[KC-103] 추론 실행 배치 수정

src/main/java/com/kamco/cd/kamcoback/inference/dto/InferenceResultDto.java

@@ -23,7 +23,9 @@ import org.springframework.data.domain.Pageable;
 
 public class InferenceResultDto {
 
-  /** 목록조회 dto */
+  /**
+   * 목록조회 dto
+   */
   @Getter
   @Setter
   @AllArgsConstructor
@@ -35,11 +37,15 @@ public class InferenceResultDto {
     private String status;
     private String mapSheetCnt;
     private Long detectingCnt;
-    @JsonFormatDttm private ZonedDateTime startTime;
+    @JsonFormatDttm
-    @JsonFormatDttm private ZonedDateTime endTime;
+    private ZonedDateTime startTime;
-    @JsonFormatDttm private ZonedDateTime elapsedTime;
+    @JsonFormatDttm
+    private ZonedDateTime endTime;
+    @JsonFormatDttm
+    private ZonedDateTime elapsedTime;
     private Boolean applyYn;
-    @JsonFormatDttm private ZonedDateTime applyDttm;
+    @JsonFormatDttm
+    private ZonedDateTime applyDttm;
 
     @JsonProperty("statusName")
     public String statusName() {
@@ -47,7 +53,9 @@ public class InferenceResultDto {
     }
   }
 
-  /** 목록조회 검색 조건 dto */
+  /**
+   * 목록조회 검색 조건 dto
+   */
   @Getter
   @Setter
   @NoArgsConstructor
@@ -69,7 +77,9 @@ public class InferenceResultDto {
     }
   }
 
-  /** 탐지 데이터 옵션 dto */
+  /**
+   * 탐지 데이터 옵션 dto
+   */
   @Getter
   @AllArgsConstructor
   public enum MapSheetScope implements EnumType {
@@ -90,7 +100,9 @@ public class InferenceResultDto {
     }
   }
 
-  /** 분석대상 도엽 enum */
+  /**
+   * 분석대상 도엽 enum
+   */
   @Getter
   @AllArgsConstructor
   public enum DetectOption implements EnumType {
@@ -138,7 +150,9 @@ public class InferenceResultDto {
     }
   }
 
-  /** 변화탐지 실행 정보 저장 요청 정보 */
+  /**
+   * 변화탐지 실행 정보 저장 요청 정보
+   */
   @Getter
   @Setter
   @NoArgsConstructor
@@ -205,17 +219,25 @@ public class InferenceResultDto {
     private Integer targetYyyy;
     private String detectOption;
     private String mapSheetScope;
-    @JsonFormatDttm private ZonedDateTime inferStartDttm;
+    @JsonFormatDttm
-    @JsonFormatDttm private ZonedDateTime inferEndDttm;
+    private ZonedDateTime inferStartDttm;
+    @JsonFormatDttm
+    private ZonedDateTime inferEndDttm;
     private Long detectingCnt = 0L;
     private Long detectingEndCnt = 0L;
 
-    @JsonFormatDttm private ZonedDateTime m1ModelStartDttm;
+    @JsonFormatDttm
-    @JsonFormatDttm private ZonedDateTime m1ModelEndDttm;
+    private ZonedDateTime m1ModelStartDttm;
-    @JsonFormatDttm private ZonedDateTime m2ModelStartDttm;
+    @JsonFormatDttm
-    @JsonFormatDttm private ZonedDateTime m2ModelEndDttm;
+    private ZonedDateTime m1ModelEndDttm;
-    @JsonFormatDttm private ZonedDateTime m3ModelStartDttm;
+    @JsonFormatDttm
-    @JsonFormatDttm private ZonedDateTime m3ModelEndDttm;
+    private ZonedDateTime m2ModelStartDttm;
+    @JsonFormatDttm
+    private ZonedDateTime m2ModelEndDttm;
+    @JsonFormatDttm
+    private ZonedDateTime m3ModelStartDttm;
+    @JsonFormatDttm
+    private ZonedDateTime m3ModelEndDttm;
 
     private String model1Ver;
     private String model2Ver;
@@ -342,9 +364,12 @@ public class InferenceResultDto {
   public static class InferenceServerStatusDto {
 
     private String serverName;
-    @JsonIgnore private float cpu_user;
+    @JsonIgnore
-    @JsonIgnore private float cpu_system;
+    private float cpu_user;
-    @JsonIgnore private float memused;
+    @JsonIgnore
+    private float cpu_system;
+    @JsonIgnore
+    private float memused;
     private Long kbmemused;
     private float gpuUtil;
 
