eis/eqpalg/define/public.h

#pragma once
/**
 * @file eqpalg/define/public.h
 * @brief eqpalg项目下常用公共定义
 * @author Cat (null.null.null@qq.com)
 * @version 0.1
 * @date 2021-07-08
 *
 * Company: Baosight Co. Ltd.
 * DO NOT COPY/USE WITHOUT PERMISSION
 *
 */
#include "mix_cc/json.h"
#include <chrono>
#include <string>

#define EVENT_NO_MAX 2019
#define EVENT_NO_MIN 2000
#define STASTIC_DAYS 7
/**
 * @brief 进程类型
 */
enum class ProcessType {
  kNull = 0,
  kMon,
  kCron,
  kTask
};
/**
 * @brief 其他算法与机器学习类算法的临界算法号
 */
struct AlgId_ML {
  static const int AlgIdMLMin = 110;
};
/**
 * @brief 表达式类型
 */
struct ExpType {
  static const int Logic = 1;
  static const int Bound = 2;
  static const int ActionFeedBack = 3;
  static const int CondBound = 4;
  static const int BoundHoldTime = 5;
  static const int HoldTimeAcc = 6;
  static const int OccTimesAcc = 7;
  static const int PolyFit = 12;
  static const int PEAR = 13;
  static const int Trend = -1;
  static const int OuterPer = 16;
  static const int OuterAct = 18;
};
/**
 * @brief 区间模式
 */
struct DistMode {
  static const int Manual = 0;
  static const int Online = 1;
  static const int Offline = 2;
};
/**
 * @brief 检测模式
 */
struct DetectMode {
  static const int Default = 0;
  static const int OnlyLeft = 1;
  static const int OnlyRight = 2;
  static const int ErrorMode = 3;
};
/**
 * @brief 规则运行前提条件
 */
struct PRR {
  static const int None = 0;
  static const int Exp = 1;
};

/**
 * @brief
 */
struct StatConst {
  static const int64_t CronUpdateDelay = 24;
  static const int StatClassCount = 50;
};

/**
 * @brief 样本类型
 */
struct SampleType {
  static constexpr char T_SAMPLE_FIT[] = "T_SAMPLE_FIT";
  static constexpr char T_SAMPLE_STAT[] = "T_SAMPLE_STAT";
};

/**
 * @brief 导入模型标记位
 */
struct CreateModels {
  bool create_new_models = false;
};

/**
 * @brief 对应控制过程的事件号
 */
struct EventCase {
  static const int kDelete = 0;
  static const int kCreate = 1;
  static const int kUpdate = 2;
  static const int kEnable = 3;
  static const int kReset = 4;
  static const int kExec = 10;
};

/**
 * @brief 数据来源
 * 包含IHDB和共享内存
 */
struct DataSource {
  static const int16_t IHDB = 0;
  static const int16_t MEMORY = 1;
};

/**
 * @brief 存储消息类型
 */
struct MsgLevel {
  static constexpr char INFO[] = "INFO";
  static constexpr char WARN[] = "WARN";
  static constexpr char ERROR[] = "ERROR";
};

/**
 * @brief 报警信息结构体
 */
struct AlarmInfo {
  /**
   * @brief Construct a new Alarm Info object
   */
  AlarmInfo() {
    alarmed = false;
    auto tmp = std::chrono::system_clock::now();
    alarm_start_time = tmp;
    alarm_end_time = tmp;
  }

  ~AlarmInfo() {}

  bool alarmed;
  mix_cc::json content;
  std::chrono::system_clock::time_point alarm_start_time;
  std::chrono::system_clock::time_point alarm_end_time;
  /**
   * @brief 规则配置信息
   */
  struct ConfigInfo {
    std::string id;
    std::string name;
    std::string group;
    std::string remark;
    std::string descName;
  };
  ConfigInfo cfg_info;
};

/**
 * @brief sample2D二维数据 数据结构
 *
 */
struct sample2D {
  int orders = 1;
  std::vector<std::vector<double>> fit_coefs;
  std::vector<double> scores;
  double pear_coefs;
  std::string method;

  bool init() {
    fit_coefs.clear();
    scores.clear();
    return true;
  }

  nlohmann::json invert2json() {
    nlohmann::json js1;
    js1["orders"] = orders;
    js1["fit_coefs"] = fit_coefs;
    js1["scores"] = scores;
    js1["pear_coefs"] = pear_coefs;
    js1["method"] = method;
    return js1;
  }
};
/**
 * @brief sample_stat统计数据 数据结构
 */
struct SampleStat {
  double ci_left = 0;
  double ci_right = 0;
  double mean = 0;
  double stddev = 0;
  double skewness = 0;
  double kurtosis = 0;
  double variance = 0;
  double range = 0;
  double init_value = 0;
  double min = 0;
  double max = 0;
  nlohmann::json invert2json() {
    nlohmann::json js1;
    js1["ci_left"] = limit_precision(ci_left);
    js1["ci_right"] = limit_precision(ci_right);
    js1["mean"] = limit_precision(mean, 3);
    js1["min"] = limit_precision(min, 3);
    js1["max"] = limit_precision(max, 3);
    js1["stddev"] = stddev;
    js1["kurtosis"] = kurtosis;
    js1["variance"] = variance;
    js1["skewness"] = skewness;
    js1["range"] = limit_precision(range, 3);
    js1["init_value"] = limit_precision(init_value, 3);
    return js1;
  }
  double limit_precision(double data, int precision = 2) {
    double factor = std::pow(10, precision);
    return std::round(data * factor) / factor;
  }
};

struct TaskReturnType {
  bool is_valid = false;
  double value = 0;
};