diff --git a/v1_apache-hop dash mapping/predictive_metrics/data_collection.hpl b/v1_apache-hop dash mapping/predictive_metrics/data_collection.hpl new file mode 100644 index 0000000..ed25b40 --- /dev/null +++ b/v1_apache-hop dash mapping/predictive_metrics/data_collection.hpl @@ -0,0 +1,207 @@ + + + + data_collection + Y + + + + Normal + + + N + 1000 + 100 + - + 2025/02/27 12:45:14.184 + - + 2025/02/27 12:45:14.184 + + + + + + Table input + Merge join + Y + + + Table input 2 + Merge join + Y + + + Merge join + Table output + Y + + + + Merge join + MergeJoin + + Y + + 1 + + none + + + INNER + + recruitment_id + + + idm_id + + Table input + Table input 2 + + + 768 + 368 + + + + Table input + TableInput + + Y + + 1 + + none + + + postgres.person_registry + N + 0 + WITH year_series AS ( + SELECT generate_series( + date_trunc('year', CURRENT_DATE) - INTERVAL '54 years', + date_trunc('year', CURRENT_DATE) - INTERVAL '1 year', + INTERVAL '1 year' + )::date AS year_date +) +SELECT + COALESCE(r.target_recruitment_id, r.current_recruitment_id) as recruitment_id, + ys.year_date AS year, + COALESCE(COUNT(r.id) FILTER ( + WHERE r.military_registration_date >= ys.year_date + AND r.military_registration_date < ys.year_date + INTERVAL '1 year' + AND (r.vu_unset_date IS NULL OR r.vu_unset_date < ys.year_date) + ), 0) AS count_all, + COALESCE(COUNT(r.id) FILTER ( + WHERE r.military_registration_date >= ys.year_date + AND r.military_registration_date < ys.year_date + INTERVAL '1 year' + AND r.registration_reasons @> '["3"]'::jsonb + ), 0) AS reaching_17_age, + COALESCE(COUNT(r.id) FILTER ( + WHERE r.military_registration_date >= ys.year_date + AND r.military_registration_date < ys.year_date + INTERVAL '1 year' + AND r.registration_reasons @> '["5"]'::jsonb + ), 0) AS women_military_specialty, + 'Department' as schema +FROM year_series ys +LEFT JOIN recruits r ON 1=1 -- Соединяем со всеми записями, фильтрация происходит в FILTER +GROUP BY ys.year_date, COALESCE(r.target_recruitment_id, r.current_recruitment_id) +ORDER BY recruitment_id,year DESC; + N + + + 544 + 304 + + + + Table input 2 + TableInput + + Y + + 1 + + none + + + ervu-dashboard + N + 0 + -- Берем только записи с максимальной версией для каждого idm_id +SELECT DISTINCT ON (idm_id) idm_id +FROM ervu_dashboard.recruitment +WHERE schema = 'Department' +ORDER BY idm_id, updated_at DESC + + N + + + 544 + 496 + + + + Table output + TableOutput + + Y + + 1 + + none + + + 1000 + ervu-dashboard + + + recruitment_id + recruitment_id + + + recording_date + year + + + count_all + count_all + + + reaching_17_age + reaching_17_age + + + women_military_specialty + women_military_specialty + + + schema + schema + + + N + N + N + N + + Y + + N + forecast + Y + registered_citizens
+ + N + Y + Y + Y + + + 960 + 368 + + + + + + diff --git a/v1_apache-hop dash mapping/predictive_metrics/next_lvl_org_pm.hpl b/v1_apache-hop dash mapping/predictive_metrics/next_lvl_org_pm.hpl new file mode 100644 index 0000000..d868ab0 --- /dev/null +++ b/v1_apache-hop dash mapping/predictive_metrics/next_lvl_org_pm.hpl @@ -0,0 +1,177 @@ + + + + next_lvl_org_pm + Y + + + + Normal + + + N + 1000 + 100 + - + 2025/02/27 12:55:12.966 + - + 2025/02/27 12:55:12.966 + + + + + + Table input + Table output + Y + + + + Table input + TableInput + + Y + + 1 + + none + + + ervu-dashboard + N + 0 + WITH