A method for analyzing the closed trajectory of a cyclotron

Abstract

This invention provides a method for analyzing the closed orbit of a cyclotron, relating to the field of cyclotron design and optimization technology. The method includes: preprocessing the acquired raw particle orbit data of the cyclotron by removing noise and formatting it to obtain preprocessed orbit data; performing trajectory decomposition on the preprocessed orbit data using numerical analysis methods to obtain orbit decomposition data describing the particle's motion state; analyzing the motion characteristics of the orbit decomposition data to obtain analysis results including frequency, amplitude, and phase; and sorting the orbit decomposition data based on the analysis results to calculate the center point of the distribution. This invention improves the accuracy and data security of cyclotron closed orbit analysis.

Description

Technical Field

[0001] This invention relates to the field of cyclotron design and optimization technology, and in particular to a method for analyzing the closed trajectory of a cyclotron. Background Technology

[0002] In the fields of aerospace technology, cutting-edge scientific research, and biomedicine, cyclotrons have become key equipment due to their particle acceleration capabilities. Especially in medical settings, isochronous cyclotrons are widely used to produce short-lived positron emission isotopes required for positron emission tomography (PET) imaging due to their compactness and high flux. Superconducting isochronous cyclotrons can also output proton beams for tumor radiotherapy. The accurate analysis of closed orbits is a core link to ensure the quality of cyclotron beams and the efficiency of isotope production and the precision of proton therapy. Currently, there are technical shortcomings in the closed orbit analysis process.

[0003] Taking the closed orbit analysis practice of a medical equipment company for a 60MeV superconducting isochronous cyclotron as an example, the company collected the original particle orbit data through orbit position sensors and magnetic field sensors, used conventional mean filtering to remove noise, and then extracted particle motion parameters through simple time domain decomposition. The stability assessment was based solely on whether the radius and radial momentum at the beginning and end of the orbit were consistent. Furthermore, the analyzed closed orbit data was directly stored in an industrial database without encryption protection.

[0004] The case study revealed significant technical deficiencies. The filtering algorithm used in the raw data preprocessing stage was not adapted to the periodic motion characteristics of particles, easily filtering out effective motion signals or retaining magnetic field fluctuation noise, leading to deviations in trajectory decomposition accuracy. The trajectory decomposition did not utilize the N-fold rotational symmetry of the cyclotron, failing to accurately extract the fundamental and harmonic components of particle motion, affecting the accuracy of key characteristic parameters such as frequency and amplitude. Stability assessment relied solely on the initial and final position parameters of the orbit, failing to use multi-symmetry point correlation analysis to determine the long-term stability of the orbit, easily misclassifying numerically pseudo-closed orbits as physically valid orbits. Furthermore, the core orbital data lacked a hierarchical encrypted storage mechanism, posing a risk of unauthorized access leading to the leakage of equipment design parameters, thereby affecting the precision of proton beam quality control and posing a potential threat to the safety and effectiveness of tumor treatment. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for analyzing the closed trajectory of a cyclotron, which improves the accuracy and data security of the analysis.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] A first aspect is a method for analyzing the closed trajectory of a cyclotron, the method comprising:

[0008] The raw data of the cyclotron particle orbits were preprocessed to remove noise and format the data, resulting in preprocessed orbit data.

[0009] The preprocessed orbital data is decomposed using numerical analysis methods to obtain orbital decomposition data describing the particle's motion state.

[0010] By analyzing the motion characteristics of the orbit decomposition data, analysis results including frequency, amplitude and phase are obtained. Based on the analysis results, the orbit decomposition data are sorted and the center point of the distribution is calculated.

[0011] Based on the center point, calculate the distance from each orbit decomposition data point to the center point; reorder the orbit decomposition data based on the distance from the center point, and evaluate the correlation between each data point and the center point to obtain the orbit stability assessment results;

[0012] Based on the motion characteristic analysis results and track stability assessment results, the track decomposition data is classified and stored to obtain a structured set of data to be processed; the structured set of data to be processed is then encrypted using a data encryption algorithm to obtain the initial encrypted data.

[0013] The initial encrypted data is used as the input plaintext, and a secondary encryption is performed using an asymmetric encryption algorithm to construct an enhanced encrypted data block. The enhanced encrypted data block is then written into a secure database for persistent storage, ultimately resulting in protected closed-loop secure storage data.

[0014] Furthermore, the acquired raw cyclotron particle orbit data is preprocessed to remove noise and format, resulting in preprocessed orbit data, including:

[0015] The raw data stream of particle orbits collected from the cyclotron sensor is obtained through the data receiving interface;

[0016] A digital filtering algorithm is applied to the raw particle orbital data stream to remove noise, resulting in denoised orbital data.

[0017] By performing data normalization processing on the denoised orbital data, including data format conversion and numerical normalization, preprocessed orbital data is obtained.

[0018] Furthermore, the preprocessed orbital data is decomposed using numerical analysis methods to obtain orbital decomposition data describing the particle's motion state, including:

[0019] Based on the preprocessed orbital data, the instantaneous position and velocity components of the particle are obtained through kinematic parameter extraction.

[0020] Based on the instantaneous position and velocity components, frequency domain decomposition is performed using Fourier transform to obtain the fundamental frequency component and harmonic components of the particle motion.

[0021] Based on the fundamental frequency component and harmonic components, an orbit decomposition dataset is constructed using a data synthesis method. This dataset describes the periodic motion state of particles in a cyclotron.

[0022] Furthermore, by analyzing the motion characteristics of the orbit decomposition data, analysis results including frequency, amplitude, and phase are obtained. Based on the analysis results, the orbit decomposition data are sorted, and the center point of the distribution is calculated, including:

[0023] By using the orbit decomposition dataset and based on the kinematic property analysis process, the frequency, amplitude, and phase parameters of particle motion are extracted to obtain the motion property analysis results.

[0024] Based on the amplitude parameters in the motion characteristic analysis results, the data points in the orbit decomposition dataset are sorted by amplitude to obtain an ordered dataset arranged by amplitude.

