数据云平台系统架构。 - 实践
这个系统整合了数据云、计算云和调度云三大平台，提供全流程的遥感数据服务能力。
1. 系统总体架构
python
import json
import hashlib
import time
from datetime import datetime
from typing import Dict, List, Optional, Tuple
import asyncio
from dataclasses import dataclass
from enum import Enum
import redis
import zarr
from dask.distributed import Client, LocalCluster
import minio
from minio import Minio
from minio.error import S3Error
import postgresql
import cryptography
from cryptography.fernet import Fernet
class PlatformType(Enum):
DATA_CLOUD = "data_cloud"
COMPUTE_CLOUD = "compute_cloud"
SCHEDULE_CLOUD = "schedule_cloud"
class DataSecurityLevel(Enum):
PUBLIC = "public"
INTERNAL = "internal"
CONFIDENTIAL = "confidential"
SECRET = "secret"
@dataclass
class SystemConfig:
"""系统配置类"""
region: str = "hainan"
backup_locations: List[str] = None
security_policy: str = "multi-level"
max_concurrent_jobs: int = 1000
data_retention_years: int = 10
def __post_init__(self):
if self.backup_locations is None:
self.backup_locations = ["haikou", "sanya", "beijing_backup"]2. 数据云软件系统
python
class RemoteSensingDataCloud:
"""遥感数据云平台"""
def __init__(self, config: SystemConfig):
self.config = config
self.metadata_db = self._init_metadata_database()
self.object_storage = self._init_object_storage()
self.catalog_system = DataCatalogSystem()
self.security_system = DataSecuritySystem()
self.backup_system = DataBackupSystem(config)
def _init_metadata_database(self):
"""初始化元数据库"""
# 使用PostgreSQL存储元数据
import psycopg2
conn = psycopg2.connect(
dbname="rs_metadata",
user="admin",
password="secure_password",
host="metadata-db.hainan.rs"
)
return conn
def _init_object_storage(self):
"""初始化对象存储"""
client = Minio(
"object-storage.hainan.rs:9000",
access_key="minioadmin",
secret_key="minioadmin",
secure=False
)
return client
def ingest_remote_sensing_data(self,
file_path: str,
metadata: Dict,
security_level: DataSecurityLevel = DataSecurityLevel.INTERNAL) -> str:
"""
遥感数据入库处理
"""
try:
# 1. 数据校验和完整性检查
file_hash = self._calculate_file_hash(file_path)
file_size = os.path.getsize(file_path)
# 2. 元数据编目
catalog_id = self.catalog_system.create_catalog_entry(
metadata, file_hash, file_size, security_level
)
# 3. 数据加密存储
encrypted_data = self.security_system.encrypt_data(file_path, security_level)
# 4. 主存储
object_name = f"{catalog_id}/{file_hash}.zarr"
self.object_storage.fput_object(
"rs-primary", object_name, encrypted_data
)
# 5. 自动备份
self.backup_system.create_backup(catalog_id, encrypted_data)
# 6. 更新索引
self._update_search_index(catalog_id, metadata)
return catalog_id
except Exception as e:
print(f"数据入库失败: {e}")
return None
def search_data(self,
query: Dict,
date_range: Tuple[datetime, datetime] = None,
spatial_filter: Dict = None,
data_type: str = None) -> List[Dict]:
"""
多维度数据检索
"""
search_params = {
'query': query,
'date_range': date_range,
'spatial_filter': spatial_filter,
'data_type': data_type
}
results = self.catalog_system.advanced_search(search_params)
# 应用安全策略过滤
filtered_results = self.security_system.apply_access_control(
results, self._get_user_context()
)
return filtered_results
def download_data(self, catalog_id: str, download_format: str = "original") -> str:
"""
安全数据下载
"""
# 验证下载权限
if not self.security_system.check_download_permission(catalog_id):
