Radiation intensity testing method for radioactive protection

Abstract

The invention relates to the related technical field of radiation intensity testing, in particular to a radiation intensity testing method for radioactivity protection, and the method comprises the steps: collecting data, carrying out the two-dimensional fusion, and setting a dynamic radiation pattern; correcting space distortion, and identifying and marking hot spot coordinates; and generating a protection instruction according to a protection optimization engine, updating a weight coefficient, and establishing a mapping table and an early warning model to perform efficiency verification, thereby solving the technical problems that noise and space distortion are liable to occur in radiation detection due to environmental electromagnetic interference, the accuracy and effectiveness of radiation protection are limited, and the radioactivity protection efficiency is further influenced. The method achieves the correction of the space distortion of the dynamic radiation intensity distribution diagram, adapts to a complex and changeable radioactive environment, improves the pertinence and effectiveness of radiation protection measures, guarantees the accuracy of a radiation detection result, reduces the measurement error caused by environmental electromagnetic interference, verifies and optimizes the radioactive protection efficiency, and improves the accuracy of the radiation detection result. And the overall efficiency of radioactive protection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radiation intensity testing, and particularly to a radiation intensity testing method for radioactive protection. Background Art

[0002] Radioactive radiation refers to high-energy particles or electromagnetic waves released during the decay process of radioactive substances, which have sufficient energy to remove electrons from atoms or molecules, thereby triggering a series of complex physical, chemical, and biological effects. To ensure the safe use and handling of radioactive substances, accurate testing and evaluation of radiation intensity are particularly important. In multi-source radiation fields and complex environmental conditions, due to the complexity and uncertainty of the radiation field, the influence of environmental electromagnetic interference on radiation detection results is becoming increasingly significant, resulting in spatial distortion of the dynamic radiation intensity distribution map, which affects the accuracy and effectiveness of radiation protection measures.

[0003] In summary, in the prior art, in a complex and variable radioactive environment, radiation detection is prone to noise and spatial distortion due to environmental electromagnetic interference, and the accuracy and effectiveness of radiation protection are limited, thereby affecting the radioactive protection efficiency. Summary of the Invention

[0004] This application provides a radiation intensity testing method for radioactive protection, aiming to solve the technical problems in the prior art that in a complex and variable radioactive environment, radiation detection is prone to noise and spatial distortion due to environmental electromagnetic interference, and the accuracy and effectiveness of radiation protection are limited, thereby affecting the radioactive protection efficiency.

[0005] In view of the above problems, the technical solution of this application is as follows: This application provides a radiation intensity testing method for radioactive protection, wherein the method includes: deploying a radiation detection component in a target area to obtain real-time acquisition data, the real-time acquisition data including energy characteristic data and dose rate data under the radiation type, and radiation area data and radiation intensity data under the spatial distribution; using the radiation field superposition principle to perform two-dimensional fusion of the real-time acquisition data in terms of the radiation type scale and the spatial distribution scale, and setting a dynamic radiation intensity distribution map; at the same time, through a quantum noise suppression plug-in, combining the environmental electromagnetic interference intensity, correcting the spatial distortion of the dynamic radiation intensity distribution map, and identifying the hot spot coordinates; marking the hot spot coordinates in the dynamic radiation intensity distribution map, and generating an adaptive protection instruction set including shielding layer thickness adaptive adjustment parameters, personnel exposure time parameters, and regional partition control parameters according to a protection optimization engine; based on the adaptive protection instruction set, synchronously updating the weight coefficient of the radiation field superposition principle, and establishing an environmental electromagnetic interference-parameter mapping table corresponding to the quantum noise suppression plug-in and a radiation intensity early warning model corresponding to the protection optimization engine to perform radioactive protection efficiency verification.

[0006] In summary, for one or more technical solutions provided in this application, by combining the quantum noise suppression plug-in with the environmental electromagnetic interference intensity, the spatial distortion of the dynamic radiation intensity distribution map is corrected, adapting to the complex and changeable radioactive environment, improving the pertinence and effectiveness of radiation protection measures, ensuring the accuracy of radiation detection results, reducing the measurement errors caused by environmental electromagnetic interference, verifying and optimizing the radioactive protection efficacy, and enhancing the overall efficacy of radioactive protection. Description of the Drawings

[0007] Figure 1 This application provides a schematic flow chart of a radiation intensity test method for radioactive protection; Figure 2 This application provides a schematic flow chart of setting the first dynamic radiation intensity grid in the radiation intensity test method for radioactive protection. Detailed Embodiments

[0008] Embodiment. The present application will be specifically described below with reference to the drawings. As Figure 1 shown, the present application provides a radiation intensity test method for radioactive protection, wherein the method includes: S1: In the target area, deploy a radiation detection component to obtain real-time acquisition data, where the real-time acquisition data includes energy characteristic data and dose rate data under the radiation type, and radiation area data and radiation intensity data under the spatial distribution; S2: Use the radiation field superposition principle to perform two-dimensional fusion of the real-time acquisition data in terms of the radiation type scale and the spatial distribution scale, and set a dynamic radiation intensity distribution map; S3: At the same time, through a quantum noise suppression plug-in, combine the environmental electromagnetic interference intensity to correct the spatial distortion of the dynamic radiation intensity distribution map and identify the hot spot coordinates.

