6d5a711d2c
功能修复： 1. 患者创建空名称验证：后端添加 name.trim().is_empty() 检查 2. 仪表盘统计容错：单个查询失败返回零值而非 500 3. FHIR 路由修复：从 /fhir 移到 /api/v1/fhir 保持一致 4. 冻结模块后端中间件：新增 frozen_module_middleware 拦截冻结路径 5. 积分端点权限码：health.health-data.list → health.points.list 6. 角色权限迁移：护士补充 devices.list，运营补充 points.list/manage 7. 测试结果文档：R01-R05 角色测试 + T00/T10 结果归档 Clippy 全 workspace 清零（14→0 errors）： - erp-core: 修复 empty doc line、collapsible if、redundant closure 等 9 处 - erp-health: 修复 too_many_arguments、unused var、unnecessary parens 等 58 处 - erp-ai: 修复 dead_code、unused import 等 11 处 - erp-plugin: 修复 too_many_arguments、wildcard pattern 等 11 处 - erp-server-migration: 修复 enum_variant_names 5 处 - erp-auth/config/workflow/message: 各 1-3 处 工程改进： - lint-staged 配置迁移到 .lintstagedrc.js（函数式避免文件列表传给 clippy） - cargo fmt 统一格式化
561 lines
20 KiB
Rust
561 lines
20 KiB
Rust
use async_trait::async_trait;
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use chrono::Datelike;
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use erp_core::crypto::{self as pii, PiiCrypto};
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use erp_core::error::{AppError, AppResult};
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use erp_core::health_provider::{
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AnomalyInfo, HealthDataProvider, HealthReportDto, LabItemDto, LabReportDto,
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MetricTrendAnalysis, PatientSummaryDto, RegressionStats, ReportSectionDto, TimeRange,
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TrendAnalysisDto, TrendDirection, VitalSignDto,
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};
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use num_traits::ToPrimitive;
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use sea_orm::{ColumnTrait, EntityTrait, QueryFilter, QueryOrder};
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use uuid::Uuid;
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use crate::entity::{
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diagnosis, health_record, lab_report, medication_record, patient, vital_signs,
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};
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pub struct HealthDataProviderImpl {
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pub db: sea_orm::DatabaseConnection,
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pub crypto: PiiCrypto,
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}
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fn compute_age_group(birth_date: Option<chrono::NaiveDate>) -> String {
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let Some(bd) = birth_date else {
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return "未知".to_string();
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};
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let age = chrono::Utc::now().date_naive().year() - bd.year();
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match age {
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a if a < 14 => "儿童",
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a if a < 36 => "青年",
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a if a < 56 => "中年",
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_ => "老年",
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}
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.to_string()
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}
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async fn find_patient(
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db: &sea_orm::DatabaseConnection,
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tenant_id: Uuid,
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patient_id: Uuid,
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) -> AppResult<patient::Model> {
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patient::Entity::find_by_id(patient_id)
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.filter(patient::Column::TenantId.eq(tenant_id))
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.filter(patient::Column::DeletedAt.is_null())
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.one(db)
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.await?
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.ok_or_else(|| AppError::NotFound(format!("患者 {patient_id} 不存在")))
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}
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async fn find_lab_report(
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db: &sea_orm::DatabaseConnection,
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tenant_id: Uuid,
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report_id: Uuid,
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) -> AppResult<lab_report::Model> {
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lab_report::Entity::find_by_id(report_id)
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.filter(lab_report::Column::TenantId.eq(tenant_id))
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.filter(lab_report::Column::DeletedAt.is_null())
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.one(db)
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.await?
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.ok_or_else(|| AppError::NotFound(format!("化验报告 {report_id} 不存在")))
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}
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fn parse_lab_items(items_json: &Option<serde_json::Value>) -> Vec<LabItemDto> {
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let Some(arr) = items_json.as_ref().and_then(|v| v.as_array()) else {
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return vec![];
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};
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arr.iter()
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.filter_map(|item| {
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// 兼容两种存储格式：item_name/name
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let name = item
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.get("item_name")
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.or_else(|| item.get("name"))?
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.as_str()?
