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

Abstract

The invention relates to the technical field of energy spectrum analysis and measurement, and particularly discloses a miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite plane detector, and the system comprises an FPGA signal processing unit which is used for amplifying a detector signal, carrying out the processing of polarity conversion, frequency extraction, waveform differential and a delay module, extracting a peak value, counting, and packaging into original energy spectrum data; the upper computer unit is used for smoothing the original energy spectrum data, dividing a full-energy peak region and an escape peak region, deducting a background through polynomial fitting, extracting a net escape peak count, dynamically mapping to a full-energy peak, correcting a count loss and generating real energy spectrum data; the energy spectrum analysis unit is used for performing analysis such as energy resolution calculation and nuclide identification based on the corrected real energy spectrum data; through cooperation of hardware and an algorithm, escape peak interference of the perovskite detector is solved, and miniaturized and high-precision energy spectrum analysis is realized.

Description

Technical Field

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

[0002] Although high-purity germanium (HPGe) detectors maintain the industry benchmark with an energy resolution of 0.2% (662 keV), their strict dependence on a liquid helium cryogenic environment greatly limits 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; cadmium zinc telluride (CdZnTe) detectors can work at room temperature, but the complex crystal growth process and high manufacturing cost hinder large-scale promotion. The all-inorganic metal halide perovskite CsPbBr3, with its excellent optoelectronic properties, long-term stability, low cost, and easy preparation, has opened up a new path for room-temperature nuclear detection technology. However, the traditional flat electrode design has low carrier separation efficiency and large dark current, making it difficult to handle high-throughput ray measurements; in addition, if the 75 keV K X-ray excited by the 662 keV ray escapes, it will form a 75 keV escape peak in the energy spectrum, causing energy measurement deviation. γ The 75 keV K α X-ray generated by the excitation of Pb will cause an energy measurement deviation if it escapes and forms a 75 keV escape peak in the energy spectrum.

[0003] Therefore, the present invention has developed a miniaturized energy spectrum analysis 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 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: A miniaturized energy spectrum analysis measurement system based on an all-inorganic perovskite planar detector includes: An FPGA processing unit: including a polarity selection module, a frequency extraction module, a waveform differentiation module, and a delay module; Obtain a detection signal, input the detection signal into an amplifier, and the amplifier outputs an analog signal; through the polarity selection module and the frequency extraction module, convert the analog signal into a filtered signal; The waveform differentiation module and the delay module respectively count and peak-seek the filtered signal to obtain a count statistical result and a peak result; and the data packet obtained by packing the count statistical result and the peak result is recorded as the original energy spectrum data; An upper computer unit: perform smoothing processing on the original energy spectrum data and display the energy spectrum of the original energy spectrum data; Divide the original energy spectrum data into a full-energy peak region and an escape peak region, correct the escape peak region to obtain the net escape peak count, and map the net escape peak count to the full-energy peak region according to the energy scale relationship to obtain the true energy spectrum data; Energy spectrum analysis unit: including an energy spectrum analysis module, which performs energy spectrum analysis measurement on the true energy spectrum data.

[0006] As a further solution of the present invention: the process of obtaining the filtered signal includes: Convert the analog signal into a digital signal, input the digital signal 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, and filter the positive signal through the frequency extraction module to obtain a filtered signal.

[0007] As a further solution of the present invention: the process of the frequency extraction module filtering the positive signal includes: The principle formula for the frequency extraction module to filter the positive signal 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.

[0008] As a further solution of the present invention: the process of the waveform differentiation module counting the filtered signal includes: The waveform differentiation module performs fast trapezoidal shaping on the filtered signal, and the trapezoidal formula for the fast trapezoidal shaping is: , where y´(n) represents the output signal obtained after the filtered signal is subjected to fast trapezoidal shaping, n a is the number of sampling points at the rising edge of the filtered signal, n c is the number of points from the rising edge of the filtered signal to the end of the trapezoidal falling edge, n c =n a +n b , , T s is the sampling rate of the positive signal, τ is the attenuation constant, n 1— sub = [y(n - 1) - y(n - n a - 1) - y(n - n b - 1) + y(n - n c - 1)], delay n 1— sub by one clock cycle to obtain n 2— sub, and n 2—sub = [y(n - 2) - y(n - n a - 2) - y(n - n b - 2) + y(n - n 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 and statistically analyzing the input signal; The output signal after differentiating the filtered signal is denoted as the differential output signal; a zero-crossing judgment is performed on the differential output signal to obtain a zero-crossing judgment result, and the zero-crossing judgment result is input into the counting module to obtain a counting and statistical result.