[0025] Based on an ordered dataset, the coordinates of the center point of the distribution are calculated using a center point statistical algorithm. The center point coordinates represent the reference position of the closed track.

[0026] Furthermore, based on the center point, the distance from each orbital decomposition data point to the center point is calculated; the orbital decomposition data is then reordered based on the distance to the center point, and the correlation between each data point and the center point is evaluated to obtain the orbital stability assessment results, including:

[0027] Based on the center point coordinates, the distance from each orbital decomposition data point to the center point is calculated using the Euclidean distance calculation method.

[0028] Based on the distance value from each orbit decomposition data point to the center point, the data points in the orbit decomposition dataset are sorted by distance to obtain an ordered distance dataset arranged in ascending order of distance value.

[0029] Based on each distance value in the ordered distance dataset, an initial association value is obtained through inverse proportional calculation, where the smaller the distance value, the larger the initial association value.

[0030] Based on the initial correlation degree value, normalization is performed to obtain the standardized correlation degree value; based on the standardized correlation degree value, the correlation coefficient between each orbital decomposition data and the center point is obtained.

[0031] Based on the correlation coefficient, the orbital stability assessment results are obtained, where the correlation coefficient is positively correlated with orbital stability.

[0032] Furthermore, based on the motion characteristic analysis results and track stability assessment results, the track decomposition data is classified and stored to obtain a structured set of data to be processed; the structured set of data to be processed is then encrypted using a data encryption algorithm to obtain initial encrypted data, including:

[0033] Based on the results of motion characteristic analysis and track stability assessment, the track decomposition data is classified by a data classification unit to obtain a classified dataset.

[0034] Based on the categorized dataset, the data is written to a specified storage area through a data storage interface to obtain a structured dataset to be processed.

[0035] Based on a structured set of data to be processed, obtain a pre-configured symmetric encryption key;

[0036] Based on a symmetric encryption key, an encryption operation is performed on a structured set of data to be processed using an Advanced Encryption Standard (AES) algorithm to obtain an encrypted data stream; based on the encrypted data stream, initial encrypted data is obtained.

[0037] Furthermore, the initial encrypted data is used as input plaintext, and a second encryption is performed using an asymmetric encryption algorithm to construct an enhanced encrypted data block. This enhanced encrypted data block is then written into a secure database for persistent storage, ultimately resulting in protected closed-loop secure storage data, including:

[0038] Based on the initial encrypted data, a secondary encryption process is performed using an asymmetric encryption algorithm to obtain enhanced encrypted data;

[0039] Based on the enhanced encrypted data, an enhanced encrypted data block is obtained through a data block construction method;

[0040] Based on enhanced encrypted data blocks, persistent storage operations are performed through the storage interface of the secure database to obtain protected closed-loop secure storage data.

[0041] Secondly, a cyclotron closed-track analysis system includes:

[0042] The acquisition module is used to preprocess the acquired raw orbital data of cyclotron particles, remove noise and format it to obtain preprocessed orbital data.

[0043] The calculation module is used to decompose the preprocessed orbit data using numerical analysis methods to obtain orbit decomposition data describing the particle's motion state; by analyzing the motion characteristics of the orbit decomposition data, it obtains analysis results including frequency, amplitude, and phase, and sorts the orbit decomposition data based on the analysis results and calculates the center point of the distribution.

[0044] The evaluation module is used to calculate the distance from each orbit decomposition data point to the center point; reorder the orbit decomposition data based on the distance to the center point; and evaluate the correlation between each data point and the center point to obtain the orbit stability evaluation result.

[0045] The encryption module is used to classify and store the track decomposition data based on the motion characteristic analysis results and track stability assessment results, resulting in a structured set of data to be processed; the structured set of data to be processed is then encrypted using a data encryption algorithm to obtain the initial encrypted data.

[0046] The processing module takes the initial encrypted data as input plaintext, performs secondary encryption using an asymmetric encryption algorithm to construct an enhanced encrypted data block, and writes the enhanced encrypted data block into a secure database for persistent storage, ultimately obtaining protected closed-track secure storage data.

[0047] Thirdly, a computing device includes:

[0048] One or more processors;

[0049] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0050] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0051] The above-described solution of the present invention has at least the following beneficial effects:

[0052] This approach employs digital filtering algorithms for noise reduction and data normalization preprocessing, kinematic parameter extraction combined with Fourier transform frequency domain decomposition and data synthesis for trajectory decomposition, amplitude sorting combined with center point statistical algorithms for baseline positioning, Euclidean distance calculation combined with inverse correlation ratio and normalization for stability assessment, and structured secure storage methods including data classification and storage, advanced encryption standards (symmetric and asymmetric encryption for secondary protection), and persistent secure database storage. Therefore, it overcomes the technical problems of traditional cyclotron closed-track analysis, such as noise interference and format chaos in raw data, inaccurate capture of particle periodic motion characteristics by trajectory decomposition, difficulty in determining the baseline position of the closed track, lack of quantitative assessment standards for track stability, and lack of structured management and secure storage of core track data. This results in improved track data quality, accurate extraction of the fundamental frequency and harmonic components of particle motion, clear determination of the baseline position of the closed track, quantitative assessment of track stability, and ensured structured management and multiple security protections for core track data. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a closed trajectory analysis method for a cyclotron provided by an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a closed orbit analysis system for a cyclotron provided in an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0056] like Figure 1 As shown, an embodiment of the present invention proposes a method for analyzing the closed trajectory of a cyclotron, the method comprising the following steps:

[0057] Step 1: Preprocess the acquired raw cyclotron particle orbit data by removing noise and formatting it to obtain preprocessed orbit data.

[0058] Step 2: The preprocessed orbit data is decomposed using numerical analysis methods to obtain orbit decomposition data describing the particle's motion state.

[0059] Step 3: By analyzing the motion characteristics of the orbit decomposition data, we obtain analysis results including frequency, amplitude and phase, and sort the orbit decomposition data based on the analysis results and calculate the center point of the distribution.

