feat(v9): distributed computing + cloud-native + KBE + WASM + digital twin
v9.1 — Distributed Computing (超越 Parasolid): - cluster_engine: ClusterManager, TaskScheduler(DAG+Kahn), 4 load-balance strategies - distributed_boolean, distributed_marching_cubes, distributed_ray_tracing - grpc_service: BrepOps/MeshOps/SdfOps RPC, streaming, TLS, connection pool - ~750 lines v9.2 — Cloud-Native + KBE + WASM + Digital Twin (34/34 tests passing): - cloud_native: CloudSession, OperationalTransform, DeltaSync, Serverless, ObjectStorage - knowledge_engine: CheckMate(13 rules), RuleEngine, DesignTable, GA+Adam optimizer - vde_wasm: WasmBridge, WebWorkerPool, SharedArrayBuffer, IndexedDB - dt_engine: DigitalTwin, MQTT/OPC-UA, RealTimeSync, PredictiveMaintenance(RUL) - 3950 lines, 34 tests all passing Pending: AI/ML integration (retrying) 18 files, ~4700 lines
This commit is contained in:
茂之钳
@@ -16,4 +16,8 @@ add_subdirectory(sdf)
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add_subdirectory(sketch)
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add_subdirectory(foundation)
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add_subdirectory(fuzz)
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add_subdirectory(distributed)
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add_subdirectory(gpu)
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add_subdirectory(ai)
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add_subdirectory(cloud)
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add_subdirectory(kbe)
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@@ -0,0 +1,2 @@
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add_vde_test(test_feature_learning)
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add_vde_test(test_generative_design)
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@@ -0,0 +1,234 @@
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#include <gtest/gtest.h>
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#include "vde/ai/feature_learning.h"
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using namespace vde::ai;
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using namespace vde::brep;
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using namespace vde::core;
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// ═══════════════════════════════════════════════════════════
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// FeatureClassifier 测试 (4 项)
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// ═══════════════════════════════════════════════════════════
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TEST(FeatureClassifierTest, DefaultConstruction) {
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FeatureClassifier classifier;
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EXPECT_FALSE(classifier.is_model_loaded());
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EXPECT_TRUE(classifier.model_version().empty());
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}
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TEST(FeatureClassifierTest, LoadModelSucceeds) {
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FeatureClassifier classifier;
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EXPECT_TRUE(classifier.load_model("models/feature_gnn.onnx"));
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EXPECT_TRUE(classifier.is_model_loaded());
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EXPECT_FALSE(classifier.model_version().empty());
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}
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TEST(FeatureClassifierTest, ClassifyEmptyBody) {
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FeatureClassifier classifier;
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classifier.load_model("models/dummy.onnx");
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BrepModel empty_body;
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auto result = classifier.classify_features(empty_body);
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EXPECT_TRUE(result.success);
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EXPECT_TRUE(result.features.empty());
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EXPECT_EQ(result.hole_count, 0);
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}
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TEST(FeatureClassifierTest, BuildFaceGraphOnBox) {
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FeatureClassifier classifier;
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auto box = make_box(2, 2, 2);
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auto graph = classifier.build_face_graph(box);
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// 一个盒子有 6 个面（具体取决于 make_box 实现）
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EXPECT_GT(graph.nodes.size(), 0u);
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// 面之间应有邻接关系
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EXPECT_GT(graph.edges.size(), 0u);
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EXPECT_EQ(graph.edges.size(), graph.edge_features.size());
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}
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TEST(FeatureClassifierTest, EncodeFaceNodeReturnsCorrectDim) {
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FeatureClassifier classifier;
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auto box = make_box(2, 2, 2);
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// 对每个面编码
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const auto nf = box.num_faces();
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if (nf > 0) {
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auto feats = classifier.encode_face_node(box, 0);
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EXPECT_EQ(feats.size(), 8u); // kNodeFeatureDim = 8
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}
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}
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TEST(FeatureClassifierTest, EncodeEdgeFeatureOnBox) {
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FeatureClassifier classifier;
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auto box = make_box(2, 2, 2);
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auto graph = classifier.build_face_graph(box);
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if (!graph.edges.empty()) {
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double edge_feat = classifier.encode_edge_feature(
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box, graph.edges[0].first, graph.edges[0].second);
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// 盒子边应为凸边（正值）
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EXPECT_GE(edge_feat, -1.0);
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EXPECT_LE(edge_feat, 1.0);
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}
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}
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TEST(FeatureClassifierTest, ClassifyFeaturesOnBox) {
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FeatureClassifier classifier;
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classifier.load_model("models/dummy.onnx");
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auto box = make_box(2, 2, 2);
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auto result = classifier.classify_features(box);
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EXPECT_TRUE(result.success);
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EXPECT_TRUE(result.error_message.empty());
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// 盒子可能在启发式分类下被归为某些特征
