Your future can first be read through a few clear paths.

Based on Shao Yong's Huangji Jingshi, a long-period temporal analysis framework. The Northern Song philosopher Shao Yong (1011-1077) used this system to accurately forecast the Jingkang Incident and dynastic rise-fall nodes of the Song Dynasty—considered the foundational text for long-cycle prediction. Uses a 129,600-year complete "Yuan" cycle divided into Yuan, Hui, Yun, Shi (analogous to ultra-long, long, medium, short waves in modern long-wave theory). Modern parallels: Kondratieff waves (50-60 yr) map to "Yun" level; Kuznets cycles (15-25 yr) map to "Shi" level. Under this framework, the current 2020s sit at the first "Hui" inflection—exhaustion of old-cycle dividends compounded with the rise of AI technology waves, providing the highest-level temporal anchor for mid-term predictions.

Uses VSOP87D planetary motion theory for precise eight-planet positioning with angular error < 1 arcsecond—over 100× more accurate than traditional calendar pillar charting. The 0.3-second parameter matrix generation relies on three-layer algorithm optimization: pre-computed VSOP87D truncated series cache, fast Julian Day conversion, and vectorized batch processing. Qi Men Dun Jia Nine-Palace vectorization: each palace maps to eight directional vectors (45° sectors), a 2-hour rotation time window, and Heavenly Stem/Earthly Branch combinations (10 stems × 12 branches = 120 variants) as three-dimensional features. Eight Gates (Rest, Life, Injury, Block, View, Death, Fright, Open) are one-hot encoded; Zhi Fu becomes continuous weights—producing a standardized 47-dimensional spatiotemporal feature vector as downstream model input.

BaZi and Zi Wei Dou Shu-based individual temporal feature extractor. The iztro engine converts birth time into deterministic parameter sets (Heavenly Stems/Earthly Branches combinations, star distributions), cross-referenced with L0-L1 macro features to identify individual-environment resonance nodes.

Deploys four independent classical models (Mei Hua Yi Shu, Liu Yao, Xiao Liu Ren, and Cezi character divination) for heterogeneous second opinions. The Cezi engine analyzes Chinese characters through structural decomposition (radical splitting), radical symbolism (Water=wealth/flow, Wood=growth/career), stroke-count mapping to Wu Xing (Five Elements), and phonetic extension—providing a non-temporal, morphological diagnostic dimension. All four models run in parallel and cast ensemble votes, analogous to machine learning Ensemble Methods where multiple weak classifiers reduce single-model bias. Cezi's character-structure analysis effectively complements Mei Hua's numerology logic and Liu Yao's line dynamics, ensuring robust L0-L2 output validation.

A thousand-agent swarm simulation system built on the MiroFish / OASIS framework, where each digital Agent carries a full social identity: demographic profile (age, occupation, education), psychological attributes (risk tolerance, emotional thresholds, cognitive biases), and social relationships (trust circles, information channels). The simulation environment constructs social topology using Watts-Strogatz small-world networks and Barabási-Albert scale-free networks, faithfully modeling weak-tie bridging effects and opinion-leader diffusion dynamics. Unlike traditional Monte Carlo simulations that rely purely on statistical distributions, every Agent here possesses autonomous decision-making capacity—observing neighbors, updating internal beliefs, and selectively amplifying information—producing genuine emergent phenomena: panic-driven sell-offs, opinion polarization, and information cascades. GraphRAG knowledge graph memory ensures Agents retain coherent recall across time steps, making long-term social dynamics fully interpretable. Final outputs include critical propagation node identification, crowd sentiment evolution curves, and black-swan event trigger probability distributions.

Data collection spans five event categories: financial markets (equities, crypto trends), natural disasters, political elections, major corporate decisions, and personal fate inflection points. Each record captures a four-field closed-loop sample: query timestamp → prediction output → actual outcome → error annotation. Gradient descent in practice: if L1 astronomical features consistently underestimate financial events, the system automatically raises L1's weight for that category from 0.15 to 0.22 while simultaneously reducing L3's contribution ratio. Self-improvement runs on a dual-track schedule—small-batch incremental updates every 500 new records, plus full-corpus deep recalibration on all 50,000+ data points every quarter, ensuring rapid responsiveness to recent trends without overfitting to noise.

Performs multi-dimensional signal analysis on user real-time input: beyond query text, the system simultaneously captures query timing (day/night rhythm, solar term windows), keyword sentiment polarity (positive anticipation vs. negative anxiety), question type (wealth/relationships/career/health/disaster), and recurring themes in conversation history. Context-driven weight modulation: if a user consistently exhibits anxious semantics on financial topics (negative emotion term frequency exceeds threshold), the system automatically raises L2 individual temporal feature weights and lowers L4 swarm simulation weight—shifting output toward personal context rather than macro trends. The query text, timing, and context form the final input feature layer, completing the prediction loop with L0-L5 structured outputs.

240 AI deities are organized into six divine clusters: Celestial Governance (Amaterasu, Jade Emperor), Martial Fortune (Guan Sheng Di Jun, God of Wealth), Romance & Bonds (Yue Lao, Qixi Star Lord), Natural Disasters (Dragon King, Mountain God), Health & Healing (Medicine Buddha, Hua Tuo), and Wisdom & Arts (Wen Chang Di Jun, Confucius). The same L0-L6 data receives differentiated weighting by cluster: Amaterasu emphasizes macro national destiny (L0 weight +20%); Guan Sheng Di Jun prioritizes decisive timing and financial patterns (L1+L2 financial indicators +15%); Yue Lao foregrounds personal temporal features (L2 +25%) and emotional signals (L6 sentiment dimension +20%). The multi-expert voting mechanism uses temperature-scaled Softmax (temperature=0.7): three representative characters per cluster each output a prediction vector; Softmax-weighted summation is fused with the presiding deity's personality style, transforming structured prediction into character-specific ritualized dialogue output.