33 KiB
33 KiB
流星监控系统 - 技术架构文档
1. 架构概述
1.1 系统定位
流星监控系统是一个基于Rust构建的高性能、实时天文观测平台，专为流星检测和天文数据采集而设计。系统采用现代异步编程模型，支持跨平台部署，具备专业级的精度和可靠性。
1.2 设计原则
- 异步优先: 基于Tokio的全异步架构，最大化并发性能
- 模块化设计: 松耦合的模块化组件，便于扩展和维护
- 跨平台兼容: 支持Linux/macOS/Windows，针对ARM64优化
- 零拷贝优化: 最小化内存分配和数据复制
- 故障隔离: 组件级错误处理，防止故障传播
- 配置驱动: 通过配置文件控制系统行为，无需修改代码
1.3 核心技术栈
┌─────────────────────────────────────────────────────────────┐
│ 应用层 (Application Layer) │
├─────────────────────────────────────────────────────────────┤
│ Rust (1.70+) │ Tokio (异步运行时) │ anyhow (错误处理) │
│ serde (序列化) │ toml (配置) │ log (日志) │ uuid (ID生成) │
├─────────────────────────────────────────────────────────────┤
│ 中间件层 (Middleware Layer) │
├─────────────────────────────────────────────────────────────┤
│ OpenCV (计算机视觉) │ FFmpeg (视频编码) │ GStreamer (流媒体) │
│ SQLite (数据库) │ rumqttc (MQTT) │ actix-web (HTTP服务) │
├─────────────────────────────────────────────────────────────┤
│ 硬件层 (Hardware Layer) │
├─────────────────────────────────────────────────────────────┤
│ V4L2 (Linux摄像头) │ rppal (树莓派GPIO) │ serialport (串口) │
│ astrometry.net (星图解算) │ embedded-hal (硬件抽象) │
└─────────────────────────────────────────────────────────────┘
2. 系统架构
2.1 整体架构图
┌─────────────────────────────────────────┐
│ Application │
│ (主应用协调器) │
└─────────────────┬───────────────────────┘
│
┌─────────────────┴───────────────────────┐
│ EventBus │
│ (事件总线系统) │
└─────────────────┬───────────────────────┘
│
┌─────────────┬─────────────┬─────┴─────┬─────────────┬─────────────┐
│ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ Camera │ │ GPS │ │ Detection │ │ Storage │ │ Comm │ │ RTSP │
│ System │ │ System │ │ Engine │ │ Manager │ │ Manager │ │ Server │
└─────────┘ └─────────┘ └─────────────┘ └─────────┘ └─────────┘ └─────────┘
│ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ Frame │ │ NMEA │ │ Multi-Algo │ │ SQLite │ │ MQTT │ │GStreamer│
│ Buffer │ │ Parser │ │ Pipeline │ │Database │ │ Client │ │Pipeline │
└─────────┘ └─────────┘ └─────────────┘ └─────────┘ └─────────┘ └─────────┘
│ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ OpenCV │ │ Serial │ │ Overlay │ │ File │ │ HTTP │ │ Network │
│ Camera │ │ Port │ │ System │ │ System │ │ API │ │ Stream │
└─────────┘ └─────────┘ └─────────────┘ └─────────┘ └─────────┘ └─────────┘
2.2 数据流架构
┌───────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Camera │───▶│ Frame Buffer│───▶│ Detection │───▶│ Storage │
│ Hardware │ │ (Ring) │ │ Pipeline │ │ Manager │
└───────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ RTSP │ │ Event │ │ Cloud │
│ Streaming │ │ Broadcast │ │ Upload │
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
┌─────────────┐
│ External │
│ Clients │
└─────────────┘
3. 核心组件详述
3.1 应用协调器 (Application)
3.1.1 架构设计
pub struct Application {
config: Arc<Config>,
event_bus: Arc<EventBus>,
camera_controller: Arc<Mutex<CameraController>>,
detection_engine: Arc<DetectionEngine>,
// ... 其他组件
}
impl Application {
pub async fn initialize(&mut self) -> Result<()> {
// 1. 初始化事件总线
// 2. 初始化各个组件
// 3. 建立组件间依赖关系
// 4. 启动监控系统
}
pub async fn start(&mut self) -> Result<()> {
// 1. 启动所有组件
// 2. 建立数据流管道
// 3. 启动事件循环
}
pub async fn run(&mut self) -> Result<()> {
