Performance monitoring and evaluating method for hard X-ray imager of advanced space-based solar observatory

Abstract

The invention provides a performance monitoring and evaluation method of an advanced space-based solar observatory hard X-ray imager, which comprises the following steps: acquiring temperature and voltage data related to HXI, analyzing the variation trend of the data along with time, and screening out effective data in a normal working voltage range; further screening the data in combination with a low counting rate region of the observation data, removing abnormal data, and finally obtaining case-by-case and energy spectrum data; and carrying out characteristic peak fitting and energy calibration on the obtained energy spectrum data to obtain gain, peak area, energy resolution and other performance parameters. And finally, determining and evaluating abnormal points by analyzing the temperature, the voltage, the gain, the peak area and the energy resolution. Through the systematized data processing and anomaly detection method, the efficiency and accuracy of HXI on-orbit monitoring are improved; and through multi-parameter comprehensive analysis, the real-time monitoring capability of the state of the instrument is enhanced, and stable operation of the instrument and acquisition of high-quality data are ensured.

Description

Technical Field

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

[0002] At present, hard X-ray imagers are increasingly used in space exploration, especially in the field of solar physics research, where they can provide important observation data. However, due to the complex space environment, the performance of hard X-ray imagers may be affected by a variety of 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 regular ground calibration and simple data analysis methods, which makes it difficult to monitor and dynamically evaluate the performance changes of imagers in real time. This method has the following shortcomings: 1) Poor real-time performance: Existing methods often cannot obtain and process data in real time, resulting in a delayed response to changes in the performance of hard X-ray imagers; 2) Incomplete data analysis: Existing technologies often use a single parameter or simple statistical analysis, lack comprehensive analysis of multiple parameters, and are difficult to comprehensively evaluate the performance changes of imagers; 3) Insufficient environmental adaptability: The space environment is complex and changeable, and existing technologies are difficult to effectively monitor and adjust the performance of imagers under different environmental conditions; 4) Low degree of automation: Many monitoring and evaluation tasks need to rely on manual work, which is inefficient and prone to human errors.

[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 enhance the scientific value of space exploration missions. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a performance monitoring and evaluation method for a hard X-ray imager of an advanced space-based solar observatory. By comprehensively processing and analyzing a variety of data, accurate monitoring and evaluation of imager performance changes can be achieved, thereby improving its working reliability and data quality in a complex space environment.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

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

[0008] Obtain engineering parameters of satellite platforms, engineering parameters of HXI, and scientific data of HXI;

[0009] The temperature values ​​of each temperature measurement point on HXI at different times and the voltage values ​​of the FEE board at different times are obtained from the engineering parameters of the satellite platform, and a time series diagram of temperature and voltage is established; the time-varying temperature information of the calorimeter probe and the FEE board is obtained from the engineering parameters of HXI, and a time series diagram of temperature is established; the observation data is screened from the scientific data of HXI, and the case-by-case spectrum and energy spectrum are obtained from them, and the total counts of each probe in each frame are obtained, and a time series diagram of counts is established;

[0010] According to the time series diagram of voltage and the time series diagram of count, the observation data are filtered in combination with the set working voltage interval, count interval and frame interval;

[0011] Perform nonlinear correction on the case-by-case spectrum and energy spectrum of the screened observation data; perform energy calibration using the case-by-case spectrum after nonlinear correction to determine the relationship between the number of ADC channels and energy; obtain a time series diagram of the gain of each probe based on the relationship between the number of ADC channels and energy, and fit the characteristic peaks in the energy spectrum to obtain the fitting parameters of the characteristic peaks; obtain a time series diagram of the peak area and energy resolution of the characteristic peaks based on the fitting parameters of the characteristic peaks;

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

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

[0014] Furthermore, the engineering parameters of the satellite platform include the temperatures of the collimator frame, the pointing mirror, the satellite mounting substrate and the calorimeter base, and the voltage of the FEE board; the engineering parameters of the HXI include the temperatures of the probe and the FEE board; when reading the scientific data of the HXI, the data format is determined according to the packet type identifier at a specific byte position, and the observation data is filtered out accordingly.

