RuView v2 中 Signal 與 MAT Crate 的 ruvector 集成實(shí)戰(zhàn)ADR-017 七個(gè)集成點(diǎn)的源碼級(jí)拆解【免費(fèi)下載鏈接】RuViewπ RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.項(xiàng)目地址: https://gitcode.com/GitHub_Trending/wi/RuView導(dǎo)讀本文以 RuView 倉(cāng)庫(kù)內(nèi) ADR-017-ruvector-signal-mat-integration.md 為絕對(duì)主線深入解析 WiFi-DensePose 信號(hào)處理與災(zāi)難檢測(cè)兩套生產(chǎn)級(jí) Rust crate 與 ruvector v2.0.4 系列算法的融合方案。ADR-017 共設(shè)計(jì)七個(gè)集成點(diǎn)橫跨wifi-densepose-signalSOTA 信號(hào)處理子載波選擇、頻譜圖、身體速度輪廓 BVP、菲涅爾區(qū)幾何求解與wifi-densepose-matMass Casualty Assessment Tool多 AP 三角定位、呼吸/心跳波形檢測(cè)并同時(shí)修正了 ADR-002 中指向不存在的虛構(gòu) crate 的依賴錯(cuò)誤。讀完本文你將掌握五個(gè) ruvector 發(fā)布 crate 各自的核心 API 與算法優(yōu)勢(shì)、七個(gè)集成點(diǎn)的動(dòng)機(jī)/替代實(shí)現(xiàn)/ruvector 方案/復(fù)雜度對(duì)比、MAT 內(nèi)存壓縮的量化收益以及在當(dāng)前倉(cāng)庫(kù) v2/Cargo.toml 工作區(qū)中真實(shí)落地的依賴配置與 feature 門(mén)控方式。1. 背景為何需要第二輪 ruvector 集成ADR-016 已將五個(gè) ruvector v2.0.4 crate 接入訓(xùn)練管線 cratewifi-densepose-trainmodel.rs、dataset.rs、subcarrier.rs、metrics.rs。但兩個(gè)更早誕生的生產(chǎn) crate 仍處于未接入狀態(tài)盡管它們存在明確的高價(jià)值接入點(diǎn)wifi-densepose-signal承載 ADR-014 定義的 SOTA 信號(hào)處理算法——共軛相乘、Hampel 濾波、菲涅爾區(qū)呼吸模型、CSI 頻譜圖、子載波靈敏度選擇、身體速度輪廓BVP。其共性瓶頸是大量逐元素獨(dú)立運(yùn)算或暴力窮舉搜索缺乏亞多項(xiàng)式級(jí)優(yōu)化空間。wifi-densepose-mat承載 ADR-001 的災(zāi)難檢測(cè)能力——多 AP 三角定位、呼吸/心跳波形檢測(cè)、分診分類(lèi)。其共性瓶頸是時(shí)間序列數(shù)據(jù)無(wú)壓縮、定位采用閉式幾何而非迭代求解。與此同時(shí)ADR-002 的依賴策略小節(jié)引用了四個(gè)并不存在的 crate 名ruvector-core、ruvector-data-framework、ruvector-consensus、ruvector-wasm版本號(hào)0.1在 crates.io 上無(wú)法解析。ADR-016 已確認(rèn) v2.0.4 實(shí)際發(fā)布的 crate 名單ADR-017 決定以真實(shí) crate 替換虛構(gòu)依賴。1.1 五個(gè)已核實(shí)發(fā)布的 cratev2.0.4ADR 依據(jù)對(duì) ruvnet/ruvector 源碼及 crates.io 的核查列出五個(gè)可用 crateCrate關(guān)鍵 API算法優(yōu)勢(shì)ruvector-mincutDynamicMinCut、MinCutBuilderO(n^1.5 log n) 動(dòng)態(tài)圖劃分ruvector-attn-mincutattn_mincut(q,k,v,d,seq,λ,τ,ε)注意力 mincut 門(mén)控單遍完成ruvector-temporal-tensorTemporalTensorCompressor、segment::decode分層量化內(nèi)存降低 50–75%ruvector-solverNeumannSolver::new(tol,max_iter).solve(CsrMatrix,[f32])O(√n) Neumann 級(jí)數(shù)收斂ruvector-attentionScaledDotProductAttention::new(d).compute(q,ks,vs)小 d 場(chǎng)景的次線性注意力需要特別說(shuō)明的是ADR-002 中描述的 RVF 認(rèn)知容器ADR-003、HNSW 搜索ADR-004、SONA 自學(xué)習(xí)ADR-005、GNN 模式ADR-006、后量子密碼ADR-007、Raft 共識(shí)ADR-008、WASM 邊緣運(yùn)行時(shí)ADR-009等能力在 v2.0.4 中屬于 ruvector內(nèi)部架構(gòu)能力而非獨(dú)立發(fā)布 crate。這些 ADR 保留為前瞻性架構(gòu)指引其實(shí)現(xiàn)路徑在適用處以五個(gè)發(fā)布 crate 為構(gòu)建塊。1.2 集成全景圖ADR-017 給出了逐文件映射原文中的目錄結(jié)構(gòu)可直接在倉(cāng)庫(kù)中對(duì)應(yīng)wifi-densepose-signal/ ├── subcarrier_selection.rs ← ruvector-mincut (DynamicMinCut partitions) ├── spectrogram.rs ← ruvector-attn-mincut (attention-gated STFT tokens) ├── bvp.rs ← ruvector-attention (cross-subcarrier BVP attention) └── fresnel.rs ← ruvector-solver (Fresnel geometry system) wifi-densepose-mat/ ├── localization/ │ └── triangulation.rs ← ruvector-solver (multi-AP TDoA equations) └── detection/ ├── breathing.rs ← ruvector-temporal-tensor (tiered waveform compression) └── heartbeat.rs ← ruvector-temporal-tensor (tiered micro-Doppler compression)2. 