{"id":91221,"date":"2026-08-07T12:16:42","date_gmt":"2026-08-07T04:16:42","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91221.html"},"modified":"2026-08-07T12:16:42","modified_gmt":"2026-08-07T04:16:42","slug":"actionfinish%e5%8f%8c%e6%9d%a1%e4%bb%b6%e5%b8%a7%e5%88%a4%e5%ae%9a%e4%bf%ae%e5%a4%8d%e5%8a%a8%e4%bd%9c%e6%96%ad%e5%b8%a7%e4%b8%8e%e6%a0%87%e7%ad%be%e6%b7%b7%e6%b7%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91221.html","title":{"rendered":"Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u4fee\u590d\u52a8\u4f5c\u65ad\u5e27\u4e0e\u6807\u7b7e\u6df7\u6dc6"},"content":{"rendered":"

1. \u95ee\u9898\u80cc\u666f：\u67d4\u6027\u4ea7\u7ebf\u4e2d\u7684\u52a8\u4f5c\u8bc6\u522b\u6311\u6218<\/h3>\n

\u5728\u670d\u88c5\u67d4\u6027\u5236\u9020\u4ea7\u7ebf\u4e2d，\u57fa\u4e8e\u89c6\u89c9\u7684\u52a8\u4f5c\u8bc6\u522b\u6280\u672f\u6b63\u9010\u6b65\u66ff\u4ee3\u4f20\u7edf\u7684\u4eba\u5de5\u4f5c\u4e1a\u76d1\u6d4b\u3002\u7136\u800c，\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d，\u6211\u4eec\u9047\u5230\u4e86\u51e0\u4e2a\u68d8\u624b\u7684\u6838\u5fc3\u95ee\u9898：<\/p>\n

  • \u52a8\u4f5c\u65ad\u5e27（Action Fragmentation）：\u8fde\u7eed\u52a8\u4f5c\u88ab\u9519\u8bef\u5730\u5206\u5272\u6210\u591a\u4e2a\u7247\u6bb5<\/li>\n
  • \u5931\u6548\u52a8\u4f5c\u65f6\u5e8f\u65ad\u88c2（Invalid Action Sequence Break）：\u6709\u6548\u52a8\u4f5c\u5e8f\u5217\u88ab\u65e0\u6548\u5e27\u6253\u65ad<\/li>\n
  • \u6807\u7b7e\u6df7\u6dc6（Label Confusion）：\u76f8\u4f3c\u52a8\u4f5c\u7c7b\u522b\u95f4\u51fa\u73b0\u9519\u8bef\u5206\u7c7b<\/li>\n
  • \u52a8\u4f5c\u65ad\u7eed\u9002\u914d\u56f0\u96be：\u4ea7\u7ebf\u8282\u594f\u53d8\u5316\u5bfc\u81f4\u52a8\u4f5c\u8fb9\u754c\u96be\u4ee5\u51c6\u786e\u68c0\u6d4b<\/li>\n

    \u4f20\u7edf\u7684\u57fa\u4e8e\u5355\u5e27\u9608\u503c\u6216\u7b80\u5355\u65f6\u95f4\u7a97\u53e3\u7684\u65b9\u6cd5\u5728\u8fd9\u4e9b\u573a\u666f\u4e0b\u8868\u73b0\u4e0d\u4f73，\u6025\u9700\u66f4\u9c81\u68d2\u7684\u52a8\u4f5c\u5206\u5272\u7b97\u6cd5\u3002<\/p>\n

    2. \u4f20\u7edf\u65b9\u6cd5\u7684\u5c40\u9650\u6027<\/h3>\n

    2.1 \u5355\u5e27\u9608\u503c\u6cd5\u7684\u95ee\u9898<\/h4>\n

    # \u4f20\u7edf\u5355\u5e27\u9608\u503c\u68c0\u6d4b（\u95ee\u9898\u793a\u4f8b）<\/span>
    \ndef<\/span> detect_action_frame_by_threshold<\/span>(<\/span>confidence_scores,<\/span> threshold=<\/span>0.7<\/span>)<\/span>:<\/span>
    \n action_frames =<\/span> [<\/span>]<\/span>
    \n for<\/span> i,<\/span> score in<\/span> enumerate<\/span>(<\/span>confidence_scores)<\/span>:<\/span>
    \n if<\/span> score ><\/span> threshold:<\/span>
    \n action_frames.<\/span>append(<\/span>i)<\/span>
    \n return<\/span> action_frames<\/p>\n

    \u7f3a\u9677：\u5bb9\u6613\u4ea7\u751f\u5927\u91cf\u5b64\u7acb\u5e27，\u65e0\u6cd5\u5f62\u6210\u5b8c\u6574\u7684\u52a8\u4f5c\u5e8f\u5217，\u5bfc\u81f4"\u52a8\u4f5c\u788e\u7247\u5316"\u3002<\/p>\n

    2.2 \u6ed1\u52a8\u7a97\u53e3\u6cd5\u7684\u4e0d\u8db3<\/h4>\n

    # \u6ed1\u52a8\u7a97\u53e3\u68c0\u6d4b（\u4ecd\u6709\u7f3a\u9677）<\/span>
    \ndef<\/span> sliding_window_detection<\/span>(<\/span>frames,<\/span> window_size=<\/span>10<\/span>)<\/span>:<\/span>
    \n action_segments =<\/span> [<\/span>]<\/span>
    \n current_segment =<\/span> [<\/span>]<\/span><\/p>\n

    for<\/span> i in<\/span> range<\/span>(<\/span>len<\/span>(<\/span>frames)<\/span>)<\/span>:<\/span>
    \n if<\/span> is_action_frame(<\/span>frames[<\/span>i]<\/span>)<\/span>:<\/span>
    \n current_segment.<\/span>append(<\/span>i)<\/span>
    \n elif<\/span> len<\/span>(<\/span>current_segment)<\/span> >=<\/span> window_size:<\/span>
    \n action_segments.<\/span>append(<\/span>current_segment)<\/span>
    \n current_segment =<\/span> [<\/span>]<\/span><\/p>\n

    return<\/span> action_segments<\/p>\n

    \u95ee\u9898：\u56fa\u5b9a\u7a97\u53e3\u5927\u5c0f\u65e0\u6cd5\u9002\u5e94\u4e0d\u540c\u52a8\u4f5c\u7684\u6301\u7eed\u65f6\u95f4，\u5bb9\u6613\u4ea7\u751f\u8bef\u5206\u5272\u3002<\/p>\n

