Radiation intensity test methods for radiological protection

Abstract

The present invention relates to the technical field related to radiation intensity testing, and specifically includes a radiation intensity testing method for radioactive protection. The method includes: collecting data and performing two-dimensional fusion to set a dynamic radiation map; correcting spatial distortion, identifying and marking hotspot coordinates; generating protection instructions according to a protection optimization engine, updating weight coefficients, and establishing a mapping table and an early warning model to perform performance verification. The method solves the technical problem that radiation detection is prone to noise and spatial distortion due to environmental electromagnetic interference, which limits the accuracy and effectiveness of radiation protection and thus affects the effectiveness of radioactive protection. The method realizes the correction of spatial distortion of a dynamic radiation intensity distribution map, adapts to complex and changeable radioactive environments, improves the pertinence and effectiveness of radiation protection measures, ensures the accuracy of radiation detection results, reduces measurement errors caused by environmental electromagnetic interference, verifies and optimizes the effectiveness of radioactive protection, and improves the overall effectiveness of radioactive protection.

Description

Technical Field

[0001] The present invention relates to the technical field related to radiation intensity testing, and in particular 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 of radioactive materials. They have enough 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 materials, accurate testing and evaluation of radiation intensity is particularly important. In multi-source radiation fields and complex environmental conditions, due to the complexity and uncertainty of the radiation field, the impact of environmental electromagnetic interference on radiation detection results is increasingly significant, resulting in spatial distortion of the dynamic radiation intensity distribution diagram, affecting the accuracy and effectiveness of radiation protection measures.

[0003] In summary, the existing technology has technical problems that in complex and changeable radioactive environments, radiation detection is prone to noise and spatial distortion due to environmental electromagnetic interference, which limits the accuracy and effectiveness of radiation protection and thus affects the effectiveness of radiation protection. Summary of the Invention

[0004] This application provides a radiation intensity testing method for radioactive protection, aiming to solve the technical problem in the prior art that in a complex and changeable radioactive environment, radiation detection is prone to noise and spatial distortion due to environmental electromagnetic interference, which limits the accuracy and effectiveness of radiation protection and thus affects the effectiveness of radioactive protection.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] The present 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 collected data, wherein the real-time collected 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; using the radiation field superposition principle, the real-time collected data is subjected to a two-dimensional fusion of the radiation type scale and the 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 hotspot coordinates are identified; the hotspot coordinates are marked in the dynamic radiation intensity distribution map, and according to the protection optimization engine, an adaptive protection instruction set including shielding layer thickness adaptive adjustment parameters, personnel exposure time parameters, and regional partition control parameters is generated; based on the adaptive protection instruction set, the weight coefficient of the radiation field superposition principle is synchronously updated, and 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 are established to verify the effectiveness of radioactive protection.

[0007] In summary, the one or more technical solutions provided in this application, through the quantum noise suppression plug-in combined with the environmental electromagnetic interference intensity, correct the spatial distortion of the dynamic radiation intensity distribution diagram, adapt to the complex and changeable radioactive environment, improve the pertinence and effectiveness of radiation protection measures, ensure the accuracy of radiation detection results, reduce the measurement errors caused by environmental electromagnetic interference, verify and optimize the radiation protection effectiveness, and improve the overall effectiveness of radiation protection. Technical effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a radiation intensity testing method for radioactive protection is provided for this application;

[0009] Figure 2 A schematic flow chart of setting a first dynamic radiation intensity grid in a radiation intensity testing method for radioactive protection is provided for this application. DETAILED DESCRIPTION

[0010] Embodiment: The present application will be described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a radiation intensity testing method for radioactive protection, wherein the method comprises:

[0011] S1: Deploy radiation detection components in the target area to obtain real-time collected data, which includes energy characteristic data and dose rate data under radiation type, and radiation area data and radiation intensity data under spatial distribution; S2: Use the radiation field superposition principle to perform two-dimensional fusion of the real-time collected data in terms of radiation type scale and spatial distribution scale, and set a dynamic radiation intensity distribution map; S3: At the same time, through the quantum noise suppression plug-in, combined with the environmental electromagnetic interference intensity, correct the spatial distortion of the dynamic radiation intensity distribution map and identify the hotspot coordinates.

