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基于大数据的农产品供应价格数据可视化分析系统介绍

《基于大数据的农产品供应价格数据可视化分析系统》是一套运用现代大数据技术栈构建的综合性农产品价格分析平台，该系统采用Hadoop分布式存储框架作为底层数据存储架构，结合Apache Spark大数据处理引擎实现海量农产品价格数据的高效处理与分析，通过HDFS分布式文件系统确保数据的可靠存储和快速访问，并运用Spark SQL进行复杂的数据查询与计算操作。系统在技术实现上提供Python和Java两套开发方案，后端分别采用Django和Spring Boot框架构建RESTful API服务，前端基于Vue.js框架搭配ElementUI组件库打造现代化用户界面，集成Echarts可视化图表库实现丰富的数据展示效果，同时运用Pandas和NumPy等Python科学计算库进行深度数据分析处理。在功能架构方面，系统提供完整的用户管理模块包含个人信息管理和密码修改功能，核心业务模块涵盖数据大屏可视化展示、多维度数据分析功能，具体包括产品维度分析用于追踪不同农产品的价格变化趋势，价格维度分析实现价格波动规律挖掘，地域维度分析展现不同地区的价格分布特征，商家维度分析帮助了解供应商价格策略，所有分析结果通过直观的图表形式在数据大屏中实时展示，为农产品供应链管理和价格决策提供科学的数据支撑，整个系统基于MySQL关系型数据库存储结构化数据，确保数据的一致性和查询效率。