@@ -458,5 +483,9 @@ public class InferenceResultDto {
     private ZonedDateTime modelEndDttm;
     private Long updateUid;
     private String runningModelType;
+    private Integer pendingJobs;
+    private Integer runningJobs;
+    private Integer completedJobs;
+    private Integer failedJobs;
   }
 }

src/main/java/com/kamco/cd/kamcoback/inference/service/InferenceResultService.java

@@ -140,6 +140,7 @@ public class InferenceResultService {
       throw new CustomApiException("NOT_FOUND_COMPARE_YEAR", HttpStatus.NOT_FOUND);
     }
 
+    // 사용할 영상파일 년도 기록 및 추론에 포함되는지 설정
     for (MngListDto target : targetList) {
       for (Map<String, Object> map : totalNumList) {
         if (target.getMapSheetNum().equals(map.get("mapSheetNum").toString())) {
@@ -271,14 +272,14 @@ public class InferenceResultService {
       throw new CustomApiException("BAD_REQUEST", HttpStatus.BAD_REQUEST);
     }
 
-    // 1) 요청 로그 (debug 권장)
+    // 1) 요청 로그
     try {
       log.debug("Inference request dto={}", objectMapper.writeValueAsString(dto));
     } catch (JsonProcessingException e) {
       log.warn("Failed to serialize inference dto", e);
     }
 
-    // 2) local 환경 임시 처리 (NPE 방어)
+    // 2) local 환경 임시 처리
     if ("local".equals(profile)) {
       if (dto.getPred_requests_areas() == null) {
         throw new IllegalStateException("pred_requests_areas is null");
@@ -303,7 +304,8 @@ public class InferenceResultService {
     // 4) 응답 파싱
     try {
       List<Map<String, Object>> list =
-          objectMapper.readValue(result.body(), new TypeReference<>() {});
+        objectMapper.readValue(result.body(), new TypeReference<>() {
+        });
 
       if (list.isEmpty()) {
         throw new IllegalStateException("Inference response is empty");

src/main/java/com/kamco/cd/kamcoback/postgres/core/InferenceResultCoreService.java

@@ -29,6 +29,7 @@ import java.time.ZonedDateTime;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.UUID;
+import java.util.function.Consumer;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.log4j.Log4j2;
 import org.springframework.dao.DataAccessException;
@@ -234,91 +235,95 @@ public class InferenceResultCoreService {
       .toList();
   }
 
+  /**
+   * learn 테이블 update
+   *
+   * @param request 추론 실행 정보
+   */
   public void update(SaveInferenceAiDto request) {
     MapSheetLearnEntity entity =
-        mapSheetLearnRepository
+      mapSheetLearnRepository.getInferenceResultByUuid(request.getUuid())
-            .getInferenceResultByUuid(request.getUuid())
+        .orElseThrow(EntityNotFoundException::new);
-            .orElseThrow(() -> new EntityNotFoundException());
 
+    // M1/M2/M3 영역 업데이트
     if (request.getType() != null) {
-      switch (request.getType()) {
+      applyModelUpdate(entity, request);
-        case "M1" -> {
-          if (request.getBatchId() != null) {
-            entity.setM1ModelBatchId(request.getBatchId());
     }
 