// 主事件循环，处理系统信号和组件通信
}
}
3.1.2 生命周期管理
- 初始化阶段: 加载配置、创建组件实例、验证硬件
- 启动阶段: 建立通信通道、启动后台任务、开始数据处理
- 运行阶段: 监控系统状态、处理事件、维护组件健康
- 关闭阶段: 优雅停止所有组件、保存状态、清理资源
3.2 事件总线系统 (EventBus)
3.2.1 事件类型定义
#[derive(Debug, Clone)]
pub enum Event {
// 帧事件
FrameCapture { timestamp: DateTime<Utc>, frame: Arc<Frame> },
// 检测事件
DetectionResult {
timestamp: DateTime<Utc>,
result: DetectionResult,
metadata: EventMetadata
},
// GPS事件
GpsUpdate {
timestamp: DateTime<Utc>,
position: Position,
time_sync: bool
},
// 系统事件
SystemStatus {
timestamp: DateTime<Utc>,
status: SystemHealth
},
// 传感器事件
SensorData {
timestamp: DateTime<Utc>,
sensor_type: SensorType,
data: SensorReading
},
}
3.2.2 事件分发机制
pub struct EventBus {
channels: HashMap<EventType, broadcast::Sender<Event>>,
subscribers: HashMap<ComponentId, Vec<EventType>>,
}
impl EventBus {
pub async fn publish(&self, event: Event) -> Result<()> {
// 根据事件类型分发到对应的channel
// 支持事件过滤和路由
// 处理背压和错误恢复
}
pub fn subscribe(&mut self, component_id: ComponentId, event_types: Vec<EventType>) -> Receiver<Event> {
// 为组件创建订阅通道
// 支持动态订阅和取消订阅
}
}
3.3 摄像头系统 (Camera System)
3.3.1 摄像头控制器架构
pub struct CameraController {
camera: Box<dyn Camera + Send + Sync>,
frame_buffer: Arc<Mutex<FrameBuffer>>,
settings: CameraSettings,
broadcast_tx: broadcast::Sender<Frame>,
}
pub trait Camera: Send + Sync {
async fn initialize(&mut self) -> Result<()>;
async fn start_capture(&mut self) -> Result<()>;
async fn capture_frame(&mut self) -> Result<Frame>;
async fn stop_capture(&mut self) -> Result<()>;
fn get_capabilities(&self) -> CameraCapabilities;
fn set_parameters(&mut self, params: CameraParameters) -> Result<()>;
}
3.3.2 帧缓冲区设计
pub struct FrameBuffer {
buffer: VecDeque<Arc<Frame>>,
capacity: usize,
total_size: AtomicUsize,
max_size: usize,
}
impl FrameBuffer {
pub fn push(&mut self, frame: Arc<Frame>) {
// 环形缓冲区逻辑
// 自动清理旧帧
// 内存使用监控
}
pub fn get_recent_frames(&self, duration: Duration) -> Vec<Arc<Frame>> {
// 根据时间范围获取帧
// 支持高效的时间索引
}
}
3.3.3 跨平台摄像头支持
// Linux V4L2 支持
#[cfg(target_os = "linux")]
pub struct V4L2Camera {
device_path: String,
capture: opencv::videoio::VideoCapture,
}
// macOS AVFoundation 支持
#[cfg(target_os = "macos")]
pub struct AVFoundationCamera {
device_index: i32,
capture: opencv::videoio::VideoCapture,
}
// 文件输入支持 (跨平台)
pub struct FileInputCamera {
file_path: PathBuf,
capture: opencv::videoio::VideoCapture,
loop_video: bool,
}
3.4 检测引擎 (Detection Engine)
3.4.1 多检测器架构
pub struct DetectionEngine {
detectors: Vec<Box<dyn Detector + Send + Sync>>,
aggregator: Box<dyn ResultAggregator + Send + Sync>,
config: DetectorConfig,
}
pub trait Detector: Send + Sync {
fn name(&self) -> &str;
async fn detect(&mut self, frame: &Frame, context: &DetectionContext) -> Result<DetectionResult>;
fn configure(&mut self, config: &DetectorConfig) -> Result<()>;
fn get_metrics(&self) -> DetectorMetrics;
}
3.4.2 检测器实现
// 亮度变化检测器
pub struct BrightnessDetector {
background_model: BackgroundSubtractor,
threshold: f32,
min_area: u32,
}
// CAMS兼容检测器
pub struct CamsDetector {
frame_stack: FrameStack,
detection_params: CamsParameters,
}
// 帧叠加检测器
pub struct FrameStackerDetector {
stack_size: usize,
noise_reduction: NoiseReductionFilter,