基于大数据的农产品供应价格数据可视化分析系统演示视频

基于大数据的农产品供应价格数据可视化分析系统演示图片

基于大数据的农产品供应价格数据可视化分析系统代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, max, min, desc, asc, date_format, when
from django.http import JsonResponse
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("AgriculturePriceAnalysis").config("spark.some.config.option", "some-value").getOrCreate()
def data_dashboard_visualization(request):
    df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agriculture_db").option("dbtable", "price_data").option("user", "root").option("password", "password").load()
    total_products = df.select("product_name").distinct().count()
    total_suppliers = df.select("supplier_name").distinct().count()
    avg_price_today = df.filter(col("date") == datetime.now().strftime("%Y-%m-%d")).agg(avg("price")).collect()[0][0]
    price_trend_data = df.groupBy("date").agg(avg("price").alias("avg_price")).orderBy("date").collect()
    product_distribution = df.groupBy("product_name").agg(count("*").alias("count")).orderBy(desc("count")).limit(10).collect()
    regional_data = df.groupBy("region").agg(avg("price").alias("avg_price"), count("*").alias("total_records")).collect()
    supplier_ranking = df.groupBy("supplier_name").agg(avg("price").alias("avg_price"), count("*").alias("supply_count")).orderBy("supply_count").collect()
    price_range_distribution = df.select(when(col("price") < 10, "低价").when((col("price") >= 10) & (col("price") < 50), "中价").otherwise("高价").alias("price_range")).groupBy("price_range").count().collect()
    monthly_trend = df.withColumn("month", date_format("date", "yyyy-MM")).groupBy("month").agg(avg("price").alias("monthly_avg")).orderBy("month").collect()
    top_expensive_products = df.groupBy("product_name").agg(max("price").alias("max_price")).orderBy(desc("max_price")).limit(5).collect()
    supply_stability = df.groupBy("supplier_name").agg(count("*").alias("supply_frequency")).filter(col("supply_frequency") > 10).collect()
    result_data = {"total_products": total_products, "total_suppliers": total_suppliers, "avg_price_today": float(avg_price_today) if avg_price_today else 0, "price_trend": [{"date": row["date"], "price": float(row["avg_price"])} for row in price_trend_data], "product_distribution": [{"name": row["product_name"], "value": row["count"]} for row in product_distribution], "regional_data": [{"region": row["region"], "avg_price": float(row["avg_price"]), "records": row["total_records"]} for row in regional_data], "supplier_ranking": [{"supplier": row["supplier_name"], "avg_price": float(row["avg_price"]), "count": row["supply_count"]} for row in supplier_ranking], "price_distribution": [{"range": row["price_range"], "count": row["count"]} for row in price_range_distribution], "monthly_trend": [{"month": row["month"], "avg_price": float(row["monthly_avg"])} for row in monthly_trend], "top_products": [{"product": row["product_name"], "max_price": float(row["max_price"])} for row in top_expensive_products]}
    return JsonResponse(result_data)
def price_dimension_analysis(request):
    df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agriculture_db").option("dbtable", "price_data").option("user", "root").option("password", "password").load()
    product_name = request.GET.get('product_name', '')
    start_date = request.GET.get('start_date', '')
    end_date = request.GET.get('end_date', '')
    filtered_df = df.filter((col("product_name") == product_name) & (col("date") >= start_date) & (col("date") <= end_date))
    price_statistics = filtered_df.agg(avg("price").alias("avg_price"), max("price").alias("max_price"), min("price").alias("min_price"), count("*").alias("total_records")).collect()[0]
    daily_price_trend = filtered_df.groupBy("date").agg(avg("price").alias("daily_avg")).orderBy("date").collect()
    price_volatility = filtered_df.select("price").rdd.map(lambda row: row[0]).collect()
    price_std = np.std(price_volatility) if price_volatility else 0
    price_variance = np.var(price_volatility) if price_volatility else 0
    price_ranges = filtered_df.select(when(col("price") < price_statistics["avg_price"] * 0.8, "低于平均").when(col("price") > price_statistics["avg_price"] * 1.2, "高于平均").otherwise("接近平均").alias("price_level")).groupBy("price_level").count().collect()
    weekly_analysis = filtered_df.withColumn("week", date_format("date", "yyyy-ww")).groupBy("week").agg(avg("price").alias("weekly_avg"), max("price").alias("weekly_max"), min("price").alias("weekly_min")).orderBy("week").collect()
    supplier_price_comparison = filtered_df.groupBy("supplier_name").agg(avg("price").alias("supplier_avg"), count("*").alias("supply_count")).filter(col("supply_count") > 3).orderBy("supplier_avg").collect()
    price_change_analysis = filtered_df.orderBy("date").select("date", "price").collect()
    price_changes = []
    for i in range(1, len(price_change_analysis)):
        prev_price = price_change_analysis[i-1]["price"]
        curr_price = price_change_analysis[i]["price"]
        change_rate = ((curr_price - prev_price) / prev_price * 100) if prev_price > 0 else 0
        price_changes.append({"date": price_change_analysis[i]["date"], "change_rate": change_rate})
    peak_prices = filtered_df.orderBy(desc("price")).limit(5).select("date", "price", "supplier_name").collect()
    bottom_prices = filtered_df.orderBy(asc("price")).limit(5).select("date", "price", "supplier_name").collect()