-          if (request.getModelStartDttm() != null) {
+    // 공통 영역 업데이트
-            entity.setM1ModelStartDttm(request.getModelStartDttm());
+    applyIfNotNull(request.getRunningModelType(), entity::setRunningModelType);
-          }
+    applyIfNotNull(request.getInferStartDttm(), entity::setInferStartDttm);
-
+    applyIfNotNull(request.getInferEndDttm(), entity::setInferEndDttm);
-          if (request.getModelEndDttm() != null) {
+    applyIfNotNull(request.getModelComparePath(), entity::setModelComparePath);
-            entity.setM1ModelEndDttm(request.getModelEndDttm());
+    applyIfNotNull(request.getModelTargetPath(), entity::setModelTargetPath);
-          }
+    applyIfNotNull(request.getDetectEndCnt(), entity::setDetectEndCnt);
-        }
+    applyIfNotNull(request.getStatus(), entity::setStatus);
-        case "M2" -> {
+    applyIfNotNull(request.getUpdateUid(), entity::setUpdatedUid);
-          if (request.getBatchId() != null) {
-            entity.setM2ModelBatchId(request.getBatchId());
-          }
-
-          if (request.getModelStartDttm() != null) {
-            entity.setM2ModelStartDttm(request.getModelStartDttm());
-          }
-
-          if (request.getModelEndDttm() != null) {
-            entity.setM2ModelEndDttm(request.getModelEndDttm());
-          }
-        }
-        case "M3" -> {
-          if (request.getBatchId() != null) {
-            entity.setM3ModelBatchId(request.getBatchId());
-          }
-
-          if (request.getModelStartDttm() != null) {
-            entity.setM3ModelStartDttm(request.getModelStartDttm());
-          }
-
-          if (request.getModelEndDttm() != null) {
-            entity.setM3ModelEndDttm(request.getModelEndDttm());
-          }
-        }
-      }
-    }
-
-    if (request.getRunningModelType() != null) {
-      entity.setRunningModelType(request.getRunningModelType());
-    }
-
-    if (request.getInferStartDttm() != null) {
-      entity.setInferStartDttm(request.getInferStartDttm());
-    }
-
-    if (request.getInferEndDttm() != null) {
-      entity.setInferEndDttm(request.getInferEndDttm());
-    }
-
-    if (request.getModelComparePath() != null) {
-      entity.setModelComparePath(request.getModelComparePath());
-    }
-
-    if (request.getModelTargetPath() != null) {
-      entity.setModelTargetPath(request.getModelTargetPath());
-    }
-
-    if (request.getDetectEndCnt() != null) {
-      entity.setDetectEndCnt(request.getDetectEndCnt());
-    }
-
-    if (request.getStatus() != null) {
-      entity.setStatus(request.getStatus());
-    }
-
-    if (request.getUpdateUid() != null) {
-      entity.setUpdatedUid(request.getUpdateUid());
-    }
 
     entity.setUpdatedDttm(ZonedDateTime.now());
   }
 
+  private void applyModelUpdate(MapSheetLearnEntity entity, SaveInferenceAiDto request) {
+    switch (request.getType()) {
+      case "M1" -> applyModelFields(
+        request,
+        entity::setM1ModelBatchId,
+        entity::setM1ModelStartDttm,
+        entity::setM1ModelEndDttm,
+        entity::setM1PendingJobs,
+        entity::setM1RunningJobs,
+        entity::setM1CompletedJobs,
+        entity::setM1FailedJobs
+      );
+      case "M2" -> applyModelFields(
+        request,
+        entity::setM2ModelBatchId,
+        entity::setM2ModelStartDttm,
+        entity::setM2ModelEndDttm,
+        entity::setM2PendingJobs,
+        entity::setM2RunningJobs,
+        entity::setM2CompletedJobs,
+        entity::setM2FailedJobs
+      );
+      case "M3" -> applyModelFields(
+        request,
+        entity::setM3ModelBatchId,
+        entity::setM3ModelStartDttm,
+        entity::setM3ModelEndDttm,
+        entity::setM3PendingJobs,
+        entity::setM3RunningJobs,
+        entity::setM3CompletedJobs,
+        entity::setM3FailedJobs
+      );
+      default -> throw new IllegalArgumentException("Unknown type: " + request.getType());
+    }
+  }
+
+  private void applyModelFields(
+    SaveInferenceAiDto request,
+    Consumer<Long> setBatchId,
+    Consumer<ZonedDateTime> setStart,
+    Consumer<ZonedDateTime> setEnd,
+    Consumer<Integer> setPending,
+    Consumer<Integer> setRunning,
+    Consumer<Integer> setCompleted,
+    Consumer<Integer> setFailed
+  ) {
+    applyIfNotNull(request.getBatchId(), setBatchId);
+    applyIfNotNull(request.getModelStartDttm(), setStart);
+    applyIfNotNull(request.getModelEndDttm(), setEnd);
+    applyIfNotNull(request.getPendingJobs(), setPending);
+    applyIfNotNull(request.getRunningJobs(), setRunning);
+    applyIfNotNull(request.getCompletedJobs(), setCompleted);
+    applyIfNotNull(request.getFailedJobs(), setFailed);
+  }
+
+  private <T> void applyIfNotNull(T value, Consumer<T> setter) {
+    if (value != null) {
+      setter.accept(value);
+    }
+  }
+
   public List<InferenceServerStatusDto> getInferenceServerStatusList() {
     return mapSheetLearnRepository.getInferenceServerStatusList();
   }
@@ -345,7 +350,14 @@ public class InferenceResultCoreService {
     return inferenceBatchSheet;
   }
 
