum. Since the 59.5keV peak is not a perfect Gaussian shape, there is a small bulge on its low-energy side and there is a continuous spectrum contribution below the Gaussian peak. In this embodiment, by comparing the fitting results, it is finally determined to use a quadratic function to fit the continuous spectrum, and deduct the continuous spectrum component from the energy spectrum before further fitting. The fitting interval is 53keV to 100keV, including two characteristic peaks of 59.5keV and 81keV. Finally, double Gaussian fitting is used to obtain the fitting parameters of the two characteristic peaks.

[0051] Step 3.4: After obtaining the fitting parameters of the 59.5 keV characteristic peak (peak value A, peak position E, standard deviation σ), according to the Gaussian function area formula: And the energy resolution expression: Calculate the peak area S and energy resolution η of the 59.5 keV characteristic peak and plot their time series. Since the 59.5 keV characteristic peak originates from Am241, which has a half-life of 432.6 years and remains essentially constant over the satellite's four-year mission cycle, changes in peak area can be used to characterize the HXI probe's detection efficiency.

[0052] Finally, by monitoring and evaluating the temporal changes in HXI parameters such as temperature, voltage, gain, detection efficiency, and energy resolution, we can understand the on-orbit performance of HXI. First, we average the temperature and voltage to obtain a time series of daily average temperature and voltage to eliminate the impact of short-term fluctuations on long-term characteristics.

[0053] For temperature and voltage, we use the z-score method to calculate the standard score for each data point. We set a threshold (e.g., z-score > 3 or z-score < -3) to identify outliers. Data points exceeding the threshold are marked, and the time and specific value of the anomaly are recorded.

[0054] For gain, peak area, and energy resolution, the relative change rate (RCR) was used to assess daily changes: RCR = |(P n -P n-1 ) / P n-1 |*100%, where P n is the performance parameter value on day n, P n-1 The performance parameter values ​​for the previous day are set; an RCR threshold is set (e.g., RCR>10%), and when the threshold is exceeded, it is marked as an abnormal change. The correlation between the parameters is analyzed to determine the source of the abnormal change and propose reasonable countermeasures.

[0055] After completing all data processing and analysis, the relevant operating procedures are integrated into a script file, and the script execution is triggered by monitoring the data file directory, thereby achieving fully automatic, real-time performance detection and analysis.

[0056] Next, the performance monitoring and evaluation method of the advanced space-based solar observatory hard X-ray imager proposed in the present invention is described with reference to specific examples.

[0057] 1. Data acquisition and preliminary processing.

[0058] Data includes engineering data and scientific data. Engineering data is divided into satellite platform engineering data and HXI payload engineering data. This example requires temperature and voltage data from the engineering data, as well as event-by-event data and energy spectrum data from the scientific data.

[0059] The satellite platform engineering data is the 05-level fits format data after preliminary processing, one file per day, and the file name contains date information. Reading the file gets the time of each frame, the temperature value of each temperature measuring point and the voltage value of the 8 FEE boards. Using a python script, with the help of astropy library to read fits file, get time data time_data, temperature data temp_data and voltage data voltage_data. Filter the data with voltage in the range of 700-900V.

[0060] Traverse all fits files, get temperature and voltage values, and sort them by time, draw the temperature and voltage changes. By averaging daily voltage and temperature, get the long time scale changes chart, see Figure 3a 、 3b and Figure 4a 、 4b .

[0061] HXI load engineering data is 0C-level CSv format data, extract the probe and FEE board temperature data at different times each day, calculate the average temperature each day, and draw the average temperature change chart over time, see Figure 5 .

[0062] HXI scientific data is 00-level raw data, compressed as fits.gz format files by hour. Scientific data is divided into 5 categories, and each data packet starts with a 4-byte "EB90EB90" identifier. According to the packet type of the last 4 bits of the 11th byte of each packet, filter out the FEE observation data, and the first 4 bits of the 11th byte represent the FEE to which the data packet belongs. The 5th to 10th bytes of the data packet are the time code, the first 4 bits represent the number of seconds since January 1, 2019, and the last 2 bits represent the number of microseconds.

[0063] Spectrum data starts from the 19th byte and contains 26624 bytes, with each two bytes storing the count of an ADC channel, a total of 13 channels of FEE, each channel 2048 bytes, storing the count of 1024 ADC channels. A frame of data contains 8 FEE data packets, each packet length is 30990 bytes, and the first 4 seconds of data are stored in the regular mode.

