💖💖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
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基于大数据的农产品数据可视化分析系统介绍
基于大数据的农产品数据可视化分析系统是一套集数据采集、存储、处理与可视化展示于一体的综合性农业数据分析平台，该系统采用Hadoop分布式文件系统HDFS作为底层数据存储架构，结合Spark大数据处理引擎实现对海量农产品数据的高效计算和分析处理。系统后端采用Python Django框架和Java Spring Boot框架双重技术栈支持，通过Spark SQL、Pandas、NumPy等数据处理工具对农产品相关数据进行深度挖掘和统计分析，前端基于Vue.js框架结合ElementUI组件库构建用户交互界面，利用Echarts图表库实现数据的动态可视化展示。系统核心功能模块包括农产品价格趋势分析、生产结构分析、宏观影响因素分析以及综合关联性分析，用户可以通过直观的图表形式查看不同时间维度下农产品价格波动规律、各地区农产品产量分布情况、气候环境等宏观因素对农产品产出的影响程度，以及多种农产品之间的关联性变化趋势。系统还提供完善的用户管理功能，包括个人信息维护、密码修改、系统简介查看等基础服务模块，整体架构采用MySQL关系型数据库存储用户信息和处理结果，通过大数据技术栈的深度整合为农业生产决策、市场预测和政策制定提供科学的数据支撑和分析依据。
基于大数据的农产品数据可视化分析系统演示视频
基于大数据的农产品数据可视化分析系统演示图片
基于大数据的农产品数据可视化分析系统代码展示
spark = SparkSession.builder.appName("AgriculturalDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def analyze_price_trend(product_name, start_date, end_date):
price_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agricultural_db").option("dbtable", "product_prices").option("user", "root").option("password", "password").load()
filtered_df = price_df.filter((price_df.product_name == product_name) & (price_df.date >= start_date) & (price_df.date <= end_date))
daily_avg = filtered_df.groupBy("date").agg({"price": "avg", "volume": "sum"}).withColumnRenamed("avg(price)", "avg_price").withColumnRenamed("sum(volume)", "total_volume")
trend_data = daily_avg.withColumn("price_change", daily_avg.avg_price - lag("avg_price").over(Window.orderBy("date"))).withColumn("change_rate", (daily_avg.avg_price - lag("avg_price").over(Window.orderBy("date"))) / lag("avg_price").over(Window.orderBy("date")) * 100)
monthly_trend = daily_avg.withColumn("month", date_format("date", "yyyy-MM")).groupBy("month").agg({"avg_price": "avg", "total_volume": "avg"}).withColumnRenamed("avg(avg_price)", "monthly_avg_price").withColumnRenamed("avg(total_volume)", "monthly_avg_volume")
seasonal_analysis = filtered_df.withColumn("season", when((month("date") >= 3) & (month("date") <= 5), "春季").when((month("date") >= 6) & (month("date") <= 8), "夏季").when((month("date") >= 9) & (month("date") <= 11), "秋季").otherwise("冬季"))
seasonal_stats = seasonal_analysis.groupBy("season").agg({"price": "avg", "price": "max", "price": "min"}).withColumnRenamed("avg(price)", "season_avg").withColumnRenamed("max(price)", "season_max").withColumnRenamed("min(price)", "season_min")
volatility_calc = trend_data.select(stddev("avg_price").alias("price_volatility"), variance("avg_price").alias("price_variance"))
trend_direction = trend_data.select(corr("date", "avg_price").alias("trend_correlation"))
result_data = {"daily_trend": trend_data.collect(), "monthly_trend": monthly_trend.collect(), "seasonal_stats": seasonal_stats.collect(), "volatility": volatility_calc.collect()[0], "trend_direction": trend_direction.collect()[0]}
support_resistance = trend_data.agg({"avg_price": "max", "avg_price": "min"}).collect()[0]
result_data["support_level"] = support_resistance["min(avg_price)"]
result_data["resistance_level"] = support_resistance["max(avg_price)"]
moving_avg_7 = trend_data.withColumn("ma_7", avg("avg_price").over(Window.orderBy("date").rowsBetween(-6, 0)))
moving_avg_30 = moving_avg_7.withColumn("ma_30", avg("avg_price").over(Window.orderBy("date").rowsBetween(-29, 0)))
result_data["moving_averages"] = moving_avg_30.select("date", "avg_price", "ma_7", "ma_30").collect()
return result_data
def analyze_production_structure(region, year):
production_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agricultural_db").option("dbtable", "production_data").option("user", "root").option("password", "password").load()
region_data = production_df.filter((production_df.region == region) & (production_df.year == year))
total_production = region_data.agg({"output": "sum"}).collect()[0]["sum(output)"]
crop_distribution = region_data.groupBy("crop_type").agg({"output": "sum", "area": "sum"}).withColumnRenamed("sum(output)", "total_output").withColumnRenamed("sum(area)", "total_area")
crop_percentage = crop_distribution.withColumn("output_percentage", (crop_distribution.total_output / total_production) * 100).withColumn("yield_per_area", crop_distribution.total_output / crop_distribution.total_area)
top_crops = crop_percentage.orderBy(desc("output_percentage")).limit(10)
productivity_analysis = crop_percentage.select("crop_type", "yield_per_area").orderBy(desc("yield_per_area"))
area_efficiency = region_data.groupBy("crop_type").agg({"area": "avg", "output": "avg"}).withColumnRenamed("avg(area)", "avg_area").withColumnRenamed("avg(output)", "avg_output")
efficiency_ratio = area_efficiency.withColumn("efficiency_score", area_efficiency.avg_output / area_efficiency.avg_area)
seasonal_production = region_data.groupBy("crop_type", "season").agg({"output": "sum"}).withColumnRenamed("sum(output)", "seasonal_output")
production_stability = region_data.groupBy("crop_type").agg(stddev("output").alias("output_std"), avg("output").alias("output_avg")).withColumn("stability_index", col("output_avg") / col("output_std"))