raise PermissionError("无数据下载权限")
# 从主存储或备份恢复
data_path = self._retrieve_data(catalog_id, download_format)
# 解密数据
decrypted_data = self.security_system.decrypt_data(data_path)
# 格式转换（如需要）
if download_format != "original":
decrypted_data = self._convert_format(decrypted_data, download_format)
# 记录下载日志
self._log_download_activity(catalog_id)
return decrypted_data3. 计算云系统
python
class RemoteSensingComputeCloud:
"""遥感计算云平台"""
def __init__(self, config: SystemConfig):
self.config = config
self.dask_cluster = self._init_distributed_cluster()
self.gpu_resources = self._init_gpu_resources()
self.algorithm_library = AlgorithmLibrary()
self.batch_processing_system = BatchProcessingSystem()
def _init_distributed_cluster(self):
"""初始化分布式计算集群"""
cluster = LocalCluster(
n_workers=8,
threads_per_worker=4,
memory_limit='32GB',
processes=True
)
client = Client(cluster)
return client
def _init_gpu_resources(self):
"""初始化GPU计算资源"""
gpu_nodes = [
{"host": "gpu-node1", "gpu_count": 8, "memory": "64GB"},
{"host": "gpu-node2", "gpu_count": 8, "memory": "64GB"},
{"host": "gpu-node3", "gpu_count": 4, "memory": "32GB"}
]
return gpu_nodes
def create_compute_environment(self,
environment_spec: Dict) -> str:
"""
创建计算环境
"""
env_id = hashlib.md5(json.dumps(environment_spec).encode()).hexdigest()[:16]
# 配置计算环境
compute_config = {
'cpu_cores': environment_spec.get('cpu_cores', 4),
'memory_gb': environment_spec.get('memory_gb', 16),
'gpu_count': environment_spec.get('gpu_count', 0),
'software_stack': environment_spec.get('software_stack', 'default'),
'data_access': environment_spec.get('data_access', [])
}
# 部署计算环境
self._deploy_compute_environment(env_id, compute_config)
return env_id
def execute_analysis(self,
algorithm_name: str,
input_data: List[str],
parameters: Dict,
compute_env: str = None) -> str:
"""
执行遥感分析算法
"""
job_id = f"job_{int(time.time())}_{hashlib.md5(algorithm_name.encode()).hexdigest()[:8]}"
# 准备计算任务
task_spec = {
'job_id': job_id,
'algorithm': algorithm_name,
'input_data': input_data,
'parameters': parameters,
'compute_environment': compute_env,
'priority': parameters.get('priority', 'normal')
}
# 提交到分布式计算系统
future = self.dask_cluster.submit(
self._execute_algorithm_task,
task_spec
)
# 监控任务状态
self._monitor_job_status(job_id, future)
return job_id
def _execute_algorithm_task(self, task_spec: Dict) -> Dict:
"""
执行算法任务
"""
algorithm = self.algorithm_library.get_algorithm(task_spec['algorithm'])
# 加载输入数据
input_data = []
for data_id in task_spec['input_data']:
data = self._load_data_for_computation(data_id)
input_data.append(data)
# 执行算法
try:
result = algorithm.execute(input_data, task_spec['parameters'])
# 保存计算结果
result_id = self._store_computation_result(result, task_spec)
return {
'status': 'success',
'result_id': result_id,
'execution_time': result.get('execution_time', 0),
'output_size': result.get('output_size', 0)
}
except Exception as e:
return {
'status': 'failed',
'error': str(e),
'execution_time': 0
}
def batch_process_datasets(self,
dataset_ids: List[str],
algorithm_pipeline: List[Dict],
output_spec: Dict) -> str:
"""
批量处理数据集
"""
batch_id = f"batch_{int(time.time())}"
# 创建批量处理任务
batch_job = {
'batch_id': batch_id,
'datasets': dataset_ids,
'pipeline': algorithm_pipeline,
'output_spec': output_spec,