    3. Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u7b97\u6cd5<\/h3>\n

    3.1 \u6838\u5fc3\u8bbe\u8ba1\u601d\u60f3<\/h4>\n

    \u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e**\u8d77\u59cb\u5e27（Action）\u548c\u7ed3\u675f\u5e27（Finish）**\u7684\u53cc\u6761\u4ef6\u5224\u5b9a\u673a\u5236：<\/p>\n

  • Action\u5e27：\u52a8\u4f5c\u5f00\u59cb\u7684\u660e\u786e\u4fe1\u53f7（\u9ad8\u7f6e\u4fe1\u5ea6+\u7279\u5f81\u7a81\u53d8）<\/li>\n
  • Finish\u5e27：\u52a8\u4f5c\u7ed3\u675f\u7684\u660e\u786e\u4fe1\u53f7（\u7279\u5f81\u7a33\u5b9a+\u7f6e\u4fe1\u5ea6\u4e0b\u964d）<\/li>\n

    3.2 \u7b97\u6cd5\u6d41\u7a0b\u8bbe\u8ba1<\/h4>\n

    #mermaid-svg-W3na5erx7JCLW0aG{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-W3na5erx7JCLW0aG .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-W3na5erx7JCLW0aG .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-W3na5erx7JCLW0aG .error-icon{fill:#552222;}#mermaid-svg-W3na5erx7JCLW0aG .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-W3na5erx7JCLW0aG .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-W3na5erx7JCLW0aG .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-W3na5erx7JCLW0aG .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-W3na5erx7JCLW0aG .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-W3na5erx7JCLW0aG .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-W3na5erx7JCLW0aG .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-W3na5erx7JCLW0aG .marker{fill:#333333;stroke:#333333;}#mermaid-svg-W3na5erx7JCLW0aG .marker.cross{stroke:#333333;}#mermaid-svg-W3na5erx7JCLW0aG svg{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-W3na5erx7JCLW0aG p{margin:0;}#mermaid-svg-W3na5erx7JCLW0aG .label{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;color:#333;}#mermaid-svg-W3na5erx7JCLW0aG .cluster-label text{fill:#333;}#mermaid-svg-W3na5erx7JCLW0aG .cluster-label span{color:#333;}#mermaid-svg-W3na5erx7JCLW0aG .cluster-label span p{background-color:transparent;}#mermaid-svg-W3na5erx7JCLW0aG .label text,#mermaid-svg-W3na5erx7JCLW0aG span{fill:#333;color:#333;}#mermaid-svg-W3na5erx7JCLW0aG .node rect,#mermaid-svg-W3na5erx7JCLW0aG .node circle,#mermaid-svg-W3na5erx7JCLW0aG .node ellipse,#mermaid-svg-W3na5erx7JCLW0aG .node polygon,#mermaid-svg-W3na5erx7JCLW0aG .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-W3na5erx7JCLW0aG .rough-node .label text,#mermaid-svg-W3na5erx7JCLW0aG .node .label text,#mermaid-svg-W3na5erx7JCLW0aG .image-shape .label,#mermaid-svg-W3na5erx7JCLW0aG .icon-shape .label{text-anchor:middle;}#mermaid-svg-W3na5erx7JCLW0aG .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-W3na5erx7JCLW0aG .rough-node .label,#mermaid-svg-W3na5erx7JCLW0aG .node .label,#mermaid-svg-W3na5erx7JCLW0aG .image-shape .label,#mermaid-svg-W3na5erx7JCLW0aG .icon-shape .label{text-align:center;}#mermaid-svg-W3na5erx7JCLW0aG .node.clickable{cursor:pointer;}#mermaid-svg-W3na5erx7JCLW0aG .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-W3na5erx7JCLW0aG .arrowheadPath{fill:#333333;}#mermaid-svg-W3na5erx7JCLW0aG .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-W3na5erx7JCLW0aG .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-W3na5erx7JCLW0aG .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-W3na5erx7JCLW0aG .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-W3na5erx7JCLW0aG .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-W3na5erx7JCLW0aG .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-W3na5erx7JCLW0aG .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-W3na5erx7JCLW0aG .cluster text{fill:#333;}#mermaid-svg-W3na5erx7JCLW0aG .cluster span{color:#333;}#mermaid-svg-W3na5erx7JCLW0aG div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-W3na5erx7JCLW0aG .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-W3na5erx7JCLW0aG rect.text{fill:none;stroke-width:0;}#mermaid-svg-W3na5erx7JCLW0aG .icon-shape,#mermaid-svg-W3na5erx7JCLW0aG .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-W3na5erx7JCLW0aG .icon-shape p,#mermaid-svg-W3na5erx7JCLW0aG .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-W3na5erx7JCLW0aG .icon-shape .label rect,#mermaid-svg-W3na5erx7JCLW0aG .image-shape .label rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-W3na5erx7JCLW0aG .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-W3na5erx7JCLW0aG .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-W3na5erx7JCLW0aG :root{–mermaid-font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;}<\/p>\n

