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Preprint . 2026
License: CC BY
Data sources: Datacite
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基于丙式方程3.0sy的历史动力学量化模型: 从历史空缺补充到未来趋势推演

基于丙式方程3.0sy的历史动力学量化模型: 从历史空缺补充到未来趋势推演

Abstract

The core proposition of historical dynamics is to realize the closed-loop deduction of "historical replay–gap completion–future prediction". Based on Zhao Futao’s Axiom of Material Memory Units, this paper integrates the three underlying hypotheses of "material constancy, class persistence, and tool hierarchization", introduces legal systems, documentary records, and folk culture as social dimension supplements, and constructs a quantitative historical model centered on C-Type Equation 3.0sy. By transforming historical elements into multi-dimensional quantitative parameters, the model generates dynamic evolution curves with historical traces, fits and completes historical gaps based on curve laws, and transfers historical laws to deduce future human behaviors and social trends. Taking the period from the Western Zhou Dynasty to the Spring and Autumn and Warring States Periods as an empirical object, this study verifies the effectiveness of the model in historical slice quantification, unknown scene completion, and future trend prediction, providing an interdisciplinary research paradigm of "quantification + visualization" for historical research and social prediction.

Keywords

丙式方程3.0sy;历史动力学;物质记忆元;量化模型;动态曲线;趋势推演

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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