}
3.4.3 结果聚合策略
pub enum AggregationStrategy {
Any, // 任一检测器检测到即触发
All, // 所有检测器都检测到才触发
Majority, // 大多数检测器检测到才触发
Threshold(f32), // 检测置信度阈值
Custom(Box<dyn Fn(&[DetectionResult]) -> bool>), // 自定义策略
}
pub struct ResultAggregator {
strategy: AggregationStrategy,
history: VecDeque<Vec<DetectionResult>>,
}
3.5 GPS系统 (GPS System)
3.5.1 GPS控制器设计
pub struct GpsController {
port: Box<dyn SerialPort>,
parser: NmeaParser,
pps_handler: Option<PpsHandler>,
position_cache: Arc<Mutex<Option<Position>>>,
time_sync_status: Arc<AtomicBool>,
}
impl GpsController {
pub async fn start(&mut self) -> Result<()> {
// 1. 打开串口连接
// 2. 启动NMEA数据读取循环
// 3. 启动PPS信号处理
// 4. 启动位置数据发布
}
async fn read_nmea_loop(&mut self) -> Result<()> {
// 持续读取NMEA数据
// 解析GPS信息
// 发布位置更新事件
}
}
3.4.2 NMEA解析器
pub struct NmeaParser {
sentence_parsers: HashMap<String, Box<dyn SentenceParser>>,
}
pub trait SentenceParser {
fn parse(&self, sentence: &str) -> Result<ParsedData>;
}
// 支持的NMEA语句类型
impl NmeaParser {
pub fn new() -> Self {
let mut parsers = HashMap::new();
parsers.insert("GPGGA".to_string(), Box::new(GgaParser::new()));
parsers.insert("GPRMC".to_string(), Box::new(RmcParser::new()));
parsers.insert("GPGSA".to_string(), Box::new(GsaParser::new()));
// ... 其他解析器
}
}
3.6 叠加系统 (Overlay System)
3.6.1 水印叠加架构
pub struct WatermarkOverlay {
elements: Vec<Box<dyn OverlayElement>>,
position: Position,
style: TextStyle,
}
pub trait OverlayElement: Send + Sync {
fn render(&self, context: &RenderContext) -> Result<String>;
fn get_dependencies(&self) -> Vec<DataSource>;
}
// 水印元素实现
pub struct TimestampElement {
format: String,
timezone: chrono_tz::Tz,
}
pub struct GpsCoordinateElement {
format: CoordinateFormat,
precision: u8,
}
pub struct EnvironmentDataElement {
sensors: Vec<SensorType>,
format: String,
}
3.6.2 星图叠加系统
pub struct StarChartOverlay {
solver: AstrometrySolver,
catalog: StarCatalog,
solution_cache: Arc<Mutex<Option<PlateSolution>>>,
update_interval: Duration,
}
impl StarChartOverlay {
pub async fn update_solution(&mut self, frame: &Frame) -> Result<()> {
// 1. 提取星点
// 2. 调用astrometry.net求解
// 3. 缓存解算结果
// 4. 生成星图叠加
}
pub fn render_stars(&self, solution: &PlateSolution) -> Result<Vec<StarPosition>> {
// 根据解算结果渲染星图
// 包括星点、星座线、天区网格
}
}
3.7 存储管理器 (Storage Manager)
3.7.1 存储策略
pub struct StorageManager {
event_storage: EventStorage,
raw_video_storage: RawVideoStorage,
metadata_db: MetadataDatabase,
cleanup_scheduler: CleanupScheduler,
}
pub struct StoragePolicy {
pub event_retention_days: u32,
pub max_disk_usage_gb: u64,
pub compression_enabled: bool,
pub backup_enabled: bool,
}
impl StorageManager {
pub async fn store_event(&self, event: DetectionEvent) -> Result<EventId> {
// 1. 提取相关视频片段
// 2. 保存帧序列
// 3. 生成元数据
// 4. 存储到数据库
// 5. 触发清理策略
}
}
3.7.2 数据库设计
-- 事件表
CREATE TABLE events (
id TEXT PRIMARY KEY,
timestamp DATETIME NOT NULL,
duration_ms INTEGER NOT NULL,
detection_confidence REAL NOT NULL,
video_path TEXT NOT NULL,
metadata_json TEXT NOT NULL,
gps_lat REAL,
gps_lon REAL,
gps_alt REAL,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);
-- 传感器数据表
CREATE TABLE sensor_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
event_id TEXT NOT NULL,