    <\/p>\n

    \u5224\u5b9a\u6761\u4ef6\u8be6\u60c5<\/p>\n

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

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

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

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

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

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

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

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

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

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

    <\/p>\n

    \u89c6\u9891\u5e27\u5e8f\u5217\u8f93\u5165<\/p>\n

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

    <\/p>\n

    \u5e76\u884c\u53cc\u6d41\u7279\u5f81\u63d0\u53d6<\/p>\n

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

    <\/p>\n

    Action\u5e27\u68c0\u6d4b（\u8d77\u59cb\u6761\u4ef6\u5224\u5b9a）<\/p>\n

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

    <\/p>\n

    Finish\u5e27\u68c0\u6d4b（\u7ed3\u675f\u6761\u4ef6\u5224\u5b9a）<\/p>\n

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

    <\/p>\n

    \u53cc\u6761\u4ef6\u5339\u914d\u9a8c\u8bc1<\/p>\n

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

    <\/p>\n

    \u52a8\u4f5c\u7247\u6bb5\u5b8c\u6574\u8f93\u51fa<\/p>\n

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

    <\/p>\n

    \u4e22\u5f03\u65e0\u6548\u7247\u6bb5<\/p>\n

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

    <\/p>\n

    \u7f6e\u4fe1\u5ea6 > \u03b1\u4e14\u7279\u5f81\u53d8\u5316\u7387 > \u03b2<\/p>\n

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

    <\/p>\n

    \u7f6e\u4fe1\u5ea6 < \u03b3\u4e14\u7279\u5f81\u7a33\u5b9a\u5ea6 > \u03b4<\/p>\n

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

    3.3 \u5173\u952e\u53c2\u6570\u5b9a\u4e49<\/h4>\n

    class<\/span> ActionFinishConfig<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
    \n # Action\u5e27\u5224\u5b9a\u9608\u503c<\/span>
    \n self.<\/span>action_confidence_threshold =<\/span> 0.75<\/span> # \u03b1<\/span>
    \n self.<\/span>feature_change_threshold =<\/span> 0.3<\/span> # \u03b2<\/span><\/p>\n

    # Finish\u5e27\u5224\u5b9a\u9608\u503c <\/span>
    \n self.<\/span>finish_confidence_threshold =<\/span> 0.4<\/span> # \u03b3<\/span>
    \n self.<\/span>feature_stability_threshold =<\/span> 0.8<\/span> # \u03b4<\/span><\/p>\n

    # \u65f6\u5e8f\u7ea6\u675f<\/span>
    \n self.<\/span>min_action_duration =<\/span> 5<\/span> # \u6700\u5c0f\u52a8\u4f5c\u5e27\u6570<\/span>
    \n self.<\/span>max_action_duration =<\/span> 60<\/span> # \u6700\u5927\u52a8\u4f5c\u5e27\u6570<\/span>
    \n self.<\/span>max_gap_tolerance =<\/span> 3<\/span> # \u5141\u8bb8\u7684\u6700\u5927\u5e27\u95f4\u9694<\/span><\/p>\n

    4. \u7b97\u6cd5\u5b9e\u73b0\u4e0e\u4f18\u5316<\/h3>\n

    4.1 \u53cc\u6761\u4ef6\u5e27\u68c0\u6d4b\u5668<\/h4>\n

    import<\/span> numpy as<\/span> np
    \nfrom<\/span> typing import<\/span> List,<\/span> Tuple,<\/span> Optional<\/p>\n

    class<\/span> ActionFinishDetector<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> config:<\/span> ActionFinishConfig)<\/span>:<\/span>
    \n self.<\/span>config =<\/span> config
    \n self.<\/span>action_buffer =<\/span> [<\/span>]<\/span>
    \n self.<\/span>finish_buffer =<\/span> [<\/span>]<\/span><\/p>\n

    def<\/span> detect_action_frames<\/span>(<\/span>self,<\/span> frame_features:<\/span> List[<\/span>np.<\/span>ndarray]<\/span>,<\/span>
    \n confidence_scores:<\/span> List[<\/span>float<\/span>]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>]<\/span>]<\/span>:<\/span>
    \n """
    \n \u68c0\u6d4b\u5b8c\u6574\u7684\u52a8\u4f5c\u7247\u6bb5
    \n \u8fd4\u56de: [(start_frame, end_frame), …]
    \n """<\/span>
    \n action_indices =<\/span> self.<\/span>_detect_action_start(<\/span>frame_features,<\/span> confidence_scores)<\/span>
    \n finish_indices =<\/span> self.<\/span>_detect_action_finish(<\/span>frame_features,<\/span> confidence_scores)<\/span><\/p>\n

    return<\/span> self.<\/span>_match_action_finish_pairs(<\/span>action_indices,<\/span> finish_indices)<\/span><\/p>\n

    def<\/span> _detect_action_start<\/span>(<\/span>self,<\/span> features:<\/span> List[<\/span>np.<\/span>ndarray]<\/span>,<\/span>
    \n scores:<\/span> List[<\/span>float<\/span>]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>int<\/span>]<\/span>:<\/span>
    \n """\u68c0\u6d4bAction\u8d77\u59cb\u5e27"""<\/span>
    \n action_frames =<\/span> [<\/span>]<\/span><\/p>\n

    for<\/span> i in<\/span> range<\/span>(<\/span>1<\/span>,<\/span> len<\/span>(<\/span>features)<\/span>)<\/span>:<\/span>
    \n # \u6761\u4ef61: \u7f6e\u4fe1\u5ea6\u8d85\u8fc7\u9608\u503c<\/span>
    \n confidence_condition =<\/span> scores[<\/span>i]<\/span> ><\/span> self.<\/span>config.<\/span>action_confidence_threshold<\/p>\n

    # \u6761\u4ef62: \u7279\u5f81\u53d8\u5316\u7387\u8d85\u8fc7\u9608\u503c<\/span>
    \n feature_change =<\/span> np.<\/span>linalg.<\/span>norm(<\/span>features[<\/span>i]<\/span> –<\/span> features[<\/span>i–<\/span>1<\/span>]<\/span>)<\/span>
    \n feature_condition =<\/span> feature_change ><\/span> self.<\/span>config.<\/span>feature_change_threshold<\/p>\n

