Real-time radiation monitoring system for accelerator solid target beam uniformity

Abstract

The application relates to the technical field of radiation monitoring, and discloses a real-time radiation monitoring system for the beam current uniformity of an accelerator solid target, which comprises a polarization characteristic sequence acquisition module, an environment compensation calibration module, a polarization angle distribution analysis module, a uniformity analysis module, an environment sensitivity analysis module and a comprehensive monitoring report generation module. First, the polarization characteristic sequence corresponding to the beam current received by the solid target is acquired; the polarization characteristic sequence is compensated and calibrated based on the temperature and the magnetic field intensity of the accelerator vacuum chamber, so that the calibrated polarization characteristic sequence is obtained; the calibrated polarization characteristic sequence is decomposed, so that the polarization angle distribution parameters are obtained; the distribution uniformity is calculated, so that the uniformity index is obtained; the environment correlation analysis is carried out in combination with the uniformity index, so that the environment sensitivity index is obtained; and finally, the comprehensive monitoring report is generated. The application can improve the precision of real-time radiation monitoring of the beam current uniformity of the accelerator solid target.

Description

Technical Field

[0001] This invention relates to the field of radiation monitoring technology, and in particular to a real-time radiation monitoring system for the uniformity of solid target beams in accelerators. Background Technology

[0002] During accelerator operation, the uniformity of the beam received by the solid target directly affects the stability of secondary rays and the quality of experimental data. However, existing monitoring systems generally lack effective real-time data purification mechanisms, making it difficult to eliminate measurement deviations caused by temperature changes and magnetic field fluctuations. Temperature changes in the vacuum chamber can cause thermal expansion of optical components, resulting in polarization angle measurement drift. Magnetic field fluctuations can alter the trajectory of charged particles through the Lorentz force, introducing systematic errors. These environmental factors result in a low signal-to-noise ratio of the original polarization signal, making it difficult to accurately reflect the true beam state and seriously affecting the reliability of subsequent analysis results.

[0003] Meanwhile, traditional methods often ignore the physical information contained in different frequency components of the signal, resulting in an overly coarse description of uniformity. They lack systematic methods based on wavelet transform multi-scale decomposition and information entropy quantification, making it difficult for monitoring systems to accurately characterize subtle changes in beam polarization angle distribution and unable to provide sufficient decision support for accelerator operation optimization. Summary of the Invention

[0004] This invention provides a real-time radiation monitoring system for the uniformity of solid target beams in accelerators, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a real-time radiation monitoring system for the uniformity of solid-state target beams in accelerators, wherein:

[0006] The polarization feature sequence acquisition module is used to acquire the polarization feature sequence corresponding to the beam received by the solid target;

[0007] The environmental compensation calibration module is used to perform environmental compensation calibration on the polarization feature sequence based on the temperature and magnetic field strength of the accelerator vacuum chamber, so as to obtain the calibrated polarization feature sequence of the beam received by the solid target.

[0008] The polarization angle distribution analysis module is used to decompose the calibrated polarization feature sequence to obtain the polarization angle distribution parameters of the beam received by the solid target.

[0009] The uniformity analysis module is used to calculate the distribution uniformity of the polarization angle distribution parameters and obtain the uniformity index of the beam received by the solid target.

[0010] The environmental sensitivity analysis module is used to perform environmental correlation analysis on the uniformity index based on the temperature and the magnetic field strength to obtain the environmental sensitivity index of the solid target receiving beam.

[0011] The comprehensive monitoring report generation module is used to integrate the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid target receiving beam.

[0012] In a preferred embodiment, obtaining the polarization feature sequence corresponding to the beam received by the solid target is specifically used for:

[0013] The original polarization signal dataset corresponding to the beam received by the solid target is acquired from the preset polarization detection device;

[0014] The original polarization signal dataset is subjected to time-domain filtering to obtain the preprocessed polarization signal set of the beam received by the solid target;

[0015] The preprocessed polarization signal set is subjected to frame segmentation to obtain a polarization signal subset containing multiple time frames;

[0016] Obtain the arithmetic mean of the polarization intensity in the polarization signal subset, and generate the frame feature sequence of the polarization signal subset;

[0017] The frame feature sequences of all time frames are aggregated to generate the polarization feature sequence of the beam received by the solid target.

[0018] In a preferred embodiment, the step of performing environmental compensation calibration on the polarization feature sequence based on the temperature and magnetic field strength of the accelerator vacuum chamber to obtain the calibrated polarization feature sequence of the beam received by the solid target is specifically used for:

[0019] The temperature and magnetic field strength of the accelerator vacuum chamber were obtained;

[0020] Temperature influence analysis was performed on the solid target receiving beam to obtain the temperature compensation factor of the solid target receiving beam.

[0021] The magnetic field strength influence of the solid target receiving beam is analyzed to obtain the magnetic field strength compensation factor of the solid target receiving beam.

[0022] The polarization feature sequence is calibrated frame by frame based on the temperature compensation factor and the magnetic field strength compensation factor to obtain the calibrated polarization feature sequence of the solid target receiving beam.

[0023] In a preferred embodiment, the step of decomposing the calibrated polarization feature sequence to obtain the polarization angular distribution parameters of the beam received by the solid target is specifically used for:

[0024] Based on the polarization intensity values ​​in the calibrated polarization feature sequence, the wavelet transform scaling parameters are set.

[0025] Based on the wavelet transform scale parameters, the calibrated polarization feature sequence is decomposed into multi-scale values ​​to obtain the detail coefficients of the calibrated polarization feature sequence.

[0026] The detailed coefficients are then subjected to angle mapping transformation to obtain the polarization angle distribution parameters of the beam received by the solid target.

[0027] In a preferred embodiment, setting the wavelet transform scaling parameter based on the polarization intensity values ​​in the calibrated polarization feature sequence is specifically used for:

[0028] Frequency domain energy analysis was performed on the calibrated polarization feature sequence to obtain the power spectral density distribution;

[0029] Based on the power spectral density distribution and the preset polarization scale division rules, the wavelet transform scale parameters are effectively extracted to obtain the effective value range of the wavelet transform scale parameters.

