作者：计算机编程小咖
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、小代码、安卓、算法。平常会做一些工程定制化开发、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编写中遇到的问题的解决办法，也喜欢交流工艺，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！
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目录
- 基于大内容的水产品安全信息可视化分析系统介绍
- 基于大数据的水产品安全信息可视化分析系统演示视频
- 基于大素材的水产品安全信息可视化分析系统演示图片
- 基于大资料的水产品安全信息可视化分析系统代码展示
- 基于大信息的水产品安全信息可视化分析系统文档展示
基于大数据的水产品安全信息可视化分析系统介绍
基于大数据的水产品安全信息可视化分析系统是一套集素材采集、存储、分析和可视化展示于一体的综合性大数据分析平台，该系统采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术底座，通过HDFS分布式文件系统实现海量水产品安全数据的可靠存储，利用Spark SQL进行高效的内容清洗、转换和分析处理，结合Pandas和NumPy等Python科学计算库进行深度数据挖掘。系统前端采用Vue.js框架搭配ElementUI组件库构建现代化的用户界面，通过Echarts图表库实现丰富的数据可视化效果，后端基于Django框架献出RESTful API接口服务，数据持久化采用MySQL关系型数据库。系统核心功能模块包括大屏可视化展示、安全评估分析、供应链全链路追踪分析、检测体系数据分析以及消费者行为特征分析等，通过多维度的数据分析为水产品安全监管供应科学决策支持。系统还具备完善的用户管理体系，包括个人信息管理、权限控制和系统配置等基础功能，整体架构设计充分体现了大素材技术在食品安全领域的实际应用价值，为相关监管部门和企业提供了一套完整的水产品安全信息化解决方案。
基于大数据的水产品安全信息可视化分析系统演示视频
基于大信息的水产品安全信息可视化分析系统【Hadoop、spark、可视化大屏、课程毕设、毕业选题、数据分析、数据爬取、数据可视化】
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 *
import pandas as pd
import numpy as np
from django.http import JsonResponse
spark = SparkSession.builder.appName("WaterProductSafetyAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def safety_evaluation_analysis(request):
safety_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "safety_records").option("user", "root").option("password", "123456").load()
quality_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "quality_tests").option("user", "root").option("password", "123456").load()
joined_data = safety_data.join(quality_data, "product_id", "inner")
risk_scores = joined_data.withColumn("risk_score", when(col("contamination_level") >
0.8, 10).when(col("contamination_level") >
0.6, 8).when(col("contamination_level") >
0.4, 6).when(col("contamination_level") >
0.2, 4).otherwise(2))
regional_risk = risk_scores.groupBy("region").agg(avg("risk_score").alias("avg_risk"), count("product_id").alias("product_count"), sum(when(col("risk_score") >= 8, 1).otherwise(0)).alias("high_risk_count"))
trend_analysis = risk_scores.withColumn("month", date_format(col("test_date"), "yyyy-MM")).groupBy("month").agg(avg("risk_score").alias("monthly_risk"), count("product_id").alias("monthly_tests"))
category_risk = risk_scores.groupBy("product_category").agg(avg("risk_score").alias("category_risk"), stddev("risk_score").alias("risk_variance"))
risk_prediction = joined_data.withColumn("predicted_risk", when(col("ph_value") <
6.5, col("risk_score") * 1.2).when(col("ph_value") >
8.5, col("risk_score") * 1.1).otherwise(col("risk_score")))
alert_products = risk_prediction.filter(col("predicted_risk") >= 8).select("product_id", "product_name", "predicted_risk", "region").orderBy(desc("predicted_risk"))
seasonal_pattern = risk_scores.withColumn("season", when(month(col("test_date")).isin([12, 1, 2]), "winter").when(month(col("test_date")).isin([3, 4, 5]), "spring").when(month(col("test_date")).isin([6, 7, 8]), "summer").otherwise("autumn")).groupBy("season").agg(avg("risk_score").alias("seasonal_risk"))
compliance_rate = joined_data.withColumn("compliant", when(col("contamination_level") <= 0.3, 1).otherwise(0)).groupBy("region", "product_category").agg(avg("compliant").alias("compliance_rate"))
result_data = {
"regional_risk": regional_risk.toPandas().to_dict('records'), "trend_analysis": trend_analysis.toPandas().to_dict('records'), "category_risk": category_risk.toPandas().to_dict('records'), "alert_products": alert_products.limit(20).toPandas().to_dict('records'), "seasonal_pattern": seasonal_pattern.toPandas().to_dict('records'), "compliance_rate": compliance_rate.toPandas().to_dict('records')
}
return JsonResponse(result_data)
def supply_chain_analysis(request):
supply_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "supply_chain").option("user", "root").option("password", "123456").load()
