USE [JSDataMiddlePlatform];
SET NOCOUNT ON;

DECLARE @start_date date = DATEADD(day, -29, CAST(GETDATE() AS date));
DECLARE @end_date date = CAST(GETDATE() AS date);
DECLARE @start_time datetime2 = CAST(@start_date AS datetime2);
DECLARE @end_time datetime2 = DATEADD(day, 1, CAST(@end_date AS datetime2));
DECLARE @shop nvarchar(100) = N''; -- 可填店铺关键词，例如 N'亿家达'
DECLARE @limit int = 20;

IF OBJECT_ID('tempdb..#chat_base') IS NOT NULL
    DROP TABLE #chat_base;

SELECT
    c.id,
    NULLIF(c.orderNo, '') AS order_no,
    ISNULL(NULLIF(c.shopName, ''), N'未知店铺') AS shop_name,
    c.createdAt,
    c.updatedAt,
    c.state,
    ISNULL(NULLIF(LTRIM(RTRIM(c.mainReason)), ''), N'未标注') AS main_reason,
    ISNULL(NULLIF(LTRIM(RTRIM(c.secondReason)), ''), N'未标注') AS second_reason,
    ISNULL(NULLIF(LTRIM(RTRIM(c.thirdReason)), ''), N'未标注') AS third_reason,
    CAST(c.content AS nvarchar(max)) AS content_text,
    CAST(c.reasonContent AS nvarchar(max)) AS reason_text
INTO #chat_base
FROM dbo.pc_kfChatLog AS c WITH (NOLOCK)
WHERE c.createdAt >= @start_time
  AND c.createdAt < @end_time
  AND (@shop = N'' OR c.shopName LIKE N'%' + @shop + N'%');

IF OBJECT_ID('tempdb..#chat_norm') IS NOT NULL
    DROP TABLE #chat_norm;

SELECT
    b.*,
    CASE WHEN b.content_text IS NULL OR LTRIM(RTRIM(b.content_text)) IN (N'', N'[]') THEN 1 ELSE 0 END AS is_empty_content,
    CASE WHEN b.main_reason = N'未标注' OR b.second_reason = N'未标注' THEN 1 ELSE 0 END AS is_missing_reason,
    CASE WHEN b.main_reason = N'无对应分类' OR b.second_reason = N'无对应分类' THEN 1 ELSE 0 END AS is_no_match_reason,
    CASE WHEN b.content_text LIKE N'%欢迎%' OR b.content_text LIKE N'%您好%' OR b.content_text LIKE N'%亲,%' OR b.content_text LIKE N'%亲，%' THEN 1 ELSE 0 END AS has_greeting,
    CASE WHEN b.content_text LIKE N'%抱歉%' OR b.content_text LIKE N'%不好意思%' OR b.content_text LIKE N'%理解您%' THEN 1 ELSE 0 END AS has_empathy,
    CASE WHEN b.content_text LIKE N'%马上%' OR b.content_text LIKE N'%尽快%' OR b.content_text LIKE N'%给您处理%' OR b.content_text LIKE N'%为您处理%' THEN 1 ELSE 0 END AS has_commitment,
    CASE WHEN b.content_text LIKE N'%感谢%' OR b.content_text LIKE N'%祝您%' OR b.content_text LIKE N'%辛苦%' THEN 1 ELSE 0 END AS has_closing,
    CASE WHEN b.content_text LIKE N'%不知道%' OR b.content_text LIKE N'%没办法%' OR b.content_text LIKE N'%无法处理%' OR b.content_text LIKE N'%不清楚%' OR b.content_text LIKE N'%等不了%' OR b.content_text LIKE N'%太慢%' THEN 1 ELSE 0 END AS has_risk_phrase
INTO #chat_norm
FROM #chat_base AS b;

