电子束流诊断：

1）电子储存环中多参数逐束团诊断

研究重点包括束流信号的新型调制、检测与分析方法，以及用于从单个宽带束流探针信号中提取多维逐束团信息的新型采样、调节与处理技术。在全球范围内首次实现了对电子储存环中单个束团的三维位置、束团长度和电荷量的同步精确测量，包括注入束团三维位置信息的分离。使用与镜像电流和可见同步辐射结合的纽扣电极作为信号源，以高速数字示波器作为数据采集设备，由离线软件包分析示波器采集的海量数据，从而能够精确测量电荷量和寿命[1-2]、横向位置[3-4]、纵向位置[5-7]、横向截面尺寸[8-9]和纵向长度[10-11]等多个参数。在此基础上，开发了一款名为HOTCAP的开源软件包[14-15]，可同时测量多种参数[12-13]，并适用于任何高能电子储存环。其横向位置分辨率超过5 μm，纵向相位分辨率优于0.2 ps，束团长度分辨率超过1 ps。还进行了多项应用研究，包括注入瞬态过程性能评估[16-17]、补充束团阻尼振荡观测[18]、新注入束团纵向相空间初始参数提取[19]、束流横向尾场分析[20]、束流负载效应评估[21]以及光学参数原位诊断[22]。

5D BYB（bunch-by-bunch）监测器系统图

使用5D BYB监测器重建注入束团纵向分布的结果

Reference

[1] Bo Gao, Fangzhou Chen, Yimei Zhou, Yongbin Leng*, Bunch-by-bunch beam lifetime measurement at SSRF, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1015, 1 November 2021, 165758, https://doi.org/10.1016/j.nima.2021.165758

[2] Chen, F., Chen, Z., Zhou, Y. Leng Y. (*) et al. Touschek lifetime study based on the precise bunch-by-bunch BCM system at SSRF. NUCL SCI TECH 30, 144 (2019)

[3] Zhang Ning, Leng Yongbin*, Chen Zhichu, et al. Development of a Bunch-by-Bunch Position Monitoring System Based on Oscilloscope-Embedded IOC Technology [J]. Nuclear Techniques, 2012. (in Chinese)

[4] Xing Yang, Hong Shuang Wang, Yi Mei Zhou, Yong Bin Leng*, Determining beam transverse absolute position by triangulation of multielectrode signal phase differences, Nuclear Science and Techniques, 23 July 2024, Volume 35, article number 89, (2024), https:// doi.org/10.1007/s41365-024-01498-y

[5] Zhou Y M, Chen H J, Cao S S, et al. Bunch-by-bunch longitudinal phase monitor at SSRF[J]. Nuclear Science and Techniques, 2018, 29(8): 113.

[6] Zhou Yimei, Leng Yongbin*, Xu Xinyi, Gao Bo, Cao Shanshan. Optimization of Signal Processing Algorithms for Storage Ring Bunch-by-Bunch Phase Measurement System [J]. High Power Laser and Particle Beams, 2020, 32: 000000. DOI: 10.11884/HPLPB202032.190033 (in Chinese)

[7] YouMing Deng, YongBin Leng*, XingYi Xu, Jian Chen, YiMei Zhou, Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation, Nuclear Science and Techniques, 3 June 2024, Volume 35, article number 133, (2024), https://doi.org/10.1007/s41365-024-01444-y

[8] Chen H J, Chen J, Gao B, Leng Y. (*) et al. Bunch-by-bunch beam size measurement during injection at Shanghai Synchrotron Radiation Facility[J]. Nuclear Science and Techniques, 2018, 29(6): 79.

[9] Yimei Zhou, Yongbin Leng*, Sang Wu, Huizhong Bai, Longwei Lai, Fangzhou Chen and Ning Zhang, A novel approach to beam size measurement at SSRF, Journal of Physics: Conference Series 3010 (2025) 012093

[10] Duan L W, Leng Y B*, Yuan R X, et al. Injection transient study using a two-frequency bunch length measurement system at the SSRF[J]. Nuclear Science and Techniques, 2017, 28(7): 93.