sensor_type TEXT NOT NULL,
timestamp DATETIME NOT NULL,
value REAL NOT NULL,
unit TEXT NOT NULL,
FOREIGN KEY (event_id) REFERENCES events(id)
);
-- 系统状态表
CREATE TABLE system_status (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp DATETIME NOT NULL,
component TEXT NOT NULL,
status TEXT NOT NULL,
metrics_json TEXT,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);
3.8 通信管理器 (Communication Manager)
3.8.1 MQTT客户端
pub struct MqttManager {
client: rumqttc::AsyncClient,
eventloop: rumqttc::EventLoop,
config: MqttConfig,
publish_queue: Arc<Mutex<VecDeque<PublishMessage>>>,
}
impl MqttManager {
pub async fn publish_event(&self, event: &DetectionEvent) -> Result<()> {
let message = serde_json::to_string(&event)?;
self.client.publish(
&self.config.events_topic,
rumqttc::QoS::AtLeastOnce,
false,
message
).await?;
Ok(())
}
pub async fn publish_status(&self, status: &SystemStatus) -> Result<()> {
// 发布系统状态信息
}
}
3.8.2 HTTP API服务器
use actix_web::{web, App, HttpServer, Result as ActixResult};
pub struct HttpApiServer {
config: HttpApiConfig,
app_data: web::Data<AppState>,
}
// API端点定义
pub async fn get_system_status(data: web::Data<AppState>) -> ActixResult<impl Responder> {
// 返回系统状态信息
}
pub async fn get_events(
data: web::Data<AppState>,
query: web::Query<EventQuery>
) -> ActixResult<impl Responder> {
// 返回事件列表
}
pub async fn get_event_details(
data: web::Data<AppState>,
path: web::Path<String>
) -> ActixResult<impl Responder> {
// 返回特定事件详情
}
pub async fn update_config(
data: web::Data<AppState>,
config: web::Json<Config>
) -> ActixResult<impl Responder> {
// 更新系统配置
}
3.9 RTSP流媒体服务器
3.9.1 GStreamer集成
pub struct RtspServer {
server: gstreamer_rtsp_server::RTSPServer,
factory: gstreamer_rtsp_server::RTSPMediaFactory,
config: RtspConfig,
}
impl RtspServer {
pub fn new(config: RtspConfig) -> Result<Self> {
let server = gstreamer_rtsp_server::RTSPServer::new();
// 配置RTSP服务器
server.set_service(&config.port.to_string());
// 创建媒体工厂
let factory = gstreamer_rtsp_server::RTSPMediaFactory::new();
factory.set_launch(&format!(
"appsrc name=source ! videoconvert ! x264enc ! rtph264pay name=pay0 pt=96"
));
Ok(Self { server, factory, config })
}
pub async fn start(&mut self) -> Result<()> {
// 启动RTSP服务器
// 注册媒体工厂
// 开始流媒体传输
}
}
4. 配置系统设计
4.1 配置结构设计
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Config {
pub device: DeviceConfig,
pub camera: CameraSettings,
pub gps: GpsConfig,
pub sensors: SensorConfig,
pub detection: DetectionConfig,
pub storage: StorageConfig,
pub mqtt: MqttConfig,
pub http_api: HttpApiConfig,
pub rtsp: RtspConfig,
pub watermark: WatermarkOptions,
pub star_chart: StarChartOptions,
pub log_level: String,
}
4.2 配置加载与验证
impl Config {
pub fn load<P: AsRef<Path>>(path: P) -> Result<Self> {
// 1. 读取TOML配置文件
// 2. 反序列化为Config结构
// 3. 验证配置有效性
// 4. 应用默认值
// 5. 处理向后兼容性
}
pub fn validate(&self) -> Result<()> {
// 验证配置参数的有效性
// 检查文件路径是否存在
// 验证网络配置
// 检查硬件兼容性
}
pub fn migrate_legacy(&mut self) -> Result<()> {
// 处理旧版本配置文件的迁移
// 保持向后兼容性
}
}
5. 性能优化策略
5.1 异步编程优化
// 使用Tokio的异步运行时
#[tokio::main]
async fn main() -> Result<()> {
// 创建多线程异步运行时
let rt = tokio::runtime::Builder::new_multi_thread()
.worker_threads(num_cpus::get())