-- 1) 时间段客服体验总览
SELECT
    @start_date AS start_date,
    @end_date AS end_date,
    COUNT(*) AS session_count,
    COUNT(DISTINCT order_no) AS order_count,
    COUNT(DISTINCT shop_name) AS shop_count,
    SUM(is_empty_content) AS empty_content_count,
    CAST(100.0 * SUM(is_empty_content) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS empty_content_rate_pct,
    SUM(is_missing_reason) AS missing_reason_count,
    CAST(100.0 * SUM(is_missing_reason) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS missing_reason_rate_pct,
    SUM(is_no_match_reason) AS no_match_reason_count,
    CAST(100.0 * SUM(is_no_match_reason) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS no_match_reason_rate_pct,
    SUM(CASE WHEN state = 1 THEN 1 ELSE 0 END) AS state_1_count,
    SUM(CASE WHEN state = 2 THEN 1 ELSE 0 END) AS state_2_count
FROM #chat_norm;

-- 2) 用户反馈问题结构 TOP（一级/二级）
SELECT TOP (@limit)
    main_reason,
    second_reason,
    COUNT(*) AS session_count,
    COUNT(DISTINCT order_no) AS order_count,
    CAST(100.0 * COUNT(*) / NULLIF((SELECT COUNT(*) FROM #chat_norm), 0) AS decimal(6,2)) AS session_share_pct
FROM #chat_norm
GROUP BY main_reason, second_reason
ORDER BY session_count DESC, order_count DESC;

-- 3) 客服体验店铺对比（体验分）
WITH shop_agg AS (
    SELECT
        shop_name,
        COUNT(*) AS session_count,
        SUM(is_empty_content) AS empty_content_count,
        SUM(is_missing_reason) AS missing_reason_count,
        SUM(is_no_match_reason) AS no_match_reason_count
    FROM #chat_norm
    GROUP BY shop_name
)
SELECT TOP (@limit)
    s.shop_name,
    s.session_count,
    CAST(100.0 * s.empty_content_count / NULLIF(s.session_count, 0) AS decimal(6,2)) AS empty_content_rate_pct,
    CAST(100.0 * s.missing_reason_count / NULLIF(s.session_count, 0) AS decimal(6,2)) AS missing_reason_rate_pct,
    CAST(100.0 * s.no_match_reason_count / NULLIF(s.session_count, 0) AS decimal(6,2)) AS no_match_reason_rate_pct,
    CASE
        WHEN (
            100.0
            - (100.0 * s.missing_reason_count / NULLIF(s.session_count, 0)) * 0.4
            - (100.0 * s.no_match_reason_count / NULLIF(s.session_count, 0)) * 0.4
            - (100.0 * s.empty_content_count / NULLIF(s.session_count, 0)) * 0.2
        ) < 0 THEN CAST(0 AS decimal(6,2))
        ELSE CAST(
            100.0
            - (100.0 * s.missing_reason_count / NULLIF(s.session_count, 0)) * 0.4
            - (100.0 * s.no_match_reason_count / NULLIF(s.session_count, 0)) * 0.4
            - (100.0 * s.empty_content_count / NULLIF(s.session_count, 0)) * 0.2
            AS decimal(6,2)
        )
    END AS experience_score
FROM shop_agg AS s
ORDER BY experience_score DESC, s.session_count DESC;

-- 4) 话术覆盖分析
SELECT
    COUNT(*) AS session_count,
    SUM(has_greeting) AS greeting_hit_count,
    CAST(100.0 * SUM(has_greeting) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS greeting_hit_rate_pct,
    SUM(has_empathy) AS empathy_hit_count,
    CAST(100.0 * SUM(has_empathy) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS empathy_hit_rate_pct,
    SUM(has_commitment) AS commitment_hit_count,
    CAST(100.0 * SUM(has_commitment) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS commitment_hit_rate_pct,
    SUM(has_closing) AS closing_hit_count,
    CAST(100.0 * SUM(has_closing) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS closing_hit_rate_pct,
    SUM(has_risk_phrase) AS risk_phrase_hit_count,
    CAST(100.0 * SUM(has_risk_phrase) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS risk_phrase_hit_rate_pct
FROM #chat_norm;