[11] HongShuang Wang, Xing Yang, YongBin Leng*, YiMei Zhou, JiGang Wang, Bunchlength measurement at a bunchbybunch rate based on time–frequencydomain joint analysis techniques and its application, Nuclear Science and Techniques, 24 May 2024, Volume 35, article number 80, (2024), https://doi.org/10.1007/s41365-024-01443-z

[12] Xingyi Xu,Yongbin Leng*, Yimei Zhou, Bo Gao, Jian Chen, and Shanshan Cao，Bunch-by-bunch three-dimensional position and charge measurement in a storage ring，PHYSICAL REVIEW ACCELERATORS AND BEAMS 24, 032802 (2021)

[13] Youming Deng, Yongbin Leng*, Yimei Zhou et al., Research Progress on Bunch-by-Bunch Diagnosis Technology in Electron Storage Rings, Nuclear Techniques, October 2024, Vol. 47, No. 10. (in Chinese)

[14] Xing-Yi Xu,Yong-Bin Leng*,Bo Gao，etc.,HOTCAP: a new software package for high-speed oscilloscope based three-dimensional bunch charge and position measurement,NUCL SCI TECH (2021) 32:131

[15] Xing Yang, Yongbin Leng*, Yimei Zhou, Real-Time Performance Optimization of the HOTCAP Software for Three-Dimensional Bunch Information Extraction, Nuclear Techniques, February 2024, Vol. 47, No. 2, pp. 020102-1-8. (in Chinese)

[16] Yong Y, Yong-Bin L*, Ying-Bing Y, et al. Injection performance evaluation for SSRF storage ring[J]. Chinese physics C, 2015, 39(9): 097003.

[17] Yimei Zhou, Yongbin Leng* et al., Extraction of Three-Dimensional Position Information of Replenishment Charge During Injection in Electron Storage Rings, Atomic Energy Science and Technology, Vol. 54, No. 11, 2020. (in Chinese)

[18] Yimei Zhou(†); Zhichu Chen; Bo Gao; Ning Zhang; Yongbin Leng(*), Bunch-by-bunch phase study of the transient state during injection, NIMA,955(2020) 163273, https://doi.org/10.1016/j.nima.2019.163273

[19] Hongshuang Wang, Yongbin Leng*, andYimei Zhou, Technique for Extracting Initial Parameters of Longitudinal Phase Space of Freshly Injected Bunches in Storage Rings, and Its Applications, Instruments 2025, 9, 17, https://www.mdpi.com/2410-390X/9/3/17

[20] Chen Z, Yang Y, Leng Y*, et al. Wakefield measurement using principal component analysis on bunch-by-bunch information during transient state of injection in a storage ring[J]. PRAB, 2014, 17(11): 112803.

[21] Yimei Zhou, Yongbin Leng*, Longwei Lai, Xingyi Xu, Tianlong He, Experimental verification and analysis of the beam loading effect based on precise bunch-by-bunch 3D position measurement, Nuclear Inst. and Methods in Physics Research, A, 1051 (2023) 168201

[22] Xingyi Xu,Yongbin Leng*, New noninvasive measurement method of optics parameters in a storage ring using bunch-by-bunch 3D beam position measurement data，PHYSICAL REVIEW ACCELERATORS AND BEAMS 24, 062802 (2021)