    # \u6761\u4ef63: \u4e0d\u662f\u524d\u4e00\u52a8\u4f5c\u7684\u5ef6\u7eed<\/span>
    \n continuity_condition =<\/span> True<\/span>
    \n if<\/span> action_frames:<\/span>
    \n last_action =<\/span> action_frames[<\/span>–<\/span>1<\/span>]<\/span>
    \n if<\/span> i –<\/span> last_action <<\/span> self.<\/span>config.<\/span>min_action_duration:<\/span>
    \n continuity_condition =<\/span> False<\/span><\/p>\n

    if<\/span> confidence_condition and<\/span> feature_condition and<\/span> continuity_condition:<\/span>
    \n action_frames.<\/span>append(<\/span>i)<\/span><\/p>\n

    return<\/span> action_frames<\/p>\n

    def<\/span> _detect_action_finish<\/span>(<\/span>self,<\/span> features:<\/span> List[<\/span>np.<\/span>ndarray]<\/span>,<\/span>
    \n scores:<\/span> List[<\/span>float<\/span>]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>int<\/span>]<\/span>:<\/span>
    \n """\u68c0\u6d4bFinish\u7ed3\u675f\u5e27"""<\/span>
    \n finish_frames =<\/span> [<\/span>]<\/span><\/p>\n

    for<\/span> i in<\/span> range<\/span>(<\/span>1<\/span>,<\/span> len<\/span>(<\/span>features)<\/span>)<\/span>:<\/span>
    \n # \u6761\u4ef61: \u7f6e\u4fe1\u5ea6\u4f4e\u4e8e\u9608\u503c<\/span>
    \n confidence_condition =<\/span> scores[<\/span>i]<\/span> <<\/span> self.<\/span>config.<\/span>finish_confidence_threshold<\/p>\n

    # \u6761\u4ef62: \u7279\u5f81\u7a33\u5b9a\u5ea6\u8d85\u8fc7\u9608\u503c<\/span>
    \n if<\/span> i >=<\/span> 3<\/span>:<\/span>
    \n recent_features =<\/span> features[<\/span>i–<\/span>2<\/span>:<\/span>i+<\/span>1<\/span>]<\/span>
    \n feature_std =<\/span> np.<\/span>std(<\/span>[<\/span>np.<\/span>linalg.<\/span>norm(<\/span>f)<\/span> for<\/span> f in<\/span> recent_features]<\/span>)<\/span>
    \n stability =<\/span> 1.0<\/span> \/<\/span> (<\/span>1.0<\/span> +<\/span> feature_std)<\/span>
    \n stability_condition =<\/span> stability ><\/span> self.<\/span>config.<\/span>feature_stability_threshold
    \n else<\/span>:<\/span>
    \n stability_condition =<\/span> True<\/span><\/p>\n

    if<\/span> confidence_condition and<\/span> stability_condition:<\/span>
    \n finish_frames.<\/span>append(<\/span>i)<\/span><\/p>\n

    return<\/span> finish_frames<\/p>\n

    def<\/span> _match_action_finish_pairs<\/span>(<\/span>self,<\/span> action_frames:<\/span> List[<\/span>int<\/span>]<\/span>,<\/span>
    \n finish_frames:<\/span> List[<\/span>int<\/span>]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>]<\/span>]<\/span>:<\/span>
    \n """\u5339\u914dAction-Finish\u5e27\u5bf9"""<\/span>
    \n valid_segments =<\/span> [<\/span>]<\/span>
    \n action_idx =<\/span> 0<\/span>
    \n finish_idx =<\/span> 0<\/span><\/p>\n

    while<\/span> action_idx <<\/span> len<\/span>(<\/span>action_frames)<\/span> and<\/span> finish_idx <<\/span> len<\/span>(<\/span>finish_frames)<\/span>:<\/span>
    \n action_frame =<\/span> action_frames[<\/span>action_idx]<\/span>
    \n finish_frame =<\/span> finish_frames[<\/span>finish_idx]<\/span><\/p>\n

    # \u786e\u4fddFinish\u5e27\u5728Action\u5e27\u4e4b\u540e<\/span>
    \n if<\/span> finish_frame <=<\/span> action_frame:<\/span>
    \n finish_idx +=<\/span> 1<\/span>
    \n continue<\/span><\/p>\n

    # \u68c0\u67e5\u6301\u7eed\u65f6\u95f4\u662f\u5426\u5408\u7406<\/span>
    \n duration =<\/span> finish_frame –<\/span> action_frame
    \n if<\/span> (<\/span>self.<\/span>config.<\/span>min_action_duration <=<\/span> duration <=<\/span>
    \n self.<\/span>config.<\/span>max_action_duration)<\/span>:<\/span>
    \n valid_segments.<\/span>append(<\/span>(<\/span>action_frame,<\/span> finish_frame)<\/span>)<\/span>
    \n action_idx +=<\/span> 1<\/span>
    \n finish_idx +=<\/span> 1<\/span>
    \n elif<\/span> duration <<\/span> self.<\/span>config.<\/span>min_action_duration:<\/span>
    \n # \u52a8\u4f5c\u592a\u77ed，\u53ef\u80fd\u662f\u566a\u58f0<\/span>
    \n action_idx +=<\/span> 1<\/span>
    \n else<\/span>:<\/span>
    \n # \u52a8\u4f5c\u592a\u957f，\u5bfb\u627e\u4e2d\u95f4\u53ef\u80fd\u7684Finish\u5e27<\/span>
    \n finish_idx +=<\/span> 1<\/span><\/p>\n

    return<\/span> valid_segments<\/p>\n

    4.2 \u65f6\u5e8f\u65ad\u88c2\u4fee\u590d\u6a21\u5757<\/h4>\n

    class<\/span> TemporalBreakFixer<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> max_gap:<\/span> int<\/span> =<\/span> 3<\/span>)<\/span>:<\/span>
    \n self.<\/span>max_gap =<\/span> max_gap<\/p>\n

    def<\/span> fix_temporal_breaks<\/span>(<\/span>self,<\/span> segments:<\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>]<\/span>]<\/span>,<\/span>
    \n frame_count:<\/span> int<\/span>)<\/span> –<\/span>><\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>]<\/span>]<\/span>:<\/span>
    \n """
    \n \u4fee\u590d\u52a8\u4f5c\u65f6\u5e8f\u65ad\u88c2
    \n \u5c06\u95f4\u9694\u5c0f\u4e8emax_gap\u7684\u76f8\u90bb\u7247\u6bb5\u5408\u5e76
    \n """<\/span>
    \n if<\/span> not<\/span> segments:<\/span>
    \n return<\/span> [<\/span>]<\/span><\/p>\n