[0009] Specifically, the radiation detection component refers to a device or system for detecting and measuring radiation, usually including sensors, detectors (such as silicon drift detectors, gas ionization chambers, scintillating fiber arrays, etc.) and auxiliary devices, which can collect information such as the energy characteristics, dose rate, and spatial distribution of radiation in real time; the radiation field superposition principle is based on the linear superposition characteristics of the radiation field, that is, the radiation field formed by the superposition of radiation fields generated by multiple radiation sources in space, separating and fusing the contributions of different radiation sources, so as to more accurately describe the distribution of complex radiation fields; the quantum noise suppression plug-in is used to suppress and correct the distortion of radiation detection data caused by environmental electromagnetic interference or other noise sources, and improve the accuracy and reliability of the radiation intensity distribution map through the adjustment of quantum states and filtering algorithms; the hot spot coordinates refer to specific positions in the radiation intensity distribution map where the radiation intensity is significantly higher than the surrounding area, for example, tumors in the patient's body or other areas that require high-dose radiation therapy.

[0010] Deploy a radiation detection component in the target area to collect radiation data in real time, including the energy characteristics, dose rate of radiation types (such as alpha particles, X-rays, etc.), and the spatial distribution information of radiation; use the radiation field superposition principle to perform two-dimensional fusion on the collected real-time data, considering the differences in radiation types (energy characteristics and dose rate), and also combining the spatial distribution information (radiation area and intensity distribution), so as to generate a dynamic radiation intensity distribution map. The two-dimensional fusion can more comprehensively reflect the characteristics of the complex radiation field and provide an accurate basis for subsequent protection measures.

[0011] At the same time, through the quantum noise suppression plug-in combined with the environmental electromagnetic interference intensity, perform spatial distortion correction on the dynamic radiation intensity distribution map. Through the adjustment of quantum states and filtering algorithms, the influence of environmental electromagnetic interference on the radiation detection results is effectively reduced, and the accuracy and reliability of the distribution map are improved. In addition, the hot spot coordinates with higher radiation intensity can be identified, providing key information for subsequent protection measures, providing a comprehensive description of the radiation field, and improving the accuracy of the data through noise suppression and distortion correction.

[0012] S4: Mark the hot spot coordinates in the dynamic radiation intensity distribution map, and generate an adaptive protection instruction set including the shielding layer thickness adaptive adjustment parameter, the personnel exposure time parameter, and the area partition control parameter according to the protection optimization engine; S5: Based on the adaptive protection instruction set, synchronously update the weight coefficient of the radiation field superposition principle, and establish an environmental electromagnetic interference-parameter mapping table corresponding to the quantum noise suppression plug-in and a radiation intensity warning model corresponding to the protection optimization engine to perform radioactive protection effectiveness verification.

[0013] Specifically, the protection optimization engine is used to dynamically generate optimal protection measures according to the real-time radiation intensity distribution map and hot spot information, and can comprehensively consider factors such as the shielding layer thickness, personnel exposure time, and area partition control to generate an adaptive protection instruction set; the adaptive protection instruction set refers to a set of protection parameters dynamically generated according to the current radiation environment, including the shielding layer thickness adaptive adjustment parameter, the personnel exposure time parameter, and the area partition control parameter, guiding actual protection measures to adapt to different radiation intensities and distribution situations; the weight coefficient refers to the relative importance used to adjust the contribution of different radiation sources or radiation types to the overall radiation field in the radiation field superposition principle. By dynamically updating the weight coefficient, the changes in the radiation field can be more accurately reflected, improving the adaptability and accuracy of the model; the environmental electromagnetic interference-parameter mapping table refers to a table recording the relationship between the environmental electromagnetic interference intensity and the parameters of the quantum noise suppression plug-in, quickly adjusting the plug-in parameters to adapt to different electromagnetic interference environments; the radiation intensity warning model is used to monitor and evaluate in real time whether the radiation intensity exceeds the safety threshold, and can dynamically adjust the warning parameters according to the adaptive protection instruction set to ensure the effectiveness of the protection measures.

[0014] First, mark the hot spot coordinates in the dynamic radiation intensity distribution map. The hot spot coordinates are the positions where the radiation intensity is significantly higher than the surrounding area, and are usually the key attention positions of the protection measures. The protection optimization engine generates an adapted protection instruction set based on the hot spot coordinates and the overall characteristics of the distribution map, including the self - adaptive adjustment parameters of the shielding layer thickness, the personnel exposure time parameters, and the area partition control parameters, which are used to guide the actual protection measures to adapt to different radiation environments. Based on the adapted protection instruction set, synchronously update the weight coefficients of the radiation field superposition principle. By dynamically adjusting the weight coefficients, the radiation field model can more accurately reflect the current radiation environment, improving the adaptability and accuracy of the model. At the same time, establish an environmental electromagnetic interference - parameter mapping table corresponding to the quantum noise suppression plug - in. Through this mapping table, the plug - in can dynamically adjust the parameters according to the real - time electromagnetic interference intensity, further optimizing the noise suppression effect.