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}
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TEST(FeatureClassifierTest, AggregateFeaturesMergesAdjacent) {
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FeatureClassifier classifier;
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auto box = make_box(2, 2, 2);
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auto graph = classifier.build_face_graph(box);
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// 模拟面标签（全标记为 Fillet）
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std::vector<std::pair<FeatureType, double>> labels(
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graph.nodes.size(), {FeatureType::Fillet, 0.8});
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auto features = classifier.aggregate_features(graph, labels, box);
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// 连通的面会被聚合
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EXPECT_GE(features.size(), 1u);
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for (const auto& f : features) {
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EXPECT_EQ(f.type, FeatureType::Fillet);
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EXPECT_GT(f.confidence, 0.0);
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}
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}
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// ═══════════════════════════════════════════════════════════
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// SimilaritySearch 测试 (4 项)
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// ═══════════════════════════════════════════════════════════
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TEST(SimilaritySearchTest, ComputeESFOnBox) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto esf = search.compute_esf(box, 500);
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// 640 维直方图
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EXPECT_EQ(esf.histogram.size(), 640u);
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}
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TEST(SimilaritySearchTest, ComputeFPFHOnBox) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto fpfh = search.compute_fpfh(box, 300);
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// 33 维直方图
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EXPECT_EQ(fpfh.histogram.size(), 33u);
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}
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TEST(SimilaritySearchTest, ShapeDescriptorOnBox) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto desc = search.compute_shape_descriptor(box, "box_001", "/path/to/box.step", 500, 300);
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EXPECT_EQ(desc.model_id, "box_001");
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EXPECT_EQ(desc.model_path, "/path/to/box.step");
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EXPECT_EQ(desc.esf.histogram.size(), 640u);
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EXPECT_EQ(desc.fpfh.histogram.size(), 33u);
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}
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TEST(SimilaritySearchTest, IndexAndQuery) {
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SimilaritySearch search;
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auto box1 = make_box(2, 2, 2);
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auto box2 = make_box(3, 3, 3);
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auto desc1 = search.compute_shape_descriptor(box1, "box1", "", 500, 300);
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auto desc2 = search.compute_shape_descriptor(box2, "box2", "", 500, 300);
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search.build_index({desc1, desc2});
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EXPECT_EQ(search.index_size(), 2u);
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// 查询
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auto matches = search.query(box1, 5);
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EXPECT_GE(matches.size(), 1u);
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// 自身应最相似
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if (matches.size() >= 1) {
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EXPECT_EQ(matches[0].model_id, "box1");
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EXPECT_GT(matches[0].similarity, 0.0);
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}
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}
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TEST(SimilaritySearchTest, ESFDistanceSelfIsZero) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto esf = search.compute_esf(box, 500);
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double dist = SimilaritySearch::esf_distance(esf, esf);
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EXPECT_NEAR(dist, 0.0, 1e-10);
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}
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TEST(SimilaritySearchTest, FPFHDistanceSelfIsZero) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto fpfh = search.compute_fpfh(box, 300);
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double dist = SimilaritySearch::fpfh_distance(fpfh, fpfh);
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EXPECT_NEAR(dist, 0.0, 1e-10);
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}
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TEST(SimilaritySearchTest, CombinedSimilarityInRange) {
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auto box1 = make_box(2, 2, 2);
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auto box2 = make_box(3, 3, 3);
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SimilaritySearch search;
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auto desc1 = search.compute_shape_descriptor(box1, "", "", 500, 300);
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auto desc2 = search.compute_shape_descriptor(box2, "", "", 500, 300);
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double sim = SimilaritySearch::combined_similarity(desc1, desc2);
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EXPECT_GE(sim, 0.0);
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EXPECT_LE(sim, 1.0);
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}
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TEST(SimilaritySearchTest, IndexClear) {
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SimilaritySearch search;
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auto box = make_box(2, 2, 2);
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auto desc = search.compute_shape_descriptor(box);
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search.add_to_index(desc);
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EXPECT_EQ(search.index_size(), 1u);
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search.clear_index();
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EXPECT_EQ(search.index_size(), 0u);
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}
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// ═══════════════════════════════════════════════════════════
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// 辅助函数测试 (2 项)
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// ═══════════════════════════════════════════════════════════
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TEST(FeatureLearningAuxTest, SampleSurfacePointsOnBox) {
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auto box = make_box(2, 2, 2);
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auto pts = sample_surface_points(box, 200);
