490 lines
17 KiB
C++
490 lines
17 KiB
C++
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#include <eqpalg/algs/exp_sample2D.h>
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#include <eqpalg/define/stat.h>
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#include <eqpalg/exp_macro/get_macro_replaced_exp.h>
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#include <eqpalg/table_struct/t_sample_fit.h>
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#include <eqpalg/table_struct/t_sample_mag.h>
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#include <eqpalg/table_struct/t_sample_stat.h>
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#include <eqpalg/utility/build_alarm_info.h>
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#include <mix_cc/sql.h>
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#include <mix_cc/sql/database/db2_t.h>
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using namespace mix_cc::sql;
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extern ProcessType glob_process_type;
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ExpSample2D::ExpSample2D(const string &name, const mix_cc::json &rule_json,
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const string &ruleId, size_t exp_type)
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: ExpBase(name, rule_json, ruleId, exp_type) {
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is_running_ = false;
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logger_.reset(
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new LOG("ExpSample2D-" + std::to_string(exp_type) + ":" + rule_name_,
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AUTO_CATCH_PID));
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}
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ExpSample2D::~ExpSample2D() {
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// ExpBase::~ExpBase();
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}
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int ExpSample2D::init() {
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int ret = 0;
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ret += AlgBase::init();
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// 重新载入数据源配置信息
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ret += this->reload_config_data_source();
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// 在载入数据源信息完成后，载入表达式配置之前，必须刷新变量，把变量信息初始化到mm_vars内
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// if (glob_process_type == ProcessType::kMon) {
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ret += this->first_fill_mm_vars(); /*4.数据项*/
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// }
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// 必须在刷新变量后，才可以初始化表达式
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ret += this->reload_config_exp_act();
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ret += this->reload_samples();
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sample_type_ = SampleType::T_SAMPLE_FIT;
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if (this->reload_param() == 0) {
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is_running_ = true;
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}
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if (ret == 0) {
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this->exp_is_wrong_ = false;
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}
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return ret;
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}
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AlarmInfo ExpSample2D::mon_proc() {
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if (!is_running_) {
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return AlarmInfo{};
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} else {
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switch (exp_type_) {
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case ExpType::PolyFit:
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if (exp_act_->evaluate() && check_polyFit()) {
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this->rule_stat_.alarm_value = this->rule_stat_.current_value;
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auto msg = rule_name_ + this->error_str_ + " Y表达式当前值：" +
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DAA::double2str(this->rule_stat_.current_value) +
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",拟合的合理区间:[" + DAA::double2strLimit(limit_down_) +
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"," + DAA::double2strLimit(limit_up_) + "]";
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logger_->Debug() << msg << endl;
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this->query_time_range_.set_left(query_time_range_.get_right() -
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delay_time_); //
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return utility::build_alarm_info(
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utility::get_msg_level(limit_down_, limit_up_,
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this->rule_stat_.current_value),
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rule_id_, rule_name_, "ExpSample2D", msg, query_time_range_);
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}
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break;
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case ExpType::PEAR:
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if (check_pear()) {
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this->rule_stat_.alarm_value = this->rule_stat_.current_value;
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auto msg = rule_name_ + this->error_str_ + " 当前线性相关性系数：" +
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DAA::double2str(this->rule_stat_.current_value) +
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",合理区间:[" + DAA::double2strLimit(limit_down_) + "," +
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DAA::double2strLimit(limit_up_) + "]";
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logger_->Debug() << msg << endl;
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this->query_time_range_.set_left(query_time_range_.get_right() -
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milliseconds(min_len_ * 50)); //
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logger_->Debug() << rule_name_ << ",stime:"
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<< mix_cc::mix_time_t(query_time_range_.get_left())
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.to_formatted_time()
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<< ",etime:"
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<< mix_cc::mix_time_t(query_time_range_.get_right())
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.to_formatted_time()
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<< std::endl;
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return utility::build_alarm_info(
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utility::get_msg_level(limit_down_, limit_up_,
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this->rule_stat_.current_value),
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rule_id_, rule_name_, "ExpSample2D", msg, query_time_range_);
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}
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break;
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default:
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break;
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}
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return AlarmInfo{};
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}
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}
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int ExpSample2D::reload_samples() {
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/*
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sample_X绑定 exp_feedback_
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sample_Y绑定 exp_result_
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*/
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auto tmp_exp =
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rule_json_.at("function").at("sample_X").at("value").get<std::string>();
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exp_str_ = get_macro_replaced_exp(tmp_exp);
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if (exp_feedback_ == nullptr && exp_str_ != "") {
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try {
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exp_feedback_ =
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std::make_unique<mix_cc::matheval::Expression>(exp_str_, &mm_vars);
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logger_->Debug() << "sample_X:" << exp_str_ << "="
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<< exp_feedback_->evaluate() << endl;
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} catch (const std::exception &e) {
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logger_->Error() << "sample_X:" << exp_str_ << "计算出错：" << e.what()
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<< ",location:" << BOOST_CURRENT_LOCATION << endl;
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return -1;
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}
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}
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tmp_exp =
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rule_json_.at("function").at("sample_Y").at("value").get<std::string>();