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.to_string();
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// 兼容 value 为字符串或数字
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let value = item
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.get("value")
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.and_then(|v| v.as_f64())
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.or_else(|| item.get("value")?.as_str()?.parse::<f64>().ok())
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.unwrap_or(0.0);
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let unit = item
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.get("unit")
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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// 兼容两种参考范围格式：reference_range(string) 或 reference_low/reference_high(number)
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let reference_range = item
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.get("reference_range")
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.and_then(|v| v.as_str())
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.map(|s| s.to_string())
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.unwrap_or_else(|| {
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let low = item
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.get("reference_low")
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.and_then(|v| v.as_f64())
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.map(|l| l.to_string());
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let high = item
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.get("reference_high")
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.and_then(|v| v.as_f64())
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.map(|h| h.to_string());
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match (low, high) {
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(Some(l), Some(h)) => format!("{l}-{h}"),
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(Some(l), None) => format!(">={l}"),
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(None, Some(h)) => format!("<={h}"),
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_ => "-".to_string(),
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}
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});
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let is_abnormal = item
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.get("is_abnormal")
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.and_then(|v| v.as_bool())
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.unwrap_or(false);
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Some(LabItemDto {
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name,
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value,
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unit,
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reference_range,
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is_abnormal,
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})
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})
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.collect()
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}
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fn report_type_to_department(report_type: &str) -> &str {
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match report_type {
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"kidney_function" => "肾内科",
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"blood_routine" => "血液科",
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"electrolyte" => "检验科",
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"liver_function" => "肝胆科",
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_ => "检验科",
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}
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}
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#[async_trait]
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impl HealthDataProvider for HealthDataProviderImpl {
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async fn get_lab_report(&self, tenant_id: Uuid, report_id: Uuid) -> AppResult<LabReportDto> {
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let report = find_lab_report(&self.db, tenant_id, report_id).await?;
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let patient = find_patient(&self.db, tenant_id, report.patient_id).await?;
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// 解密 items：加密时存储为 Value::String(ciphertext)
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let kek = self.crypto.kek();
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let decrypted_items = report
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.items
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.as_ref()
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.and_then(|v| v.as_str())
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.and_then(|s| pii::decrypt(kek, s).ok())
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.and_then(|s| serde_json::from_str(&s).ok())
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.or(report.items.clone());
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Ok(LabReportDto {
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age_group: compute_age_group(patient.birth_date),
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sex: patient.gender.unwrap_or_else(|| "未知".to_string()),
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department: report_type_to_department(&report.report_type).to_string(),
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report_date: report.report_date.to_string(),
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items: parse_lab_items(&decrypted_items),
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})
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}
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async fn get_vital_signs(
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&self,
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tenant_id: Uuid,
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patient_id: Uuid,
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metrics: &[String],
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range: &TimeRange,
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) -> AppResult<Vec<VitalSignDto>> {
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let _ = find_patient(&self.db, tenant_id, patient_id).await?;
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let start_date = range.start.date_naive();
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let end_date = range.end.date_naive();
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let records = vital_signs::Entity::find()
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.filter(vital_signs::Column::TenantId.eq(tenant_id))
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.filter(vital_signs::Column::PatientId.eq(patient_id))
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.filter(vital_signs::Column::DeletedAt.is_null())
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.filter(vital_signs::Column::RecordDate.gte(start_date))
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.filter(vital_signs::Column::RecordDate.lte(end_date))
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.order_by_asc(vital_signs::Column::RecordDate)
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.all(&self.db)
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.await?;
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let metric_extractors: [(&str, Box<dyn Fn(&vital_signs::Model) -> Option<f64>>); 8] = [
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(
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"systolic_bp_morning",
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Box::new(|r| r.systolic_bp_morning.map(|v| v as f64)),
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),
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(
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"diastolic_bp_morning",
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Box::new(|r| r.diastolic_bp_morning.map(|v| v as f64)),
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),
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("heart_rate", Box::new(|r| r.heart_rate.map(|v| v as f64))),
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(
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"weight",
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Box::new(|r| r.weight.map(|v| v.to_f64().unwrap_or(0.0))),
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),
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(
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"blood_sugar",
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Box::new(|r| r.blood_sugar.map(|v| v.to_f64().unwrap_or(0.0))),