[0015] Furthermore, the operating voltage interval is 700 to 900 V, the counting interval is 300 to 600 per frame, and the frame interval is not less than 4 seconds.

[0016] Furthermore, the nonlinear correction of the case-by-case spectrum and the energy spectrum of the screened observation data is specifically performed by rebinning the case-by-case spectrum and the energy spectrum using the nonlinear calibration data of the ground calibration.

[0017] Furthermore, the energy calibration is performed using the nonlinearly corrected case-by-case spectrum to determine the relationship between the ADC channel number and the energy, specifically by performing peak search and Gaussian fitting on the characteristic peaks of the nonlinearly corrected case-by-case spectrum to obtain the ADC channel number of each energy peak, and performing linear fitting in combination with the energy value corresponding to the ADC channel number to determine the relationship between the ADC channel number and the energy.

[0018] Furthermore, the characteristic peaks in the energy spectrum are fitted to obtain fitting parameters of the characteristic peaks, specifically: the energy spectrum is converted into energy units according to energy calibration, a quadratic function is used to fit the energy spectrum continuum, the continuum is deducted from the energy spectrum, and then a double Gaussian fitting is performed on the interval containing the characteristic peaks to obtain the fitting parameters of the characteristic peaks.

[0019] Furthermore, the time series diagram of the peak area and energy resolution of the characteristic peak is obtained according to the fitting parameters of the characteristic peak, 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, where 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.

[0020] Furthermore, the abnormal temperature and voltage data are detected through the time series diagram of temperature and voltage, specifically: the standard score of each temperature and voltage data point is calculated using the z-score method, and a threshold is set to determine the abnormal point; the data points exceeding the threshold are marked, and the time and specific value of the abnormality are recorded.

[0021] Furthermore, the abnormal data of gain, peak area and energy resolution are detected by using the time series diagram of gain, peak area and 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%, where P n is the performance parameter value on the nth day, P n-1 is the performance parameter value of the previous day; set the RCR threshold, and when the RCR of gain, peak area, and energy resolution exceeds the RCR threshold, it is marked as an abnormal change.

[0022] Accordingly, the present invention proposes a performance evaluation method for a hard X-ray imager of an advanced space-based solar observatory, characterized in that the working state and performance changes of the HXI are evaluated through the results obtained by the performance monitoring method as described above.

[0023] The beneficial effects of the present invention are as follows: the present invention realizes the performance monitoring and evaluation of the on-orbit operation of the hard X-ray imager through systematic data processing, comprehensive data analysis, real-time anomaly detection and high-precision fitting. Specifically, by systematically processing the temperature and voltage data, a detailed change image is generated; by combining the temperature, voltage, case-by-case data and energy spectrum data, a multi-parameter comprehensive analysis is performed to provide comprehensive performance monitoring and evaluation; by screening the time period data of normal working voltage and low background particle flow, abnormal data is detected and eliminated in real time to ensure the accuracy and reliability of the data; a variety of fitting methods are used to analyze the characteristic peaks, determine the changes in gain, peak area and energy resolution, and provide high-precision performance evaluation; by monitoring data triggering the script to run, fully automatic and real-time performance detection and analysis is achieved. Through these innovations, the present invention improves the accuracy of data analysis, enhances the real-time monitoring capability, provides reliable performance evaluation, and optimizes the data processing process, thereby significantly improving the performance monitoring and evaluation level of the on-orbit operation of the hard X-ray imager, and providing reliable data support for solar physics research. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a flow chart of performance monitoring and evaluation of the hard X-ray imager of the advanced space-based solar observatory.

[0025] Figure 2 This is a comparison of the event-by-event spectrum and energy spectrum of the same probe.

[0026] Figure 3a and Figure 3b They are the time series diagrams of the temperature of the temperature measurement points before and after the collimator and the voltage of FEE 0.

[0027] Figure 4a and Figure 4b They are the time series diagrams of the average temperature of the temperature measurement points before and after the collimator and the average voltage of FEE 0, respectively.