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exp_str_ = get_macro_replaced_exp(tmp_exp);
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if (exp_result_ == nullptr && exp_str_ != "") {
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try {
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exp_result_ =
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std::make_unique<mix_cc::matheval::Expression>(exp_str_, &mm_vars);
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logger_->Debug() << "sample_Y:" << exp_str_ << "="
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<< exp_result_->evaluate() << endl;
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} catch (const std::exception &e) {
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logger_->Error() << "sample_Y:" << exp_str_ << "计算出错：" << e.what()
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<< ",location:" << BOOST_CURRENT_LOCATION << endl;
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return -1;
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}
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}
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tmp_exp = rule_json_.at("function")
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.at("sample_Y")
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.at("param")
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.at("scale")
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.at("value")
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.get<std::string>();
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try {
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scale_ = std::stod(tmp_exp);
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logger_->Debug() << "scale_:" << scale_ << std::endl;
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} catch (const std::exception &e) {
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logger_->Error() << "scale_:" << tmp_exp << "stod出错：" << e.what()
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<< ",location:" << BOOST_CURRENT_LOCATION << endl;
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scale_ = 0.3;
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return -1;
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}
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if (exp_type_ == ExpType::PEAR) {
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tmp_exp = rule_json_.at("function")
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.at("sample_Y")
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.at("param")
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.at("min_len")
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.at("value")
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.get<std::string>();
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try {
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min_len_ = std::stod(tmp_exp);
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if (min_len_ < 1000) {
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min_len_ = 1000;
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}
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if (min_len_ > 200000) {
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min_len_ = 200000;
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}
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logger_->Debug() << "min_len_:" << min_len_ << std::endl;
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} catch (const std::exception &e) {
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logger_->Error() << "min_len_:" << tmp_exp << "stod出错：" << e.what()
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<< endl;
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min_len_ = 1000;
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return -1;
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}
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}
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return 0;
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}
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bool ExpSample2D::check_polyFit() {
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double X = exp_feedback_->evaluate(); /* SampleX*/
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double Y = exp_result_->evaluate(); /* SampleY*/
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double Y_Fit = PolyFitValue(X, this->fit_coefs_, this->orders_);
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limit_down_ = Y_Fit - abs(Y_Fit) * scale_;
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limit_up_ = Y_Fit + abs(Y_Fit) * scale_;
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this->rule_stat_.limit_down = limit_down_;
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this->rule_stat_.limit_up = limit_up_;
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this->rule_stat_.current_value = Y;
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logger_->Debug() << "X:" << X << ",Y:" << Y << ",Y_Fit:" << Y_Fit
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<< "fit_coefs:" << fit_coefs_[0] << "," << fit_coefs_[1]
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<< ",orders:" << orders_ << endl;
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return (abs(Y_Fit - Y) > abs(Y_Fit) * scale_) ? true : false;
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}
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double ExpSample2D::PolyFitValue(double x, std::vector<double> &fit_coefs,
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size_t orders) {
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if (fit_coefs.size() != orders + 1) {
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logger_->Debug() << "拟合次数不匹配！" << endl;
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}
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try {
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double res = 0;
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for (int i = 0; i < orders + 1; i++) {
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res += std::pow(x, orders - i) * fit_coefs[i];
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}
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return res;
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} catch (const std::exception &e) {
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logger_->Error() << "PolyFitValue() 出错：" << e.what()
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<< ",location:" << BOOST_CURRENT_LOCATION << endl;
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return -1;
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}
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}
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bool ExpSample2D::check_pear() {
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if (this->data_len_ < min_len_) {
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if (exp_act_->evaluate()) {
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SampleX_.push_back(exp_feedback_->evaluate());
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SampleY_.push_back(exp_result_->evaluate());
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data_len_ = SampleX_.size();
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} else {
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reset_SampleXY();
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}
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return false;
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}
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limit_down_ = pear_coefs_ - abs(pear_coefs_) * scale_;
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limit_up_ = pear_coefs_ + abs(pear_coefs_) * scale_;
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if (pear_coefs_ > 0) {
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limit_up_ = 1;
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} else {
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limit_down_ = -1;
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}
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this->rule_stat_.limit_down = limit_down_;
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this->rule_stat_.limit_up = limit_up_;
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double pear_now = PearValue(SampleX_, SampleY_);
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reset_SampleXY();
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if (pear_now > 1) {
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return false;
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}
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this->rule_stat_.current_value = pear_now;
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if (pear_coefs_ > 0) {
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return (pear_now < this->limit_down_) ? true : false;
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} else {
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return (pear_now > this->limit_up_) ? true : false;
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}
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return false;
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}
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double ExpSample2D::PearValue(std::vector<double> &X, std::vector<double> &Y) {
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if (X.size() != Y.size() || X.size() < 2) {
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logger_->Debug() << "样本异常!"