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),
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(
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"body_temperature",
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Box::new(|r| r.body_temperature.map(|v| v.to_f64().unwrap_or(0.0))),
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),
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("spo2", Box::new(|r| r.spo2.map(|v| v as f64))),
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(
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"urine_output_ml",
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Box::new(|r| r.urine_output_ml.map(|v| v as f64)),
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),
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];
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let mut result = Vec::new();
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for (metric_name, extractor) in &metric_extractors {
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if !metrics.is_empty() && !metrics.iter().any(|m| m == *metric_name) {
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continue;
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}
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let values: Vec<(String, f64)> = records
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.iter()
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.filter_map(|r| extractor(r).map(|v| (r.record_date.to_string(), v)))
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.collect();
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if values.is_empty() {
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continue;
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}
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let unit = match *metric_name {
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"systolic_bp_morning" | "diastolic_bp_morning" => "mmHg",
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"heart_rate" => "bpm",
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"weight" => "kg",
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"blood_sugar" => "mmol/L",
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"body_temperature" => "°C",
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"spo2" => "%",
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"urine_output_ml" => "ml",
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_ => "",
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};
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result.push(VitalSignDto {
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metric: metric_name.to_string(),
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values,
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unit: unit.to_string(),
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});
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}
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Ok(result)
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}
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async fn get_patient_summary(
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&self,
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tenant_id: Uuid,
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patient_id: Uuid,
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) -> AppResult<PatientSummaryDto> {
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let patient = find_patient(&self.db, tenant_id, patient_id).await?;
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let diagnoses: Vec<String> = diagnosis::Entity::find()
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.filter(diagnosis::Column::TenantId.eq(tenant_id))
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.filter(diagnosis::Column::PatientId.eq(patient_id))
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.filter(diagnosis::Column::DeletedAt.is_null())
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.filter(diagnosis::Column::Status.eq("active"))
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.order_by_desc(diagnosis::Column::DiagnosedDate)
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.all(&self.db)
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.await?
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.iter()
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.map(|d| format!("{}({})", d.diagnosis_name, d.icd_code))
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.collect();
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let medications: Vec<String> = medication_record::Entity::find()
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.filter(medication_record::Column::TenantId.eq(tenant_id))
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.filter(medication_record::Column::PatientId.eq(patient_id))
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.filter(medication_record::Column::DeletedAt.is_null())
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.filter(medication_record::Column::IsCurrent.eq(true))
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.all(&self.db)
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.await?
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.iter()
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.map(|m| {
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let mut s = m.medication_name.clone();
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if let Some(ref dosage) = m.dosage {
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s.push_str(&format!(" {dosage}"));
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}
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s
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})
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.collect();
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let family_history = patient
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.medical_history_summary
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.as_ref()
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.map(|h| {
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h.split('；')
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.chain(h.split(';'))
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.map(|s| s.trim().to_string())
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.filter(|s| !s.is_empty())
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.collect()
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})
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.unwrap_or_default();
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let last_checkup = health_record::Entity::find()
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.filter(health_record::Column::TenantId.eq(tenant_id))
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.filter(health_record::Column::PatientId.eq(patient_id))
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.filter(health_record::Column::DeletedAt.is_null())
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.order_by_desc(health_record::Column::RecordDate)
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.one(&self.db)
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.await?;
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let last_checkup_date = last_checkup
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.map(|r| r.record_date.to_string())
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.unwrap_or_else(|| "无".to_string());
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Ok(PatientSummaryDto {
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age_group: compute_age_group(patient.birth_date),
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sex: patient.gender.unwrap_or_else(|| "未知".to_string()),
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chronic_conditions: diagnoses,
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medications,
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family_history,
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last_checkup_date,
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})
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}
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async fn get_full_report(
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&self,
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tenant_id: Uuid,
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report_id: Uuid,
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) -> AppResult<HealthReportDto> {
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let record = health_record::Entity::find_by_id(report_id)
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.filter(health_record::Column::TenantId.eq(tenant_id))
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.filter(health_record::Column::DeletedAt.is_null())
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.one(&self.db)
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.await?