[0064] By reading the scientific data of each hour, we can get the time of each frame and the spectrum data of each channel of each FEE. Sum the count of 1024 ADC channels of each channel to get the total count of each channel (i.e. corresponding probe) of each FEE per frame.

[0065] The following is the step of obtaining data by Python script:

[0066] S1: Read the HXI scientific data file using the fits function of the astropy library, and store the data in the variable data;

[0067] S2: Determine whether the 4 bits after the 11th byte of the data packet are 5, and filter the FEE observation data;

[0068] S3: Extract the time information from the 5th to the 10th byte and convert it to datetime format;

[0069] S4: Traverse each FEE data of each frame to obtain the energy spectrum data counts_per_channel;

[0070] S5: Sum the counts of the 1024 ADC channels of each channel of each FEE to obtain the total counts summed_counts of each channel of each FEE of each frame;

[0071] S6, store the time and total count data into processed_data.

[0072] By plotting the total count change of each frame of each probe, as shown in Figure 6 , it is found that the count of each frame of the non-radiation band is basically in the range of 300-600. According to this count interval, the frame number that meets the condition is selected, and the corresponding time information is obtained.

[0073] II. Acquisition of spectrum and energy spectrum per event.

[0074] Before acquiring the data, the data is first screened. The 6 bytes starting from the 5th byte of each frame of the observation data packet store the cutoff time of the current data packet. First, the time difference between adjacent data packets is 4 seconds to screen the data packets in the normal mode; then, according to the time range jointly limited by the normal working voltage and the non-radiation band count, the data packets are further screened, and finally the observation data of the non-burst mode and the non-radiation band region under the normal working voltage are obtained.

[0075] In the observation data packet, the per-event data is 4096 bytes of data starting from the 26643rd byte, and each 4 bytes record one per-event data, a total of 1024 events. In the 4 bytes of each event: the first 4 bits of the first byte record the channel number, and the last 4 bits and the second byte together form 12 bits to record the ADC channel. The first 4 bits of the third byte record the trigger information, and the last 4 bits and the fourth byte form 12 bits of space to record the time code with a precision of 2us.

[0076] Take the acquisition of a day's event spectrum and energy spectrum as an example. Scientific data is classified by day, and each day's folder contains 24 fit.gz files classified by hour. Assume that the data of January 1, 2023 is read, and the data is stored under the directory ' / sci_data / 2023 / 01 / 01'.

[0077] All files under this directory are traversed by the Python script using os.walk(), and the valid_times_dic that meets the normal working voltage and non-radiation band counting time range is obtained by the load_selected_times function. Then the fits function of the astropy library is used to read each hour data file, and the observation data and normal mode data are filtered out. Then the data of the 8 FEE boards is traversed to obtain the energy spectrum data and the event spectrum data. According to valid_times_dic, data filtering and accumulation operations are performed for each FEE channel to obtain the hourly event spectrum eveData, energy spectrum speData and effective frame number frame_num. Finally, the hourly data is accumulated to obtain the daily event spectrum all_eve_dat, energy spectrum all_spe_dat and total effective frame number all_frame_num, and the average energy spectrum per frame is calculated. The specific implementation steps are as follows:

[0078] S1: Traverse the data file: traverse all files under the directory, read the data file and filter out the observation data;

[0079] S2: Filter time data: filter out the data in the normal mode according to the time information;

[0080] S3: Obtain event spectrum and energy spectrum data:

[0081] S4: For each FEE board, read and parse the event spectrum data and energy spectrum data;

[0082] S5: Data filtering according to valid_times_dic;

[0083] S6: Accumulate the data of each channel to obtain the hourly event spectrum and energy spectrum;

[0084] S7: Accumulate daily data: accumulate the hourly data to obtain the daily event spectrum and energy spectrum.

[0085] Through the above steps, the event spectrum and energy spectrum diagram of a day can be drawn, as shown in Figure 2 From the figure, it can be seen that the shape of several characteristic peaks is obvious, the statistical fluctuation of the energy spectrum is smaller than that of the event spectrum, but the energy range of the event spectrum is larger and contains the 356keV peak that the energy spectrum does not have.

[0086] III. Fitting of the event-by-event spectrum and the energy spectrum and obtaining of parameters.