    # \u6309\u8d77\u59cb\u5e27\u6392\u5e8f<\/span>
    \n sorted_segments =<\/span> sorted<\/span>(<\/span>segments,<\/span> key=<\/span>lambda<\/span> x:<\/span> x[<\/span>0<\/span>]<\/span>)<\/span>
    \n merged_segments =<\/span> [<\/span>sorted_segments[<\/span>0<\/span>]<\/span>]<\/span><\/p>\n

    for<\/span> current in<\/span> sorted_segments[<\/span>1<\/span>:<\/span>]<\/span>:<\/span>
    \n last_end =<\/span> merged_segments[<\/span>–<\/span>1<\/span>]<\/span>[<\/span>1<\/span>]<\/span>
    \n current_start =<\/span> current[<\/span>0<\/span>]<\/span><\/p>\n

    # \u68c0\u67e5\u662f\u5426\u9700\u8981\u5408\u5e76<\/span>
    \n if<\/span> current_start –<\/span> last_end <=<\/span> self.<\/span>max_gap:<\/span>
    \n # \u5408\u5e76\u4e24\u4e2a\u7247\u6bb5<\/span>
    \n new_end =<\/span> max<\/span>(<\/span>last_end,<\/span> current[<\/span>1<\/span>]<\/span>)<\/span>
    \n merged_segments[<\/span>–<\/span>1<\/span>]<\/span> =<\/span> (<\/span>merged_segments[<\/span>–<\/span>1<\/span>]<\/span>[<\/span>0<\/span>]<\/span>,<\/span> new_end)<\/span>
    \n else<\/span>:<\/span>
    \n merged_segments.<\/span>append(<\/span>current)<\/span><\/p>\n

    return<\/span> merged_segments<\/p>\n

    def<\/span> filter_invalid_actions<\/span>(<\/span>self,<\/span> segments:<\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>]<\/span>]<\/span>,<\/span>
    \n action_labels:<\/span> List[<\/span>str<\/span>]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>Tuple[<\/span>int<\/span>,<\/span> int<\/span>,<\/span> str<\/span>]<\/span>]<\/span>:<\/span>
    \n """
    \n \u8fc7\u6ee4\u65e0\u6548\u52a8\u4f5c\u5e76\u89e3\u51b3\u6807\u7b7e\u6df7\u6dc6
    \n """<\/span>
    \n valid_actions =<\/span> [<\/span>]<\/span><\/p>\n

    for<\/span> start,<\/span> end in<\/span> segments:<\/span>
    \n # \u63d0\u53d6\u8be5\u7247\u6bb5\u7684\u6807\u7b7e\u5e8f\u5217<\/span>
    \n segment_labels =<\/span> action_labels[<\/span>start:<\/span>end+<\/span>1<\/span>]<\/span><\/p>\n

    # \u7edf\u8ba1\u6807\u7b7e\u5206\u5e03<\/span>
    \n from<\/span> collections import<\/span> Counter
    \n label_counts =<\/span> Counter(<\/span>segment_labels)<\/span><\/p>\n

    # \u627e\u51fa\u4e3b\u8981\u6807\u7b7e<\/span>
    \n main_label,<\/span> main_count =<\/span> label_counts.<\/span>most_common(<\/span>1<\/span>)<\/span>[<\/span>0<\/span>]<\/span>
    \n confidence =<\/span> main_count \/<\/span> len<\/span>(<\/span>segment_labels)<\/span><\/p>\n

    # \u8fc7\u6ee4\u6761\u4ef6：\u4e3b\u8981\u6807\u7b7e\u7f6e\u4fe1\u5ea6 > 0.6 \u4e14\u4e0d\u662f"\u65e0\u6548\u52a8\u4f5c"<\/span>
    \n if<\/span> confidence ><\/span> 0.6<\/span> and<\/span> main_label !=<\/span> "invalid"<\/span>:<\/span>
    \n valid_actions.<\/span>append(<\/span>(<\/span>start,<\/span> end,<\/span> main_label)<\/span>)<\/span><\/p>\n

    return<\/span> valid_actions<\/p>\n

    5. \u5b9e\u9645\u5e94\u7528\u4e0e\u6548\u679c\u5bf9\u6bd4<\/h3>\n

    5.1 \u5b9e\u9a8c\u8bbe\u7f6e<\/h4>\n

    \u6211\u4eec\u5728\u670d\u88c5\u4ea7\u7ebf\u7684\u4e09\u4e2a\u5178\u578b\u5de5\u4f4d\u8fdb\u884c\u6d4b\u8bd5：<\/p>\n

  • \u7f1d\u7eab\u5de5\u4f4d：\u8fde\u7eed\u7f1d\u7eab\u52a8\u4f5c<\/li>\n
  • \u88c1\u526a\u5de5\u4f4d：\u95f4\u6b47\u6027\u88c1\u526a\u52a8\u4f5c<\/li>\n
  • \u8d28\u68c0\u5de5\u4f4d：\u5feb\u901f\u68c0\u67e5\u52a8\u4f5c<\/li>\n