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.ok_or_else(|| AppError::NotFound(format!("健康报告 {report_id} 不存在")))?;
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let patient = find_patient(&self.db, tenant_id, record.patient_id).await?;
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let mut sections = Vec::new();
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let findings: Vec<String> = record
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.overall_assessment
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.as_ref()
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.map(|a| {
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a.split('；')
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.chain(a.split(';'))
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.map(|s| s.trim().to_string())
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.filter(|s| !s.is_empty())
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.collect()
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})
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.unwrap_or_default();
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if !findings.is_empty() {
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sections.push(ReportSectionDto {
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title: "总体评估".to_string(),
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findings,
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abnormal_items: vec![],
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});
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}
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let report_diagnoses = diagnosis::Entity::find()
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.filter(diagnosis::Column::TenantId.eq(tenant_id))
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.filter(diagnosis::Column::PatientId.eq(record.patient_id))
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.filter(diagnosis::Column::DeletedAt.is_null())
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.filter(
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diagnosis::Column::HealthRecordId
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.eq(report_id)
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.or(diagnosis::Column::Status.eq("active")),
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)
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.all(&self.db)
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.await?;
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if !report_diagnoses.is_empty() {
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let (abnormal, findings): (Vec<_>, Vec<_>) =
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report_diagnoses.iter().partition(|d| d.status == "active");
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sections.push(ReportSectionDto {
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title: "诊断记录".to_string(),
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findings: findings
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.iter()
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.map(|d| {
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format!(
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"{}({}) — {}",
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d.diagnosis_name, d.icd_code, d.diagnosed_date
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)
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})
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.collect(),
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abnormal_items: abnormal
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.iter()
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.map(|d| format!("{}({})", d.diagnosis_name, d.icd_code))
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.collect(),
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});
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}
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let lab_reports = lab_report::Entity::find()
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.filter(lab_report::Column::TenantId.eq(tenant_id))
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.filter(lab_report::Column::PatientId.eq(record.patient_id))
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.filter(lab_report::Column::DeletedAt.is_null())
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.filter(
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lab_report::Column::ReportDate.gte(record.record_date - chrono::Duration::days(30)),
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)
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.filter(lab_report::Column::ReportDate.lte(record.record_date))
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.order_by_desc(lab_report::Column::ReportDate)
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.all(&self.db)
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.await?;
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for lr in &lab_reports {
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let items = parse_lab_items(&lr.items);
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let abnormal: Vec<String> = items
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.iter()
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.filter(|i| i.is_abnormal)
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.map(|i| format!("{} {}{}", i.name, i.value, i.unit))
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.collect();
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let findings: Vec<String> = items
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.iter()
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.map(|i| format!("{}: {}{} ({})", i.name, i.value, i.unit, i.reference_range))
