Miniaturized energy spectrum analysis and measurement system based on all-inorganic perovskite planar detector

Abstract

The present invention relates to the technical field of energy spectrum analysis and measurement, and specifically discloses a miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector, comprising: an FPGA signal processing unit: after the detector signal is amplified, it is processed through polarity conversion, frequency extraction, waveform differentiation and delay modules to extract peak values ​​and counts, and package them into original energy spectrum data; a host computer unit: smoothing the original energy spectrum data, dividing the total energy peak and escape peak regions, deducting the background through polynomial fitting, extracting the net escape peak counts, and dynamically mapping them to the total energy peak, correcting the count loss, and generating real energy spectrum data; an energy spectrum analysis unit: performing energy resolution calculation, nuclide identification and other analyses based on the corrected real energy spectrum data; the present invention solves the problem of escape peak interference of the perovskite detector through the collaboration of hardware and algorithms, and realizes miniaturized and high-precision energy spectrum analysis.

Description

Technical Field

[0001] The present invention relates to the field of energy spectrum analysis and measurement technology, and in particular to a miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector. Background Art

[0002] Although high-purity germanium (HPGe) detectors maintain their industry benchmark status with an energy resolution of 0.2% (662keV), their strict dependence on the cryogenic environment of liquid helium greatly limits their portable applications; silicon carbide (SiC) detectors are limited by the low average atomic number and the bottleneck of single crystal material preparation technology, making it difficult to achieve effective absorption of high-energy rays; although cadmium zinc telluride (CdZnTe) detectors can operate at room temperature, the complex crystal growth process and high manufacturing costs hinder large-scale promotion. The all-inorganic metal halide perovskite CsPbBr3 has opened up a new path for room-temperature nuclear detection technology due to its excellent photoelectric properties and long-term stability, as well as low cost and easy preparation. However, the traditional flat electrode design is difficult to cope with high-flux ray measurements due to its low carrier separation efficiency and large dark current; in addition, 662keV γ 75keVK produced by ray excitation of Pb α If X-rays escape, a 75keV escape peak will be formed in the energy spectrum, causing energy measurement deviation.

[0003] Therefore, the present invention develops a miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite (CsPbBr3) planar detector to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector, including:

[0007] FPGA processing unit: including polarity selection module, frequency extraction module, waveform differentiation module and delay module;

[0008] Acquire a detection signal, input the detection signal into an amplifier, and the amplifier outputs an analog signal; convert the analog signal into a filtered signal through the polarity selection module and the frequency extraction module;

[0009] The waveform differentiation module and the delay module respectively count and search for peaks on the filtered signal to obtain counting statistics and peak results; and the data group obtained by packaging the counting statistics and peak results is recorded as original energy spectrum data;

[0010] The host computer unit performs smoothing processing on the raw energy spectrum data and performs energy spectrum display on the raw energy spectrum data;

[0011] The original energy spectrum data is divided into a full energy peak region and an escape peak region, and the escape peak region is corrected to obtain a net escape peak count, and the net escape peak count is mapped to the full energy peak region according to an energy scale relationship to obtain true energy spectrum data;

[0012] Energy spectrum analysis unit: includes an energy spectrum analysis module, which performs energy spectrum analysis measurement on real energy spectrum data.

[0013] As a further solution of the present invention: the process of obtaining the filtered signal includes:

[0014] The analog signal is converted into a digital signal, and the digital signal is input into the polarity selection module. The polarity selection module converts the analog signal into a positive signal, and transmits the positive signal to the frequency extraction module. The positive signal is filtered by the frequency extraction module to obtain a filtered signal.

[0015] As a further solution of the present invention: the process of filtering the positive signal by the frequency extraction module includes:

[0016] The principle formula for filtering the positive signal by the frequency extraction module is:

[0017] , where n represents the nth sampling point on the positive signal, m is the total number of sampling points, y(n) represents the filtered signal, and x(n) represents the positive signal.