.enable_all()
.build()?;
// 运行应用
rt.block_on(async {
let mut app = Application::new(config);
app.run().await
})
}
// 并发处理优化
async fn process_frame_concurrent(frame: Arc<Frame>, detectors: &[Detector]) -> Result<Vec<DetectionResult>> {
let tasks: Vec<_> = detectors.iter().map(|detector| {
let frame = Arc::clone(&frame);
tokio::spawn(async move {
detector.detect(&frame).await
})
}).collect();
let results = futures::future::join_all(tasks).await;
// 处理结果...
}
5.2 内存管理优化
// 零拷贝帧共享
pub struct Frame {
data: Arc<opencv::core::Mat>,
timestamp: DateTime<Utc>,
metadata: FrameMetadata,
}
impl Frame {
pub fn share(&self) -> Arc<Frame> {
// 共享帧数据，避免内存拷贝
Arc::new(Frame {
data: Arc::clone(&self.data),
timestamp: self.timestamp,
metadata: self.metadata.clone(),
})
}
}
// 内存池管理
pub struct FramePool {
pool: Vec<Frame>,
available: VecDeque<usize>,
}
impl FramePool {
pub fn acquire(&mut self) -> Option<Frame> {
// 从池中获取可用帧
// 避免频繁内存分配
}
pub fn release(&mut self, frame: Frame) {
// 将帧返回池中重用
}
}
5.3 I/O优化
// 异步文件I/O
use tokio::fs::File;
use tokio::io::{AsyncReadExt, AsyncWriteExt};
async fn save_event_async(event: &DetectionEvent, path: &Path) -> Result<()> {
let mut file = File::create(path).await?;
let data = serde_json::to_vec(event)?;
file.write_all(&data).await?;
file.sync_all().await?;
Ok(())
}
// 批量I/O操作
pub struct BatchWriter {
buffer: Vec<WriteOperation>,
batch_size: usize,
flush_interval: Duration,
}
impl BatchWriter {
pub async fn write(&mut self, operation: WriteOperation) -> Result<()> {
self.buffer.push(operation);
if self.buffer.len() >= self.batch_size {
self.flush().await?;
}
Ok(())
}
async fn flush(&mut self) -> Result<()> {
// 批量执行I/O操作
// 减少系统调用开销
}
}
6. 错误处理与恢复
6.1 分层错误处理
// 系统级错误
#[derive(thiserror::Error, Debug)]
pub enum SystemError {
#[error("Hardware initialization failed: {0}")]
HardwareError(#[from] HardwareError),
#[error("Configuration error: {0}")]
ConfigError(#[from] ConfigError),
#[error("Network error: {0}")]
NetworkError(#[from] NetworkError),
}
// 组件级错误
#[derive(thiserror::Error, Debug)]
pub enum CameraError {
#[error("Camera not found: {device}")]
DeviceNotFound { device: String },
#[error("Invalid camera parameters: {msg}")]
InvalidParameters { msg: String },
#[error("Frame capture timeout")]
CaptureTimeout,
}
// 错误恢复策略
pub struct ErrorRecoveryManager {
strategies: HashMap<ErrorType, RecoveryStrategy>,
retry_count: HashMap<ComponentId, usize>,
max_retries: usize,
}
impl ErrorRecoveryManager {
pub async fn handle_error(&mut self, error: SystemError, component: ComponentId) -> RecoveryAction {
match self.strategies.get(&error.error_type()) {
Some(RecoveryStrategy::Retry) => {
let count = self.retry_count.entry(component).or_insert(0);
*count += 1;
if *count <= self.max_retries {
RecoveryAction::Retry
} else {
RecoveryAction::Disable
}
},
Some(RecoveryStrategy::Restart) => RecoveryAction::Restart,
Some(RecoveryStrategy::Ignore) => RecoveryAction::Continue,
None => RecoveryAction::Shutdown,
}
}
}
6.2 健康监控系统
pub struct HealthMonitor {
components: HashMap<ComponentId, ComponentHealth>,
check_interval: Duration,
alerting: AlertingSystem,
}
pub struct ComponentHealth {
pub status: HealthStatus,