2）机器学习技术在束流诊断中的应用

探索机器学习技术在粒子加速器束流测量与反馈控制中的应用。近期主要研究包括：基于多维束流参数测量构建加速器运行状态空间，运用聚类算法分析状态，进行运行状态评估与预测[1-2]；以宽带探针采样的多束团、多圈电压波形原始数据作为输入，设计融合时空特征的混合神经网络架构，通过共享特征提取层与专用预测分支的协同设计，实现束团长度、相位和横向位置的端到端联合预测；针对束流信号传输中可能存在的反射与串扰问题，利用卷积神经网络（Convolutional Neural Network，CNN）处理具有相似特征的多通道束流采样信号，求解反射与串扰系数，从而将源信号与干扰分离[3]；利用CNN分析束流横向或纵向振荡的监测数据，实现多个加速器与束流参数的快速精准拟合[4]；尝试在合肥红外自由电子激光装置（Hefei Infrared FELFacility）上，采用强化学习中的深度确定性策略梯度（Deep Deterministic Policy Gradient，DDPG）和SAC（Soft Actor-Critic）算法训练智能体，用于运行过程中的参数自动优化；为解决注入束团测量纵向分布数据的快速分析问题，通过MobileNetV2网络处理数据图像并提取特征向量，与模拟标准数据库进行匹配，实现电子储存环注入过程中初始束流参数的反向提取[5]。采用深度全连接神经网络方法（FEL功率预测器），对波荡器上下游BPM获取的多束团横向位置、电荷量及纵向相位数据进行分析，构建高精度激光功率预测模型，解决了红外自由电子激光（Free-Electron Laser，FEL）输出功率的实时无损监测难题，为装置参数优化提供了数据驱动的指导[6]。

Reference

[1] Zikun Fang, Tianyu Jiang, Yimei Zhou, Yongbin Leng (*), Machine Learning Based Injection Quality Assessment and Anomaly Detection in Electron Storage Rings, Nuclear Techniques (Under Review, in Chinese).

[2] JIANG Ruitao, YANG Xing, DENG Youming, LENG Yongbin∗, Diagnostic Method for Beam Position Monitor Based on Clustering by Fast Search and Find of Density Peaks, J. Shanghai Jiao Tong Univ. (Sci.), 2022,  https://doi.org/10.1007/s12204-022-2546-y

[3] Chen J, Leng Y B *, Yu L Y, et al. Study of the crosstalk evaluation for cavity BPM[J]. Nuclear Science and Techniques, 2018, 29(6): 83.

[4] Xinyi Xu(†); Yimei Zhou; Yongbin Leng(*), Machine learning based image processing technology application in bunch longitudinal phase information extraction, PRAB 23, 032805 (2020)

[5] Tianyu Jiang, Jerry Jin, Yimei Zhou, Hongshuang Wang, Xing Yang and Yongbin  Leng, Analysis of Longitudinal Phase Space Evolution of  Fresh Injected Beam in Electron Storage Ring Based on Image Matching, JINST(under review)

[6] C. Liu, X. Yang, Y. M. Deng, Y. B. Leng†, PREDICTION OF FEL PERFORMANCE USING BPM MEASUREMENTS AND MACHINE LEARNING, IBIC2025

3）基于腔体的多参数诊断技术

包括谐振腔探针的优化设计方法、腔体探针多参数信息提取的改进算法，以及基于腔体探针的束流测量系统集成与优化技术。为满足自由电子激光装置对束团位置精确测量的需求，研制了C波段腔体探针射频信号调理前端[1]。对腔体探针信号的优化幅度提取算法进行了理论分析与实验验证[2]。在此基础上，相关研究探讨了此类集成系统内各模块间参数匹配的优化设计方法[3]，完成了系统集成[4]，并开展了精确电荷量测量[5-6]、到达时间测量[7-10]以及束团轨迹偏转角测量[11]的应用研究。

Reference

[1] Jian Chen, Fangzhou Chen, Yongbin Leng*, Development of a Radio Frequency Signal Conditioning Front-End for the Cavity-Based CBPM System, Atomic Energy Science and Technology, October 2022, Vol. 56, No. 10. (in Chinese)