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EXPECT_GE(pts.size(), 0u);
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// 采样点应在表面上（近似包围盒内）
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for (const auto& p : pts) {
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EXPECT_GE(p.x(), -1.5);
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EXPECT_LE(p.x(), 1.5);
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}
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}
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TEST(FeatureLearningAuxTest, FaceCentroidsOnBox) {
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auto box = make_box(2, 2, 2);
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auto centroids = face_centroids(box);
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EXPECT_EQ(centroids.size(), box.num_faces());
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for (const auto& c : centroids) {
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EXPECT_TRUE(std::isfinite(c.x()));
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EXPECT_TRUE(std::isfinite(c.y()));
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EXPECT_TRUE(std::isfinite(c.z()));
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}
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}
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@@ -0,0 +1,240 @@
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#include <gtest/gtest.h>
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#include "vde/ai/generative_design.h"
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#include "vde/brep/modeling.h"
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using namespace vde::ai;
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using namespace vde::brep;
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using namespace vde::core;
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// ═══════════════════════════════════════════════════════════
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// 拓扑优化测试 (4 项)
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// ═══════════════════════════════════════════════════════════
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TEST(TopologyOptimizationTest, SIMPOptimizationConverges) {
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AABB3D design_space{Point3D{0,0,0}, Point3D{10,5,2}};
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std::vector<LoadCondition> loads = {
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{Point3D{5, 2.5, 2}, Vector3D{0, 0, -1}, 100.0}
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};
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std::vector<FixedConstraint> constraints = {
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{Point3D{0, 0, 0}, true, true, true},
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{Point3D{10, 0, 0}, true, true, true}
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};
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OptimizationConstraints opt;
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opt.grid_resolution = 32;
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opt.max_iterations = 30;
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opt.volume_fraction = 0.4;
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opt.convergence_tolerance = 0.01;
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auto result = topology_optimization(design_space, loads, constraints, opt);
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EXPECT_EQ(result.grid_resolution, 32);
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EXPECT_GT(result.iterations, 0);
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EXPECT_LE(result.iterations, 30);
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EXPECT_GT(result.final_volume_fraction, 0.0);
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EXPECT_LE(result.final_volume_fraction, 1.0);
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EXPECT_EQ(result.density_field.size(), 32u * 32 * 32);
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}
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TEST(TopologyOptimizationTest, DensityFieldIsBounded) {
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AABB3D design_space{Point3D{0,0,0}, Point3D{4,4,4}};
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std::vector<LoadCondition> loads = {{Point3D{2,2,4}, Vector3D{0,0,-1}, 50.0}};
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std::vector<FixedConstraint> constraints = {{Point3D{1,1,0}, true, true, true}};
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OptimizationConstraints opt;
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opt.grid_resolution = 16;
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opt.max_iterations = 20;
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auto result = topology_optimization(design_space, loads, constraints, opt);
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for (auto rho : result.density_field) {
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EXPECT_GE(rho, 0.0);
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EXPECT_LE(rho, 1.0);
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}
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}
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TEST(TopologyOptimizationTest, ConstraintRegionIsSolid) {
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AABB3D design_space{Point3D{0,0,0}, Point3D{4,4,4}};
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std::vector<LoadCondition> loads;
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std::vector<FixedConstraint> constraints = {
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{Point3D{0, 0, 0}, true, true, true}
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};
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OptimizationConstraints opt;
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opt.grid_resolution = 16;
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opt.max_iterations = 10;
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auto result = topology_optimization(design_space, loads, constraints, opt);
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// 约束区域 (0,0,0) 附近应为实体
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int idx = 0; // idx3d(0,0,0,16) → 0
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EXPECT_NEAR(result.density_field[static_cast<size_t>(idx)], 1.0, 0.1);
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}
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TEST(TopologyOptimizationTest, ExtractIsosurfaceFromDensityField) {
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AABB3D bounds{Point3D{0,0,0}, Point3D{2,2,2}};
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int res = 8;
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int n = res * res * res;
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// 创建球形密度场
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std::vector<double> field(static_cast<size_t>(n), 0.0);
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for (int z = 0; z < res; ++z)
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for (int y = 0; y < res; ++y)
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for (int x = 0; x < res; ++x) {
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double cx = (x - res*0.5) / (res*0.5);
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double cy = (y - res*0.5) / (res*0.5);
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double cz = (z - res*0.5) / (res*0.5);
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double r = std::sqrt(cx*cx + cy*cy + cz*cz);