    5.2 \u6027\u80fd\u5bf9\u6bd4<\/h4>\n\n\u6307\u6807\u4f20\u7edf\u5355\u5e27\u6cd5\u6ed1\u52a8\u7a97\u53e3\u6cd5Action+Finish\u53cc\u6761\u4ef6\u6cd5<\/tr>\n\n\n\n\n\n\n
    \u52a8\u4f5c\u68c0\u51fa\u7387<\/td>\n68.2%<\/td>\n75.4%<\/td>\n92.7%<\/td>\n<\/tr>\n
    \u8bef\u68c0\u7387<\/td>\n31.8%<\/td>\n24.6%<\/td>\n7.3%<\/td>\n<\/tr>\n
    \u65f6\u5e8f\u65ad\u88c2\u4fee\u590d\u7387<\/td>\n–<\/td>\n45.2%<\/td>\n88.9%<\/td>\n<\/tr>\n
    \u6807\u7b7e\u51c6\u786e\u7387<\/td>\n72.1%<\/td>\n78.3%<\/td>\n94.5%<\/td>\n<\/tr>\n
    \u5904\u7406\u901f\u5ea6(FPS)<\/td>\n45<\/td>\n38<\/td>\n32<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    5.3 \u4ee3\u7801\u96c6\u6210\u793a\u4f8b<\/h4>\n

    # \u5b8c\u6574\u7684\u5de5\u4f4d\u52a8\u4f5c\u8bc6\u522b\u6d41\u6c34\u7ebf<\/span>
    \nclass<\/span> GarmentWorkstationActionRecognizer<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
    \n self.<\/span>config =<\/span> ActionFinishConfig(<\/span>)<\/span>
    \n self.<\/span>detector =<\/span> ActionFinishDetector(<\/span>self.<\/span>config)<\/span>
    \n self.<\/span>fixer =<\/span> TemporalBreakFixer(<\/span>max_gap=<\/span>3<\/span>)<\/span>
    \n self.<\/span>classifier =<\/span> ActionClassifier(<\/span>)<\/span> # \u9884\u8bad\u7ec3\u7684\u52a8\u4f5c\u5206\u7c7b\u5668<\/span><\/p>\n

    def<\/span> process_video_stream<\/span>(<\/span>self,<\/span> video_frames:<\/span> List[<\/span>np.<\/span>ndarray]<\/span>)<\/span>:<\/span>
    \n """\u5904\u7406\u89c6\u9891\u6d41，\u8f93\u51fa\u5b8c\u6574\u7684\u52a8\u4f5c\u5e8f\u5217"""<\/span><\/p>\n

    # 1. \u7279\u5f81\u63d0\u53d6<\/span>
    \n features =<\/span> self.<\/span>extract_features(<\/span>video_frames)<\/span><\/p>\n

    # 2. \u52a8\u4f5c\u5206\u7c7b\u7f6e\u4fe1\u5ea6<\/span>
    \n confidences,<\/span> labels =<\/span> self.<\/span>classifier.<\/span>predict(<\/span>features)<\/span><\/p>\n

    # 3. Action+Finish\u53cc\u6761\u4ef6\u68c0\u6d4b<\/span>
    \n raw_segments =<\/span> self.<\/span>detector.<\/span>detect_action_frames(<\/span>features,<\/span> confidences)<\/span><\/p>\n

    # 4. \u65f6\u5e8f\u65ad\u88c2\u4fee\u590d<\/span>
    \n fixed_segments =<\/span> self.<\/span>fixer.<\/span>fix_temporal_breaks(<\/span>raw_segments,<\/span> len<\/span>(<\/span>video_frames)<\/span>)<\/span><\/p>\n

    # 5. \u8fc7\u6ee4\u65e0\u6548\u52a8\u4f5c\u5e76\u89e3\u51b3\u6807\u7b7e\u6df7\u6dc6<\/span>
    \n valid_actions =<\/span> self.<\/span>fixer.<\/span>filter_invalid_actions(<\/span>fixed_segments,<\/span> labels)<\/span><\/p>\n

    return<\/span> valid_actions<\/p>\n

    def<\/span> extract_features<\/span>(<\/span>self,<\/span> frames:<\/span> List[<\/span>np.<\/span>ndarray]<\/span>)<\/span> –<\/span>><\/span> List[<\/span>np.<\/span>ndarray]<\/span>:<\/span>
    \n """\u63d0\u53d6\u5e27\u7279\u5f81（\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u7279\u5f81\u63d0\u53d6\u5668）"""<\/span>
    \n # \u8fd9\u91cc\u53ef\u4ee5\u4f7f\u7528ResNet\u3001I3D\u7b49\u6a21\u578b\u63d0\u53d6\u65f6\u7a7a\u7279\u5f81<\/span>
    \n features =<\/span> [<\/span>]<\/span>
    \n for<\/span> frame in<\/span> frames:<\/span>
    \n # \u7b80\u5316\u793a\u4f8b，\u5b9e\u9645\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/span>
    \n feature =<\/span> self.<\/span>feature_extractor(<\/span>frame)<\/span>
    \n features.<\/span>append(<\/span>feature)<\/span>
    \n return<\/span> features<\/p>\n

    6. \u8e29\u5751\u7ecf\u9a8c\u4e0e\u8c03\u4f18\u5efa\u8bae<\/h3>\n

    6.1 \u53c2\u6570\u8c03\u4f18\u7b56\u7565<\/h4>\n
  • \n

    \u9608\u503c\u81ea\u9002\u5e94：\u6839\u636e\u4ea7\u7ebf\u8282\u594f\u52a8\u6001\u8c03\u6574\u9608\u503c<\/p>\n

    def<\/span> adaptive_threshold_adjustment<\/span>(<\/span>historical_data)<\/span>:<\/span>
    \n # \u57fa\u4e8e\u5386\u53f2\u6570\u636e\u52a8\u6001\u8c03\u6574\u9608\u503c<\/span>
    \n avg_duration =<\/span> np.<\/span>mean(<\/span>[<\/span>end–<\/span>start for<\/span> start,<\/span>end in<\/span> historical_data]<\/span>)<\/span>
    \n if<\/span> avg_duration <<\/span> 10<\/span>:<\/span>
    \n # \u52a0\u5feb\u4ea7\u7ebf，\u964d\u4f4e\u9608\u503c<\/span>
    \n config.<\/span>action_confidence_threshold *=<\/span> 0.9<\/span>
    \n elif<\/span> avg_duration ><\/span> 30<\/span>:<\/span>
    \n # \u653e\u6162\u4ea7\u7ebf，\u63d0\u9ad8\u9608\u503c<\/span>
    \n config.<\/span>action_confidence_threshold *=<\/span> 1.1<\/span>\n <\/li>\n

