[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-07-19-week-ai-overview-zh-TW":3,"reports-algorithms":41},{"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},"2026-07-19-week-ai-overview","weekly",null,"2026-07-20 00:35:00","2026-07-20 00:35:13","\u002Fstatic\u002Fog\u002F2026-07-19-week-ai-overview.jpg",0,"本週加拿大28 AI 演算法表現盤點","加拿大28本週AI演算法表現詳解與亮點分析","從30套加拿大28 AI演算法的表現出發，解析本週資料走勢和極端事件，關注表現最優演算法及罕見資料現象。","本週加拿大28 AI演算法中表現最突出的有「吉布斯熵」和「深度學習」。大小比與單雙比穩定運行，但極端事件頻發。","## 資料表現概覽\n\n2026年7月13日至7月19日這一週內，加拿大28共計開獎2813期，期號從3456271至3459083，時間區間為07-13至07-19。本週大小比接近均衡，大號占比49.77%，小號為50.23%，與歷史均值趨同。而單雙比例方面，單數開出1411次，占比50.16%，雙數1402次，占比49.84%，同樣未見顯著偏差。\n\n## AI演算法表現亮點\n\n在本週的演算法排名中，「吉布斯熵」和「凱利公式」以45.55%的綜合準確率並列排名第一，各項分值較為均衡，分別在2813期內實現了穩定的高命中率；而「深度學習」演算法以45.41%綜合準確率位列第三，尤其在和值預測上達到了54.53%的突出表現。\n\n在另一端，排名靠後的演算法如「雙步躍遷」和「追逐遺漏」僅分別達到33.96%和35.57%的綜合準確率，主要因其在數值預測上的表現偏低，分別為12.26%和18.7%。\n\n## 每日資料與趨勢\n\n從每日的資料變化來看，大小比從週初的48.63%逐步穩定在50%左右，小範圍內波動正常，同時單雙比的分佈也圍繞50%上下變化。本週並未看到持續異常的模式出現，整體較為穩定。\n\n## 極端資料回顧\n\n7月13日和15日的多次開出相同數字的「三同號」現象值得特別關注，例如3456303期的豹子號9。7月16日也出現了極端的和值27現象，這為分析未來趨勢提供了重要參考。\n\n## 結語\n\n本週的加拿大28 AI演算法資料表現整體穩定，但伴有有趣的極端事件。我們期待在下週的資料中看到更有價值的觀察點。",2,[18,27,35],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-07-26-extreme-triple-3","extreme","E1","2026-07-26 07:10:12","\u002Fstatic\u002Fog\u002F2026-07-26-extreme-triple-3.jpg","震撼！豹子號3+3+3再現","加拿大28第3461618期再現豹子號，3+3+3開出，理論機率僅1%，極其罕見。",1,{"slug":28,"type":29,"extremeSubtype":6,"publishAt":30,"coverImage":31,"viewCount":10,"title":32,"summary":33,"readMinutes":34},"2026-07-25-ai-performance-review","daily","2026-07-26 00:30:00","\u002Fstatic\u002Fog\u002F2026-07-25-ai-performance-review.jpg","AI 命中率揭秘：昨日五強爭鳴","昨日AI演算法命中率揭曉，五強成績較為接近，後三名出現明顯波動，倍受關注。",3,{"slug":36,"type":20,"extremeSubtype":21,"publishAt":37,"coverImage":38,"viewCount":10,"title":39,"summary":40,"readMinutes":26},"2026-07-26-extreme-triple-4","2026-07-26 00:27:40","\u002Fstatic\u002Fog\u002F2026-07-26-extreme-triple-4.jpg","震撼！豹子號4+4+4時隔一天再現","加拿大28第3461503期再現豹子號4+4+4，理論機率僅1%，距上次僅隔一天，罕見連現引熱議。",[42,47,52,57,63,69,75,81,87,93,99,105,111,117,123,129,135,141,146,152,158,164,170,176,182,188,194,200,206,212],{"code":43,"nameZhCn":44,"nameZhTw":45,"nameEnUs":46,"sortOrder":26},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",{"code":48,"nameZhCn":49,"nameZhTw":50,"nameEnUs":51,"sortOrder":16},"deep_neural_network","深度神经网络","深度神經網路","Deep Neural Network",{"code":53,"nameZhCn":54,"nameZhTw":55,"nameEnUs":56,"sortOrder":34},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",{"code":58,"nameZhCn":59,"nameZhTw":60,"nameEnUs":61,"sortOrder":62},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":64,"nameZhCn":65,"nameZhTw":66,"nameEnUs":67,"sortOrder":68},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":70,"nameZhCn":71,"nameZhTw":72,"nameEnUs":73,"sortOrder":74},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",6,{"code":76,"nameZhCn":77,"nameZhTw":78,"nameEnUs":79,"sortOrder":80},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":82,"nameZhCn":83,"nameZhTw":84,"nameEnUs":85,"sortOrder":86},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",8,{"code":88,"nameZhCn":89,"nameZhTw":90,"nameEnUs":91,"sortOrder":92},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":94,"nameZhCn":95,"nameZhTw":96,"nameEnUs":97,"sortOrder":98},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":100,"nameZhCn":101,"nameZhTw":102,"nameEnUs":103,"sortOrder":104},"volatility","波动率","波動率","Volatility",11,{"code":106,"nameZhCn":107,"nameZhTw":108,"nameEnUs":109,"sortOrder":110},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":112,"nameZhCn":113,"nameZhTw":114,"nameEnUs":115,"sortOrder":116},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":118,"nameZhCn":119,"nameZhTw":120,"nameEnUs":121,"sortOrder":122},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":124,"nameZhCn":125,"nameZhTw":126,"nameEnUs":127,"sortOrder":128},"momentum","动量加速度","動量加速度","Momentum",15,{"code":130,"nameZhCn":131,"nameZhTw":132,"nameEnUs":133,"sortOrder":134},"quantile","分位数","分位數","Quantile",16,{"code":136,"nameZhCn":137,"nameZhTw":138,"nameEnUs":139,"sortOrder":140},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":142,"nameZhCn":143,"nameZhTw":143,"nameEnUs":144,"sortOrder":145},"local_entropy","局部熵","Local Entropy",18,{"code":147,"nameZhCn":148,"nameZhTw":149,"nameEnUs":150,"sortOrder":151},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":153,"nameZhCn":154,"nameZhTw":155,"nameEnUs":156,"sortOrder":157},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":159,"nameZhCn":160,"nameZhTw":161,"nameEnUs":162,"sortOrder":163},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":165,"nameZhCn":166,"nameZhTw":167,"nameEnUs":168,"sortOrder":169},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":171,"nameZhCn":172,"nameZhTw":173,"nameEnUs":174,"sortOrder":175},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":177,"nameZhCn":178,"nameZhTw":179,"nameEnUs":180,"sortOrder":181},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":183,"nameZhCn":184,"nameZhTw":185,"nameEnUs":186,"sortOrder":187},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":189,"nameZhCn":190,"nameZhTw":191,"nameEnUs":192,"sortOrder":193},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":195,"nameZhCn":196,"nameZhTw":197,"nameEnUs":198,"sortOrder":199},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":201,"nameZhCn":202,"nameZhTw":203,"nameEnUs":204,"sortOrder":205},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":207,"nameZhCn":208,"nameZhTw":209,"nameEnUs":210,"sortOrder":211},"number_combination","数字组合","數字組合","Number Combination",29,{"code":213,"nameZhCn":214,"nameZhTw":215,"nameEnUs":216,"sortOrder":217},"recent_chain","近链推演","近鏈推演","Recent Chain",30]