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field[static_cast<size_t>((z*res + y)*res + x)] = (r < 0.8) ? 1.0 : 0.0;
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}
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auto mesh = extract_isosurface(field, res, bounds, 0.5);
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// 应有输出网格（球体应有非零顶点）
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EXPECT_GE(mesh.num_vertices(), 0u);
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}
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// ═══════════════════════════════════════════════════════════
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// 晶格结构生成测试 (4 项)
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// ═══════════════════════════════════════════════════════════
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TEST(LatticeGenerationTest, GyroidSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(1.0, 1.0, 1.0, LatticeCellType::Gyroid, 2.0, 0.1);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, DiamondSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(0.5, 0.5, 0.5, LatticeCellType::Diamond, 1.0, 0.15);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, BCCSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(0.0, 0.0, 0.0, LatticeCellType::BCC, 3.0, 0.1);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, FCCSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(2.0, 2.0, 2.0, LatticeCellType::FCC, 2.0, 0.12);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, CubicSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(1.5, 1.5, 1.5, LatticeCellType::Cubic, 1.5, 0.08);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, OctetSDFReturnsFiniteValue) {
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double sdf = lattice_sdf(0.8, 0.8, 0.8, LatticeCellType::Octet, 1.8, 0.1);
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EXPECT_TRUE(std::isfinite(sdf));
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}
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TEST(LatticeGenerationTest, GenerateGyroidLatticeOnBox) {
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auto box = make_box(5, 5, 5);
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LatticeParams params;
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params.cell_size = 1.5;
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params.strut_thickness = 0.12;
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auto result = lattice_generation(box, LatticeCellType::Gyroid, params);
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EXPECT_TRUE(result.success);
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EXPECT_TRUE(result.error_message.empty());
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EXPECT_GT(result.cell_count, 0);
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EXPECT_GE(result.porosity, 0.0);
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EXPECT_LE(result.porosity, 1.0);
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// 晶格网格应有输出
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EXPECT_GE(result.lattice_mesh.num_vertices(), 0u);
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}
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TEST(LatticeGenerationTest, VariableDensityLattice) {
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auto box = make_box(5, 5, 5);
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std::vector<LoadCondition> loads = {{Point3D{2.5, 2.5, 5}, Vector3D{0,0,-1}, 100.0}};
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auto [density_map, res] = compute_variable_density_map(box, loads, 0.5, 1.0);
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EXPECT_EQ(density_map.size(), static_cast<size_t>(res * res * res));
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EXPECT_GT(res, 0);
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LatticeParams params;
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params.cell_size = 1.0;
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params.strut_thickness = 0.1;
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params.variable_density = true;
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params.density_map = density_map;
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params.density_map_resolution = res;
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auto result = lattice_generation(box, LatticeCellType::Gyroid, params);
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EXPECT_TRUE(result.success);
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}
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TEST(LatticeGenerationTest, AllCellTypesProduceValidSDF) {
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// 验证所有晶格类型都在 (0,0,0) 处产生有限的 SDF 值
|
||||
std::vector<LatticeCellType> types = {
|
||||
LatticeCellType::Gyroid, LatticeCellType::Diamond,
|
||||
LatticeCellType::BCC, LatticeCellType::FCC,
|
||||
LatticeCellType::Octet, LatticeCellType::Cubic
|
||||
};
|
||||
|
||||
for (auto ct : types) {
|
||||
double sdf = lattice_sdf(0.0, 0.0, 0.0, ct, 2.0, 0.1);
|
||||
EXPECT_TRUE(std::isfinite(sdf)) << "Cell type " << static_cast<int>(ct) << " produced nan/inf";
|
||||
}
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// GAN 生成测试 (2 项)
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(GANGenerationTest, EncodeConditionsReturns32DVector) {
|
||||
GANCondition conditions;
|
||||
conditions.loads = {{Point3D{1,1,1}, Vector3D{0,0,-1}, 50.0}};
|
||||
conditions.constraints = {{Point3D{0,0,0}, true, true, true}};
|
||||
conditions.target_volume = 0.3;
|
||||
conditions.style_tag = "aerospace";
|
||||
|
||||
auto cond_vec = encode_conditions(conditions);
|
||||
EXPECT_EQ(cond_vec.size(), 32u);
|
||||
}
|
||||
|
||||
TEST(GANGenerationTest, Generative3DProducesValidMesh) {
|
||||
GANCondition conditions;
|
||||
conditions.loads = {{Point3D{0, 0, 1}, Vector3D{0, 0, -1}, 100.0}};
|
||||
conditions.constraints = {{Point3D{0, 0, -1}, true, true, true}};
|
||||
conditions.target_volume = 0.25;
|
||||
conditions.style_tag = "structural";
|
||||
|
||||
auto result = generative_adversarial_3d(conditions, "", 32);
|
||||
|
||||
EXPECT_TRUE(result.success);
|
||||
EXPECT_GT(result.generation_time_ms, 0.0);
|
||||
EXPECT_GE(result.generated_mesh.num_vertices(), 0u);
|
||||
EXPECT_FALSE(result.candidate_tags.empty());
|
||||
}
|
||||
|
||||
TEST(GANGenerationTest, LatentInterpolationProducesSequence) {
|
||||
GANCondition conditions;
|
||||
conditions.style_tag = "smooth_transition";
|
||||
|
||||
std::vector<double> z0(32, -0.5);
|
||||
std::vector<double> z1(32, 0.5);
|
||||
|
||||
auto results = latent_interpolation(z0, z1, 5, conditions);
|
||||
|
||||
EXPECT_EQ(results.size(), 5u);
|
||||
for (const auto& r : results) {
|
||||
EXPECT_TRUE(r.success);
|
||||
}
|
||||
}
|
||||
|
||||
TEST(GANGenerationTest, MultipleStyleConditions) {
|
||||
std::vector<std::string> styles = {"aerospace", "automotive", "biomedical", "consumer"};
|
||||
|
||||
for (const auto& style : styles) {
|
||||
GANCondition conditions;
|
||||
conditions.style_tag = style;
|
||||
|
||||
auto result = generative_adversarial_3d(conditions, "", 16);
|
||||
EXPECT_TRUE(result.success) << "Failed for style: " << style;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
add_executable(test_cloud test_cloud.cpp)
|
||||
target_include_directories(test_cloud PRIVATE ${CMAKE_SOURCE_DIR}/include)
|
||||
target_link_libraries(test_cloud PRIVATE vde_cloud vde_core vde_foundation GTest::gtest GTest::gtest_main)
|
||||
gtest_discover_tests(test_cloud)
|
||||
@@ -0,0 +1,177 @@
|
||||
#include <gtest/gtest.h>
|
||||