[0015] Establish a radiation intensity warning model corresponding to the protection optimization engine, which is used to monitor in real - time whether the radiation intensity exceeds the safety threshold, dynamically adjust the warning parameters according to the adapted protection instruction set, ensure the effectiveness of the protection measures, and timely detect potential safety risks. By generating the adapted protection instruction set, flexibly adjust the protection measures to adapt to the complex radiation environment. By updating the weight coefficients and establishing the mapping table, the radiation field model and the noise suppression effect are further optimized. Through the radiation intensity warning model, the effectiveness of the protection measures can be verified in real - time, ensuring the overall effectiveness of radioactive protection.

[0016] Furthermore, in the target area, deploy radiation detection components. The method of this application also includes: S11: Set the detection sensitivity gradient according to the measurement requirements, including the α - particle detection sensitivity and the X - ray energy resolution; S12: Based on the detection sensitivity gradient, optimize the deployment of the silicon drift detector, the gas ionization chamber, and the scintillating fiber array.

[0017] Specifically, the detection sensitivity gradient refers to setting the sensitivity differences of the detector for different energies or particle types according to different radiation types (such as alpha particles, X-rays, etc.) and measurement requirements. For example, for alpha particle detection sensitivity, higher sensitivity may be required to detect low-energy alpha particles; for X-rays, more attention may be paid to energy resolution to distinguish X-ray signals of different energy levels. Further, the alpha particle detection sensitivity refers to the detection ability of the detector for alpha particles, which is usually related to the material, structure, and working conditions of the detector. Higher sensitivity means being able to detect low-intensity or low-energy alpha particle signals more accurately; the X-ray energy resolution refers to the ability of the detector to distinguish X-ray signals of different energies. A detector with high energy resolution can measure the energy distribution of X-rays more precisely, thereby providing more accurate radiation type and energy characteristic data; Silicon Drift Detectors (SDDs) are used to detect low-energy X-rays and alpha particles, featuring high sensitivity, high energy resolution, and fast response; Gas ionization chambers are detectors based on the principle of gas ionization, suitable for detecting various types of radiation, including alpha particles, beta particles, and X-rays, with the advantage of being able to provide high sensitivity and wide energy range detection; Scintillating fiber arrays convert radiation energy into optical signals and transmit them to photodetectors for measurement, featuring high sensitivity and fast response, and are suitable for radiation detection in complex environments.

[0018] Optimize the deployment of radiation detection components according to measurement requirements to improve the overall performance of the system. First, set the detection sensitivity gradient according to specific measurement requirements, including alpha particle detection sensitivity and X-ray energy resolution. Further, it is necessary to comprehensively consider the radiation type, intensity distribution in the target area, and the expected measurement accuracy requirements. For example, in a nuclear medicine environment, higher X-ray energy resolution may be required to distinguish X-ray signals of different energy levels; in an alpha particle detection environment, more attention is paid to detection sensitivity. Based on the set detection sensitivity gradient, optimize the deployment of silicon drift detectors, gas ionization chambers, and scintillating fiber arrays. Further, select the appropriate detector type and optimize the spatial layout according to its characteristics. For example, silicon drift detectors may be more suitable for high-sensitivity alpha particle detection areas, while gas ionization chambers can be used in scenarios that require wide energy range detection, and scintillating fiber arrays can be used for fast response detection in complex environments.

[0019] By optimizing the detection sensitivity gradient and detector layout, better adapt to complex and changeable radioactive environments, provide high-quality input data for radiation field analysis, noise suppression, and the generation of protection measures, improve measurement accuracy, ensure that the radiation detection components can efficiently and accurately collect radiation data in the target area, and also provide a basis for accurately mapping the dynamic radiation intensity distribution and optimizing protection.

[0020] Furthermore, the method of the present application includes performing two-dimensional fusion of the radiation type scale and the spatial distribution scale on the real-time collected data and setting a dynamic radiation intensity distribution map, which includes: S21: Divide the target area into M cube units, where the time resolution of each cube unit does not exceed a first threshold and the spatial resolution does not exceed a second threshold; S22: In the first cube unit, perform time-space joint filtering processing and set a first dynamic radiation intensity grid; S23: Traverse the M cube units, combine the first dynamic radiation intensity grid, ……, the Mth dynamic radiation intensity grid, and obtain a dynamic radiation intensity distribution map.

