💖💖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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大气和海洋动力学数据分析与可视化系统介绍
《基于大数据的大气和海洋动力学数据分析与可视化系统》是一套融合了现代大数据技术栈的综合性数据处理平台，该系统采用Hadoop分布式存储框架配合Spark大数据计算引擎作为核心技术架构，实现对海量大气和海洋动力学数据的高效存储、处理与分析。系统支持Python和Java两套开发语言方案，其中Python版本基于Django框架构建，Java版本采用Spring Boot全家桶（Spring+SpringMVC+MyBatis）进行开发，前端统一使用Vue.js结合ElementUI组件库搭建现代化用户界面，通过ECharts图表库实现丰富的数据可视化效果。在数据处理层面，系统深度整合了HDFS分布式文件系统用于大规模数据存储，利用Spark SQL进行复杂数据查询分析，结合Pandas和NumPy科学计算库处理数值型数据运算，所有结构化数据统一存储在MySQL数据库中。系统功能模块涵盖用户权限管理、个人信息维护、大气海洋动力数据的增删改查操作、多维度数据可视化分析展示、专业的大气和海洋动力学数据大屏监控、温室气体变化趋势分析、宏观气候数据趋势预测、极地动态变化监测以及海洋动力学深度分析等核心业务场景，为大气海洋科学研究提供了完整的大数据技术解决方案，充分体现了大数据技术在环境科学领域的实际应用价值。
大气和海洋动力学数据分析与可视化系统演示视频
大气和海洋动力学数据分析与可视化系统演示图片
大气和海洋动力学数据分析与可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, max, min, when, desc, asc, year, month, dayofmonth
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType, IntegerType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("AtmosphereOceanAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def atmosphere_ocean_data_analysis(request):
try:
request_data = json.loads(request.body)
start_date = request_data.get('start_date')
end_date = request_data.get('end_date')
region_code = request_data.get('region_code')
analysis_type = request_data.get('analysis_type', 'temperature')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/atmosphere_ocean_db").option("dbtable", "atmosphere_ocean_data").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("record_date") >= start_date) & (col("record_date") <= end_date) & (col("region_code") == region_code))
if analysis_type == 'temperature':
result_df = filtered_df.groupBy("region_code", year("record_date").alias("year"), month("record_date").alias("month")).agg(avg("temperature").alias("avg_temp"), max("temperature").alias("max_temp"), min("temperature").alias("min_temp"), count("*").alias("record_count"))
elif analysis_type == 'pressure':
result_df = filtered_df.groupBy("region_code", year("record_date").alias("year"), month("record_date").alias("month")).agg(avg("atmospheric_pressure").alias("avg_pressure"), max("atmospheric_pressure").alias("max_pressure"), min("atmospheric_pressure").alias("min_pressure"), count("*").alias("record_count"))
else:
result_df = filtered_df.groupBy("region_code", year("record_date").alias("year"), month("record_date").alias("month")).agg(avg("wind_speed").alias("avg_wind"), max("wind_speed").alias("max_wind"), min("wind_speed").alias("min_wind"), count("*").alias("record_count"))
trend_analysis = result_df.orderBy("year", "month")
pandas_df = trend_analysis.toPandas()
trend_data = []
for index, row in pandas_df.iterrows():
trend_data.append({
'year': int(row['year']),
'month': int(row['month']),
'avg_value': float(row[pandas_df.columns[3]]),
'max_value': float(row[pandas_df.columns[4]]),
'min_value': float(row[pandas_df.columns[5]]),
'record_count': int(row['record_count'])
})
return JsonResponse({'status': 'success', 'trend_data': trend_data, 'analysis_type': analysis_type})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
@csrf_exempt
def data_visualization_dashboard(request):
try:
request_data = json.loads(request.body)
chart_type = request_data.get('chart_type', 'line')
time_range = request_data.get('time_range', 30)
end_date = datetime.now()
start_date = end_date - timedelta(days=time_range)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/atmosphere_ocean_db").option("dbtable", "atmosphere_ocean_data").option("user", "root").option("password", "password").load()
recent_df = df.filter((col("record_date") >= start_date.strftime('%Y-%m-%d')) & (col("record_date") <= end_date.strftime('%Y-%m-%d')))
if chart_type == 'line':
chart_data = recent_df.groupBy(dayofmonth("record_date").alias("day")).agg(avg("temperature").alias("avg_temp"), avg("humidity").alias("avg_humidity"), avg("atmospheric_pressure").alias("avg_pressure")).orderBy("day")
elif chart_type == 'bar':
chart_data = recent_df.groupBy("region_code").agg(count("*").alias("total_records"), avg("temperature").alias("avg_temp"), avg("wind_speed").alias("avg_wind")).orderBy(desc("total_records"))
else:
chart_data = recent_df.groupBy("weather_type").agg(count("*").alias("type_count")).orderBy(desc("type_count"))
pandas_result = chart_data.toPandas()
visualization_data = {
'chart_type': chart_type,
'labels': pandas_result.iloc[:, 0].tolist(),
'datasets': []
}
for col_name in pandas_result.columns[1:]:
dataset = {
'label': col_name,
'data': pandas_result[col_name].fillna(0).tolist(),
'backgroundColor': f'rgba({np.random.randint(0, 255)}, {np.random.randint(0, 255)}, {np.random.randint(0, 255)}, 0.6)'