-  public InferenceProgressDto getInferenceAiResultById(Long id, String type, UUID modelUuid) {
+  /**
+   * 추론 실행 api 파라미터 조회
+   *
+   * @param id
+   * @param modelUuid
+   * @return
+   */
+  public InferenceProgressDto getInferenceAiResultById(Long id, UUID modelUuid) {
     return mapSheetLearnRepository.getInferenceAiResultById(id, modelUuid);
   }
 

src/main/java/com/kamco/cd/kamcoback/postgres/repository/label/LabelAllocateRepositoryImpl.java

@@ -34,7 +34,6 @@ import com.querydsl.core.types.dsl.CaseBuilder;
 import com.querydsl.core.types.dsl.Expressions;
 import com.querydsl.core.types.dsl.NumberExpression;
 import com.querydsl.core.types.dsl.StringExpression;
-import com.querydsl.jpa.JPAExpressions;
 import com.querydsl.jpa.impl.JPAQueryFactory;
 import jakarta.persistence.EntityManager;
 import jakarta.persistence.EntityNotFoundException;

src/main/java/com/kamco/cd/kamcoback/scheduler/service/MapSheetInferenceJobService.java

@@ -11,7 +11,6 @@ import com.kamco.cd.kamcoback.inference.dto.InferenceProgressDto;
 import com.kamco.cd.kamcoback.inference.dto.InferenceResultDto.SaveInferenceAiDto;
 import com.kamco.cd.kamcoback.inference.dto.InferenceResultDto.Status;
 import com.kamco.cd.kamcoback.inference.dto.InferenceSendDto;
-import com.kamco.cd.kamcoback.inference.dto.InferenceSendDto.pred_requests_areas;
 import com.kamco.cd.kamcoback.postgres.core.InferenceResultCoreService;
 import com.kamco.cd.kamcoback.scheduler.dto.JobStatusDto;
 import jakarta.transaction.Transactional;
@@ -48,137 +47,247 @@ public class MapSheetInferenceJobService {
   @Value("${inference.url}")
   private String inferenceUrl;
 
-  /** 추론 진행 배치 1분 */
+  /**
+   * 추론 진행 배치 1분
+   */
   @Scheduled(fixedDelay = 60_000)
   @Transactional
   public void runBatch() {
-
+    if (isLocalProfile()) {
-    if ("local".equalsIgnoreCase(profile)) {
       return;
     }
 
     try {
-      InferenceBatchSheet batchSheet =
+      // 진행중 배치 조회
-          inferenceResultCoreService.getInferenceResultByStatus(Status.IN_PROGRESS.getId());
+      InferenceBatchSheet sheet = findInProgressSheet();
-
+      if (sheet == null) {
-      if (batchSheet == null) {
         return;
       }
 
-      HttpHeaders headers = new HttpHeaders();
+      // 배치 아이디 가져오기
-      headers.setContentType(MediaType.APPLICATION_JSON);
+      Long batchId = resolveBatchId(sheet);
-      headers.setAccept(List.of(MediaType.APPLICATION_JSON));
+      if (batchId == null || batchId == 0L) {
-
-      Long batchId = 0L;
-
-      if (batchSheet.getM3BatchId() != null) {
-        batchId = batchSheet.getM3BatchId();
-      } else if (batchSheet.getM2BatchId() != null) {
-        batchId = batchSheet.getM2BatchId();
-      } else if (batchSheet.getM1BatchId() != null) {
-        batchId = batchSheet.getM1BatchId();
-      }
-
-      if (batchId == 0L) {
         return;
       }
 