[0030] The wavelet transform scaling parameters are defined based on the effective value range.

[0031] In a preferred embodiment, calculating the uniformity of the polarization angle distribution parameters to obtain the uniformity index of the beam received by the solid target is specifically used for:

[0032] The polarization angles in the polarization angle distribution parameters are statistically analyzed to obtain an angle probability distribution sequence;

[0033] The randomness of the angle probability distribution sequence is analyzed to obtain the random distribution information entropy of the polarization angle distribution parameters;

[0034] The random distribution information entropy is subjected to convergence mapping to generate the uniformity index of the beam received by the solid target.

[0035] In a preferred embodiment, the step of performing a randomness analysis on the angular probability distribution sequence to obtain the random distribution information entropy of the polarization angle distribution parameters is specifically used for:

[0036] Extract the angle distribution probability values ​​from the angle probability distribution sequence;

[0037] The probability values ​​of the angle distribution are subjected to random quantization processing to obtain the random distribution information entropy of the polarization angle distribution parameters.

[0038] In a preferred embodiment, the environmental correlation analysis of the uniformity index based on the temperature and the magnetic field strength to obtain the environmental sensitivity index of the solid target receiving beam is specifically used for:

[0039] Based on the uniformity index, the time series of the temperature, and the time series of the magnetic field strength, an environmental correlation analysis is performed on the solid target receiving beam to obtain the temperature sensitivity coefficient and the magnetic field sensitivity coefficient.

[0040] The temperature sensitivity coefficient and the magnetic field sensitivity coefficient are weighted and fused to obtain the environmental sensitivity index of the solid target receiving beam.

[0041] In a preferred embodiment, the integration of the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid-state target received beam is specifically used for:

[0042] The uniformity index and the environmental sensitivity index are normalized to obtain a comprehensive analysis dataset of the solid target receiving beam.

[0043] Based on the comprehensive analysis dataset, a comprehensive score is calculated for the solid target receiving beam to obtain a comprehensive monitoring index;

[0044] Based on the comprehensive monitoring index, a real-time trend analysis of the solid target receiving beam is performed to obtain the dynamic prediction trend result of the solid target receiving beam.

[0045] A comprehensive monitoring report of the solid target receiving beam is generated based on the comprehensive monitoring index and the dynamic prediction trend results.

[0046] In a preferred embodiment, the step of performing real-time trend analysis on the comprehensive monitoring index to obtain the dynamic prediction trend result of the solid target receiving beam is specifically used for:

[0047] Obtain the time series data of the comprehensive monitoring index to obtain the comprehensive monitoring index sequence;

[0048] Trend analysis is performed on the comprehensive monitoring index sequence to obtain the dynamic trend slope of the solid target receiving beam.

[0049] The dynamic prediction trend of the solid target receiving beam is generated based on the dynamic trend slope.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. This invention significantly improves the accuracy and reliability of monitoring data by introducing a real-time data purification mechanism based on environmental compensation calibration. The system collects the temperature and magnetic field strength of the accelerator vacuum chamber in real time, calculates the corresponding temperature compensation factor and magnetic field strength compensation factor, and performs frame-by-frame dynamic calibration on the polarization feature sequence. This mechanism effectively eliminates measurement drift caused by thermal expansion effect and Lorentz force interference, ensuring that the polarization signals acquired under different operating conditions are comparable and stable. The data purification process not only improves the signal-to-noise ratio, but also provides high-quality input data for subsequent uniformity analysis, making the monitoring results more reflective of the true beam state.

[0052] 2. This invention employs a multi-scale uniformity quantization method based on wavelet transform and information entropy to achieve a fine characterization of beam uniformity. By decomposing the calibrated polarization feature sequence into components of different scales through wavelet transform, it can simultaneously capture the macroscopic trend and microscopic fluctuations of the signal. Combined with information entropy, the randomness of the polarization angle distribution is quantified to generate an intuitive uniformity index. This multi-scale analysis method not only improves the resolution and sensitivity of uniformity assessment but also effectively identifies subtle changes in beam state. Compared with traditional single-scale analysis, this method provides more comprehensive physical information, offering a scientific basis for accelerator operation optimization and fault early warning, and significantly improving the system's monitoring accuracy and decision support capabilities. Attached Figure Description

[0053] Figure 1 This is a system architecture diagram of a real-time radiation monitoring system for the uniformity of a solid target beam in an accelerator, provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0056] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0057] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0058] In practice, the server-side equipment deployed in the real-time radiation monitoring system for accelerator solid-state target beam uniformity may consist of one or more devices. This system can be implemented as a service instance, a virtual machine, or a hardware device. For example, it can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node to provide real-time radiation monitoring of accelerator solid-state target beam uniformity to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage each user terminal. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide real-time radiation monitoring of accelerator solid-state target beam uniformity to various user terminals.

[0059] In terms of implementation, the real-time radiation monitoring system for accelerator solid-state target beam uniformity and the user terminal are mutually compatible. That is, if the real-time radiation monitoring system for accelerator solid-state target beam uniformity is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the real-time radiation monitoring system for accelerator solid-state target beam uniformity is implemented as a website, then the user terminal is implemented as a webpage; or if the real-time radiation monitoring system for accelerator solid-state target beam uniformity is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0060] like Figure 1 The figure shown is a system architecture diagram of a real-time radiation monitoring system for the uniformity of accelerator solid target beams provided in an embodiment of the present invention.

[0061] The real-time radiation monitoring system 100 for the uniformity of solid target beam in accelerators described in this invention can be located on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the real-time radiation monitoring system 100 for the uniformity of solid target beam in accelerators may include a polarization feature sequence acquisition module 101, an environmental compensation calibration module 102, a polarization angle distribution analysis module 103, a uniformity analysis module 104, an environmental sensitivity analysis module 105, and a comprehensive monitoring report generation module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, stored in the memory of the electronic device.

[0062] In this embodiment of the invention, in the real-time radiation monitoring system for accelerator solid target beam uniformity, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. This "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the real-time radiation monitoring system for accelerator solid target beam uniformity provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the real-time radiation monitoring system for accelerator solid target beam uniformity. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0063] The following describes, with reference to specific embodiments, each component and its specific workflow of the real-time radiation monitoring system for the uniformity of solid target beams in accelerators:

[0064] The polarization feature sequence acquisition module 101 is used to acquire the polarization feature sequence corresponding to the beam received by the solid target.