[0012] Specifically, a radiation detection component refers to a device or system used to detect and measure radiation, usually including sensors, detectors (such as silicon drift detectors, gas ionization chambers, scintillating fiber arrays, etc.) and auxiliary equipment, which can collect radiation energy characteristics, dose rates, spatial distribution and other information 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, and the contributions of different radiation sources are separated and fused 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 quantum state adjustment and filtering algorithms; hotspot coordinates refer to specific locations in the radiation intensity distribution map where the radiation intensity is significantly higher than the surrounding area, such as tumors or other areas in the patient's body that require high-dose radiation therapy.

[0013] Radiation detection components are deployed in the target area to collect radiation data in real time, including the energy characteristics and dose rate of the radiation type (such as alpha particles, X-rays, etc.), as well as the spatial distribution information of the radiation. The collected real-time data is fused in two dimensions using the principle of radiation field superposition, taking into account the differences in radiation types (energy characteristics and dose rates) and combining spatial distribution information (radiation area and intensity distribution) 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 protective measures.

[0014] At the same time, the quantum noise suppression plug-in is combined with the environmental electromagnetic interference intensity to perform spatial distortion correction on the dynamic radiation intensity distribution map. Through the adjustment of the quantum state and the filtering algorithm, the impact 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 coordinates of hot spots with high radiation intensity can be identified, providing key information for subsequent protective measures, providing a comprehensive description of the radiation field, and improving the accuracy of the data through noise suppression and distortion correction.

[0015] S4: Mark the hotspot 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 zoning control parameters based on 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 early warning model corresponding to the protection optimization engine to verify the radiation protection effectiveness.

[0016] Specifically, the protection optimization engine is used to dynamically generate optimal protection measures based on real-time radiation intensity distribution maps and hotspot information. It can comprehensively consider factors such as shielding layer thickness, personnel exposure time, and regional zoning 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 shielding layer thickness adaptive adjustment parameters, personnel exposure time parameters, and regional zoning control parameters, to guide actual protection measures to adapt to different radiation intensities and distributions; the weight coefficient refers to the principle of radiation field superposition, which is used to adjust the relative importance of the contribution of different radiation sources or radiation types to the overall radiation field. By dynamically updating the weight coefficient, it can more accurately reflect changes in the radiation field and improve the adaptability and accuracy of the model; the environmental electromagnetic interference-parameter mapping table refers to a table that records the relationship between environmental electromagnetic interference intensity and quantum noise suppression plug-in parameters, and quickly adjusts 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. It can dynamically adjust the warning parameters according to the adaptive protection instruction set to ensure the effectiveness of the protection measures.

[0017] First, the hotspot coordinates are marked in the dynamic radiation intensity distribution map. The hotspot coordinates are locations where the radiation intensity is significantly higher than that of the surrounding areas, and are usually the focus of protective measures. The protection optimization engine generates an adaptive protection instruction set based on the hotspot coordinates and the overall characteristics of the distribution map, including shielding layer thickness adaptive adjustment parameters, personnel exposure time parameters, and area zoning control parameters, which are used to guide actual protective measures to adapt to different radiation environments. Based on the adaptive protection instruction set, the weight coefficient of the radiation field superposition principle is synchronously updated. By dynamically adjusting the weight coefficient, the radiation field model can more accurately reflect the current radiation environment, improve the adaptability and accuracy of the model, and 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 to further optimize the noise suppression effect.

[0018] A radiation intensity warning model corresponding to the protection optimization engine is established to monitor in real time whether the radiation intensity exceeds the safety threshold, dynamically adjust the warning parameters according to the adaptive protection instruction set, ensure the effectiveness of protection measures, and promptly discover potential safety risks; by generating an adaptive protection instruction set, the protection measures can be flexibly adjusted to adapt to complex radiation environments; by updating the weight coefficients and establishing a mapping table, the radiation field model and noise suppression effect are further optimized; through the radiation intensity warning model, the effectiveness of protection measures can be verified in real time to ensure the overall effectiveness of radioactive protection.