growth_trend = production_df.filter(production_df.region == region).groupBy("crop_type", "year").agg({"output": "sum"}).withColumnRenamed("sum(output)", "yearly_output")
year_over_year = growth_trend.withColumn("growth_rate", (col("yearly_output") - lag("yearly_output").over(Window.partitionBy("crop_type").orderBy("year"))) / lag("yearly_output").over(Window.partitionBy("crop_type").orderBy("year")) * 100)
diversity_index = crop_distribution.select(sum((col("total_output") / total_production) * log2(col("total_output") / total_production))).collect()[0][0]
production_summary = {"crop_distribution": crop_percentage.collect(), "top_crops": top_crops.collect(), "productivity_ranking": productivity_analysis.collect(), "efficiency_analysis": efficiency_ratio.collect(), "seasonal_patterns": seasonal_production.collect(), "stability_metrics": production_stability.collect(), "growth_trends": year_over_year.collect(), "diversity_index": abs(diversity_index) if diversity_index else 0}
return production_summary
def analyze_comprehensive_correlation(start_date, end_date):
price_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agricultural_db").option("dbtable", "product_prices").option("user", "root").option("password", "password").load()
weather_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agricultural_db").option("dbtable", "weather_data").option("user", "root").option("password", "password").load()
production_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/agricultural_db").option("dbtable", "production_data").option("user", "root").option("password", "password").load()
date_filtered_price = price_df.filter((price_df.date >= start_date) & (price_df.date <= end_date))
date_filtered_weather = weather_df.filter((weather_df.date >= start_date) & (weather_df.date <= end_date))
price_weather_joined = date_filtered_price.join(date_filtered_weather, ["date", "region"], "inner")
price_temp_corr = price_weather_joined.select(corr("price", "temperature").alias("price_temp_correlation"))
price_rainfall_corr = price_weather_joined.select(corr("price", "rainfall").alias("price_rainfall_correlation"))
price_humidity_corr = price_weather_joined.select(corr("price", "humidity").alias("price_humidity_correlation"))
monthly_price_avg = date_filtered_price.withColumn("month", date_format("date", "yyyy-MM")).groupBy("product_name", "month").agg({"price": "avg"}).withColumnRenamed("avg(price)", "monthly_avg_price")
monthly_production = production_df.withColumn("month", date_format("date", "yyyy-MM")).groupBy("crop_type", "month").agg({"output": "sum"}).withColumnRenamed("sum(output)", "monthly_production").withColumnRenamed("crop_type", "product_name")
price_production_joined = monthly_price_avg.join(monthly_production, ["product_name", "month"], "inner")
supply_demand_corr = price_production_joined.select(corr("monthly_avg_price", "monthly_production").alias("price_production_correlation"))
cross_product_analysis = date_filtered_price.groupBy("date").pivot("product_name").agg({"price": "avg"})
product_columns = [col for col in cross_product_analysis.columns if col != "date"]
correlation_matrix = {}
for i in range(len(product_columns)):
for j in range(i+1, len(product_columns)):
corr_val = cross_product_analysis.select(corr(product_columns[i], product_columns[j]).alias("correlation")).collect()[0]["correlation"]
correlation_matrix[f"{product_columns[i]}_{product_columns[j]}"] = corr_val if corr_val else 0
regional_price_diff = date_filtered_price.groupBy("product_name", "region").agg({"price": "avg"}).withColumnRenamed("avg(price)", "regional_avg_price")
price_spread_analysis = regional_price_diff.groupBy("product_name").agg({"regional_avg_price": "max", "regional_avg_price": "min"}).withColumn("price_spread", col("max(regional_avg_price)") - col("min(regional_avg_price)"))
seasonal_correlation = price_weather_joined.withColumn("season", when((month("date") >= 3) & (month("date") <= 5), "春季").when((month("date") >= 6) & (month("date") <= 8), "夏季").when((month("date") >= 9) & (month("date") <= 11), "秋季").otherwise("冬季"))
seasonal_price_weather = seasonal_correlation.groupBy("season").agg(corr("price", "temperature").alias("seasonal_temp_corr"), corr("price", "rainfall").alias("seasonal_rain_corr"))
comprehensive_results = {"weather_correlations": {"temperature": price_temp_corr.collect()[0]["price_temp_correlation"], "rainfall": price_rainfall_corr.collect()[0]["price_rainfall_correlation"], "humidity": price_humidity_corr.collect()[0]["price_humidity_correlation"]}, "supply_demand_correlation": supply_demand_corr.collect()[0]["price_production_correlation"], "cross_product_correlations": correlation_matrix, "regional_price_analysis": price_spread_analysis.collect(), "seasonal_correlations": seasonal_price_weather.collect()}
return comprehensive_results
基于大数据的农产品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