[0018] As a further solution of the present invention, the process of the waveform differentiation module counting the filtered signal includes:

[0019] The waveform differentiation module performs fast trapezoidal shaping on the filtered signal. The trapezoidal formula of the fast trapezoidal shaping is:

[0020] , where y´(n) represents the output signal obtained after the filtered signal is fast ladder-formed, n a is the number of rising edge sampling points of the filtered signal, n c is the number of points from the rising edge to the falling edge of the trapezoidal filter signal, n c =n a +n b , , T s is the sampling rate of the positive signal, τ is the decay constant, n1— sub=[y(n-1)-y(nn a -1)-y(nn b -1)+y(nn c -1)], n 1— Sub delays one clock cycle to get n 2— sub, and n 2— sub=[y(n-2)-y(nn a -2)-y(nn b -2)+y(nn c -2)];

[0021] The waveform differentiation module differentiates the filtered signal, and the differentiation formula is:

[0022] , where y´´(n) represents the output signal after differentiating the filtered signal;

[0023] The FPGA processing unit further includes a counting module for counting input signals;

[0024] The output signal after the filter signal is differentiated is recorded as the differential output signal; the differential output signal is subjected to zero-crossing judgment to obtain a zero-crossing judgment result, and the zero-crossing judgment result is input into the counting module to obtain a counting statistical result.

[0025] As a further solution of the present invention, the process of the delay module performing peak search on the filtered signal includes:

[0026] The delay module performs trapezoidal shaping on the filtered signal to compress the filtered signal into a fast signal; obtains a baseline of the fast signal, and restores the baseline to obtain a restored signal, wherein the restoration process is based on a sliding average method;

[0027] A zero-crossing detection is performed on the restored signal, and after the zero-crossing detection is completed, the recovery of the baseline is frozen until the trapezoidal formation is completed; and a pile-up judgment is performed on the restored signal. If the judgment is passed, a peak search is performed on the restored signal.

[0028] As a further solution of the present invention, the process of performing accumulation judgment on the recovery signal includes:

[0029] Obtain the differential output signal and the recovery signal, and respectively obtain the zero-crossing points of the differential output signal and the recovery signal, and record them as the differential zero-crossing point T1 and the recovery zero-crossing point T2 respectively; if |T1-T2|<T shaping , then the filtered signal is a pile-up signal, the judgment fails at this time, and the filtered signal is discarded; if |T1-T2|≥Tshaping , there is no accumulation of the filtered signal, and the judgment is passed at this time, and the filtered signal is input to the upper computer unit.

[0030] As a further solution of the present invention: the process of dividing the original energy spectrum data into a full energy peak region and an escape peak region includes:

[0031] A sliding average method is used to remove the peak of the original energy spectrum data, extract the low-frequency background trend, and perform cubic polynomial fitting with the low-frequency background trend as the background point to obtain a fitting formula; the original energy spectrum data is fitted by the fitting formula to obtain fitting data, the fitting data is subtracted from the original energy spectrum data to obtain characteristic peak net data, and Gaussian fitting is performed on the characteristic peak net data to obtain a full energy peak area and an escape peak area.

[0032] As a further solution of the present invention: the process of correcting the escape peak area includes:

[0033] Perform trinomial fitting on the escape peak region to obtain a new fitting formula, and perform Gaussian fitting on the escape peak region to obtain the half-height width C E , according to the half-height width C E , define the energy range E escape ±C E , where E escape represents the central energy value corresponding to the escape peak area;

[0034] The original energy spectrum data is fitted by the new fitting formula to obtain new fitting data B (E), and the net escape peak count N is obtained by subtracting the new fitting data from the original energy spectrum data. escape (E) = N raw (E)-B(E), where N raw (E) represents the original energy spectrum data.

[0035] Beneficial effects of the present invention:

[0036] The present invention accurately identifies and corrects Pb escape peaks (such as the 587keV false peak caused by 75keV characteristic X-rays) through polynomial fitting and Gaussian fitting, migrates the escape peak counts back to the full-energy peak (such as 662keV), and restores the true energy spectrum shape; and adopts sliding average + cubic polynomial fitting to effectively separate the Compton platform and characteristic peaks, reducing noise interference in the low-energy region; the present invention integrates high-voltage bias, FPGA processing, Ethernet communication and other modules into three 50mm×50mm circuit boards, with a volume of only 1 / 4 of the traditional solution; uses FPGA algorithms to replace traditional analog circuits (CR-RC filtering, amplifiers, etc.), reducing hardware complexity and power consumption; in addition, the present invention compresses the original pulse width from 500μs to the microsecond level, significantly improving signal throughput and avoiding pulse accumulation; through accumulation judgment, the accumulated signal is discarded in real time to ensure the accuracy of the counting rate; through baseline recovery and differential enhancement, noise interference is suppressed and the weak signal detection capability is improved.