[0019] Furthermore, in the target area, a radiation detection component is deployed, and the method of the present application further includes:

[0020] S11: According to the measurement requirements, the detection sensitivity gradient is set, including alpha particle detection sensitivity and X-ray energy resolution; S12: Based on the detection sensitivity gradient, the deployment of the silicon drift detector, gas ionization chamber and scintillating fiber array is optimized.

[0021] Specifically, the detection sensitivity gradient refers to setting the difference in detector sensitivity to 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; while for X-rays, more emphasis may be placed on energy resolution to distinguish X-ray signals of different energy levels. Furthermore, alpha particle detection sensitivity refers to the detector's ability to detect alpha particles, which is usually related to the detector's material, structure and working conditions. Higher sensitivity means that low-intensity or low-energy alpha particle signals can be detected more accurately; X-ray energy resolution refers to the detector's ability to distinguish between X-rays of different energies. The ability of high-energy-resolution detectors to detect line signals, can more accurately measure the energy distribution of X-rays, 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, and are characterized by high sensitivity, high energy resolution and fast response; Gas ionization chambers refer to detectors based on the principle of gas ionization, which are suitable for detecting various types of radiation, including alpha particles, beta particles and X-rays, and have the advantage of providing high sensitivity and wide energy range detection; Scintillating fiber arrays convert radiation energy into light signals and transmit them to photodetectors for measurement. They have the characteristics of high sensitivity and fast response and are suitable for radiation detection in complex environments.

[0022] 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 the specific measurement requirements, including α-particle detection sensitivity and X-ray energy resolution. Furthermore, it is necessary to comprehensively consider the radiation type, intensity distribution and expected measurement accuracy requirements of the target area. For example, in a nuclear medicine environment, a higher X-ray energy resolution may be required to distinguish X-ray signals of different energy levels; while in an α-particle detection environment, more emphasis is placed on detection sensitivity. Based on the set detection sensitivity gradient, the deployment of silicon drift detectors, gas ionization chambers and scintillating fiber arrays is optimized. Furthermore, the appropriate detector type is selected and the spatial layout is optimized according to its characteristics. For example, a silicon drift detector may be more suitable for high-sensitivity α-particle detection areas, while a gas ionization chamber can be used in scenarios requiring wide energy range detection, and a scintillating fiber array can be used for fast response detection in complex environments.

[0023] By optimizing the detection sensitivity gradient and detector layout, we can better adapt to the complex and changeable radioactive environment, provide high-quality input data for radiation field analysis, noise suppression and protection measures generation, improve measurement accuracy, ensure that the radiation detection components can efficiently and accurately collect radiation data in the target area, and provide a basis for the accurate drawing of dynamic radiation intensity distribution maps and protection optimization.

[0024] Furthermore, the real-time collected data is subjected to dual-dimensional fusion of radiation type scale and spatial distribution scale to set a dynamic radiation intensity distribution map. The method of this application includes:

[0025] S21: Divide the target area into M cubic units, where the time resolution of each cubic unit does not exceed a first threshold and the spatial resolution does not exceed a second threshold; S22: Perform time-space joint filtering processing in the first cubic unit and set a first dynamic radiation intensity grid; S23: Traverse the M cubic units, combine the first dynamic radiation intensity grid, ..., the Mth dynamic radiation intensity grid, and obtain a dynamic radiation intensity distribution map.

[0026] Specifically, the cube unit refers to dividing the target area into multiple small cubes, and each cube is used as an independent unit to measure and analyze the radiation intensity, which helps to improve the spatial resolution and more accurately describe the spatial distribution of the radiation field; the time resolution refers to the minimum time interval for distinguishing two adjacent events in the time dimension. In radiation measurement, high time resolution means that the instantaneous changes in radiation intensity can be captured more accurately; the spatial resolution refers to the minimum distance between two adjacent positions in the spatial dimension. In radiation measurement, high spatial resolution means that the distribution of radiation intensity in space can be more accurately described; time-space joint filtering processing refers to filtering that considers both 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 radiation intensity; the dynamic radiation intensity grid refers to the radiation intensity data obtained by time-space joint filtering processing in each cube unit, represented as a grid. The dynamic radiation intensity grid describes the distribution of radiation intensity within a limited time and space range.