[2] Jian Chen, Shanshan Cao, Yongbin Leng*，Study of the optimal amplitude extraction algorithm for cavity BPM，Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment Volume 1012, 1 October 2021, 165627, https://doi.org/10.1016/j.nima.2021.165627

[3] Jian Chen, Yongbin Leng*, Shanshan Cao, Longwei Lai, Renxian Yuan, Ruitao Jiang, Xing Yang, Optimized design method study for cavity BPM system, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1044, 1 December 2022, 167509, https://doi.org/10.1016/j.nima.2022.167509

[4] Jian Chen, Yongbin Leng* et al., Shanghai Soft X-ray Free-Electron Laser Cavity-Based Beam Position Monitoring System, Atomic Energy Science and Technology, Vol. 54, No. 10, 2020. (in Chinese)

[5] Shanshan Cao, Yongbin Leng*, Renxian Yuan, Longwei Lai, Jian Chen. Study on High-Resolution Bunch Charge Measurement Method Based on Cavity Probes [J]. Nuclear Techniques, 2021, Vol. 44, No. 4. DOI: 10.11889/j.0253-3219.2021.hjs.44.040101. (in Chinese)

[6] Shanshan Cao, Yongbin Leng*, Renxian Yuan, et al.. Accurate Beam Current Measurement Based on Dual-Cavity Probes [J]. Nuclear Techniques, 2019, 42(4): 40101-040101.  (in Chinese)

[7] Shanshan Cao, Yongbin Leng*, Renxian Yuan, and Jian Chen, Optimization of beam arrival and flight time measurement system based on cavity monitors at the SXFEL, IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 68, NO. 1, JANUARY 2021

[8] Shanshan Cao, Yongbin Leng∗, Renxian Yuan, Jian Chen, An application of a cavity-based beam arrival time measurement system: Beam energy measurement, Nuclear Inst. And Methods in Physics Research, A, Volume 1045, 1 January 2023, 167456, https://doi.org/10.1016/j.nima.2022.167456

[9] Cao, S., Yuan, R., Chen, J., Leng Y. (*) et al. Dual-cavity beam arrival time monitor design for the Shanghai soft X-ray FEL facility. NUCL SCI TECH 30, 72 (2019)

[10] Yimei Zhou, Yongbin Leng*, Jian Chen, Shanshan Cao, Xingyi Xu, Longwei Lai, Algorithm Optimization for Bunch Arrival Time Measurement Based on Cavity Probes, Atomic Energy Science and Technology, October 2022, Vol. 56, No. 10, pp. 2104-2112. (in Chinese)

[11] Jian Chen, Shanshan Cao, Luyang Yu, Longwei Lai, Renxian Yuan, Fangzhou Chen, and Yongbin Leng*, In situ beam trajectory tilt measurement based on single cavity beam position monitor, Phys. Rev. Accel. Beams 26, 102802

4）基于光学技术的束流诊断

致力于发展新型同步辐射光调制方法，结合计算成像技术，探索束流截面分布和束团纵向分布表征的高精度、高速诊断方法，旨在解决同步辐射光利用效率低以及测量精度与速度之间固有矛盾等难题。通过复杂掩模编码技术，实现对宽带、多空间频率同步辐射的直接操控。利用彩色相机或单像素成像系统，可一次性获取多波长干涉图或高灵敏度强度分布，随后通过计算反演算法高效提取参数。目前，已开发了一款专用的波动光学数值模拟工具，并利用多色空间干涉仪完成了概念验证实验，同时完成了彩色相机和单像素探测器配置的理论建模与优化。

Reference

[1] Gao B, Leng Y B*, Chen H J, et al. Upgrade of the X-ray pinhole camera system at SSRF[J]. Nuclear Science and Techniques, 2018, 29(8): 115.