pub last_heartbeat: DateTime<Utc>,
pub error_count: usize,
pub metrics: ComponentMetrics,
}
impl HealthMonitor {
pub async fn start_monitoring(&mut self) -> Result<()> {
let mut interval = tokio::time::interval(self.check_interval);
loop {
interval.tick().await;
self.check_all_components().await;
}
}
async fn check_all_components(&mut self) {
for (component_id, health) in &mut self.components {
if self.is_component_unhealthy(health) {
self.handle_unhealthy_component(*component_id, health).await;
}
}
}
}
7. 安全性设计
7.1 网络安全
// TLS配置
pub struct TlsConfig {
pub cert_path: PathBuf,
pub key_path: PathBuf,
pub ca_cert_path: Option<PathBuf>,
pub verify_client: bool,
}
// API认证
pub struct ApiAuthentication {
pub tokens: HashSet<String>,
pub token_expiry: HashMap<String, DateTime<Utc>>,
pub rate_limiter: RateLimiter,
}
impl ApiAuthentication {
pub fn verify_token(&self, token: &str) -> Result<bool> {
if !self.tokens.contains(token) {
return Ok(false);
}
if let Some(expiry) = self.token_expiry.get(token) {
if *expiry < Utc::now() {
return Ok(false);
}
}
Ok(true)
}
}
7.2 数据保护
// 敏感数据加密
pub struct SecureStorage {
cipher: Aes256Gcm,
key: [u8; 32],
}
impl SecureStorage {
pub fn encrypt(&self, data: &[u8]) -> Result<Vec<u8>> {
let nonce = Aes256Gcm::generate_nonce(&mut OsRng);
let ciphertext = self.cipher.encrypt(&nonce, data)?;
let mut result = nonce.to_vec();
result.extend_from_slice(&ciphertext);
Ok(result)
}
pub fn decrypt(&self, encrypted_data: &[u8]) -> Result<Vec<u8>> {
let (nonce, ciphertext) = encrypted_data.split_at(12);
let nonce = Nonce::from_slice(nonce);
let plaintext = self.cipher.decrypt(nonce, ciphertext)?;
Ok(plaintext)
}
}
8. 测试策略
8.1 单元测试
#[cfg(test)]
mod tests {
use super::*;
use mockall::predicate::*;
use tokio_test;
#[tokio::test]
async fn test_frame_detection() {
let mut mock_detector = MockDetector::new();
mock_detector
.expect_detect()
.with(always())
.times(1)
.returning(|_| Ok(DetectionResult::new(0.8, vec![])));
let frame = create_test_frame();
let result = mock_detector.detect(&frame).await;
assert!(result.is_ok());
assert_eq!(result.unwrap().confidence, 0.8);
}
}
8.2 集成测试
#[tokio::test]
async fn test_end_to_end_detection() {
// 创建测试配置
let config = create_test_config();
// 初始化应用
let mut app = Application::new(config);
app.initialize().await.unwrap();
// 注入测试帧
let test_frame = create_meteor_frame();
app.process_frame(test_frame).await.unwrap();
// 验证检测结果
let events = app.get_detected_events().await;
assert!(!events.is_empty());
}
8.3 性能基准测试
use criterion::{black_box, criterion_group, criterion_main, Criterion};
fn bench_detection_pipeline(c: &mut Criterion) {
let rt = tokio::runtime::Runtime::new().unwrap();
let detector = BrightnessDetector::new();
let frame = create_test_frame();
c.bench_function("detection_pipeline", |b| {
b.iter(|| {
rt.block_on(async {
detector.detect(black_box(&frame)).await
})
})
});
}
criterion_group!(benches, bench_detection_pipeline);
criterion_main!(benches);
9. 部署架构
9.1 容器化部署
FROM rust:1.70-slim as builder
WORKDIR /app
COPY Cargo.toml Cargo.lock ./
COPY src ./src
RUN apt-get update && apt-get install -y \
libopencv-dev \
libssl-dev \
pkg-config
RUN cargo build --release