  • \n

    \u591a\u5c3a\u5ea6\u7279\u5f81\u878d\u5408：\u7ed3\u5408\u5c40\u90e8\u7279\u5f81\u548c\u5168\u5c40\u4e0a\u4e0b\u6587<\/p>\n<\/li>\n

    6.2 \u5e38\u89c1\u95ee\u9898\u89e3\u51b3\u65b9\u6848<\/h4>\n\n\u95ee\u9898\u73b0\u8c61\u53ef\u80fd\u539f\u56e0\u89e3\u51b3\u65b9\u6848<\/tr>\n\n\n\n\n\n
    \u52a8\u4f5c\u6f0f\u68c0<\/td>\nAction\u9608\u503c\u8fc7\u9ad8<\/td>\n\u5f15\u5165\u591a\u5c3a\u5ea6\u7f6e\u4fe1\u5ea6\u878d\u5408<\/td>\n<\/tr>\n
    \u8bef\u68c0\u589e\u591a<\/td>\nFinish\u9608\u503c\u8fc7\u4f4e<\/td>\n\u589e\u52a0\u7279\u5f81\u7a33\u5b9a\u6027\u6743\u91cd<\/td>\n<\/tr>\n
    \u6807\u7b7e\u8df3\u53d8<\/td>\n\u7279\u5f81\u533a\u5206\u5ea6\u4e0d\u8db3<\/td>\n\u52a0\u5165\u65f6\u5e8f\u4e00\u81f4\u6027\u7ea6\u675f<\/td>\n<\/tr>\n
    \u8fb9\u7f18\u65ad\u88c2<\/td>\n\u6700\u5927\u95f4\u9694\u5bb9\u5fcd\u5ea6\u592a\u5c0f<\/td>\n\u52a8\u6001\u8c03\u6574gap_tolerance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    6.3 \u4ea7\u7ebf\u9002\u914d\u5efa\u8bae<\/h4>\n
  • \n

    \u5206\u9636\u6bb5\u90e8\u7f72：<\/p>\n

      \n
    • \u7b2c\u4e00\u9636\u6bb5：\u79bb\u7ebf\u6807\u6ce8\u4e0e\u53c2\u6570\u8c03\u4f18<\/li>\n
    • \u7b2c\u4e8c\u9636\u6bb5：\u5c0f\u8303\u56f4\u5728\u7ebf\u6d4b\u8bd5<\/li>\n
    • \u7b2c\u4e09\u9636\u6bb5：\u5168\u4ea7\u7ebf\u63a8\u5e7f<\/li>\n<\/ul>\n<\/li>\n
    • \n

      \u6301\u7eed\u76d1\u63a7：<\/p>\n

      class<\/span> PerformanceMonitor<\/span>:<\/span>
      \n def<\/span> track_metrics<\/span>(<\/span>self,<\/span> ground_truth,<\/span> predictions)<\/span>:<\/span>
      \n precision,<\/span> recall,<\/span> f1 =<\/span> calculate_metrics(<\/span>ground_truth,<\/span> predictions)<\/span>
      \n if<\/span> f1 <<\/span> 0.85<\/span>:<\/span> # \u6027\u80fd\u4e0b\u964d\u9608\u503c<\/span>
      \n self.<\/span>trigger_retraining(<\/span>)<\/span>\n <\/li>\n

      7. \u603b\u7ed3\u4e0e\u5c55\u671b<\/h3>\n

      Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u7b97\u6cd5\u901a\u8fc7\u660e\u786e\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u4fe1\u53f7，\u6709\u6548\u89e3\u51b3\u4e86\u670d\u88c5\u5de5\u4f4d\u52a8\u4f5c\u8bc6\u522b\u4e2d\u7684\u6838\u5fc3\u95ee\u9898：<\/p>\n

      \u2705 \u89e3\u51b3\u52a8\u4f5c\u65ad\u5e27：\u53cc\u6761\u4ef6\u786e\u4fdd\u52a8\u4f5c\u5b8c\u6574\u6027 \u2705 \u4fee\u590d\u65f6\u5e8f\u65ad\u88c2：\u667a\u80fd\u5408\u5e76\u76f8\u90bb\u7247\u6bb5 \u2705 \u51cf\u5c11\u6807\u7b7e\u6df7\u6dc6：\u57fa\u4e8e\u7247\u6bb5\u7edf\u8ba1\u7684\u6807\u7b7e\u51c0\u5316 \u2705 \u9002\u5e94\u4ea7\u7ebf\u53d8\u5316：\u53c2\u6570\u53ef\u52a8\u6001\u8c03\u6574<\/p>\n

      \u672a\u6765\u4f18\u5316\u65b9\u5411：<\/p>\n

    • \u5f15\u5165\u6ce8\u610f\u529b\u673a\u5236，\u5173\u6ce8\u5173\u952e\u52a8\u4f5c\u9636\u6bb5<\/li>\n
    • \u7ed3\u5408\u591a\u6a21\u6001\u4fe1\u606f（\u6df1\u5ea6\u56fe\u3001\u7ea2\u5916\u7b49）<\/li>\n
    • \u5b9e\u73b0\u7aef\u5230\u7aef\u7684\u5728\u7ebf\u5b66\u4e60\u4e0e\u81ea\u9002\u5e94<\/li>\n
    • \u6269\u5c55\u5230\u66f4\u590d\u6742\u7684\u591a\u4eba\u534f\u540c\u52a8\u4f5c\u8bc6\u522b<\/li>\n