集成點(diǎn)一DynamicMinCut 驅(qū)動(dòng)的子載波靈敏度選擇目標(biāo)文件wifi-densepose-signal/src/subcarrier_selection.rs引入 crateruvector-mincut2.1 現(xiàn)狀與問(wèn)題傳統(tǒng)方案對(duì)所有子載波按variance_motion / variance_static比值排序后取 top-K復(fù)雜度為 O(n log n) 的靜態(tài)排序。該邏輯在當(dāng)前源碼中依然保留為基線實(shí)現(xiàn) subcarrier_selection.rsselect_sensitive_subcarriers計(jì)算每個(gè)子載波的靈敏度得分sensitivity var(motion[k]) / (var(static[k]) 1e-12)隨后降序排序并受SubcarrierSelectionConfig默認(rèn)top_k: 20、min_sensitivity: 1.5約束。靜態(tài)排序的問(wèn)題在于環(huán)境一旦變化家具移動(dòng)、新住戶出現(xiàn)就必須全量重新計(jì)算排序。2.2 ruvector 集成方案將子載波建模為相似圖頂點(diǎn)邊權(quán)重編碼方差比相似度|sensitivity_i ? sensitivity_j|^?1。DynamicMinCut求最小二分把高靈敏度運(yùn)動(dòng)響應(yīng)型與低靈敏度噪聲主導(dǎo)型子載波切到兩側(cè)。新靜態(tài)/運(yùn)動(dòng)測(cè)量到達(dá)時(shí)通過(guò)insert_edge/delete_edge增量更新劃分?jǐn)傔€復(fù)雜度 O(n^1.5 log n)無(wú)需全量重排。ADR 中給出的核心函數(shù)原型如下在當(dāng)前倉(cāng)庫(kù)的 subcarrier_selection.rs 中已有落地實(shí)現(xiàn)mincut_subcarrier_partitionuse ruvector_mincut::{DynamicMinCut, MinCutBuilder}; /// Partition subcarriers into sensitive/insensitive groups via min-cut. /// Returns (sensitive_indices, insensitive_indices). pub fn mincut_subcarrier_partition( sensitivity: [f32], ) - (Vecusize, Vecusize) { let n sensitivity.len(); // Build fully-connected similarity graph (prune edges threshold) let threshold 0.1_f64; let mut edges Vec::new(); for i in 0..n { for j in (i 1)..n { let diff (sensitivity[i] - sensitivity[j]).abs() as f64; let weight if diff 1e-9 { 1.0 / diff } else { 1e6 }; if weight threshold { edges.push((i as u64, j as u64, weight)); } } } let mc MinCutBuilder::new().exact().with_edges(edges).build(); let (side_a, side_b) mc.partition(); // side with higher mean sensitivity sensitive let mean_a: f32 side_a.iter().map(|i| sensitivity[i as usize]).sum::f32() / side_a.len() as f32; let mean_b: f32 side_b.iter().map(|i| sensitivity[i as usize]).sum::f32() / side_b.len() as f32; if mean_a mean_b { (side_a.into_iter().map(|x| x as usize).collect(), side_b.into_iter().map(|x| x as usize).collect()) } else { (side_b.into_iter().map(|x| x as usize).collect(), side_a.into_iter().map(|x| x as usize).collect()) } }2.3 倉(cāng)庫(kù)源碼印證從源碼看該集成點(diǎn)已經(jīng)真實(shí)落地且實(shí)現(xiàn)較 ADR 草案做了一些工程化細(xì)節(jié)補(bǔ)充實(shí)際代碼使用MinCutBuilderuse ruvector_mincut::MinCutBuilder;邊權(quán)重剪枝閾值取0.5_f64并包含小輸入兜底n 4時(shí)視為全部敏感與全等邊兜底edges.is_empty()時(shí)按中位數(shù)切分這些健壯性分支正是生產(chǎn)代碼與設(shè)計(jì)草案的典型差異。配套測(cè)試 subcarrier_selection.rs 中的mincut_partition_separates_high_low構(gòu)造[0.9, 0.85, 0.92, 0.1, 0.12, 0.08]兩組靈敏度假數(shù)據(jù)斷言切出的兩組sens_mean insens_meanmincut_partition_small_input則驗(yàn)證小輸入下不 panic、元素總數(shù)守恒。該文件完整提供了三種可切換的選擇策略經(jīng)典方差比 top-K 排序、純方差在線選擇select_by_variance、以及 mincut 劃分mincut_subcarrier_partition方便調(diào)用方按場(chǎng)景權(quán)衡。源碼頭注還引用了 WiDanceMobiCom 2017與 WiGestSenSys 2015作為算法譜系來(lái)源。2.4 優(yōu)勢(shì)動(dòng)態(tài)劃分能跟蹤靈敏度變化O(n^1.5 log n) vs O(n^2) 重掃設(shè)備搬動(dòng)或新人員入場(chǎng)后無(wú)需人工重調(diào)參數(shù)即可自動(dòng)重劃敏感子載波集合。3. 集成點(diǎn)二Attention-Gated CSI 頻譜圖目標(biāo)文件wifi-densepose-signal/src/spectrogram.rs引入 crateruvector-attn-mincut3.1 現(xiàn)狀與問(wèn)題原實(shí)現(xiàn)逐子載波獨(dú)立計(jì)算 STFT再堆疊為[freq_bins × time_frames]二維矩陣送入下游 CNN 時(shí)所有時(shí)頻 bin等權(quán)。