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<< "X size:" << X.size() << ",Y size" << Y.size() << endl;
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return 2;
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}
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Eigen::Map<Eigen::VectorXd> sampleX(X.data(), X.size());
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Eigen::Map<Eigen::VectorXd> sampleY(Y.data(), Y.size());
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double m1 = sampleX.mean();
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double m2 = sampleY.mean();
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int num_data = sampleX.size();
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double sigma1 = 0;
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double sigma2 = 0;
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double sigma12 = 0;
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for (int i = 0; i < num_data; i++) {
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sigma12 += (sampleX(num_data - 1 - i) - m1) *
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(sampleY(num_data - 1 - i) - m2) / num_data;
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sigma1 += (sampleX(num_data - 1 - i) - m1) *
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(sampleX(num_data - 1 - i) - m1) / num_data;
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sigma2 += (sampleY(num_data - 1 - i) - m2) *
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(sampleY(num_data - 1 - i) - m2) / num_data;
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}
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if (sigma1 * sigma2 != 0) {
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double dis1 = sigma1 / m1;
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double dis2 = sigma2 / m2;
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if (dis1 > 1 || dis2 > 1) {
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return sigma12 / sqrt(sigma1 * sigma2);
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} else {
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logger_->Debug() << "存在离散度小于1" << endl;
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return 2;
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}
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} else {
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logger_->Debug() << "存在方差为0" << endl;
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return 2;
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}
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}
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int ExpSample2D::reload_param() {
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T_SAMPLE_MAG tsm;
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auto sql_statement =
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select(tsm.result())
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.from(tsm)
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.where(tsm.ruleId() == this->rule_id_, tsm.usable() == 1);
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auto sample_list_maybe = exec<db2_t, T_SAMPLE_MAG>(sql_statement);
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if (sample_list_maybe.is_just()) {
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auto sample_list = sample_list_maybe.unsafe_get_just();
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if (sample_list.empty()) {
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logger_->Info() << "无有效样本" << std::endl;
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return -1;
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} else {
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try {
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sample_param_ = mix_cc::json::parse(sample_list[0].result);
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this->orders_ = sample_param_["orders"];
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this->fit_coefs_ =