[0060] Step 4: Calculate the distance from each orbit decomposition data point to the center point; reorder the orbit decomposition data based on the distance to the center point, and evaluate the correlation between each data point and the center point to obtain the orbit stability assessment results.

[0061] Step 5: Based on the motion characteristic analysis results and track stability assessment results, classify and store the track decomposition data to obtain a structured set of data to be processed; encrypt the structured set of data to be processed using a data encryption algorithm to obtain the initial encrypted data.

[0062] Step 6: Using the initial encrypted data as input plaintext, perform secondary encryption using an asymmetric encryption algorithm to construct an enhanced encrypted data block; write the enhanced encrypted data block into a secure database for persistent storage, and finally obtain the protected closed-track secure storage data.

[0063] In this embodiment of the invention, the following technical means are employed: noise reduction and formatting preprocessing of the acquired cyclotron particle orbit data; trajectory decomposition of the preprocessed data using numerical analysis methods; analysis of the motion characteristics of the orbit decomposition data to extract frequency, amplitude, and phase and calculate the distribution center point; calculation of the distance between each orbit decomposition data based on the center point; evaluation of correlation to obtain orbit stability results; classification and storage of the orbit decomposition data according to the analysis results; generation of initial encrypted data using an encryption algorithm; secondary encryption of the initial encrypted data using an asymmetric encryption algorithm; and persistent storage of the enhanced encrypted data block in a secure database. Therefore, this approach overcomes the technical problems of traditional cyclotron closed orbit analysis, such as high noise interference in the original data, inconsistent formats, inaccurate description of particle motion states by trajectory decomposition, unclear closed orbit reference positions, lack of quantitative assessment basis for orbit stability, and unstructured management and insufficient storage security of orbit data. This improves the quality of the original orbit data, accurately obtains particle motion state information, clarifies the closed orbit reference position, achieves quantitative assessment of orbit stability, and ensures structured management and secure storage of orbit data, providing reliable support for accurate analysis of cyclotron closed orbits and secure application of core data.

[0064] In a preferred embodiment of the present invention, step 1 above may include:

[0065] Step 1.1: Obtain the raw particle orbit data stream collected from the cyclotron sensors through the data receiving interface. Specifically, this includes: first, building a data receiving interface compatible with the orbit position sensor and magnetic field sensor mounted on the 60MeV superconducting isochronous cyclotron, with the interface being compatible with the signal output types of both sensors; during the operation of the cyclotron, receiving the position parameters of the particles at different times collected by the orbit position sensor and the magnetic field strength parameters related to the particle orbit collected by the magnetic field sensor in real time through this interface, and integrating the parameters to form a continuous raw particle orbit data stream.

[0066] Step 1.2 involves applying a digital filtering algorithm to remove noise from the raw particle orbit data stream to obtain denoised orbit data. Specifically, this includes: based on the acquired raw particle orbit data stream, a digital filtering algorithm adapted to the periodic motion characteristics of particles within the cyclotron is used for noise removal. The algorithm pre-determines the periodic cyclotron behavior of particles in the 60MeV superconducting isochronous cyclotron, such as the time period for each cyclotron cycle and the periodic changes in the trajectory. During the filtering process, interference noise caused by magnetic field fluctuations and random interference noise generated by the orbit position sensor and magnetic field sensor are identified and removed. At the same time, the effective signals reflecting the true motion state of the particles in the data stream are fully preserved, such as the position change signal of the periodic particle motion and the stable change signal of the magnetic field strength related to the orbit, ultimately yielding the denoised orbit data.

[0067] Step 1.3 involves data normalization of the denoised orbital data, including data format conversion and numerical normalization, to obtain preprocessed orbital data. Specifically, this includes: firstly, performing data format conversion on the denoised orbital data, transforming the coordinate data output by the orbital position sensor (such as radial coordinates and azimuth coordinates) and the magnetic field strength data output by the magnetic field sensor from their original output formats (such as the discrete coordinate format of the position sensor and the continuous intensity data format of the magnetic field sensor) into the standard data format for trajectory decomposition analysis; after completing the format conversion, performing numerical normalization, adjusting different types of parameters in the converted orbital data to the same numerical range according to the particle motion parameter range of the 60MeV superconducting isochronous cyclotron, eliminating the influence of differences in magnitude between different parameters on the analysis, and finally obtaining the preprocessed orbital data.

[0068] In this embodiment of the invention, the technical means of acquiring the raw particle orbit data stream collected by the cyclotron sensor through a data receiving interface, applying a digital filtering algorithm to filter out noise from the raw particle orbit data stream, and performing data normalization processing on the denoised data including data format conversion and numerical normalization are adopted. Therefore, it overcomes the problems in traditional cyclotron closed orbit analysis, such as the lack of a unified interface for acquiring raw particle orbit data, resulting in scattered and disordered data sources, noise from magnetic field fluctuations and sensor interference in the raw data affecting the accuracy of subsequent analysis, inconsistent data formats, and huge differences in numerical ranges. Thus, it achieves the goals of ensuring stable and controllable sources of raw particle orbit data, effectively removing noise to improve data purity, and ensuring that the preprocessed data format is unified and the numerical magnitude is coordinated, thereby guaranteeing the accuracy and efficiency of the overall closed orbit analysis process.

[0069] In a preferred embodiment of the present invention, step 2 above may include:

[0070] Step 2.1: Based on the preprocessed orbital data, the instantaneous position and velocity components of the particles are obtained through kinematic parameter extraction. Specifically, this includes: First, based on the obtained preprocessed orbital data, the position information of the particles recorded in the data at different times is filtered out. The position information has been denoised and normalized to accurately reflect the trajectory of the particles in the 60MeV superconducting isochronous cyclotron accelerator. Then, the kinematic parameter extraction operation is carried out. First, the particle position coordinates corresponding to each recording time are determined as the instantaneous position parameters of the particles. Then, based on the time interval between two adjacent recording times and the coordinate difference of the instantaneous position of the particles at these two times, the velocity components of the particles at the corresponding times are obtained by calculating the ratio of the change in position at adjacent times to the time interval, ensuring that each instantaneous position can be matched with the corresponding velocity component, and completely obtaining the key kinematic parameters in the particle motion process.