這導(dǎo)致噪聲幀與多徑偽影被同等放大抑制了真實(shí)體動(dòng)段的信噪貢獻(xiàn)。3.2 ruvector 集成方案STFT 之后把每個(gè)時(shí)間幀視作一個(gè)序列 tokend n_freq_binsseq_len n_time_frames調(diào)用attn_mincut對(duì)譜圖輸出做門(mén)控抑制噪聲幀與多徑偽影、放大體動(dòng)時(shí)段的貢獻(xiàn)。use ruvector_attn_mincut::attn_mincut; /// Apply attention gating to a computed spectrogram. /// spectrogram: [n_freq_bins × n_time_frames] row-major f32 pub fn gate_spectrogram( spectrogram: [f32], n_freq: usize, n_time: usize, lambda: f32, // 0.1 mild gating, 0.5 aggressive ) - Vecf32 { // Q K V spectrogram (self-attention over time frames) let out attn_mincut( spectrogram, spectrogram, spectrogram, n_freq, // d feature dimension (freq bins) n_time, // seq_len number of time frames lambda, /*tau*/ 2, /*eps*/ 1e-7, ); out.output }3.3 關(guān)鍵參數(shù)lambda門(mén)控強(qiáng)度。ADR 注釋給出經(jīng)驗(yàn)區(qū)間——0.1對(duì)應(yīng)輕度門(mén)控0.5對(duì)應(yīng)激進(jìn)門(mén)控。lambda 只調(diào)這一個(gè)標(biāo)量即可連續(xù)調(diào)節(jié)去噪力度無(wú)需單獨(dú)維護(hù)去噪或時(shí)序平滑管線。taumincut 門(mén)控中的溫度/閾值類(lèi)超參數(shù)示例取2。eps數(shù)值穩(wěn)定項(xiàng)示例取1e-7。3.4 優(yōu)勢(shì)自注意力 mincut 可以識(shí)別出相干的時(shí)序段體動(dòng)區(qū)間并門(mén)控掉不相關(guān)幀環(huán)境噪聲、瞬時(shí)干擾。該方案無(wú)需為頻譜圖額外串接去噪模塊。工程提示ADR-017 將本集成點(diǎn)定為 P3——因?yàn)樗膭?dòng)頻譜圖輸出分布后下游 CNN 需要重新訓(xùn)練因此優(yōu)先級(jí)低于不改變模型輸入的方案。4. 集成點(diǎn)三跨子載波 BVP 注意力加權(quán)目標(biāo)文件wifi-densepose-signal/src/bvp.rs引入 crateruvector-attention4.1 現(xiàn)狀與問(wèn)題身體速度輪廓Body Velocity ProfileBVP原實(shí)現(xiàn)為對(duì)全部子載波 STFT 幅值做均勻累加BVP[v,t] Σ_k |STFT_k[v,t]|等權(quán)求和意味著不敏感子載波會(huì)稀釋速度估計(jì)——處于多徑零點(diǎn)或噪聲主導(dǎo)頻段的子載波其貢獻(xiàn)本應(yīng)被壓低。4.2 ruvector 集成方案改用ScaledDotProductAttention完成跨子載波的加權(quán)聚合每個(gè)子載波貢獻(xiàn)一個(gè) key其靈敏度分布與一個(gè) value其 STFT 行query 為當(dāng)前速度 bin。注意力權(quán)重會(huì)自動(dòng)放大對(duì)查詢速度區(qū)間響應(yīng)強(qiáng)的子載波無(wú)需手工挑選或單獨(dú)維護(hù)靈敏度步驟。use ruvector_attention::ScaledDotProductAttention; /// Compute attention-weighted BVP aggregation across subcarriers. /// stft_rows: Vec of n_subcarriers rows, each [n_velocity_bins] f32 /// sensitivity: sensitivity score per subcarrier [n_subcarriers] f32 pub fn attention_weighted_bvp( stft_rows: [Vecf32], sensitivity: [f32], n_velocity_bins: usize, ) - Vecf32 { let d n_velocity_bins; let attn ScaledDotProductAttention::new(d); // Mean sensitivity row as query (overall body motion profile) let query: Vecf32 (0..d).map(|v| { stft_rows.iter().zip(sensitivity.iter()) .map(|(row, s)| row[v] * s) .sum::f32() / sensitivity.iter().sum::f32() }).collect(); // Keys STFT rows (each subcarriers velocity profile) // Values STFT rows (same, weighted by attention) let keys: Vec[f32] stft_rows.iter().map(|r| r.as_slice()).collect(); let values: Vec[f32] stft_rows.iter().map(|r| r.as_slice()).collect(); attn.compute(query, keys, values) .unwrap_or_else(|_| vec![0.0; d]) }4.3 倉(cāng)庫(kù)源碼印證該集成點(diǎn)已實(shí)際落地于 bvp.rs文件頂部use ruvector_attention::ScaledDotProductAttention;其中注釋明確寫(xiě)著 Uses ScaledDotProductAttention to weight each subcarriers velocity profile并以ScaledDotProductAttention::new(n_velocity_bins)構(gòu)造注意力實(shí)例。實(shí)現(xiàn)還包含unwrap_or_else(|_| vec![0.0; d])錯(cuò)誤兜底說(shuō)明compute返回Result生產(chǎn)代碼對(duì)失敗路徑做了顯式處理。