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sample_param_["fit_coefs"]
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.get<std::vector<std::vector<double>>>()[orders_ - 1];
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this->pear_coefs_ = sample_param_["pear_coefs"];
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logger_->Debug() << rule_id_ << ",orders_:" << orders_
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<< ",pear_coefs_:" << pear_coefs_ << std::endl;
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return 0;
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} catch (const std::exception &e) {
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logger_->Error() << rule_id_ << "," << e.what()
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<< ",location:" << BOOST_CURRENT_LOCATION << endl;
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return -1;
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}
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}
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return 0;
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} else {
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logger_->Error() << "无样本，请检查数据库连接" << std::endl;
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return -1;
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}
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}
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int ExpSample2D::insert_mag(std::string sample_id) {
|
|||
|
|
T_SAMPLE_MAG tsm;
|
|||
|
|
auto inset_ret = exec<db2_t, size_t>(insert_into(tsm).set(
|
|||
|
|
tsm.ruleId() = this->rule_id_, tsm.result() = this->sample_param_.dump(),
|
|||
|
|
tsm.type() = SampleType_, tsm.sampleid() = sample_id, tsm.usable() = 0,
|
|||
|
|
tsm.starttime() = mix_cc::mix_time_t(this->query_time_range_.get_left()),
|
|||
|
|
tsm.endtime() = mix_cc::mix_time_t(query_time_range_.get_right())));
|
|||
|
|
if (inset_ret.is_nothing()) {
|
|||
|
|
std::string error_msg = "ruleid size:" + std::to_string(rule_id_.size()) +
|
|||
|
|
",result:" + this->sample_param_.dump() +
|
|||
|
|
",type:" + SampleType_ +
|
|||
|
|
"smpleid size:" + std::to_string(sample_id.size());
|
|||
|
|
gb_logger_->log_info("T_SAMPLE_MAG 表插入失败:" + rule_name_ + "," +
|
|||
|
|
error_msg);
|
|||
|
|
return -1;
|
|||
|
|
}
|
|||
|
|
return 0;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/*移至ExpBase*/
|
|||
|
|
// string ExpSample2D::get_id() {
|
|||
|
|
// return this->rule_id_.substr(0, 30) + "_" +
|
|||
|
|
// std::to_string(system_clock::now().time_since_epoch().count());
|
|||
|
|
// }
|
|||
|
|
|
|||
|
|
std::vector<AlarmInfo> ExpSample2D::exec_task(mix_cc::time_range_t time_range) {
|
|||
|
|
/**
|
|||
|
|
* 1.查数据,生成SampleX_、SampleY_
|
|||
|
|
* 2.计算参数
|
|||
|
|
* 3.存数据至 db2 T_SAMPLE_FIT表
|
|||
|
|
* 4.存样本管理 db2 T_SAMPLE_MAG
|
|||
|
|
*/
|
|||
|
|
bool cal_vlid = true;
|
|||
|
|
logger_->Debug()
|
|||
|
|
<< "stime:"
|
|||
|
|
<< mix_cc::mix_time_t(time_range.get_left()).to_formatted_time()
|
|||
|
|
<< ",endtime:"
|
|||
|
|
<< mix_cc::mix_time_t(time_range.get_right()).to_formatted_time() << endl;
|
|||
|
|
std::string sample_id = this->get_id(time_range);
|
|||
|
|
this->sample_id_ = sample_id;
|
|||
|
|
reset_SampleXY();
|
|||
|
|
if ((time_range.get_right() - time_range.get_left()) <
|
|||
|
|
this->query_interval_time_) {
|
|||
|
|
this->query_time_range_ = time_range;
|
|||
|
|
task_mon_pro();
|
|||
|
|
} else {
|
|||
|
|
for (auto t = time_range.get_left(); t < time_range.get_right();
|
|||
|
|
t += query_interval_time_) {
|
|||
|
|
this->query_time_range_.set_left(t);
|
|||
|
|
if (time_range.get_right() - t > query_interval_time_) {
|
|||
|
|
this->query_time_range_.set_right(t + query_interval_time_);
|
|||
|
|
} else {
|
|||
|
|
this->query_time_range_.set_right(time_range.get_right());
|
|||
|
|
}
|
|||
|
|
task_mon_pro();
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (data_len_ > 100) {
|
|||
|
|
sample2D sample2d;
|
|||
|
|
if (lsm_ptr_ == nullptr) {
|
|||
|
|
for (int i = 0; i < data_len_; i++) {