[0071] Step 2.2: Based on the instantaneous position and velocity components, frequency domain decomposition is performed using Fourier transform to obtain the fundamental frequency component and harmonic components of the particle motion. Specifically, this includes: using the obtained instantaneous position and velocity components of the particle as a basis, firstly, the parameters are arranged into a continuous motion parameter sequence in time order. This sequence can completely represent the motion process of the particle within the 60MeV superconducting isochronous cyclotron accelerator. Then, Fourier transform is applied to perform frequency domain decomposition on this motion parameter sequence. During the decomposition process, the calculation process of frequency domain decomposition is calibrated by combining the N-fold rotational symmetry characteristics of the 60MeV superconducting isochronous cyclotron accelerator and referring to the law of particle motion at the magnetic pole symmetry point. Through decomposition, the fundamental frequency component corresponding to the main cyclotron motion and the harmonic components corresponding to the free oscillation motion are separated, ensuring that the fundamental frequency component can accurately reflect the periodic main motion of the particle around the closed orbit.

[0072] Step 2.3: Based on the fundamental frequency component and harmonic components, an orbital decomposition dataset is constructed using a data synthesis method. This dataset describes the periodic motion state of particles in the cyclotron. Specifically, it includes: firstly, for the obtained fundamental frequency component and harmonic components of particle motion, sorting out the parameter information corresponding to each component, including the amplitude and frequency of the fundamental frequency component at different times, and the amplitude, frequency, and phase of the harmonic components; then, using a data synthesis method, the fundamental frequency component parameters and harmonic component parameters at the same time are correlated and integrated, so that the particle motion state at each time can be described by the corresponding fundamental frequency component and harmonic component; simultaneously, combining the periodic characteristics of particle motion in the 60MeV superconducting isochronous cyclotron, the integrated motion state data at each time is arranged in chronological order to construct the orbital decomposition dataset.

[0073] In this embodiment of the invention, the technical means of obtaining the instantaneous position and velocity components of the particle through kinematic parameter extraction based on preprocessed orbit data, obtaining the fundamental frequency component and harmonic component of the particle motion through frequency domain decomposition by Fourier transform based on the instantaneous position and velocity components, and constructing an orbit decomposition dataset describing the periodic motion state of the particle through data synthesis based on the fundamental frequency component and harmonic component, thus overcoming the technical problems in traditional cyclotron closed orbit analysis that are difficult to accurately extract the core parameters of particle motion, cannot effectively separate the motion components corresponding to the main cyclotron motion and free oscillation, and are difficult to clearly describe the periodic motion state of the particle in the cyclotron, thereby affecting the accuracy of isochronous analysis and oscillation parameter calculation. Thus, it achieves the accurate acquisition of key parameters of particle motion, clear distinction between the main cyclotron motion and free oscillation characteristics of the particle, and construction of an orbit decomposition dataset that accurately reflects the periodic motion law of the particle, thereby improving the overall accuracy of closed orbit analysis.

[0074] In a preferred embodiment of the present invention, step 3 above may include:

[0075] Step 3.1: Using the orbit decomposition dataset and based on the kinematic characteristic analysis process, extract the frequency, amplitude, and phase parameters of particle motion to obtain the motion characteristic analysis results. Specifically, this includes: first, retrieving the constructed orbit decomposition dataset, which contains the fundamental frequency and harmonic component data of particle motion within a 60 MeV superconducting isochronous cyclotron. Then, initiating the kinematic characteristic analysis process, the analysis logic is calibrated by incorporating the N-fold rotational symmetry of the cyclotron and referencing the particle motion patterns at the magnetic pole symmetry points. For the frequency parameter, the frequency of the particle's main cyclotron motion is calculated by identifying the periodic variation period of the fundamental frequency component, and the frequency corresponding to the free oscillation is obtained by identifying the periodic variation period of the harmonic components. For the amplitude parameter, the maximum amplitude and fluctuation range of the fundamental frequency and harmonic components at different times are statistically analyzed to determine the amplitude corresponding to each component. For the phase parameter, the phase difference between the fundamental frequency and harmonic components at different times is compared, and the phase data corresponding to each time point is extracted by combining the particle motion time series. The extracted frequency, amplitude, and phase parameters are integrated to form the motion characteristic analysis results.

[0076] Step 3.2: Based on the amplitude parameters in the motion characteristic analysis results, sort the data points in the orbit decomposition dataset by amplitude to obtain an ordered dataset arranged by amplitude. Specifically, this includes: selecting the amplitude parameters corresponding to all data points from the obtained motion characteristic analysis results. The amplitude parameters cover the amplitude information of the fundamental frequency component and harmonic components in the orbit decomposition dataset; then, sorting each data point in the orbit decomposition dataset based on the magnitude of the amplitude parameters; during sorting, first compare the values ​​of the amplitude parameters of each data point, and arrange them in order from smallest to largest. If there are data points with the same amplitude value, the time sequence corresponding to the data points is combined to assist in the sorting, finally forming an ordered dataset arranged by amplitude.

[0077] Step 3.3: Based on the ordered dataset, the center point coordinates of the distribution are calculated using a center point statistical algorithm. The center point coordinates represent the reference position of the closed orbit. Specifically, this involves: First, removing data points with abnormally large amplitude values ​​from the ordered dataset, as these data points often correspond to abnormal oscillations in particle motion and are unsuitable for calculating the reference position of the closed orbit; then, starting the center point statistical algorithm, extracting the particle position coordinates corresponding to each remaining data point in the ordered dataset, including radial and azimuth coordinates, and then, combining the N-fold rotational symmetry of the cyclotron, performing preliminary statistical analysis on the position coordinates of data points in different magnetic pole symmetry regions; finally, calculating the average value of the position coordinates of all valid data points, using the average value as the coordinate value of the center point, and verifying whether this coordinate value conforms to the physical characteristics of the closed orbit within the 60MeV superconducting isochronous cyclotron, ensuring that it can accurately represent the reference position of the closed orbit when the particle is in stable motion, and finally obtaining the center point coordinates.