4.4 優(yōu)勢(shì)用靈敏度感知的加權(quán)替代均勻求和多徑零點(diǎn)處或噪聲頻段的子載波被自動(dòng)分配低注意力權(quán)重。ADR-017 將其列為 P2理由是直接提升活動(dòng)分類(lèi)的準(zhǔn)確率且不涉及模型重訓(xùn)。5. 集成點(diǎn)四菲涅爾區(qū)幾何系統(tǒng)的 NeumannSolver 求解目標(biāo)文件wifi-densepose-signal/src/fresnel.rs引入 crateruvector-solver5.1 現(xiàn)狀與問(wèn)題原實(shí)現(xiàn)依賴已知 TX-RX-人體幾何的閉式菲涅爾區(qū)半徑公式。實(shí)際部署中 d1TX→body與 d2body→RX通常未知——只有 AP 布放決定的直線距離 D 已知。5.2 ruvector 集成方案當(dāng)多個(gè)子載波在同一胸腔位移處觀察到不同的菲涅爾區(qū)穿越事件時(shí)可以把未知幾何 (d1, d2, Δd) 組織成超定線性系統(tǒng)求解。矩陣稀疏用CsrMatrixCOO 組裝NeumannSolver高效處理正則化法方程(A?A λI) x A?b。use ruvector_solver::neumann::NeumannSolver; use ruvector_solver::types::CsrMatrix; /// Estimate TX-body and body-RX distances from multi-subcarrier Fresnel observations. /// observations: Vec of (wavelength_m, observed_amplitude_variation) /// Returns (d1_estimate_m, d2_estimate_m) pub fn solve_fresnel_geometry( observations: [(f32, f32)], d_total: f32, // Known TX-RX straight-line distance in metres ) - Option(f32, f32) { let n observations.len(); if n 3 { return None; } // System: A·[d1, d2]^T b // From Fresnel: A_k |sin(2π·2·Δd / λ_k)|, observed ~ A_k // Linearize: use log-magnitude ratios as rows // Normal equations: (A^T A λI) x A^T b let lambda_reg 0.05_f32; let mut coo Vec::new(); let mut rhs vec![0.0_f32; 2]; for (k, (wavelength, amplitude)) in observations.iter().enumerate() { // Row k: [1/wavelength, -1/wavelength] · [d1; d2] ≈ log(amplitude 1) let coeff 1.0 / wavelength; coo.push((k, 0, coeff)); coo.push((k, 1, -coeff)); let _ amplitude; // used implicitly via b vector } // Build normal equations let ata_csr CsrMatrix::f32::from_coo(2, 2, vec![ (0, 0, lambda_reg observations.iter().map(|(w, _)| 1.0 / (w * w)).sum::f32()), (1, 1, lambda_reg observations.iter().map(|(w, _)| 1.0 / (w * w)).sum::f32()), ]); let atb: Vecf32 vec![ observations.iter().map(|(w, a)| a / w).sum::f32(), -observations.iter().map(|(w, a)| a / w).sum::f32(), ]; let solver NeumannSolver::new(1e-5, 300); match solver.solve(ata_csr, atb) { Ok(result) { let d1 result.solution[0].abs().clamp(0.1, d_total - 0.1); let d2 (d_total - d1).clamp(0.1, d_total - 0.1); Some((d1, d2)) } Err(_) None, } }5.3 倉(cāng)庫(kù)源碼印證從源碼看該集成點(diǎn)已落地 fresnel.rs 頂部use ruvector_solver::neumann::NeumannSolver;并在求解器構(gòu)造處使用NeumannSolver::new(1e-5, 300)——與 ADR 示例中的容差1e-5、最大迭代300完全一致。值得注意的擴(kuò)展應(yīng)用同目錄下的 CIR 稀疏恢復(fù)模塊 cir.rs 也復(fù)用了NeumannSolver作為 ISTA 迭代的warm-start——先對(duì)對(duì)角占優(yōu) CSR 近似(Φ?Φ εI)做一次 Neumann 求解得到初始解再進(jìn)入稀疏恢復(fù)主循環(huán)對(duì)應(yīng) ADR-134 對(duì) CIR 模塊的收斂性要求。這證明ruvector-solver在 signal crate 內(nèi)部被多個(gè)算法路徑復(fù)用。5.4 優(yōu)勢(shì)把菲涅爾模型從「單次固定幾何公式」升級(jí)為「數(shù)據(jù)驅(qū)動(dòng)的幾何估計(jì)器」只要 3 個(gè)子載波不同頻率提供觀測(cè)NeumannSolver 即可在 O(√n) 次迭代內(nèi)收斂——這對(duì) 100 Hz 實(shí)時(shí)呼吸檢測(cè)至關(guān)重要。本集成點(diǎn)被列為 P3用于未知幾何場(chǎng)景下改善呼吸檢測(cè)。6. 集成點(diǎn)五NeumannSolver 求解多 AP 三角定位目標(biāo)文件wifi-densepose-mat/src/localization/triangulation.rs引入 crateruvector-solver6.1 現(xiàn)狀與問(wèn)題多 AP 定位采用成對(duì) TDoA到達(dá)時(shí)間差轉(zhuǎn)換為雙曲方程組。解 N-AP 系統(tǒng)需要線性化 最小二乘原實(shí)現(xiàn)通過(guò)高斯消元做暴力法方程求解O(n3)。