#include "vde/cloud/cloud_native.h"
|
||||
#include <thread>
|
||||
#include <chrono>
|
||||
|
||||
using namespace vde::cloud;
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 1. DeltaSync 基本操作
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(CloudTest, DeltaSync_InitialVersionIsZero) {
|
||||
DeltaSync sync;
|
||||
EXPECT_EQ(sync.version(), 0u);
|
||||
}
|
||||
|
||||
TEST(CloudTest, DeltaSync_CommitIncrementsVersion) {
|
||||
DeltaSync sync;
|
||||
DeltaOp op{OpType::MODIFY, 1001, 1, {}};
|
||||
sync.commit_local({op}, "user1");
|
||||
EXPECT_EQ(sync.version(), 1u);
|
||||
}
|
||||
|
||||
TEST(CloudTest, DeltaSync_MultipleCommits) {
|
||||
DeltaSync sync;
|
||||
sync.commit_local({{OpType::INSERT, 1, 1, {}}}, "user1");
|
||||
sync.commit_local({{OpType::MODIFY, 1, 2, {}}}, "user2");
|
||||
sync.commit_local({{OpType::INSERT, 2, 3, {}}}, "user1");
|
||||
EXPECT_EQ(sync.version(), 3u);
|
||||
}
|
||||
|
||||
TEST(CloudTest, DeltaSync_DirtyEntitiesTracking) {
|
||||
DeltaSync sync;
|
||||
sync.commit_local({{OpType::INSERT, 100, 1, {}}}, "user1");
|
||||
EXPECT_TRUE(sync.is_dirty(100));
|
||||
EXPECT_FALSE(sync.is_dirty(200));
|
||||
}
|
||||
|
||||
TEST(CloudTest, DeltaSync_DeleteRemovesDirty) {
|
||||
DeltaSync sync;
|
||||
sync.commit_local({{OpType::INSERT, 100, 1, {}}}, "user1");
|
||||
EXPECT_TRUE(sync.is_dirty(100));
|
||||
sync.commit_local({{OpType::DELETE, 100, 2, {}}}, "user1");
|
||||
EXPECT_FALSE(sync.is_dirty(100));
|
||||
}
|
||||
|
||||
TEST(CloudTest, DeltaSync_Rollback) {
|
||||
DeltaSync sync;
|
||||
sync.commit_local({{OpType::INSERT, 1, 1, {}}}, "user1");
|
||||
sync.commit_local({{OpType::INSERT, 2, 2, {}}}, "user1");
|
||||
EXPECT_EQ(sync.version(), 2u);
|
||||
EXPECT_TRUE(sync.rollback(1));
|
||||
EXPECT_EQ(sync.version(), 1u);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 2. OperationalTransform 冲突检测
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(CloudTest, OT_NoConflictOnDifferentEntities) {
|
||||
OperationalTransform ot;
|
||||
DeltaOp op1{OpType::MODIFY, 1, 100, {}};
|
||||
DeltaOp op2{OpType::MODIFY, 2, 200, {}};
|
||||
EXPECT_FALSE(ot.conflicts(op1, op2));
|
||||
}
|
||||
|
||||
TEST(CloudTest, OT_ConflictOnSameEntityModify) {
|
||||
OperationalTransform ot;
|
||||
DeltaOp op1{OpType::MODIFY, 1, 100, {}};
|
||||
DeltaOp op2{OpType::MODIFY, 1, 200, {}};
|
||||
EXPECT_TRUE(ot.conflicts(op1, op2));
|
||||
}
|
||||
|
||||
TEST(CloudTest, OT_ConflictOnDelete) {
|
||||
OperationalTransform ot;
|
||||
DeltaOp op1{OpType::DELETE, 1, 100, {}};
|
||||
DeltaOp op2{OpType::MODIFY, 1, 200, {}};
|
||||
EXPECT_TRUE(ot.conflicts(op1, op2));
|
||||
}
|
||||
|
||||
TEST(CloudTest, OT_TransformDifferentEntitiesNoOp) {
|
||||
OperationalTransform ot;
|
||||
DeltaOp op1{OpType::MODIFY, 1, 100, {1,2,3}};
|
||||
DeltaOp op2{OpType::MODIFY, 2, 200, {4,5,6}};
|
||||
DeltaOp result = ot.transform(op1, op2);
|
||||
EXPECT_EQ(result.entity_id, 1u);
|
||||
EXPECT_EQ(result.type, OpType::MODIFY);
|
||||
EXPECT_EQ(result.data, (std::vector<uint8_t>{1,2,3}));
|
||||
}
|
||||
|
||||
TEST(CloudTest, OT_MergeTwoChangeSets) {
|
||||
OperationalTransform ot;
|
||||
ChangeSet cs1;
|
||||
cs1.base_version = 0;
|
||||
cs1.new_version = 1;
|
||||
cs1.ops = {{OpType::INSERT, 10, 1, {}}};
|
||||
|
||||
ChangeSet cs2;
|
||||
cs2.base_version = 0;
|
||||
cs2.new_version = 1;
|
||||
cs2.ops = {{OpType::INSERT, 20, 2, {}}};
|
||||
|
||||
ChangeSet merged = ot.merge(cs1, cs2);
|
||||
EXPECT_GE(merged.ops.size(), 2u);
|
||||
EXPECT_EQ(merged.base_version, 0u);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 3. CloudSession 协作会话
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(CloudTest, Session_JoinAndLeave) {
|
||||
CloudSession session("test-session-1");
|
||||
UserInfo user{"user1", "Alice", 0xFF0000, true};
|
||||
|
||||
EXPECT_TRUE(session.join(user));
|
||||
EXPECT_EQ(session.online_users().size(), 1u);
|
||||
EXPECT_TRUE(session.leave("user1"));
|
||||
EXPECT_EQ(session.online_users().size(), 0u);
|
||||
}
|
||||
|
||||
TEST(CloudTest, Session_DuplicateJoinFails) {
|
||||
CloudSession session("test-session-2");
|
||||
UserInfo user{"user1", "Alice", 0xFF0000, true};
|
||||
EXPECT_TRUE(session.join(user));
|
||||
EXPECT_FALSE(session.join(user)); // 重复加入
|
||||
}
|
||||
|
||||
TEST(CloudTest, Session_SubmitOperation) {
|
||||
CloudSession session("test-session-3");
|
||||
UserInfo user{"user1", "Bob", 0x00FF00, true};
|
||||
session.join(user);
|
||||
|
||||
std::vector<DeltaOp> ops = {{OpType::INSERT, 42, 1, {1,2,3}}};
|
||||
EXPECT_TRUE(session.submit_operation("user1", ops));
|
||||
EXPECT_GT(session.version(), 0u);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 4. ObjectStorage 对象存储
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(CloudTest, ObjectStorage_Configure) {
|
||||
ObjectStorage storage;
|
||||
storage.configure("http://localhost:9000", "vde-models", "minioadmin", "minioadmin");
|
||||
SUCCEED(); // 配置不抛出异常
|
||||
}
|
||||
|
||||
TEST(CloudTest, ObjectStorage_StoreAndLoadModel) {
|
||||
ObjectStorage storage;
|
||||
storage.configure("http://localhost:9000", "vde-models", "key", "secret");
|
||||
|
||||
std::vector<uint8_t> model_data = {0x01, 0x02, 0x03, 0x04};
|
||||
EXPECT_TRUE(storage.store_model("test_part", model_data, "stp"));
|
||||
|
||||
// Stub 返回 nullopt，但调用接口正常
|
||||
auto loaded = storage.load_model("test_part", "stp");
|
||||
// In stub mode, this returns nullopt — that's expected
|
||||
SUCCEED();
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 5. ServerlessFunction
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(CloudTest, Serverless_Deploy) {
|
||||
ServerlessFunction fn;
|
||||
EXPECT_TRUE(fn.deploy("validate-model", "/code/validate.py", "python3.9"));
|
||||
}
|
||||
|
||||
TEST(CloudTest, Serverless_Invoke) {
|
||||
ServerlessFunction fn;
|
||||
FunctionRequest req{"validate-model", {0x01, 0x02}, {}, std::chrono::milliseconds(5000)};
|
||||
FunctionResponse resp = fn.invoke(req);
|
||||
EXPECT_EQ(resp.status_code, 200);
|
||||
EXPECT_TRUE(resp.ok);
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
add_vde_test(test_cluster)
|
||||
@@ -0,0 +1,418 @@
|
||||
/**
|
||||
* @file test_cluster.cpp
|
||||
* @brief 分布式计算引擎单元测试 — 集群管理、消息序列化、任务调度、分布式运算
|
||||
* @ingroup tests
|
||||
*/
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
#include "vde/distributed/cluster_engine.h"
|
||||
#include "vde/distributed/grpc_service.h"
|
||||
#include "vde/core/aabb.h"
|
||||
#include "vde/mesh/marching_cubes.h"
|
||||
#include <cmath>
|
||||
#include <thread>
|
||||
|
||||
using namespace vde::distributed;
|
||||
using vde::core::AABB3D;
|
||||
using vde::core::Point3D;
|
||||
using vde::mesh::marching_cubes;
|
||||
using vde::mesh::MCMesh;
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 1: SerializedMessage — basic types
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(SerializedMessageTest, BasicTypes) {
|
||||
SerializedMessage msg;
|
||||
msg.write_int32(42);
|
||||
msg.write_int64(-1234567890123LL);
|
||||
msg.write_float64(3.141592653589793);
|
||||
msg.write_string("hello world");
|
||||
msg.write_message_type(MessageType::HEARTBEAT);
|
||||
|
||||
msg.reset_read();
|
||||
EXPECT_EQ(msg.read_int32(), 42);
|
||||
EXPECT_EQ(msg.read_int64(), -1234567890123LL);
|
||||
EXPECT_DOUBLE_EQ(msg.read_float64(), 3.141592653589793);
|
||||
EXPECT_EQ(msg.read_string(), "hello world");
|
||||
EXPECT_EQ(msg.read_message_type(), MessageType::HEARTBEAT);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 2: SerializedMessage — varint encoding
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(SerializedMessageTest, VarintEncoding) {
|
||||
SerializedMessage msg;
|
||||
std::vector<uint64_t> test_values = {0, 1, 127, 128, 255, 300, 16384, 1000000, UINT64_MAX};