}
visualization_data['datasets'].append(dataset)
statistical_summary = recent_df.agg(count("*").alias("total_records"), avg("temperature").alias("avg_temperature"), max("temperature").alias("max_temperature"), min("temperature").alias("min_temperature"), avg("humidity").alias("avg_humidity"), avg("atmospheric_pressure").alias("avg_pressure")).collect()[0]
summary_stats = {
'total_records': statistical_summary['total_records'],
'avg_temperature': round(float(statistical_summary['avg_temperature']), 2),
'max_temperature': round(float(statistical_summary['max_temperature']), 2),
'min_temperature': round(float(statistical_summary['min_temperature']), 2),
'avg_humidity': round(float(statistical_summary['avg_humidity']), 2),
'avg_pressure': round(float(statistical_summary['avg_pressure']), 2)
}
return JsonResponse({'status': 'success', 'visualization_data': visualization_data, 'summary_stats': summary_stats})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
@csrf_exempt
def greenhouse_gas_analysis(request):
try:
request_data = json.loads(request.body)
analysis_period = request_data.get('period', 'monthly')
gas_type = request_data.get('gas_type', 'CO2')
region_filter = request_data.get('region', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/atmosphere_ocean_db").option("dbtable", "greenhouse_gas_data").option("user", "root").option("password", "password").load()
if region_filter != 'all':
df = df.filter(col("monitoring_region") == region_filter)
if analysis_period == 'monthly':
grouped_df = df.groupBy(year("measurement_date").alias("year"), month("measurement_date").alias("month")).agg(avg(f"{gas_type.lower()}_concentration").alias("avg_concentration"), max(f"{gas_type.lower()}_concentration").alias("max_concentration"), min(f"{gas_type.lower()}_concentration").alias("min_concentration"), count("*").alias("measurement_count"))
elif analysis_period == 'yearly':
grouped_df = df.groupBy(year("measurement_date").alias("year")).agg(avg(f"{gas_type.lower()}_concentration").alias("avg_concentration"), max(f"{gas_type.lower()}_concentration").alias("max_concentration"), min(f"{gas_type.lower()}_concentration").alias("min_concentration"), count("*").alias("measurement_count"))
else:
grouped_df = df.groupBy(year("measurement_date").alias("year"), month("measurement_date").alias("month"), dayofmonth("measurement_date").alias("day")).agg(avg(f"{gas_type.lower()}_concentration").alias("avg_concentration"), max(f"{gas_type.lower()}_concentration").alias("max_concentration"), min(f"{gas_type.lower()}_concentration").alias("min_concentration"), count("*").alias("measurement_count"))
trend_df = grouped_df.orderBy("year", "month") if analysis_period != 'yearly' else grouped_df.orderBy("year")
pandas_trend = trend_df.toPandas()
concentration_trend = []
for index, row in pandas_trend.iterrows():
trend_point = {
'period': f"{row['year']}-{row.get('month', 1):02d}" if 'month' in row else str(row['year']),
'avg_concentration': round(float(row['avg_concentration']), 3),
'max_concentration': round(float(row['max_concentration']), 3),
'min_concentration': round(float(row['min_concentration']), 3),
'measurement_count': int(row['measurement_count'])
}
concentration_trend.append(trend_point)
growth_rate_data = []
for i in range(1, len(concentration_trend)):
current_avg = concentration_trend[i]['avg_concentration']
previous_avg = concentration_trend[i-1]['avg_concentration']
growth_rate = ((current_avg - previous_avg) / previous_avg) * 100
growth_rate_data.append({
'period': concentration_trend[i]['period'],
'growth_rate': round(growth_rate, 2),
'concentration_change': round(current_avg - previous_avg, 3)
})
current_level = df.agg(avg(f"{gas_type.lower()}_concentration").alias("current_avg")).collect()[0]['current_avg']
historical_avg = df.filter(year("measurement_date") < 2020).agg(avg(f"{gas_type.lower()}_concentration").alias("historical_avg")).collect()[0]['historical_avg']
comparison_result = {
'current_level': round(float(current_level), 3),
'historical_average': round(float(historical_avg), 3) if historical_avg else 0,
'change_percentage': round(((float(current_level) - float(historical_avg)) / float(historical_avg)) * 100, 2) if historical_avg else 0
}
return JsonResponse({'status': 'success', 'gas_type': gas_type, 'concentration_trend': concentration_trend, 'growth_rate_analysis': growth_rate_data, 'comparison_result': comparison_result, 'analysis_period': analysis_period})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
大气和海洋动力学数据分析与可视化系统文档展示
💖💖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