[0009] As a further solution of the present invention: The process of the delay module performing peak searching on the filtered signal includes: The delay module performs trapezoidal shaping on the filtered signal to compress the filtered signal into a fast signal; the baseline of the fast signal is obtained, and the baseline is restored to obtain a restored signal, and the restoration process is based on a moving average method; Zero-crossing detection is performed on the restored signal, and after the zero-crossing detection is completed, the restoration of the baseline is frozen until the trapezoidal shaping is completed; and a stacking judgment is performed on the restored signal. If the judgment passes, peak searching is performed on the restored signal.

[0010] As a further solution of the present invention: The process of performing a stacking judgment on the restored signal includes: The differential output signal and the restored signal are obtained, and the zero-crossing points of the differential output signal and the restored signal are respectively obtained and denoted as the differential zero-crossing point T1 and the restored zero-crossing point T2; if |T1 - T2| < T shaping , then the filtered signal is a stacked signal, at this time the judgment fails, and the filtered signal is discarded; if |T1 - T2| ≥ T shaping , there is no stacking in the filtered signal, at this time the judgment passes, and the filtered signal is input into the host computer unit.

[0011] 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: The peak of the original energy spectrum data is removed by the moving average method, and the low-frequency background trend is extracted. Taking the low-frequency background trend as the background point, a cubic polynomial fitting is performed to obtain a fitting formula. The original energy spectrum data is fitted through the fitting formula to obtain fitting data. The original energy spectrum data is subtracted from the fitting data to obtain the net data of the characteristic peak, and the net data of the characteristic peak is subjected to Gaussian fitting to obtain the full-energy peak region and the escape peak region.

[0012] As a further solution of the present invention: the process of correcting the escape peak region includes: Perform a 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 full width at half maximum C E , according to the full width at half maximum C E , define the energy range E escape ±C E , where E escape represents the central energy value corresponding to the escape peak region; The original energy spectrum data is fitted through the new fitting formula to obtain new fitting data B(E). The original energy spectrum data is subtracted from the new fitting data to obtain the net escape peak count N escape (E)=N raw (E)-B(E), where N raw (E) represents the original energy spectrum data.

[0013] Advantages of the present invention: The present invention accurately identifies and corrects the Pb escape peak (such as the 587 keV false peak caused by the 75 keV characteristic X-ray) through polynomial fitting and Gaussian fitting, migrates the escape peak count back to the full-energy peak (such as 662 keV), and restores the true energy spectrum shape; and adopts moving average + cubic polynomial fitting to effectively separate the Compton plateau and the characteristic peak, reducing the noise interference in the low-energy region; the present invention integrates modules such as high-voltage bias, FPGA processing, and Ethernet communication on three circuit boards of 50 mm × 50 mm, and the volume is only 1 / 4 of the traditional solution; uses FPGA algorithms to replace traditional analog circuits (such as CR-RC filters, amplifiers, etc.), reducing the hardware complexity and power consumption; in addition, the present invention compresses the original pulse width from 500 μs to the microsecond level, significantly improving the signal throughput and avoiding pulse pile-up; through pile-up judgment, the piled-up signals are discarded in real time to ensure the accuracy of the counting rate; through baseline recovery and differential enhancement, the noise interference is suppressed and the weak signal detection ability is improved.

[0014] The present invention includes a main electronics system: adopting a high-density integrated design architecture to achieve functions such as bias voltage output within 1 KV, IV conversion, high-bandwidth data acquisition, deployment of an FPGA core processor, 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 decimation, waveform differentiation, fast shaping, anti-pileup processing, and energy spectrum calculation. The host computer unit: smooths the original energy spectrum data, divides the full-energy peak and escape peak regions, deducts the background through polynomial fitting, extracts the net escape peak count, and dynamically maps it to the full-energy peak to correct the full-energy peak count loss, generating real energy spectrum data. On this basis, analyses such as energy resolution calculation and nuclide identification are carried out; through the cooperation of hardware and algorithms, the present invention solves problems such as the overly slow original signal, low throughput, escape peak interference, and noise problems of planar perovskite detectors, realizes the miniaturization and high-precision energy spectrum analysis of planar perovskite detectors, and provides the possibility for the wide application of this type of detector in the field of energy spectrum measurement.