[0028] Figure 5 This is a time series diagram of the average temperature of some HXI probes.

[0029] Figure 6 Time series plot of counts for all HXI probes for one day.

[0030] Figure 7 This is a comparison of the energy spectra before and after nonlinear correction.

[0031] Figure 8 This is a diagram showing the fitting results of different characteristic peaks of each case spectrum.

[0032] Fig. 9 This is the time series diagram of the probe gain of the HXI part.

[0033] Fig.10 This is the time series diagram of the 59.5keV characteristic peak area of ​​the HXI part probe.

[0034] Fig.11 This is a time series diagram of the energy resolution of the 59.5keV characteristic peak of some HXI probes. DETAILED DESCRIPTION

[0035] The present invention will now be described in further detail with reference to the accompanying drawings.

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

[0037] Considering that the present invention is implemented based on a hard X-ray imager (HXI), the following is a brief introduction to the structure and working principle of HXI. The HXI body consists of three parts: a collimator, a calorimeter and an electric control box. The collimator and the calorimeter are both installed on a satellite mounting substrate (also called a base). The collimator consists of a support structure, a front and rear grating array and a pointing mirror. The support structure is a titanium alloy frame. The two substrates in front and behind the frame are used to install the grating array, and the pointing mirror system is installed in the middle of the rear substrate. The calorimeter facing the back of the collimator is used to measure the X-ray flux and count passing through the collimator. It consists of a carbon fiber support structure (i.e., a calorimeter base), an array of 99 LaBr3 scintillator probes, a front-end electronics system (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 an electronic basic circuit. The FEEs then amplify the pulse signal, and then digitally process it into ADC channel numbers and record it. The above is the general structure and basic working principle of HXI in this field, and there may be differences in size, number, shape, and detector type.

[0038] Regarding the performance monitoring and evaluation method of the hard X-ray imager of the advanced space-based solar observatory, the first thing to do is to obtain data. The data used for performance analysis is included in the engineering parameters and observation data. The engineering parameters include temperature and voltage data, and the observation data includes energy spectrum data and case-by-case data. There are several steps to obtain them:

[0039] Step 1.1: Obtain the engineering parameters of the satellite platform, including key temperature and voltage information of HXI, such as the temperature measurement point data on the collimator frame, pointing mirror, base, and calorimeter base, as well as the voltage data of the eight 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: Get the scientific data of HXI. HXI scientific data has four formats. When reading, the data format is determined according to the packet type identifier at a specific byte position, and the observation data is filtered out accordingly. The observation data includes energy spectrum data and case-by-case data, and is decoded according to the specified format of the observation data.

[0042] After obtaining the required data, the data is then processed and analyzed initially, which involves the creation of time series images and data screening. 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 a time series diagram of temperature and voltage, and determine the change characteristics and abnormal changes of temperature and voltage over time.

[0044] Step 2.2: Obtain the temperature time-varying information of the probe and FEE board from the engineering parameters of HXI, establish a temperature time series diagram, analyze the temperature change trend and spatial distribution, and understand the temperature difference and its relationship with spatial distribution.

[0045] Step 2.3: Filter observation data from HXI scientific data to obtain case-by-case data and energy spectrum data. Case-by-case data is data collected at a high frequency, and the recording mode occupies a large storage space, so no more than 100 cases are recorded per frame; the energy spectrum data is recorded at a lower frequency, and the particle counts at the corresponding energy are represented by the counts on the ADC channels, so the energy spectrum of each frame can be recorded. The total counts of each frame of the energy spectrum are accumulated, and a time series graph of the counts of each frame per day is established based on this.

[0046] Step 2.4: According to the voltage range (700 to 900V) and the count range (300 to 600 per frame), filter out the time period data of normal operating voltage and low environmental particle flow. According to the orbital particle environment of HXI, low environmental particle flow generally refers to the particle flow in the low geomagnetic latitude area. According to the change of HXI's on-orbit per-frame count with the orbit, for all HXI probes, 600 counts per frame is a suitable flow upper limit. Therefore, whether it is a low environmental particle flow is determined by whether the count per frame is in the range of 300 to 600. In addition, the data during the flare outbreak needs to be eliminated. When the flare erupts, HXI enters the burst mode, and the interval between each frame changes from 4 seconds to 0.125 seconds. The frames with a time interval of less than 4 seconds are eliminated to achieve the elimination of flare data. Finally, the observation data under normal operating voltage, conventional mode and low background flow are obtained.

