[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-05-28-ai-performance-dip-en-US":3,"reports-algorithms":40},{"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},"2026-05-28-ai-performance-dip","daily",null,"2026-05-29 00:30:00","2026-05-29 00:30:15","\u002Fstatic\u002Fog\u002F2026-05-28-ai-performance-dip.jpg",0,"AI Algorithm Performance Diverges, Overall Accuracy Declines","Canada 28: AI Algorithm Performance Trends and Insights","Yesterday's Canada 28 AI algorithm accuracy showed notable divergence. Analyze cold and hot number trends and uncover irregular patterns.","Yesterday's Canada 28 AI algorithms struggled overall, with deep learning leading at 47.39% accuracy. Cold numbers remained frozen, and extreme events were frequent.","## AI Algorithm Performance Diverges Yesterday\n\nYesterday's Canada 28 data shows a clear divergence in AI algorithm performance. Across 30 algorithms, the average accuracy stood at 41.48%, slightly below recent averages. Leading the pack was the \"Anti-Martingale\" algorithm with an impressive accuracy of 47.39%, excelling in number predictions at 56.97%. Close behind was the \"Monte Carlo\" algorithm at 46.02%. On the lower end, the \"Two-Step Transition\" algorithm lagged significantly with only 34.20% accuracy, highlighting a stark performance gap.\n\n### Data Distribution and Analysis\n\nTurning to data distribution, yesterday's big-to-small ratio was 204:198, equating to 50.75% versus 49.25%. This indicates a stable distribution without significant deviation from the norm. However, the odd-to-even ratio showed a slight imbalance at 217:185, with odd numbers dominating at 53.98%, marking a minor shift from typical patterns.\n\n### Hot and Cold Number Dynamics\n\nExamining hot and cold numbers, hot numbers like 16 appeared 34 times, accounting for 8.46% of total draws. Other hot numbers such as 11 and 15 appeared 32 and 31 times respectively, hovering around the 8% mark. On the cold side, values like 0, 24, and 27 have now gone over 230 draws without appearing, maintaining their frozen status.\n\n### Frequent Extreme Events\n\nNoteworthy were several extreme occurrences, including triple numbers like 9+9+9 and span values hitting the maximum of 9. These anomalies add unpredictability to the draw outcomes, posing challenges for predictive algorithms.\n\n## Summary\n\nOverall, yesterday's data showcased a mix of stability and irregular fluctuations. The \"Anti-Martingale\" algorithm stood out, while lower-performing algorithms struggled to keep up. Cold numbers remain elusive, and extreme events continue to challenge prediction models.",6,[18,27,34],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-06-03-extreme-triple-5","extreme","E1","2026-06-03 02:01:48","\u002Fstatic\u002Fog\u002F2026-06-03-extreme-triple-5.jpg","Rare Triple Number Emerges: 5+5+5 Stuns in Canada 28","Today's Canada 28 draw 3440224 featured the rare triple number 5+5+5, with an extreme probability of only 1%, marking the second consecutive day of such phenomena.",2,{"slug":28,"type":5,"extremeSubtype":6,"publishAt":29,"coverImage":30,"viewCount":10,"title":31,"summary":32,"readMinutes":33},"2026-06-02-extreme-events","2026-06-03 00:30:00","\u002Fstatic\u002Fog\u002F2026-06-02-extreme-events.jpg","Rare Triple Numbers Appear Frequently, AI Algorithm Shines","Yesterday's Canada 28 draws featured three rare triple numbers, while AI algorithms achieved an impressive 41.6% hit rate, showcasing remarkable performance.",8,{"slug":35,"type":20,"extremeSubtype":21,"publishAt":36,"coverImage":37,"viewCount":10,"title":38,"summary":39,"readMinutes":26},"2026-06-02-extreme-triple-7","2026-06-02 16:43:49","\u002Fstatic\u002Fog\u002F2026-06-02-extreme-triple-7.jpg","Rare Triple Number Appears: 7+7+7 Stuns in Draw","Draw #3440074 of Canada 28 featured the rare triple number 7+7+7, with a theoretical probability of just 1%, making it an exceptional occurrence.",[41,47,52,58,64,70,75,81,86,92,98,104,110,116,122,128,134,140,145,151,157,163,169,175,181,187,193,199,205,211],{"code":42,"nameZhCn":43,"nameZhTw":44,"nameEnUs":45,"sortOrder":46},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",1,{"code":48,"nameZhCn":49,"nameZhTw":50,"nameEnUs":51,"sortOrder":26},"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":57},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",3,{"code":59,"nameZhCn":60,"nameZhTw":61,"nameEnUs":62,"sortOrder":63},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":65,"nameZhCn":66,"nameZhTw":67,"nameEnUs":68,"sortOrder":69},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":71,"nameZhCn":72,"nameZhTw":73,"nameEnUs":74,"sortOrder":16},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",{"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":33},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",{"code":87,"nameZhCn":88,"nameZhTw":89,"nameEnUs":90,"sortOrder":91},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":93,"nameZhCn":94,"nameZhTw":95,"nameEnUs":96,"sortOrder":97},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":99,"nameZhCn":100,"nameZhTw":101,"nameEnUs":102,"sortOrder":103},"volatility","波动率","波動率","Volatility",11,{"code":105,"nameZhCn":106,"nameZhTw":107,"nameEnUs":108,"sortOrder":109},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":111,"nameZhCn":112,"nameZhTw":113,"nameEnUs":114,"sortOrder":115},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":117,"nameZhCn":118,"nameZhTw":119,"nameEnUs":120,"sortOrder":121},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":123,"nameZhCn":124,"nameZhTw":125,"nameEnUs":126,"sortOrder":127},"momentum","动量加速度","動量加速度","Momentum",15,{"code":129,"nameZhCn":130,"nameZhTw":131,"nameEnUs":132,"sortOrder":133},"quantile","分位数","分位數","Quantile",16,{"code":135,"nameZhCn":136,"nameZhTw":137,"nameEnUs":138,"sortOrder":139},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":141,"nameZhCn":142,"nameZhTw":142,"nameEnUs":143,"sortOrder":144},"local_entropy","局部熵","Local Entropy",18,{"code":146,"nameZhCn":147,"nameZhTw":148,"nameEnUs":149,"sortOrder":150},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":152,"nameZhCn":153,"nameZhTw":154,"nameEnUs":155,"sortOrder":156},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":158,"nameZhCn":159,"nameZhTw":160,"nameEnUs":161,"sortOrder":162},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":164,"nameZhCn":165,"nameZhTw":166,"nameEnUs":167,"sortOrder":168},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":170,"nameZhCn":171,"nameZhTw":172,"nameEnUs":173,"sortOrder":174},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":176,"nameZhCn":177,"nameZhTw":178,"nameEnUs":179,"sortOrder":180},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":182,"nameZhCn":183,"nameZhTw":184,"nameEnUs":185,"sortOrder":186},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":188,"nameZhCn":189,"nameZhTw":190,"nameEnUs":191,"sortOrder":192},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":194,"nameZhCn":195,"nameZhTw":196,"nameEnUs":197,"sortOrder":198},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":200,"nameZhCn":201,"nameZhTw":202,"nameEnUs":203,"sortOrder":204},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":206,"nameZhCn":207,"nameZhTw":208,"nameEnUs":209,"sortOrder":210},"number_combination","数字组合","數字組合","Number Combination",29,{"code":212,"nameZhCn":213,"nameZhTw":214,"nameEnUs":215,"sortOrder":216},"recent_chain","近链推演","近鏈推演","Recent Chain",30]