[0015] In summary, through hardware miniaturization and algorithm intelligence, the present invention solves three core problems of perovskite detectors in energy spectrum analysis: 1. Solving escape peak interference by dynamically correcting to restore the real spectrum shape; 2. Solving signal pileup by improving the counting rate through fast ramp shaping; 3. Solving the lack of portability by replacing bulky equipment with a high-integration design. Finally, a radiation detection system with room-temperature operation, high resolution, and low cost is realized, providing a breakthrough solution for fields such as nuclear safety and medical imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a schematic flow chart of the method of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 2 It is a schematic flow chart of the FPGA processing unit in the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 3 It is the perovskite original Cs137 energy spectrum diagram in the experimental results of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 4 It is the perovskite original Eu152 energy spectrum diagram in the experimental results of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 5 It is the energy channel address fitting diagram in the experimental results of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 6It is the escape peak polynomial and Gaussian fitting diagram in the experimental effect of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 7 It is the corrected Cs137 spectrum in the experimental effect of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector of the present invention; Figure 8 It is a schematic diagram of straight line fitting in the experimental effect of the miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention designs a miniaturized energy spectrum analysis system for a perovskite planar detector, which is used to solve the escape peak problem generated during the use of the detector and the accumulation problem caused by the original signal being too slow. The system is divided into a highly integrated hardware body and hardware algorithm and a dedicated host computer software with a dynamic energy scale correction function.

[0020] Part I Hardware Design: In order to realize the miniaturization design of the equipment, the hardware system adopts a high-density integrated architecture, and integrates the core functional modules such as high-voltage bias circuit, power management module, Gigabit Ethernet communication unit, low-noise preamplifier circuit and FPGA processing unit on three 50mm×50mm functional printed circuit boards through modular design. Each functional board adopts a vertical stacking structure and uses precision connectors to achieve high-speed signal interconnection between boards. While ensuring the functional integrity of the system, the overall volume is compressed to 1 / 4 of the traditional solution, which significantly improves the portability and environmental adaptability of the equipment. The design effectively reduces signal transmission loss through three-dimensional spatial layout optimization, and reserves standardized interfaces for subsequent expansion to meet the flexible deployment requirements in special scenarios.

[0021] The second part of FPGA code design: The entire pulse width of the signal is about 500 microseconds. In order to increase the throughput of the signal, it is necessary to quickly shape the signal, so that the overall pulse width becomes narrower, 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 such as 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 serves as a smoothing filter module. In the present invention, the original pulse is smoothed and filtered according to the window size set by the host computer, which can not only reduce the amount of the original waveform data and reduce the FPGA resource consumption, but also filter the original data. The principle of the frequency extraction module is as shown in the formula: , 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; After being processed by the frequency extraction module, the signal synchronously enters the waveform differentiation module and the delay module. The waveform differentiation module integrates two major functions: fast ramp shaping and differentiation operation. The fast ramp shaping link performs fast shaping on the original signal to achieve the separation of the stacked signals; the differentiation operation converts the original unipolar signal into a bipolar signal and transmits it to the zero-crossing detection module. The fast ramp shaping formula is as shown in the formula: , where y´(n) represents the output signal obtained after the fast ramp shaping of the filtered signal, n a is the number of sampling points at the rising edge of the filtered signal, n c is the number of points from the rising edge of the filtered signal to the end of the trapezoidal falling edge, n c =n a +n b , , T s is the sampling rate of the positive signal, τ is the attenuation constant and τ =RC; where n 1— sub = [y(n - 1) - y(n - n a - 1) - y(n - n b - 1) + y(n - n c - 1)], delay n 1— sub by one clock cycle to get n 2— sub, and n 2— sub = [y(n - 2) - y(n - n a - 2) - y(n - n b - 2) + y(n - n c - 2)]; The differentiation formula is as shown in the formula: , where y´´(n) represents the output signal after differentiating the filtered signal; By adjusting the distance between two differential sampling points, the amplitude change between the two differential sampling points is enlarged. After waveform differentiation is completed, zero-crossing judgment is performed on the data, and finally the result is sent to the counting module for counting statistics.