[0027] The target area is divided into multiple cubic units and each unit is subjected to a time-space joint filtering process to obtain a dynamic radiation intensity distribution map. Specifically, the target area is divided into M cubic units, and the time resolution of each unit does not exceed the first threshold, and the spatial resolution does not exceed the second threshold. The division method is used to improve the measurement accuracy and resolution. In the first cubic unit, a time-space joint filtering process is performed, and a first dynamic radiation intensity grid is set. By filtering out noise, the dynamic changes and spatial distribution of the radiation intensity in the first cubic unit are extracted. All M cubic units are traversed, and the above process is repeated for each unit to obtain M dynamic radiation intensity grids. The M dynamic radiation intensity grids are combined to obtain a dynamic radiation intensity distribution map corresponding to the target area. Through the processing and combination of the sub-units, the dynamic changes and spatial distribution of the complex radiation field can be described more accurately, and a high-precision dynamic radiation intensity distribution map can be generated, thereby improving the pertinence and effectiveness of protective measures.

[0028] Furthermore, if Figure 2 As shown, in the first cubic unit, a time-space joint filtering process is performed to set a first dynamic radiation intensity grid. The method of the present application includes:

[0029] S221: In the first cubic 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 tensor of the α-particle and the contribution tensor of the X-ray; S223: Based on the α-particle effective flux matrix and the X-ray effective flux matrix, map the α-particle contribution tensor and the X-ray contribution tensor in combination to obtain the first dynamic radiation intensity grid.

[0030] Specifically, the alpha particle pulse sequence refers to a series of electrical pulse signals output by the alpha particle detector, each pulse represents an alpha particle detection event, and contains the time and spatial distribution information of the alpha particle; X-ray energy spectrum data refers to the energy spectrum data output by the X-ray detector, which contains the energy distribution information of the X-rays. By analyzing the energy spectrum data, the intensity and energy characteristics of the X-rays are obtained; 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 radiation intensity; the radiation intensity data obtained by time-space joint filtering processing is expressed in matrix form, and the effective flux matrix describes the distribution of radiation intensity within a limited time and space range; the contribution tensor refers to the parameter used to describe the relative importance of the contribution of different radiation sources or radiation types to the radiation field corresponding to the target area in the radiation field superposition principle. By evaluating the contribution tensor, the contribution of different radiation sources can be more accurately reflected.

[0031] Through time-space joint filtering processing and contribution tensor evaluation, the effective flux matrix of α particles and X-rays is generated, and the first dynamic radiation intensity grid is obtained. Specifically, the α particle pulse sequence and X-ray energy spectrum data are subjected to time-space joint filtering processing to generate the α particle effective flux matrix and the X-ray effective flux matrix. By filtering out noise, the distribution of α particles and X-rays in time and space is extracted; the contribution tensor of α particles and the contribution tensor of X-rays are evaluated, and by analyzing the contributions of different radiation sources, a parameter basis is provided for the subsequent radiation field superposition.

[0032] Based on the α-particle effective flux matrix and the X-ray effective flux matrix, the α-particle contribution tensor and the X-ray contribution tensor are combined for mapping to obtain the first dynamic radiation intensity grid. By weighting the contribution tensor, the contributions of different radiation sources are merged into one grid to obtain a comprehensive radiation intensity distribution, which provides a basis for the subsequent generation of a 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 protective measures.

[0033] Furthermore, the present application method also includes:

[0034] S221-1: Based on the α-particle pulse sequence, configure a first counting noise index and a second counting noise index according to the detector dead time and the particle flight time difference; S221-2: Generate an α-particle effective flux matrix according to the first counting noise index and the second counting noise index.