latest_hierarchy AS ( + -- Берем только записи с максимальной версией для каждого idm_id + SELECT DISTINCT ON (idm_id) * + FROM ervu_dashboard.recruitment + ORDER BY idm_id, updated_at DESC +), +hierarchy_cte AS ( + -- Строим связи между уровнями (Department → Organization → Region → Ministry) + SELECT + h.idm_id AS department_id, + h.parent_id AS organization_id, + h2.parent_id AS region_id, + h3.parent_id AS ministry_id + FROM latest_hierarchy h + LEFT JOIN latest_hierarchy h2 ON h.parent_id = h2.idm_id -- Organization + LEFT JOIN latest_hierarchy h3 ON h2.parent_id = h3.idm_id -- Region + WHERE h.schema = 'Department' -- Начинаем с Department +), +aggregated_counts AS ( + -- Агрегируем для Organization + SELECT + h.organization_id AS level_id, + SUM(r.reaching_17_age) AS total_reaching_17_age, + SUM(r.women_military_specialty) AS total_women_military_specialty, + SUM(r.count_all) AS total_count_all, + recording_date AS recording_date, + 'Organization' AS level + FROM forecast.registered_citizens r + JOIN hierarchy_cte h ON r.recruitment_id = h.department_id + WHERE h.organization_id IS NOT NULL + GROUP BY h.organization_id, recording_date + + UNION ALL + + -- Агрегируем для Region + SELECT + h.region_id AS level_id, + SUM(r.reaching_17_age) AS total_reaching_17_age, + SUM(r.women_military_specialty) AS total_women_military_specialty, + SUM(r.count_all) AS total_count_all, + recording_date AS recording_date, + 'Region' AS level + FROM forecast.registered_citizens r + JOIN hierarchy_cte h ON r.recruitment_id = h.department_id + WHERE h.region_id IS NOT NULL + GROUP BY h.region_id, recording_date + + UNION ALL + + -- Агрегируем для Ministry + SELECT + h.ministry_id AS level_id, + SUM(r.reaching_17_age) AS total_reaching_17_age, + SUM(r.women_military_specialty) AS total_women_military_specialty, + SUM(r.count_all) AS total_count_all, + recording_date AS recording_date, + 'Ministry' AS level + FROM forecast.registered_citizens r + JOIN hierarchy_cte h ON r.recruitment_id = h.department_id + WHERE h.ministry_id IS NOT NULL + GROUP BY h.ministry_id, recording_date +) +SELECT * FROM aggregated_counts; + N + + + 672 + 304 + + + + Table output + TableOutput + + Y + + 1 + + none + + + 1000 + ervu-dashboard + + + recruitment_id + level_id + + + reaching_17_age + total_reaching_17_age + + + women_military_specialty + total_women_military_specialty + + + count_all + total_count_all + + + recording_date + recording_date + + + schema + level + + + N + N + N + N + + Y + + N + forecast + Y + registered_citizens
+ + N + Y + N + Y + + + 912 + 304 + + + + + + diff --git a/v1_apache-hop dash mapping/predictive_metrics/predictive.hwf b/v1_apache-hop dash mapping/predictive_metrics/predictive.hwf new file mode 100644 index 0000000..a307c17 --- /dev/null +++ b/v1_apache-hop dash mapping/predictive_metrics/predictive.hwf @@ -0,0 +1,165 @@ + + + predictive + Y + + + + - + 2025/02/27 13:51:40.861 + - + 2025/02/27 13:51:40.861 + + + + + Start + + SPECIAL + + 1 + 12 + 60 + 0 + 0 + N + 0 + 1 + N + 304 + 352 + + + + data_collection.hpl + + PIPELINE + + N + N + N + N + N + N + ${PROJECT_HOME}/predictive_metrics/data_collection.hpl + + + Basic + + Y + + N + local + N + N + Y + N + 448 + 352 + + + + next_lvl_org_pm.hpl + + PIPELINE + + N + N + N + N + N + N + ${PROJECT_HOME}/predictive_metrics/next_lvl_org_pm.hpl + + + Basic + + Y + + N + local + N + N + Y + N + 592 + 352 + + + + predictive_data.hpl + + PIPELINE + + N + N + N + N + N + N + ${PROJECT_HOME}/predictive_metrics/predictive_data.hpl + + + Basic + + Y + + N + local + N + N + Y + N + 736 + 352 + + + + Success + + SUCCESS + + N + 880 + 352 + + + + + + Start + data_collection.hpl + Y + Y + Y + + + data_collection.hpl + next_lvl_org_pm.hpl + Y + Y + N + + + next_lvl_org_pm.hpl + predictive_data.hpl + Y + Y + N + + + predictive_data.hpl + Success + Y + Y + N + + + + + + + debug_plugin + + + diff --git a/v1_apache-hop dash mapping/predictive_metrics/predictive_data.hpl b/v1_apache-hop dash mapping/predictive_metrics/predictive_data.hpl new file mode 100644 index 0000000..12cf2d3 --- /dev/null +++ b/v1_apache-hop dash mapping/predictive_metrics/predictive_data.hpl @@ -0,0 +1,480 @@ + + + + predictive_data + Y + + + + Normal + + + N + 1000 + 100 + - + 2025/02/27 13:48:40.269 + - + 2025/02/27 13:48:40.269 + + + + + + Table input + Table output + Y + + + + Table input + TableInput + + Y + + 1 + + none + + + ervu-dashboard + N + 0 + -- Адаптированный запрос для таблицы forecast.registered_citizens с учетом указанной структуры +-- recording_date - год наблюдения +-- recruitment_id - идентификатор военкомата +-- count_all - всего на учете +-- reaching_17_age - поставлено на учет по достижении 17 лет +-- women_military_specialty - женщины, получившие ВУС +-- schema - уровень военкомата + +-- 1. Проверяем наличие данных за весь необходимый период и создаем полную сетку лет +WITH year_range AS ( + SELECT + EXTRACT(YEAR FROM CURRENT_DATE) - 54 AS min_year, -- Для первых двух показателей + EXTRACT(YEAR FROM CURRENT_DATE) - 34 AS min_year_females, -- Для женщин с ВУС + EXTRACT(YEAR FROM CURRENT_DATE) AS max_year +), + +all_years AS ( + SELECT generate_series(min_year, max_year) AS year + FROM year_range +), + +all_years_females AS ( + SELECT generate_series(min_year_females, max_year) AS year + FROM year_range +), + +all_regions AS ( + SELECT DISTINCT recruitment_id, schema FROM forecast.registered_citizens +), + +-- 2. Создаем полную сетку регион-год для всех показателей +full_grid_males AS ( + SELECT + r.recruitment_id, + r.schema, + y.year + FROM all_regions r + CROSS JOIN all_years y +), + +full_grid_females AS ( + SELECT + r.recruitment_id, + r.schema, + y.year + FROM all_regions r + CROSS JOIN all_years_females y +), + +-- 3. Объединяем сетку с имеющимися данными, чтобы выявить пропуски +males_data AS ( + SELECT + g.recruitment_id, + g.schema, + g.year, + COALESCE(h.count_all, NULL) AS count_all, + COALESCE(h.reaching_17_age, NULL) AS reaching_17_age + FROM full_grid_males g + LEFT JOIN forecast.registered_citizens h ON g.recruitment_id = h.recruitment_id AND g.year = EXTRACT(YEAR FROM h.recording_date) +), + +females_data AS ( + SELECT + g.recruitment_id, + g.schema, + g.year, + COALESCE(h.women_military_specialty, NULL) AS women_military_specialty + FROM full_grid_females g + LEFT JOIN forecast.registered_citizens h ON g.recruitment_id = h.recruitment_id AND g.year = EXTRACT(YEAR FROM h.recording_date) +), + +-- 4. Рассчитываем статистики для выявления выбросов +males_stats AS ( + SELECT + recruitment_id, + schema, + AVG(count_all) AS avg_count_all, + STDDEV(count_all) * 2 AS stddev_count_all, -- 2σ для фильтрации выбросов + AVG(reaching_17_age) AS