💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
基于大数据的水产品安全信息可视化分析系统介绍
本系统是一套面向水产品安全监管的大数据可视化分析平台，通过Hadoop分布式存储和Spark计算引擎处理海量水产品检测数据。系统采用Python+Django作为后端框架，前端使用Vue+ElementUI+Echarts实现交互式数据展示。核心功能涵盖安全评估分析、供应链追溯分析、检测体系监控以及消费者行为特征挖掘四大模块。技术架构上，利用HDFS存储水产品检验检疫记录、养殖环境监测数据等非结构化信息，通过Spark SQL对多源异构数据进行清洗转换，结合Pandas和NumPy完成统计分析计算。系统能够实时呈现不同品类水产品的合格率趋势、重金属超标区域分布、养殖企业信用评级等关键指标，帮助监管部门快速定位食品安全风险点。可视化层面，Echarts图表支持地理热力图、时间序列折线图、多维雷达图等多种展现形式，让复杂的检测数据变得直观易懂，为决策提供数据支撑。
基于大数据的水产品安全信息可视化分析系统演示视频
基于大数据的水产品安全信息可视化分析系统演示图片
基于大数据的水产品安全信息可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, year, month, desc, lit
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("WaterProductSafetyAnalysis").config("spark.sql.warehouse.dir", "/user/hive/warehouse").config("spark.executor.memory", "2g").config("spark.driver.memory", "1g").getOrCreate()
class SafetyEvaluationView(View):
def post(self, request):
params = json.loads(request.body)
start_date = params.get('start_date')
end_date = params.get('end_date')
product_type = params.get('product_type', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "detection_records").option("user", "root").option("password", "123456").load()
df = df.filter((col("detection_date") >= start_date) & (col("detection_date") <= end_date))
if product_type:
df = df.filter(col("product_type") == product_type)
df = df.withColumn("is_qualified", when(col("detection_result") == "合格", 1).otherwise(0))
total_count = df.count()
qualified_count = df.filter(col("is_qualified") == 1).count()
qualification_rate = round((qualified_count / total_count * 100), 2) if total_count > 0 else 0
risk_items_df = df.filter(col("is_qualified") == 0).groupBy("risk_item").agg(count("*").alias("risk_count")).orderBy(desc("risk_count")).limit(5)
risk_items = [{"item": row["risk_item"], "count": row["risk_count"]} for row in risk_items_df.collect()]
product_stats_df = df.groupBy("product_type").agg(count("*").alias("total"), sum("is_qualified").alias("qualified")).withColumn("rate", (col("qualified") / col("total") * 100))
product_stats = [{"type": row["product_type"], "total": row["total"], "qualified": row["qualified"], "rate": round(row["rate"], 2)} for row in product_stats_df.collect()]
monthly_trend_df = df.withColumn("year_month", concat(year("detection_date"), lit("-"), month("detection_date"))).groupBy("year_month").agg(count("*").alias("total"), sum("is_qualified").alias("qualified")).withColumn("rate", (col("qualified") / col("total") * 100)).orderBy("year_month")
monthly_trend = [{"month": row["year_month"], "rate": round(row["rate"], 2)} for row in monthly_trend_df.collect()]
return JsonResponse({"qualification_rate": qualification_rate, "total_samples": total_count, "risk_items": risk_items, "product_stats": product_stats, "monthly_trend": monthly_trend})
class SupplyChainView(View):
def post(self, request):
params = json.loads(request.body)
product_id = params.get('product_id')
supply_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "supply_chain").option("user", "root").option("password", "123456").load()
detection_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "detection_records").option("user", "root").option("password", "123456").load()
product_chain = supply_df.filter(col("product_id") == product_id).orderBy("stage_order")
chain_data = [{"stage": row["stage_name"], "location": row["location"], "operator": row["operator_name"], "timestamp": str(row["operation_time"]), "status": row["status"]} for row in product_chain.collect()]
risk_nodes_df = supply_df.filter(col("product_id") == product_id).join(detection_df, supply_df.batch_id == detection_df.batch_id, "left").filter(col("detection_result") == "不合格")
