br_maths/

spc.rs

use json::{JsonValue, object};
pub struct Spc {
    /// 数据
    pub data: Vec<f64>,
    /// 数据长度
    pub data_len: usize,
    /// 样本分组数量
    pub group_len: usize,
    /// 规格上限
    pub upper: f64,
    /// 规格下限
    pub lower: f64,
    /// 标准值
    pub value: f64,

    /// 最大值
    pub max: f64,
    /// 最小值
    pub min: f64,
    /// 平均值
    pub avg: f64,
    /// 总值
    pub total: f64,
    /// 组均值
    pub group_avg: Vec<f64>,
    /// 组极差
    pub group_range: Vec<f64>,
    /// 组极差-最大
    pub group_range_max: f64,
    /// 组极差-最小
    pub group_range_min: f64,

    /// 组极差-r-均值集合
    pub group_r_avgs: Vec<f64>,
    /// 组极差-x-均值集合
    pub group_x_avgs: Vec<f64>,
    /// 组极差-r-均值
    pub group_r_avg: f64,
    /// 组极差-x-均值
    pub group_x_avg: f64,

    pub group_r_avg_sum: f64,
    pub group_x_avg_sum: f64,

    /// 组极差-合计-集合
    pub group_sums: Vec<f64>,

    /// 组数据
    pub group_data: Vec<Vec<f64>>,
    /// 小数位
    pub decimal: usize,
    /// 能力指数上限(CPU)
    pub cpu: f64,
    /// 能力指数下限(CPL)
    pub cpl: f64,
    /// 控制 A2 系数
    pub a2: f64,
    /// 控制 d2 系数
    pub d2: f64,
    /// 控制 D3 系数
    pub d3: f64,
    /// 控制 D4 系数
    pub d4: f64,

    /// 能力指数 (Cp)
    pub cp: f64,
    /// 过程能力指数 (Cpk)
    pub cpk: f64,
    /// 过程能力比值 (CR)
    pub cr: f64,
    /// 标准差 (n-1) 样本标准差
    pub stdev: f64,
    /// 标准差 (n) 总体标准差
    pub stdevp: f64,
    /// 变差 (n-1)
    pub var: f64,
    /// 变差 (n)
    pub varp: f64,
    /// 性能指数 (PP)
    pub pp: f64,
    /// 性能比率 (PR)
    pub pr: f64,
    /// 性能指数 (PPK)
    pub ppk: f64,

    /// 西格玛
    pub sigma: f64,
    /// 西格玛数组
    pub sigma_list: Vec<f64>,

    /// R 图 UCLr指标
    pub uclr: f64,
    /// R 图 极差值均值
    pub clr: f64,
    /// R 图 LCLr指标
    pub lclr: f64,

    /// X 图 UCLx 指标
    pub uclx: f64,
    /// X 图 CLx 均值指标
    pub clx: f64,
    /// X 图 UCLx 指标
    pub lclx: f64,
}

impl Spc {
    /// 初始化
    ///
    /// * usl 上限值
    /// * lsl 下限值
    /// * value 标准值
    /// * array 测量数据
    /// * group_len 样本组数量
    /// * decimal 小数位
    pub fn new(upper: f64, lower: f64, value: f64, array: Vec<f64>, group_len: usize, decimal: usize) -> Self {
        Self {
            data: array.clone(),
            data_len: array.len(),
            total: 0.0,
            upper,
            value,
            lower,
            // stddev,
            max: 0.0,
            min: 0.0,
            avg: 0.0,
            // cpk,
            // ca: ca * 100.0,
            // cp,
            group_len,
            group_avg: vec![],
            group_range: vec![],
            group_range_max: 0.0,
            group_range_min: 0.0,
            group_r_avgs: vec![],
            group_data: vec![],
            decimal,
            cpu: 0.0,
            cpl: 0.0,
            a2: a2(group_len),
            d2: d2(group_len),
            d3: d3(group_len),
            d4: d4(group_len),
            cp: 0.0,
            cpk: 0.0,
            cr: 0.0,
            stdev: 0.0,
            stdevp: 0.0,
            var: 0.0,
            varp: 0.0,
            pp: 0.0,
            pr: 0.0,
            ppk: 0.0,
            sigma: 0.0,
            sigma_list: vec![],
            group_x_avgs: vec![],
            group_r_avg: 0.0,
            group_x_avg: 0.0,
            group_r_avg_sum: 0.0,
            group_x_avg_sum: 0.0,
            group_sums: vec![],
            uclr: 0.0,
            clr: 0.0,
            lclr: 0.0,
            uclx: 0.0,
            clx: 0.0,
            lclx: 0.0,
        }
    }
    pub fn compute(&mut self) -> &mut Self {
        self.max = 0.0;
        self.min = self.data[0].to_string().parse::<f64>().unwrap();
        let mut group_list = vec![];