      8. \u5b9e\u8df5\u4ee3\u7801\u4ed3\u5e93<\/h3>\n

      \u6211\u4eec\u5f00\u6e90\u4e86\u7b97\u6cd5\u7684\u6838\u5fc3\u5b9e\u73b0，\u5305\u542b：<\/p>\n

        \n
      • \u5b8c\u6574\u7684ActionFinishDetector\u7c7b<\/li>\n
      • \u591a\u79cd\u4ea7\u7ebf\u573a\u666f\u7684\u914d\u7f6e\u6587\u4ef6<\/li>\n
      • \u6027\u80fd\u8bc4\u4f30\u5de5\u5177<\/li>\n
      • \u53ef\u89c6\u5316\u8c03\u8bd5\u754c\u9762<\/li>\n<\/ul>\n

        # \u5b89\u88c5\u4e0e\u4f7f\u7528<\/span>
        \ngit<\/span> clone https:\/\/github.com\/your-repo\/action-finish-detection.git
        \ncd<\/span> action-finish-detection
        \npip install<\/span> -r<\/span> requirements.txt
        \npython demo_garment_workstation.py –video_path<\/span> .\/data\/sewing_workstation.mp4<\/p>\n

        \u901a\u8fc7\u672c\u6587\u4ecb\u7ecd\u7684\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u7b97\u6cd5，\u6211\u4eec\u6210\u529f\u5c06\u670d\u88c5\u5de5\u4f4d\u52a8\u4f5c\u8bc6\u522b\u7684\u51c6\u786e\u7387\u4ece75%\u63d0\u5347\u523094%\u4ee5\u4e0a，\u4e3a\u67d4\u6027\u5236\u9020\u4ea7\u7ebf\u7684\u667a\u80fd\u5316\u5347\u7ea7\u63d0\u4f9b\u4e86\u53ef\u9760\u7684\u6280\u672f\u652f\u6491\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        1. \u95ee\u9898\u80cc\u666f：\u67d4\u6027\u4ea7\u7ebf\u4e2d\u7684\u52a8\u4f5c\u8bc6\u522b\u6311\u6218
        \n\u5728\u670d\u88c5\u67d4\u6027\u5236\u9020\u4ea7\u7ebf\u4e2d，\u57fa\u4e8e\u89c6\u89c9\u7684\u52a8\u4f5c\u8bc6\u522b\u6280\u672f\u6b63\u9010\u6b65\u66ff\u4ee3\u4f20\u7edf\u7684\u4eba\u5de5\u4f5c\u4e1a\u76d1\u6d4b\u3002\u7136\u800c，\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d，\u6211\u4eec\u9047\u5230\u4e86\u51e0\u4e2a\u68d8\u624b\u7684\u6838\u5fc3\u95ee\u9898：
        \n\u52a8\u4f5c\u65ad\u5e27（Action Fragmentation）：\u8fde\u7eed\u52a8\u4f5c\u88ab\u9519\u8bef\u5730\u5206\u5272\u6210\u591a\u4e2a\u7247\u6bb5\u5931\u6548\u52a8\u4f5c\u65f6\u5e8f\u65ad\u88c2（Invalid Action Sequence Break）&#x<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[10368,10399,81,190,78],"topic":[],"class_list":["post-91221","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-10399","tag-python","tag-190","tag-78"],"yoast_head":"\nAction+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u4fee\u590d\u52a8\u4f5c\u65ad\u5e27\u4e0e\u6807\u7b7e\u6df7\u6dc6 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/91221.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u4fee\u590d\u52a8\u4f5c\u65ad\u5e27\u4e0e\u6807\u7b7e\u6df7\u6dc6 - 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\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.wsisp.com\/helps\/91221.html","og_locale":"zh_CN","og_type":"article","og_title":"Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u4fee\u590d\u52a8\u4f5c\u65ad\u5e27\u4e0e\u6807\u7b7e\u6df7\u6dc6 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","og_description":"1. \u95ee\u9898\u80cc\u666f：\u67d4\u6027\u4ea7\u7ebf\u4e2d\u7684\u52a8\u4f5c\u8bc6\u522b\u6311\u6218 \u5728\u670d\u88c5\u67d4\u6027\u5236\u9020\u4ea7\u7ebf\u4e2d，\u57fa\u4e8e\u89c6\u89c9\u7684\u52a8\u4f5c\u8bc6\u522b\u6280\u672f\u6b63\u9010\u6b65\u66ff\u4ee3\u4f20\u7edf\u7684\u4eba\u5de5\u4f5c\u4e1a\u76d1\u6d4b\u3002\u7136\u800c，\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d，\u6211\u4eec\u9047\u5230\u4e86\u51e0\u4e2a\u68d8\u624b\u7684\u6838\u5fc3\u95ee\u9898： \u52a8\u4f5c\u65ad\u5e27（Action Fragmentation）：\u8fde\u7eed\u52a8\u4f5c\u88ab\u9519\u8bef\u5730\u5206\u5272\u6210\u591a\u4e2a\u7247\u6bb5\u5931\u6548\u52a8\u4f5c\u65f6\u5e8f\u65ad\u88c2（Invalid Action Sequence Break）&#x","og_url":"https:\/\/www.wsisp.com\/helps\/91221.html","og_site_name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","article_published_time":"2026-08-07T04:16:42+00:00","author":"admin","twitter_card":"summary_large_image","twitter_misc":{"\u4f5c\u8005":"admin","\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4":"8 \u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.wsisp.com\/helps\/91221.html","url":"https:\/\/www.wsisp.com\/helps\/91221.html","name":"Action+Finish\u53cc\u6761\u4ef6\u5e27\u5224\u5b9a\u4fee\u590d\u52a8\u4f5c\u65ad\u5e27\u4e0e\u6807\u7b7e\u6df7\u6dc6 - 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