[0065] In this embodiment of the invention, obtaining the polarization feature sequence corresponding to the beam received by the solid target is specifically used for:

[0066] The original polarization signal dataset corresponding to the beam received by the solid target is acquired from the preset polarization detection device;

[0067] The original polarization signal dataset is subjected to time-domain filtering to obtain the preprocessed polarization signal set of the beam received by the solid target;

[0068] The preprocessed polarization signal set is subjected to frame segmentation to obtain a polarization signal subset containing multiple time frames;

[0069] Obtain the arithmetic mean of the polarization intensity in the polarization signal subset, and generate the frame feature sequence of the polarization signal subset;

[0070] The frame feature sequences of all time frames are aggregated to generate the polarization feature sequence of the beam received by the solid target.

[0071] It should be noted that the original polarization signal dataset includes time-series polarization intensity and angle values.

[0072] It should be noted that the preset polarization detection device refers to a standard hardware component that integrates a photoelectric sensor and a polarization filter. It receives secondary rays generated by the beam impact on a solid target and measures the physical effects of changes in the polarization angle and intensity of the rays in real time, thus providing a carrier for the acquisition of the original polarization signal.

[0073] It should be noted that time-domain filtering refers to low-pass digital filtering of time-series polarized signals to eliminate high-frequency noise interference in the time dimension. The role of time-domain filtering is to improve the signal-to-noise ratio and ensure the stability of subsequent processing.

[0074] Furthermore, the low-pass digital filtering operation removes high-frequency noise components from the polarization signal by setting a cutoff frequency, thereby improving the signal-to-noise ratio and ensuring the stability of subsequent framing and feature extraction.

[0075] Furthermore, the cutoff frequency is dynamically determined based on the sampling rate and noise characteristics of the polarization signal. For example, for a polarization signal with a sampling rate of 1kHz, the cutoff frequency can be set to 100Hz to filter out high-frequency environmental noise, retain the effective low-frequency components of the beam polarization, improve the signal-to-noise ratio, and ensure the stability of subsequent framing and feature extraction.

[0076] It should be noted that the preprocessed polarization signal set is a polarization intensity dataset after time-domain filtering, representing the true polarization state of the beam after eliminating environmental noise. It is used to provide standardized input for framing and prevent noise from contaminating the calculation of statistical features.

[0077] It should be noted that frame segmentation is an operation that divides a continuous polarization signal into segments of equal length. The continuous polarization signal is segmented by a fixed time window of 10ms / frame by default, which is used to convert continuous monitoring into discrete analysis units to support local feature extraction.

[0078] It should be noted that the polarization signal subset is a collection of 512 polarization intensity data points in a single frame, reflecting the short-term distribution characteristics of the beam's polarization state within that time period. As the smallest unit for calculating statistical features, it captures details of dynamic polarization changes such as peak fluctuations.

[0079] It should be noted that the arithmetic mean of polarization intensity is calculated by summing the polarization intensity values ​​of all 512 data points in the polarization signal subset and then dividing by the number of points to obtain the arithmetic mean. This arithmetic mean is used to quantify the central tendency of polarization intensity within a single frame and to support the generation of frame feature sequences.

[0080] It should be noted that the frame feature sequence refers to the ordered set of all frame feature values. It is a quantized trajectory of the time-varying characteristics of polarization intensity and is used to compress high-dimensional signals into low-dimensional time series to support polarization uniformity analysis.

[0081] Furthermore, the order of all frame feature values ​​in the frame feature sequence is sorted according to the timestamp order of the frame feature values.

[0082] It should be noted that aggregating the frame feature sequence of all time frames refers to concatenating the frame feature values ​​calculated for each time frame into an ordered sequence according to the timestamp order. This can be achieved by storing the data continuously using an array or list data structure, and is used to form a complete polarization feature sequence that reflects the time-varying characteristics of beam polarization.

[0083] It should be noted that the polarization feature sequence is the final aggregated output of the frame feature sequence, used to describe the change in polarization intensity distribution of the solid target under beam impact throughout the entire cycle, providing core input for the generation of environmental sensitivity indicators.

[0084] The environmental compensation calibration module 102 is used to perform environmental compensation calibration on the polarization feature sequence based on the temperature and magnetic field strength of the accelerator vacuum chamber, so as to obtain the calibrated polarization feature sequence of the beam received by the solid target.

[0085] In this embodiment of the invention, the step of performing environmental compensation calibration on the polarization feature sequence based on the temperature and magnetic field strength of the accelerator vacuum chamber to obtain the calibrated polarization feature sequence of the beam received by the solid target is specifically used for:

[0086] The temperature and magnetic field strength of the accelerator vacuum chamber were obtained;

[0087] Temperature influence analysis was performed on the solid target receiving beam to obtain the temperature compensation factor of the solid target receiving beam.

[0088] The magnetic field strength influence of the solid target receiving beam is analyzed to obtain the magnetic field strength compensation factor of the solid target receiving beam.

[0089] The polarization feature sequence is calibrated frame by frame based on the temperature compensation factor and the magnetic field strength compensation factor to obtain the calibrated polarization feature sequence of the solid target receiving beam.

[0090] It should be noted that the temperature and magnetic field strength of the accelerator vacuum chamber are obtained by integrating temperature sensors and Hall effect magnetic field sensors on the walls of the vacuum chamber in real time. The temperature sensor uses a platinum resistance thermometer PT100 to measure with a resolution of 0.1°C, and the magnetic field sensor captures magnetic field fluctuations with an accuracy of 0.1mT based on the anisotropic magnetoresistive principle. At the same time, the data is collected to ensure the time synchronization of environmental parameters and polarization signals.

[0091] It should be noted that the temperature compensation factor is a dimensionless coefficient obtained through calculation. It quantifies the scaling effect of temperature changes on the polarization signal intensity and is used to eliminate the polarization angle measurement drift caused by thermal expansion, thereby improving data reliability.