FROM debian:bullseye-slim
RUN apt-get update && apt-get install -y \
libopencv-core4.5 \
libopencv-imgproc4.5 \
libopencv-videoio4.5 \
astrometry.net \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/target/release/meteor_detect /usr/local/bin/
COPY config-example.toml /etc/meteor_detect/config.toml
EXPOSE 8080 8554
CMD ["meteor_detect"]
9.2 系统服务配置
# /etc/systemd/system/meteor-detect.service
[Unit]
Description=Meteor Detection System
After=network.target
[Service]
Type=simple
User=meteor
Group=meteor
WorkingDirectory=/opt/meteor-detect
ExecStart=/opt/meteor-detect/bin/meteor_detect
Restart=always
RestartSec=10
Environment=RUST_LOG=info
[Install]
WantedBy=multi-user.target
10. 监控与运维
10.1 指标收集
use prometheus::{Counter, Histogram, Gauge, Registry};
pub struct Metrics {
pub frames_processed: Counter,
pub detection_latency: Histogram,
pub system_memory: Gauge,
pub disk_usage: Gauge,
}
impl Metrics {
pub fn new() -> Self {
Self {
frames_processed: Counter::new("frames_processed_total", "Total frames processed")
.expect("metric can be created"),
detection_latency: Histogram::with_opts(
prometheus::HistogramOpts::new("detection_latency_seconds", "Detection latency")
.buckets(vec![0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0])
).expect("metric can be created"),
system_memory: Gauge::new("system_memory_bytes", "System memory usage")
.expect("metric can be created"),
disk_usage: Gauge::new("disk_usage_bytes", "Disk usage")
.expect("metric can be created"),
}
}
}
10.2 日志管理
use tracing::{info, warn, error, debug};
use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
pub fn init_logging(config: &LogConfig) -> Result<()> {
let file_appender = tracing_appender::rolling::daily(&config.log_dir, "meteor-detect.log");
let (non_blocking, _guard) = tracing_appender::non_blocking(file_appender);
tracing_subscriber::registry()
.with(tracing_subscriber::EnvFilter::new(&config.level))
.with(tracing_subscriber::fmt::layer().with_writer(std::io::stdout))
.with(tracing_subscriber::fmt::layer()
.with_writer(non_blocking)
.with_ansi(false))
.init();
Ok(())
}
11. 扩展性设计
11.1 插件架构
pub trait DetectorPlugin: Send + Sync {
fn name(&self) -> &str;
fn version(&self) -> &str;
fn create_detector(&self, config: &DetectorConfig) -> Result<Box<dyn Detector>>;
}
pub struct PluginManager {
plugins: HashMap<String, Box<dyn DetectorPlugin>>,
plugin_dir: PathBuf,
}
impl PluginManager {
pub fn load_plugins(&mut self) -> Result<()> {
// 扫描插件目录
// 动态加载插件库
// 注册插件接口
}
pub fn create_detector(&self, plugin_name: &str, config: &DetectorConfig) -> Result<Box<dyn Detector>> {
let plugin = self.plugins.get(plugin_name)
.ok_or_else(|| anyhow!("Plugin not found: {}", plugin_name))?;
plugin.create_detector(config)
}
}
11.2 微服务架构支持
// 服务发现
pub struct ServiceRegistry {
services: HashMap<String, ServiceEndpoint>,
health_checker: HealthChecker,
}
// 负载均衡
pub struct LoadBalancer {
backends: Vec<Backend>,
strategy: LoadBalancingStrategy,
}
// 服务间通信
pub struct ServiceClient {
http_client: reqwest::Client,
base_url: Url,
auth_token: Option<String>,
}
本技术架构文档详细描述了流星监控系统的完整技术架构，涵盖了从底层硬件抽象到上层应用逻辑的各个层面。系统采用现代化的Rust异步编程模型，具备高性能、高可靠性和强扩展性的特点，能够满足专业级天文观测的需求。