-      String url = batchUrl + "/" + batchId;
+      // 추론실행 상태 정보 가져오기
-
+      JobStatusDto job = fetchJobStatus(batchId);
-      ExternalCallResult<String> result =
+      if (job == null) {
-          externalHttpClient.call(url, HttpMethod.GET, null, headers, String.class);
-
-      int status = result.statusCode();
-      if (status < 200 || status >= 300) {
         return;
       }
 
-      String json = result.body();
+      if (isCompleted(job)) {
-      JobStatusDto dto = objectMapper.readValue(json, JobStatusDto.class);
+        // 완료 처리
-
+        onCompleted(sheet, job);
-      int totalJobs = dto.getTotalJobs();
-      int completedJobs = dto.getCompletedJobs();
-      int failedJobs = dto.getFailedJobs();
-
-      // 성공, 실패 값 더해서 total 과 같으면 완료
-      String inferStatus = setStatus(totalJobs, completedJobs, failedJobs);
-
-      if ("COMPLETED".equals(inferStatus)) {
-        String type = batchSheet.getRunningModelType();
-
-        if (type.equals("M1")) {
-          // M1 완료되었으면 M2 실행
-          startInference(
-              batchSheet.getId(), batchSheet.getUuid(), "M2", batchSheet.getM2ModelUuid());
-          // 종료시간
-          updateProcessingEndTimeByModel(batchSheet.getUuid(), ZonedDateTime.now(), "M1");
-        } else if (type.equals("M2")) {
-          // M2 완료되었으면 M3 실행
-          startInference(
-              batchSheet.getId(), batchSheet.getUuid(), "M3", batchSheet.getM3ModelUuid());
-          // 종료시간
-          updateProcessingEndTimeByModel(batchSheet.getUuid(), ZonedDateTime.now(), "M2");
-        } else if (type.equals("M3")) {
-          // 완료
-          SaveInferenceAiDto saveInferenceAiDto = new SaveInferenceAiDto();
-          saveInferenceAiDto.setUuid(batchSheet.getUuid());
-          saveInferenceAiDto.setStatus(Status.END.getId());
-          saveInferenceAiDto.setInferEndDttm(ZonedDateTime.now());
-          saveInferenceAiDto.setType(type);
-          inferenceResultCoreService.update(saveInferenceAiDto);
-          // 종료시간
-          updateProcessingEndTimeByModel(batchSheet.getUuid(), ZonedDateTime.now(), "M3");
-        }
       } else {
-        SaveInferenceAiDto saveInferenceAiDto = new SaveInferenceAiDto();
+        // 진행중 처리
-        saveInferenceAiDto.setUuid(batchSheet.getUuid());
+        onProcessing(sheet, job);
-        saveInferenceAiDto.setStatus(Status.IN_PROGRESS.getId());
-        saveInferenceAiDto.setDetectEndCnt((long) (completedJobs + failedJobs));
-        inferenceResultCoreService.update(saveInferenceAiDto);
       }
 
     } catch (JsonProcessingException e) {
-      Thread.currentThread().interrupt();
+      // JSON 파싱 오류는 interrupt 대상 아님
-      log.error("배치 중 인터럽트 발생", e);
+      log.error("배치 중 JSON 파싱 오류", e);
+    } catch (Exception e) {
+      log.error("배치 처리 중 예외", e);
     }
   }
 
