[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-10-10-extreme-triple-7-zh-TW":3,"reports-algorithms":37},{"slug":4,"type":5,"extremeSubtype":6,"publishAt":7,"updateTime":8,"coverImage":9,"viewCount":10,"title":11,"metaTitle":12,"metaDesc":13,"summary":14,"content":15,"readMinutes":16,"related":17},"2026-10-10-extreme-triple-7","extreme","E1","2026-10-10 10:11:48","2026-10-10 10:11:47","\u002Fstatic\u002Fog\u002F2026-10-10-extreme-triple-7.jpg",0,"加拿大28豹子號777現身，機率僅1%","加拿大28極端事件速報：豹子號777現身，機率僅1%","罕見事件！在2026年10月10日第3492222期，加拿大28開出了豹子號777，理論機率僅1%。上次類似事件距今僅8小時。","剛剛，加拿大28在第3492222期開出豹子號777，罕見機率僅1%，上次出現於8小時前。","## 豹子號777突現\n\n2026年10月10日10:08，加拿大28第3492222期意外開出了罕見的豹子號：7+7+7，總和值為21。這樣的結果令人矚目，屬於極端事件中的E1類型。\n\n## 出現機率僅為1%\n\n很多人認為豹子號是常常發生的，但實際情況恰恰相反。這次開出的777，其理論機率僅為1%。有趣的是，上一次出現類似事件是在今天凌晨2:29的第3492221期，當時的豹子號為888。不難發現，這樣的連續現象非常少見。\n\n## 是否暗示規律？\n\n在極端事件發生後，很多用戶會試圖尋找模式或規律。然而資料表明，這類事件更可能是隨機性的一部分，而非固定走勢。是否真的存在深層規律值得繼續觀察，您怎麼看？",1,[18,25,31],{"slug":19,"type":5,"extremeSubtype":20,"publishAt":21,"coverImage":22,"viewCount":10,"title":23,"summary":24,"readMinutes":16},"2026-10-11-extreme-sum-zero-v2","E2","2026-10-11 11:56:58","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-sum-zero-v2.jpg","加拿大28第3492654期驚現和值0","加拿大28第3492654期開出總和值為0的極值，罕見機率僅0.1%。最近一次出現是在3492637期。",{"slug":26,"type":5,"extremeSubtype":6,"publishAt":27,"coverImage":28,"viewCount":10,"title":29,"summary":30,"readMinutes":16},"2026-10-11-extreme-triple-zero","2026-10-11 11:56:49","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-triple-zero.jpg","加拿大28豹子號000再度出現，罕見機率僅1%","加拿大28第3492654期出現豹子號000，理論機率僅為1%。距離上次開出僅間隔約1小時。",{"slug":32,"type":5,"extremeSubtype":20,"publishAt":33,"coverImage":34,"viewCount":10,"title":35,"summary":36,"readMinutes":16},"2026-10-11-extreme-sum-zero","2026-10-11 10:46:56","\u002Fstatic\u002Fog\u002F2026-10-11-extreme-sum-zero.jpg","加拿大28再現和值極端0事件","10月11日JND28第3492634期開獎出現極端和值0，理論機率僅0.1%，兩天內的第二次發生。",[38,43,49,55,61,67,73,79,85,91,97,103,109,115,121,127,133,139,144,150,156,162,168,174,180,186,192,198,204,210],{"code":39,"nameZhCn":40,"nameZhTw":41,"nameEnUs":42,"sortOrder":16},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",{"code":44,"nameZhCn":45,"nameZhTw":46,"nameEnUs":47,"sortOrder":48},"deep_neural_network","深度神经网络","深度神經網路","Deep Neural Network",2,{"code":50,"nameZhCn":51,"nameZhTw":52,"nameEnUs":53,"sortOrder":54},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",3,{"code":56,"nameZhCn":57,"nameZhTw":58,"nameEnUs":59,"sortOrder":60},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":62,"nameZhCn":63,"nameZhTw":64,"nameEnUs":65,"sortOrder":66},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":68,"nameZhCn":69,"nameZhTw":70,"nameEnUs":71,"sortOrder":72},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",6,{"code":74,"nameZhCn":75,"nameZhTw":76,"nameEnUs":77,"sortOrder":78},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":80,"nameZhCn":81,"nameZhTw":82,"nameEnUs":83,"sortOrder":84},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",8,{"code":86,"nameZhCn":87,"nameZhTw":88,"nameEnUs":89,"sortOrder":90},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":92,"nameZhCn":93,"nameZhTw":94,"nameEnUs":95,"sortOrder":96},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":98,"nameZhCn":99,"nameZhTw":100,"nameEnUs":101,"sortOrder":102},"volatility","波动率","波動率","Volatility",11,{"code":104,"nameZhCn":105,"nameZhTw":106,"nameEnUs":107,"sortOrder":108},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":110,"nameZhCn":111,"nameZhTw":112,"nameEnUs":113,"sortOrder":114},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":116,"nameZhCn":117,"nameZhTw":118,"nameEnUs":119,"sortOrder":120},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":122,"nameZhCn":123,"nameZhTw":124,"nameEnUs":125,"sortOrder":126},"momentum","动量加速度","動量加速度","Momentum",15,{"code":128,"nameZhCn":129,"nameZhTw":130,"nameEnUs":131,"sortOrder":132},"quantile","分位数","分位數","Quantile",16,{"code":134,"nameZhCn":135,"nameZhTw":136,"nameEnUs":137,"sortOrder":138},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":140,"nameZhCn":141,"nameZhTw":141,"nameEnUs":142,"sortOrder":143},"local_entropy","局部熵","Local Entropy",18,{"code":145,"nameZhCn":146,"nameZhTw":147,"nameEnUs":148,"sortOrder":149},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":151,"nameZhCn":152,"nameZhTw":153,"nameEnUs":154,"sortOrder":155},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":157,"nameZhCn":158,"nameZhTw":159,"nameEnUs":160,"sortOrder":161},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":163,"nameZhCn":164,"nameZhTw":165,"nameEnUs":166,"sortOrder":167},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":169,"nameZhCn":170,"nameZhTw":171,"nameEnUs":172,"sortOrder":173},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":175,"nameZhCn":176,"nameZhTw":177,"nameEnUs":178,"sortOrder":179},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":181,"nameZhCn":182,"nameZhTw":183,"nameEnUs":184,"sortOrder":185},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":187,"nameZhCn":188,"nameZhTw":189,"nameEnUs":190,"sortOrder":191},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":193,"nameZhCn":194,"nameZhTw":195,"nameEnUs":196,"sortOrder":197},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":199,"nameZhCn":200,"nameZhTw":201,"nameEnUs":202,"sortOrder":203},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":205,"nameZhCn":206,"nameZhTw":207,"nameEnUs":208,"sortOrder":209},"number_combination","数字组合","數字組合","Number Combination",29,{"code":211,"nameZhCn":212,"nameZhTw":213,"nameEnUs":214,"sortOrder":215},"recent_chain","近链推演","近鏈推演","Recent Chain",30]