avg_reaching_17_age, + STDDEV(reaching_17_age) * 2 AS stddev_reaching_17_age -- 2σ для фильтрации выбросов + FROM males_data + WHERE count_all IS NOT NULL AND reaching_17_age IS NOT NULL + GROUP BY recruitment_id, schema +), + +females_stats AS ( + SELECT + recruitment_id, + schema, + AVG(women_military_specialty) AS avg_women_military_specialty, + STDDEV(women_military_specialty) * 2 AS stddev_women_military_specialty -- 2σ для фильтрации выбросов + FROM females_data + WHERE women_military_specialty IS NOT NULL + GROUP BY recruitment_id, schema +), + +-- 5. Выявляем выбросы и помечаем их для интерполяции +males_outliers AS ( + SELECT + m.recruitment_id, + m.schema, + m.year, + m.count_all, + CASE + WHEN m.count_all IS NULL OR ABS(m.count_all - s.avg_count_all) > s.stddev_count_all + THEN TRUE ELSE FALSE + END AS count_all_is_outlier, + m.reaching_17_age, + CASE + WHEN m.reaching_17_age IS NULL OR ABS(m.reaching_17_age - s.avg_reaching_17_age) > s.stddev_reaching_17_age + THEN TRUE ELSE FALSE + END AS reaching_17_age_is_outlier + FROM males_data m + JOIN males_stats s ON m.recruitment_id = s.recruitment_id AND m.schema = s.schema +), + +females_outliers AS ( + SELECT + f.recruitment_id, + f.schema, + f.year, + f.women_military_specialty, + CASE + WHEN f.women_military_specialty IS NULL OR ABS(f.women_military_specialty - s.avg_women_military_specialty) > s.stddev_women_military_specialty + THEN TRUE ELSE FALSE + END AS women_military_specialty_is_outlier + FROM females_data f + JOIN females_stats s ON f.recruitment_id = s.recruitment_id AND f.schema = s.schema +), + +-- 6. Подготовка данных для интерполяции (находим ближайшие НЕ выбросы) +males_interpolation_prep AS ( + SELECT + mo.recruitment_id, + mo.schema, + mo.year, + mo.count_all, + mo.count_all_is_outlier, + mo.reaching_17_age, + mo.reaching_17_age_is_outlier, + -- Для интерполяции "Всего на учете" ищем ближайшие не-выбросы + (SELECT MAX(year) FROM males_outliers + WHERE recruitment_id = mo.recruitment_id AND schema = mo.schema AND year < mo.year AND NOT count_all_is_outlier) AS prev_year_count_all, + (SELECT MIN(year) FROM males_outliers + WHERE recruitment_id = mo.recruitment_id AND schema = mo.schema AND year > mo.year AND NOT count_all_is_outlier) AS next_year_count_all, + -- Для интерполяции "Поставлено на учет по достижении 17 лет" ищем ближайшие не-выбросы + (SELECT MAX(year) FROM males_outliers + WHERE recruitment_id = mo.recruitment_id AND schema = mo.schema AND year < mo.year AND NOT reaching_17_age_is_outlier) AS prev_year_reaching_17_age, + (SELECT MIN(year) FROM males_outliers + WHERE recruitment_id = mo.recruitment_id AND schema = mo.schema AND year > mo.year AND NOT reaching_17_age_is_outlier) AS next_year_reaching_17_age + FROM males_outliers mo +), + +females_interpolation_prep AS ( + SELECT + fo.recruitment_id, + fo.schema, + fo.year, + fo.women_military_specialty, + fo.women_military_specialty_is_outlier, + -- Для интерполяции "Женщины, получившие ВУС" ищем ближайшие не-выбросы + (SELECT MAX(year) FROM females_outliers + WHERE recruitment_id = fo.recruitment_id AND schema = fo.schema AND year < fo.year AND NOT women_military_specialty_is_outlier) AS prev_year_women_military_specialty, + (SELECT MIN(year) FROM females_outliers + WHERE recruitment_id = fo.recruitment_id AND schema = fo.schema AND year > fo.year AND NOT women_military_specialty_is_outlier) AS