        for x in self.data.clone() {
            self.total += x;
            if x > self.max {
                self.max = x;
            }
            if x < self.min {
                self.min = x;
            }
            group_list.push(x);
            if group_list.len() == self.group_len {
                self.get_group_list_info(group_list.clone());
                self.group_data.push(group_list.clone());
                group_list = vec![];
            }
        }
        // 补数组不够的
        if !group_list.is_empty() {
            self.get_group_list_info(group_list.clone());
            self.group_data.push(group_list.clone());
        }

        self.uclr = self.d4 * self.group_r_avg;
        self.uclr = format!("{0:.1$}", self.uclr, self.decimal).parse::<f64>().unwrap();

        self.clr = self.group_r_avg;
        self.clr = format!("{0:.1$}", self.clr, self.decimal).parse::<f64>().unwrap();

        self.lclr = self.d3 * self.group_r_avg;
        self.lclr = format!("{0:.1$}", self.lclr, self.decimal).parse::<f64>().unwrap();


        self.uclx = self.group_x_avg + self.a2 * self.group_r_avg;
        self.uclx = format!("{0:.1$}", self.uclx, self.decimal).parse::<f64>().unwrap();

        self.clx = self.group_x_avg;
        self.clx = format!("{0:.1$}", self.clx, self.decimal).parse::<f64>().unwrap();

        self.lclx = self.group_x_avg - self.a2 * self.group_r_avg;
        self.lclx = format!("{0:.1$}", self.lclx, self.decimal).parse::<f64>().unwrap();

        self.cp = (self.upper - self.lower) / (6.0 * (self.group_r_avg / self.d2));

        let k = (self.value - self.group_x_avg).abs() / ((self.upper - self.lower) / 2.0);
        self.cpk = (1.0 - k) * self.cp;

        self.cp = format!("{0:.1$}", self.cp, self.decimal).parse::<f64>().unwrap();
        self.cpk = format!("{0:.1$}", self.cpk, self.decimal).parse::<f64>().unwrap();

        self.avg = self.data.clone().iter().sum::<f64>() / self.data_len as f64;
        // 方差
        let variance = self.data.clone().iter().map(|value| {
            let diff = self.avg - *value;
            diff * diff
        }).sum::<f64>() / (self.data_len - 1) as f64;
        // 标准差
        self.sigma = variance.sqrt();

        self
    }
    fn get_group_list_info(&mut self, mut group_list: Vec<f64>) {
        group_list.sort_by(|a, b| a.partial_cmp(b).unwrap());

        let max = group_list.last().unwrap();
        let min = group_list.first().unwrap();

        let max_min = format!("{0:.1$}", max - min, self.decimal).parse::<f64>().unwrap();

        self.group_r_avgs.push(max_min);
        self.group_r_avg_sum = self.group_r_avgs.iter().sum();
        self.group_r_avg = self.group_r_avg_sum / self.group_r_avgs.len() as f64;

        let mut total = 0.0;
        for group in group_list.clone() {
            total += group;
        }
        self.group_sums.push(total);

