{"id":209,"date":"2022-07-17T00:13:45","date_gmt":"2022-07-16T16:13:45","guid":{"rendered":"https:\/\/www.corticalchip.com\/?p=209"},"modified":"2025-11-26T19:50:52","modified_gmt":"2025-11-26T11:50:52","slug":"%e7%8e%8b%e6%b5%b7%e5%86%b0%e4%b8%aa%e4%ba%ba%e7%ae%80%e4%bb%8b","status":"publish","type":"post","link":"https:\/\/www.corticalchip.com\/index.php\/2022\/07\/17\/%e7%8e%8b%e6%b5%b7%e5%86%b0%e4%b8%aa%e4%ba%ba%e7%ae%80%e4%bb%8b\/","title":{"rendered":"\u738b\u6d77\u51b0\u4e2a\u4eba\u7b80\u4ecb"},"content":{"rendered":"\n

                                                                                                              \"\"
                                                                                                 \u4e2a\u4eba\u90ae\u7bb1\uff1ahaibingwang@cqu.edu.cn<\/p>\n\n\n\n

      \u738b\u6d77\u51b0\uff0c2020\u5e746\u6708\u6bd5\u4e1a\u4e8e\u5e7f\u897f\u5927\u5b66\u8ba1\u7b97\u673a\u4e0e\u7535\u5b50\u4fe1\u606f\u5b66\u9662\u83b7\u5f97\u5de5\u5b66\u5b66\u58eb\u5b66\u4f4d\uff0c2023\u5e746\u6708\u6bd5\u4e1a\u4e8e\u91cd\u5e86\u5927\u5b66\u5fae\u7535\u5b50\u4e0e\u901a\u4fe1\u5de5\u7a0b\u5b66\u9662\u83b7\u5f97\u5de5\u5b66\u7855\u58eb\u5b66\u4f4d\uff0c2023\u5e749\u6708\u5c06\u4e8e\u91cd\u5e86\u5927\u5b66\u5fae\u7535\u5b50\u4e0e\u901a\u4fe1\u5de5\u7a0b\u5b66\u9662\u5f00\u59cb\u653b\u8bfb\u535a\u58eb\u5b66\u4f4d\u3002\u76ee\u524d\u7814\u7a76\u5174\u8da3\u65b9\u5411\u4e3a\u9762\u5411\u7269\u7aef\u667a\u80fd\u5e94\u7528\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7b97\u6cd5\u7814\u7a76\u548c\u795e\u7ecf\u5f62\u6001\u7c7b\u8111\u82af\u7247\u8bbe\u8ba1\uff0c\u5171\u53c2\u4e0e4\u9879\u56fd\u5bb6\u548c\u7701\u90e8\u7ea7\u79d1\u7814\u8bfe\u9898\u3002\u8363\u83b72022\u5e74\u7855\u58eb\u7814\u7a76\u751f\u56fd\u5bb6\u5956\u5b66\u91d1\u3002<\/strong>\u5df2\u53d1\u8868\u5b66\u672f\u8bba\u658720\u7bc7\uff0c\u516c\u5f00\u53d1\u660e\u4e13\u522915\u9879\u3002<\/p>\n\n\n\n