ADR-001 場(chǎng)景下 AP 數(shù)量多、測(cè)量對(duì)數(shù)量大直接消元開(kāi)銷(xiāo)可觀。6.2 ruvector 集成方案線性化后的 TDoA 系統(tǒng)是稀疏的——每條測(cè)量只涉及 2 個(gè) AP 而非全部 N 個(gè)。CsrMatrix::from_cooNeumannSolver求解稀疏法方程復(fù)雜度為 O(√nnz)其中 nnz 為非零元數(shù)遠(yuǎn)小于 N2。use ruvector_solver::neumann::NeumannSolver; use ruvector_solver::types::CsrMatrix; /// Solve multi-AP TDoA survivor localization. /// tdoa_measurements: Vec of (ap_i_idx, ap_j_idx, tdoa_seconds) /// ap_positions: Vec of (x, y) metre positions /// Returns estimated (x, y) survivor position. pub fn solve_triangulation( tdoa_measurements: [(usize, usize, f32)], ap_positions: [(f32, f32)], ) - Option(f32, f32) { let n_meas tdoa_measurements.len(); if n_meas 3 { return None; } const C: f32 3e8_f32; // speed of light let mut coo Vec::new(); let mut b vec![0.0_f32; n_meas]; // Linearize: subtract reference AP from each TDoA equation let (x_ref, y_ref) ap_positions[0]; for (row, (i, j, tdoa)) in tdoa_measurements.iter().enumerate() { let (xi, yi) ap_positions[i]; let (xj, yj) ap_positions[j]; // (xi - xj)·x (yi - yj)·y ≈ (d_ref_i - d_ref_j C·tdoa) / 2 coo.push((row, 0, xi - xj)); coo.push((row, 1, yi - yj)); b[row] C * tdoa / 2.0 ((xi * xi - xj * xj) (yi * yi - yj * yj)) / 2.0 - x_ref * (xi - xj) - y_ref * (yi - yj); } // Normal equations: (A^T A λI) x A^T b let lambda 0.01_f32; let ata CsrMatrix::f32::from_coo(2, 2, vec![ (0, 0, lambda coo.iter().filter(|e| e.1 0).map(|e| e.2 * e.2).sum::f32()), (0, 1, coo.iter().filter(|e| e.1 0).zip(coo.iter().filter(|e| e.1 1)).map(|(a, b2)| a.2 * b2.2).sum::f32()), (1, 0, coo.iter().filter(|e| e.1 1).zip(coo.iter().filter(|e| e.1 0)).map(|(a, b2)| a.2 * b2.2).sum::f32()), (1, 1, lambda coo.iter().filter(|e| e.1 1).map(|e| e.2 * e.2).sum::f32()), ]); let atb vec![ coo.iter().filter(|e| e.1 0).zip(b.iter()).map(|(e, bi)| e.2 * bi).sum::f32(), coo.iter().filter(|e| e.1 1).zip(b.iter()).map(|(e, bi)| e.2 * bi).sum::f32(), ]; NeumannSolver::new(1e-5, 500) .solve(ata, atb) .ok() .map(|r| (r.solution[0], r.solution[1])) }6.3 復(fù)雜度量化災(zāi)難現(xiàn)場(chǎng)通常部署 5–20 個(gè) AP。此時(shí) TDoA 測(cè)量對(duì)數(shù)量為 N×(N-1)/2 10–190 條但未知量只有 2 個(gè)x, y。注意法方程無(wú)論 N 多大都恒為 2×2 矩陣——規(guī)模與 N 無(wú)關(guān)。對(duì)于良態(tài)的 2×2 系統(tǒng)NeumannSolver 只需 O(1) 次迭代即收斂完全消除高斯消元的開(kāi)銷(xiāo)。6.4 優(yōu)先級(jí)與工程狀態(tài)ADR 將多 AP 三角定位列為P1理由為「安全關(guān)鍵精度改進(jìn)」。從 wifi-densepose-mat/Cargo.toml 可以看到ruvectorfeature 的定義為ruvector [dep:ruvector-solver, dep:ruvector-temporal-tensor]即ruvector-solver是 MAT crate 的可選依賴隨ruvectorfeature 一起啟用這與 ADR-017「triangulation ← ruvector-solver」的映射一致。實(shí)際消費(fèi)端可依部署形態(tài)選擇編譯開(kāi)關(guān)。7. 集成點(diǎn)六呼吸波形分層壓縮內(nèi)存大頭目標(biāo)文件wifi-densepose-mat/src/detection/breathing.rs引入 crateruvector-temporal-tensor7.1 現(xiàn)狀與問(wèn)題內(nèi)存的算術(shù)題呼吸檢測(cè)器需在內(nèi)存中維護(hù)各子載波 × 時(shí)間維的 CSI 幅度環(huán)形緩沖。按 60 秒窗口、100 Hz 采樣率、56 個(gè)子載波估算60 s × 100 Hz × 56 子載波 × 4 bytes ≈ 13.4 MB / 區(qū)域(zone)ADR-001 的多區(qū)域掃描模型下16 個(gè)并發(fā)區(qū)域 約 214 MB 僅用于呼吸緩沖。災(zāi)難救援典型硬件如 Raspberry Pi 44–8 GB 內(nèi)存難以支撐高并發(fā)區(qū)域掃描。7.2 ruvector 集成方案分層量化TemporalTensorCompressor以分層量化策略壓縮呼吸波形緩沖熱層 8-bit近期/ 溫層 5–7-bit / 冷層 3-bit整體降低內(nèi)存 50–75%。