|
|||
|
|
logger_->Debug() << "[" << this->SampleX_[i] << "," << this->SampleY_[i]
|
|||
|
|
<< "]" << endl;
|
|||
|
|
}
|
|||
|
|
lsm_ptr_ = std::make_unique<DAA::LSM>(this->SampleX_, this->SampleY_);
|
|||
|
|
} else {
|
|||
|
|
lsm_ptr_->reset(this->SampleX_, this->SampleY_);
|
|||
|
|
}
|
|||
|
|
try {
|
|||
|
|
sample2d.fit_coefs = lsm_ptr_->polyfit();
|
|||
|
|
} catch (const std::exception &e) {
|
|||
|
|
this->sample_result_ = "polyfit ERROR!";
|
|||
|
|
this->gb_logger_->log_error(rule_name_ + "exec_task:" + e.what());
|
|||
|
|
// return {};
|
|||
|
|
cal_vlid = false;
|
|||
|
|
this->update_t_sample_mag(cal_vlid);
|
|||
|
|
return {};
|
|||
|
|
}
|
|||
|
|
// sample2d.fit_coefs = lsm_ptr_->polyfit();
|
|||
|
|
sample2d.scores = lsm_ptr_->get_r2a();
|
|||
|
|
sample2d.pear_coefs = lsm_ptr_->cor();
|
|||
|
|
sample2d.orders = lsm_ptr_->get_order_best();
|
|||
|
|
sample2d.method = std::to_string(exp_type_);
|
|||
|
|
sample_param_ = sample2d.invert2json();
|
|||
|
|
/* 插入样本表 T_SAMPLE_FIT */
|
|||
|
|
for (int i = 0; i < data_len_; i++) {
|
|||
|
|
insert_fit(sample_id, SampleX_[i], SampleY_[i], i);
|
|||
|
|
}
|
|||
|
|
/* 插入样本管理表 T_SAMPLE_MAG */
|
|||
|
|
this->query_time_range_ = time_range;
|
|||
|
|
// insert_mag(sample_id);
|
|||
|
|
this->sample_result_ = this->sample_param_.dump();
|
|||
|
|
} else {
|
|||
|
|
this->gb_logger_->log_error(rule_name_ + "数据量不足，不计算！");
|
|||
|
|
cal_vlid = false;
|
|||
|
|
}
|
|||
|
|
this->update_t_sample_mag(cal_vlid);
|
|||
|
|
this->alarm_poster_.zmqp_send(912, this->sample_result_);
|
|||
|
|
return {};
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
void ExpSample2D::reset_SampleXY() {
|
|||
|
|
SampleX_.clear();
|
|||
|
|
SampleY_.clear();
|
|||
|
|
SampleX_.shrink_to_fit();
|
|||
|
|
SampleY_.shrink_to_fit();
|
|||
|
|
data_len_ = 0;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/*弃用 继承 ExpBase*/
|
|||
|
|
// void ExpSample2D::query_ihd_data() {
|
|||
|
|
// auto queried_batch_maybe = mix_cc::ihd::make_query_batch_maybe(
|
|||
|
|
// m_tags, query_time_range_, this->ihd_min_time_particles_);
|
|||
|
|
// if (queried_batch_maybe.is_nothing()) {
|
|||
|
|
// queried_time_.clear();
|
|||
|
|
// queried_data_ = typename decltype(queried_batch_maybe)::type::Mat2d{};
|
|||
|
|
// gb_logger_->log_error("Tag点没有查到相应的信息");
|
|||
|
|
// return;
|
|||
|
|
// }
|
|||
|
|
// auto queried_bath = queried_batch_maybe.unsafe_get_just();
|
|||
|
|
// auto result_maybe = mix_cc::ihd::read_data_with_time_maybe(&queried_bath);
|
|||
|
|
// if (result_maybe.is_nothing()) {
|
|||
|
|
// queried_time_.clear();
|
|||
|
|
// queried_data_ = typename decltype(queried_batch_maybe)::type::Mat2d{};
|
|||
|
|
// gb_logger_->log_error("Tag点没有查到相应的数据");
|
|||
|
|
// return;
|
|||
|
|
// }
|
|||
|
|
// std::tie(queried_time_, queried_data_) = result_maybe.unsafe_get_just();
|
|||
|
|
// }
|
|||
|
|
|
|||
|
|
void ExpSample2D::task_mon_pro() {
|
|||
|
|
if (data_len_ > MAX_STORAGE_SIZE) {
|
|||
|
|
return;
|
|||
|
|
}
|
|||
|
|
query_ihd_data();
|
|||
|
|
logger_->Debug() << rule_name_ << ",data_len_:" << data_len_
|
|||
|
|
<< ",ihd size:" << queried_data_.size() << endl;
|
|||
|
|
for (auto i = 0; i < queried_data_.rows(); i++) {
|
|||
|
|
refresh_exp_vars_ihd(i);
|
|||
|
|
if (data_len_ < MAX_STORAGE_SIZE) {
|
|||
|
|
if (exp_act_->evaluate()) {
|
|||
|
|
SampleX_.push_back(exp_feedback_->evaluate());
|
|||
|
|
SampleY_.push_back(exp_result_->evaluate());
|
|||
|
|
data_len_ = SampleX_.size();
|
|||
|
|
}
|
|||
|
|
} else {
|
|||
|
|
break;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
int ExpSample2D::insert_fit(std::string sample_id, double X, double Y,
|
|||
|
|
int seq) {
|
|||
|
|
T_SAMPLE_FIT tsf;
|
|||
|
|
auto inset_ret = exec<db2_t, size_t>(insert_into(tsf).set(
|
|||
|
|
tsf.sampleid() = sample_id, tsf.X() = X, tsf.Y() = Y, tsf.Seq() = seq));
|
|||
|
|
if (inset_ret.is_nothing()) {
|
|||
|
|
gb_logger_->log_info("T_SAMPLE_FIT 表插入失败:" + rule_name_ +
|
|||
|
|
",simpleid size:" + std::to_string(sample_id.size()));
|
|||
|
|
return -1;
|
|||
|
|
}
|
|||
|
|
return 0;
|
|||
|
|
}
|