'status': 'pending',
'created_time': datetime.now()
}
# 提交到批量处理系统
self.batch_processing_system.submit_batch_job(batch_job)
return batch_id4. 云调度系统
python
class CloudSchedulingSystem:
"""云调度系统"""
def __init__(self, data_cloud: RemoteSensingDataCloud,
compute_cloud: RemoteSensingComputeCloud):
self.data_cloud = data_cloud
self.compute_cloud = compute_cloud
self.resource_manager = ResourceManager()
self.job_scheduler = JobScheduler()
self.monitoring_system = MonitoringSystem()
def schedule_computation_job(self,
job_request: Dict) -> Dict:
"""
智能调度计算任务
"""
# 分析资源需求
resource_requirements = self._analyze_resource_requirements(job_request)
# 寻找最优计算节点
optimal_node = self.resource_manager.find_optimal_node(
resource_requirements
)
# 检查数据本地性
data_locations = self._get_data_locations(job_request['input_data'])
# 制定调度策略
scheduling_strategy = self._create_scheduling_strategy(
job_request, optimal_node, data_locations
)
# 执行调度
scheduled_job = self.job_scheduler.schedule_job(
job_request, scheduling_strategy
)
# 启动监控
self.monitoring_system.track_job(scheduled_job['job_id'])
return scheduled_job
def dynamic_resource_optimization(self):
"""
动态资源优化
"""
while True:
# 监控系统负载
system_load = self.monitoring_system.get_system_load()
# 分析资源利用率
resource_utilization = self._analyze_resource_utilization()
# 执行优化策略
if resource_utilization['cpu'] > 0.8:
self._scale_out_compute_nodes()
elif resource_utilization['cpu'] < 0.3:
self._scale_in_compute_nodes()
# 数据存储优化
self._optimize_data_placement()
time.sleep(300) # 每5分钟执行一次优化
def _create_scheduling_strategy(self,
job_request: Dict,
optimal_node: Dict,
data_locations: List[str]) -> Dict:
"""
创建调度策略
"""
strategy = {
'compute_node': optimal_node['node_id'],
'data_transfer_strategy': 'prefetch' if optimal_node['node_id'] in data_locations else 'stream',
'priority': job_request.get('priority', 'normal'),
'deadline': job_request.get('deadline'),
'retry_policy': {
'max_retries': 3,
'backoff_factor': 2
},
'fault_tolerance': {
'checkpoint_interval': 300, # 5分钟检查点
'recovery_strategy': 'restart'
}
}
return strategy5. 数据安全与备份体系
python
class DataSecuritySystem:
"""数据安全系统"""
def __init__(self):
self.encryption_keys = self._load_encryption_keys()
self.access_control = AccessControlSystem()
self.audit_logger = AuditLogger()
def encrypt_data(self, data_path: str, security_level: DataSecurityLevel) -> str:
"""数据加密"""
encryption_key = self.encryption_keys[security_level]
fernet = Fernet(encryption_key)
with open(data_path, 'rb') as file:
original_data = file.read()
encrypted_data = fernet.encrypt(original_data)
encrypted_path = data_path + '.encrypted'
with open(encrypted_path, 'wb') as file:
file.write(encrypted_data)
return encrypted_path
def multi_level_access_control(self, user: Dict, resource: Dict) -> bool:
"""多级访问控制"""
user_clearance = user.get('security_clearance', DataSecurityLevel.PUBLIC)
resource_level = resource.get('security_level', DataSecurityLevel.INTERNAL)
# 安全检查
clearance_values = {
DataSecurityLevel.PUBLIC: 1,
DataSecurityLevel.INTERNAL: 2,
DataSecurityLevel.CONFIDENTIAL: 3,
DataSecurityLevel.SECRET: 4
}
return clearance_values[user_clearance] >= clearance_values[resource_level]
class DataBackupSystem:
"""数据备份系统"""
def __init__(self, config: SystemConfig):
