# 注意：根据要求，代码需体现大数据应用，但系统技术栈为Django。# 此处假设系统集成了Spark用于处理海量历史传感器数据分析任务，与Django业务逻辑并行。# 以下为模拟的核心业务处理函数代码。from pyspark.sql import SparkSessionimport datetime# 初始化Spark会话，用于大数据分析任务spark = SparkSession.builder.appName("AgricultureDataAnalysis").getOrCreate()def process_sensor_data(sensor_data_list): # 功能1：处理并分析实时传感器数据，利用Spark进行批量聚合分析 # 将传入的传感器数据列表转换为Spark DataFrame df = spark.createDataFrame(sensor_data_list) # 按农田ID分组，计算平均温度和湿度 aggregated_df = df.groupBy("field_id").avg("temperature", "humidity") # 将Spark DataFrame转换为Pandas DataFrame以便与常规Web框架交互 result_pd = aggregated_df.toPandas() # 模拟将聚合结果写入MySQL数据库，供前端展示 for index, row in result_pd.iterrows(): field_id = row['field_id'] avg_temp = round(row['avg(temperature)'], 2) avg_humidity = round(row['avg(humidity)'], 2) # db.execute("INSERT INTO field_env_summary (field_id, avg_temp, avg_humidity, record_time) VALUES (%s, %s, %s, %s)", (field_id, avg_temp, avg_humidity, datetime.datetime.now())) print(f"Field {field_id} summary: Avg Temp={avg_temp}, Avg Humidity={avg_humidity} saved to DB.") return {"status": "success", "processed_records": len(result_pd)}def report_pest_disease(user_id, field_id, crop_id, description, image_path): # 功能2：病虫害上报与智能预警逻辑 # 1. 记录新的病虫害报告到数据库 report_id = 12345 # 模拟数据库插入后返回的报告ID # db.execute("INSERT INTO pest_reports (user_id, field_id, crop_id, description, image_path, report_time) VALUES (%s, %s, %s, %s, %s, %s)", (user_id, field_id, crop_id, description, image_path, datetime.datetime.now())) print(f"New pest report {report_id} recorded for field {field_id}.") # 2. 查询该区域近期同类报告数量，判断是否触发预警 seven_days_ago = datetime.datetime.now() - datetime.timedelta(days=7) # recent_reports_count = db.query("SELECT COUNT(*) FROM pest_reports WHERE field_id = %s AND crop_id = %s AND report_time > %s", (field_id, crop_id, seven_days_ago)) recent_reports_count = 3 # 模拟查询结果 # 3. 预警阈值判断 WARNING_THRESHOLD = 2 if recent_reports_count >= WARNING_THRESHOLD: warning_message = f"警告：农田 {field_id} 的作物 {crop_id} 在过去一周内已发生 {recent_reports_count} 起同类病虫害报告，请立即采取防治措施！" # send_notification_to_manager(warning_message) print(f"ALERT TRIGGERED: {warning_message}") return {"status": "success", "report_id": report_id, "warning": warning_message} else: return {"status": "success", "report_id": report_id, "warning": None}def update_crop_growth_stage(crop_id): # 功能3：根据种植时间自动更新作物生长阶段并提供建议 # 1. 从数据库获取作物的种植日期和当前生长阶段 # crop_info = db.query("SELECT planting_date, current_stage FROM crops WHERE id = %s", (crop_id,)) crop_info = {'planting_date': datetime.datetime(2024, 3, 1), 'current_stage': '苗期'} # 模拟数据 planting_date = crop_info['planting_date'] current_stage = crop_info['current_stage'] # 2. 计算生长天数 days_since_planting = (datetime.datetime.now() - planting_date).days new_stage = current_stage suggestion = "继续保持当前管理措施。" # 3. 根据生长天数判断新的生长阶段 if days_since_planting > 90 and current_stage != '成熟期': new_stage = '成熟期' suggestion = "作物已进入成熟期，请关注采收时机，并减少氮肥施用。" elif days_since_planting > 60 and current_stage == '苗期': new_stage = '开花期' suggestion = "作物进入开花期，是需水需肥关键期，建议增施磷钾肥，保持土壤湿润。" elif days_since_planting > 30 and current_stage == '苗期': new_stage = '生长期' suggestion = "作物进入快速生长期，建议及时进行中耕除草和追肥。" # 4. 如果生长阶段发生变化，更新数据库并生成新建议 if new_stage != current_stage: # db.execute("UPDATE crops SET current_stage = %s WHERE id = %s", (new_stage, crop_id)) print(f"Crop {crop_id} stage updated from '{current_stage}' to '{new_stage}'.") return {"status": "updated", "new_stage": new_stage, "suggestion": suggestion} else: return {"status": "unchanged", "current_stage": current_stage, "suggestion": suggestion}