[0022] After the data enters the delay module, it immediately enters the trapezoidal shaping module, and this formula is the same as formula 3. Fast trapezoidal shaping sacrifices the energy information of the original signal, compresses the signal into a fast signal to separate the stacked signals, so as to ensure 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. Therefore, compared with the fast trapezoid, its n a 、n b 、n c is slightly different. After shaping, the signal baseline will shift, so the baseline needs to be restored before peak searching. The baseline restoration module uses the moving average method for calculation, and the size of the moving window is determined by the host computer. After zero-crossing detection is completed, the baseline calculation is frozen until the trapezoidal shaping ends.

[0023] After baseline restoration is completed, pile-up judgment also needs to be performed before entering the peak searching mode. If it is found that the interval between two signals is less than the trapezoidal shaping time, it is considered a piled-up signal, and the lower computer will discard it. If there is no pile-up, it will enter the peak searching stage, and the peak value result will be sent to the host computer.

[0024] The third step is the host computer software design: After the data enters the host computer, energy spectrum display is performed. In the present invention, the following improvements are made to the perovskite energy spectrum characteristics on the basis of the traditional energy spectrum algorithm.

[0025] 1. Abandon the traditional linear fitting method for data fitting of the background, and use the polynomial fitting method for fitting. Compared with the former, the polynomial fitting method is more flexible, adapts to complex background applications, and is more accurate in solving the energy resolution. However, high-order polynomials may overfit noise, so it is particularly important when selecting the order. In the invention, polynomial fitting is realized in two steps.

[0026] Step 1: Remove the peak value by moving average and extract the low-frequency background trend. The specific implementation principle is as the formula: , where j is the local index variable of the moving average method, used to traverse the data points within the moving window, represents the background count estimate value at the i-th energy channel address in the original energy spectrum data after removing the peak value, Y(x i +j) represents the original energy spectrum data, 2k + 1 is the size of the moving window, i represents the i-th energy channel address of the original energy spectrum data, and x i +j represents the adjacent energy channel address positions offset by j energy channel addresses to the left and right with the i-th energy channel address as the center; Among the above, 2k + 1 is the sliding window size. It should be noted that k of the sliding window should be large enough to flatten sharp feature peaks.

[0027] Step 2: Use the moving average result as the background point and fit a cubic polynomial as follows: Set the standard expression of the cubic polynomial f(E)= α 1E 3 + α 2E 2 + α 3E + α 4, where α 1, α 2, α 3 and α 4 are all fitting parameters, E is the independent variable representing the energy channel address, and f(E) is the dependent variable representing the estimated background count at the energy channel address; The fitting parameters are obtained based on minimizing the sum of squared residuals. The obtaining process is that each fitting parameter satisfies , where w represents the w-th sliding window, E w represents the energy channel address of the w-th sliding window, represents the estimated background count at the energy channel address of the w-th sliding window; After completing the above two steps, the fitting formula is obtained. Subtract the fitting data from the original data to obtain the net data of the feature peak, and then perform Gaussian fitting.

[0028] 2. Correct the influence brought by the loss of full-energy peak count caused by the Pb escape peak through the dynamic energy scale correction method. 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 polynomial fitting on the escape peak region to obtain the fitting formula B(E). Step 3: Perform Gaussian fitting on the escape peak to obtain the full width at half maximum C E , define the energy range E escape ±C E . Step 4: Extract the escape peak count. Subtract the fitted background B(E) from the original energy spectrum to obtain the net escape peak count, as shown in the formula: N escape (E)=N raw (E)-B(E), where N raw (E) represents the original energy spectrum data; Step 5: Count migration. Map the escape peak count to the full-energy peak according to the energy scale relationship.

[0029] Fourth, experimental results: After connecting the all-inorganic perovskite planar detector to the energy spectrum analysis measurement system, use the Cs137 and Eu152 radiation sources to perform tests respectively. The results are asFigure 3 , Figure 4 as shown.

[0030] By fitting the channel addresses corresponding to the above 40 keV, 121 keV, 244 keV, 344 keV, and 662 keV, the energy-channel address correspondence is obtained, as Figure 5 shown.