self.config = config
self.backup_locations = config.backup_locations
self.backup_strategy = self._create_backup_strategy()
def _create_backup_strategy(self) -> Dict:
"""创建备份策略"""
return {
'full_backup_interval': '7d', # 每周全量备份
'incremental_backup_interval': '24h', # 每日增量备份
'retention_policy': {
'full_backups': 4, # 保留4个全量备份
'incremental_backups': 30 # 保留30天增量备份
},
'backup_locations': self.backup_locations,
'encryption': True,
'compression': True
}
def create_backup(self, catalog_id: str, data_path: str):
"""创建备份"""
backup_metadata = {
'catalog_id': catalog_id,
'backup_time': datetime.now(),
'backup_type': 'incremental',
'data_size': os.path.getsize(data_path),
'checksum': self._calculate_checksum(data_path)
}
# 多位置备份
for location in self.backup_locations:
self._backup_to_location(data_path, location, backup_metadata)
# 更新备份索引
self._update_backup_index(catalog_id, backup_metadata)
def disaster_recovery(self, catalog_id: str, recovery_point: datetime = None):
"""灾难恢复"""
if recovery_point is None:
recovery_point = datetime.now()
# 查找最近的可用备份
available_backups = self._find_available_backups(catalog_id, recovery_point)
if not available_backups:
raise ValueError("未找到可用的备份")
# 执行恢复
recovery_plan = self._create_recovery_plan(available_backups)
recovered_data = self._execute_recovery(recovery_plan)
return recovered_data6. 智能化服务门户
python
class IntelligentServicePortal:
"""智能化服务门户"""
def __init__(self, data_cloud: RemoteSensingDataCloud,
compute_cloud: RemoteSensingComputeCloud,
schedule_system: CloudSchedulingSystem):
self.data_cloud = data_cloud
self.compute_cloud = compute_cloud
self.schedule_system = schedule_system
self.user_management = UserManagementSystem()
self.service_catalog = ServiceCatalog()
def search_and_analyze(self,
search_query: Dict,
analysis_algorithms: List[str],
output_format: str = "geotiff") -> Dict:
"""
一站式搜索分析服务
"""
# 1. 数据搜索
search_results = self.data_cloud.search_data(search_query)
if not search_results:
return {"error": "未找到匹配数据"}
# 2. 准备分析任务
data_ids = [result['catalog_id'] for result in search_results]
# 3. 调度分析任务
analysis_results = {}
for algorithm in analysis_algorithms:
job_id = self.compute_cloud.execute_analysis(
algorithm_name=algorithm,
input_data=data_ids,
parameters=search_query.get('parameters', {})
)
analysis_results[algorithm] = {
'job_id': job_id,
'status': 'processing'
}
return {
'search_results': search_results,
'analysis_jobs': analysis_results,
'portal_session_id': self._create_session()
}
def create_user_workspace(self, user_id: str, workspace_config: Dict) -> str:
"""创建用户工作空间"""
workspace_id = f"workspace_{user_id}_{int(time.time())}"
workspace_spec = {
'workspace_id': workspace_id,
'user_id': user_id,
'storage_quota': workspace_config.get('storage_quota', '100GB'),
'compute_quota': workspace_config.get('compute_quota', '1000CPUh'),
'data_collections': workspace_config.get('data_collections', []),
'analysis_tools': workspace_config.get('analysis_tools', []),
'created_time': datetime.now()
}
# 分配资源
self._allocate_workspace_resources(workspace_spec)
return workspace_id
def get_system_status_dashboard(self) -> Dict:
"""获取系统状态仪表板"""
return {
'data_cloud': {
'total_datasets': self.data_cloud.get_total_datasets(),
'storage_used': self.data_cloud.get_storage_usage(),