[0035] Specifically, detector dead time refers to the time it takes for the detector to recover to a state capable of detecting the next event after detecting a particle event. This period of time is called dead time. The existence of dead time will cause the detector to miss some events at high counting rates, thereby affecting the accuracy of the counting; 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 time for particles emitted by different sources to arrive at the detector is different. 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 detector dead time and 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 alpha particle effective flux matrix refers to the matrix generated by the corrected pulse sequence, which reflects the flux distribution of alpha particles within a specific time and space range. The effective flux matrix eliminates the influence of noise and detector characteristics, and more accurately describes the actual distribution of alpha particles.

[0036] By analyzing the alpha particle pulse sequence and combining the detector dead time and the particle flight time difference, an effective flux matrix of alpha particles is generated. Specifically, the first counting noise index and the second counting noise index are calculated based on the detector dead time and the particle flight time difference. 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. The first counting noise index and the second counting noise index are used to quantify the counting error caused by the detector characteristics. The first counting noise index and the second counting noise index are used to correct the alpha particle pulse sequence. The effective flux matrix of alpha particles is generated through the corrected pulse sequence, eliminating the influence of noise and detector characteristics and more accurately reflecting the distribution of alpha particles in time and space.

[0037] By correcting the errors caused by detector dead time and particle flight time difference, a more reliable α-particle effective flux matrix is ​​generated, providing a reliable data basis for the generation of dynamic radiation intensity distribution diagrams. 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 radiation protection program.

[0038] Furthermore, according to the first counting noise index and the second counting noise index, an alpha particle effective flux matrix is ​​generated. The method of the present application includes:

[0039] S221-21: Denote the effective particle flux as , construct the confidence interval of the α-particle pulse sequence [ , ],in, is the noise standard deviation, k is the significance factor; S221-22: according to the effective particle flux , configure the sliding time window, and perform the test outside the confidence interval [ , ] is attenuated to generate the effective flux matrix of α particles after smoothing in the time dimension.

[0040] Specifically, the effective particle flux is denoted as It refers to the particle flux obtained by correction after considering factors such as detector dead time and particle flight time difference, which more accurately reflects the actual particle emission rate; the confidence interval [ , ] is used to identify outliers in alpha particle pulse trains; the noise standard deviation It indicates the degree of discreteness 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 window of fixed length on the time series. It is used to smooth the alpha particle pulse sequence.

[0041] By constructing confidence intervals and applying a sliding time window, the α-particle pulse sequence is smoothed to generate the α-particle effective flux matrix after time dimension smoothing. Specifically, according to the effective particle flux, it is recorded as , construct the confidence interval of the α-particle pulse sequence [ , ], confidence interval [ , ] is used to identify outliers in the sequence, configure a sliding time window, and attenuate abnormal pulses that exceed the confidence interval. It smoothes the data by moving a window of fixed length on the time series, thereby reducing the impact of outliers. According to the smoothed α-particle pulse sequence, the α-particle effective flux matrix after smoothing in the time dimension is generated. The α-particle effective flux matrix after smoothing in the time dimension more accurately reflects the distribution of α particles in time, providing a high-quality data foundation for the subsequent generation of dynamic radiation intensity distribution diagrams.

[0042] By constructing confidence intervals and applying sliding time windows, outliers can be effectively identified and processed, and the accuracy and stability of α-particle flux data can be improved, thereby generating a smoother and more reliable α-particle effective flux matrix, providing a high-quality data basis for the subsequent generation of dynamic radiation intensity distribution maps, thereby improving the reliability and effectiveness of the entire radioactive protection program.

[0043] Furthermore, the present application method includes:

[0044] S221-23: The effective particle flux ,in, is the raw count rate, The detector dead time constant associated with the first counting noise indicator, The particle flight time difference associated with the second counting noise indicator, The time calibration factor associated with the ambient temperature.

[0045] Specifically, the effective particle flux refers to the corrected particle flux, which reflects the actual particle flow after taking into account factors such as detector dead time, particle flight time difference and ambient temperature; the raw count rate refers to the particle count rate directly measured by the detector without any correction processing; the detector dead time constant is associated with the detector dead time and is used to quantify the counting loss caused by the recovery time of the detector at high count rates; the particle flight time difference refers to the time difference between different particles arriving at the detector, which is used to distinguish particles from different sources or paths; the ambient temperature-related time calibration coefficient is associated with the ambient temperature and is used to correct the impact of temperature changes on detector performance to ensure the accuracy of the measurement results.