-- 5) 风险话术命中 TOP
WITH phrase_hits AS (
    SELECT
        n.id,
        n.order_no,
        n.shop_name,
        v.risk_phrase
    FROM #chat_norm AS n
    CROSS APPLY (VALUES
        (CASE WHEN n.content_text LIKE N'%不知道%' THEN N'不知道' END),
        (CASE WHEN n.content_text LIKE N'%没办法%' THEN N'没办法' END),
        (CASE WHEN n.content_text LIKE N'%无法处理%' THEN N'无法处理' END),
        (CASE WHEN n.content_text LIKE N'%不清楚%' THEN N'不清楚' END),
        (CASE WHEN n.content_text LIKE N'%太慢%' THEN N'太慢' END),
        (CASE WHEN n.content_text LIKE N'%等不了%' THEN N'等不了' END)
    ) AS v(risk_phrase)
    WHERE v.risk_phrase IS NOT NULL
)
SELECT TOP (@limit)
    risk_phrase,
    COUNT(*) AS hit_count,
    COUNT(DISTINCT id) AS session_count,
    COUNT(DISTINCT order_no) AS order_count
FROM phrase_hits
GROUP BY risk_phrase
ORDER BY session_count DESC, hit_count DESC;

-- 6) 每日体验趋势
SELECT
    CONVERT(date, createdAt) AS stat_date,
    COUNT(*) AS session_count,
    CAST(100.0 * SUM(is_no_match_reason) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS no_match_reason_rate_pct,
    CAST(100.0 * SUM(is_missing_reason) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS missing_reason_rate_pct,
    CAST(100.0 * SUM(is_empty_content) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS empty_content_rate_pct,
    CAST(100.0 * SUM(has_risk_phrase) / NULLIF(COUNT(*), 0) AS decimal(6,2)) AS risk_phrase_hit_rate_pct
FROM #chat_norm
GROUP BY CONVERT(date, createdAt)
ORDER BY stat_date ASC;

-- 7) 对话案例（风险话术）+ 推荐回复
WITH case_pool AS (
    SELECT
        n.id,
        n.order_no,
        n.shop_name,
        n.main_reason,
        n.second_reason,
        n.content_text,
        CASE
            WHEN n.content_text LIKE N'%不清楚%' THEN N'不清楚'
            WHEN n.content_text LIKE N'%不知道%' THEN N'不知道'
            WHEN n.content_text LIKE N'%没办法%' THEN N'没办法'
            WHEN n.content_text LIKE N'%太慢%' THEN N'太慢'
            WHEN n.content_text LIKE N'%等不了%' THEN N'等不了'
            WHEN n.content_text LIKE N'%无法处理%' THEN N'无法处理'
            ELSE NULL
        END AS risk_tag
    FROM #chat_norm AS n
)
SELECT TOP (@limit)
    order_no,
    shop_name,
    main_reason,
    second_reason,
    risk_tag,
    LEFT(REPLACE(REPLACE(ISNULL(content_text, N''), CHAR(13), N' '), CHAR(10), N' '), 220) AS dialogue_sample,
    CASE
        WHEN risk_tag IN (N'不清楚', N'不知道')
            THEN N'建议回复：我先帮您核实一下该问题，预计5分钟内给您明确答复。'
        WHEN risk_tag = N'没办法'
            THEN N'建议回复：这个情况我来帮您申请处理方案，先给您两个可行选项并同步时效。'
        WHEN risk_tag = N'无法处理'
            THEN N'建议回复：我已升级给对应专员处理，预计今天内给您处理结果。'
        WHEN risk_tag IN (N'太慢', N'等不了')
            THEN N'建议回复：抱歉让您久等，我现在优先为您加急处理，并在10分钟内回报进展。'
        ELSE N'建议回复：收到您的问题，我先确认订单与处理路径，稍后给您明确方案。'
    END AS suggested_reply
FROM case_pool
WHERE risk_tag IS NOT NULL
ORDER BY id DESC;