[0092] Furthermore, the temperature compensation factor is calculated by multiplying a preset temperature influence coefficient by the difference between the real-time temperature and the reference temperature and then adding 1, wherein the reference temperature is 25°C by default.

[0093] Furthermore, the preset temperature influence coefficient ranges from 0.001 to 0.01°C. -1 , is the linear influence factor of unit temperature change on polarization signal intensity, used to quantify thermal expansion effect, and used in temperature compensation factor calculation to eliminate measurement drift.

[0094] The magnetic field strength compensation factor is obtained by exponentially multiplying the preset magnetic field sensitivity coefficient by the product of the difference between the real-time magnetic field strength and the reference magnetic field strength. It represents the degree of interference of the magnetic field on the polarization direction. Essentially, it is used to counteract the electron trajectory deflection caused by the Lorentz force and reduce the error of the beam uniformity assessment caused by magnetic field fluctuations. The reference magnetic field strength is 0mT by default.

[0095] Furthermore, the preset magnetic field sensitivity coefficient ranges from 0.05 to 0.2 mT. -1 , is the nonlinear interference coefficient of a unit magnetic field change on the polarization direction, used to represent the effect of the Lorentz force.

[0096] Furthermore, the Lorentz force effect refers to the force exerted by the magnetic field on moving charged particles, causing electron trajectory deflection. In radiation monitoring, this manifests as a deviation in polarization direction measurement. It is the essential mechanism by which magnetic field interferes with polarization angle. This effect is offset by a magnetic field strength compensation factor to reduce beam uniformity assessment errors.

[0097] It should be noted that frame-by-frame environmental compensation calibration is achieved by multiplying the temperature compensation factor and magnetic field strength compensation factor with the feature values ​​of each frame of the polarization feature sequence. This is used to dynamically eliminate environmental noise and ensure the comparability of polarization signals under different operating conditions.

[0098] It should be noted that the polarization intensity time series after environmental compensation processing of the calibrated polarization feature sequence represents the true beam polarization state after eliminating temperature and magnetic field interference. As input for polarization angle distribution analysis, it provides a clean data basis for uniformity analysis and avoids environmental factors from misleading monitoring results.

[0099] The polarization angle distribution analysis module 103 is used to decompose the calibrated polarization feature sequence to obtain the polarization angle distribution parameters of the beam received by the solid target.

[0100] In this embodiment of the invention, the step of decomposing the calibrated polarization feature sequence to obtain the polarization angle distribution parameters of the solid target received beam is specifically used for:

[0101] Based on the polarization intensity values ​​in the calibrated polarization feature sequence, the wavelet transform scaling parameters are set.

[0102] Based on the wavelet transform scale parameters, the calibrated polarization feature sequence is decomposed into multi-scale values ​​to obtain the detail coefficients of the calibrated polarization feature sequence.

[0103] The detailed coefficients are then subjected to angle mapping transformation to obtain the polarization angle distribution parameters of the beam received by the solid target.

[0104] It should be noted that multi-scale decomposition is a signal processing technique that uses wavelet transform to decompose the calibrated polarization feature sequence into components of different scales in order to analyze the local features of the signal at multiple resolutions. The specific decomposition method is as follows: the signal is separated into approximate coefficients representing low-frequency components and detail coefficients representing high-frequency components. Its role is to enhance the fine-grained analysis of polarization intensity changes, avoid noise interference, provide clean input for angle mapping, and thus improve the accuracy of beam uniformity monitoring.

[0105] Furthermore, only the calibrated polarization feature sequence corresponding to scale 2 in the wavelet transform scale parameter participates in the multi-scale decomposition. This is because scale 2 covers the mid-frequency band, which concentrates the main energy characteristics of beam polarization, avoiding the high-frequency noise of scale 1 and the low-frequency baseline drift of scale 3, thereby optimizing the decomposition efficiency and focusing on key physical processes.

[0106] Furthermore, wavelet transform is a mathematical transformation tool that performs time-frequency analysis on signals by scaling and translating the mother wavelet. Its function is to transform the polarization feature sequence from the time domain to the scale-time domain in order to capture the local details of non-stationary signals.

[0107] Furthermore, wavelet transform overcomes the limitations of Fourier transform, providing both time localization and frequency information simultaneously, improving sensitivity to dynamic changes in beam polarization, reducing the impact of environmental fluctuations, and thus enhancing the robustness of real-time monitoring.

[0108] It should be noted that the approximation coefficient and detail coefficient are the output results of wavelet transform multi-scale decomposition. The approximation coefficient represents the low-frequency components of the signal, indicating the overall trend of polarization intensity or baseline information, while the detail coefficient represents the high-frequency components.

[0109] Furthermore, approximation coefficients are used to preserve the main characteristics of beam polarization, while detail coefficients are used for further angle mapping transformations to extract the polarization angle distribution. The combination of the two ensures that the analysis is both comprehensive and focused on key changes.

[0110] Furthermore, the range of values ​​for the approximation coefficient and the detail coefficient depends on the wavelet transform scale and the signal energy. The approximation coefficient range covers the baseline value of the polarization intensity and is set from 0 to the maximum signal amplitude. The detail coefficient range is set from -1 to 1. The approximation coefficient and the detail coefficient represent low-frequency trends and high-frequency fluctuations, respectively, and are used for signal separation in multi-scale analysis.

[0111] It should be noted that angle mapping transformation is a mathematical process that transforms detail coefficients from the scale domain to the angle domain. It uses a linear transformation to map detail coefficient values ​​to polarization angle values. Angle mapping transformation quantifies the local changes in polarization direction and is used to convert high-frequency signal components into statistically significant angular distribution parameters, providing direct input for uniformity analysis.

[0112] Furthermore, the linear transformation to map detail coefficient values ​​to polarization angle values ​​is obtained by calculating the sum of the product of the detail coefficients and the scaling parameter and the offset parameter, and is used to convert high-frequency components into physical angles.

[0113] Furthermore, the scaling parameter and the offset parameter are key parameters in the linear transformation of the angle mapping conversion. The scaling parameter scales the detail coefficient values ​​to an angle range of 0° to 180°, and the offset parameter adjusts the angle reference point to achieve a physical mapping from the detail coefficients to the polarization angle, ensuring the accuracy of the distribution parameter calculation.