\u53d1\u8868\u8bba\u6587<\/strong><\/p>\n\n\n\n

    \n
  1. MorphBungee-Tiny: An Ultra-Low-Cost FPGA Implementation of Neuromorphic Architecture With High-Accuracy On-Chip E-Prop Learning<\/a><\/mark><\/mark><\/mark>
    \u00a0 \u00a0Zhengqing Zhong, Jialin Yang, Haibing Wang<\/strong>, Ying Jiang, Yingcheng Lin, Ping Gan, Liyuan Liu, Liang Liang, Cong Shi*. IEEE Transactions On Circuits and Systems II: Express Briefs<\/strong><\/em>, 2025, 72(12), 2012-2016.\uff08SCI\uff0c 1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  2. A Visual-Cortex-Mimetic Tiny Neuromorphic Vision Processor Based on Reconfigurable Cortical Neuron Unit<\/a><\/mark>
        Mingju Chen, Junxian He, Haibing Wang<\/strong>, Tengxiao Wang, Haoran Gao, Liyuan Liu, Ying Wang, Cong Shi*. IEEE Transactions <\/strong><\/em>On Circuits and Systems II: Express Briefs<\/em><\/strong><\/strong>, <\/strong>2025, 72(7), 943-947.\uff08SCI\uff0c1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  3. An Edge Neuromorphic Processor With High-Accuracy On-Chip Aggregate-Label Learning<\/a>
        Guanyu Chen, Minju Chen, Tengxiao Wang, Haibing Wang<\/strong>, Xiang Fu, Yingcheng Liu, Liyuan Liu, Cong Shi*. IEEE Transactions <\/strong><\/em>On Circuits and Systems II: Express Briefs<\/em><\/strong>, <\/strong>2025, 72(3), 509-513.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  4. MorphBungee-Lite: An Edge Neuromorphic Architecture With Balanced Cross-Core Workloads Based on Layer-Wise Event-Batch Learning\/Inference<\/a><\/mark>
        Zhengqing Zhong, Haibing Wang<\/strong>, Mingju Chen, Yingcheng Lin, Min Tian, Tengxiao Wang, Liyuan Liu, Cong Shi*. IEEE Transactions On Circuits and Systems Part II: Express Briefs<\/em><\/strong>, 2025, 72(1), 293 – 297\uff08SCI, 2\u533a<\/strong>\uff09

    <\/li>\n\n\n\n
  5. A Visual Cortex-Inspired Edge Neuromorphic Hardware Architecture With On-Chip Multi-Layer STDP Learning<\/a><\/mark>
        Junxian He, Min Tian, Ying Jiang, Haibing Wang<\/strong>, Tengxiao Wang, Xichuan Zhou, Liyuan Liu, Nanjian Wu, Ying Wang, Cong Shi*. Computers and Electrical Engineering<\/em><\/strong>, 2024, 120:109806.\uff08SCI, 1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  6. NeuroCam: A Neuromorphic Intelligent CameraSystem for Face Recognition<\/a><\/mark>
        Jialin Yang, Haibing Wang<\/strong>*, Zhengging Zhong, Mingju Chen, Ying Jiang, Yangfan Huang,Cong Shi. 2024 4th<\/em><\/strong> International Conference on Intelligent Technology and Embedded Systems (ICITES)<\/em><\/strong>, September 20-23, 2024, Chengdu, China.

    <\/li>\n\n\n\n
  7. MorphBungee: A 65-nm 7.2-mm2<\/sup> 27-\u03bcJ\/image Digital Edge Neuromorphic Chip with On-Chip 802-frame\/s Multi-Layer Spiking Neural Network Learning<\/a><\/mark>
        Tengxiao Wang, Min Tian, Haibing Wang<\/strong>, Zhengqing Zhong, Junxian He, Fang Tang, Xichuan Zhou, Yingcheng Lin, Shuang-Ming Yu, Liyuan Liu, Cong Shi*. IEEE Transactions on Biomedical Circuits and Systems<\/strong><\/em>, <\/strong>2024, doi: 10.1109\/TBCAS.2024.3412908 (Early Access)\uff08SCI\uff0c1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  8. Ghost Reservoir: A Memory-Efficient Low-Power and Real-Time Neuromorphic Processor of Liquid State Machine with On-Chip Learning<\/a><\/mark>
        Cong Shi, Xiang Fu, Haibing Wang<\/strong>, Yingcheng Lin, Ying Jiang, Liyuan Liu, Nanjian Wu, Min Tian*. <\/strong>IEEE Transactions on Circuits and Systems II: Express Briefs<\/strong><\/em>:  2024, 71(10), 4526-4530.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  9. NORP: A Compact Neuromorphic Olfactory Recognition Processor with On-Chip Hybrid Learning<\/a><\/mark>
         Cong Shi, Yuxuan Wang, Fengchun Tian, Xiang Fu, Haibing Wang<\/strong>, Jingya Zhang, Haoran Gao, Shukai Duan, Lidan Wang, Min Tian*. IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)<\/em><\/strong>, 2023, 31-32.