[0037] The present invention includes a main electronics system that utilizes a high-density integrated design architecture to implement functions such as bias output within 1KV, IV conversion, high-bandwidth data acquisition, FPGA core processor deployment, and gigabit network communication. The FPGA signal processing unit improves the signal throughput and energy resolution of the original detector through specialized algorithms such as frequency extraction, waveform differentiation, fast prototyping, anti-pile-up processing, and energy spectrum calculation. The host computer unit smoothes the raw energy spectrum data, divides it into full-energy peak and escape peak regions, subtracts the background through polynomial fitting, extracts the net escape peak counts, and dynamically maps them to the full-energy peak to correct for full-energy peak count loss, generating true energy spectrum data. Based on this, energy resolution calculation and nuclide identification are performed. Through the synergy of hardware and algorithms, the present invention solves the problems of slow raw signal, low throughput, escape peak interference, and noise in planar perovskite detectors, achieving miniaturization and high-precision energy spectrum analysis of planar perovskite detectors, and opening up the possibility of widespread application of this type of detector in the field of energy spectrum measurement.

[0038] In summary, this invention addresses three core challenges of perovskite detectors in energy spectrum analysis through hardware miniaturization and intelligent algorithms: 1. Restoring the true spectral shape through dynamic correction addresses escape peak interference; 2. Rapid ladder formation improves count rate to address signal accumulation; and 3. Highly integrated design replaces bulky equipment to address portability. The result is a room-temperature, high-resolution, and low-cost radiation detection system, providing a breakthrough solution for nuclear safety, medical imaging, and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below with reference to the accompanying drawings.

[0040] Figure 1This is a schematic diagram of the method flow of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0041] Figure 2 It is a flow chart of the FPGA processing unit in the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0042] Figure 3 This is the original Cs137 energy spectrum of perovskite in the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0043] Figure 4 This is the original Eu152 energy spectrum of perovskite in the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0044] Figure 5 This is an energy channel address fitting diagram of the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0045] Figure 6 It is the escape peak polynomial and Gaussian fitting diagram in the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0046] Figure 7 This is the corrected Cs137 spectrum in the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention;

[0047] Figure 8 It is a schematic diagram of straight line fitting in the experimental results of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] See also Figure 1 As shown in the figure, the present invention designs a miniaturized energy spectrum analysis system for perovskite planar detectors, designed to address the problems of escape peaks and accumulation caused by slow raw signal processing during detector operation. The system consists of a highly integrated hardware core and hardware algorithms, and dedicated host computer software with dynamic energy calibration correction capabilities.

[0050] Part I Hardware Design:

[0051] To achieve the device's miniaturization, the hardware system adopts a high-density integrated architecture. Core functional modules such as the high-voltage bias circuit, power management module, Gigabit Ethernet communication unit, low-noise preamplifier circuit, and FPGA processing unit are integrated onto three 50mm x 50mm functional printed circuit boards through a modular design. Each functional board adopts a vertically stacked structure, and high-speed signal interconnection between boards is achieved using precision connectors. While ensuring the functional integrity of the system, the overall volume is compressed to 1 / 4 of that of traditional solutions, significantly improving the device's portability and environmental adaptability. This design effectively reduces signal transmission loss through three-dimensional spatial layout optimization and reserves standardized interfaces for subsequent expansion, meeting the flexible deployment requirements in special scenarios.