[2] Gao Bo, Leng Yongbin*, Chen Hanjiao, Chen Jie. Rapid Grating Pitch Scanning System for Synchrotron Light Spatial Interferometer [J]. Nuclear Techniques, 2018, 41(8): 080101. DOI: 10.11889/j.0253-3219.2018.hjs.41.080101. (in Chinese)

[3] Yong-Bin L*, Guo-Qing H, Man-Zhou Z, et al. The beam-based calibration of an X-ray pinhole camera at SSRF[J]. Chinese physics C, 2012, 36(1): 80.

[4] Chen Jie, Ye Kairong, Leng Yongbin*. Development of Spatial Interferometer for Shanghai Synchrotron Radiation Facility [J]. Nuclear Techniques, January 2011, Vol. 23, No. 1. (in Chinese)

5）束流信号处理器的开发与应用

涉及基于FPGA与高速ADC/DAC（采样率从数百MHz至GHz）的专用信号处理器开发与应用，重点包括软硬件架构设计、实现以及新型信号处理算法在FPGA内的优化。已成功研制出满足合肥先进光源储存环轨道测量需求的超低延迟专用束流信号处理器，采用双导频技术进行通道一致性补偿，在2 Hz带宽下位置测量分辨率优于20 nm，在10 kHz带宽（FA模式）下优于200 nm，逐圈位置分辨率低于500 nm，FA数据处理延迟低于90 μs，达到国际先进水平。另一款面向合肥红外自由电子激光装置的逐束团束流信号处理器已完成，其横向位置分辨率优于20 μm，纵向相位分辨率优于1 ps，性能达到世界领先水平。在中国科学院重大科技基础设施维修改造项目的支持下，启动了通用束流控制信号处理平台的研制。该方案采用一块数字信号处理主板集成两块FMC信号输入/输出子板，以适应大多数控制与测量应用需求。

低延迟DBPM处理器原型

DBPM处理器原型的位置分辨率

注入期间利用DBPM原型捕获的水平位置波形

Reference

[1] Lai Longwei, Leng Yongbin*, Yan Yingbing. Development and Application of Digital Beam Position Signal Processor for Free-Electron Laser Facility [J]. Nuclear Techniques, July 2018, 41(7): 070402. (in Chinese)

[2] Chen Fangzhou, Lai Longwei, Yan Yingbing, Leng Yongbin*. Development of Test Platform for Digital Beam Position Signal Processor at Shanghai Synchrotron Radiation Facility [J]. Nuclear Techniques, November 2017, Vol. 40, Issue 11. (in Chinese)

[3] Lai Longwei, Leng Yongbin*, Yan Yingbing. Progress in Development of Digital BPM Signal Processor [J]. Atomic Energy Science and Technology, October 2015, Supplement to Vol. 49: 607–610. (in Chinese)

[4] Sun Xudong, Leng Yongbin*. Implementation and Integration of a Systematic DBPM Calibration with PLL Frequency Synthesis and FPGA [J]. NST, 2014, 25(2): 20401-020401.

[5] Lai Longwei, Leng Yongbin*, Yi Xing. Optimization of Digital Beam Position Signal Processing Algorithms [J]. High Power Laser and Particle Beams, January 2013, 25(1): 109–113. (in Chinese)

[6] Yi Xing, Leng Yongbin*, Lai Longwei. Novel Digital Beam Position Processor Based on Software Radio [J]. Nuclear Techniques, May 2012, 35(5): 346–351. (in Chinese)

[7] Longwei L, Yongbin L*, Xing Y, et al. DBPM Signal Processing with Field Programmable Gate Arrays [J]. NST, 2013, 22(3): 129–133.

[8] Leng Yongbin*, Yi Xing, Lai Longwei. Progress in Development of Novel Digital BPM Signal Processor [J]. Nuclear Techniques, May 2011, 34(5): 326–330. (in Chinese)

[9] Xing Y, Yongbin L*, Longwei L, et al. RF Front-End for Digital Beam Position Monitor Signal Processor [J]. NST, 2011, 22(2011): 65–69.

