49-误差预算卡 Template v1.0 | 第12章 附录（表单/清单/模板）

目录 ／ 文档-技术白皮书 ／ 49-误差预算卡 Template v1.0

第12章 附录（表单/清单/模板）

I. 目的与范围（Purpose & Scope）

II. 附录目录索引（Index）
A. 误差预算表 error_budget.csv（表头）
B. 不确定度区间表 uncertainty_intervals.csv（表头）
C. 合规对照表 compliance_table.csv（表头）
D. 报告清单 report_manifest.yaml
E. 协方差配置 cov_config.yaml / 协方差块结构 cov_blocks.json
F. Δ法配置 delta_config.yaml / MC 配置 mc_config.yaml / 自举配置 bootstrap_config.yaml
G. 样本量与功效 power_plan.yaml / 结果摘要 sample_size_report.md
H. UQ-DoE 设计 uq_doe.yaml / 敏感度报告 sensitivity_report.md / 表 sensitivity_table.csv
I. 质量门配置 gates.yaml / 阈值映射 gate_map.yaml / 回退策略 fallbacks.yaml
J. 引用注册表 references.yml
K. 审计轨迹示例 audit.jsonl（单行）
L. 发布版目录结构与一页式发布自检清单

III. 表单与模板（可直接落库）

A. error_budget.csv（表头与示例行）

source,symbol,unit,type,estimate,distribution,correlation,gate,see[]

TIM-01,δt_abs,s,A,2.5e-8,normal,"corr:with Δτ_ch",G5,"Metrology.Sync v1.0:PPS"

PTH-01,u(d ell),m,B,5.0e-4,uniform,"kernel:exp(Lc=25m)",G3,"Core.DataSpec v1.0:TARR"

MED-01,u(n_eff),1,A/B,3.0e-3,gaussian,"kernel:exp(Lc=50m)",G4,"Core.Equations v1.1:S20-1"

B. uncertainty_intervals.csv（发布表）

target,point,lower,upper,unit,method,coverage,notes

T_arr,1.23e-8,9.9e-9,1.47e-8,s,delta,"k=2 (95%)","Σ from exp kernel; delta_form=general"

Phi,0.035,0.028,0.043,rad,mc,"quantile [2.5,97.5]%","B=10000; aligned window"

r_phi,0.72,0.61,0.80,1,bootstrap,"Fisher-z 95%","stratified by device"

C. compliance_table.csv（发布表）

metric,point,interval_or_band,gate,threshold,decision,notes

DeltaT_arr,1.2e-9,"±U(k=2)",G4/G5,"<= tau_T_s",pass,"dim OK; freshness OK"

r_phi,0.72,"[0.61,0.80]",G6,">= r_phi_min",pass,"Fisher-z back-transform"

epsilon_flux,0.006,"P95=0.011",G7,"<= 0.02",pass,"paraxial guard satisfied"

Q_res,0.13,"band in [0,0.2]",G6,"in-band",pass,"robust surrogate used"

p_dim,1.0,"—",G4,"= 1.0",pass,"check_dim_report attached"

D. report_manifest.yaml（报告清单）

version: "1.0.0"

bundle:

tables:

- "tables/error_budget.csv"

- "tables/uncertainty_intervals.csv"

- "tables/compliance_table.csv"

figures:

- "figs/dashboard.pdf"

- "figs/residual_ba.pdf"

- "figs/path_profile.svg"

- "figs/sobol_bar.pdf"

reports:

- "reports/check_dim_report.json"

- "reports/audit.jsonl"

metadata:

dataset_id: "ptn-demo"

method_version: "2.0.0"

anchor_coverage_min: 0.90

see:

- "EFT.WP.Core.Equations v1.1:S20-1"

- "EFT.WP.Core.Metrology v1.0:check_dim"

E. cov_config.yaml（协方差配置）

version: "1.0.0"

domains: ["path","time","channel"]

kernels:

path: { name: "exp", params: { sigma2: 3.0e-3, L_c_m: 25.0 } }

time: { name: "ar1", params: { sigma2: 1.0e-4, phi: 0.92 } }

channel: { name: "block_toeplitz", params: { rho_matrix: [[1,0.35],[0.35,1]] } }

nonstationary:

segmentation:

path:

- { start: 0.0, end: 120.0, L_c_m: 20.0 }

- { start: 120.0, end: 300.0, L_c_m: 35.0 }

numerics: { jitter: 1.0e-6, method: "chol_block" }

see:

- "EFT.WP.Core.Equations v1.1:S20-1"

- "EFT.WP.Core.Metrology v1.0:check_dim"

F. cov_blocks.json（块结构示意）

{

"path_indices": [0, 300],

"time_indices": [0, 400],

"channel_blocks": 2,

"structures": ["toeplitz", "ar1", "block"],

"jitter": 1e-6

}

G. delta_config.yaml / mc_config.yaml / bootstrap_config.yaml

# delta_config.yaml

version: "1.0.0"

model:

f: "T_arr = ∫ ( n_eff / c_ref ) d ell"

jacobian: "auto" # or "manual"

covariance: { from: "cov_config.yaml" }

coverage: { type: "confidence", k: 2 }

see:

- "EFT.WP.Core.Equations v1.1:S20-1"

- "EFT.WP.Core.Metrology v1.0:check_dim"

# mc_config.yaml

version: "1.0.0"

draws: 10000

sampler: "chol" # chol|spectral|state-space

seed: 20250924

covariance: { from: "cov_config.yaml" }

targets: ["T_arr","Phi","ε_flux"]

summaries: ["mean","std","p2.5","p50","p97.5"]

# bootstrap_config.yaml

version: "1.0.0"

B: 10000

scheme: "stratified" # paired|stratified|residual

align_phase_window: true

strata: ["batch","device","region"]

robust: { loss: "huber", delta: 1.345 }

targets: ["DeltaT_arr","r_phi","ε_flux"]

H. power_plan.yaml / sample_size_report.md

version: "1.0.0"

targets:

- { name: "DeltaT_arr", type: "means_diff", alpha: 0.01, power: 0.90, effect_size_d: 0.35, design: { paired: true, deff: 1.00 } }

- { name: "r_phi", type: "correlation", alpha: 0.01, power: 0.90, r_target: 0.60, fisher_z: true }

- { name: "epsilon_flux", type: "threshold_rate", alpha: 0.05, power: 0.80, p0: 0.15, p1: 0.07 }

robust: { loss: "huber", inflate_factor: 1.20 }

correlation: { path: { kernel: "exp", L_c_m: 25.0 } }

sequential: { scheme: "obrien_fleming", looks: 2 }

# Sample Size & Power Report

- Targets and effect sizes; alpha/power; DEFF & ν_eff.

- Methods: z-approx / Fisher-z / exact-proportion; robust inflate ×1.20.

- Outputs: N per group/stratum; sequential spending; bootstrap power curve.

I. uq_doe.yaml / sensitivity_report.md / sensitivity_table.csv

version: "1.0.0"

targets: ["T_arr","Phi","r_phi","ε_flux"]

design:

sampler: "lhs" # lhs|sobol|halton

n_init: 200

strata: ["batch","device","region"]

path_refine: { metric: "grad_n_eff", rule: "adaptive_subdivide" }

screening: { method: "morris", levels: 6, trajectories: 12 }

global: { method: "sobol", draws: 5000, robust: { variance: "huber", delta: 1.345 } }

sequential: { scheme: "obrien_fleming", looks: 2 }

see:

- "EFT.WP.Core.Equations v1.1:S20-1"

- "EFT.WP.Core.Metrology v1.0:check_dim"

# UQ-DoE Sensitivity Report

- Design: LHS n=200; strata={batch,device,region}; adaptive path refine.

- Screening: Morris μ,σ; Global: Sobol S/ST (robust variance).