'recent_ingestions': self.data_cloud.get_recent_activity()
},
'compute_cloud': {
'active_jobs': self.compute_cloud.get_active_job_count(),
'cluster_utilization': self.compute_cloud.get_cluster_utilization(),
'gpu_utilization': self.compute_cloud.get_gpu_utilization()
},
'scheduling_system': {
'queue_length': self.schedule_system.get_queue_length(),
'average_wait_time': self.schedule_system.get_average_wait_time(),
'resource_availability': self.schedule_system.get_resource_availability()
},
'user_activity': {
'active_users': self.user_management.get_active_user_count(),
'recent_downloads': self.data_cloud.get_recent_downloads(),
'api_requests': self._get_api_request_stats()
}
}7. 系统部署与集成
python
class HainanRemoteSensingBigDataSystem:
"""海南遥感大数据系统主类"""
def __init__(self, config: SystemConfig):
self.config = config
self.data_cloud = RemoteSensingDataCloud(config)
self.compute_cloud = RemoteSensingComputeCloud(config)
self.schedule_system = CloudSchedulingSystem(
self.data_cloud, self.compute_cloud
)
self.service_portal = IntelligentServicePortal(
self.data_cloud, self.compute_cloud, self.schedule_system
)
# 启动后台服务
self._start_background_services()
def _start_background_services(self):
"""启动后台服务"""
# 资源优化服务
asyncio.create_task(
self.schedule_system.dynamic_resource_optimization()
)
# 数据备份服务
asyncio.create_task(
self.data_cloud.backup_system.run_scheduled_backups()
)
# 系统监控服务
asyncio.create_task(
self._monitor_system_health()
)
async def _monitor_system_health(self):
"""系统健康监控"""
while True:
system_health = {
'timestamp': datetime.now(),
'component_status': self._check_component_status(),
'resource_usage': self._get_resource_usage(),
'security_events': self._get_security_events(),
'performance_metrics': self._get_performance_metrics()
}
# 报警处理
if not system_health['component_status']['all_healthy']:
self._trigger_alert(system_health)
await asyncio.sleep(60) # 每分钟检查一次
def initialize_application_services(self) -> Dict:
"""初始化多领域应用服务"""
application_services = {
'agriculture': AgricultureRemoteSensingService(self),
'environment': EnvironmentMonitoringService(self),
'urban_planning': UrbanPlanningService(self),
'disaster_response': DisasterResponseService(self),
'marine_monitoring': MarineMonitoringService(self)
}
return application_services
# 系统初始化
def initialize_hainan_system():
"""初始化海南遥感大数据系统"""
config = SystemConfig(
region="hainan",
backup_locations=["haikou_primary", "sanya_backup", "beijing_disaster"],
security_policy="multi-level-aes-256",
max_concurrent_jobs=5000,
data_retention_years=15
)
hainan_system = HainanRemoteSensingBigDataSystem(config)
print("XX遥感大数据系统初始化完成")
print("包含功能:")
print("- 数据云软件系统: 海量数据编目、存档、保藏、检索")
print("- 计算云系统: 分布式计算、GPU加速、算法库")
print("- 云调度系统: 智能资源调度、动态优化")
print("- 安全备份体系: 多级安全、异地容灾")
print("- 智能化门户: 多领域应用服务支撑")
return hainan_system
if __name__ == "__main__":
# 启动系统
system = initialize_hainan_system()
# 获取系统状态
dashboard = system.service_portal.get_system_status_dashboard()
print("系统仪表板:", json.dumps(dashboard, indent=2, default=str))系统特色与优势
三位一体架构: 数据云、计算云、调度云深度融合
完善安全体系: 多级安全控制、加密存储、审计日志
智能资源调度: 动态优化、负载均衡、故障恢复
多领域应用: 农业、环保、城市规划、灾害应急等
海南特色: 针对热带海洋环境优化的遥感处理算法
公众服务: 提供开放API和服务门户，支撑空间信息产业
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