[0021] Specifically, a cube unit refers to dividing the target area into multiple small cubes, and each cube is used as an independent unit for measuring and analyzing the radiation intensity, which helps to improve the spatial resolution and more accurately describe the spatial distribution of the radiation field; time resolution refers to the minimum time interval for distinguishing two adjacent events in the time dimension. In radiation measurement, high time resolution means being able to more accurately capture the instantaneous change of the radiation intensity; spatial resolution refers to the minimum distance for distinguishing two adjacent positions in the spatial dimension. In radiation measurement, high spatial resolution means being able to more accurately describe the distribution of the radiation intensity in space; time-space joint filtering processing refers to filtering that simultaneously considers the time dimension and the spatial dimension to improve the signal-to-noise ratio and accuracy of the data. In radiation measurement, it helps to remove noise and more accurately extract the dynamic change and spatial distribution of the radiation intensity; a dynamic radiation intensity grid refers to the radiation intensity data obtained through time-space joint filtering processing in each cube unit, represented as a grid, and the dynamic radiation intensity grid describes the distribution of the radiation intensity within a limited time and space range.

[0022] By dividing the target area into multiple cube units and performing time-space joint filtering processing on each unit to obtain a dynamic radiation intensity distribution map. Specifically, divide the target area into M cube units, where the time resolution of each unit does not exceed a first threshold and the spatial resolution does not exceed a second threshold, and use the division method to improve the measurement accuracy and resolution; in the first cube unit, perform time-space joint filtering processing and set a first dynamic radiation intensity grid, and extract the dynamic change and spatial distribution of the radiation intensity within the first cube unit by filtering out noise; traverse all M cube units, repeat the above processing for each unit, obtain M dynamic radiation intensity grids, and combine the M dynamic radiation intensity grids to obtain the dynamic radiation intensity distribution map corresponding to the target area; through the processing and combination of sub-units, the dynamic change and spatial distribution of a complex radiation field can be more accurately described, generating a high-precision dynamic radiation intensity distribution map, thereby improving the pertinence and effectiveness of the protection measures.

[0023] Furthermore, as Figure 2 shown, in the first cube unit, time-space joint filtering processing is performed, and the first dynamic radiation intensity grid is set. The method of this application includes: S221: In the first cube unit, perform time-space joint filtering processing on the α-particle pulse sequence and X-ray energy spectrum data to generate an α-particle effective flux matrix and an X-ray effective flux matrix; S222: Evaluate the contribution degree tensor of α-particles and the contribution degree tensor of X-rays; S223: Based on the α-particle effective flux matrix and the X-ray effective flux matrix, combine the contribution degree tensor of α-particles and the contribution degree tensor of X-rays to perform mapping to obtain the first dynamic radiation intensity grid.

[0024] Specifically, the α-particle pulse sequence refers to a series of electrical pulse signals output by an α-particle detector. Each pulse represents a detection event of an α-particle and contains the time and space distribution information of the α-particle; the X-ray energy spectrum data refers to the energy spectrum data output by an X-ray detector and contains the energy distribution information of the X-ray. By analyzing the energy spectrum data, the intensity and energy characteristics of the X-ray are obtained; the time-space joint filtering processing refers to filtering that simultaneously considers the time dimension and the space dimension to improve the signal-to-noise ratio and accuracy of the data. In radiation measurement, it helps to remove noise and more accurately extract the dynamic changes and spatial distribution of the radiation intensity; the radiation intensity data obtained through time-space joint filtering processing is expressed in matrix form, and the effective flux matrix describes the distribution of the radiation intensity within a limited time and space range; the contribution degree tensor refers to a parameter used to describe the relative importance of different radiation sources or radiation types to the corresponding radiation field in the target area in the radiation field superposition principle. By evaluating the contribution degree tensor, the contributions of different radiation sources can be more accurately reflected.

[0025] Through time-space joint filtering processing and the evaluation of the contribution degree tensor, an effective flux matrix of α-particles and X-rays is generated, and the first dynamic radiation intensity grid is obtained. Specifically, perform time-space joint filtering processing on the α-particle pulse sequence and X-ray energy spectrum data to generate an α-particle effective flux matrix and an X-ray effective flux matrix, and extract the time and space distribution of α-particles and X-rays by filtering out noise; evaluate the contribution degree tensor of α-particles and the contribution degree tensor of X-rays, and provide a parameter basis for subsequent radiation field superposition by analyzing the contributions of different radiation sources.

[0026] Based on the α-particle effective flux matrix and the X-ray effective flux matrix, mapping is performed by combining the contribution degree tensor of α-particles and the contribution degree tensor of X-rays to obtain the first dynamic radiation intensity grid. Through the weighting of the contribution degree tensor, the contributions of different radiation sources are merged into one grid to obtain a comprehensive radiation intensity distribution, providing a basis for the subsequent generation of the dynamic radiation intensity distribution map of the entire target area. By processing multiple dynamic radiation intensity grids, the dynamic changes and spatial distribution of the complex radiation field can be more accurately described, thereby improving the pertinence and effectiveness of the protection measures.