[0078] In this embodiment of the invention, the frequency, amplitude, and phase parameters of particle motion are extracted through kinematic characteristic analysis based on the orbit decomposition dataset. The amplitude parameters in the orbit decomposition dataset are sorted according to the amplitude parameters in the kinematic characteristic analysis results. Then, the coordinates of the center point representing the reference position of the closed orbit are calculated based on the ordered dataset using a center point statistical algorithm. Therefore, this method overcomes the technical problems of inaccurate extraction of key characteristic parameters of particle motion, low analysis efficiency due to lack of orderly sorting of orbit data, and difficulty in clearly defining the reference position of the closed orbit in traditional cyclotron closed orbit analysis. Thus, it achieves the following: accurately obtaining the core characteristic parameters of particle motion to support subsequent characteristic analysis, making the orbit data present an orderly distribution to improve subsequent processing efficiency, and clearly determining the reference position of the closed orbit to provide a clear reference basis.

[0079] In a preferred embodiment of the present invention, step 4 above may include:

[0080] Step 4.1: Based on the center point coordinates, calculate the distance from each orbit decomposition data point to the center point using the Euclidean distance calculation method. Specifically, this includes: retrieving the calculated center point coordinates, which are the reference position coordinates of the closed orbit of the 60MeV superconducting isochronous cyclotron, including the radial and azimuth coordinates of the particle in the mid-plane; then obtaining the particle position coordinates corresponding to each data point in the constructed orbit decomposition dataset. The coordinates have been preprocessed and decomposed to accurately reflect the actual position of the particle's motion. Based on the Euclidean distance calculation method, calculate the straight-line distance between the position coordinates of each orbit decomposition data point and the center point coordinates. During the calculation process, the N-fold rotational symmetry of the cyclotron is taken into account to ensure that the same distance calculation logic is used for data points in different magnetic pole symmetry regions, and finally obtain the distance value corresponding to each orbit decomposition data point.

[0081] Step 4.2: Based on the distance value from each orbit decomposition data point to the center point, sort the data points in the orbit decomposition dataset by distance to obtain an ordered distance dataset arranged in ascending order of distance value. Specifically, this includes: collecting the distance values ​​corresponding to all the obtained orbit decomposition data points and associating each distance value with the corresponding orbit decomposition data point; then, sorting all the data points in the orbit decomposition dataset according to the magnitude of the distance value, arranging the data points in order from smallest to largest distance value; if there are data points with the same distance value, then combining the particle motion time series corresponding to the data points, and performing auxiliary sorting on these data points according to the time sequence to ensure the uniqueness and orderliness of the sorting results; finally, an ordered distance dataset arranged in ascending order of distance value is formed, and the dataset can clearly show the distribution pattern of each data point's distance from the closed orbit reference position.

[0082] Step 4.3: Based on each distance value in the ordered distance dataset, an initial correlation degree value is obtained through inverse proportional calculation. The smaller the distance value, the larger the initial correlation degree value. Specifically, this includes: performing inverse proportional calculation for each distance value in the obtained ordered distance dataset; using the distance value as the denominator and selecting an appropriate constant as the numerator. The constant needs to be determined based on the particle motion parameter range of the 60MeV superconducting isochronous cyclotron accelerator to ensure that the initial correlation degree value is within a reasonable range; obtaining the initial correlation degree value corresponding to each distance value by dividing the numerator by the distance value; verifying the calculation logic during the calculation process to ensure that the smaller the distance value, the larger the initial correlation degree value, thus reflecting the closeness of the correlation between the data point and the closed orbit reference position. The closer the distance, the closer the correlation, and the higher the initial correlation degree value.

[0083] Step 4.4: Based on the initial correlation coefficient values, normalization is performed to obtain standardized correlation coefficient values. Based on the standardized correlation coefficient values, the correlation coefficient between each orbital decomposition data point and the center point is obtained. Specifically, this includes: first, calculating the maximum and minimum values ​​of all obtained initial correlation coefficient values ​​to determine the distribution range of the initial correlation coefficient values; then, normalization is performed based on this distribution range, scaling each initial correlation coefficient value to a numerical range of 0 to 1 by the same proportion, maintaining the relative magnitude relationship between the initial correlation coefficient values ​​during the scaling process to avoid distortion of the correlation coefficient comparison due to differences in numerical ranges; after completing the normalization process, the standardized correlation coefficient value corresponding to each orbital decomposition data point is obtained, and the standardized correlation coefficient value is directly used as the correlation coefficient between each orbital decomposition data point and the center point.

[0084] Step 4.5: Based on the correlation coefficient, the orbital stability assessment result is obtained. The correlation coefficient is positively correlated with orbital stability. Specifically, this involves: first, setting an assessment threshold for the correlation coefficient according to the actual operating requirements and beam quality standards of the 60MeV superconducting isochronous cyclotron accelerator; for example, a correlation coefficient greater than 0.8 is considered high stability, between 0.5 and 0.8 is considered medium stability, and less than 0.5 is considered low stability. Then, the correlation coefficient of each orbital decomposition data is compared with the assessment threshold to determine the stability level corresponding to each data point. Next, the stability level distribution of all data points is combined to comprehensively judge the stability state of the entire particle orbit. If the proportion of high-stability data points exceeds a preset ratio, such as 80%, the overall orbital stability is considered good; if the proportion of low-stability data points is too high, the orbit is considered to have stability risks. Finally, an orbital stability assessment result is formed, which includes the stability level of each data point and the overall orbital stability state.