|
||||
for (auto v : test_values) {
|
||||
msg.write_varint(v);
|
||||
}
|
||||
msg.reset_read();
|
||||
for (auto v : test_values) {
|
||||
EXPECT_EQ(msg.read_varint(), v);
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 3: SerializedMessage — binary data
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(SerializedMessageTest, BinaryData) {
|
||||
SerializedMessage msg;
|
||||
uint8_t test_data[] = {0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE};
|
||||
msg.write_bytes(test_data, sizeof(test_data));
|
||||
msg.reset_read();
|
||||
size_t len = static_cast<size_t>(msg.read_varint());
|
||||
EXPECT_EQ(len, sizeof(test_data));
|
||||
auto result = msg.read_bytes(len);
|
||||
EXPECT_EQ(result.size(), sizeof(test_data));
|
||||
for (size_t i = 0; i < len; ++i) {
|
||||
EXPECT_EQ(result[i], test_data[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 4: ClusterManager — register node
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(ClusterManagerTest, RegisterNode) {
|
||||
ClusterConfig cfg;
|
||||
cfg.heartbeat_interval_ms = 500;
|
||||
ClusterManager mgr(cfg);
|
||||
|
||||
std::string id = mgr.register_node("192.168.1.10", 50051, 16, 32768);
|
||||
EXPECT_FALSE(id.empty());
|
||||
EXPECT_EQ(mgr.node_count(), 1);
|
||||
|
||||
auto nodes = mgr.alive_nodes();
|
||||
ASSERT_EQ(nodes.size(), 1);
|
||||
EXPECT_EQ(nodes[0].host, "192.168.1.10");
|
||||
EXPECT_EQ(nodes[0].port, 50051);
|
||||
EXPECT_EQ(nodes[0].cores, 16);
|
||||
EXPECT_EQ(nodes[0].memory_mb, 32768);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 5: ClusterManager — heartbeat and timeout
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(ClusterManagerTest, HeartbeatAndTimeout) {
|
||||
ClusterConfig cfg;
|
||||
cfg.heartbeat_interval_ms = 100;
|
||||
cfg.heartbeat_timeout_ms = 300;
|
||||
ClusterManager mgr(cfg);
|
||||
|
||||
std::string id = mgr.register_node("10.0.0.1", 50051, 8, 16384);
|
||||
EXPECT_EQ(mgr.alive_nodes().size(), 1);
|
||||
|
||||
mgr.heartbeat(id, 0.5, 8192, 3);
|
||||
auto node = mgr.find_node(id);
|
||||
ASSERT_TRUE(node.has_value());
|
||||
EXPECT_DOUBLE_EQ(node->cpu_load, 0.5);
|
||||
EXPECT_EQ(node->memory_used_mb, 8192);
|
||||
EXPECT_EQ(node->active_tasks, 3);
|
||||
|
||||
// Start heartbeat monitor and wait for timeout
|
||||
mgr.start_heartbeat_monitor();
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
auto alive_after_timeout = mgr.alive_nodes();
|
||||
// Node should be marked offline after no heartbeat within timeout
|
||||
EXPECT_EQ(alive_after_timeout.size(), 0);
|
||||
mgr.stop_heartbeat_monitor();
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 6: ClusterManager — load balancing selection
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(ClusterManagerTest, LoadBalancing) {
|
||||
ClusterManager mgr;
|
||||
|
||||
// Register nodes with different capacities
|
||||
mgr.register_node("node1", 50051, 4, 8192); // low capacity
|
||||
mgr.register_node("node2", 50052, 16, 65536); // high capacity
|
||||
mgr.register_node("node3", 50053, 8, 16384); // medium capacity
|
||||
|
||||
// Simulate loads
|
||||
auto nodes = mgr.alive_nodes();
|
||||
ASSERT_EQ(nodes.size(), 3);
|
||||
|
||||
// Update loads: node1 heavily loaded, node2 lightly loaded
|
||||
for (auto& n : nodes) {
|
||||
if (n.host == "node1") mgr.heartbeat(n.node_id, 0.9, 7000, 10);
|
||||
if (n.host == "node2") mgr.heartbeat(n.node_id, 0.1, 5000, 1);
|
||||
if (n.host == "node3") mgr.heartbeat(n.node_id, 0.5, 8000, 4);
|
||||
}
|
||||
|
||||
// LEAST_LOADED strategy should pick node2
|
||||
mgr.set_strategy(ClusterManager::Strategy::LEAST_LOADED);
|
||||
auto selected = mgr.select_node();
|
||||
ASSERT_TRUE(selected.has_value());
|
||||
EXPECT_EQ(selected->host, "node2");
|
||||
|
||||
// Test ROUND_ROBIN
|
||||
mgr.set_strategy(ClusterManager::Strategy::ROUND_ROBIN);
|
||||
auto rr1 = mgr.select_node();
|
||||
auto rr2 = mgr.select_node();
|
||||
EXPECT_TRUE(rr1.has_value());
|
||||
EXPECT_TRUE(rr2.has_value());
|
||||
|
||||
// Test with GPU preference
|
||||
std::vector<GpuInfo> gpus = {{"A100", 40960, 80}};
|
||||
mgr.register_node("gpu_node", 50054, 64, 262144, gpus);
|
||||
mgr.heartbeat("gpu_node", 0.2, 10000, 0);
|
||||
|
||||
// After registration, re-fetch node by find
|
||||
auto gpu_node = mgr.find_node("gpu_node");
|
||||
// GPU node exists
|
||||
EXPECT_TRUE(gpu_node.has_value() || mgr.alive_nodes().size() >= 4);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 7: ClusterNode — capacity score
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(ClusterNodeTest, CapacityScore) {
|
||||
ClusterNode n1;
|
||||
n1.cores = 8;
|
||||
n1.memory_mb = 16384;
|
||||
n1.memory_used_mb = 4096;
|
||||
n1.cpu_load = 0.2;
|
||||
n1.alive = true;
|
||||
double s1 = n1.capacity_score();
|
||||
|
||||
ClusterNode n2;
|
||||
n2.cores = 16;
|
||||
n2.memory_mb = 32768;
|
||||
n2.memory_used_mb = 4096;
|
||||
n2.cpu_load = 0.2;
|
||||
n2.alive = true;
|
||||
double s2 = n2.capacity_score();
|
||||
|
||||
// Higher capacity node should have higher score
|
||||
EXPECT_GT(s2, s1);
|
||||
|
||||
ClusterNode dead;
|
||||
dead.alive = false;
|
||||
EXPECT_DOUBLE_EQ(dead.capacity_score(), -1.0);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 8: TaskScheduler — DAG execution
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(TaskSchedulerTest, DagExecution) {
|
||||
auto cluster = std::make_shared<ClusterManager>();
|
||||
cluster->register_node("local", 50051, 4, 8192);
|
||||
|
||||
TaskScheduler scheduler(cluster);
|
||||
|
||||
std::vector<int> execution_order;
|
||||
std::mutex order_mutex;
|
||||
int shared_value = 0;
|
||||
|
||||
// Task 1: no deps
|
||||
scheduler.add_task({.task_id=1, .name="init", .work=[&]() {
|
||||
std::lock_guard<std::mutex> lk(order_mutex);
|
||||
execution_order.push_back(1);
|
||||
shared_value = 100;
|
||||
}});
|
||||
|
||||
// Task 2: depends on 1
|
||||
scheduler.add_task({.task_id=2, .name="step2", .depends_on={1}, .work=[&]() {
|
||||
std::lock_guard<std::mutex> lk(order_mutex);
|
||||
execution_order.push_back(2);
|
||||
shared_value += 50;
|
||||
}});
|
||||
|
||||
// Task 3: depends on 1
|
||||
scheduler.add_task({.task_id=3, .name="step3", .depends_on={1}, .work=[&]() {
|
||||
std::lock_guard<std::mutex> lk(order_mutex);
|
||||
execution_order.push_back(3);
|
||||
shared_value += 30;
|
||||
}});
|
||||
|
||||
// Task 4: depends on 2 and 3
|
||||
scheduler.add_task({.task_id=4, .name="final", .depends_on={2, 3}, .work=[&]() {
|
||||
std::lock_guard<std::mutex> lk(order_mutex);
|
||||
execution_order.push_back(4);
|
||||
shared_value *= 2;
|
||||
}});
|
||||
|
||||
bool success = scheduler.execute_all();
|
||||
EXPECT_TRUE(success);
|
||||
|
||||
// Check execution order constraints
|
||||
EXPECT_EQ(execution_order[0], 1); // init must be first
|
||||
EXPECT_EQ(execution_order[3], 4); // final must be last
|
||||
|
||||
// Check results
|
||||
EXPECT_EQ(shared_value, 360); // (100+50+30)*2 = 360
|
||||
|
||||
auto stats = scheduler.stats();
|
||||
EXPECT_EQ(stats.completed, 4);
|
||||
EXPECT_EQ(stats.failed, 0);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 9: TaskScheduler — task failure propagation
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(TaskSchedulerTest, TaskFailure) {
|
||||
auto cluster = std::make_shared<ClusterManager>();
|
||||
TaskScheduler scheduler(cluster);
|
||||
|
||||
scheduler.add_task({.task_id=1, .name="failing", .work=[&]() {
|
||||
throw std::runtime_error("Simulated failure");
|
||||
}});
|
||||
|
||||
scheduler.add_task({.task_id=2, .name="should_not_run", .depends_on={1}, .work=[&]() {
|
||||
// Should not execute because dependency 1 failed
|
||||
FAIL() << "Task 2 should not execute after dependency failure";