-- 8) 客服能力评分（参考天猫服务保障维度，按可用字段做代理映射）
;WITH score_base AS (
    SELECT
        shop_name,
        COUNT(*) AS session_count,
        CAST(SUM(has_commitment) AS float) / NULLIF(COUNT(*), 0) AS commitment_rate,
        CAST(SUM(has_empathy) AS float) / NULLIF(COUNT(*), 0) AS empathy_rate,
        CAST(SUM(has_closing) AS float) / NULLIF(COUNT(*), 0) AS closing_rate,
        CAST(SUM(has_risk_phrase) AS float) / NULLIF(COUNT(*), 0) AS risk_phrase_rate,
        CAST(SUM(CASE WHEN second_reason LIKE N'%催退款%' OR second_reason LIKE N'%催取件%' THEN 1 ELSE 0 END) AS float) / NULLIF(COUNT(*), 0) AS refund_urge_rate,
        CAST(SUM(is_no_match_reason) AS float) / NULLIF(COUNT(*), 0) AS no_match_rate,
        CAST(SUM(is_missing_reason) AS float) / NULLIF(COUNT(*), 0) AS missing_reason_rate
    FROM #chat_norm
    GROUP BY shop_name
),
score_calc AS (
    SELECT
        sb.*,
        100.0 * (0.70 * sb.commitment_rate + 0.30 * (1.0 - sb.risk_phrase_rate)) AS resp_ability_score,
        100.0 * (0.45 * sb.empathy_rate + 0.35 * sb.closing_rate + 0.20 * (1.0 - sb.risk_phrase_rate)) AS satisfaction_score,
        100.0 * (1.0 - sb.refund_urge_rate) AS refund_efficiency_score,
        100.0 * (1.0 - (0.80 * sb.no_match_rate + 0.20 * sb.missing_reason_rate)) AS help_risk_control_score
    FROM score_base AS sb
)
SELECT TOP (@limit)
    shop_name,
    session_count,
    CAST(resp_ability_score AS decimal(6,2)) AS resp_ability_score,
    CAST(satisfaction_score AS decimal(6,2)) AS satisfaction_score,
    CAST(refund_efficiency_score AS decimal(6,2)) AS refund_efficiency_score,
    CAST(help_risk_control_score AS decimal(6,2)) AS help_risk_control_score,
    CAST(
        0.30 * resp_ability_score
        + 0.30 * satisfaction_score
        + 0.20 * refund_efficiency_score
        + 0.20 * help_risk_control_score
        AS decimal(6,2)
    ) AS service_capability_score,
    CASE
        WHEN (
            0.30 * resp_ability_score
            + 0.30 * satisfaction_score
            + 0.20 * refund_efficiency_score
            + 0.20 * help_risk_control_score
        ) >= 90 THEN N'S'
        WHEN (
            0.30 * resp_ability_score
            + 0.30 * satisfaction_score
            + 0.20 * refund_efficiency_score
            + 0.20 * help_risk_control_score
        ) >= 80 THEN N'A'
        WHEN (
            0.30 * resp_ability_score
            + 0.30 * satisfaction_score
            + 0.20 * refund_efficiency_score
            + 0.20 * help_risk_control_score
        ) >= 70 THEN N'B'
        WHEN (
            0.30 * resp_ability_score
            + 0.30 * satisfaction_score
            + 0.20 * refund_efficiency_score
            + 0.20 * help_risk_control_score
        ) >= 60 THEN N'C'
        ELSE N'D'
    END AS service_grade,
    N'映射说明: 响应能力≈旺旺响应能力; 满意度≈旺旺满意度; 退款效率≈退款处理体验; 求助风险控制≈平台求助风险(代理口径)' AS score_mapping_note
FROM score_calc
ORDER BY service_capability_score DESC, session_count DESC;