[0085] In this embodiment of the invention, the distance from each orbit decomposition data point to the center point is calculated using the Euclidean distance calculation method based on the center point coordinates; the data points in the orbit decomposition dataset are sorted in ascending order based on the distance values ​​to obtain an ordered distance dataset; the initial correlation value is obtained through inverse distance calculation; the initial correlation value is normalized to obtain a standardized correlation value and the correlation coefficient between each orbit decomposition data point and the center point; and the orbit stability assessment result is obtained based on the correlation coefficient. Therefore, this method overcomes the technical problems in traditional cyclotron closed orbit analysis, where stability assessment relies solely on the orbit's initial and final position parameters, lacks quantitative distance and correlation calculation basis, cannot accurately determine long-term orbit stability, and is prone to misjudging numerically pseudo-closed orbits as physically valid orbits. This method achieves quantitative characterization of the deviation of each orbit decomposition data point from the closed orbit reference position, orderly sorting out the distance distribution pattern of data points, obtaining accurate quantitative correlation coefficients, realizing a scientific assessment of orbit stability, and effectively avoiding misjudgment of pseudo-closed orbits.

[0086] In a preferred embodiment of the present invention, step 5 above may include:

[0087] Step 5.1: Based on the motion characteristic analysis results and orbital stability assessment results, the orbital decomposition data is classified using a data classification unit to obtain a classified dataset. Specifically, this includes the obtained motion characteristic analysis results and orbital stability assessment results. The motion characteristic analysis results contain the frequency, amplitude, and phase parameters of particle motion within the 60MeV superconducting isochronous cyclotron accelerator. The orbital stability assessment results contain the correlation coefficients and stability levels (high, medium, and low) corresponding to each orbital decomposition data. The data classification unit is then activated, using both motion characteristic parameters and stability levels as dual classification criteria. First, major categories are defined based on frequency ranges in the motion characteristics, such as frequency intervals corresponding to different acceleration stages of the particles. Then, subcategories are defined under each major frequency category based on amplitude. Finally, subcategories are defined under each amplitude subcategory based on stability levels (high, medium, and low). The orbital decomposition data are assigned to their corresponding categories one by one, ensuring that each category clearly reflects the particle motion characteristics and stability state, ultimately resulting in a classified dataset.

[0088] Step 5.2: Based on the classified dataset, the data is written to a designated storage area through a data storage interface to obtain a structured dataset to be processed. Specifically, this includes: creating a storage directory structure matching the classification logic for the obtained classified dataset; naming the first-level directory by frequency range, the second-level directory by amplitude, and the third-level directory by stability level; writing the data of each category in the classified dataset to the corresponding storage directory through a preset data storage interface, synchronously recording data association information such as data acquisition time and corresponding particle acceleration energy range during the writing process, and setting access permission identifiers for each storage directory; after the data writing is completed, a structured data index table is automatically generated, containing the storage path, data volume, and key characteristic parameter summary for each type of data, ultimately forming a structured dataset to be processed.

[0089] Step 5.3: Based on the structured set of data to be processed, obtain the pre-configured symmetric encryption key. Specifically, this includes: first, confirming the device identifier of the 60MeV superconducting isochronous cyclotron accelerator corresponding to the structured set of data to be processed. The device identifier is bound to the pre-configured symmetric encryption key and stored in the device's security key management. The encryption processing unit sends a key acquisition request to the security key management, carrying the storage directory identifier of the structured set of data to be processed and the device identifier in the request. The security key management verifies the legality of the request and, after confirming that the encryption processing unit has authorized access rights, extracts the symmetric encryption key bound to the device identifier from the encrypted storage area and sends the key to the encryption processing unit through the encrypted transmission channel to ensure that the key is not leaked during transmission. Finally, the acquisition of the pre-configured symmetric encryption key is completed.

[0090] Step 5.4: Based on the symmetric encryption key, the structured dataset to be processed is encrypted using the Advanced Encryption Standard (AES) algorithm to obtain an encrypted data stream. Based on the encrypted data stream, initial encrypted data is obtained, specifically including: After receiving the acquired symmetric encryption key, the encryption processing unit reads the track decomposition data files in each storage directory of the structured dataset to be processed, and sequentially performs AES encryption operations on each data file: first, the data file is converted into a continuous data stream, and then the data stream is grouped and encrypted using the symmetric encryption key. Each group of data is processed according to the fixed length required by the algorithm, and a data checksum is generated synchronously during the encryption process. After encrypting a single data file, the encrypted data stream corresponding to the file is obtained. All encrypted data streams are integrated in the order of the storage directories, and an encryption header is attached, including the key identifier, encryption time, and data checksum summary, to finally form the initial encrypted data.

[0091] In this embodiment of the invention, the orbit decomposition data is classified and processed by a data classification unit based on the motion characteristic analysis results and the orbit stability assessment results. The classified data set is written to a designated storage area through a data storage interface to form a structured data set to be processed. A pre-configured symmetric encryption key is obtained, and an encryption operation is performed on the structured data set to be processed using an advanced encryption standard algorithm based on the key to obtain the initial encrypted data. Therefore, this invention overcomes the technical problems in traditional cyclotron closed orbit analysis, such as the lack of targeted classification of orbit decomposition data according to motion characteristics and stability levels, resulting in messy data, and the lack of effective encryption protection for core orbit data, which makes it easy to be illegally accessed and thus leak equipment design parameters. This invention achieves clear classification of orbit decomposition data according to motion characteristics and stability levels, forming a structured data set to significantly improve the efficiency of subsequent data retrieval and processing. At the same time, the symmetric encryption of the advanced encryption standard algorithm builds the first layer of security protection for core orbit data, effectively reducing the risk of data being illegally stolen or tampered with.

[0092] In a preferred embodiment of the present invention, step 6 above may include:

[0093] Step 6.1: Based on the initial encrypted data, a secondary encryption process is performed using an asymmetric encryption algorithm to obtain enhanced encrypted data. Specifically, this includes: first, retrieving the initial encrypted data, which is the result of encrypting the core orbital data of the 60MeV superconducting isochronous cyclotron accelerator using an advanced encryption standard algorithm, including encrypted orbital decomposition data classified according to motion characteristics and stability levels; extracting the asymmetric encryption key pair bound to the cyclotron from the device security key management, including the public key and private key, with the private key stored in the hardware encryption module; initiating the asymmetric encryption processing flow, dividing the initial encrypted data into segments of fixed length, and sequentially encrypting each segment using the extracted public key, while simultaneously generating a digital digest for each segment during the encryption process; integrating the encrypted segmented data with the corresponding digital digests in sequence, and adding an encryption identifier to the data header, including the asymmetric encryption algorithm type, key number, and segmentation information, ultimately forming the enhanced encrypted data.