    <\/li>\n\n\n\n
  10. An 8-T Processing-in-Memory SRAM Cell-Based Pixel-Parallel Array Processor for Vision Chips<\/a><\/mark>
         Leyi Chen, Cong Shi, Junxian He, Jianyi Yu, Haibing Wang<\/strong>, Jing Lu, Liyuan Liu, Nanjian Wu, Min Tian*. IEEE Transactions on Circuits and Systems I: Regular Papers<\/em><\/strong>: 2023, 70(11), 4249-4259.\uff08SCI\uff0c1\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  11. MorphBungee: A 65nm 7.2mm2<\/sup> 27\u03bcJ\/image Digital Edge Neuromorphic Chip with On-Chip 802 frame\/s Multi-layer Spiking Neural Network Learning<\/a><\/mark>
        Tengxiao Wang, Min Tian, Zhengqing Zhong, Haibing Wang<\/strong>, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Nanjian Wu, Liyuan Liu, Cong Shi*<\/sup>, 2023 IEEE Asian Solid-State Circuits Conference (ASSCC)<\/em><\/strong>, 2023, 1-3.

    <\/li>\n\n\n\n
  12. Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip<\/a>
        Zhengqing Zhong, Tengxiao Wang, Haibing Wang<\/strong>, Zhihua Zhou, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi*. <\/strong>2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)<\/strong><\/em>, 2023, 1-2.

    <\/li>\n\n\n\n
  13. High-Accuracy Deep ANN-to-SNN Conversion Using Quantization-Aware Training Framework and Calcium-Gated Bipolar Leaky Integrate & Fire Neuron<\/a><\/mark>
         Haoran Gao, Junxian He, Haibing Wang<\/strong>, Tengxiao Wang, Zhengqing Zhong, Jianyi Yu, Ying Wang, Min Tian, Cong Shi*. Frontiers in Neuroscience<\/em><\/strong>: 2023, 17, 1141701.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  14. A Configurable On-Chip Spike Encoding Network Based on Dual-Mode Integrate & Fire Neurons<\/a><\/mark>
         Zhengqing Zhong, Yunpeng Tuo*, Haibing Wang<\/strong>, Tengxiao Wang, Junxian He, Min Tian, Cong Shi. IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference(ITNEC)<\/em><\/strong>: 2023, 1445-1449.

    <\/li>\n\n\n\n
  15. An 8-T Processing-in-Memory SRAM Cell-Based <\/mark>Pixel-Parallel Array Processor for Vision Chip<\/mark>s<\/a>
         Leyi Chen, Junxian He, Jianyi Yu, Haibing Wang<\/strong>, Jing Lu, Liyuan Liu, Nanjian Wu, Cong Shi, Min Tian*. IEEE Asia Pacific Conference on Circuits and Systems<\/strong><\/em> (APCCAS)<\/em><\/strong>: 2022, 1-5.(Best Paper Award)<\/strong>

    <\/li>\n\n\n\n
  16. TEDOP: a Tiny Event-Driven Neural Network Hardware Core Enabling On-Chip Spike-Driven Synaptic Plasticity<\/a><\/mark>
         Cong Shi, Sihao Chen, Haibing Wang<\/strong>, Zhengqing Zhong, Ping Li, Junxian He, Tengxiao Wang, Jianyi Yu, Min Tian*. IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)<\/em><\/strong>: 2022, pp. 62-63.

    <\/li>\n\n\n\n
  17. MorphBungee: An Edge Neuromorphic Chip for High-Accuracy On-Chip Learning of Multiple-Layer Spiking Neural Networks<\/a><\/mark>
        Tengxiao Wang, Haibing Wang<\/strong>, Junxian He, Zhengqing Zhong, Fang Tang, Xichuan Zhou, Shuang-Ming Yu, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi*. IEEE Biomedical Circuits and Systems Conference (BioCAS)<\/em><\/strong>: 2022, 255-259.

    <\/li>\n\n\n\n
  18. TripleBrain: A Compact Neuromorphic Hardware Core with Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity<\/a>
        Haibing Wang<\/strong>, Zhen He, Tengxiao Wang, Junxian He, Xichuan Zhou, Ying Wang, Liyuan Liu, Nanjian Wu, Min Tian, Cong Shi. IEEE Transactions on Biomedical Circuits and Systems<\/em><\/strong>. 2022, 16(4), 636-650.\uff08SCI\uff0c2\u533a\uff09<\/strong>

    <\/li>\n\n\n\n
  19. A Lightweight Spiking GAN Model for Memristor-Centric Silicon Circuit with On-Chip Reinforcement Adversarial Learning<\/a><\/mark>
        Min Tian, Jing Lu*, Haoran Gao, Haibing Wang<\/strong>, Jianyi Yu, Cong Shi. IEEE International Symposium on Circuits & Systems (ISCAS)<\/strong><\/em>: 2022, 3388-3392.