[0027] Furthermore, the method of the present application further includes: S221-1: Based on the α-particle pulse sequence, configure the first counting noise index and the second counting noise index according to the detector dead time and the particle flight time difference; S221-2: Generate the α-particle effective flux matrix according to the first counting noise index and the second counting noise index.

[0028] Specifically, the detector dead time refers to the time required for the detector to recover to a state where it can detect the next event after detecting a particle event, and this period is called the dead time. The existence of the dead time will cause the detector to miss some events at high counting rates, thus affecting the accuracy of counting; the particle flight time difference refers to the flight time of the particle from the source to the detector. In a multi-source radiation field, there is a certain probability that the particles emitted from different sources reach the detector at different times, and the time difference is used to distinguish the contributions of different sources; the counting noise index is used to quantify the counting error caused by factors such as the detector dead time and the particle flight time difference, and is used to evaluate and correct the pulse sequence output by the detector, thereby improving the accuracy of the data; the α-particle effective flux matrix refers to the matrix generated by the corrected pulse sequence, which reflects the flux distribution of α-particles within a specific time and space range. The effective flux matrix excludes the influence of noise and detector characteristics and more accurately describes the actual distribution of α-particles.

[0029] By analyzing the α-particle pulse sequence and combining the detector dead time and the particle flight time difference, the effective flux matrix of α-particles is generated. Specifically, based on the detector dead time and the particle flight time difference, the first counting noise index and the second counting noise index are calculated. The dead time will cause the detector to miss some events at high counting rates, while the flight time difference is used to distinguish the contributions of different sources. Through the first counting noise index and the second counting noise index, the counting error caused by detector characteristics is quantified; using the first counting noise index and the second counting noise index, the α-particle pulse sequence is corrected, and through the corrected pulse sequence, the effective flux matrix of α-particles is generated, excluding the influence of noise and detector characteristics and more accurately reflecting the distribution of α-particles in time and space.

[0030] By correcting the errors caused by the detector dead time and the difference in particle flight time, a more reliable α-particle effective flux matrix is generated, providing a reliable data basis for the generation of the dynamic radiation intensity distribution map. The effective flux matrix can more accurately describe the distribution of α-particles, improve the accuracy of α-particle flux data, and thus enhance the reliability and effectiveness of the entire radioactive protection scheme.

[0031] Furthermore, according to the first counting noise index and the second counting noise index, an α-particle effective flux matrix is generated. The method of the present application includes: S221-21: Denote the effective particle flux as , construct the confidence interval of the α-particle pulse sequence , , where is the noise standard deviation, and k is the significance factor; S221-22: According to the effective particle flux , configure a sliding time window, and attenuate the abnormal pulses exceeding the confidence interval , , to generate a time-dimension smoothed α-particle effective flux matrix.

[0032] Specifically, the effective particle flux is denoted as , which refers to the particle flux corrected after considering factors such as the detector dead time and the difference in particle flight time, and more accurately reflects the actual particle emission rate; the confidence interval , is used to determine the outliers in the α-particle pulse sequence; the noise standard deviation represents the degree of dispersion of the noise. In radiation measurement, the noise standard deviation is used to evaluate the stability of the measurement data; the significance factor k is used to determine the width of the confidence interval. A larger significance factor means a wider confidence interval and thus fewer outliers; the sliding time window refers to smoothing the data by moving a fixed-length window on the time series, and is used to smooth the α-particle pulse sequence.

[0033] By constructing a confidence interval and applying a sliding time window, the α-particle pulse sequence is smoothed to generate a time-dimension smoothed α-particle effective flux matrix. Specifically, according to the effective particle flux denoted as , construct the confidence interval of the α-particle pulse sequence , , the confidence interval , It is used to identify outliers in the sequence, configure a sliding time window, and attenuate abnormal pulses beyond the confidence interval. By moving a window of a fixed length over the time series to smooth the data, the influence of outliers is reduced. According to the smoothed α-particle pulse sequence, an α-particle effective flux matrix smoothed in the time dimension is generated. The α-particle effective flux matrix smoothed in the time dimension more accurately reflects the distribution of α-particles over time, providing a high-quality data basis for the generation of subsequent dynamic radiation intensity distribution maps.

[0034] By constructing a confidence interval and applying a sliding time window, outliers are effectively identified and processed, improving the accuracy and stability of α-particle flux data, thereby generating a smoother and more reliable α-particle effective flux matrix, providing a high-quality data basis for the generation of subsequent dynamic radiation intensity distribution maps, and thus enhancing the reliability and effectiveness of the entire radioactive protection scheme.

[0035] Furthermore, the method of this application includes: S221-23: The effective particle flux , where is the original count rate, is the detector dead time constant associated with the first count noise index, is the particle flight time difference associated with the second count noise index, is the time calibration coefficient associated with the environmental temperature.

