前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
一.开发工具简介
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
二.系统内容简介
《大气和海洋动力学数据分析与可视化系统》是一套基于Python Django框架开发的大数据处理与可视化平台，专门针对大气和海洋动力学相关数据进行深度分析和直观展示。系统采用Hadoop分布式存储架构结合Spark大数据计算引擎，能够高效处理海量的气象和海洋观测数据。前端采用Vue框架配合ElementUI组件库和Echarts可视化图表库，为用户提供友好的交互界面和丰富的数据展示效果。系统集成了用户管理、数据管理、可视化分析、数据大屏展示等核心功能模块，支持温室气体分析、宏观趋势分析、极地动态分析以及海洋动力学分析等多个专业分析维度。通过运用Pandas和NumPy进行数据处理，结合Spark SQL进行大数据查询，系统能够实现从数据采集、存储、处理到可视化展示的完整数据分析流程，为气象和海洋科学研究提供了一个集成化的数据分析工具平台。
三.系统功能演示
Python Django大数据毕设：大气和海洋动力学数据系统实战案例
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from django.http import JsonResponse
import pandas as pd
import numpy as np
from django.views.decorators.csrf import csrf_exempt
import json
from models import AtmosphericData, OceanData, GreenhouseGasData
spark = SparkSession.builder.appName("AtmosphericOceanAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def atmospheric_ocean_data_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
region = data.get('region')
atmospheric_data = AtmosphericData.objects.filter(date__range=[start_date, end_date], region=region)
ocean_data = OceanData.objects.filter(date__range=[start_date, end_date], region=region)
atm_df = pd.DataFrame(list(atmospheric_data.values()))
ocean_df = pd.DataFrame(list(ocean_data.values()))
if not atm_df.empty and not ocean_df.empty:
spark_atm_df = spark.createDataFrame(atm_df)
spark_ocean_df = spark.createDataFrame(ocean_df)
spark_atm_df.createOrReplaceTempView("atmospheric_data")
spark_ocean_df.createOrReplaceTempView("ocean_data")
correlation_query = """
SELECT a.date, a.temperature as atm_temp, a.pressure, a.humidity,
o.temperature as ocean_temp, o.salinity, o.current_speed
FROM atmospheric_data a
JOIN ocean_data o ON a.date = o.date AND a.region = o.region
"""
correlation_result = spark.sql(correlation_query)
correlation_pandas = correlation_result.toPandas()
temp_correlation = np.corrcoef(correlation_pandas['atm_temp'], correlation_pandas['ocean_temp'])[0,1]
pressure_analysis = correlation_pandas['pressure'].describe()
humidity_trends = correlation_pandas.groupby(correlation_pandas['date'].dt.month)['humidity'].mean()
salinity_distribution = correlation_pandas['salinity'].quantile([0.25, 0.5, 0.75])
current_anomalies = correlation_pandas[correlation_pandas['current_speed'] > correlation_pandas['current_speed'].quantile(0.95)]
analysis_result = {
'temperature_correlation': float(temp_correlation),
'pressure_statistics': pressure_analysis.to_dict(),
'monthly_humidity_trends': humidity_trends.to_dict(),
'salinity_distribution': salinity_distribution.to_dict(),
'current_anomalies_count': len(current_anomalies),
'total_records': len(correlation_pandas)
}
return JsonResponse({'status': 'success', 'data': analysis_result})
else:
return JsonResponse({'status': 'error', 'message': 'No data found for the specified parameters'})
@csrf_exempt
def greenhouse_gas_trend_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
gas_type = data.get('gas_type', 'CO2')
time_period = data.get('time_period', 'yearly')
gas_data = GreenhouseGasData.objects.filter(gas_type=gas_type).order_by('date')
gas_df = pd.DataFrame(list(gas_data.values()))
if not gas_df.empty:
spark_gas_df = spark.createDataFrame(gas_df)
spark_gas_df.createOrReplaceTempView("greenhouse_data")
if time_period == 'yearly':
trend_query = """
SELECT YEAR(date) as year, AVG(concentration) as avg_concentration,
MAX(concentration) as max_concentration, MIN(concentration) as min_concentration,
COUNT(*) as record_count
FROM greenhouse_data
GROUP BY YEAR(date)
ORDER BY year
"""
elif time_period == 'monthly':
trend_query = """
SELECT YEAR(date) as year, MONTH(date) as month,
AVG(concentration) as avg_concentration,
STDDEV(concentration) as std_concentration
FROM greenhouse_data
GROUP BY YEAR(date), MONTH(date)
ORDER BY year, month
"""
trend_result = spark.sql(trend_query)
trend_pandas = trend_result.toPandas()
if time_period == 'yearly':
growth_rates = []
for i in range(1, len(trend_pandas)):
current_conc = trend_pandas.iloc[i]['avg_concentration']