[0114] It should be noted that the polarization angle distribution parameter is a quantitative indicator describing the statistical characteristics of the polarization angle. It represents the concentration or dispersion of the beam polarization angle distribution and reflects the directional uniformity of the secondary rays generated when the beam impacts a solid target. As the basis for calculating the uniformity index, the polarization angle distribution parameter directly supports the decision output of the radiation monitoring system, ensuring the reliability and interpretability of the monitoring results.

[0115] In this embodiment of the invention, setting the wavelet transform scaling parameter based on the polarization intensity values ​​in the calibrated polarization feature sequence is specifically used for:

[0116] Frequency domain energy analysis was performed on the calibrated polarization feature sequence to obtain the power spectral density distribution;

[0117] Based on the power spectral density distribution and the preset polarization scale division rules, the wavelet transform scale parameters are effectively extracted to obtain the effective value range of the wavelet transform scale parameters.

[0118] The wavelet transform scaling parameters are defined based on the effective value range.

[0119] It should be noted that frequency domain energy analysis is a signal processing method that uses Fourier transform to convert time-domain polarized signals into frequency-domain representations to analyze the distribution of signal energy at different frequency components.

[0120] Furthermore, frequency domain energy analysis was used to quantify the frequency characteristics of the polarization feature sequence, identify the frequency bands where the main energy is concentrated, provide a basis for setting wavelet transform scale parameters, optimize the targeting of subsequent multi-scale decomposition through frequency domain features, and improve the accuracy of beam uniformity monitoring.

[0121] It should be noted that the power spectral density distribution represents the density function of signal power as a function of frequency. It is the signal power value within a unit frequency bandwidth and reflects the frequency domain energy distribution characteristics of the polarized signal. In radiation monitoring, the power spectral density distribution is associated with the frequency stability of the beam polarization state. High-frequency components correspond to noise or fluctuations, while low-frequency components correspond to the main characteristics of beam uniformity. It is used to determine the effective scale range of wavelet transform based on energy distribution, avoid invalid frequency band interference, and ensure that the decomposition focuses on key physical processes.

[0122] It should be noted that the preset polarization scale division rules are a set of predefined logical criteria based on the power spectral density distribution. The content is to dynamically divide the range of wavelet transform scale parameters according to the peak frequency, bandwidth and other characteristics of the power spectrum. For example, the scale range is set to the corresponding value covering the main energy band of the power spectrum. This is used to adapt to the multi-resolution analysis requirements of polarization signals through regular scale selection, enhance the ability of wavelet decomposition to capture beam uniformity characteristics, and reduce the arbitrariness of subjective parameter settings.

[0123] It should be noted that the effective range of the wavelet transform scale parameter refers to the range of values ​​allowed for the scale parameter in polarization signal analysis. This means that the scale is inversely proportional to the frequency, with large scales corresponding to low-frequency components and small scales corresponding to high-frequency components. This range defines the frequency band boundary of signal decomposition, which is used to constrain the wavelet transform to operate within the physical correlation band, ensuring that the approximation coefficients and detail coefficients can effectively characterize the macroscopic trend and microscopic fluctuations of beam polarization, thereby improving the reliability and efficiency of the radiation monitoring system.

[0124] Furthermore, in the wavelet transform scaling parameters, scale 1 corresponds to the high frequency band, in the range of 50 to 100 Hz; scale 2 corresponds to the mid frequency band, in the range of 20 to 50 Hz; and scale 3 corresponds to the low frequency band, in the range of 5 to 20 Hz, covering the main energy region of the signal and used to adapt to the multi-resolution decomposition requirements of polarization signals.

[0125] The uniformity analysis module 104 is used to calculate the distribution uniformity of the polarization angle distribution parameters and obtain the uniformity index of the beam received by the solid target.

[0126] In this embodiment of the invention, calculating the uniformity of the polarization angle distribution parameters to obtain the uniformity index of the beam received by the solid target is specifically used for:

[0127] The polarization angles in the polarization angle distribution parameters are statistically analyzed to obtain an angle probability distribution sequence;

[0128] The randomness of the angle probability distribution sequence is analyzed to obtain the random distribution information entropy of the polarization angle distribution parameters;

[0129] The random distribution information entropy is subjected to convergence mapping to generate the uniformity index of the beam received by the solid target.

[0130] It should be noted that probability distribution statistics calculate the probability of each angle value by counting and normalizing the frequency of the angle values ​​in the polarization angle distribution parameters, thereby forming a probability distribution function. This function represents the statistical distribution law of the quantified polarization angle, reflects the randomness of the beam polarization state, and provides basic data for subsequent randomness analysis. By revealing the concentration or dispersion trend of angle values ​​through probability distribution, it supports the calculation of the uniformity index and improves the objectivity of radiation monitoring.

[0131] Furthermore, frequency counting involves counting the number of occurrences of each angle value in the polarization angle distribution parameters to form a frequency distribution;

[0132] The normalization process performed after frequency counting obtains a probability value by dividing the frequency of each angle value by the sum of the total frequencies, which is used to provide standardized input for probability distribution statistics.

[0133] It should be noted that the angular probability distribution sequence is the output of probability distribution statistics. It represents an ordered set of polarization angle probability values, where each element in the sequence corresponds to the probability of a specific angle value occurring. Physically, in radiation monitoring, it characterizes the consistency of the polarization direction of secondary rays generated by the beam impacting a solid target; high probability values ​​indicate concentrated angles, while low probability values ​​indicate dispersed angles. Its function is to transform the angular distribution into a quantifiable sequence, providing input for information entropy calculation and directly related to the assessment of beam uniformity.

[0134] It should be noted that convergence mapping maps the random distribution information entropy to a finite interval through a scaling function. For example, it converts the entropy value into an exponent in the range of 0 to 1. This process normalizes the information entropy, eliminates the influence of dimensions, and is used to transform randomness measures into intuitive uniformity indicators, enhance the comparability and interpretability of the exponent, and ensure the stability of monitoring results under different conditions.