use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy}; use ruvector_temporal_tensor::segment; pub struct CompressedBreathingBuffer { compressor: TemporalTensorCompressor, encoded: Vecu8, n_subcarriers: usize, frame_count: u64, } impl CompressedBreathingBuffer { pub fn new(n_subcarriers: usize, zone_id: u64) - Self { Self { compressor: TemporalTensorCompressor::new( TierPolicy::default(), n_subcarriers, zone_id, ), encoded: Vec::new(), n_subcarriers, frame_count: 0, } } pub fn push_frame(mut self, amplitudes: [f32]) { self.compressor.push_frame(amplitudes, self.frame_count, mut self.encoded); self.frame_count 1; } pub fn flush(mut self) { self.compressor.flush(mut self.encoded); } /// Decode all frames for frequency analysis. pub fn to_vec(self) - Vecf32 { let mut out Vec::new(); segment::decode(self.encoded, mut out); out } /// Get single frame for real-time display. pub fn get_frame(self, idx: usize) - OptionVecf32 { segment::decode_single_frame(self.encoded, idx) } }7.3 倉(cāng)庫(kù)源碼印證該集成點(diǎn)已真實(shí)落地于 breathing.rs文件頭部注釋明確標(biāo)注 Integration 6: CompressedBreathingBuffer (ADR-017, ruvector feature)所有 ruvector 引用TemporalTensorCompressor、TierPolicy、segment均以#[cfg(feature ruvector)]門(mén)控測(cè)試用例#[cfg(all(test, feature ruvector))]同樣按 feature 條件編譯。這意味著不帶ruvectorfeature 構(gòu)建 MAT 時(shí)壓縮邏輯完全不進(jìn)入二進(jìn)制保持最小依賴面。7.4 內(nèi)存收益指標(biāo)壓縮前壓縮后單區(qū)域呼吸緩沖13.4 MB3.4–6.7 MB50–75% 下降16 區(qū)域合計(jì)≈ 214 MB≈ 54–107 MB結(jié)論同一臺(tái)救援設(shè)備可支持2–4 倍并發(fā)掃描區(qū)域或者為更多區(qū)域內(nèi)聯(lián)告警/分診邏輯騰出內(nèi)存。8. 集成點(diǎn)七心跳微多普勒頻譜分層壓縮目標(biāo)文件wifi-densepose-mat/src/detection/heartbeat.rs引入 crateruvector-temporal-tensor8.1 現(xiàn)狀與問(wèn)題心跳檢測(cè)使用微多普勒頻譜圖對(duì) CSI 幅度時(shí)間序列做滑動(dòng) STFT。每區(qū)域頻譜圖形狀為[n_freq_bins128, n_time600]60 秒、10 Hz 輸出率128 × 600 × 4 bytes ≈ 307 KB / 區(qū)域16 個(gè)區(qū)域合計(jì) 4.9 MB絕對(duì)內(nèi)存尚可接受但心跳頻譜圖是全系統(tǒng)訪問(wèn)最密集的數(shù)據(jù)——每次分診更新都會(huì)查詢因此壓縮收益主要體現(xiàn)為降低隨機(jī)訪問(wèn)成本與緩存壓力。8.2 ruvector 集成方案把頻譜圖行視作時(shí)間演化幀每個(gè)頻率 bin 維護(hù)一個(gè)獨(dú)立的TemporalTensorCompressor熱層最近 10 秒8-bit 高保真、溫層10–30 秒5-bit、冷層30 秒以上3-bit。近期心跳周期保持高保真歷史數(shù)據(jù)壓縮約 5 倍。pub struct CompressedHeartbeatSpectrogram { /// One compressor per frequency bin bin_buffers: VecTemporalTensorCompressor, encoded: VecVecu8, n_freq_bins: usize, frame_count: u64, } impl CompressedHeartbeatSpectrogram { pub fn new(n_freq_bins: usize) - Self { let bin_buffers: Vec_ (0..n_freq_bins) .map(|i| TemporalTensorCompressor::new(TierPolicy::default(), 1, i as u64)) .collect(); let encoded vec![Vec::new(); n_freq_bins]; Self { bin_buffers, encoded, n_freq_bins, frame_count: 0 } } /// Push one column of the spectrogram (one time step, all frequency bins). pub fn push_column(mut self, column: [f32]) { for (i, (val, buf)) in column.iter().zip(self.bin_buffers.iter_mut()).enumerate() { buf.push_frame([val], self.frame_count, mut self.encoded[i]); } self.frame_count 1; } /// Extract heartbeat frequency band power (0.8–1.5 Hz) from recent frames. pub fn heartbeat_band_power(self, low_bin: usize, high_bin: usize) - f32 { (low_bin..high_bin.min(self.n_freq_bins - 1)) .map(|b| { let mut out Vec::new(); segment::decode(self.encoded[b], mut out); out.iter().rev().take(100).map(|x| x * x).sum::f32() }) .sum::f32() / (high_bin - low_bin 1) as f32 } }8.3 倉(cāng)庫(kù)源碼印證與使用細(xì)節(jié)落地代碼位于 heartbeat.rs同樣以#[cfg(feature ruvector)]門(mén)控。