+  /**
+   * 진행중 배치 조회
+   *
+   * @return
+   */
+  private InferenceBatchSheet findInProgressSheet() {
+    return inferenceResultCoreService.getInferenceResultByStatus(Status.IN_PROGRESS.getId());
+  }
+
+  /**
+   * batchId 결정
+   *
+   * @param sheet
+   * @return
+   */
+  private Long resolveBatchId(InferenceBatchSheet sheet) {
+    // M3 > M2 > M1
+    if (sheet.getM3BatchId() != null) {
+      return sheet.getM3BatchId();
+    }
+    if (sheet.getM2BatchId() != null) {
+      return sheet.getM2BatchId();
+    }
+    if (sheet.getM1BatchId() != null) {
+      return sheet.getM1BatchId();
+    }
+    return 0L;
+  }
+
+  /**
+   * 추론실행 상태 정보 가져오기
+   *
+   * @param batchId
+   * @return
+   * @throws JsonProcessingException
+   */
+  private JobStatusDto fetchJobStatus(Long batchId) throws JsonProcessingException {
+    String url = batchUrl + "/" + batchId;
+
+    ExternalCallResult<String> result =
+      externalHttpClient.call(url, HttpMethod.GET, null, jsonHeaders(), String.class);
+
+    int status = result.statusCode();
+    if (status < 200 || status >= 300) {
+      return null;
+    }
+
+    return objectMapper.readValue(result.body(), JobStatusDto.class);
+  }
+
+  private HttpHeaders jsonHeaders() {
+    HttpHeaders headers = new HttpHeaders();
+    headers.setContentType(MediaType.APPLICATION_JSON);
+    headers.setAccept(List.of(MediaType.APPLICATION_JSON));
+    return headers;
+  }
+
+  /**
+   * 완료 판단
+   *
+   * @param dto
+   * @return
+   */
+  private boolean isCompleted(JobStatusDto dto) {
+    return dto.getTotalJobs() <= (dto.getCompletedJobs() + dto.getFailedJobs());
+  }
+
+  /**
+   * 완료 처리
+   *
+   * @param sheet
+   * @param job
+   */
+  private void onCompleted(InferenceBatchSheet sheet, JobStatusDto job) {
+    String currentType = sheet.getRunningModelType();
+    ZonedDateTime now = ZonedDateTime.now();
+
+    // 현재 모델 종료 업데이트
+    updateProcessingEndTimeByModel(job, sheet.getUuid(), now, currentType);
+
+    // M3이면 전체 종료
+    if ("M3".equals(currentType)) {
+      endAll(sheet, now);
+      return;
+    }
+
+    // 다음 모델 실행 (M1->M2, M2->M3)
+    String nextType = nextModelType(currentType);
+    UUID nextModelUuid = resolveModelUuid(sheet, nextType);
+
+    // 다음 모델 추론 실행
+    startInference(sheet.getId(), sheet.getUuid(), nextType, nextModelUuid);
+  }
+
+  /**
+   * 추론 종료 할때 update
+   *
+   * @param sheet
+   * @param now
+   */
+  private void endAll(InferenceBatchSheet sheet, ZonedDateTime now) {
+    SaveInferenceAiDto save = new SaveInferenceAiDto();
+    save.setUuid(sheet.getUuid());
+    save.setStatus(Status.END.getId());
+    save.setInferEndDttm(now);
+    save.setType("M3"); // 마지막 모델 기준
+    inferenceResultCoreService.update(save);
+  }
+
+  /**
+   * 다음 실행할 모델 타입 조회
+   *
+   * @param currentType
+   * @return
+   */
+  private String nextModelType(String currentType) {
+    if ("M1".equals(currentType)) {
+      return "M2";
+    }
+    if ("M2".equals(currentType)) {
+      return "M3";
+    }
+    throw new IllegalArgumentException("Unknown runningModelType: " + currentType);
+  }
+
+  /**
+   * 모델 정보 UUID 가져오기
+   *
+   * @param sheet
+   * @param type
+   * @return
+   */
+  private UUID resolveModelUuid(InferenceBatchSheet sheet, String type) {
+    if ("M1".equals(type)) {
+      return sheet.getM1ModelUuid();
+    }
+    if ("M2".equals(type)) {
+      return sheet.getM2ModelUuid();
+    }
+    if ("M3".equals(type)) {
+      return sheet.getM3ModelUuid();
+    }
+    throw new IllegalArgumentException("Unknown type: " + type);
+  }
+
+  /**
+   * 진행중 처리
+   *
+   * @param sheet
+   * @param job
+   */
+  private void onProcessing(InferenceBatchSheet sheet, JobStatusDto job) {
+    SaveInferenceAiDto save = new SaveInferenceAiDto();
+    save.setUuid(sheet.getUuid());
+    save.setStatus(Status.IN_PROGRESS.getId());
+    save.setPendingJobs(job.getPendingJobs());
+    save.setRunningJobs(job.getRunningJobs());
+    save.setCompletedJobs(job.getCompletedJobs());
+    save.setFailedJobs(job.getFailedJobs());
+    inferenceResultCoreService.update(save);
+  }
+
+  /**
+   * 다음 모델 추론 실행
+   *
+   * @param id
+   * @param uuid
+   * @param type
+   * @param modelUuid
+   */
   private void startInference(Long id, UUID uuid, String type, UUID modelUuid) {
 