[0135] It should be noted that the uniformity index is a scalar value that quantifies the uniformity of the beam polarization angle distribution. The higher the value, the more uniform the distribution. In radiation monitoring, it directly reflects the uniformity of the beam impact on the solid target. A high index indicates that the secondary rays are in a consistent direction, while a low index indicates that the direction is scattered.

[0136] Furthermore, the uniformity index, as a core evaluation indicator, is integrated into the comprehensive monitoring report to provide a basis for decision-making in accelerator operation optimization and improve the efficiency and reliability of radiation monitoring.

[0137] In this embodiment of the invention, the step of performing randomness analysis on the angle probability distribution sequence to obtain the random distribution information entropy of the polarization angle distribution parameters is specifically used for:

[0138] Extract the angle distribution probability values ​​from the angle probability distribution sequence;

[0139] The probability values ​​of the angle distribution are subjected to random quantization processing to obtain the random distribution information entropy of the polarization angle distribution parameters.

[0140] It should be noted that extracting the angular distribution probability values ​​involves reading the probability value corresponding to each angle value one by one from the angular probability distribution sequence, and then normalizing the process to ensure that the sum of all probability values ​​is 1. For example, the range of probability values ​​can be adjusted using the minimum-maximum scaling method. This is a standardized extraction of the probability distribution, used to eliminate differences in data units, provide clean input for randomness quantification, enhance the accuracy of subsequent information entropy calculations, and ensure the reliability of radiation monitoring.

[0141] It should be noted that randomness quantization is performed by calculating the probability value of the angle distribution using the information entropy formula. This process measures the discreteness of the polarization angle distribution and converts the probability distribution into a scalar entropy value, which intuitively reflects the uniformity of beam polarization. A high entropy value indicates a uniform distribution, while a low entropy value indicates a concentrated distribution, providing a core metric for generating the uniformity index.

[0142] Furthermore, the mathematical expression for the information entropy formula is as follows:

[0143]

[0144] In the formula, To represent the entropy of a random distribution, The total number of angle values. Indexed by angle value. For the first The probability distribution of each angle value.

[0145] It should be noted that the random distribution information entropy is the output of randomness quantification. It is a scalar value that quantifies the randomness of the angular probability distribution. It is calculated based on probability values. The higher the value, the more random the distribution. In radiation monitoring, it is directly related to the consistency of the polarization direction of the secondary rays generated by the beam impacting the solid target. High entropy indicates angular dispersion, while low entropy indicates angular concentration.

[0146] Furthermore, the random distribution information entropy, as a key indicator for uniformity assessment, is transformed into actionable monitoring data through entropy value mapping, thereby improving the system's decision-making efficiency.

[0147] The environmental sensitivity analysis module 105 is used to perform environmental correlation analysis on the uniformity index based on the temperature and the magnetic field strength to obtain the environmental sensitivity index of the solid target receiving beam.

[0148] In this embodiment of the invention, the environmental correlation analysis of the uniformity index based on the temperature and the magnetic field strength to obtain the environmental sensitivity index of the solid target receiving beam is specifically used for:

[0149] Based on the uniformity index, the time series of the temperature, and the time series of the magnetic field strength, an environmental correlation analysis is performed on the solid target receiving beam to obtain the temperature sensitivity coefficient and the magnetic field sensitivity coefficient.

[0150] The temperature sensitivity coefficient and the magnetic field sensitivity coefficient are weighted and fused to obtain the environmental sensitivity index of the solid target receiving beam.

[0151] It should be noted that acquiring the time-series data of the temperature and magnetic field strength is a data acquisition operation. This involves real-time acquisition of temperature and magnetic field strength values ​​by temperature sensors and Hall effect magnetic field sensors integrated into the walls of the accelerator vacuum chamber, and recording them in chronological order to form a continuous sequence. This ensures the time synchronization of environmental parameters with polarization signals, providing basic data for subsequent environmental compensation calibration and correlation analysis, and improving the accuracy of radiation monitoring.

[0152] It should be noted that environmental correlation analysis quantifies the impact of environmental factors on beam uniformity by calculating the correlation coefficient between the uniformity index and the time series of temperature and magnetic field. Environmental correlation analysis is used to assess how changes in temperature and magnetic field cause fluctuations in the uniformity index, identify key environmental interference sources, provide a basis for the calculation of sensitivity coefficients, and enhance the adaptability of the monitoring system to operating conditions.

[0153] Furthermore, the correlation coefficient between the uniformity index and the time series of temperature and magnetic field is calculated using the Pearson correlation coefficient formula, which calculates the product of the covariance of the uniformity index series and the temperature or magnetic field series and their respective standard deviations, in order to quantify the degree of linear influence of environmental factors on the uniformity index.

[0154] It should be noted that the temperature sensitivity coefficient is a dimensionless quantitative index that represents the intensity of the uniformity index's response to temperature changes. The higher the value, the greater the influence of temperature. In radiation monitoring, the temperature sensitivity coefficient is correlated with the polarization angle measurement drift caused by thermal expansion. It is used to quantify temperature interference, guide environmental compensation strategies, and reduce the error of thermal effects in beam uniformity assessment.

[0155] It should be noted that the magnetic field sensitivity coefficient is a dimensionless coefficient that represents the sensitivity of the uniformity index to changes in the magnetic field, reflecting the degree of interference of magnetic field fluctuations on the polarization direction.

[0156] Furthermore, the magnetic field sensitivity coefficient corresponds to the electron trajectory deflection effect caused by the Lorentz force, and is used to assess the magnetic field stability requirements, provide feedback for accelerator magnetic field control, and ensure the reliability of radiation monitoring.

[0157] It should be noted that weighted fusion involves multiplying the temperature sensitivity coefficient and the magnetic field sensitivity coefficient by their respective preset weights, and then summing them to obtain a comprehensive index. The weighted fusion process is used to balance the contributions of different environmental factors, generate a unified environmental sensitivity index, simplify multi-parameter evaluation, and improve monitoring efficiency. The preset weights for the temperature sensitivity coefficient and the magnetic field sensitivity coefficient each account for half.