[0046] The effective particle flux Raw count rate After correction, the counting noise introduced by the detector dead time constant and the particle flight time difference is further deducted from the original counting rate. At the same time, the flux is corrected by the time calibration coefficient associated with the ambient temperature to compensate for the influence of temperature changes on the detector performance. The detector dead time constant is used to correct the events missed by the detector at high counting rates; the particle flight time difference is used to distinguish the contributions of different sources; the time calibration coefficient associated with the ambient temperature is used to correct the influence of temperature changes on the detector response, so that the effective particle flux is closer to the true value.

[0047] The effective particle flux is calculated by comprehensively considering the original counting rate, detector dead time constant, particle flight time difference, and the time calibration coefficient related to the ambient temperature. The impact of various factors on detector performance is fully considered, so that the effective particle flux can more realistically reflect the dynamic changes of the radiation field and provide more accurate particle flux data. It provides a reliable data basis for the subsequent generation of radiation intensity distribution maps and optimization of protective measures, thereby improving the accuracy and reliability of the entire radiation protection plan.

[0048] Furthermore, the spatial distortion of the dynamic radiation intensity distribution diagram is corrected by combining the quantum noise suppression plug-in with the ambient electromagnetic interference intensity. The method of the present application includes:

[0049] S31: Based on the quantum noise suppression plug-in and combined with the environmental electromagnetic interference intensity, the initial quantum state is configured; S32: According to the initial quantum state, an adaptive Kalman filter is used to iteratively compensate for the phase distortion and establish a solution space; S33: Based on the solution space, the reconstruction process of the dynamic radiation intensity distribution map is optimized by back propagation, and the spatial distortion correction result is output to the protection optimization engine.

[0050] Specifically, 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 quantum state adjustment and filtering algorithms; the original quantum state refers to the removal or correction of quantum state distortion caused by factors such as environmental electromagnetic interference during the quantum noise suppression process, thereby obtaining 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 environmental electromagnetic interference or other factors, which will affect the accuracy of the radiation intensity distribution map; the solution space refers to the set of all possible solutions in the optimization process, and the solution space is used to store the results after iterative compensation by the Kalman filter; backpropagation optimization refers to optimizing the performance of the system by calculating the gradient of the error and backpropagating the adjustment parameters, and is used to optimize the reconstruction process of the dynamic radiation intensity distribution map.

[0051] Through the quantum noise suppression plug-in and adaptive Kalman filter, the spatial distortion in the dynamic radiation intensity distribution map is corrected, and the correction results are output to the protection optimization engine. Specifically, based on the quantum noise suppression plug-in, combined with the environmental electromagnetic interference intensity, it is initialized to the original quantum state. The original quantum state is the basis for subsequent noise suppression and data correction, and can effectively suppress the impact of environmental electromagnetic interference on radiation detection data; according to the initial quantum state, the adaptive Kalman filter is used to iteratively compensate for phase distortion. The Kalman filter gradually reduces the impact of phase distortion on the radiation intensity distribution map by dynamically adjusting the filtering parameters. During each iteration, the filter will update the filtering parameters according to real-time detection data to optimize the compensation effect.

[0052] During each iterative compensation process, the compensation results generated by the Kalman filter are stored in the solution space. Based on the data in the solution space, the back propagation optimization algorithm is used to optimize the reconstruction process of the dynamic radiation intensity distribution map. The back propagation 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 results; improves the accuracy and reliability of the dynamic radiation intensity distribution map, and through the combination of the quantum noise suppression plug-in and the adaptive Kalman filter, it can effectively correct the spatial distortion caused by environmental electromagnetic interference, thereby providing more accurate data support for subsequent protection optimization. Back propagation optimization further improves the reconstruction accuracy of the distribution map, ensuring that the protection measures can adapt to the complex radioactive environment more accurately and improve the overall effectiveness of radioactive protection.