[0047] After screening 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: Nonlinear correction of the event-by-event spectrum and energy spectrum. Ideally, the width of each ADC channel should be the same, but in reality it is not uniform, which introduces nonlinearity in the observed spectrum. First, calibrate the nonlinearity of the electronics on the ground to obtain the nonlinear calibration data of each probe. Re-bin all ADC channels with equal spacing according to the calibration data, and redistribute the counts in the old bins to the new bins according to the overlap ratio, so as to obtain the event-by-event spectrum and energy spectrum after nonlinear correction.

[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, its characteristic peaks are still obvious, and compared with the energy spectrum, it covers a larger energy range, including the 356keV characteristic peak of Ba133, which is the only energy peak that can be used for calibration in the high energy range. On-orbit calibration is performed using a mixed source of Am241 and Ba133, and the number of ADC channels of each energy peak is obtained by Gaussian fitting of the characteristic peaks (26.4keV, 59.5keV, 37.4keV, 81keV and 356keV). Linear fitting is performed in combination with the corresponding energy values ​​to determine the relationship between ADC channels 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 for 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 59.5keV 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 59.5keV characteristic peak and draw their time series graphs. Since the 59.5keV characteristic peak comes from Am241, its half-life is 432.6 years, and its activity remains basically unchanged during the satellite's 4-year mission cycle, the change in peak area can be used to characterize the change in the detection efficiency of the HXI probe.

[0052] Finally, the on-orbit performance of HXI is understood by monitoring and evaluating the changes of HXI's temperature, voltage, gain, detection efficiency and energy resolution over time. First, the temperature and voltage are averaged to obtain the time series of the average temperature and voltage for each day to eliminate the impact of short-term drastic fluctuations on long-term variation characteristics.

[0053] For temperature and voltage, the z-score method is used to calculate the standard score of each data point, and a threshold (such as z-score>3 or z-score<-3) is set to determine the abnormal point. The data points that exceed the threshold are marked, and the time and specific value of the abnormality 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 the nth day, P n-1 The performance parameter value of the previous day is set; the RCR threshold is set (such as RCR>10%), and when the threshold is exceeded, it is marked as an abnormal change. The correlation between the parameters is analyzed, the source of the abnormal change is determined, and reasonable countermeasures are proposed.

[0055] After completing the processing and analysis of all data, the relevant operating procedures are integrated into a script file, and the script operation is triggered by monitoring the data file directory, thereby realizing fully automatic and 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] The 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 in engineering data, and case-by-case data and energy spectrum data in scientific data.

[0059] The satellite platform engineering data is preliminarily processed in the 05-level fits format, with one file per day and the file name containing the date information. Read the file to obtain the time of each frame, the temperature value of each temperature measurement point, and the voltage value of the 8 FEE boards. Use the python script to read the fits file with the help of the astropy library to obtain the time data time_data, temperature data temp_data, and voltage data voltage_data. Filter the data with a voltage range of 700 to 900V.

[0060] Traverse all fits files, obtain temperature and voltage values, sort them by time, and plot the changes in temperature and voltage. By averaging daily voltage and temperature, we can get a long-term change graph, see Figure 3a , 3b and Figure 4a , 4b .

[0061] The engineering data of HXI load is 0C level CSv format data. Extract the temperature data of the probe and FEE board at different times of the day, calculate the average temperature of each day, and draw a graph of the average temperature changing with time. Figure 5 .