[0158] It should be noted that the environmental sensitivity index is the output of the weighted fusion and is a scalar value that represents the total impact of temperature and magnetic field on beam uniformity. The environmental sensitivity index is used to quantify the overall interference level of environmental factors, serving as a key input for the comprehensive monitoring report, providing decision support for optimizing accelerator operating parameters, and enhancing the robustness of the radiation monitoring system.

[0159] The comprehensive monitoring report generation module 106 is used to integrate the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid target receiving beam.

[0160] In this embodiment of the invention, the integration of the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid target receiving beam is specifically used for:

[0161] The uniformity index and the environmental sensitivity index are normalized to obtain a comprehensive analysis dataset of the solid target receiving beam.

[0162] Based on the comprehensive analysis dataset, a comprehensive score is calculated for the solid target receiving beam to obtain a comprehensive monitoring index;

[0163] Based on the comprehensive monitoring index, a real-time trend analysis of the solid target receiving beam is performed to obtain the dynamic prediction trend result of the solid target receiving beam.

[0164] A comprehensive monitoring report of the solid target receiving beam is generated based on the comprehensive monitoring index and the dynamic prediction trend results.

[0165] It should be noted that the comprehensive analysis dataset includes a normalized uniformity index and a normalized environmental sensitivity index.

[0166] It should be noted that normalization is achieved by mapping the uniformity index and environmental sensitivity index to the [0,1] interval using a minimum-maximum scaling method. This eliminates the incomparability caused by differences in dimensions and value ranges. The normalization process is a standardization transformation of heterogeneous indicators, ensuring the fairness of weighted fusion. It is used to improve the accuracy of comprehensive score calculation, prevent a single indicator from overly dominating the evaluation results, and enhance the robustness of the monitoring system to data fluctuations.

[0167] Furthermore, the main reason for normalizing the uniformity index and the environmental sensitivity index is to eliminate the incomparability caused by the difference in the dimensions and value ranges between the two indicators, and to ensure fair and accurate weighted fusion in the subsequent comprehensive score calculation. The uniformity index is usually calculated based on information entropy, and its value range may be affected by the specific characteristics of the polarization angle distribution. The environmental sensitivity index is derived through the correlation coefficient, and its value range has statistical characteristics. If it is not normalized, direct fusion may distort the comprehensive evaluation result due to the large difference in the magnitude of the indicators, such as causing one indicator to overly dominate the score. The normalization process maps the two to the [0,1] interval through minimum-maximum scaling, making the weight allocation meaningful and enhancing the robustness of the algorithm to data fluctuations, which is more in line with the real-time and stability requirements of the monitoring system.

[0168] It should be noted that generating the comprehensive monitoring report of the solid target receiving beam is a data integration operation. The method is to first calculate the comprehensive monitoring index by comprehensively scoring the normalized uniformity index and environmental sensitivity index, then perform real-time trend analysis on the index to obtain dynamic prediction trend results, and finally integrate the index and trend results into a structured report.

[0169] Furthermore, the process of generating a comprehensive monitoring report is a multi-step automated report generation process used to provide real-time, visualized beam status assessments, providing a basis for decision-making in accelerator operation optimization.

[0170] It should be noted that the comprehensive score calculation is a weighted sum of the normalized evenness index and the normalized environmental sensitivity index to obtain the comprehensive monitoring index, which is used to integrate multiple indicators into a single score, support the generation of comprehensive monitoring reports, and improve assessment efficiency. The evenness index has a weight of 0.6, and the environmental sensitivity index has a weight of 0.4.

[0171] It should be noted that the comprehensive monitoring report is a document that includes comprehensive monitoring indices and dynamic forecast trends. It is used to quantify the overall status of beam uniformity and environmental impact. In radiation monitoring, it reflects the uniformity of beam impact on the solid target and the cumulative effect of environmental interference. It serves as a reference tool for accelerator operators, supports fault diagnosis and parameter adjustment, and improves the efficiency and reliability of radiation monitoring.

[0172] In this embodiment of the invention, the step of performing real-time trend analysis on the comprehensive monitoring index to obtain the dynamic prediction trend result of the solid target receiving beam is specifically used for:

[0173] Obtain the time series data of the comprehensive monitoring index to obtain the comprehensive monitoring index sequence;

[0174] Trend analysis is performed on the comprehensive monitoring index sequence to obtain the dynamic trend slope of the solid target receiving beam.

[0175] The dynamic prediction trend of the solid target receiving beam is generated based on the dynamic trend slope.

[0176] It should be noted that obtaining the time series data of the comprehensive monitoring index refers to the process of recording the value of the comprehensive monitoring index every second and storing it in chronological order to form a continuous data sequence. This data is used to provide a historical data basis for trend analysis, ensure time consistency, and support the accuracy of real-time monitoring and prediction.

[0177] Furthermore, obtaining the time series data of the comprehensive monitoring index is to analyze the changing pattern of the index over time, identify the long-term trend or short-term fluctuations of beam uniformity, capture the dynamic behavior of the monitoring index, provide input for the prediction model, and enhance the system's responsiveness to changes in beam state.

[0178] It should be noted that trend analysis is the process of fitting a comprehensive monitoring index sequence to extract the direction and magnitude of changes in data points. It is used to quantify the overall trend of the time series, reveal the tendency of beam uniformity to improve or deteriorate, and enhance the predictability of monitoring.

[0179] It should be noted that the dynamic trend slope is the slope value of the fitted trend line, which represents the rate of change of the comprehensive monitoring index over time. In radiation monitoring, it is related to the degree of change in beam uniformity. A positive slope indicates improvement, while a negative slope indicates deterioration. It is used to quantify the rate of change, to set early warning thresholds, and to support real-time decision-making.

[0180] It should be noted that the process of generating dynamic prediction trend results is based on the dynamic trend slope to generate comprehensive monitoring index values ​​for future time points through extrapolation, forming a prediction sequence. This process is an estimate of the future beam state, used to provide forward-looking information, help operators adjust accelerator settings in advance, prevent runaway uniformity, and ensure the initiative and reliability of radiation monitoring.

