💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
基于机器学习的农产品价格数据分析可视化系统介绍
农产品价格数据分析可视化系统是一个基于大数据技术栈构建的智能分析平台，该系统充分利用Hadoop分布式存储和Spark大数据处理能力，对农产品市场价格数据进行深度挖掘与分析。系统采用Python语言开发核心算法模块，结合Django框架构建稳定的后端服务架构，前端使用Vue框架配合ElementUI组件库和Echarts图表库实现用户交互界面。系统核心功能涵盖蔬菜信息管理、水产品信息管理、惠农网数据管理、蔬菜价格预测分析等模块，通过HDFS分布式文件系统存储海量农产品价格历史数据，运用Spark SQL进行复杂数据查询和统计分析，结合Pandas和NumPy科学计算库实现价格趋势预测算法。系统能够实时采集和处理来自不同渠道的农产品价格信息，通过机器学习算法分析价格波动规律，为用户提供直观的数据可视化展示和准确的价格预测结果，帮助农业从业者和相关企业做出更加科学的决策。
基于机器学习的农产品价格数据分析可视化系统演示视频
基于机器学习的农产品价格数据分析可视化系统演示图片
基于机器学习的农产品价格数据分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("AgriculturePriceAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def vegetable_price_prediction(vegetable_type, days_ahead):
"""蔬菜价格预测核心算法"""
price_data = spark.sql(f"SELECT date, price, market_supply, weather_factor FROM vegetable_prices WHERE vegetable_type='{vegetable_type}' ORDER BY date")
price_df = price_data.withColumn("date_numeric", unix_timestamp("date").cast("double"))
price_df = price_df.withColumn("day_of_year", dayofyear("date"))
price_df = price_df.withColumn("month", month("date"))
price_df = price_df.withColumn("price_lag1", lag("price", 1).over(Window.orderBy("date")))
price_df = price_df.withColumn("price_lag7", lag("price", 7).over(Window.orderBy("date")))
price_df = price_df.withColumn("price_ma7", avg("price").over(Window.orderBy("date").rowsBetween(-6, 0)))
price_df = price_df.withColumn("price_volatility", stddev("price").over(Window.orderBy("date").rowsBetween(-13, 0)))
feature_cols = ["date_numeric", "day_of_year", "month", "market_supply", "weather_factor", "price_lag1", "price_lag7", "price_ma7", "price_volatility"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_data = assembler.transform(price_df.na.drop())
lr = LinearRegression(featuresCol="features", labelCol="price")
model = lr.fit(feature_data)
latest_data = feature_data.orderBy(desc("date")).limit(1)
future_date = datetime.now() + timedelta(days=days_ahead)
future_numeric = future_date.timestamp()
prediction_row = latest_data.withColumn("date_numeric", lit(future_numeric))
prediction_row = prediction_row.withColumn("day_of_year", lit(future_date.timetuple().tm_yday))
prediction_row = prediction_row.withColumn("month", lit(future_date.month))
prediction_features = assembler.transform(prediction_row)
prediction_result = model.transform(prediction_features)
predicted_price = prediction_result.select("prediction").collect()[0]["prediction"]
confidence_interval = abs(predicted_price * 0.15)
return {"predicted_price": round(predicted_price, 2), "confidence_range": [round(predicted_price - confidence_interval, 2), round(predicted_price + confidence_interval, 2)], "prediction_date": future_date.strftime("%Y-%m-%d")}
def price_data_analysis_engine(start_date, end_date, product_category):
"""农产品价格数据深度分析引擎"""
raw_data = spark.sql(f"SELECT * FROM agriculture_prices WHERE date BETWEEN '{start_date}' AND '{end_date}' AND category='{product_category}'")
daily_stats = raw_data.groupBy("date").agg(avg("price").alias("avg_price"), min("price").alias("min_price"), max("price").alias("max_price"), count("*").alias("record_count"))
monthly_trends = raw_data.withColumn("year_month", date_format("date", "yyyy-MM")).groupBy("year_month").agg(avg("price").alias("monthly_avg"), stddev("price").alias("monthly_volatility"))
regional_analysis = raw_data.groupBy("region").agg(avg("price").alias("region_avg_price"), count("*").alias("region_count")).orderBy(desc("region_avg_price"))
price_volatility = raw_data.withColumn("price_change", col("price") - lag("price", 1).over(Window.partitionBy("product_name").orderBy("date")))
volatility_stats = price_volatility.groupBy("product_name").agg(stddev("price_change").alias("volatility_index"), avg("price_change").alias("avg_change"))
seasonal_pattern = raw_data.withColumn("season", when(month("date").isin([12, 1, 2]), "winter").when(month("date").isin([3, 4, 5]), "spring").when(month("date").isin([6, 7, 8]), "summer").otherwise("autumn"))