    <\/li>\n\n\n\n
  20. TripleBrain: An Edge Neuromorphic Architecture for High-accuracy Single-layer Spiking Neural Network with On-chip Self-organizing and Reinforcement Learning<\/a>
         Haibing Wang<\/strong>, Zhen He, Jinsong Rao, Tengxiao Wang, Junxian He, Min Tian, Xichuan Zhou, Liyuan Liu, Nanjian Wu, Cong Shi*. IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)<\/em><\/strong>: 2021, 88-89.<\/li>\n<\/ol>\n\n\n\n

    <\/p>\n\n\n\n

    \u53d1\u660e\u4e13\u5229<\/strong><\/p>\n\n\n\n

      \n
    1. \u7f51\u7edc\u7ed3\u6784\u53ef\u914d\u7f6e\u7684\u7c7b\u8111\u82af\u7247\u67b6\u6784.
      \u77f3\u5306*, \u738b\u817e\u9704, \u7530\u654f, \u738b\u6d77\u51b0<\/strong>, \u949f\u6b63\u9752, \u4f55\u4fca\u8d24, \u848b\u9896. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN117273100A\uff0c2023-12-22

      <\/li>\n\n\n\n
    2. \u4e8b\u4ef6\u9a71\u52a8\u7c7b\u578b\u82af\u7247\u4e2d\u7a81\u89e6\u6743\u91cd\u66f4\u65b0\u65b9\u6cd5\u3001\u82af\u7247\u3001\u7535\u5b50\u8bbe\u5907.
      \u77f3\u5306*, \u738b\u6d77\u51b0<\/strong>, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136, \u5f20\u9756\u96c5, \u9648\u4e50\u6bc5, \u9648\u601d\u8c6a, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116629331A\uff0c2023-08-22

      <\/li>\n\n\n\n
    3. \u57fa\u4e8eCa-LIF\u795e\u7ecf\u5143\u6a21\u578b\u7684Spike-BP\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u77f3\u5306*, \u9ad8\u704f\u7136, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u55bb\u5251\u4f9d, \u738b\u6d77\u51b0<\/strong>, \u9648\u601d\u8c6a, \u5f20\u9756\u96c5, \u5eb9\u4e91\u9e4f, \u9648\u4e50\u6bc5. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116629344A\uff0c2023-08-22

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    4. \u57fa\u4e8e\u5168\u8109\u51b2HMAX\u6a21\u578b\u7684\u591a\u5c42\u5377\u79ef\u7c7b\u8111\u82af\u7247.
      \u77f3\u5306*, \u4f55\u4fca\u8d24, \u738b\u6d77\u51b0<\/strong>, \u738b\u817e\u9704, \u5f20\u9756\u96c5, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562350A\uff0c2023-08-08

      <\/li>\n\n\n\n
    5. \u4e00\u79cd\u57fa\u4e8e\u6db2\u4f53\u72b6\u6001\u673a\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u67b6\u6784.
      \u77f3\u5306*, \u9648\u4e50\u6bc5, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u738b\u6d77\u51b0<\/strong>, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562354A\uff0c2023-08-08

      <\/li>\n\n\n\n
    6. \u6df1\u5c42\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u53ca\u6df1\u5c42SNN\u7247\u4e0a\u5b9e\u65f6\u5b66\u4e60\u5904\u7406\u5668.
      \u77f3\u5306*\uff0c\u5f20\u9756\u96c5, \u7530\u654f, \u738b\u817e\u9704, \u4f55\u4fca\u8d24, \u55bb\u5251\u4f9d, \u9ad8\u704f\u7136, \u738b\u6d77\u51b0<\/strong>, \u9648\u4e50\u6bc5, \u9648\u601d\u8c6a, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116562344A\uff0c2023-08-08