[0053] Furthermore, the first dynamic radiation intensity grid, ..., and the Mth dynamic radiation intensity grid are combined to obtain a dynamic radiation intensity distribution diagram. The method of the present application includes:

[0054] S231: During each iterative compensation process, the current quantum state parameters are predicted, and the Kalman gain is dynamically updated based on the real-time data collected by the radiation detection component. S232: Phase coherence constraints are used to perform graph structure sparsification, screen valid quantum state branches, spatially align M dynamic radiation intensity grids, and determine the dynamic radiation intensity distribution graph.

[0055] 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 the measurement data and the predicted data, and dynamically adjust the weight of the filter. The update of the Kalman gain can optimize the performance of the filter, making it better adapt to the dynamically changing environment; the phase coherence constraint is used to optimize the reconstruction process of the dynamic radiation intensity distribution map to ensure that the phase of the signal remains consistent and stable during the processing process; graph structure sparsification processing refers to reducing redundant information in the graph and retaining key information to screen effective quantum state branches, remove noise and interference, and improve the efficiency and accuracy of data processing; spatial alignment refers to spatial alignment between 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 to generate an accurate dynamic radiation intensity distribution map.

[0056] During each iterative compensation process, the reconstruction process of the dynamic radiation intensity distribution map is optimized by dynamically updating the Kalman gain and graph structure sparsification. Specifically, during each iterative compensation process, predictions are made based on the current quantum state parameters, and then the Kalman gain is dynamically updated based on the real-time data collected by the radiation detection component. The update of the Kalman gain can optimize the performance of the filter, making it better adapt to the dynamically changing environment, thereby improving the accuracy of phase distortion compensation; the dynamic radiation intensity grid is subjected to graph structure sparsification using the phase coherence constraint condition to screen effective quantum state branches, remove noise and interference, and retain key information. The phase coherence constraint condition ensures that the phase of the signal remains consistent and stable during the processing process, thereby improving the accuracy of the distribution map; all dynamic radiation intensity grids that have been sparsely processed are spatially aligned. 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.

[0057] By dynamically updating the Kalman gain, it can better adapt to the dynamically changing environment; through graph structure sparse processing and phase coherence constraints, it can effectively remove noise and interference and retain key information; through spatial alignment, the reconstruction process of the dynamic radiation intensity distribution map is further optimized, and a consistent dynamic radiation intensity distribution map is generated to ensure 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 radiation protection.

[0058] In summary, the beneficial effects of the embodiments of the present application are:

[0059] Due to the deployment of radiation detection components, real-time collected data is obtained; using the radiation field superposition principle, the real-time collected data is integrated in two dimensions, namely, the radiation type scale and the spatial distribution scale, to set a dynamic radiation intensity distribution map; at the same time, through the 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 hotspot 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 effectiveness of radioactive protection. This application provides a radiation intensity testing method for radioactive protection, which uses a quantum noise suppression plug-in combined with the environmental electromagnetic interference intensity to correct the spatial distortion of the dynamic radiation intensity distribution map, adapt to complex and changeable radioactive environments, improve the pertinence and effectiveness of radiation protection measures, ensure the accuracy of radiation detection results, reduce measurement errors caused by environmental electromagnetic interference, verify and optimize the effectiveness of radioactive protection, and improve the overall effectiveness of radioactive protection.

[0060] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory and can be called and recognized by an unlimited computer processor, without any unnecessary restrictions.

[0061] Furthermore, the above technical solution only reflects the preferred technical solution of the technical solution of the embodiment of the present application. Some changes that may be made to certain parts thereof by technical personnel in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technical personnel in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A radiation intensity test method for radioactive protection, characterized in that: The method comprises: Deploy radiation detection components in the target area to acquire real-time data, including energy characteristic data and dose rate data under radiation type, and radiation area data and radiation intensity data under spatial distribution; Using the radiation field superposition principle, the real-time collected data is fused in two dimensions: radiation type scale and spatial distribution scale, to set a dynamic radiation intensity distribution map; At the same time, the quantum noise suppression plug-in is used to correct the spatial distortion of the dynamic radiation intensity distribution diagram and identify the hotspot coordinates in combination with the environmental electromagnetic interference intensity. Marking the hotspot 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 area zoning control parameters based on a protection optimization engine; Based on the adaptive protection instruction set, the weight coefficient of the radiation field superposition principle is synchronously updated, and 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 are established to perform radioactive protection effectiveness verification.

