239 lines
8.0 KiB
GDScript
239 lines
8.0 KiB
GDScript
extends Node
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## 资源加载调试器
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## 用于诊断和监控资源加载问题
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class_name ResourceLoadingDebugger
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# 调试信息收集
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var loading_stats: Dictionary = {}
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var failed_resources: Array = []
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var performance_metrics: Dictionary = {}
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## 初始化调试器
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func _ready():
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print("[ResourceLoadingDebugger] 资源加载调试器已启动")
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_init_performance_monitoring()
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## 初始化性能监控
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func _init_performance_monitoring():
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performance_metrics = {
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"total_load_time": 0.0,
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"average_load_time": 0.0,
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"peak_memory_usage": 0,
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"current_memory_usage": 0,
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"successful_loads": 0,
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"failed_loads": 0
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}
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## 记录资源加载开始
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func start_loading_resource(resource_path: String) -> int:
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var start_time = Time.get_ticks_msec()
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var load_id = randi()
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loading_stats[load_id] = {
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"path": resource_path,
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"start_time": start_time,
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"status": "loading"
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}
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return load_id
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## 记录资源加载完成
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func finish_loading_resource(load_id: int, success: bool, resource: Resource = null):
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if not loading_stats.has(load_id):
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return
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var end_time = Time.get_ticks_msec()
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var load_info = loading_stats[load_id]
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var load_time = end_time - load_info["start_time"]
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load_info["end_time"] = end_time
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load_info["load_time"] = load_time
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load_info["success"] = success
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load_info["status"] = "completed"
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# 更新性能指标
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performance_metrics["total_load_time"] += load_time
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if success:
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performance_metrics["successful_loads"] += 1
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if resource:
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load_info["resource_size"] = _get_resource_memory_size(resource)
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else:
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performance_metrics["failed_loads"] += 1
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failed_resources.append(load_info["path"])
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# 计算平均加载时间
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var total_loads = performance_metrics["successful_loads"] + performance_metrics["failed_loads"]
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if total_loads > 0:
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performance_metrics["average_load_time"] = performance_metrics["total_load_time"] / total_loads
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# 更新内存使用情况
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_update_memory_usage()
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## 获取资源内存大小估算
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func _get_resource_memory_size(resource: Resource) -> int:
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if resource is Texture2D:
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var texture = resource as Texture2D
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var size = texture.get_size()
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# 估算：RGBA * 宽 * 高 * 4字节
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return size.x * size.y * 4
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return 0
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## 更新内存使用情况
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func _update_memory_usage():
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var current_memory = OS.get_static_memory_usage()
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performance_metrics["current_memory_usage"] = current_memory
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if current_memory > performance_metrics["peak_memory_usage"]:
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performance_metrics["peak_memory_usage"] = current_memory
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## 检测设备资源加载能力
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func detect_device_capabilities() -> Dictionary:
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var capabilities = {}
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# 设备基本信息
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capabilities["platform"] = OS.get_name()
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capabilities["processor_count"] = OS.get_processor_count()
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capabilities["memory_total"] = OS.get_static_memory_usage()
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# 图形信息
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var rendering_device = RenderingServer.get_rendering_device()
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if rendering_device:
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capabilities["gpu_name"] = rendering_device.get_device_name()
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capabilities["gpu_vendor"] = rendering_device.get_device_vendor_name()
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# 存储信息
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capabilities["user_data_dir"] = OS.get_user_data_dir()
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# 性能测试
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capabilities["texture_loading_speed"] = _benchmark_texture_loading()
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return capabilities
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## 基准测试纹理加载速度
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func _benchmark_texture_loading() -> float:
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var test_path = "res://assets/作物/默认/0.webp"
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if not ResourceLoader.exists(test_path):
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return -1.0
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var start_time = Time.get_ticks_msec()
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var iterations = 10
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for i in range(iterations):
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var texture = ResourceLoader.load(test_path)
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if not texture:
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return -1.0
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var end_time = Time.get_ticks_msec()
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return float(end_time - start_time) / iterations
