💖💖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
基于大数据的牛油果数据分析系统介绍
牛油果大数据分析平台是一个基于现代大数据技术栈构建的综合性数据分析系统，采用Django作为后端开发框架，结合Vue+ElementUI+Echarts构建响应式前端界面，通过MySQL数据库进行数据存储管理。系统核心运用Hadoop+Spark大数据处理框架，利用HDFS分布式文件系统实现海量牛油果数据的可靠存储，通过Spark SQL引擎完成复杂数据查询与分析任务，同时集成Pandas、NumPy等Python科学计算库进行深度数据挖掘。平台提供完整的用户权限管理体系，支持牛油果基础数据的增删改查操作，具备多维度数据概览分析功能，能够对牛油果的物理特性、颜色特征进行专业化统计分析，并通过多维特征分析模块实现数据间的关联性探索。系统采用前后端分离架构设计，通过RESTful API接口实现数据交互，利用Echarts图表库将分析结果以直观的可视化形式展现，为用户提供便捷高效的牛油果数据分析解决方案，满足农业数据分析、市场研究等多种应用场景需求。
基于大数据的牛油果数据分析系统演示视频
基于大数据的牛油果数据分析系统演示图片
基于大数据的牛油果数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, max, min, sum, when, desc, asc
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("AvocadoDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def avocado_overview_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "123456").load()
total_count = df.count()
avg_weight = df.select(avg(col("weight")).alias("avg_weight")).collect()[0]["avg_weight"]
max_weight = df.select(max(col("weight")).alias("max_weight")).collect()[0]["max_weight"]
min_weight = df.select(min(col("weight")).alias("min_weight")).collect()[0]["min_weight"]
region_stats = df.groupBy("region").agg(count("*").alias("count"), avg("weight").alias("avg_weight")).orderBy(desc("count"))
region_data = []
for row in region_stats.collect():
region_data.append({"region": row["region"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)})
quality_distribution = df.groupBy("quality_grade").agg(count("*").alias("count")).orderBy(desc("count"))
quality_data = []
for row in quality_distribution.collect():
quality_data.append({"grade": row["quality_grade"], "count": row["count"]})
monthly_trend = df.groupBy("harvest_month").agg(count("*").alias("count"), avg("weight").alias("avg_weight")).orderBy(asc("harvest_month"))
trend_data = []
for row in monthly_trend.collect():
trend_data.append({"month": row["harvest_month"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)})
result_data = {"total_count": total_count, "avg_weight": round(avg_weight, 2), "max_weight": max_weight, "min_weight": min_weight, "region_stats": region_data, "quality_distribution": quality_data, "monthly_trend": trend_data}
return JsonResponse({"code": 200, "message": "概览分析成功", "data": result_data})
def avocado_physical_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "123456").load()
weight_ranges = df.select(when(col("weight") < 100, "轻量级").when((col("weight") >= 100) & (col("weight") < 200), "中等级").when((col("weight") >= 200) & (col("weight") < 300), "重量级").otherwise("超重级").alias("weight_category")).groupBy("weight_category").agg(count("*").alias("count"))
weight_data = []
for row in weight_ranges.collect():
weight_data.append({"category": row["weight_category"], "count": row["count"]})
size_correlation = df.select(col("length"), col("width"), col("height"), col("weight")).toPandas()
correlation_matrix = size_correlation.corr().to_dict()
length_stats = df.select(avg("length").alias("avg_length"), max("length").alias("max_length"), min("length").alias("min_length")).collect()[0]
width_stats = df.select(avg("width").alias("avg_width"), max("width").alias("max_width"), min("width").alias("min_width")).collect()[0]
height_stats = df.select(avg("height").alias("avg_height"), max("height").alias("max_height"), min("height").alias("min_height")).collect()[0]
size_distribution = df.groupBy(when(col("length") > 10, "大型").when((col("length") >= 7) & (col("length") <= 10), "中型").otherwise("小型").alias("size_category")).agg(count("*").alias("count"), avg("weight").alias("avg_weight"))
size_data = []
for row in size_distribution.collect():
size_data.append({"size": row["size_category"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)})