2. The radiation intensity testing method for radioactive protection according to claim 1, wherein: Deploying a radiation detection assembly in a target area, the method further comprising: According to the measurement requirements, set the detection sensitivity gradient, including α particle detection sensitivity and X-ray energy resolution; Based on the detection sensitivity gradient, the deployment of silicon drift detectors, gas ionization chambers and scintillating fiber arrays is optimized.

3. The radiation intensity testing method for radioactive protection according to claim 1, wherein: Performing dual-dimensional fusion of radiation type scale and spatial distribution scale on the real-time collected data to set a dynamic radiation intensity distribution map, the method comprising: Dividing the target area into M cubic units, wherein the temporal resolution of each cubic unit does not exceed a first threshold and the spatial resolution does not exceed a second threshold; In the first cubic unit, a time-space joint filtering process is performed to set a first dynamic radiation intensity grid; The M cubic units are traversed, and the first dynamic radiation intensity grid, ..., and the Mth dynamic radiation intensity grid are combined to obtain a dynamic radiation intensity distribution diagram.

4. The radiation intensity testing method for radioactive protection according to claim 3, wherein: In a first cubic unit, a time-space joint filtering process is performed to set a first dynamic radiation intensity grid, the method comprising: In the first cubic unit, the α-particle pulse sequence and X-ray energy spectrum data are subjected to time-space joint filtering to generate the α-particle effective flux matrix and the X-ray effective flux matrix; Evaluate the alpha particle contribution tensor and the X-ray contribution tensor; Based on the α-particle effective flux matrix and the X-ray effective flux matrix, mapping is performed in combination with the α-particle contribution tensor and the X-ray contribution tensor to obtain the first dynamic radiation intensity grid.

5. The radiation intensity testing method for radioactive protection according to claim 4, characterized in that: Based on the alpha particle pulse sequence, configuring a first counting noise index and a second counting noise index according to a detector dead time and a particle flight time difference; An alpha particle effective flux matrix is ​​generated according to the first counting noise index and the second counting noise index.

6. The radiation intensity testing method for radioactive protection according to claim 5, characterized in that: Generating an alpha particle effective flux matrix according to the first counting noise index and the second counting noise index, the method comprising: The effective particle flux is denoted as , construct the confidence interval of the α-particle pulse sequence [ , ],in, is the noise standard deviation, k is the significance factor; According to the effective particle flux , configure the sliding time window, and perform the test outside the confidence interval [ , ] is used to attenuate the abnormal pulses and generate the effective flux matrix of α particles after smoothing in the time dimension.

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

8. The radiation intensity testing method for radioactive protection according to claim 3, wherein: Correcting the spatial distortion of the dynamic radiation intensity distribution diagram by using a quantum noise suppression plug-in in combination with the environmental electromagnetic interference intensity, the method comprising: Based on the quantum noise suppression plug-in and in combination with the strength of the environmental electromagnetic interference, the original quantum state is configured; According to the initial quantum state, an adaptive Kalman filter is used to iteratively compensate for phase distortion and establish a solution space; Based on the solution space, the reconstruction process of the dynamic radiation intensity distribution map is optimized by back propagation, and the spatial distortion correction result is output to the protection optimization engine.

9. The radiation intensity testing method for radioactive protection according to claim 8, characterized in that: Combining the first dynamic radiation intensity grid, ..., and the Mth dynamic radiation intensity grid to obtain a dynamic radiation intensity distribution diagram, the method comprising: During each iterative compensation process, the current quantum state parameters are predicted, and the Kalman gain is dynamically updated based on the real-time data collected by the radiation detection component. Phase coherence constraints are used to perform graph structure sparsification processing, effective quantum state branches are screened, M dynamic radiation intensity grids are spatially aligned, and the dynamic radiation intensity distribution graph is determined.

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