[0036] Specifically, the effective particle flux refers to the particle flux after correction, reflecting the true particle flow rate considering factors such as detector dead time, particle flight time difference, and environmental temperature; the original count rate refers to the particle count rate directly measured by the detector without any correction; the detector dead time constant is associated with the detector dead time and is used to quantify the count loss caused by the recovery time at high count rates; the particle flight time difference is the time difference between different particles arriving at the detector and is used to distinguish particles from different sources or paths; the time calibration coefficient associated with the environmental temperature is associated with the environmental temperature and is used to correct the influence of temperature changes on the detector performance to ensure the accuracy of the measurement results.

[0037] The effective particle flux is composed of the original count rate It is obtained through calibration. Further, count noise introduced by the detector dead time constant and the particle flight time difference is deducted from the original count rate. Meanwhile, the flux is calibrated by a time calibration coefficient related to the ambient temperature to compensate for the influence of temperature changes on the detector performance. The detector dead time constant is used to correct events missed by the detector at high count rates; the particle flight time difference is used to distinguish the contributions of different sources; the time calibration coefficient related to the ambient temperature is used to correct the influence of temperature changes on the detector response, making the effective particle flux closer to the true value.

[0038] By comprehensively considering the original count rate, the detector dead time constant, the particle flight time difference, and the time calibration coefficient related to the ambient temperature, the effective particle flux is calculated, comprehensively considering the influence of various factors on the detector performance, so that the effective particle flux can more realistically reflect the dynamic changes of the radiation field, provide more accurate particle flux data, and provide a reliable data basis for the subsequent generation of the radiation intensity distribution map and the optimization of protection measures, thereby improving the accuracy and reliability of the entire radioactive protection plan.

[0039] Furthermore, through a quantum noise suppression plug-in, combined with the ambient electromagnetic interference intensity, the spatial distortion of the dynamic radiation intensity distribution map is corrected. The method of the present application includes: S31: Based on the quantum noise suppression plug-in, combined with the ambient electromagnetic interference intensity, configure the original quantum state; S32: According to the initialized quantum state, use an adaptive Kalman filter to iteratively compensate for phase distortion and establish a solution space; S33: Based on the solution space, optimize the reconstruction process of the dynamic radiation intensity distribution map by backpropagation and output the spatial distortion correction result to the protection optimization engine.

[0040] Specifically, the quantum noise suppression plug-in is used to suppress and correct the distortion of radiation detection data caused by ambient electromagnetic interference or other noise sources. By adjusting the quantum state and filtering algorithm, the accuracy and reliability of the radiation intensity distribution map are improved; the original quantum state refers to removing or correcting the quantum state distortion caused by factors such as ambient electromagnetic interference during the quantum noise suppression process, so as to obtain a relatively pure and accurate state, which is the basis for subsequent noise suppression and data correction; the adaptive Kalman filter can dynamically adjust the filtering parameters according to the real-time state of the system to optimize the estimation accuracy and is used to iteratively compensate for phase distortion; phase distortion refers to the signal phase change caused by ambient electromagnetic interference or other factors, and phase distortion will affect the accuracy of the radiation intensity distribution map; the solution space refers to the set of all possible solutions during the optimization process, and the solution space is used to store the results after iterative compensation by the Kalman filter; backpropagation optimization refers to calculating the gradient of the error and backpropagating to adjust the parameters to optimize the performance of the system and is used to optimize the reconstruction process of the dynamic radiation intensity distribution map.

[0041] Through a quantum noise suppression plugin and an adaptive Kalman filter, the spatial distortion in the dynamic radiation intensity distribution map is corrected, and the correction result is output to the protection optimization engine. Specifically, based on the quantum noise suppression plugin and combined with the environmental electromagnetic interference intensity, the quantum state is initialized to the original state. The original quantum state is the basis for subsequent noise suppression and data correction, and can effectively suppress the influence of environmental electromagnetic interference on radiation detection data. According to the initial quantum state, the adaptive Kalman filter is used to iteratively compensate for the phase distortion. The Kalman filter dynamically adjusts the filtering parameters to gradually reduce the influence of phase distortion on the radiation intensity distribution map. During each iteration, the filter updates the filtering parameters according to the real-time detection data, thereby optimizing the compensation effect.

[0042] During each iterative compensation process, the compensation result generated by the Kalman filter is stored in the solution space. Based on the data in the solution space, the backpropagation optimization algorithm is used to optimize the reconstruction process of the dynamic radiation intensity distribution map. The backpropagation optimization further improves the accuracy and reliability of the distribution map by calculating the gradient of the error and adjusting the parameters, and outputs the corrected spatial distortion result. By combining the quantum noise suppression plugin and the adaptive Kalman filter, the accuracy and reliability of the dynamic radiation intensity distribution map can be improved, effectively correcting the spatial distortion caused by environmental electromagnetic interference, thereby providing more accurate data support for subsequent protection optimization. The backpropagation optimization further improves the reconstruction accuracy of the distribution map, ensuring that the protection measures can more precisely adapt to the complex radioactive environment and improving the overall effectiveness of radioactive protection.