next_year_women_military_specialty + FROM females_outliers fo +), + +-- 7. Выполняем линейную интерполяцию для выбросов и отсутствующих значений +males_interpolated AS ( + SELECT + m.recruitment_id, + m.schema, + m.year, + CASE + WHEN m.count_all_is_outlier AND m.prev_year_count_all IS NOT NULL AND m.next_year_count_all IS NOT NULL THEN + -- Linear interpolation formula: y = y1 + ((x - x1) / (x2 - x1)) * (y2 - y1) + (SELECT p.count_all FROM males_outliers p + WHERE p.recruitment_id = m.recruitment_id AND p.schema = m.schema AND p.year = m.prev_year_count_all) + + ((m.year - m.prev_year_count_all)::float / (m.next_year_count_all - m.prev_year_count_all)) * + ((SELECT n.count_all FROM males_outliers n + WHERE n.recruitment_id = m.recruitment_id AND n.schema = m.schema AND n.year = m.next_year_count_all) - + (SELECT p.count_all FROM males_outliers p + WHERE p.recruitment_id = m.recruitment_id AND p.schema = m.schema AND p.year = m.prev_year_count_all)) + ELSE m.count_all + END AS count_all, + CASE + WHEN m.reaching_17_age_is_outlier AND m.prev_year_reaching_17_age IS NOT NULL AND m.next_year_reaching_17_age IS NOT NULL THEN + -- Linear interpolation formula: y = y1 + ((x - x1) / (x2 - x1)) * (y2 - y1) + (SELECT p.reaching_17_age FROM males_outliers p + WHERE p.recruitment_id = m.recruitment_id AND p.schema = m.schema AND p.year = m.prev_year_reaching_17_age) + + ((m.year - m.prev_year_reaching_17_age)::float / (m.next_year_reaching_17_age - m.prev_year_reaching_17_age)) * + ((SELECT n.reaching_17_age FROM males_outliers n + WHERE n.recruitment_id = m.recruitment_id AND n.schema = m.schema AND n.year = m.next_year_reaching_17_age) - + (SELECT p.reaching_17_age FROM males_outliers p + WHERE p.recruitment_id = m.recruitment_id AND p.schema = m.schema AND p.year = m.prev_year_reaching_17_age)) + ELSE m.reaching_17_age + END AS reaching_17_age + FROM males_interpolation_prep m +), + +females_interpolated AS ( + SELECT + f.recruitment_id, + f.schema, + f.year, + CASE + WHEN f.women_military_specialty_is_outlier AND f.prev_year_women_military_specialty IS NOT NULL AND f.next_year_women_military_specialty IS NOT NULL THEN + -- Linear interpolation formula: y = y1 + ((x - x1) / (x2 - x1)) * (y2 - y1) + (SELECT p.women_military_specialty FROM females_outliers p + WHERE p.recruitment_id = f.recruitment_id AND p.schema = f.schema AND p.year = f.prev_year_women_military_specialty) + + ((f.year - f.prev_year_women_military_specialty)::float / (f.next_year_women_military_specialty - f.prev_year_women_military_specialty)) * + ((SELECT n.women_military_specialty FROM females_outliers n + WHERE n.recruitment_id = f.recruitment_id AND n.schema = f.schema AND n.year = f.next_year_women_military_specialty) - + (SELECT p.women_military_specialty FROM females_outliers p + WHERE p.recruitment_id = f.recruitment_id AND p.schema = f.schema AND p.year = f.prev_year_women_military_specialty)) + ELSE f.women_military_specialty + END AS women_military_specialty + FROM females_interpolation_prep f +), + +-- 8. Объединяем интерполированные данные +combined_data AS ( + SELECT + m.recruitment_id, + m.schema, + m.year, + m.count_all, + m.reaching_17_age, + f.women_military_specialty + FROM males_interpolated m + LEFT JOIN females_interpolated f ON m.recruitment_id = f.recruitment_id AND m.schema = f.schema AND m.year = f.year +), + + + +-- 9. Рассчитываем параметры линейной регрессии +average_values AS ( + SELECT + recruitment_id, + schema, + year, + count_all, + reaching_17_age, + women_military_specialty, + -- Вычисляем средние значения отдельно + AVG(year) FILTER (WHERE count_all IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_x_count_all, + AVG(count_all) FILTER (WHERE count_all IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_y_count_all, + AVG(year) FILTER (WHERE reaching_17_age IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_x_reaching_17_age, + AVG(reaching_17_age) FILTER (WHERE reaching_17_age IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_y_reaching_17_age, + AVG(year) FILTER (WHERE women_military_specialty IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_x_women_military_specialty, + AVG(women_military_specialty) FILTER (WHERE women_military_specialty IS NOT NULL) OVER (PARTITION BY recruitment_id, schema) AS avg_y_women_military_specialty + FROM combined_data +), +regression_stats AS ( + SELECT + recruitment_id, + schema, + + -- count_all + COUNT(*) FILTER (WHERE count_all IS NOT NULL) AS n_count_all, + AVG(year) FILTER (WHERE count_all IS NOT NULL) AS avg_x_count_all, + AVG(count_all) FILTER (WHERE count_all IS NOT NULL) AS avg_y_count_all, + SUM((year - avg_x_count_all) * (count_all - avg_y_count_all)) FILTER (WHERE count_all IS NOT NULL) AS sum_xy_count_all, + SUM(POWER(year - avg_x_count_all, 2)) FILTER (WHERE count_all IS NOT NULL) AS sum_x_squared_count_all, + + -- reaching_17_age + COUNT(*) FILTER (WHERE reaching_17_age IS NOT NULL) AS n_reaching_17_age, + AVG(year) FILTER (WHERE reaching_17_age IS NOT NULL) AS avg_x_reaching_17_age, + AVG(reaching_17_age) FILTER (WHERE reaching_17_age IS NOT NULL) AS avg_y_reaching_17_age, + SUM((year - avg_x_reaching_17_age) * (reaching_17_age - avg_y_reaching_17_age)) FILTER (WHERE reaching_17_age IS NOT NULL) AS sum_xy_reaching_17_age, + SUM(POWER(year - avg_x_reaching_17_age, 2)) FILTER (WHERE reaching_17_age IS NOT NULL) AS sum_x_squared_reaching_17_age, + + -- women_military_specialty + COUNT(*) FILTER (WHERE women_military_specialty IS NOT NULL) AS n_women_military_specialty, + AVG(year) FILTER (WHERE women_military_specialty IS NOT NULL) AS avg_x_women_military_specialty, + AVG(women_military_specialty) FILTER (WHERE women_military_specialty IS NOT NULL) AS avg_y_women_military_specialty, + SUM((year - avg_x_women_military_specialty) * (women_military_specialty - avg_y_women_military_specialty)) FILTER (WHERE women_military_specialty IS NOT NULL) AS sum_xy_women_military_specialty, + SUM(POWER(year - avg_x_women_military_specialty, 2)) FILTER (WHERE women_military_specialty IS NOT NULL) AS sum_x_squared_women_military_specialty + + FROM average_values + GROUP BY recruitment_id, schema +), + +-- 10. Вычисляем параметры уравнения линейной регрессии +regression_params AS ( + SELECT + recruitment_id, + schema, + -- Параметры для count_all + CASE + WHEN sum_x_squared_count_all <> 0 THEN sum_xy_count_all / sum_x_squared_count_all + ELSE 0 + END AS k_count_all, + avg_y_count_all - (CASE + WHEN sum_x_squared_count_all <> 0 THEN sum_xy_count_all / sum_x_squared_count_all + ELSE 0 + END) * avg_x_count_all AS b_count_all, + + -- Параметры для reaching_17_age + CASE + WHEN sum_x_squared_reaching_17_age <> 0 THEN sum_xy_reaching_17_age / sum_x_squared_reaching_17_age + ELSE 0 + END AS k_reaching_17_age, + avg_y_reaching_17_age - (CASE + WHEN sum_x_squared_reaching_17_age <> 0 THEN sum_xy_reaching_17_age / sum_x_squared_reaching_17_age + ELSE 0 + END) * avg_x_reaching_17_age AS b_reaching_17_age, + + -- Параметры для women_military_specialty + CASE + WHEN sum_x_squared_women_military_specialty <> 0 THEN sum_xy_women_military_specialty / sum_x_squared_women_military_specialty + ELSE 0 + END AS k_women_military_specialty, + avg_y_women_military_specialty - (CASE + WHEN sum_x_squared_women_military_specialty <> 0 THEN sum_xy_women_military_specialty / sum_x_squared_women_military_specialty + ELSE 0 + END) * avg_x_women_military_specialty AS b_women_military_specialty + FROM regression_stats +) + +-- 11. Рассчитываем прогнозные значения на текущий и следующий годы +SELECT + recruitment_id, + schema, + MAKE_DATE(EXTRACT(YEAR FROM CURRENT_DATE)::int, 1, 1) AS current_year, + ROUND(k_count_all * EXTRACT(YEAR FROM CURRENT_DATE) + b_count_all) AS count_all, + ROUND(k_reaching_17_age * EXTRACT(YEAR FROM CURRENT_DATE) + b_reaching_17_age) AS reaching_17_age, + ROUND(k_women_military_specialty * EXTRACT(YEAR FROM CURRENT_DATE) + b_women_military_specialty) AS women_military_specialty, + 'Текущий год' AS prediction_type +FROM regression_params + +UNION ALL + +SELECT + recruitment_id, + schema, + MAKE_DATE(EXTRACT(YEAR FROM CURRENT_DATE)::int + 1, 1, 1) AS next_year, + ROUND(k_count_all * (EXTRACT(YEAR FROM CURRENT_DATE) + 1) + b_count_all) AS count_all, + ROUND(k_reaching_17_age * (EXTRACT(YEAR FROM CURRENT_DATE) + 1) + b_reaching_17_age) AS reaching_17_age, + ROUND(k_women_military_specialty * (EXTRACT(YEAR FROM CURRENT_DATE) + 1) + b_women_military_specialty) AS women_military_specialty, + 'Следующий год' AS prediction_type +FROM regression_params + +UNION ALL +SELECT + recruitment_id, + schema, + MAKE_DATE(EXTRACT(YEAR FROM CURRENT_DATE)::int + 2, 1, 1) AS next_year, + ROUND(k_count_all * (EXTRACT(YEAR FROM CURRENT_DATE) + 2) + b_count_all) AS count_all, + ROUND(k_reaching_17_age * (EXTRACT(YEAR FROM CURRENT_DATE) + 2) + b_reaching_17_age) AS reaching_17_age, + ROUND(k_women_military_specialty * (EXTRACT(YEAR FROM CURRENT_DATE) + 2) + b_women_military_specialty) AS women_military_specialty, + 'Следующий год' AS prediction_type +FROM regression_params + +UNION ALL + +SELECT + recruitment_id, + schema, + MAKE_DATE(EXTRACT(YEAR FROM CURRENT_DATE)::int + 3, 1, 1) AS next_year, + ROUND(k_count_all * (EXTRACT(YEAR FROM CURRENT_DATE) + 3) + b_count_all) AS count_all, + ROUND(k_reaching_17_age * (EXTRACT(YEAR FROM CURRENT_DATE) + 3) + b_reaching_17_age) AS reaching_17_age, + ROUND(k_women_military_specialty * (EXTRACT(YEAR FROM CURRENT_DATE) + 3) + b_women_military_specialty) AS women_military_specialty, + 'Следующий год' AS prediction_type +FROM regression_params +ORDER BY schema, recruitment_id, current_year; + + + N + + + 720 + 304 + + + + Table output + TableOutput + + Y + + 1 + + none + + + 1000 + ervu-dashboard + + + recruitment_id + recruitment_id + + + schema + schema + + + recording_date + current_year + + + count_all + count_all + + + reaching_17_age + reaching_17_age + + + women_military_specialty + women_military_specialty + + + N + N + N + N + Y + N + forecast + Y + registered_citizens
+ N + Y + N + Y + + + 928 + 304 + + + + + +