previous_conc = trend_pandas.iloc[i-1]['avg_concentration']
growth_rate = ((current_conc - previous_conc) / previous_conc) * 100
growth_rates.append(growth_rate)
trend_pandas['growth_rate'] = [0] + growth_rates
seasonal_analysis = trend_pandas.groupby('month' if time_period == 'monthly' else 'year')['avg_concentration'].agg(['mean', 'std']).reset_index() if time_period == 'monthly' else None
concentration_forecast = np.polyfit(range(len(trend_pandas)), trend_pandas['avg_concentration'], 2)
forecast_values = np.polyval(concentration_forecast, range(len(trend_pandas), len(trend_pandas) + 5))
analysis_result = {
'trend_data': trend_pandas.to_dict('records'),
'seasonal_analysis': seasonal_analysis.to_dict('records') if seasonal_analysis is not None else None,
'forecast_next_5_periods': forecast_values.tolist(),
'total_growth_rate': float(growth_rates[-1]) if growth_rates else 0,
'analysis_period': time_period
}
return JsonResponse({'status': 'success', 'data': analysis_result})
else:
return JsonResponse({'status': 'error', 'message': 'No greenhouse gas data found'})
@csrf_exempt
def ocean_dynamics_visualization(request):
if request.method == 'POST':
data = json.loads(request.body)
depth_range = data.get('depth_range', [0, 1000])
analysis_type = data.get('analysis_type', 'current_patterns')
ocean_data = OceanData.objects.filter(depth__range=depth_range)
ocean_df = pd.DataFrame(list(ocean_data.values()))
if not ocean_df.empty:
spark_ocean_df = spark.createDataFrame(ocean_df)
spark_ocean_df.createOrReplaceTempView("ocean_dynamics")
if analysis_type == 'current_patterns':
current_query = """
SELECT latitude, longitude, depth, current_speed, current_direction,
temperature, salinity,
CASE
WHEN current_speed > 1.5 THEN 'Strong'
WHEN current_speed > 0.8 THEN 'Moderate'
ELSE 'Weak'
END as current_intensity
FROM ocean_dynamics
ORDER BY depth, current_speed DESC
"""
elif analysis_type == 'temperature_distribution':
current_query = """
SELECT depth, AVG(temperature) as avg_temp,
STDDEV(temperature) as temp_variance,
MAX(temperature) as max_temp, MIN(temperature) as min_temp,
COUNT(*) as measurement_count
FROM ocean_dynamics
GROUP BY depth
ORDER BY depth
"""
dynamics_result = spark.sql(current_query)
dynamics_pandas = dynamics_result.toPandas()
if analysis_type == 'current_patterns':
intensity_distribution = dynamics_pandas['current_intensity'].value_counts().to_dict()
directional_analysis = dynamics_pandas.groupby('current_direction')['current_speed'].mean().to_dict()
depth_current_correlation = np.corrcoef(dynamics_pandas['depth'], dynamics_pandas['current_speed'])[0,1]
temperature_current_correlation = np.corrcoef(dynamics_pandas['temperature'], dynamics_pandas['current_speed'])[0,1]
visualization_data = {
'current_vectors': dynamics_pandas[['latitude', 'longitude', 'current_speed', 'current_direction']].to_dict('records'),
'intensity_distribution': intensity_distribution,
'directional_patterns': directional_analysis,
'depth_correlation': float(depth_current_correlation),
'temperature_correlation': float(temperature_current_correlation)
}
else:
thermal_layers = dynamics_pandas[dynamics_pandas['temp_variance'] > dynamics_pandas['temp_variance'].quantile(0.7)]
temperature_gradient = []
for i in range(1, len(dynamics_pandas)):
gradient = (dynamics_pandas.iloc[i]['avg_temp'] - dynamics_pandas.iloc[i-1]['avg_temp']) / (dynamics_pandas.iloc[i]['depth'] - dynamics_pandas.iloc[i-1]['depth'])
temperature_gradient.append(gradient)
visualization_data = {
'temperature_profile': dynamics_pandas.to_dict('records'),
'thermal_layers': thermal_layers.to_dict('records'),
'temperature_gradients': temperature_gradient,
'thermocline_depth': float(dynamics_pandas.loc[dynamics_pandas['temp_variance'].idxmax(), 'depth'])
}
return JsonResponse({'status': 'success', 'data': visualization_data, 'analysis_type': analysis_type})
else:
return JsonResponse({'status': 'error', 'message': 'No ocean dynamics data found for the specified depth range'})
六.系统文档展示
结束
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