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## 诊断资源加载问题
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func diagnose_loading_issues() -> Dictionary:
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var diagnosis = {
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"issues_found": [],
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"recommendations": [],
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"severity": "normal"
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}
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# 检查失败率
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var total_loads = performance_metrics["successful_loads"] + performance_metrics["failed_loads"]
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if total_loads > 0:
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var failure_rate = float(performance_metrics["failed_loads"]) / total_loads
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if failure_rate > 0.1: # 失败率超过10%
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diagnosis["issues_found"].append("资源加载失败率过高: %.1f%%" % (failure_rate * 100))
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diagnosis["recommendations"].append("检查资源文件完整性和路径正确性")
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diagnosis["severity"] = "warning"
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if failure_rate > 0.3: # 失败率超过30%
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diagnosis["severity"] = "critical"
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diagnosis["recommendations"].append("考虑降低资源质量或减少同时加载的资源数量")
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# 检查加载速度
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if performance_metrics["average_load_time"] > 100: # 平均加载时间超过100ms
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diagnosis["issues_found"].append("资源加载速度较慢: %.1fms" % performance_metrics["average_load_time"])
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diagnosis["recommendations"].append("考虑使用多线程加载或预加载常用资源")
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if diagnosis["severity"] == "normal":
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diagnosis["severity"] = "warning"
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# 检查内存使用
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var memory_mb = performance_metrics["peak_memory_usage"] / (1024 * 1024)
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if memory_mb > 500: # 峰值内存使用超过500MB
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diagnosis["issues_found"].append("内存使用过高: %.1fMB" % memory_mb)
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diagnosis["recommendations"].append("实施LRU缓存清理或降低缓存大小")
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if diagnosis["severity"] == "normal":
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diagnosis["severity"] = "warning"
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# 检查平台特定问题
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var platform = OS.get_name()
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if platform in ["Android", "iOS"]:
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if performance_metrics["failed_loads"] > 5:
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diagnosis["issues_found"].append("移动设备上资源加载失败较多")
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diagnosis["recommendations"].append("移动设备内存有限，建议降低资源质量或实施更积极的缓存清理")
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return diagnosis
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## 生成资源加载报告
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func generate_loading_report() -> String:
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var report = "=== 资源加载调试报告 ===\n\n"
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# 基本统计
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report += "基本统计:\n"
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report += " 成功加载: %d\n" % performance_metrics["successful_loads"]
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report += " 失败加载: %d\n" % performance_metrics["failed_loads"]
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report += " 平均加载时间: %.1fms\n" % performance_metrics["average_load_time"]
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report += " 总加载时间: %.1fs\n" % (performance_metrics["total_load_time"] / 1000.0)
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report += " 峰值内存使用: %.1fMB\n\n" % (performance_metrics["peak_memory_usage"] / (1024 * 1024))
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# 设备信息
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var capabilities = detect_device_capabilities()
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report += "设备信息:\n"
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report += " 平台: %s\n" % capabilities.get("platform", "未知")
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report += " CPU核心数: %d\n" % capabilities.get("processor_count", 0)
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report += " 纹理加载速度: %.1fms\n\n" % capabilities.get("texture_loading_speed", -1)
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# 失败的资源
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if failed_resources.size() > 0:
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report += "加载失败的资源:\n"
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for i in range(min(10, failed_resources.size())): # 只显示前10个
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report += " - %s\n" % failed_resources[i]
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if failed_resources.size() > 10:
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report += " ... 还有 %d 个失败的资源\n" % (failed_resources.size() - 10)
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report += "\n"
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# 诊断结果
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var diagnosis = diagnose_loading_issues()
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report += "诊断结果 [%s]:\n" % diagnosis["severity"].to_upper()
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for issue in diagnosis["issues_found"]:
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report += " 问题: %s\n" % issue
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for recommendation in diagnosis["recommendations"]:
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report += " 建议: %s\n" % recommendation
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return report
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## 清理调试数据
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func clear_debug_data():
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loading_stats.clear()
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failed_resources.clear()
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_init_performance_monitoring()
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print("[ResourceLoadingDebugger] 调试数据已清理")
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## 导出调试数据到文件
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func export_debug_data_to_file():
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var report = generate_loading_report()
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var datetime = Time.get_datetime_dict_from_system()
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var filename = "resource_loading_debug_%04d%02d%02d_%02d%02d%02d.txt" % [
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datetime.year, datetime.month, datetime.day,
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datetime.hour, datetime.minute, datetime.second
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]
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var file_path = OS.get_user_data_dir() + "/" + filename
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var file = FileAccess.open(file_path, FileAccess.WRITE)
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if file:
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file.store_string(report)
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file.close()
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print("[ResourceLoadingDebugger] 调试报告已导出到: ", file_path)
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return file_path
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else:
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print("[ResourceLoadingDebugger] 导出调试报告失败")
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return "" |