+    // 추론 실행 api 파라미터 조회
     InferenceProgressDto progressDto =
-        inferenceResultCoreService.getInferenceAiResultById(id, type, modelUuid);
+      inferenceResultCoreService.getInferenceAiResultById(id, modelUuid);
 
-    String inferenceType = "";
+    // ai 에 맞는 모델 명으로 변경
+    String inferenceType = modelToInferenceType(type);
 
-    if (type.equals("M1")) {
+    InferenceSendDto.pred_requests_areas predRequestsAreas = new InferenceSendDto.pred_requests_areas();
-      inferenceType = "G1";
-    } else if (type.equals("M2")) {
-      inferenceType = "G2";
-    } else if (type.equals("M3")) {
-      inferenceType = "G3";
-    }
-
-    pred_requests_areas predRequestsAreas = new pred_requests_areas();
     predRequestsAreas.setInput1_year(progressDto.getPred_requests_areas().getInput1_year());
     predRequestsAreas.setInput2_year(progressDto.getPred_requests_areas().getInput2_year());
-    predRequestsAreas.setInput1_scene_path(
+    predRequestsAreas.setInput1_scene_path(progressDto.getPred_requests_areas().getInput1_scene_path());
-        progressDto.getPred_requests_areas().getInput1_scene_path());
+    predRequestsAreas.setInput2_scene_path(progressDto.getPred_requests_areas().getInput2_scene_path());
-    predRequestsAreas.setInput2_scene_path(
-        progressDto.getPred_requests_areas().getInput2_scene_path());
 
     InferenceSendDto m = new InferenceSendDto();
     m.setPred_requests_areas(predRequestsAreas);
     m.setModel_version(progressDto.getModelVersion());
     m.setCd_model_path(progressDto.getCdModelPath() + "/" + progressDto.getCdModelFileName());
-    m.setCd_model_config(
+    m.setCd_model_config(progressDto.getCdModelConfigPath() + "/" + progressDto.getCdModelConfigFileName());
-        progressDto.getCdModelConfigPath() + "/" + progressDto.getCdModelConfigFileName());
+    m.setCls_model_path(progressDto.getCdModelClsPath() + "/" + progressDto.getCdModelClsFileName());
-    m.setCls_model_path(
-        progressDto.getCdModelClsPath() + "/" + progressDto.getCdModelClsFileName());
     m.setCls_model_version(progressDto.getClsModelVersion());
     m.setCd_model_type(inferenceType);
     m.setPriority(progressDto.getPriority());
 
-    // 추론 다음모델 실행
+    // 추론 실행 api 호출
     Long batchId = ensureAccepted(m);
 
     SaveInferenceAiDto saveInferenceAiDto = new SaveInferenceAiDto();
@@ -191,71 +300,111 @@ public class MapSheetInferenceJobService {
     inferenceResultCoreService.update(saveInferenceAiDto);
   }
 
-  private Long ensureAccepted(InferenceSendDto dto) {
+  /**
-    log.info("dto null? {}", dto == null);
+   * ai 에 맞는 모델 명으로 변경
-    ObjectMapper om = new ObjectMapper();
+   *
-    try {
+   * @param type 모델 타입
-      log.info("dto json={}", om.writeValueAsString(dto));
+   * @return String
-    } catch (Exception e) {
+   */
-      log.error(e.getMessage());
+  private String modelToInferenceType(String type) {
+    if ("M1".equals(type)) {
+      return "G1";
+    }
+    if ("M2".equals(type)) {
+      return "G2";
+    }
+    if ("M3".equals(type)) {
+      return "G3";
+    }
+    throw new IllegalArgumentException("Unknown type: " + type);
   }
 
-    HttpHeaders headers = new HttpHeaders();
+  /**
-    headers.setContentType(MediaType.APPLICATION_JSON);
+   * api 호출
-    headers.setAccept(List.of(MediaType.APPLICATION_JSON));
+   *
+   * @param dto
+   * @return
+   */
+  private Long ensureAccepted(InferenceSendDto dto) {
+    if (dto == null) {
+      log.warn("not InferenceSendDto dto");
+      throw new CustomApiException("BAD_REQUEST", HttpStatus.BAD_REQUEST);
+    }
 