seasonal_analysis = seasonal_pattern.groupBy("season").agg(avg("price").alias("seasonal_avg_price"), count("*").alias("seasonal_count"))
correlation_data = raw_data.select("price", "market_supply", "demand_index", "weather_score").toPandas()
correlation_matrix = correlation_data.corr().to_dict()
outlier_detection = raw_data.withColumn("z_score", abs((col("price") - mean("price").over(Window.partitionBy("product_name"))) / stddev("price").over(Window.partitionBy("product_name"))))
outliers = outlier_detection.filter(col("z_score") > 2.5).select("date", "product_name", "price", "z_score")
trend_analysis = raw_data.withColumn("row_number", row_number().over(Window.partitionBy("product_name").orderBy("date")))
trend_data = trend_analysis.withColumn("trend_slope", (col("price") - first("price").over(Window.partitionBy("product_name").orderBy("date"))) / col("row_number"))
analysis_results = {"daily_statistics": daily_stats.toPandas().to_dict("records"), "monthly_trends": monthly_trends.toPandas().to_dict("records"), "regional_analysis": regional_analysis.toPandas().to_dict("records"), "volatility_analysis": volatility_stats.toPandas().to_dict("records"), "seasonal_patterns": seasonal_analysis.toPandas().to_dict("records"), "correlation_matrix": correlation_matrix, "outliers": outliers.toPandas().to_dict("records"), "trend_indicators": trend_data.groupBy("product_name").agg(avg("trend_slope").alias("avg_trend")).toPandas().to_dict("records")}
return analysis_results
def visualization_data_processor(chart_type, product_list, time_range):
"""可视化数据处理与图表生成引擎"""
base_query = f"SELECT date, product_name, price, volume, market_name FROM market_data WHERE date >= '{time_range['start']}' AND date <= '{time_range['end']}'"
if product_list:
product_filter = "'" + "','".join(product_list) + "'"
base_query += f" AND product_name IN ({product_filter})"
market_data = spark.sql(base_query)
if chart_type == "line_chart":
line_data = market_data.groupBy("date", "product_name").agg(avg("price").alias("avg_price")).orderBy("date")
chart_data = line_data.toPandas()
result = {"xAxis": sorted(chart_data['date'].unique().tolist()), "series": []}
for product in chart_data['product_name'].unique():
product_data = chart_data[chart_data['product_name'] == product]
result["series"].append({"name": product, "data": product_data['avg_price'].tolist(), "type": "line"})
elif chart_type == "bar_chart":
bar_data = market_data.groupBy("product_name").agg(avg("price").alias("avg_price"), sum("volume").alias("total_volume")).orderBy(desc("avg_price"))
chart_data = bar_data.toPandas()
result = {"xAxis": chart_data['product_name'].tolist(), "series": [{"name": "平均价格", "data": chart_data['avg_price'].tolist(), "type": "bar"}]}
elif chart_type == "pie_chart":
pie_data = market_data.groupBy("product_name").agg(sum("volume").alias("total_volume"))
total_volume = pie_data.agg(sum("total_volume")).collect()[0][0]
chart_data = pie_data.withColumn("percentage", round((col("total_volume") / total_volume) * 100, 2)).toPandas()
result = {"series": [{"name": "市场占比", "type": "pie", "data": [{"name": row["product_name"], "value": row["percentage"]} for _, row in chart_data.iterrows()]}]}
elif chart_type == "heatmap":
heatmap_data = market_data.withColumn("month", month("date")).withColumn("day", dayofmonth("date"))
heatmap_agg = heatmap_data.groupBy("month", "day").agg(avg("price").alias("avg_price")).orderBy("month", "day")
chart_data = heatmap_agg.toPandas()
result = {"xAxis": list(range(1, 32)), "yAxis": list(range(1, 13)), "data": [[row["day"]-1, row["month"]-1, row["avg_price"]] for _, row in chart_data.iterrows()]}
price_range = market_data.agg(min("price").alias("min_price"), max("price").alias("max_price")).collect()[0]
result["metadata"] = {"price_range": {"min": price_range["min_price"], "max": price_range["max_price"]}, "data_points": market_data.count(), "time_span": time_range, "products_included": product_list}
return result
基于机器学习的农产品价格数据分析可视化系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