    result = {"statistics": {"avg_price": float(price_statistics["avg_price"]), "max_price": float(price_statistics["max_price"]), "min_price": float(price_statistics["min_price"]), "total_records": price_statistics["total_records"], "price_std": float(price_std), "price_variance": float(price_variance)}, "daily_trend": [{"date": str(row["date"]), "price": float(row["daily_avg"])} for row in daily_price_trend], "price_ranges": [{"level": row["price_level"], "count": row["count"]} for row in price_ranges], "weekly_analysis": [{"week": row["week"], "avg": float(row["weekly_avg"]), "max": float(row["weekly_max"]), "min": float(row["weekly_min"])} for row in weekly_analysis], "supplier_comparison": [{"supplier": row["supplier_name"], "avg_price": float(row["supplier_avg"]), "count": row["supply_count"]} for row in supplier_price_comparison], "price_changes": price_changes, "peak_prices": [{"date": str(row["date"]), "price": float(row["price"]), "supplier": row["supplier_name"]} for row in peak_prices], "bottom_prices": [{"date": str(row["date"]), "price": float(row["price"]), "supplier": row["supplier_name"]} for row in bottom_prices]}
    return JsonResponse(result)
def regional_dimension_analysis(request):
    df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agriculture_db").option("dbtable", "price_data").option("user", "root").option("password", "password").load()
    region_name = request.GET.get('region', '')
    time_period = request.GET.get('period', '30')
    end_date = datetime.now()
    start_date = end_date - timedelta(days=int(time_period))
    regional_df = df.filter((col("region") == region_name) & (col("date") >= start_date.strftime("%Y-%m-%d")) & (col("date") <= end_date.strftime("%Y-%m-%d")))
    regional_overview = regional_df.agg(count("*").alias("total_records"), avg("price").alias("avg_price"), max("price").alias("max_price"), min("price").alias("min_price")).collect()[0]
    product_regional_analysis = regional_df.groupBy("product_name").agg(avg("price").alias("avg_price"), count("*").alias("product_count"), max("price").alias("max_price")).orderBy(desc("avg_price")).collect()
    regional_supplier_analysis = regional_df.groupBy("supplier_name").agg(avg("price").alias("supplier_avg"), count("*").alias("supply_frequency")).filter(col("supply_frequency") > 2).orderBy("supplier_avg").collect()
    regional_price_trend = regional_df.groupBy("date").agg(avg("price").alias("daily_avg")).orderBy("date").collect()
    cross_regional_comparison = df.filter(col("date") >= start_date.strftime("%Y-%m-%d")).groupBy("region").agg(avg("price").alias("region_avg"), count("*").alias("region_records")).orderBy("region_avg").collect()
    seasonal_regional_pattern = regional_df.withColumn("month", date_format("date", "MM")).groupBy("month").agg(avg("price").alias("monthly_avg")).orderBy("month").collect()
    regional_price_stability = regional_df.select("price").rdd.map(lambda row: row[0]).collect()
    regional_std = np.std(regional_price_stability) if regional_price_stability else 0
    regional_coefficient_variation = (regional_std / regional_overview["avg_price"]) if regional_overview["avg_price"] > 0 else 0
    high_value_products = regional_df.filter(col("price") > regional_overview["avg_price"] * 1.5).groupBy("product_name").agg(count("*").alias("high_price_count")).orderBy(desc("high_price_count")).collect()
    regional_supply_chain = regional_df.groupBy("supplier_name", "product_name").agg(avg("price").alias("chain_avg")).orderBy("supplier_name", "product_name").collect()
    market_concentration = regional_df.groupBy("supplier_name").agg(count("*").alias("market_share")).orderBy(desc("market_share")).collect()
    total_supplies = sum([row["market_share"] for row in market_concentration])
    market_concentration_ratio = [(row["supplier_name"], row["market_share"], (row["market_share"] / total_supplies * 100)) for row in market_concentration[:5]]
    regional_price_distribution = regional_df.select(when(col("price") < regional_overview["avg_price"] * 0.7, "低价区间").when(col("price") > regional_overview["avg_price"] * 1.3, "高价区间").otherwise("正常区间").alias("price_segment")).groupBy("price_segment").count().collect()
    result = {"overview": {"total_records": regional_overview["total_records"], "avg_price": float(regional_overview["avg_price"]), "max_price": float(regional_overview["max_price"]), "min_price": float(regional_overview["min_price"]), "price_stability": float(regional_std), "variation_coefficient": float(regional_coefficient_variation)}, "product_analysis": [{"product": row["product_name"], "avg_price": float(row["avg_price"]), "count": row["product_count"], "max_price": float(row["max_price"])} for row in product_regional_analysis], "supplier_analysis": [{"supplier": row["supplier_name"], "avg_price": float(row["supplier_avg"]), "frequency": row["supply_frequency"]} for row in regional_supplier_analysis], "price_trend": [{"date": str(row["date"]), "price": float(row["daily_avg"])} for row in regional_price_trend], "cross_regional": [{"region": row["region"], "avg_price": float(row["region_avg"]), "records": row["region_records"]} for row in cross_regional_comparison], "seasonal_pattern": [{"month": row["month"], "avg_price": float(row["monthly_avg"])} for row in seasonal_regional_pattern], "high_value_products": [{"product": row["product_name"], "count": row["high_price_count"]} for row in high_value_products], "supply_chain": [{"supplier": row["supplier_name"], "product": row["product_name"], "avg_price": float(row["chain_avg"])} for row in regional_supply_chain], "market_concentration": [{"supplier": item[0], "share": item[1], "percentage": item[2]} for item in market_concentration_ratio], "price_distribution": [{"segment": row["price_segment"], "count": row["count"]} for row in regional_price_distribution]}
    return JsonResponse(result)

基于大数据的农产品供应价格数据可视化分析系统文档展示

💖💖作者：计算机毕业设计小途
💙💙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