density_analysis = df.withColumn("density", col("weight") / (col("length") * col("width") * col("height"))).select(avg("density").alias("avg_density"), max("density").alias("max_density"), min("density").alias("min_density")).collect()[0]
physical_data = {"weight_distribution": weight_data, "correlation_matrix": correlation_matrix, "length_stats": {"avg": round(length_stats["avg_length"], 2), "max": length_stats["max_length"], "min": length_stats["min_length"]}, "width_stats": {"avg": round(width_stats["avg_width"], 2), "max": width_stats["max_width"], "min": width_stats["min_width"]}, "height_stats": {"avg": round(height_stats["avg_height"], 2), "max": height_stats["max_height"], "min": height_stats["min_height"]}, "size_distribution": size_data, "density_stats": {"avg": round(density_analysis["avg_density"], 4), "max": round(density_analysis["max_density"], 4), "min": round(density_analysis["min_density"], 4)}}
return JsonResponse({"code": 200, "message": "物性分析完成", "data": physical_data})
def avocado_color_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "123456").load()
color_distribution = df.groupBy("skin_color").agg(count("*").alias("count"), avg("weight").alias("avg_weight")).orderBy(desc("count"))
color_data = []
for row in color_distribution.collect():
color_data.append({"color": row["skin_color"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)})
brightness_ranges = df.select(when(col("brightness") < 30, "暗淡").when((col("brightness") >= 30) & (col("brightness") < 60), "适中").when((col("brightness") >= 60) & (col("brightness") < 80), "明亮").otherwise("极亮").alias("brightness_category")).groupBy("brightness_category").agg(count("*").alias("count"))
brightness_data = []
for row in brightness_ranges.collect():
brightness_data.append({"brightness": row["brightness_category"], "count": row["count"]})
color_quality_relation = df.groupBy("skin_color", "quality_grade").agg(count("*").alias("count")).orderBy("skin_color", "quality_grade")
quality_relation_data = []
for row in color_quality_relation.collect():
quality_relation_data.append({"color": row["skin_color"], "quality": row["quality_grade"], "count": row["count"]})
saturation_stats = df.select(avg("saturation").alias("avg_saturation"), max("saturation").alias("max_saturation"), min("saturation").alias("min_saturation")).collect()[0]
hue_distribution = df.select(when(col("hue") < 60, "红黄色调").when((col("hue") >= 60) & (col("hue") < 120), "黄绿色调").when((col("hue") >= 120) & (col("hue") < 180), "绿色调").when((col("hue") >= 180) & (col("hue") < 240), "绿蓝色调").when((col("hue") >= 240) & (col("hue") < 300), "蓝紫色调").otherwise("紫红色调").alias("hue_category")).groupBy("hue_category").agg(count("*").alias("count"), avg("brightness").alias("avg_brightness"))
hue_data = []
for row in hue_distribution.collect():
hue_data.append({"hue_range": row["hue_category"], "count": row["count"], "avg_brightness": round(row["avg_brightness"], 2)})
color_maturity_analysis = df.groupBy("skin_color").agg(avg("maturity_level").alias("avg_maturity"), count("*").alias("count")).orderBy(desc("avg_maturity"))
maturity_data = []
for row in color_maturity_analysis.collect():
maturity_data.append({"color": row["skin_color"], "avg_maturity": round(row["avg_maturity"], 2), "count": row["count"]})
color_analysis_result = {"color_distribution": color_data, "brightness_ranges": brightness_data, "color_quality_relation": quality_relation_data, "saturation_stats": {"avg": round(saturation_stats["avg_saturation"], 2), "max": saturation_stats["max_saturation"], "min": saturation_stats["min_saturation"]}, "hue_distribution": hue_data, "color_maturity_analysis": maturity_data}
return JsonResponse({"code": 200, "message": "颜色分析完成", "data": color_analysis_result})
基于大数据的牛油果数据分析系统文档展示
💖💖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