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.collect();
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if !findings.is_empty() {
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sections.push(ReportSectionDto {
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title: format!("化验报告 — {} ({})", lr.report_type, lr.report_date),
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findings,
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abnormal_items: abnormal,
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});
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}
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}
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Ok(HealthReportDto {
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age_group: compute_age_group(patient.birth_date),
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sex: patient.gender.unwrap_or_else(|| "未知".to_string()),
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department: record.record_type.clone(),
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report_date: record.record_date.to_string(),
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sections,
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})
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}
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async fn get_trend_analysis_data(
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&self,
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tenant_id: Uuid,
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patient_id: Uuid,
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metrics: &[String],
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range: &TimeRange,
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) -> AppResult<TrendAnalysisDto> {
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let _ = find_patient(&self.db, tenant_id, patient_id).await?;
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let start_date = range.start.date_naive();
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let end_date = range.end.date_naive();
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let records = vital_signs::Entity::find()
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.filter(vital_signs::Column::TenantId.eq(tenant_id))
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.filter(vital_signs::Column::PatientId.eq(patient_id))
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.filter(vital_signs::Column::DeletedAt.is_null())
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.filter(vital_signs::Column::RecordDate.gte(start_date))
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.filter(vital_signs::Column::RecordDate.lte(end_date))
|
||
.order_by_asc(vital_signs::Column::RecordDate)
|
||
.all(&self.db)
|
||
.await?;
|
||
|
||
let metric_extractors: [(&str, Box<dyn Fn(&vital_signs::Model) -> Option<f64>>); 8] = [
|
||
(
|
||
"systolic_bp_morning",
|
||
Box::new(|r| r.systolic_bp_morning.map(|v| v as f64)),
|
||
),
|
||
(
|
||
"diastolic_bp_morning",
|
||
Box::new(|r| r.diastolic_bp_morning.map(|v| v as f64)),
|
||
),
|
||
("heart_rate", Box::new(|r| r.heart_rate.map(|v| v as f64))),
|
||
(
|
||
"weight",
|
||
Box::new(|r| r.weight.map(|v| v.to_f64().unwrap_or(0.0))),
|
||
),
|
||
(
|
||
"blood_sugar",
|
||
Box::new(|r| r.blood_sugar.map(|v| v.to_f64().unwrap_or(0.0))),
|
||
),
|
||
(
|
||
"body_temperature",
|
||
Box::new(|r| r.body_temperature.map(|v| v.to_f64().unwrap_or(0.0))),
|
||
),
|
||
("spo2", Box::new(|r| r.spo2.map(|v| v as f64))),
|
||
(
|
||
"urine_output_ml",
|
||
Box::new(|r| r.urine_output_ml.map(|v| v as f64)),
|
||
),
|
||
];
|
||
|
||
let mut metric_results = Vec::new();
|
||
|
||
for (metric_name, extractor) in &metric_extractors {
|
||
if !metrics.is_empty() && !metrics.iter().any(|m| m == *metric_name) {
|
||
continue;
|
||
}
|
||
|
||
// 构建时间序列数据
|
||
let time_series: Vec<(chrono::NaiveDate, f64)> = records
|
||
.iter()
|
||
.filter_map(|r| extractor(r).map(|v| (r.record_date, v)))
|
||
.collect();
|
||
|
||
let data_point_count = time_series.len();
|
||
if data_point_count == 0 {
|
||
continue;
|
||
}
|
||
|
||
// 线性回归
|
||
let regression = crate::service::trend_stats::compute_linear_regression(&time_series)
|
||
.map(|r| RegressionStats {
|
||
slope: r.slope,
|
||
intercept: r.intercept,
|
||
r_squared: r.r_squared,
|
||
direction: match r.direction {
|
||
crate::service::trend_stats::TrendDirection::Rising => {
|
||
TrendDirection::Rising
|
||
}
|
||
crate::service::trend_stats::TrendDirection::Falling => {
|
||
TrendDirection::Falling
|
||
}
|
||
crate::service::trend_stats::TrendDirection::Stable => {
|
||
TrendDirection::Stable
|
||
}
|
||
},
|
||
daily_change: r.daily_change,
|
||
period_change: r.period_change,
|
||
});
|
||
|
||
// 异常检测（使用 2.0 倍标准差阈值）
|
||
let anomaly_points = crate::service::trend_stats::detect_anomalies(&time_series, 2.0);
|
||
let anomalies: Vec<AnomalyInfo> = anomaly_points
|
||
.into_iter()
|
||
.map(|a| AnomalyInfo {
|
||
date: a.date.to_string(),
|
||
value: a.value,
|
||
mean: a.mean,
|
||
std_dev: a.std_dev,
|
||
deviation: a.deviation,
|
||
})
|
||
.collect();
|
||
|
||
let unit = match *metric_name {
|
||
"systolic_bp_morning" | "diastolic_bp_morning" => "mmHg",
|
||
"heart_rate" => "bpm",
|
||
"weight" => "kg",
|
||
"blood_sugar" => "mmol/L",
|
||
"body_temperature" => "°C",
|
||
"spo2" => "%",
|
||
"urine_output_ml" => "ml",
|
||
_ => "",
|
||
};
|
||
|
||
metric_results.push(MetricTrendAnalysis {
|
||
metric: metric_name.to_string(),
|
||
unit: unit.to_string(),
|
||
data_point_count,
|
||
regression,
|
||
anomalies,
|
||
});
|
||
}
|
||
|
||
Ok(TrendAnalysisDto {
|
||
patient_id,
|
||
period_start: start_date.to_string(),
|
||
period_end: end_date.to_string(),
|
||
metrics: metric_results,
|
||
})
|
||
}
|
||
}
|