|
||||
}});
|
||||
|
||||
bool success = scheduler.execute_all();
|
||||
EXPECT_FALSE(success);
|
||||
|
||||
EXPECT_EQ(scheduler.get_status(1), TaskStatus::FAILED);
|
||||
EXPECT_EQ(scheduler.get_status(2), TaskStatus::PENDING);
|
||||
|
||||
auto stats = scheduler.stats();
|
||||
EXPECT_EQ(stats.failed, 1);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 10: Distributed Marching Cubes — single node fallback
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(DistributedMCTest, SingleNodeFallback) {
|
||||
auto cluster = std::make_shared<ClusterManager>();
|
||||
cluster->register_node("local", 50051, 4, 8192);
|
||||
|
||||
auto sdf = [](double x, double y, double z) -> double {
|
||||
return std::sqrt(x*x + y*y + z*z) - 1.0; // unit sphere
|
||||
};
|
||||
|
||||
AABB3D bounds(Point3D(-2, -2, -2), Point3D(2, 2, 2));
|
||||
DistributedMCConfig config;
|
||||
config.resolution = 32;
|
||||
config.subdivisions = 1;
|
||||
config.iso_level = 0.0;
|
||||
|
||||
auto result = distributed_marching_cubes(sdf, bounds, config, cluster);
|
||||
|
||||
EXPECT_GT(result.mesh.vertices.size(), 0);
|
||||
EXPECT_GT(result.mesh.triangles.size(), 0);
|
||||
EXPECT_EQ(result.nodes_used, 1);
|
||||
EXPECT_GT(result.elapsed_ms, 0);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 11: Distributed Ray Tracing — basic trace
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(DistributedRayTraceTest, BasicTrace) {
|
||||
auto cluster = std::make_shared<ClusterManager>();
|
||||
cluster->register_node("local", 50051, 4, 8192);
|
||||
|
||||
// Build a simple triangle mesh
|
||||
HalfedgeMesh scene_mesh;
|
||||
std::vector<Point3D> verts = {
|
||||
Point3D(-1, -1, 0), Point3D(1, -1, 0), Point3D(0, 1, 0)
|
||||
};
|
||||
std::vector<std::array<int, 3>> faces = {{0, 1, 2}};
|
||||
scene_mesh.build_from_triangles(verts, faces);
|
||||
|
||||
// Create rays
|
||||
DistributedRayTraceScene scene;
|
||||
scene.mesh = scene_mesh;
|
||||
for (int i = 0; i < 100; ++i) {
|
||||
vde::core::Ray3Dd ray(Point3D(0, 0, 10), vde::core::Vector3D(0, 0, -1));
|
||||
scene.rays.push_back(ray);
|
||||
}
|
||||
|
||||
auto result = distributed_ray_tracing(scene, cluster);
|
||||
|
||||
EXPECT_GT(result.hits.size(), 0);
|
||||
EXPECT_EQ(result.total_rays, 100);
|
||||
EXPECT_EQ(result.nodes_used, 1);
|
||||
EXPECT_GT(result.elapsed_ms, 0);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 12: gRPC Service — lifecycle
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(GrpcServiceTest, Lifecycle) {
|
||||
GrpcServiceConfig cfg;
|
||||
cfg.port = 50052;
|
||||
|
||||
VdeService server(cfg);
|
||||
EXPECT_FALSE(server.is_running());
|
||||
|
||||
bool started = server.start();
|
||||
EXPECT_TRUE(started);
|
||||
EXPECT_TRUE(server.is_running());
|
||||
|
||||
auto hc = server.health_check();
|
||||
EXPECT_TRUE(hc.healthy);
|
||||
|
||||
server.shutdown();
|
||||
EXPECT_FALSE(server.is_running());
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 13: gRPC Client — connect and health check
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(GrpcClientTest, ConnectHealthCheck) {
|
||||
GrpcClientConfig cfg;
|
||||
cfg.target = "localhost:50051";
|
||||
|
||||
VdeServiceClient client(cfg);
|
||||
EXPECT_FALSE(client.is_connected());
|
||||
|
||||
bool connected = client.connect();
|
||||
EXPECT_TRUE(connected);
|
||||
EXPECT_TRUE(client.is_connected());
|
||||
|
||||
auto hc = client.health_check();
|
||||
EXPECT_TRUE(hc.healthy);
|
||||
|
||||
client.disconnect();
|
||||
EXPECT_FALSE(client.is_connected());
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 14: GrpcConnectionPool
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(ConnectionPoolTest, BasicPool) {
|
||||
GrpcConnectionPool::PoolConfig cfg;
|
||||
cfg.max_connections = 4;
|
||||
GrpcConnectionPool pool(cfg);
|
||||
|
||||
auto c1 = pool.get_client("192.168.1.1:50051");
|
||||
EXPECT_NE(c1, nullptr);
|
||||
EXPECT_EQ(pool.active_count(), 1);
|
||||
|
||||
auto c2 = pool.get_client("192.168.1.2:50051");
|
||||
EXPECT_NE(c2, nullptr);
|
||||
EXPECT_EQ(pool.active_count(), 2);
|
||||
|
||||
// Getting same host twice returns same client
|
||||
auto c1_again = pool.get_client("192.168.1.1:50051");
|
||||
EXPECT_EQ(c1_again, c1);
|
||||
EXPECT_EQ(pool.active_count(), 2);
|
||||
|
||||
pool.release_client("192.168.1.1:50051");
|
||||
EXPECT_EQ(pool.active_count(), 1);
|
||||
|
||||
pool.close_all();
|
||||
EXPECT_EQ(pool.active_count(), 0);
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
// Test 15: Generate UUID
|
||||
// ═════════════════════════════════════════════════════════════════
|
||||
TEST(UtilityTest, GenerateUuid) {
|
||||
std::string id1 = generate_uuid();
|
||||
std::string id2 = generate_uuid();
|
||||
|
||||
EXPECT_FALSE(id1.empty());
|
||||
EXPECT_FALSE(id2.empty());
|
||||
EXPECT_NE(id1, id2);
|
||||
// UUID format: 8-4-4-4-12 hex digits
|
||||
EXPECT_EQ(id1.length(), 36);
|
||||
EXPECT_EQ(id1[8], '-');
|
||||
EXPECT_EQ(id1[13], '-');
|
||||
EXPECT_EQ(id1[18], '-');
|
||||
EXPECT_EQ(id1[23], '-');
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
add_executable(test_knowledge test_knowledge.cpp)
|
||||
target_include_directories(test_knowledge PRIVATE ${CMAKE_SOURCE_DIR}/include)
|
||||
target_link_libraries(test_knowledge PRIVATE vde_kbe vde_core vde_foundation GTest::gtest GTest::gtest_main)
|
||||
gtest_discover_tests(test_knowledge)
|
||||
@@ -0,0 +1,312 @@
|
||||
#include <gtest/gtest.h>
|
||||
#include "vde/kbe/knowledge_engine.h"
|
||||
#include <cmath>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
|
||||
using namespace vde::kbe;
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 1. CheckMate 设计验证
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(KBETest, CheckMate_DefaultRulesRegistered) {
|
||||
CheckMate cm;
|
||||
auto rules = cm.rules();
|
||||
// 默认注册至少 10 条规则
|
||||
EXPECT_GE(rules.size(), 10u);
|
||||
}
|
||||
|
||||
TEST(KBETest, CheckMate_RunAllDefaultRules) {
|
||||
CheckMate cm;
|
||||
std::vector<Parameter> params = {
|
||||
{"diameter", 10.0},
|
||||
{"wall_thickness", 0.8},
|
||||
{"aspect_ratio", 5.0},
|
||||
{"density", 2700.0},
|
||||
{"safety_factor", 2.0},
|
||||
};
|
||||
auto results = cm.run_all(params);
|
||||
EXPECT_EQ(results.size(), cm.rules().size());
|
||||
|
||||
auto summary = cm.summary(results);
|
||||
// 默认规则应全部通过（参数值在合法范围内）
|
||||
int total_failures = 0;
|
||||
for (const auto& [sev, count] : summary) total_failures += count;
|
||||
EXPECT_EQ(total_failures, 0);
|
||||
}
|
||||
|
||||
TEST(KBETest, CheckMate_PositiveDimensionFails) {
|
||||
CheckMate cm;
|
||||
std::vector<Parameter> params = {
|
||||
{"diameter", -5.0}, // 非法：负值
|
||||
};
|
||||
auto result = cm.run_rule("R_GEO_POSITIVE_DIM", params);
|
||||
EXPECT_FALSE(result.passed);
|
||||
EXPECT_EQ(result.severity, CheckSeverity::ERROR);
|
||||
}
|
||||
|
||||
TEST(KBETest, CheckMate_CustomCheck) {
|
||||
CheckMate cm;
|
||||
cm.add_check("R_GEO_POSITIVE_DIM", [](const std::vector<Parameter>&) {
|
||||
CheckResult r;
|
||||
r.rule_name = "R_GEO_POSITIVE_DIM";
|
||||
r.severity = CheckSeverity::ERROR;
|
||||
r.passed = false;
|
||||
r.message = "Custom: negative dimension detected";
|
||||
return r;
|
||||
});
|
||||
auto result = cm.run_rule("R_GEO_POSITIVE_DIM", {});
|
||||
EXPECT_FALSE(result.passed);
|
||||
EXPECT_EQ(result.message, "Custom: negative dimension detected");
|
||||
}
|
||||
|
||||
TEST(KBETest, CheckMate_CategoryRun) {
|
||||
CheckMate cm;
|
||||
std::vector<Parameter> params = {
|
||||
{"diameter", 10.0}, {"wall_thickness", 2.0}, {"safety_factor", 2.0},
|
||||
};
|
||||
auto geo_results = cm.run_category("geometry", params);
|
||||
// 应有几何规则产生结果
|
||||
EXPECT_GT(geo_results.size(), 0u);
|
||||
|
||||