risk_nodes = [{"stage": row["stage_name"], "risk_type": row["risk_item"], "detection_value": row["detection_value"]} for row in risk_nodes_df.collect()]
origin_info_df = supply_df.filter((col("product_id") == product_id) & (col("stage_name") == "养殖"))
origin_info = {"farm_name": origin_info_df.first()["operator_name"], "farm_location": origin_info_df.first()["location"], "farm_license": origin_info_df.first()["license_code"]} if origin_info_df.count() > 0 else {}
circulation_time_df = supply_df.filter(col("product_id") == product_id).agg({"operation_time": "min", "operation_time": "max"})
circulation_days = (circulation_time_df.first()[1] - circulation_time_df.first()[0]).days if circulation_time_df.count() > 0 else 0
related_batches_df = supply_df.filter((col("operator_name") == origin_info.get("farm_name")) & (col("stage_name") == "养殖")).select("product_id", "batch_id").distinct().limit(10)
related_batches = [{"product_id": row["product_id"], "batch_id": row["batch_id"]} for row in related_batches_df.collect()]
return JsonResponse({"chain_data": chain_data, "risk_nodes": risk_nodes, "origin_info": origin_info, "circulation_days": circulation_days, "related_batches": related_batches})
class DetectionSystemView(View):
def post(self, request):
params = json.loads(request.body)
year = params.get('year')
detection_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "detection_records").option("user", "root").option("password", "123456").load()
institution_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "detection_institutions").option("user", "root").option("password", "123456").load()
detection_df = detection_df.filter(year("detection_date") == year)
detection_with_inst = detection_df.join(institution_df, detection_df.institution_id == institution_df.institution_id, "left")
inst_workload_df = detection_with_inst.groupBy("institution_name", "region").agg(count("*").alias("total_tasks"), sum(when(col("detection_result") == "不合格", 1).otherwise(0)).alias("unqualified_count")).withColumn("unqualified_rate", (col("unqualified_count") / col("total_tasks") * 100))
inst_workload = [{"institution": row["institution_name"], "region": row["region"], "tasks": row["total_tasks"], "unqualified_rate": round(row["unqualified_rate"], 2)} for row in inst_workload_df.orderBy(desc("total_tasks")).collect()]
item_coverage_df = detection_with_inst.groupBy("detection_item").agg(count("*").alias("item_count"), countDistinct("institution_id").alias("inst_count"))
item_coverage = [{"item": row["detection_item"], "count": row["item_count"], "institutions": row["inst_count"]} for row in item_coverage_df.orderBy(desc("item_count")).limit(10).collect()]
regional_distribution_df = detection_with_inst.groupBy("region").agg(count("*").alias("total"), sum(when(col("detection_result") == "不合格", 1).otherwise(0)).alias("unqualified")).withColumn("rate", (col("unqualified") / col("total") * 100))
regional_distribution = [{"region": row["region"], "total": row["total"], "unqualified": row["unqualified"], "rate": round(row["rate"], 2)} for row in regional_distribution_df.collect()]
efficiency_df = detection_with_inst.withColumn("detection_month", month("detection_date")).groupBy("institution_name", "detection_month").agg(count("*").alias("monthly_tasks")).groupBy("institution_name").agg(avg("monthly_tasks").alias("avg_monthly_tasks"))
efficiency_ranking = [{"institution": row["institution_name"], "avg_monthly_tasks": round(row["avg_monthly_tasks"], 2)} for row in efficiency_df.orderBy(desc("avg_monthly_tasks")).limit(10).collect()]
return JsonResponse({"institution_workload": inst_workload, "item_coverage": item_coverage, "regional_distribution": regional_distribution, "efficiency_ranking": efficiency_ranking})
基于大数据的水产品安全信息可视化分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