[0094] Step 6.2: Based on the enhanced encrypted data, an enhanced encrypted data block is obtained through a data block construction method. Specifically, this includes: First, based on the obtained enhanced encrypted data, initiating the data block construction process. First, the segmentation information and encryption identifier in the header of the enhanced encrypted data are parsed to determine the total length, number of segments, and algorithm type of the data. Then, according to the application scenarios of the orbital data from the 60MeV superconducting isochronous cyclotron accelerator, such as the orbital analysis needs corresponding to different acceleration energy ranges and different treatment plans, the enhanced encrypted data is divided into multiple logical data blocks. Each data block corresponds to a specific type of orbital data for a particular application scenario, such as a high-stability orbital data block for the 10MeV to 20MeV energy range and a moderately stable orbital data block for the 20MeV to 30MeV energy range. Metadata information is added to each data block, including the corresponding energy range, stability level, data acquisition time, encryption algorithm version, and digital digest checksum. Finally, integrity verification is performed on each data block by comparing the digital digest in the metadata with the actual calculated digital digest of the data block. After successful verification, a clearly structured and clearly identified enhanced encrypted data block is formed.

[0095] Step 6.3: Based on the enhanced encrypted data blocks, perform persistent storage operations through the storage interface of the secure database to obtain protected closed-loop orbit secure storage data. Specifically, this includes: first, confirming the status of the secure database used to store orbit data for the 60MeV superconducting isochronous cyclotron, ensuring that the database has enabled security mechanisms such as access control and transmission encryption; obtaining authorization for the storage interface with data write permissions from the database management, binding the authorization information to the encryption processing unit to prevent unauthorized devices from accessing the data; and then writing the obtained enhanced encrypted data blocks one by one into the secure database through this storage interface, linking the metadata of each data block with the orbit data in the database during the writing process. The data is classified and indexed, which records information such as motion characteristic classification and stability assessment results for each data block. Simultaneously, the database automatically assigns a unique storage identifier to each data block. After the data block is written, the database performs a persistence confirmation operation, writing the data from memory to the physical storage medium and initiating a data backup process to synchronously back up the written data blocks to a remote secure storage node, preventing data loss due to a single storage node failure. Ultimately, a complete protected closed-loop data storage system is formed in the database, resulting in protected closed-loop secure storage data, ensuring that core track data is not illegally accessed, tampered with, or lost during long-term storage.

[0096] In this embodiment of the invention, the technical means of performing a secondary encryption process using an asymmetric encryption algorithm based on the initial encrypted data to obtain enhanced encrypted data, obtaining enhanced encrypted data blocks based on the enhanced encrypted data using a data block construction method, and performing persistent storage operations based on the enhanced encrypted data blocks through the storage interface of a secure database, overcome the technical problems of key leakage risk, lack of secondary security protection mechanism, and easy unauthorized access and leakage of equipment design parameters due to the core orbit data relying solely on a single symmetric encryption in the traditional analysis of cyclotron closed orbits, as well as the lack of reliable persistent storage and easy loss of data. This achieves the following: constructing a dual security protection system of symmetric and asymmetric encryption for the core orbit data to improve the data encryption level; realizing structured management of encrypted data through data block construction for easy retrieval and retrieval; and ensuring long-term secure data preservation and no loss through persistent storage in a secure database. Ultimately, this ensures the integrity, confidentiality, and availability of the securely stored data for the cyclotron closed orbit.

[0097] like Figure 2 As shown, embodiments of the present invention also provide a cyclotron closed orbit analysis system, comprising:

[0098] The acquisition module is used to preprocess the acquired raw orbital data of cyclotron particles, remove noise and format it to obtain preprocessed orbital data.

[0099] The calculation module is used to decompose the preprocessed orbit data using numerical analysis methods to obtain orbit decomposition data describing the particle's motion state; by analyzing the motion characteristics of the orbit decomposition data, it obtains analysis results including frequency, amplitude, and phase, and sorts the orbit decomposition data based on the analysis results and calculates the center point of the distribution.

[0100] The evaluation module is used to calculate the distance from each orbit decomposition data point to the center point; reorder the orbit decomposition data based on the distance to the center point; and evaluate the correlation between each data point and the center point to obtain the orbit stability evaluation result.

[0101] The encryption module is used to classify and store the track decomposition data based on the motion characteristic analysis results and track stability assessment results, resulting in a structured set of data to be processed; the structured set of data to be processed is then encrypted using a data encryption algorithm to obtain the initial encrypted data.

[0102] The processing module takes the initial encrypted data as input plaintext, performs secondary encryption using an asymmetric encryption algorithm to construct an enhanced encrypted data block, and writes the enhanced encrypted data block into a secure database for persistent storage, ultimately obtaining protected closed-track secure storage data.

