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GlobalScript/ResourceLoadingDebugger.gd

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