[0062] HXI's scientific data is 00-level raw data, compressed and stored in fits.gz format files by hour, stored in hexadecimal form. Scientific data is divided into 5 categories, and each data packet starts with a 4-byte "EB90EB90" identifier. FEE observation data is filtered out based on the packet type after the 4 bits of the 11th byte of each data packet. The first 4 bits of the 11th byte indicate the FEE to which the data packet belongs. The 5th to 10th bytes of the data packet are time codes, the first 4 bits indicate the number of seconds from January 1, 2019, and the last 2 bits are microseconds.

[0063] The energy spectrum data starts from the 19th byte and contains 26624 bytes. Every two bytes store the count of an ADC channel. A total of 13 channels of FEE are stored. Each channel has 2048 bytes, and the counts of 1024 ADC channels are stored. One frame of data contains 8 FEE data packets, each packet is 30990 bytes long, and one frame stores the data of the previous 4 seconds in normal mode.

[0064] By reading the scientific data every hour, we can get the time of each frame and the energy spectrum data of each channel of each FEE. The counts of the 1024 ADC channels of each channel are summed to get the total count of each channel (i.e. the corresponding probe) of each FEE in each frame.

[0065] Here are the steps to get the data through Python script:

[0066] S1: Use the fits function of the astropy library to read the HXI scientific data file, and the data is stored in the variable data;

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

[0068] S3: extract time information from the 5th to the 10th bytes and convert it into datetime format;

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

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

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

[0072] By plotting the total count changes of each probe per frame, e.g. Figure 6 As shown, it is found that the counts per frame of the non-radiation belt are basically in the range of 300 to 600. According to this count interval, the frames that meet the conditions are selected and the corresponding time information is obtained.

[0073] 2. Obtaining case-by-case spectra and energy spectra.

[0074] Before acquiring data, the data is filtered. The 6 bytes starting from the 5th byte of the data packet of each frame of the observation data store the deadline of the current data packet. First, the data packets of the normal mode are filtered by whether the time difference between adjacent data packets is 4 seconds; then, the data packets are further filtered according to the time range jointly defined by the normal working voltage and the non-radiation belt count, and finally the observation data of the non-burst mode and non-radiation belt area under the normal working voltage are obtained.

[0075] In the observation data packet, the case-by-case data is 4096 bytes starting from the 26643rd byte. Every 4 bytes record a case-by-case data, and a total of 1024 cases are recorded. In the 4 bytes of each case: the first four bits of the first byte record the channel number, and the last four bits and the second byte together form a 12-bit record of the ADC channel. The first four bits of the third byte record the trigger information, and the last four bits and the fourth byte form a 12-bit space record time code with an accuracy of 2us.

[0076] Take the case-by-case spectrum and energy spectrum of one day as an example. Scientific data is classified by day, and there are 24 fit.gz files classified by hour in each folder of each day. Assuming that the data of January 1, 2023 is read, the data is stored in the directory ' / sci_data / 2023 / 01 / 01'.

[0077] Use os.walk() through the Python script to traverse all the files in this directory, and use the load_selected_times function to obtain the time range valid_times_dic that satisfies the normal operating voltage and non-radiation belt count on that day. Then use the fits function of the astropy library to read the hourly data file, filter out the observation data and filter the data in normal mode. Then traverse the data of the 8 FEE boards to obtain the energy spectrum data and case-by-case data. According to valid_times_dic, perform data screening and accumulation operations on the channels of each FEE to obtain the hourly case-by-case spectrum eveData, energy spectrum speData and valid frame number frame_num. Finally, accumulate the hourly data to obtain the case-by-case spectrum all_eve_dat, energy spectrum all_spe_dat and valid total frame number all_frame_num for one day, and calculate the average energy spectrum of each frame. The specific implementation steps are as follows:

[0078] S1: traverse data files: traverse all files in the directory, read data files and filter observation data;

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

[0080] S3: Get event-by-event spectrum and energy spectrum data:

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

[0082] S5: filter data based on valid_times_dic;

[0083] S6: Accumulate the data of each channel to obtain the event-by-event spectrum and energy spectrum per hour;

[0084] S7: Accumulate daily data: accumulate the data for each hour to obtain the event-by-event spectrum and energy spectrum for the day.