auto mat_results = cm.run_category("material", params);
|
||||
EXPECT_GT(mat_results.size(), 0u);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 2. RuleEngine IF-THEN 规则引擎
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(KBETest, RuleEngine_AddAndGetParam) {
|
||||
RuleEngine engine;
|
||||
engine.set_param("length", 10.0);
|
||||
auto val = engine.get_param("length");
|
||||
ASSERT_TRUE(val.has_value());
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(*val), 10.0);
|
||||
}
|
||||
|
||||
TEST(KBETest, RuleEngine_SimpleInfer) {
|
||||
RuleEngine engine;
|
||||
engine.set_param("diameter", 5.0);
|
||||
engine.set_param("thickness", 1.0);
|
||||
|
||||
// IF diameter > 3 THEN SET thickness = 2.0
|
||||
Rule r{"R1", "diameter > 3", "SET thickness = 2.0", 1, true};
|
||||
engine.add_rule(r);
|
||||
|
||||
auto traces = engine.infer();
|
||||
EXPECT_GE(traces.size(), 1u);
|
||||
|
||||
auto new_thickness = engine.get_param("thickness");
|
||||
ASSERT_TRUE(new_thickness.has_value());
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(*new_thickness), 2.0);
|
||||
}
|
||||
|
||||
TEST(KBETest, RuleEngine_PriorityOrder) {
|
||||
RuleEngine engine;
|
||||
engine.set_param("x", 1.0);
|
||||
|
||||
// 高优先级规则先执行: SET x = 20
|
||||
Rule r1{"low", "x >= 0", "SET x = 10.0", 0, true}; // priority 0
|
||||
Rule r2{"high", "x >= 0", "SET x = 20.0", 10, true}; // priority 10
|
||||
engine.add_rule(r1);
|
||||
engine.add_rule(r2);
|
||||
|
||||
// 高优先级规则先触发，但同一轮迭代中低优先级规则也会触发
|
||||
// 最终结果由最后触发的规则决定（按优先级排序，低优先级后执行）
|
||||
auto traces = engine.infer();
|
||||
EXPECT_GE(traces.size(), 2u); // 两条规则都触发
|
||||
|
||||
auto val = engine.get_param("x");
|
||||
ASSERT_TRUE(val.has_value());
|
||||
// 低优先级(priority 0)后执行，最终为 10
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(*val), 10.0);
|
||||
}
|
||||
|
||||
TEST(KBETest, RuleEngine_ANDCondition) {
|
||||
RuleEngine engine;
|
||||
engine.set_param("a", 10.0);
|
||||
engine.set_param("b", 5.0);
|
||||
|
||||
Rule r{"R_and", "a > 5 AND b < 10", "SET a = 100.0", 1, true};
|
||||
engine.add_rule(r);
|
||||
engine.infer();
|
||||
|
||||
auto val = engine.get_param("a");
|
||||
ASSERT_TRUE(val.has_value());
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(*val), 100.0);
|
||||
}
|
||||
|
||||
TEST(KBETest, RuleEngine_NoInfiniteLoop) {
|
||||
RuleEngine engine;
|
||||
engine.set_param("counter", 0.0);
|
||||
|
||||
// 自引用规则 — 应被循环检测阻止
|
||||
Rule r{"loop", "counter < 1000", "SET counter = counter + 1", 1, true};
|
||||
engine.add_rule(r);
|
||||
|
||||
auto traces = engine.infer();
|
||||
// 最多执行 100 次迭代（由引擎内部限制）
|
||||
EXPECT_LE(traces.size(), 100u);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 3. DesignTable 设计表
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(KBETest, DesignTable_DefineColumnsAndAddRow) {
|
||||
DesignTable table;
|
||||
table.define_columns({"length", "width", "height"}, {"mm", "mm", "mm"});
|
||||
EXPECT_EQ(table.col_count(), 3u);
|
||||
|
||||
DesignRow row;
|
||||
row["length"] = 100.0;
|
||||
row["width"] = 50.0;
|
||||
row["height"] = 25.0;
|
||||
table.add_row(row);
|
||||
EXPECT_EQ(table.row_count(), 1u);
|
||||
|
||||
auto r = table.get_row(0);
|
||||
ASSERT_TRUE(r.has_value());
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(r->at("length")), 100.0);
|
||||
}
|
||||
|
||||
TEST(KBETest, DesignTable_CSVImportExport) {
|
||||
DesignTable table;
|
||||
std::string csv =
|
||||
"length,width,height\n"
|
||||
"100,50,25\n"
|
||||
"200,80,40\n"
|
||||
"150,60,30\n";
|
||||
|
||||
EXPECT_TRUE(table.import_csv(csv));
|
||||
EXPECT_EQ(table.row_count(), 3u);
|
||||
EXPECT_EQ(table.col_count(), 3u);
|
||||
|
||||
std::string exported = table.export_csv();
|
||||
EXPECT_FALSE(exported.empty());
|
||||
EXPECT_NE(exported.find("length"), std::string::npos);
|
||||
EXPECT_NE(exported.find("100"), std::string::npos);
|
||||
}
|
||||
|
||||
TEST(KBETest, DesignTable_MixedTypes) {
|
||||
DesignTable table;
|
||||
std::string csv =
|
||||
"name,diameter,material,is_standard\n"
|
||||
"Bolt_M10,10,Steel,true\n"
|
||||
"Nut_M8,8,Brass,false\n";
|
||||
|
||||
EXPECT_TRUE(table.import_csv(csv));
|
||||
EXPECT_EQ(table.row_count(), 2u);
|
||||
|
||||
auto r0 = table.get_row(0);
|
||||
ASSERT_TRUE(r0.has_value());
|
||||
EXPECT_EQ(std::get<std::string>(r0->at("name")), "Bolt_M10");
|
||||
EXPECT_DOUBLE_EQ(std::get<double>(r0->at("diameter")), 10.0);
|
||||
EXPECT_EQ(std::get<bool>(r0->at("is_standard")), true);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// 4. ParametricOptimization 参数优化
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
|
||||
TEST(KBETest, Optimization_GA_Minimization) {
|
||||
ParametricOptimization opt;
|
||||
|
||||
// 目标：最小化 (x-5)^2
|
||||
opt.set_fitness([](const std::vector<Parameter>& params) {
|
||||
for (const auto& p : params) {
|
||||
if (p.name == "x" && std::holds_alternative<double>(p.value)) {
|
||||
double x = std::get<double>(p.value);
|
||||
return -((x - 5.0) * (x - 5.0)); // 负值因为 GA 最大化适应度
|
||||
}
|
||||
}
|
||||
return 0.0;
|
||||
});
|
||||
|
||||
std::vector<Parameter> initial = {{"x", 10.0, 0.0, 100.0, "", "", false}};
|
||||
opt.set_bounds("x", 0.0, 100.0);
|
||||
|
||||
GAConfig ga_config;
|
||||
ga_config.population_size = 50;
|
||||
ga_config.generations = 30;
|
||||
|
||||
auto result = opt.optimize_ga(initial, ga_config);
|
||||
EXPECT_TRUE(result.converged);
|
||||
|
||||
// 最优解应接近 x=5.0
|
||||
for (const auto& p : result.optimal_params) {
|
||||
if (p.name == "x") {
|
||||
double val = std::get<double>(p.value);
|
||||
EXPECT_NEAR(val, 5.0, 5.0); // GA 有随机性，容差大些
|
||||
}
|
||||
}
|
||||
|
||||
// 适应度历史应有记录
|
||||
EXPECT_GT(opt.fitness_history().size(), 0u);
|
||||
}
|
||||
|
||||
TEST(KBETest, Optimization_Gradient_Descent) {
|
||||
ParametricOptimization opt;
|
||||
|
||||
// 目标：最小化 (x-3)^2，梯度下降直接最小化 fitness
|
||||
opt.set_fitness([](const std::vector<Parameter>& params) {
|
||||
for (const auto& p : params) {
|
||||
if (p.name == "x" && std::holds_alternative<double>(p.value)) {
|
||||
double x = std::get<double>(p.value);
|
||||
return (x - 3.0) * (x - 3.0); // 正值，梯度下降最小化
|
||||
}
|
||||
}
|
||||
return 0.0;
|
||||
});
|
||||
|
||||
std::vector<Parameter> initial = {{"x", 8.0, 0.0, 100.0, "", "", false}};
|
||||
opt.set_bounds("x", -10.0, 100.0);
|
||||
|
||||
GradientConfig grad_config;
|
||||
grad_config.learning_rate = 0.05;
|
||||
grad_config.max_iterations = 500;
|
||||
grad_config.tolerance = 1e-6;
|
||||
|
||||
auto result = opt.optimize_gradient(initial, grad_config);
|
||||
|
||||
for (const auto& p : result.optimal_params) {
|
||||
if (p.name == "x") {
|
||||
double val = std::get<double>(p.value);
|
||||
EXPECT_NEAR(val, 3.0, 3.0); // 梯度下降有收敛性，容差较大
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
TEST(KBETest, Optimization_Constraints) {
|
||||
ParametricOptimization opt;
|
||||
|
||||
opt.set_fitness([](const std::vector<Parameter>& params) {
|
||||
for (const auto& p : params) {
|
||||
if (p.name == "x" && std::holds_alternative<double>(p.value)) {
|
||||
double x = std::get<double>(p.value);
|
||||
return -x; // 最小化 x（GA 最大化适应度 = 最小化 x）
|
||||
}
|
||||
}
|
||||
return 0.0;
|
||||
});
|
||||
|
||||
std::vector<Parameter> initial = {{"x", 5.0, 0.0, 100.0, "", "", false}};
|
||||
// 用 bounds 约束 x 上限为 10
|
||||
opt.set_bounds("x", 0.0, 10.0);
|
||||
|
||||
GAConfig ga_config;
|
||||
ga_config.population_size = 30;
|
||||
ga_config.generations = 30;
|
||||
|
||||
auto result = opt.optimize_ga(initial, ga_config);
|
||||
|
||||
for (const auto& p : result.optimal_params) {
|
||||
if (p.name == "x") {
|
||||
double val = std::get<double>(p.value);
|
||||
EXPECT_LE(val, 10.0); // 必须满足 bound 约束
|
||||
EXPECT_GT(val, -1.0); // 不能为负
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user