[0181] Furthermore, the extrapolation method is based on the dynamic trend slope. It is a method to obtain the dynamic prediction trend result by calculating the product of the collection time interval and the dynamic trend slope, and adding the current comprehensive monitoring index value. This method is used to generate a dynamic prediction trend sequence.

[0182] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0183] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A real-time radiation monitoring system for the uniformity of a solid-state target beam in an accelerator, characterized in that, The system includes a polarization feature sequence acquisition module, an environmental compensation calibration module, a polarization angle distribution analysis module, a uniformity analysis module, an environmental sensitivity analysis module, and a comprehensive monitoring report generation module, wherein: The polarization feature sequence acquisition module is used to acquire the polarization feature sequence corresponding to the beam received by the solid target; The environmental compensation calibration module is used to perform environmental compensation calibration on the polarization feature sequence based on the temperature and magnetic field strength of the accelerator vacuum chamber, so as to obtain the calibrated polarization feature sequence of the beam received by the solid target. The polarization angle distribution analysis module is used to decompose the calibrated polarization feature sequence to obtain the polarization angle distribution parameters of the beam received by the solid target, including: Based on the polarization intensity values ​​in the calibrated polarization feature sequence, wavelet transform scaling parameters are set, including: Frequency domain energy analysis was performed on the calibrated polarization feature sequence to obtain the power spectral density distribution; Based on the power spectral density distribution and the preset polarization scale division rules, the wavelet transform scale parameters are effectively extracted to obtain the effective value range of the wavelet transform scale parameters. The wavelet transform scaling parameters are defined based on the effective value range; Based on the wavelet transform scale parameters, the calibrated polarization feature sequence is decomposed into multi-scale values ​​to obtain the detail coefficients of the calibrated polarization feature sequence. The detailed coefficients are then subjected to angle mapping transformation to obtain the polarization angle distribution parameters of the beam received by the solid target; The uniformity analysis module is used to calculate the distribution uniformity of the polarization angle distribution parameters to obtain the uniformity index of the beam received by the solid target, including: The polarization angles in the polarization angle distribution parameters are statistically analyzed to obtain an angle probability distribution sequence; The randomness of the angle probability distribution sequence is analyzed to obtain the random distribution information entropy of the polarization angle distribution parameters; The random distribution information entropy is mapped to a finite interval through a scaling function to generate the uniformity index of the solid target received beam. The environmental sensitivity analysis module is used to perform environmental correlation analysis on the uniformity index based on the temperature and the magnetic field strength to obtain the environmental sensitivity index of the solid target receiving beam. The comprehensive monitoring report generation module is used to integrate the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid target receiving beam.

2. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 1, characterized in that, The acquisition of the polarization feature sequence corresponding to the beam received by the solid target is specifically used for: The original polarization signal dataset corresponding to the beam received by the solid target is acquired from the preset polarization detection device; The original polarization signal dataset is subjected to time-domain filtering to obtain the preprocessed polarization signal set of the beam received by the solid target; The preprocessed polarization signal set is subjected to frame segmentation to obtain a polarization signal subset containing multiple time frames; Obtain the arithmetic mean of the polarization intensity in the polarization signal subset, and generate the frame feature sequence of the polarization signal subset; The frame feature sequences of all time frames are aggregated to generate the polarization feature sequence of the beam received by the solid target.

3. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 1, characterized in that, The polarization characteristic sequence is calibrated based on the temperature and magnetic field strength of the accelerator vacuum chamber to obtain the calibrated polarization characteristic sequence of the beam received by the solid target, specifically used for: The temperature and magnetic field strength of the accelerator vacuum chamber were obtained; Temperature influence analysis was performed on the solid target receiving beam to obtain the temperature compensation factor of the solid target receiving beam. The magnetic field strength influence of the solid target receiving beam is analyzed to obtain the magnetic field strength compensation factor of the solid target receiving beam. The polarization feature sequence is calibrated frame by frame based on the temperature compensation factor and the magnetic field strength compensation factor to obtain the calibrated polarization feature sequence of the solid target receiving beam.

4. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 1, characterized in that, The randomness analysis of the angle probability distribution sequence to obtain the random distribution information entropy of the polarization angle distribution parameters is specifically used for: Extract the angle distribution probability values ​​from the angle probability distribution sequence; The probability values ​​of the angle distribution are subjected to random quantization processing to obtain the random distribution information entropy of the polarization angle distribution parameters.

5. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 1, characterized in that, The environmental correlation analysis of the uniformity index based on the temperature and the magnetic field strength is used to obtain the environmental sensitivity index of the beam received by the solid target, specifically for: Based on the uniformity index, the time series of the temperature, and the time series of the magnetic field strength, an environmental correlation analysis is performed on the solid target receiving beam to obtain the temperature sensitivity coefficient and the magnetic field sensitivity coefficient. The temperature sensitivity coefficient and the magnetic field sensitivity coefficient are weighted and fused to obtain the environmental sensitivity index of the solid target receiving beam.

6. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 1, characterized in that, The integration of the uniformity index and the environmental sensitivity index to generate a comprehensive monitoring report of the solid target received beam is specifically used for: The uniformity index and the environmental sensitivity index are normalized to obtain a comprehensive analysis dataset of the solid target receiving beam. Based on the comprehensive analysis dataset, a comprehensive score is calculated for the solid target receiving beam to obtain a comprehensive monitoring index; Based on the comprehensive monitoring index, a real-time trend analysis of the solid target receiving beam is performed to obtain the dynamic prediction trend result of the solid target receiving beam. A comprehensive monitoring report of the solid target receiving beam is generated based on the comprehensive monitoring index and the dynamic prediction trend results.

7. The real-time radiation monitoring system for the uniformity of accelerator solid target beam as described in claim 6, characterized in that, The real-time trend analysis of the comprehensive monitoring index to obtain the dynamic prediction trend of the solid target receiving beam is specifically used for: Obtain the time series data of the comprehensive monitoring index to obtain the comprehensive monitoring index sequence; Trend analysis is performed on the comprehensive monitoring index sequence to obtain the dynamic trend slope of the solid target receiving beam. The dynamic prediction trend of the solid target receiving beam is generated based on the dynamic trend slope.

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