[0052] The second part is FPGA code design:

[0053] The entire pulse width of the signal is about 500 microseconds. In order to improve the signal throughput, it is necessary to perform fast shaping on the signal, and the overall pulse width becomes narrower during this period, thereby improving the energy resolution. In order to reduce the hardware volume, the invention abandons the traditional amplifier design idea, that is, front + excitation cancellation + amplifier + filter shaping, and all these functions are realized by digital means, that is, using FPGA algorithm. The FPGA block diagram is as follows: Figure 2 As shown;

[0054] like Figure 2 As shown, after the ADC data enters the FPGA, it first enters the polarity selection module, which converts the negative signal output by the perovskite planar detector into a positive signal, and then outputs it to the frequency extraction module. The frequency extraction module acts as a smoothing filter module. The present invention smoothes and filters the original pulse according to the window size set by the host computer, which can not only reduce the amount of original waveform data and alleviate FPGA resource consumption, but also filter the original data. The principle of the frequency extraction module is shown in the formula:

[0055] , where n represents the nth sampling point on the positive signal, m is the total number of sampling points, y(n) represents the filtered signal, and x(n) represents the positive signal;

[0056] After being processed by the frequency extraction module, the signal is simultaneously fed into the waveform differentiation module and the delay module. The waveform differentiation module integrates two major functions: fast ladder shaping and differential operation. The fast ladder shaping stage rapidly shapes the original signal to achieve accumulated signal separation; the differential operation converts the original unipolar signal into a bipolar signal, which is then fed into the zero-crossing detection module. The fast ladder shaping formula is shown as follows:

[0057] , where y´(n) represents the output signal obtained after the filtered signal is fast ladder-formed, n ais the number of rising edge sampling points of the filtered signal, n c is the number of points from the rising edge to the falling edge of the trapezoidal filter signal, n c =n a +n b , , T s is the sampling rate of the positive signal, τ is the decay constant and τ =RC;

[0058] where n 1— sub=[y(n-1)-y(nn a -1)-y(nn b -1)+y(nn c -1)], n 1— Sub delays one clock cycle to get n 2— sub, and n 2— sub=[y(n-2)-y(nn a -2)-y(nn b -2)+y(nn c -2)];

[0059] The differential formula is shown as follows:

[0060] , where y´´(n) represents the output signal after differentiating the filtered signal;

[0061] By adjusting the distance between the two differential sampling points, the amplitude change between the two differential sampling points is expanded. After completing the waveform differentiation, the data is judged to be zero-crossing, and finally the result is sent to the counting module for counting statistics.

[0062] After the data enters the delay module, it immediately enters the trapezoidal shaping module. The formula is consistent with Formula 3. Fast trapezoidal shaping compresses the signal into a fast signal by sacrificing the energy information of the original signal to separate the accumulated signal, thereby ensuring the accuracy of the counting rate. However, the purpose of the trapezoidal shaping here is to accurately extract the peak value of the original signal, so compared with the fast trapezoidal shaping, its n a 、n b 、n c Slightly different. After shaping, the signal baseline will shift, so it is necessary to restore the baseline before peak finding. The baseline recovery module uses a sliding average method for calculation. The sliding window size is determined by the host computer. After zero-crossing detection is completed, the baseline calculation is frozen until the trapezoidal shaping is completed.

[0063] After completing the baseline recovery, before entering the peak search mode, accumulation judgment is required. If the interval between two signals is less than the trapezoidal formation time, it is considered to be an accumulation signal, and the lower computer will discard it. If there is no accumulation, it enters the peak search stage and transmits the peak result to the upper computer.

[0064] The third step is the design of host computer software:

[0065] After the data enters the host computer, the energy spectrum is displayed. In the present invention, the following improvements are made on the basis of the traditional energy spectrum algorithm for the perovskite energy spectrum characteristics.

[0066] 1. The traditional linear fitting method for background data fitting is abandoned, and polynomial fitting is used instead. Compared with the former, polynomial fitting is more flexible, adaptable to complex background applications, and more accurate in solving energy resolution. However, high-order polynomials may overfit noise, so the choice of order is particularly important. The invention implements polynomial fitting in two steps.

[0067] Step 1: Use sliding average to remove peaks and extract low-frequency background trends. The specific implementation principle is as follows:

[0068] , where j is the local index variable of the sliding average method, which is used to traverse the data points in the sliding window. It represents the estimated background count value at the i-th energy channel in the original energy spectrum data after removing the peak, Y(x i +j) represents the original energy spectrum data, 2k+1 is the size of the sliding window, i represents the i-th energy channel address of the original energy spectrum data, x i +j represents the adjacent energy channel position after being offset to the left and right by j energy channels, with the i-th energy channel as the center;

[0069] In the above, 2k+1 is the sliding window size. It should be noted that the sliding window k should be large enough to flatten the sharp characteristic peaks.