[10] Lai Longwei, Leng Yongbin*, Yan Yingbing. Research on Digital BPM Signal Processing Algorithms [J]. Nuclear Techniques, October 2010, Vol. 33, Issue 10. (in Chinese)

控制技术

1）提高可用性的技术

可用性是综合了可靠性与可维护性的指标，更高的可靠性和更好的可维护性带来更高的可用性。对于加速器控制系统，提高可用性的常用方法包括冗余技术、故障分析与定位技术、报警技术以及过程自动化技术。冗余技术用于增强系统可靠性，可应用于加速器控制系统的不同层级，如服务器系统、网络系统、前端控制器和I/O设备。设计了一种基于PROFINET的全冗余EPICS控制系统架构[1]，并已应用于HLS-II人员安全系统[2]。故障分析与定位技术依赖于数据归档系统的支撑，开展了配置自动化、基于Web的可视化以及归档数据库性能等技术研究[3-6]。报警系统用于实时监测加速器的运行状态，并在合理的时间范围内将最关键的信息传递给相关人员，对保障装置稳定运行起着至关重要的作用。在报警技术方面，开发了短信、微信、网页等多种信息分发方式，并且研究了各种抑制误报的方法[7-8]。

Reference

[1] Huang Z, Song Y, Wan K, et al. A REDUNDANT EPICS CONTROL SYSTEM BASED ON PROFINET[C]. Proceedings of ICALEPCS2015, Melbourne, Australia, 2015.

[2] Huang Z Y, Xuan K, Li C, et al. Novel design of a personnel safety system for Hefei Light Source-II[J]. Nuclear Science and Techniques, 2019, 30(6): 99.

[3] Song Y F, Li C, Xuan K, et al. Automatic data archiving and visualization at HLS-II[J]. Nuclear Science and Techniques, 2018, 29(9): 129.

[4] Wang Zijian, Xuan Ke, Gan Yanfang, et al. Development of a Mobile Terminal Data Query System for HLS-II [J]. High Power Laser and Particle Beams, 2021, 33(4). (in Chinese)

[5] Chen H, Liu G F, Zhang D D, et al. A study of performance comparison of databases for HALF data archiving system[J]. Journal of Instrumentation, 2023, 18(12): P12015.

[6] Yuan X, Liu G, Zhang D, et al. Development of HALF historical data archiving system based on TimescaleDB[J]. Journal of Instrumentation, 2025, 20(06): P06043.

[7] Xu S, Liu G F, Gan Y F, et al. The development of the alarm system for HLS-II[J]. Journal of Instrumentation, 2022, 17(06): P06027.

[8] Xu S, Wang K X, Liu G F, et al. The HLS-II alarm system optimization for removing nuisance alarms[J]. Journal of Instrumentation, 2024, 19(04): P04007.

2）机器学习技术在加速器控制中的应用

近年来，机器学习技术取得了显著进展，并在各领域得到广泛应用。在加速器控制领域，我们将相关技术用于参数估计、参数校正、异常检测及预警系统等应用[1-2]。近期工作集中在以下三个方面：首先，采用LASSO回归算法实现实时调谐反馈校正，并在合肥光源储存环上进行了在线实验验证，结果表明该机器学习应用进一步提高了工作点（betatron tune）稳定性[3]。其次，利用神经网络算法对储存环的β函数进行校正，为进一步优化该校正模型的性能，开发了基于改进遗传算法（Improved Genetic Algorithm，IGA-DNN）的神经网络结构优化方法，该方法降低了训练过程中参数调节的复杂度，获得了更优化的网络结构[4]。最后，使用核岭回归算法对储存环束流轨道与β函数之间的关系进行建模，通过束流轨道数据实现β函数的实时测量，从而提高了测量效率。在线实验结果表明，所提方法得出的结果与利用LOCO和四极铁调制技术获得的结果一致[5]。

Reference

[1] Yongbo Yu, Wangbiao Ni, Gongfa Liu, Wei Xu, Chuan Li, Weiming Li, Ke Xuan, Initial Application of Machine Learning for Beam Parameter Optimization at the Hefei Light Source II, Journal of Physics: Conference Series. IOP Publishing, 2024, 2687:072002.