需要注意一處實(shí)現(xiàn)差異倉(cāng)庫(kù)中實(shí)際構(gòu)造調(diào)用為T(mén)emporalTensorCompressor::new(TierPolicy::default(), 1, i as u32)zone_id 參數(shù)為u32強(qiáng)轉(zhuǎn)與 ADR 草案示例的u64略有出入屬于編碼期對(duì) API 簽名的對(duì)齊修正——這說(shuō)明參考示例與最終庫(kù) API 之間需以實(shí)際編譯為準(zhǔn)。heartbeat_band_power的用法值得單獨(dú)解釋心跳頻帶功率提取針對(duì)0.8–1.5 Hz對(duì)應(yīng) 48–90 次/分鐘的成人心率區(qū)間調(diào)用方需傳入該頻帶對(duì)應(yīng)的low_bin/high_bin它只從每 bin 解碼后取最近 100 個(gè)樣本做平方和out.iter().rev().take(100)正好落在熱層/溫層的最近區(qū)間——這正是「近期高保真」設(shè)計(jì)意圖的直接體現(xiàn)功率統(tǒng)計(jì)只需近期高保真數(shù)據(jù)無(wú)需解碼全部歷史。9. 依賴聲明與 ADR-002 更正9.1 工作區(qū)依賴v2/Cargo.tomlADR-017 寫(xiě)明的依賴變更分兩層。第一層是工作區(qū)級(jí) v2/Cargo.toml五個(gè) crate 已在 ADR-016 時(shí)加入 workspaceruvector-mincut 2.0.4 # already present ruvector-attn-mincut 2.0.4 # already present ruvector-temporal-tensor 2.0.4 # already present ruvector-solver 2.0.4 # already present ruvector-attention 2.0.4 # already present第二層是消費(fèi)側(cè) Cargo.toml。結(jié)合當(dāng)前倉(cāng)庫(kù)源碼實(shí)際落地配置為# wifi-densepose-signal/Cargo.toml [dependencies] ruvector-mincut { workspace true } ruvector-attn-mincut { workspace true } ruvector-attention { workspace true } ruvector-solver { workspace true }# wifi-densepose-mat/Cargo.toml默認(rèn) features 含 ruvector [features] default [std, api, ruvector, ml] ruvector [dep:ruvector-solver, dep:ruvector-temporal-tensor]9.2 版本演進(jìn)提示當(dāng)前工作區(qū) v2/Cargo.toml 中的 ruvector 版本已隨后續(xù)同步迭代推進(jìn)注釋標(biāo)明「Vendored at origin/main… Bumps per ADR-152 §2.6 (2026-06-10 vendor sync survey)」實(shí)際釘版本為ruvector-mincut 2.0.6、ruvector-attn-mincut 2.0.4、ruvector-temporal-tensor 2.0.6、ruvector-solver 2.0.6、ruvector-attention 2.1.0并額外引入了ruvector-core 2.3.0、ruvector-gnn、ruvector-crv等。因此ADR-0172026-02-28 記錄所述的2.0.4是決策時(shí)點(diǎn)的已驗(yàn)證版本若你基于當(dāng)前倉(cāng)庫(kù)構(gòu)建請(qǐng)以 v2/Cargo.toml 中 workspace 實(shí)際釘住的版本為準(zhǔn)cargo build前可執(zhí)行cargo tree -i ruvector-solver核對(duì)解析結(jié)果。9.3 ADR-002 依賴策略勘誤ADR-002 原依賴策略引用了 crates.io 上不存在的虛構(gòu) crate 與版本# WRONG (ADR-002 original — these crates do not exist at crates.io) ruvector-core { version 0.1, features [hnsw, sona, gnn] } ruvector-data-framework { version 0.1, features [rvf, witness, crypto] } ruvector-consensus { version 0.1, features [raft] } ruvector-wasm { version 0.1, features [edge-runtime] }ADR-017 給出經(jīng) crates.io 核實(shí)的正確依賴# CORRECT (as of 2026-02-28, all at v2.0.4) ruvector-mincut 2.0.4 # Dynamic min-cut, O(n^1.5 log n) updates ruvector-attn-mincut 2.0.4 # Attention mincut gating ruvector-temporal-tensor 2.0.4 # Tiered temporal compression ruvector-solver 2.0.4 # NeumannSolver, sublinear convergence ruvector-attention 2.0.4 # ScaledDotProductAttentionRVF 認(rèn)知容器ADR-003、HNSWADR-004、SONAADR-005、GNNADR-006、后量子密碼ADR-007、RaftADR-008、WASM 邊緣運(yùn)行時(shí)ADR-009等被證實(shí)在 v2.0.4 不對(duì)外發(fā)布為獨(dú)立 crate因此作為前瞻性架構(gòu)指引保留其實(shí)現(xiàn)以五個(gè)發(fā)布 crate 為構(gòu)建塊。10. 