[0085] Through the above steps, we can draw the event-by-event spectrum and energy spectrum of a day, see Figure 2 From the figure, we can clearly see the shapes of several characteristic peaks. The statistical fluctuation of the energy spectrum is smaller than that of the event-by-event spectrum, but the energy range of the event-by-event spectrum is larger and contains a 356keV peak that is not in the energy spectrum.

[0086] 3. Fitting and parameter acquisition of case-by-case spectrum and energy spectrum.

[0087] Before fitting, the observed data is first corrected for nonlinearity. According to the ground-based electronic nonlinear calibration data 'DNLCorData', the ADC channels are re-binned with equal spacing, and the counts of each ADC channel of the observed spectrum 'spe' are redistributed according to the overlap between the old bin and the new bin.

[0088] The comparison of energy spectra before and after nonlinear correction is as follows: Figure 7 As shown in the figure, in order to observe the difference between the two curves more clearly, the energy spectrum intensity before and after correction was slightly adjusted so that the two curves can be staggered. From the figure, it can be seen that the energy spectrum after correction has significantly reduced a lot of burrs, and the peak position has shifted to a certain extent compared with before correction, which shows that the nonlinear 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 and make them correspond one to one, the event-by-event spectrum ADC channels are first merged in pairs, and the first 128 channels are discarded, so that the starting point is the 129th ADC channel. Then the event-by-event spectrum is nonlinearly corrected, and the data is smoothed using Gaussian filtering to reduce noise. The 'find_peaks' algorithm of the scipy library is used to automatically find the peaks that need to be fitted. However, since the spectrum count decreases with the energy power law, the count difference between the high-energy end and the low-energy end is large, so it is not easy to use unified parameters for smoothing and peak finding. Therefore, the event-by-event spectrum is divided into two parts from the 700th ADC channel, and smoothing and peak finding operations are performed on each part, and the required peak finding parameters 'peaks' are screened out. The 'curve_fit' function of the scipy library is further used to perform Gaussian fitting on the found peaks to obtain the ADC channel number of the peak position.

[0090] The fitting results of the case-by-case spectrum are as follows Figure 8 As shown in the figure, after obtaining the peak values ​​on the ADC channel corresponding to several characteristic peaks, 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. Therefore, the 26.34keV peak is excluded and fitting is performed again. The slope of the obtained fitting parameters is the gain 'Gain'.

[0091] The above method can be used to obtain the gain of all probes, and then draw a time series graph of the gain. Fig. 9 The figure shows how the gain of the probes corresponding to different channels on FEEO changes over time.

[0092] According to the obtained gain and offset, the energy spectrum is converted to energy units. The energy spectrum is first corrected nonlinearly, then converted to energy units, and the energy range of the continuous spectrum is selected, and a quadratic function is used to fit the fitting function. The continuous spectrum is then deducted from all energy segments of the energy spectrum, and then the 53keV to 100keV range of the energy spectrum is selected for double Gaussian fitting.

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

[0094] After obtaining the peak area and energy resolution for each day, we can further draw a graph showing the changes in peak area and energy resolution over time, such as Fig.10 and Fig.11 shown.

[0095] 4. Anomaly detection

[0096] Abnormal detection uses the z-score method and relative change rate (RCR). The z-score detection is to first perform a moving average on the temperature and voltage data 'T_V_data' according to a certain window width to obtain the average mean 'T_V_mean' and standard deviation 'T_V_std', and then obtain the z-score according to (T_V_data-T_V_mean) / T_V_std. Finally, the abnormal data is judged according to -3<z-score<3; RCR detection is first RCR=|(P n -P n-1 ) / P n-1 |Get the RCR of gain, peak area or energy resolution, and then judge abnormal data according to RCR>10%.

[0097] Finally, all the above operations are implemented through the script script.sh. On the server, the changes in the specific data file directory are monitored to trigger the execution of the above script. The screen tool is used to implement the background operation of the above script, and the htop command is used to check whether the script is running normally. In this way, fully automatic and real-time performance monitoring and analysis can be achieved.