logistics_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "logistics_records").option("user", "root").option("password", "123456").load()
supplier_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "suppliers").option("user", "root").option("password", "123456").load()
full_chain = supply_data.join(logistics_data, "batch_id", "left").join(supplier_data, "supplier_id", "left")
transport_time = full_chain.withColumn("transport_duration", (unix_timestamp("delivery_time") - unix_timestamp("departure_time")) / 3600)
temperature_violations = full_chain.withColumn("temp_violation", when((col("temperature") >
4) | (col("temperature") <
-2), 1).otherwise(0))
supplier_performance = full_chain.groupBy("supplier_id", "supplier_name").agg(count("batch_id").alias("total_batches"), avg("transport_duration").alias("avg_transport_time"), sum("temp_violation").alias("temp_violations"), avg(when(col("quality_grade") == "A", 1).otherwise(0)).alias("grade_a_rate"))
route_efficiency = full_chain.groupBy("transport_route").agg(avg("transport_duration").alias("avg_duration"), count("batch_id").alias("route_usage"), avg("fuel_consumption").alias("avg_fuel"))
traceability_gaps = full_chain.withColumn("missing_records", when(col("departure_time").isNull() | col("delivery_time").isNull() | col("temperature").isNull(), 1).otherwise(0)).groupBy("supplier_id").agg(sum("missing_records").alias("gap_count"), count("batch_id").alias("total_records"))
quality_correlation = full_chain.withColumn("quality_score", when(col("quality_grade") == "A", 5).when(col("quality_grade") == "B", 4).when(col("quality_grade") == "C", 3).otherwise(2)).groupBy("transport_route", "supplier_id").agg(avg("quality_score").alias("avg_quality"), avg("transport_duration").alias("avg_time"))
risk_hotspots = full_chain.withColumn("risk_level", when(col("transport_duration") >
48, "high").when(col("transport_duration") >
24, "medium").otherwise("low")).groupBy("region", "risk_level").agg(count("batch_id").alias("batch_count"))
cost_analysis = full_chain.withColumn("cost_per_kg", col("transport_cost") / col("weight")).groupBy("supplier_id", "transport_route").agg(avg("cost_per_kg").alias("avg_cost_per_kg"), sum("transport_cost").alias("total_cost"))
seasonal_supply = full_chain.withColumn("month", month(col("departure_time"))).groupBy("month", "product_category").agg(sum("weight").alias("monthly_supply"), count("batch_id").alias("monthly_batches"))
disruption_analysis = full_chain.withColumn("delayed", when(col("transport_duration") >
col("planned_duration") * 1.2, 1).otherwise(0)).groupBy("supplier_id", "transport_route").agg(sum("delayed").alias("delay_count"), count("batch_id").alias("total_shipments"), (sum("delayed") * 100.0 / count("batch_id")).alias("delay_rate"))
chain_result = {
"supplier_performance": supplier_performance.toPandas().to_dict('records'), "route_efficiency": route_efficiency.toPandas().to_dict('records'), "traceability_gaps": traceability_gaps.toPandas().to_dict('records'), "quality_correlation": quality_correlation.toPandas().to_dict('records'), "risk_hotspots": risk_hotspots.toPandas().to_dict('records'), "cost_analysis": cost_analysis.toPandas().to_dict('records'), "seasonal_supply": seasonal_supply.toPandas().to_dict('records'), "disruption_analysis": disruption_analysis.toPandas().to_dict('records')
}
return JsonResponse(chain_result)
def detection_system_analysis(request):
detection_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "detection_records").option("user", "root").option("password", "123456").load()
equipment_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "detection_equipment").option("user", "root").option("password", "123456").load()
laboratory_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/waterproduct").option("dbtable", "laboratories").option("user", "root").option("password", "123456").load()
detection_full = detection_data.join(equipment_data, "equipment_id", "left").join(laboratory_data, "lab_id", "left")