集成點(diǎn)總覽表與實(shí)施優(yōu)先級(jí)ADR-017 給出了七處集成的統(tǒng)一對(duì)照表集成點(diǎn)文件crate改造前改造后子載波選擇subcarrier_selection.rsruvector-mincutO(n log n) 靜態(tài)排序O(n^1.5 log n) 動(dòng)態(tài)劃分頻譜圖門(mén)控spectrogram.rsruvector-attn-mincut均勻 STFT bins注意力門(mén)控噪聲抑制BVP 聚合bvp.rsruvector-attention均勻子載波求和靈敏度加權(quán)注意力菲涅爾幾何fresnel.rsruvector-solver固定幾何公式數(shù)據(jù)驅(qū)動(dòng)多觀測(cè)系統(tǒng)多 AP 三角定位triangulation.rsMATruvector-solverO(N3) 稠密高斯消元2×2 Neumann 系統(tǒng)O(1) 迭代呼吸緩沖breathing.rsMATruvector-temporal-tensor13.4 MB/區(qū)域3.4–6.7 MB/區(qū)域省 50–75%心跳頻譜heartbeat.rsMATruvector-temporal-tensor307 KB/區(qū)域 均勻熱/溫/冷分層實(shí)施優(yōu)先級(jí)ADR-017 原文決策優(yōu)先級(jí)集成點(diǎn)決策理由P1呼吸 心跳壓縮MAT16 區(qū)域?yàn)?zāi)難部署的內(nèi)存關(guān)鍵項(xiàng)P1多 AP 三角定位MAT安全關(guān)鍵的精度改進(jìn)P2DynamicMinCut 子載波選擇支撐動(dòng)態(tài)環(huán)境自適應(yīng)P2BVP 注意力聚合直接提升活動(dòng)分類(lèi)精度P3頻譜圖注意力門(mén)控降低 CNN 輸入噪聲需 CNN 重訓(xùn)P3菲涅爾幾何系統(tǒng)改善未知幾何下的呼吸檢測(cè)11. 決策影響評(píng)估收益與代價(jià)11.1 正向影響全生產(chǎn) crate 一致性train、signal、MAT 三系 crate 統(tǒng)一基于 ruvector 五件套降低認(rèn)知與維護(hù)成本。災(zāi)難檢測(cè)內(nèi)存銳減 50–75%16 區(qū)域并發(fā)從約 214 MB 降至約 54–107 MB救援設(shè)備可支撐 2–4 倍并發(fā)區(qū)域。動(dòng)態(tài)子載波劃分環(huán)境變化家具移動(dòng)、新占用者無(wú)需人工調(diào)參即可自動(dòng)適配。注意力加權(quán) BVP不敏感子載波不再稀釋速度估計(jì)誤差。NeumannSolver 三角定位復(fù)雜度與 AP 數(shù)解耦——無(wú)論 N 多少恒求解 2×2 系統(tǒng)。11.2 負(fù)向影響與緩解類(lèi)型橋接成本ruvector crate 全部基于 CPU 上的[f32]切片運(yùn)算而 signal/MAT crate 原生類(lèi)型為ndarray與復(fù)數(shù)num-complex必須在調(diào)用邊界做顯式轉(zhuǎn)換倉(cāng)庫(kù) Cargo.toml 中ndarray、rustfft、num-complex依賴并存正為此。有損壓縮的保真度邊界ruvector-temporal-tensor分層量化是有損的溫/冷層心跳幅度值可能丟失精細(xì)細(xì)節(jié)。緩解機(jī)制是熱層覆蓋近期窗口而heartbeat_band_power只依賴最近樣本的平方和恰好規(guī)避了對(duì)歷史精度的強(qiáng)依賴。二分類(lèi)假設(shè)局限D(zhuǎn)ynamicMinCut 方案假設(shè)子載波呈「近似二部」的可切分結(jié)構(gòu)若環(huán)境存在 3 個(gè)明顯不同的子載波組需擴(kuò)展為多路切multi-way cut才能正確建模。12. 在倉(cāng)庫(kù)中如何繼續(xù)深入以下是按 ADR-017 主題繼續(xù)閱讀本倉(cāng)庫(kù)的推薦路徑?jīng)Q策上下文ADR-016訓(xùn)練管線集成ADR-017 的前置參考實(shí)現(xiàn)、ADR-014signal 側(cè)算法來(lái)源、ADR-001MAT 側(cè)業(yè)務(wù)來(lái)源、ADR-015。signal 側(cè)實(shí)現(xiàn)subcarrier_selection.rs含 mincut 落地與測(cè)試、bvp.rs、fresnel.rs、spectrogram.rs。MAT 側(cè)實(shí)現(xiàn)breathing.rs、heartbeat.rs、triangulation.rsfeature 門(mén)控見(jiàn) wifi-densepose-mat/Cargo.toml。CIR warm-start 擴(kuò)展應(yīng)用cir.rs是 NeumannSolver 在同一 crate 內(nèi)被二次復(fù)用的實(shí)例。構(gòu)建驗(yàn)證倉(cāng)庫(kù)根目錄提供 Makefile 與 verify 等構(gòu)建/驗(yàn)證入口工作區(qū)統(tǒng)一在 v2/Cargo.toml 中維護(hù)版本。閱讀時(shí)注意 ruvector 系列 crate 版本已隨 ADR-152 vendor sync 高于 ADR-017 記錄的 2.0.4。結(jié)語(yǔ)ADR-017 的價(jià)值并不在于「引入五個(gè)依賴」而在于它把 ruvector 的圖切分、注意力、矩陣求解與分層壓縮四類(lèi)算法精準(zhǔn)映射到 Wi-Fi 感知中最痛的四類(lèi)問(wèn)題上子載波選擇從靜態(tài)排序變動(dòng)態(tài)劃分、速度聚合從等權(quán)變注意力加權(quán)、幾何/定位從閉式公式與暴力消元變 Neumann 迭代、時(shí)域/頻域緩沖從裸數(shù)組變分層量化。當(dāng)前倉(cāng)庫(kù) v2/crates 的源碼證實(shí)其中的子載波 mincut、BVP 注意力、菲涅爾求解、呼吸與心跳壓縮等集成點(diǎn)已經(jīng)以 feature 門(mén)控的真實(shí)代碼落地并配有對(duì)應(yīng)單元測(cè)試剩余的高層改造如頻譜圖注意力門(mén)控對(duì) CNN 的影響則需要結(jié)合訓(xùn)練管線在重訓(xùn)驗(yàn)證后推進(jìn)。這套「決策記錄 源碼落地」的雙層結(jié)構(gòu)恰好也是讀懂整個(gè) RuView v2 工程演進(jìn)的最佳入口?！久赓M(fèi)下載鏈接】RuViewπ RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.項(xiàng)目地址: https://gitcode.com/GitHub_Trending/wi/RuView創(chuàng)作聲明：本文部分內(nèi)容由AI輔助生成（AIGC），僅供參考