[2] Yang Rui, Yu Haishan, Sun Xiaokang, Wang Guanliang, Xuan Ke, Liu Gongfa. Analysis and Diagnosis of Beam Lifetime Based on Hefei Light Source Database [J]. Nuclear Electronics & Detector Techniques, 2024, 44(1): 131–138. (in Chinese)

[3] Yong-Bo Yu, Gong-Fa Liu, Wei Xu, Chuan Li, Wei-Min Li, Ke Xuan, Research on tune feedback of the Hefei Light Source II based on machine learning, Nuclear Science and Techniques, 2022, 33(3): 28.

[4] Y.B.Yu, B.W.Ni, K.Xuan, W.M.L, W.Xu, C.Li, G.F.Liu, Neural network structure optimization for Hefei Light Source II beta function correction, Journal of Instrumentation, 2023, 8(9): P09008.

[5] S.Y. Yang , W. Xu, F. Li, C. Li, X.K. Sun, Y.B. Yu, G.F. Liu, L. Wang , K. Xuan, Study of beta function measurement methods based on beam orbit, Journal of Instrumentation, 2025, 20(8): P08033.

3）数据采集

数据采集系统旨在实现实验控制与科学数据采集的自动化和智能化。该系统的软件架构采用分层结构，由硬件层、硬件控制层、硬件抽象层、过程控制层和实验处理层五个层级组成。其中，硬件控制层基于EPICS构建，而硬件抽象层、过程控制层和实验处理层则基于Bluesky开发。硬件层包括电机、探测器及其他相关设备。硬件控制层采用基于EPICS的分布式架构，这些IOC共同为硬件抽象层提供所需的过程变量。硬件抽象层使用Bluesky中的Ophyd将过程变量封装为Python对象。过程控制层执行Bluesky计划（如步进扫描、飞扫），并将各类实验元数据与原始数据相关联，同时以脚本形式向实验处理层提供接口。实验处理层主要用于编排和管理计划，此外还负责收集和处理实验过程中产生的数据。

Bluesky队列服务器是数据采集系统的核心。用户将计划上传至队列服务器，由队列服务器自主、按顺序执行这些计划。队列服务器以多进程服务方式运行，其中运行引擎管理器（Run Engine Manager）是负责维护和执行计划队列的核心组件。计划在运行引擎工作进程（Run Engine Worker）的独立进程中执行。队列服务器提供计划队列管理服务，在Redis中维护多个计划的队列。GUI基于Bluesky Queue Server API和Bluesky Widgets开发。

4）实验数据管理系统

实验数据管理系统负责对光源在各阶段产生的实验数据进行全生命周期管理，涵盖数据采集、存储、分析与数据发布等环节。光源每年将产生PB量级的实验数据。这些海量数据由实验数据管理系统集中存储与管理，并按照FAIR原则为用户提供数据访问服务。

实验数据全生命周期管理

光源实验数据采用NeXus/HDF5文件格式存储，该格式同时包含原始数据和全面的元数据。数据文件存储使用GPFS分布式文件系统。元数据通过Kafka消息队列采集，并由SciCat元数据目录进行管理。系统为用户提供基于Web的数据访问服务。

5）实验数据分析系统

实验数据分析系统旨在处理海量实验数据，集成了先进的数据分析与管理工具。该软件提供可扩展的分布式异构计算能力，满足多模态数据的处理需求。算法方面集成了谱学、衍射/散射和成像算法库，支持用户灵活调用。用户界面提供两种访问方式：基于PyQt5的工作台和基于JupyterLab的Web客户端，为用户提供灵活选择。