detection_accuracy = detection_full.withColumn("accurate_result", when((col("detected_value") >= col("actual_value") * 0.95) &
(col("detected_value") <= col("actual_value") * 1.05), 1).otherwise(0))
lab_performance = detection_accuracy.groupBy("lab_id", "lab_name").agg(avg("accurate_result").alias("accuracy_rate"), count("detection_id").alias("total_tests"), avg("detection_time").alias("avg_time"), stddev("detected_value").alias("precision"))
equipment_efficiency = detection_full.groupBy("equipment_id", "equipment_type").agg(count("detection_id").alias("usage_count"), avg("detection_time").alias("avg_detection_time"), sum(when(col("equipment_status") == "maintenance", 1).otherwise(0)).alias("maintenance_count"))
contamination_patterns = detection_full.withColumn("contamination_type", when(col("heavy_metals") >
0.1, "heavy_metal").when(col("pesticide_residue") >
0.05, "pesticide").when(col("bacterial_count") >
1000, "bacterial").otherwise("normal")).groupBy("contamination_type", "product_category").agg(count("detection_id").alias("case_count"))
detection_trends = detection_full.withColumn("detection_month", date_format(col("detection_date"), "yyyy-MM")).groupBy("detection_month").agg(count("detection_id").alias("monthly_tests"), avg("heavy_metals").alias("avg_heavy_metals"), avg("pesticide_residue").alias("avg_pesticide"), avg("bacterial_count").alias("avg_bacteria"))
threshold_violations = detection_full.withColumn("violation_type", when(col("heavy_metals") >
0.2, "heavy_metal_exceed").when(col("pesticide_residue") >
0.1, "pesticide_exceed").when(col("bacterial_count") >
2000, "bacteria_exceed").otherwise("compliant")).filter(col("violation_type") != "compliant")
regional_detection = detection_full.groupBy("region", "product_category").agg(count("detection_id").alias("test_count"), avg("heavy_metals").alias("regional_heavy_metals"), avg("pesticide_residue").alias("regional_pesticide"), (sum(when(col("heavy_metals") >
0.2, 1).otherwise(0)) * 100.0 / count("detection_id")).alias("violation_rate"))
equipment_maintenance = detection_full.withColumn("days_since_maintenance", datediff(col("detection_date"), col("last_maintenance_date"))).groupBy("equipment_id", "equipment_type").agg(avg("days_since_maintenance").alias("avg_maintenance_gap"), max("days_since_maintenance").alias("max_gap"))
quality_assurance = detection_full.withColumn("qa_score", when(col("calibration_status") == "valid", 5).otherwise(3) + when(col("operator_certified") == true, 3).otherwise(0) + when(col("temperature_controlled") == true, 2).otherwise(0)).groupBy("lab_id").agg(avg("qa_score").alias("avg_qa_score"))
batch_analysis = detection_full.groupBy("batch_id").agg(count("detection_id").alias("tests_per_batch"), avg("heavy_metals").alias("batch_heavy_metals"), avg("pesticide_residue").alias("batch_pesticide"), max(when(col("heavy_metals") >
0.2, 1).otherwise(0)).alias("batch_violation"))
detection_result = {
"lab_performance": lab_performance.toPandas().to_dict('records'), "equipment_efficiency": equipment_efficiency.toPandas().to_dict('records'), "contamination_patterns": contamination_patterns.toPandas().to_dict('records'), "detection_trends": detection_trends.toPandas().to_dict('records'), "threshold_violations": threshold_violations.limit(50).toPandas().to_dict('records'), "regional_detection": regional_detection.toPandas().to_dict('records'), "equipment_maintenance": equipment_maintenance.toPandas().to_dict('records'), "quality_assurance": quality_assurance.toPandas().to_dict('records'), "batch_analysis": batch_analysis.toPandas().to_dict('records')
}
return JsonResponse(detection_result)基于大素材的水产品安全信息可视化分析系统文档展示
作者：计算机编程小咖
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、微信小代码、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难题的解决办法，也喜欢交流技能，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！
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