-    // TODO 추후 삭제
+    // 1) 요청 로그
+    try {
+      log.debug("Inference request dto={}", objectMapper.writeValueAsString(dto));
+    } catch (JsonProcessingException e) {
+      log.warn("Failed to serialize inference dto", e);
+    }
+
+    // 2) local 환경 임시 처리
     if ("local".equals(profile)) {
       if (dto.getPred_requests_areas() == null) {
-        dto.setPred_requests_areas(new InferenceSendDto.pred_requests_areas());
+        throw new IllegalStateException("pred_requests_areas is null");
       }
-
       dto.getPred_requests_areas().setInput1_scene_path("/kamco-nfs/requests/2023_local.geojson");
       dto.getPred_requests_areas().setInput2_scene_path("/kamco-nfs/requests/2024_local.geojson");
     }
 
+    // 3) HTTP 호출
+    HttpHeaders headers = new HttpHeaders();
+    headers.setContentType(MediaType.APPLICATION_JSON);
+    headers.setAccept(List.of(MediaType.APPLICATION_JSON));
+
     ExternalCallResult<String> result =
       externalHttpClient.call(inferenceUrl, HttpMethod.POST, dto, headers, String.class);
 
-    int status = result.statusCode();
+    if (result.statusCode() < 200 || result.statusCode() >= 300) {
-    String body = result.body();
+      log.error("Inference API failed. status={}, body={}", result.statusCode(), result.body());
-
-    if (status < 200 || status >= 300) {
       throw new CustomApiException("BAD_GATEWAY", HttpStatus.BAD_GATEWAY);
     }
 
-    Long batchId = 0L;
+    // 4) 응답 파싱
-
     try {
       List<Map<String, Object>> list =
-          om.readValue(body, new TypeReference<List<Map<String, Object>>>() {});
+        objectMapper.readValue(result.body(), new TypeReference<>() {
+        });
 
-      Integer batchIdInt = (Integer) list.get(0).get("batch_id");
-      batchId = batchIdInt.longValue();
       if (list.isEmpty()) {
         throw new IllegalStateException("Inference response is empty");
       }
 
+      Object batchIdObj = list.get(0).get("batch_id");
+      if (batchIdObj == null) {
+        throw new IllegalStateException("batch_id not found in response");
+      }
+
+      return Long.valueOf(batchIdObj.toString());
+
     } catch (Exception e) {
-      log.error(e.getMessage());
+      log.error("Failed to parse inference response. body={}", result.body(), e);
+      throw new CustomApiException("INVALID_INFERENCE_RESPONSE", HttpStatus.BAD_GATEWAY);
+    }
   }
 
-    return batchId;
+  /**
+   * 실행중인 profile
+   *
+   * @return
+   */
+  private boolean isLocalProfile() {
+    return "local".equalsIgnoreCase(profile);
   }
 
-  private void updateProcessingEndTimeByModel(UUID uuid, ZonedDateTime dateTime, String type) {
+  /**
+   * 모델별 추론 종료 update
+   */
+  private void updateProcessingEndTimeByModel(JobStatusDto dto, UUID uuid, ZonedDateTime dateTime, String type) {
     SaveInferenceAiDto saveInferenceAiDto = new SaveInferenceAiDto();
     saveInferenceAiDto.setUuid(uuid);
     saveInferenceAiDto.setUpdateUid(0L);
     saveInferenceAiDto.setModelEndDttm(dateTime);
     saveInferenceAiDto.setType(type);
+    saveInferenceAiDto.setPendingJobs(dto.getPendingJobs());
+    saveInferenceAiDto.setRunningJobs(dto.getRunningJobs());
+    saveInferenceAiDto.setCompletedJobs(dto.getCompletedJobs());
+    saveInferenceAiDto.setFailedJobs(dto.getFailedJobs());
     inferenceResultCoreService.update(saveInferenceAiDto);
   }
-
-  private String setStatus(int totalJobs, int completedJobs, int failedJobs) {
-    if (totalJobs <= (completedJobs + failedJobs)) {
-      return "COMPLETED";
-    }
-    return "PROCESSING";
-  }
 }