[0087] Before fitting, the observed data is first corrected for non-linearity. According to the non-linearity calibration data of the electronics on the ground, 'DNLCorData', the ADC channels are re-binned with equal intervals, and the counts of each ADC channel of the observed spectrum,'spe', are re-allocated according to the overlap of the old bins and the new bins.

[0088] Comparison of the energy spectrum before and after non-linearity correction as shown in Figure 7 In order to more clearly observe the difference between the two curves, the intensity of the energy spectrum before and after correction is adjusted a little to make the two curves staggered. As can be seen from the figure, the corrected energy spectrum has significantly reduced a lot of burrs, and the peak position has shifted compared to before correction, which shows that the non-linearity correction has a significant improvement and influence on the energy spectrum.

[0089] In order to unify the ADC channels of the event-by-event spectrum and the energy spectrum, so that they correspond one by one, the event-by-event spectrum ADC channels are first merged two by two, and the first 128 channels are discarded, so that the starting point is the 129th ADC channel. Then the event-by-event spectrum is corrected for non-linearity, and the data is smoothed using Gaussian filtering to reduce noise, and the 'find_peaks' algorithm of the scipy library is used to automatically find the peaks that need to be fitted. However, due to the power-law decrease of the spectrum count with energy, the count difference between the high-energy end and the low-energy end is large, so it is not easy to use uniform parameters for smoothing and peak finding, so the event-by-event spectrum is divided into two parts from the 700th ADC channel, and each part is operated separately for smoothing and peak finding, and the required peak finding parameters 'peaks' are selected. Further use the 'curve_fit' function of the scipy library to perform Gaussian fitting on the found peaks to obtain the ADC channel number of the peak position.

[0090] The fitting results of the event-by-event spectrum are shown in Figure 8 After obtaining the peak values of several characteristic peaks on the ADC channels, the'stats.linregress' function of the scipy library can be used for linear fitting. Considering that the 26.34keV peak is not very standard, the peak value obtained by Gaussian fitting may have a large error, so the 26.34keV peak is excluded, and then the fitting is performed, and the slope in the fitting parameters is the gain 'Gain'.

[0091] Through the above method, the gain of all probes can be obtained, and the time series graph of the gain, Figure 9 shows the change of the gain of the probes corresponding to different channels on the FEEO over time.

[0092] According to the acquired gain and offset, the energy spectrum is converted to energy unit. The energy spectrum is first corrected non-linearly, then converted to energy unit, and the energy region of continuous spectrum is selected, a quadratic function is fitted to obtain a fitting function, and then the continuous spectrum is deducted from all energy sections of the energy spectrum, and then the 53keV-100keV section of the energy spectrum is selected for double Gaussian fitting.

[0093] After obtaining the fitting parameters of the 59.5keV characteristic peak, the peak area and energy resolution of the characteristic peak can be obtained according to the related formula introduced in the summary.

[0094] After obtaining the peak area and energy resolution of each day, the peak area and energy resolution change with time can be further plotted, as shown in Figure 10 and Figure 11 .

[0095] Four, anomaly detection.

[0096] The z-score method and the relative change rate (RCR) are used for anomaly detection. The z-score detection is first to obtain the average value 'T_V_mean' and the standard deviation 'T_V_std' by moving average of the temperature and voltage data 'T_V_data' according to a certain window width, then to obtain the z-score according to (T_V_data-T_V_mean) / T_V_std, and finally to judge the abnormal data according to -3 n -P n-1 ) / P n-1 | to obtain the RCR of gain, peak area or energy resolution, and then to judge the abnormal data according to RCR>10%.

[0097] Finally, the running of all the above operations is realized by the script script.sh. On the server, the running of the above script is triggered by monitoring the change of the specific data file directory. The background running of the above script is realized by the screen tool, and the normal running of the script is checked by the htop command, so that the full-automatic and real-time performance monitoring and analysis work can be realized.

[0098] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that for ordinary skilled in the art, some improvements and refinements without departing from the principles of the present application shall be considered as the protection scope of the present application.