      <\/li>\n\n\n\n
    7. \u57fa\u4e8e\u53cc\u6a21\u5f0f\u79ef\u5206\u70b9\u706b\u795e\u7ecf\u5143\u7684\u7247\u4e0a\u8109\u51b2\u7f16\u7801\u5668.
      \u77f3\u5306*, \u5eb9\u4e91\u9e4f, \u949f\u6b63\u9752, \u7530\u654f, \u738b\u6d77\u51b0<\/strong>, \u738b\u817e\u9704, \u4f55\u4fca\u8d24, \u9648\u4e50\u6bc5, \u5f20\u9756\u96c5, \u738b\u4e3d, \u9648\u601d\u8c6a, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN116050487A\uff0c2023-05-02

      <\/li>\n\n\n\n
    8. \u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60FPGA\u786c\u4ef6\u67b6\u6784\u53ca\u5176\u8bbe\u8ba1\u65b9\u6cd5.
      \u77f3\u5306*, \u5f20\u9756\u96c5, \u7530\u654f, \u738b\u817e\u9704, \u738b\u6d77\u51b0<\/strong>, \u4f55\u4fca\u8d24, \u5362\u9756, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115115039A\uff0c2022-09-27

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    9. \u4e00\u79cd\u4f4e\u5149\u7167\u5c0f\u50cf\u7d20CFA\u91c7\u6837\u4e0e\u8fb9\u7f18\u8ba1\u7b97\u8bbe\u5907\u9002\u7528\u7684\u53bb\u9a6c\u8d5b\u514b\u65b9\u6cd5.
      \u77f3\u5306*, \u4efb\u9759, \u674e\u777f, \u738b\u6d77\u51b0<\/strong>, \u9ad8\u704f\u7136, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u738b\u4e3d. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115082315A\uff0c2022-09-20

      <\/li>\n\n\n\n
    10. \u5b58\u5185\u8ba1\u7b97\u88c5\u7f6e\u53ca\u5176\u63a7\u5236\u65b9\u6cd5.
      \u77f3\u5306*, \u9648\u4e50\u6bc5, \u7530\u654f, \u738b\u6d77\u51b0<\/strong>, \u55bb\u5251\u4f9d, \u4f55\u4fca\u8d24. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115064197A\uff0c2022-09-16

      <\/li>\n\n\n\n
    11. \u57fa\u4e8e\u7b80\u5316SDSP\u7b97\u6cd5\u7684\u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668. 
      \u9648\u601d\u8c6a, \u77f3\u5306*, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u738b\u6d77\u51b0<\/strong>, \u9ad8\u704f\u7136, \u5eb9\u4e91\u9e4f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN115018058A\uff0c2022-09-06

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    12. \u57fa\u4e8e\u5fc6\u963b\u5668\u7684\u53ef\u7247\u4e0a\u5f3a\u5316\u5b66\u4e60\u8109\u51b2GAN\u6a21\u578b\u53ca\u8bbe\u8ba1\u65b9\u6cd5. 
      \u77f3\u5306*, \u5362\u9756, \u7530\u654f, \u738b\u6d77\u51b0<\/strong>, \u55bb\u5251\u4f9d, \u4f55\u4fca\u8d24, \u738b\u817e\u9704, \u9ad8\u704f\u7136. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114943329A\uff0c2022-08-25

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    13. \u4e00\u79cd\u57fa\u4e8e\u5f02\u6784\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u4eba\u8138\u8bc6\u522b\u7684FPGA\u82af\u7247
      \u5468\u653f\u534e, \u77f3\u5306*, \u949f\u6b63\u9752, \u738b\u6d77\u51b0<\/strong>, \u5468\u559c\u5ddd. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114708639A\uff0c2022-07-05

      <\/li>\n\n\n\n
    14. \u57fa\u4e8e\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u8f7b\u91cf\u7ea7\u7247\u4e0a\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u738b\u6d77\u51b0<\/strong>, \u77f3\u5306*, \u7530\u654f, \u738b\u817e\u9704, \u4f55\u4fca\u8d24, \u4f55\u796f. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114091663A\uff0c2022-2-25

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    15. \u57fa\u4e8e\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u5b9e\u65f6\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u3001\u7cfb\u7edf\u53ca\u5904\u7406\u5668.
      \u738b\u817e\u9704, \u77f3\u5306*, \u7530\u654f, \u4f55\u4fca\u8d24, \u738b\u6d77\u51b0<\/strong>, \u55bb\u5251\u4f9d. \u4e2d\u56fd\uff0c\u516c\u5f00\u53f7\uff1aCN114065922A\uff0c2022-2-18<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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