- Results: top drivers; power vs N_eff; decisions & gate mapping.

factor,S_i,ST_i,EE_mean,EE_sd,rank,notes

c_ref,0.18,0.26,0.09,0.04,2,"Delta-form=general"

lambda_ref,0.22,0.31,0.12,0.06,1,"phase-aligned window"

n_eff_profile,0.15,0.44,0.20,0.18,3,"path-kernel exp L_c=25 m"

J. gates.yaml / gate_map.yaml / fallbacks.yaml

# gates.yaml

version: "1.0.0"

gates:

G1: { schema: true }

G2: { anchor_coverage_min: 0.90, forbid_external_links: true }

G3: { path_min_len: 2, require_sync: true }

G4: { p_dim: 1.0, units: { T_arr: "s", Phi: "rad" } }

G5: { tau_calib_s_max: 86400, clock_state: "locked" }

G6: { Q_res_band: [0.0, 0.2], robust_surrogate: true }

G7: { epsilon_flux_guard_p95: 0.02 }

G8: { unique_record_id: true, unique_checksum: true }

# gate_map.yaml

version: "1.0.0"

thresholds: { tau_T_s: "3*u(T_arr)", r_phi_min: 0.60 }

decision:

DeltaT_arr: "abs(DeltaT_arr)+U_T <= tau_T_s"

r_phi: "LB_r >= r_phi_min"

epsilon_flux: "P95_epsilon <= epsilon_flux_guard_p95"

labels: { restricted: "[Restricted]" }

# fallbacks.yaml

version: "1.0.0"

triggers:

SYNC: ["clock_unlocked","abs_t_over","skew_over","allan_over"]

DIM: ["p_dim_fail","check_dim_fail"]

SMP: ["fs_below_nyquist","Delta_ell_over","path_len_short","path_desync"]

PAX: ["theta_over","coherence_fail"]

NOISE: ["Q_res_over","flux_nonconserve"]

CALIB: ["tau_calib_expired","u_theta_over"]

CITE: ["ver_missing","anchor_coverage_low","external_link_found"]

INTG: ["record_dup","checksum_dup","audit_missing"]

actions:

sync_fallback: ["GNSS","PTP","NTP"]

switch_fullwave: true

segment_adapt: { T_coh: "shrink", L_coh: "shrink", B_coh: "increase" }

robust_substitute: ["huber","quantile"]

recalibrate: true

labels: { restricted: "[Restricted]" }

K. references.yml（引用注册表）

version: "1.0.0"

refs:

core_terms_p10_3: "EFT.WP.Core.Terms v1.0:P10-3"

core_eq_s20_1: "EFT.WP.Core.Equations v1.1:S20-1"

core_met_checkdim: "EFT.WP.Core.Metrology v1.0:check_dim"

core_dataspec_tarr: "EFT.WP.Core.DataSpec v1.0:TARR"

methods_bench: "Data.Benchmarks v1.0:PROTO"

L. audit.jsonl（单行示例）

JSON json
{
  "run_id": "01JXYZABCD...",
  "started_at": "2025-09-24T16:10:00Z",
  "tools": [ { "name": "ptn-cli", "version": "1.4.2" } ],
  "random_seeds": [ 20250924 ],
  "input_hashes": [ "sha256:..." ],
  "events": [ { "ts": "...", "clock_state": "locked", "delta_t_abs_ns": 23, "allan_1s": 1.2e-11 } ],
  "references": [ "EFT.WP.Core.Equations v1.1:S20-1" ],
  "version": "1.0.0"
}

IV. 发布目录结构（建议）

PTN_EXPORT/

manifest.yaml

tables/

error_budget.csv

uncertainty_intervals.csv

compliance_table.csv

figs/

dashboard.pdf

residual_ba.pdf

path_profile.svg

sobol_bar.pdf

reports/

check_dim_report.json

audit.jsonl

configs/

cov_config.yaml

delta_config.yaml

mc_config.yaml

bootstrap_config.yaml

power_plan.yaml

uq_doe.yaml

results.md

SIGNATURE.asc

V. 一页式发布自检清单（Publish Checklist）

版权与许可（CC BY 4.0）

版权声明：除另有说明外，《能量丝理论》（含文本、图表、插图、符号与公式）的著作权由作者（“屠广林”先生）享有。
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署名格式（建议）：作者：“屠广林”；作品：《能量丝理论》；来源：energyfilament.org；许可证：CC BY 4.0。

首次发布： 2025-11-11｜当前版本：v5.1
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