[0098] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A performance monitoring method for hard X-ray imagers of advanced space-based solar observatories, It is characterized in that include: Obtain engineering parameters of satellite platforms, engineering parameters of HXI, and scientific data of HXI; The temperature values ​​of each temperature measurement point on HXI at different times and the voltage values ​​of the FEE board at different times are obtained from the engineering parameters of the satellite platform, and a time series diagram of temperature and voltage is established; the time-varying temperature information of the calorimeter probe and the FEE board is obtained from the engineering parameters of HXI, and a time series diagram of temperature is established; the observation data is screened from the scientific data of HXI, and the case-by-case spectrum and energy spectrum are obtained from them, and the total counts of each probe in each frame are obtained, and a time series diagram of counts is established; According to the time series diagram of voltage and the time series diagram of count, the observation data are filtered in combination with the set working voltage interval, count interval and frame interval; Perform nonlinear correction on the case-by-case spectrum and energy spectrum of the screened observation data; perform energy calibration using the case-by-case spectrum after nonlinear correction to determine the relationship between the number of ADC channels and energy; obtain a time series diagram of the gain of each probe based on the relationship between the number of ADC channels and energy, and fit the characteristic peaks in the energy spectrum to obtain the fitting parameters of the characteristic peaks; obtain a time series diagram of the peak area and energy resolution of the characteristic peaks based on the fitting parameters of the characteristic peaks; The abnormal data of temperature and voltage are detected through the time series graph of temperature and voltage; the abnormal data of gain, peak area and energy resolution are detected through the time series graph of gain, peak area and energy resolution.

2. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The engineering parameters of the satellite platform include the temperature of the collimator frame, pointing mirror, satellite mounting substrate and calorimeter base, and the voltage of the FEE board; the engineering parameters of the HXI include the temperature of the probe and the FEE board; when reading the scientific data of the HXI, the data format is determined according to the packet type identifier at a specific byte position, and the observation data is filtered out accordingly.

3. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The operating voltage range is 700 to 900 V, the counting range is 300 to 600 per frame, and the frame interval is not less than 4 seconds.

4. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The nonlinear correction of the case-by-case spectrum and energy spectrum of the screened observation data is specifically: re-binning the case-by-case spectrum and energy spectrum using the nonlinear calibration data of the ground calibration.

5. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The energy calibration is performed using the nonlinearly corrected case-by-case spectrum to determine the relationship between the ADC channel number and the energy. Specifically, the characteristic peaks of the nonlinearly corrected case-by-case spectrum are searched for peaks and Gaussian fit is performed to obtain the ADC channel number of each energy peak, and a linear fit is performed in combination with the energy value corresponding to the ADC channel number to determine the relationship between the ADC channel number and the energy.

6. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The method of fitting the characteristic peak in the energy spectrum to obtain the fitting parameters of the characteristic peak is specifically as follows: the energy spectrum is converted into energy units according to energy calibration, a quadratic function is used to fit the energy spectrum continuum, the continuum is deducted from the energy spectrum, and then a double Gaussian fitting is performed on the interval containing the characteristic peak to obtain the fitting parameters of the characteristic peak.

7. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: According to the fitting parameters of the characteristic peak, the time series diagram 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, where 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 performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The abnormal data of temperature and voltage are detected through the time series diagram of temperature and voltage, specifically: the standard score of each temperature and voltage data point is calculated by using the z-score method, and a threshold is set to judge the abnormal point; the data points exceeding the threshold are marked, and the time and specific value of the abnormality are recorded.

9. The performance monitoring method of the advanced space-based solar observatory hard X-ray imager according to claim 1, Features: The abnormal data of gain, peak area and energy resolution are detected by using the time series diagram of gain, peak area and 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%, where P n is the performance parameter value on the nth day, P n-1 is the performance parameter value of the previous day; set the RCR threshold, and when the RCR of gain, peak area, and energy resolution exceeds the RCR threshold, it is marked as an abnormal change.

10. A performance evaluation method for hard X-ray imagers of advanced space-based solar observatories. Features: The working state and performance change of the HXI are evaluated by the results obtained by the performance monitoring method according to any one of claims 1 to 9.

Images