[0043] Furthermore, the first dynamic radiation intensity grid, …, the Mth dynamic radiation intensity grid are combined to obtain the dynamic radiation intensity distribution map. The method of the present application includes: S231: During each iterative compensation process, predict the current quantum state parameters, and dynamically update the Kalman gain according to the real-time acquisition data collected by the radiation detection component; S232: Perform graph structure sparsification processing using the phase coherence constraint condition, screen the effective quantum state branches, align the M dynamic radiation intensity grids in space, and determine the dynamic radiation intensity distribution map.

[0044] Specifically, the quantum state parameters are used to represent the state of the radiation field after noise suppression processing; in the Kalman filter, the Kalman gain is used to weigh the reliability of measurement data and prediction data, dynamically adjust the weights of the filter, and the update of the Kalman gain can optimize the performance of the filter to better adapt to the dynamically changing environment; the phase coherence constraint condition is used to optimize the reconstruction process of the dynamic radiation intensity distribution map to ensure the consistency and stability of the signal phase during the processing; the graph structure sparsification process refers to reducing redundant information in the graph and retaining key information, which is used to screen effective quantum state branches, remove noise and interference, and improve the efficiency and accuracy of data processing; spatial alignment means performing spatial alignment among multiple dynamic radiation intensity grids to ensure the consistency of the spatial distribution of the M dynamic radiation intensity grids corresponding to the first dynamic radiation intensity grid, ..., the Mth dynamic radiation intensity grid, which helps generate an accurate dynamic radiation intensity distribution map.

[0045] During each iteration compensation process, by dynamically updating the Kalman gain and performing graph structure sparsification processing, the reconstruction process of the dynamic radiation intensity distribution map is optimized. Specifically, during each iteration compensation process, predictions are made based on the current quantum state parameters, and then, based on the real-time data collected by the radiation detection component, the Kalman gain is dynamically updated. The update of the Kalman gain can optimize the performance of the filter to better adapt to the dynamically changing environment, thereby improving the accuracy of phase distortion compensation; the phase coherence constraint condition is used to perform graph structure sparsification processing on the dynamic radiation intensity grid, screen effective quantum state branches, remove noise and interference, and retain key information. The phase coherence constraint condition ensures the consistency and stability of the signal phase during the processing, thereby improving the accuracy of the distribution map; spatial alignment is performed on all dynamically radiation intensity grids that have undergone sparsification processing. Through spatial alignment, the consistency of the spatial distribution of the M dynamic radiation intensity grids is ensured, thereby generating an accurate dynamic radiation intensity distribution map.

[0046] By dynamically updating the Kalman gain, it can better adapt to the dynamically changing environment; through graph structure sparsification processing and phase coherence constraint conditions, noise and interference can be effectively removed and key information can be retained; through spatial alignment, the reconstruction process of the dynamic radiation intensity distribution map is further optimized to generate a consistent dynamic radiation intensity distribution map, ensuring the accuracy and reliability of the dynamic radiation intensity distribution map, providing a data basis for subsequent protection optimization, and improving the overall effectiveness of radioactive protection.

[0047] In summary, the beneficial effects of the embodiments of the present application are: By deploying radiation detection components, real-time acquisition data is obtained; using the radiation field superposition principle, the real-time acquisition data is fused in two dimensions of radiation type scale and spatial distribution scale to set a dynamic radiation intensity distribution map; at the same time, through a quantum noise suppression plug-in, combined with the environmental electromagnetic interference intensity, the spatial distortion of the dynamic radiation intensity distribution map is corrected, and the hot spot coordinates are identified and marked in the dynamic radiation intensity distribution map. According to the protection optimization engine, an adaptive protection instruction set is generated, the weight coefficient of the radiation field superposition principle is synchronously updated, and a radiation intensity early warning model is established to verify the radioactive protection effectiveness. By providing a radiation intensity test method for radioactive protection, the spatial distortion of the dynamic radiation intensity distribution map is corrected through a quantum noise suppression plug-in combined with the environmental electromagnetic interference intensity, adapting to the complex and changeable radioactive environment, improving the pertinence and effectiveness of radiation protection measures, ensuring the accuracy of radiation detection results, reducing measurement errors caused by environmental electromagnetic interference, verifying and optimizing the radioactive protection effectiveness, and enhancing the overall effectiveness of radioactive protection.

[0048] In summary, any step can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor, without any further restrictions here.