Claims

1. A method for performance monitoring of an advanced space-based solar observatory hard X-ray imager, characterized by, The application relates to a method for detecting abnormal data of a high-energy X-ray imager (HXI) on a satellite platform. The method comprises the following steps: acquiring engineering parameters of the satellite platform, engineering parameters of the HXI and scientific data of the HXI; acquiring temperature values of temperature measuring points on the HXI and voltage values of a FEE board at different time points from the engineering parameters of the satellite platform, and establishing a time sequence graph of temperature and voltage; obtaining time-varying information of a quantometer probe and the FEE board from the engineering parameters of the HXI, and establishing a time sequence graph of temperature; screening observation data from the scientific data of the HXI, acquiring spectrum and energy spectrum of each case, and obtaining total counts of each probe in each frame, and establishing a time sequence graph of counts; screening the observation data according to the time sequence graph of voltage and the time sequence graph of counts, and combining a set working voltage interval, a count interval and a frame interval; nonlinearly correcting the spectrum and the energy spectrum of the screened observation data; performing energy calibration on the nonlinearly corrected spectrum to determine the relationship between ADC channel numbers and energy; obtaining a time sequence graph of the gain of each probe according to the relationship between the ADC channel numbers and the energy, and fitting the characteristic peaks in the energy spectrum to obtain fitting parameters of the characteristic peaks; and obtaining a time sequence graph of the peak area and the energy resolution of the characteristic peaks according to the fitting parameters of the characteristic peaks; 2. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: detecting abnormal data of temperature and voltage through the time sequence graph of temperature and voltage; and detecting abnormal data of gain, peak area and energy resolution through the time sequence graphs of the gain, the peak area and the energy resolution.

3. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The engineering parameters of the satellite platform include the temperature of a collimator frame, a pointing mirror, a satellite mounting substrate and a quantometer base and the voltage of the FEE board; the engineering parameters of the HXI include the temperature of a probe and the FEE board; and the scientific data of the HXI is judged according to the data format of a packet type identifier at a specific byte position when the scientific data is read, and the observation data is screened according to the data format.

4. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The working voltage interval is 700-900 V, the count interval is 300-600 per frame, and the frame interval is not less than 4 seconds.

5. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The nonlinearly corrected spectrum and the energy spectrum of the screened observation data are corrected, and the specific method is that the nonlinearly corrected spectrum and the energy spectrum are re-binned by using nonlinear calibration data of ground calibration.

6. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The energy calibration is performed on the nonlinearly corrected spectrum to determine the relationship between the ADC channel numbers and the energy, and the specific method is that the characteristic peaks of the nonlinearly corrected spectrum are searched and Gaussian fitted to obtain the ADC channel numbers of each energy peak, and the relationship between the ADC channel numbers and the energy is determined by linear fitting of the energy values corresponding to the ADC channel numbers.

7. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: According to the fitting parameters of the characteristic peak, a time series graph of the peak area and energy resolution of the characteristic peak is obtained, specifically: according to the Gaussian function area formula and the energy resolution expression The peak area S and energy resolution η of the characteristic peak are calculated respectively, wherein A represents the peak value, E represents the peak position, and σ represents the standard deviation, and a time series graph of the peak area and energy resolution is drawn accordingly.

8. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The characteristic peaks in the energy spectrum are fitted to obtain the fitting parameters of the characteristic peaks, and the specific method is that the energy spectrum is converted to an energy unit by energy calibration, a continuous spectrum of the energy spectrum is fitted by using a quadratic function, the continuous spectrum is deducted from the energy spectrum, then a double-Gaussian fitting is performed on an interval containing the characteristic peaks to obtain the fitting parameters of the characteristic peaks. The abnormal data of temperature and voltage are detected through the time sequence graph of temperature and voltage, and the specific method is that the standard scores of the temperature and voltage data points are calculated by using a z-score method, a threshold value is set to judge abnormal points, the data points exceeding the threshold value are marked, and the time and specific value of the abnormality are recorded.

9. The method of performance monitoring of an advanced space-based solar observatory hard X-ray imager as recited in claim 1, wherein: The time series graph of gain, peak area and energy resolution is used to detect abnormal data of gain, peak area and energy resolution, specifically: using relative change rate RCR to evaluate daily change, RCR = |(P n - P n-1 ) / P n-1 |*100%, wherein P n is the performance parameter value of the nth day, and P n-1 is the performance parameter value of the previous day; set the RCR threshold value, when the RCR of gain, peak area and energy resolution exceeds the RCR threshold value, mark it as abnormal change.

10. A method of performance evaluation of an advanced space-based solar observatory hard X-ray imager, characterized by: The results obtained by the performance monitoring method according to any one of claims 1 to 9 are used to assess the working state and performance changes of the HXI.

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