[0031] From Figure 5 it can be seen that there is a strong linear relationship between the channel addresses and energies measured by the system, indicating that the design of the miniaturized energy spectrum analysis and measurement system based on the all-inorganic perovskite planar detector is correct in its main functions. And using this relationship, energy calibration can also be performed to convert the channel addresses and energies to obtain the actual spectrum with energy as the abscissa.

[0032] After obtaining the actual spectrum, the influence of the Pb escape peak needs to be removed. First, filtering is performed, and then polynomial fitting and Gaussian fitting are carried out at the Compton plateau to obtain the fitting curve and the full width at half maximum, as Figure 6 shown.

[0033] Figure 6 In Figure 7 , the black 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 Figure 8 For data fitting of the background using the linear fitting method, the calculated result of the energy resolution is 0.2% worse than that of Figure 7 . Thus, it can be seen that the method of using curve fitting to subtract the background in the present invention has superiority.

[0034] The above has described a specific embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the present invention.

Claims

1. A miniaturized energy spectrum analysis measurement system based on an all-inorganic perovskite planar detector, characterized in that, including: FPGA processing unit: including a polarity selection module, a frequency extraction module, a waveform differentiation module and a delay module; Obtain a detection signal, input the detection signal into an amplifier, and the amplifier outputs an analog signal; through the polarity selection module and the frequency extraction module, convert the analog signal into a filtered signal; The waveform differentiation module and the delay module respectively perform counting and peak searching on the filtered signal to obtain a counting statistical result and a peak value result; and the data packet obtained by packing the counting statistical result and the peak value result is recorded as the original energy spectrum data; Host computer unit: perform smoothing processing on the original energy spectrum data and perform energy spectrum display on the original energy spectrum data; Divide the original energy spectrum data into a full energy peak region and an escape peak region, correct the escape peak region to obtain a net escape peak count, and map the net escape peak count to the full energy peak region according to the energy scale relationship to obtain real energy spectrum data; Energy spectrum analysis unit: including an energy spectrum analysis module, and the energy spectrum analysis module performs energy spectrum analysis measurement on the 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, wherein The process of obtaining the filtered signal includes: Convert the analog signal into a digital signal, input the digital signal 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, and filter the positive signal through the frequency extraction module to obtain a filtered signal.

3. The miniaturized energy spectrum analysis and measurement system based on an all-inorganic perovskite planar detector according to claim 2, characterized in that, The process of the frequency extraction module filtering the positive signal includes: The principle formula for the frequency extraction module to filter the positive signal 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, 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, and the trapezoidal formula for the fast trapezoidal shaping is: , where y´(n) represents the output signal obtained after fast ramp shaping of the filtered signal, and n a is the number of sampling points at the rising edge of the filtered signal, and n c is the number of points from the rising edge to the end of the trapezoidal falling edge of the filtered signal, and n c = n a + n b , , T s is the sampling rate of the positive signal, τ is the attenuation constant, and n 1— sub = [y(n - 1) - y(n - n a - 1) - y(n - n b - 1) + y(n - n c - 1)], delay n 1— sub by one clock cycle to obtain n 2— sub, and n 2— sub = [y(n - 2) - y(n - n a - 2) - y(n - n b - 2) + y(n - n 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 and statistically analyzing the input signal; The output signal after the filtered signal is differentiated is recorded as the differential output signal; perform a zero-crossing judgment on the differential output signal to obtain a zero-crossing judgment result, and input the zero-crossing judgment result into the counting module to obtain a counting statistical result.

5. The miniaturized energy spectrum analysis measurement system based on the all-inorganic perovskite planar detector according to claim 1, wherein The process of the delay module searching for a peak in the filtered signal includes: The delay module performs trapezoidal shaping on the filtered signal to compress the filtered signal into a fast signal; obtain the baseline of the fast signal, and restore the baseline to obtain a restored signal, and the restoration process is based on a moving average method; Perform zero-crossing detection on the restored signal, and freeze the restoration of the baseline until the trapezoidal shaping ends after the zero-crossing detection ends; and perform a pile-up judgment on the restored signal, and if the judgment passes, perform peak searching on the restored signal.

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

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

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

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