        let group_x_avg = format!("{0:.1$}", total / group_list.len() as f64, self.decimal).parse::<f64>().unwrap();
        self.group_x_avgs.push(group_x_avg);
        self.group_x_avg_sum = self.group_x_avgs.iter().sum();
        self.group_x_avg = self.group_x_avg_sum / self.group_x_avgs.len() as f64;
    }
    pub fn xbar_r(&self) -> JsonValue {
        object! {
            data:self.group_r_avgs.clone(),
            UCLR:self.uclr,
            CLR:self.clr,
            LCLR:self.lclr
        }
    }
    pub fn xbar_x(&self) -> JsonValue {
        object! {
            data:self.group_x_avgs.clone(),
            UCLX:self.uclx,
            CLX:self.clx,
            LCLX:self.lclx
        }
    }
    pub fn cpk_rating_criteria(&self) -> (&'static str, &'static str) {
        if self.cpk >= 2.0 {
            ("A++", "特优,可考虑降低成本。")
        } else if 1.67 <= self.cpk && self.cpk < 2.0 {
            return ("A+", "优,应当保持之。");
        } else if 1.33 <= self.cpk && self.cpk < 1.67 {
            ("A", "良,能力好，状态稳定，但应尽力提升到A+级。")
        } else if 1.00 <= self.cpk && self.cpk < 1.33 {
            ("B", "一般,制程因素稍有变异即有生产不良的危险，应利用各种资源及方法将其提升为A级。")
        } else if 0.67 <= self.cpk && self.cpk < 1.00 {
            ("C", "差,制程不良较多。须提升其能力。")
        } else {
            ("D", "不可接受，其能力太差,应考虑重新整改设计制程。")
        }
    }
    pub fn cp_rating_criteria(&self) -> (&'static str, &'static str) {
        if self.cp >= 1.67 {
            ("A+", "无缺点,可考虑降低成本。")
        } else if 1.33 <= self.cp && self.cp <= 1.67 {
            ("A", "状态良好维持现状。")
        } else if 1.00 <= self.cp && self.cp <= 1.33 {
            ("B", "可以改进为A级。")
        } else if 0.67 <= self.cp && self.cp <= 1.00 {
            ("C", "制程不良较多。须提升能力。")
        } else {
            ("D", "制程能力好差,应考虑重新整改设计制程。")
        }
    }
}

/// 计量型 X-R图 A2
fn a2(len: usize) -> f64 {
    match len {
        2 => 1.880,
        3 => 1.023,
        4 => 0.729,
        5 => 0.577,
        6 => 0.483,
        7 => 0.149,
        8 => 0.373,
        9 => 0.337,
        10 => 0.308,
        11 => 0.285,
        12 => 0.266,
        13 => 0.249,
        14 => 0.235,
        15 => 0.223,
        16 => 0.212,
        17 => 0.203,
        18 => 0.194,
        19 => 0.187,
        20 => 0.180,
        21 => 0.173,
        22 => 0.167,
        23 => 0.162,
        24 => 0.157,
        25 => 0.153,
        _ => 0.0
    }
}

/// 计量型 X-R图 d2
fn d2(len: usize) -> f64 {
    match len {
        2 => 1.128,
        3 => 1.693,
        4 => 2.059,
        5 => 2.326,
        6 => 2.534,
        7 => 2.704,
        8 => 2.847,
        9 => 2.970,
        10 => 3.078,
        11 => 3.173,
        12 => 3.258,
        13 => 3.336,
        14 => 3.407,
        15 => 3.472,
        16 => 3.532,
        17 => 3.588,
        18 => 3.640,
        19 => 3.689,
        20 => 3.735,
        21 => 3.778,
        22 => 3.819,
        23 => 3.858,
        24 => 3.895,
        25 => 3.931,
        _ => 0.0
    }
}

/// 计量型 X-R图 D3 计算控制限用的系数 LCLr
fn d3(len: usize) -> f64 {
    match len {
        7 => 0.076,
        8 => 0.136,
        9 => 0.184,
        10 => 0.223,
        11 => 0.256,
        12 => 0.283,
        13 => 0.307,
        14 => 0.328,
        15 => 0.347,
        16 => 0.363,
        17 => 0.738,
        18 => 0.391,
        19 => 0.403,
        20 => 0.415,
        21 => 0.425,
        22 => 0.434,
        23 => 0.443,
        24 => 0.451,
        25 => 0.459,
        _ => 0.0
    }
}

/// 计量型 X-R图 D4 计算控制限用的系数 UCLr
fn d4(len: usize) -> f64 {
    match len {
        2 => 3.267,
        3 => 2.574,
        4 => 2.282,
        5 => 2.114,
        6 => 2.004,
        7 => 1.924,
        8 => 1.864,
        9 => 1.816,
        10 => 1.777,
        11 => 1.744,
        12 => 1.717,
        13 => 1.693,
        14 => 1.672,
        15 => 1.653,
        16 => 1.637,
        17 => 1.622,
        18 => 1.608,
        19 => 1.597,
        20 => 1.585,
        21 => 1.575,
        22 => 1.566,
        23 => 1.557,
        24 => 1.548,
        25 => 1.541,
        _ => 0.0
    }
}