[0103] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of analyzing a closed orbit of a cyclotron, characterized by, The method includes: The raw data of the cyclotron particle orbits were preprocessed to remove noise and format the data, resulting in preprocessed orbit data. The preprocessed orbital data is decomposed using numerical analysis methods to obtain orbital decomposition data describing the particle's motion state. By analyzing the motion characteristics of the orbit decomposition data, analysis results including frequency, amplitude, and phase are obtained. Based on the analysis results, the orbit decomposition data are sorted, and the center point of the distribution is calculated, including: By using the orbit decomposition dataset and based on the kinematic property analysis process, the frequency, amplitude, and phase parameters of particle motion are extracted to obtain the motion property analysis results. Based on the amplitude parameters in the motion characteristic analysis results, the data points in the orbit decomposition dataset are sorted by amplitude to obtain an ordered dataset arranged by amplitude. Based on an ordered dataset, the coordinates of the center point of the distribution are calculated using a center point statistical algorithm. The center point coordinates represent the reference position of the closed track. Based on the center point, calculate the distance from each orbital decomposition data point to the center point; reorder the orbital decomposition data based on the distance to the center point, and evaluate the correlation between each data point and the center point to obtain the orbital stability assessment results, including: Based on the center point coordinates, the distance from each orbital decomposition data point to the center point is calculated using the Euclidean distance calculation method. Based on the distance value from each orbit decomposition data point to the center point, the data points in the orbit decomposition dataset are sorted by distance to obtain an ordered distance dataset arranged in ascending order of distance value. Based on each distance value in the ordered distance dataset, an initial association value is obtained through inverse proportional calculation, where the smaller the distance value, the larger the initial association value. Based on the initial correlation degree value, normalization is performed to obtain the standardized correlation degree value; based on the standardized correlation degree value, the correlation coefficient between each orbital decomposition data and the center point is obtained. Based on the correlation coefficient, the orbital stability assessment results are obtained, where the correlation coefficient is positively correlated with orbital stability; Based on the motion characteristic analysis results and track stability assessment results, the track decomposition data is classified and stored to obtain a structured set of data to be processed; the structured set of data to be processed is then encrypted using a data encryption algorithm to obtain the initial encrypted data. The initial encrypted data is used as the input plaintext, and a secondary encryption is performed using an asymmetric encryption algorithm to construct an enhanced encrypted data block. The enhanced encrypted data block is then written into a secure database for persistent storage, ultimately resulting in protected closed-loop secure storage data.

2. The cyclotron closed orbit analysis method according to claim 1, wherein, The acquired raw cyclotron particle orbit data is preprocessed to remove noise and format, resulting in preprocessed orbit data, including: The raw data stream of particle orbits collected from the cyclotron sensor is obtained through the data receiving interface; A digital filtering algorithm is applied to the raw particle orbital data stream to remove noise, resulting in denoised orbital data. By performing data normalization processing on the denoised orbital data, including data format conversion and numerical normalization, preprocessed orbital data is obtained.

3. The cyclotron closed orbit analysis method according to claim 2, wherein The trajectory decomposition data set is constructed by a data synthesis method based on the fundamental frequency component and the harmonic component, wherein the trajectory decomposition data set describes the periodic motion state of the particle in the cyclotron. According to the motion characteristic analysis result and the orbit stability evaluation result, the trajectory decomposition data is classified and stored to obtain a structured to-be-processed data set; The structured to-be-processed data set is encrypted by a data encryption algorithm to obtain initial encrypted data, including: Based on the motion characteristic analysis result and the orbit stability evaluation result, the trajectory decomposition data is classified by a data classification unit to obtain a classified data set; 4. The method of analyzing a closed orbit of an cyclotron according to claim 3, wherein Based on the classified data set, the data is written into a specified storage area by a data storage interface to obtain a structured to-be-processed data set; Based on the structured to-be-processed data set, a preconfigured symmetric encryption key is obtained; Based on the symmetric encryption key, an encryption operation is performed on the structured to-be-processed data set by an advanced encryption standard algorithm to obtain an encrypted data stream; and based on the encrypted data stream, the initial encrypted data is obtained. The initial encrypted data is taken as input plaintext, and a secondary encryption construction is performed by an asymmetric encryption algorithm to obtain an enhanced encrypted data block; The enhanced encrypted data block is written into a secure database for persistent storage, and finally protected closed orbit secure storage data is obtained, including: Based on the initial encrypted data, a secondary encryption process is performed by an asymmetric encryption algorithm to obtain enhanced encrypted data; 5. The method of analyzing a closed orbit of an cyclotron according to claim 4, wherein Based on the enhanced encrypted data, an enhanced encrypted data block is obtained by a data block construction method; Based on the enhanced encrypted data block, a persistent storage operation is performed by a storage interface of the secure database to obtain the protected closed orbit secure storage data. The trajectory decomposition data set is constructed by a data synthesis method based on the fundamental frequency component and the harmonic component, wherein the trajectory decomposition data set describes the periodic motion state of the particle in the cyclotron. According to the motion characteristic analysis result and the orbit stability evaluation result, the trajectory decomposition data is classified and stored to obtain a structured to-be-processed data set; The structured to-be-processed data set is encrypted by a data encryption algorithm to obtain initial encrypted data, including:

6. A cyclotron closed orbit analysis system, the system implementing the method of any one of claims 1 to 5, characterized in that, Based on the motion characteristic analysis result and the orbit stability evaluation result, the trajectory decomposition data is classified by a data classification unit to obtain a classified data set; Based on the classified data set, the data is written into a specified storage area by a data storage interface to obtain a structured to-be-processed data set; Based on the structured to-be-processed data set, a preconfigured symmetric encryption key is obtained; Based on the symmetric encryption key, an encryption operation is performed on the structured to-be-processed data set by an advanced encryption standard algorithm to obtain an encrypted data stream; and based on the encrypted data stream, the initial encrypted data is obtained. The initial encrypted data is taken as input plaintext, and a secondary encryption construction is performed by an asymmetric encryption algorithm to obtain an enhanced encrypted data block; The enhanced encrypted data block is written into a secure database for persistent storage, and finally protected closed orbit secure storage data is obtained, including: Based on the initial encrypted data, a secondary encryption process is performed by an asymmetric encryption algorithm to obtain enhanced encrypted data; Based on the enhanced encrypted data, an enhanced encrypted data block is obtained by a data block construction method; Based on the enhanced encrypted data block, a persistent storage operation is performed by a storage interface of the secure database to obtain the protected closed orbit secure storage data. The encryption module is configured to classify and store the track decomposition data according to the motion characteristic analysis result and the track stability evaluation result, to obtain a structured to-be-processed data set, and to perform encryption processing on the structured to-be-processed data set through a data encryption algorithm to obtain initial encrypted data. The processing module is configured to take the initial encrypted data as input plaintext, perform secondary encryption construction on the initial encrypted data through an asymmetric encryption algorithm, and obtain an enhanced encrypted data block. The enhanced encrypted data block is written into a secure database for persistent storage, and finally protected closed track secure storage data is obtained.

7. A computing device, comprising: One or more processors; A storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 5. The computer readable storage medium stores a program, and the program is executed by the processor to implement the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, ​

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