[0070] Step 2: Use the sliding average result as the background point and fit a cubic polynomial as follows:

[0071] Assume the standard expression of the cubic polynomial f(E) = α 1E 3 + α 2E 2 + α 3E+ α 4, among which α 1. α 2. α 3 and α 4 are all fitting parameters, E is the independent variable, which represents the energy channel address, and f(E) is the dependent variable, which represents the background count estimate at the energy channel address;

[0072] The fitting parameters are obtained based on minimizing the residual sum of squares, and the obtaining process is that each fitting parameter satisfies , where w represents the wth sliding window, E w represents the energy address of the w-th sliding window, represents the estimated background count value at the energy channel of the w-th sliding window;

[0073] After completing the above two steps, the fitting formula is obtained, and the net data of the characteristic peak is obtained by subtracting the fitting data from the original data, and then Gaussian fitting is performed.

[0074] 2. Use the dynamic energy scale correction method to correct the impact of the full energy peak count loss caused by the Pb escape peak. Step 1: Smooth the original energy spectrum to reduce the interference of statistical fluctuations on the fitting. Step 2: Use the above polynomial fitting method to perform a polynomial fit on the escape peak area to obtain the fitting formula B(E). Step 3: Perform a Gaussian fit on the escape peak to obtain the half-height width C E , define the energy range E escape ±C E Step 4: Extract the escape peak counts. Subtract the fitted background B(E) from the original energy spectrum to obtain the net escape peak counts, as shown in the formula:

[0075] N escape (E) = N raw (E)-B(E), where N raw (E) represents the original energy spectrum data;

[0076] Step 5: Count migration, map the escape peak counts to the full energy peak according to the energy scale relationship.

[0077] The fourth experimental result:

[0078] After connecting the all-inorganic perovskite planar detector to the energy spectrum analysis measurement system, the Cs137 and Eu152 radiation sources were used to test the results. Figure 3 、 Figure 4 shown.

[0079] By fitting the channel addresses corresponding to the above 40keV, 121keV, 244keV, 344keV, and 662keV, the energy channel address correspondence is obtained, as shown in the figure: Figure 5 shown.

[0080] from Figure 5It can be seen that the channel addresses and energies measured by the system have a strong linear relationship, demonstrating that the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector is correctly designed in terms of its core functionality. This relationship can also be used for energy calibration, converting channel addresses and energies to obtain an actual spectrum with energy as the horizontal axis.

[0081] After obtaining the actual spectrum, the influence of the Pb escape peak needs to be removed. First, filter the spectrum, then perform polynomial fitting and Gaussian fitting on the Compton platform to obtain the fitting curve and half-height width, as shown in the figure. Figure 6 shown.

[0082] Figure 6 In the figure, the green curve represents the background polynomial fitting curve of the escape peak, and the red curve represents the peak Gaussian fitting curve of the escape peak. Then, dynamic energy scale correction is performed to restore the true Cs137 spectrum, as shown in Figure 2. Figure 7 As shown;

[0083] Figure 8 The energy resolution calculation results are similar to those of the background data using a straight line fitting method. Figure 7 The difference is 0.2%, which shows that the background subtraction method using curve fitting in the present invention is superior.

[0084] The above is a detailed description of one embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector, characterized in that: include: FPGA processing unit: including polarity selection module, frequency extraction module, waveform differentiation module and delay module; Acquire a detection signal, input the detection signal into an amplifier, and the amplifier outputs an analog signal; convert the analog signal into a filtered signal through the polarity selection module and the frequency extraction module; The waveform differentiation module and the delay module respectively count and search for peaks on the filtered signal to obtain counting statistics and peak results; and the data group obtained by packaging the counting statistics and peak results is recorded as original energy spectrum data; The host computer unit performs smoothing processing on the raw energy spectrum data and performs energy spectrum display on the raw energy spectrum data; The original energy spectrum data is divided into a full energy peak region and an escape peak region, and the escape peak region is corrected to obtain a net escape peak count, and the net escape peak count is mapped to the full energy peak region according to an energy scale relationship to obtain true energy spectrum data; Energy spectrum analysis unit: includes an energy spectrum analysis module, which performs energy spectrum analysis measurement on real energy spectrum data.

2. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 1 is characterized in that: The process of obtaining the filtered signal includes: The analog signal is converted into a digital signal, and the digital signal is input into the polarity selection module. The polarity selection module converts the analog signal into a positive signal, and transmits the positive signal to the frequency extraction module. The positive signal is filtered by the frequency extraction module to obtain a filtered signal.

3. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 2 is characterized in that: The process of filtering the positive signal by the frequency extraction module includes: The principle formula for filtering the positive signal by the frequency extraction module is: , where n represents the nth sampling point on the positive signal, m is the total number of sampling points, y(n) represents the filtered signal, and x(n) represents the positive signal.

4. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 3 is characterized in that: The process of the waveform differentiation module counting the filtered signal includes: The waveform differentiation module performs fast trapezoidal shaping on the filtered signal. The trapezoidal formula of the fast trapezoidal shaping is: , where y´(n) represents the output signal obtained after the filtered signal is fast ladder-formed, n a is the number of rising edge sampling points of the filtered signal, n c is the number of points from the rising edge to the falling edge of the trapezoidal filter signal, n c =n a +n b , , T s is the sampling rate of the positive signal, τ is the decay constant, n 1— sub=[y(n-1)-y(nn a -1)-y(nn b -1)+y(nn c -1)], n 1— Sub delays one clock cycle to get n 2— sub, and n 2— sub=[y(n-2)-y(nn a -2)-y(nn b -2)+y(nn c -2)]; The waveform differentiation module differentiates the filtered signal, and the differentiation formula is: , where y´´(n) represents the output signal after differentiating the filtered signal; The FPGA processing unit further includes a counting module for counting input signals; The output signal after the filter signal is differentiated is recorded as the differential output signal; the differential output signal is subjected to zero-crossing judgment to obtain a zero-crossing judgment result, and the zero-crossing judgment result is input into the counting module to obtain a counting statistical result.

5. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 4 is characterized in that: The process of the delay module performing peak search on the filtered signal includes: The delay module performs trapezoidal shaping on the filtered signal to compress the filtered signal into a fast signal; obtains a baseline of the fast signal, and restores the baseline to obtain a restored signal, wherein the restoration process is based on a sliding average method; A zero-crossing detection is performed on the restored signal, and after the zero-crossing detection is completed, the recovery of the baseline is frozen until the trapezoidal formation is completed; and a pile-up judgment is performed on the restored signal. If the judgment is passed, a peak search is performed on the restored signal.

6. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 5, characterized in that: The process of performing accumulation judgment on the recovery signal includes: Obtain the differential output signal and the recovery signal, and respectively obtain the zero-crossing points of the differential output signal and the recovery signal, and record them as the differential zero-crossing point T1 and the recovery zero-crossing point T2 respectively; if |T1-T2|<T shaping , then the filtered signal is a pile-up signal, the judgment fails at this time, and the filtered signal is discarded; if |T1-T2|≥T shaping , there is no accumulation of the filtered signal, and the judgment is passed at this time, and the filtered signal is input to the upper computer unit.

7. The miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector according to claim 1 is characterized in that: The process of dividing the original energy spectrum data into a full energy peak region and an escape peak region includes: A sliding average method is used to remove the peak of the original energy spectrum data, extract the low-frequency background trend, and perform cubic polynomial fitting with the low-frequency background trend as the background point to obtain a fitting formula; the original energy spectrum data is fitted by the fitting formula to obtain fitting data, the fitting data is subtracted from the original energy spectrum data to obtain characteristic peak net data, and Gaussian fitting is performed on the characteristic peak net data to obtain a full energy peak area and an escape peak area.

8. The miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector according to claim 1, characterized in that: The process of correcting the escape peak region includes: Perform trinomial fitting on the escape peak region to obtain a new fitting formula, and perform Gaussian fitting on the escape peak region to obtain the half-height width C E , according to the half-height width C E , define the energy range E escape ±C E , where E escape represents the central energy value corresponding to the escape peak area; The original energy spectrum data is fitted by the new fitting formula to obtain new fitting data B (E), and the net escape peak count N is obtained by subtracting the new fitting data from the original energy spectrum data. escape (E) = N raw (E)-B(E), where N raw (E) represents the original energy spectrum data.

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