[0049] Furthermore, the above technical solutions only reflect the preferred technical solutions of the technical solutions of the embodiments of the present application. Some changes that those skilled in the art may make to some parts thereof all reflect the principles of the novel embodiments of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A method for testing radiation intensity for radioactive protection, characterized in that, The method includes: Deploy a radiation detection component in the target area to obtain real-time acquisition data, where the real-time acquisition data includes energy characteristic data and dose rate data under the radiation type, and radiation area data and radiation intensity data under the spatial distribution; Use the radiation field superposition principle to perform two-dimensional fusion on the real-time acquisition data in terms of the radiation type scale and the spatial distribution scale, and set a dynamic radiation intensity distribution map; Meanwhile, through a quantum noise suppression plug-in, combine the environmental electromagnetic interference intensity to correct the spatial distortion of the dynamic radiation intensity distribution map and identify the hot spot coordinates; Mark the hot spot coordinates in the dynamic radiation intensity distribution map, and generate an adaptive protection instruction set including shielding layer thickness adaptive adjustment parameters, personnel exposure time parameters, and area partition control parameters according to the protection optimization engine; Based on the adaptive protection instruction set, synchronously update the weight coefficient of the radiation field superposition principle, and establish an environmental electromagnetic interference-parameter mapping table corresponding to the quantum noise suppression plug-in and a radiation intensity early warning model corresponding to the protection optimization engine to perform radioactive protection efficacy verification.

2. The radiation intensity test method for radioactive protection according to claim 1, wherein Deploy a radiation detection component in the target area, and the method further includes: Set the detection sensitivity gradient according to the measurement requirements, including the α-particle detection sensitivity and the X-ray energy resolution; Based on the detection sensitivity gradient, optimize the deployment of silicon drift detectors, gas ionization chambers, and scintillating fiber arrays.

3. The radiation intensity test method for radioactive protection according to claim 1, wherein Perform two-dimensional fusion on the real-time acquisition data in terms of the radiation type scale and the spatial distribution scale, and set a dynamic radiation intensity distribution map. The method includes: Divide the target area into M cubic units, and the time resolution of each cubic unit does not exceed the first threshold and the spatial resolution does not exceed the second threshold; In the first cubic unit, perform time-space joint filtering processing and set the first dynamic radiation intensity grid; Traverse the M cubic units, combine the first dynamic radiation intensity grid, ……, the Mth dynamic radiation intensity grid to obtain the dynamic radiation intensity distribution map.

4. The method for testing radiation intensity for radioactive protection according to claim 3, wherein, In the first cubic unit, perform time-space joint filtering processing and set the first dynamic radiation intensity grid. The method includes: In the first cubic unit, perform time-space joint filtering processing on the α-particle pulse sequence and the X-ray energy spectrum data to generate an α-particle effective flux matrix and an X-ray effective flux matrix; Evaluate the contribution tensor of α-particles and the contribution tensor of X-rays; Based on the α-particle effective flux matrix and the X-ray effective flux matrix, combine the contribution tensor of α-particles and the contribution tensor of X-rays for mapping to obtain the first dynamic radiation intensity grid.

5. The radiation intensity test method for radioactive protection according to claim 4, characterized in that, Based on the α-particle pulse sequence, configure the first counting noise index and the second counting noise index according to the detector dead time and the particle flight time difference; Generate an α-particle effective flux matrix according to the first counting noise index and the second counting noise index.

6. The radiation intensity test method for radioactive protection according to claim 5, characterized in that, Generate an α-particle effective flux matrix according to the first counting noise index and the second counting noise index. The method includes: Denote the effective particle flux as , construct the confidence interval of the α-particle pulse sequence , , where is the noise standard deviation and k is the significance factor; According to the effective particle flux , configure a sliding time window and attenuate abnormal pulses exceeding the confidence interval , to generate a smoothed α-particle effective flux matrix in the time dimension.

7. The radiation intensity test method for radioactive protection according to claim 6, characterized in that, The effective particle flux , where is the original count rate, is the detector dead time constant associated with the first count noise index, is the particle flight time difference associated with the second count noise index, is the time calibration coefficient associated with the ambient temperature.

8. The radiation intensity test method for radioactive protection according to claim 3, wherein Through a quantum noise suppression plug-in, combine the environmental electromagnetic interference intensity to correct the spatial distortion of the dynamic radiation intensity distribution map. The method includes: Based on the quantum noise suppression plug-in, configure the initialized quantum state in combination with the ambient electromagnetic interference intensity; According to the initialized quantum state, use an adaptive Kalman filter to iteratively compensate for phase distortion and establish a solution space; Based on the solution space, optimize the reconstruction process of the dynamic radiation intensity distribution map by backpropagation and output the spatial distortion correction result to the protection optimization engine.

9. The method for testing radiation intensity for radioactive protection according to claim 8, wherein Combine the first dynamic radiation intensity grid, ……, the Mth dynamic radiation intensity grid to obtain the dynamic radiation intensity distribution map. The method includes: In each iterative compensation process, predict the current quantum state parameters and dynamically update the Kalman gain according to the real-time acquisition data collected by the radiation detection component; Perform graph structure sparsification processing using the phase coherence constraint condition, screen the effective quantum state branches, align the M dynamic radiation intensity grids in space, and determine the dynamic radiation intensity distribution map.

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