feat: 轻量化 ScaledProcessingPattern，改为最小字段与延迟计算，

减少构造时的大量临时对象分配，降低 GC 压力并提升高并发/多 provider 场景下的性能

src/main/java/com/extendedae_plus/content/ScaledProcessingPattern.java

@@ -17,27 +17,21 @@ import java.util.Objects;
  */
 public final class ScaledProcessingPattern implements IPatternDetails {
 
+    // 最小化实例字段：只保留原始样板引用、定义和倍数
     private final AEProcessingPattern original; // 原始样板引用
-    private final AEItemKey definition;                // 样板物品
+    private final AEItemKey definition;         // 样板物品（直接委托自 original）
-    private final GenericStack[] sparseInputs;         // 缩放后的稀疏输入
+    private final long multiplier;              // 乘数（外部可视为视图参数）
-    private final GenericStack[] sparseOutputs;        // 缩放后的稀疏输出
-    private final IInput[] inputs;                     // 缩放后的压缩输入
-    private final GenericStack[] condensedOutputs;     // 缩放后的压缩输出
 
-    public ScaledProcessingPattern(
+    // 延迟计算缓存（轻量化实例时避免在构造器中分配大数组）
-            AEProcessingPattern original,
+    private transient volatile IInput[] inputsCache;
-            AEItemKey definition,
+    private transient volatile GenericStack[] outputsCache;
-            GenericStack[] sparseInputs,
+    private transient volatile GenericStack[] sparseInputsCache;
-            GenericStack[] sparseOutputs,
+    private transient volatile GenericStack[] sparseOutputsCache;
-            IInput[] inputs,
+
-            GenericStack[] condensedOutputs
+    public ScaledProcessingPattern(AEProcessingPattern original, AEItemKey definition, long multiplier) {
-    ) {
         this.original = Objects.requireNonNull(original);
         this.definition = Objects.requireNonNull(definition);
-        this.sparseInputs = Objects.requireNonNull(sparseInputs);
+        this.multiplier = multiplier <= 0 ? 1L : multiplier;
-        this.sparseOutputs = Objects.requireNonNull(sparseOutputs);
-        this.inputs = Objects.requireNonNull(inputs);
-        this.condensedOutputs = Objects.requireNonNull(condensedOutputs);
     }
 
     /* -------------------- API 实现 -------------------- */
@@ -53,25 +47,91 @@ public final class ScaledProcessingPattern implements IPatternDetails {
 
     @Override
     public IInput[] getInputs() {
-        return inputs;
+        IInput[] cached = this.inputsCache;
+        if (cached == null) {
+            synchronized (this) {
+                cached = this.inputsCache;
+                if (cached == null) {
+                    var base = original.getInputs();
+                    IInput[] arr = new IInput[base.length];
+                    for (int i = 0; i < base.length; i++) {
+                        var in = base[i];
+                        // 不复制 template 数组，直接复用原始的 possible inputs；仅放大 multiplier
+                        arr[i] = new Input(in.getPossibleInputs(), in.getMultiplier() * this.multiplier);
+                    }
+                    this.inputsCache = arr;
+                    cached = arr;
+                }
+            }
+        }
+        return cached;
     }
 
     @Override
     public GenericStack[] getOutputs() {
-        return condensedOutputs;
+        GenericStack[] cached = this.outputsCache;
+        if (cached == null) {
+            synchronized (this) {
+                cached = this.outputsCache;
+                if (cached == null) {
+                    var baseOutputs = original.getOutputs();
+                    GenericStack[] arr = new GenericStack[baseOutputs.length];
+                    for (int i = 0; i < baseOutputs.length; i++) {
+                        var o = baseOutputs[i];
+                        if (o != null) arr[i] = new GenericStack(o.what(), o.amount() * this.multiplier);
+                    }
+                    this.outputsCache = arr;
+                    cached = arr;
+                }
+            }
+        }
+        return cached;
     }
 
     public GenericStack[] getSparseInputs() {
-        return sparseInputs;
+        GenericStack[] cached = this.sparseInputsCache;
+        if (cached == null) {
+            synchronized (this) {
+                cached = this.sparseInputsCache;
+                if (cached == null) {
+                    var base = original.getSparseInputs();
+                    GenericStack[] arr = new GenericStack[base.length];
+                    for (int i = 0; i < base.length; i++) {
+                        var v = base[i];
+                        if (v != null) arr[i] = new GenericStack(v.what(), v.amount() * this.multiplier);
+                    }
+                    this.sparseInputsCache = arr;
+                    cached = arr;
+                }
+            }
+        }
+        return cached;
     }
 
     public GenericStack[] getSparseOutputs() {
-        return sparseOutputs;
+        GenericStack[] cached = this.sparseOutputsCache;
+        if (cached == null) {
+            synchronized (this) {
+                cached = this.sparseOutputsCache;
+                if (cached == null) {
+                    var base = original.getSparseOutputs();
+                    GenericStack[] arr = new GenericStack[base.length];
+                    for (int i = 0; i < base.length; i++) {
+                        var v = base[i];
+                        if (v != null) arr[i] = new GenericStack(v.what(), v.amount() * this.multiplier);
+                    }
+                    this.sparseOutputsCache = arr;
+                    cached = arr;
+                }
+            }
+        }
+        return cached;
     }
 
     @Override
     public GenericStack getPrimaryOutput() {
-        if (condensedOutputs.length > 0) return condensedOutputs[0];
+        var outs = getOutputs();
+        if (outs.length > 0 && outs[0] != null) return outs[0];
         return original.getPrimaryOutput();
     }
 
@@ -82,15 +142,19 @@ public final class ScaledProcessingPattern implements IPatternDetails {
 
     @Override
     public void pushInputsToExternalInventory(KeyCounter[] inputHolder, PatternInputSink inputSink) {
-        // 保持和 AEProcessingPattern 一致，用 sparseInputs 驱动
+        // 使用 lazy 计算的 sparseInputs 与 inputs 来驱动；当两者长度一致时直接委托
-        if (sparseInputs.length == inputs.length) {
+        GenericStack[] sInputs = getSparseInputs();
+        IInput[] ins = getInputs();
+        if (sInputs.length == ins.length) {
             IPatternDetails.super.pushInputsToExternalInventory(inputHolder, inputSink);
-        } else {
+            return;
+        }
+
         KeyCounter allInputs = new KeyCounter();
         for (KeyCounter counter : inputHolder) {
             allInputs.addAll(counter);
         }
-            for (GenericStack sparseInput : sparseInputs) {
+        for (GenericStack sparseInput : sInputs) {
             if (sparseInput != null) {
                 AEKey key = sparseInput.what();
                 long amount = sparseInput.amount();
@@ -104,7 +168,6 @@ public final class ScaledProcessingPattern implements IPatternDetails {
             }
         }
     }
-    }
 
     /* -------------------- 缩放输入代理 -------------------- */
 
@@ -117,18 +180,22 @@ public final class ScaledProcessingPattern implements IPatternDetails {
             this.multiplier = multiplier;
         }
 
+        @Override
         public GenericStack[] getPossibleInputs() {
             return this.template;
         }
 
+        @Override
         public long getMultiplier() {
             return this.multiplier;
         }
 
+        @Override
         public boolean isValid(AEKey input, Level level) {
             return input.matches(this.template[0]);
         }
 
+        @Override
         public @Nullable AEKey getRemainingKey(AEKey template) {
             return null;
         }

src/main/java/com/extendedae_plus/util/PatternScaler.java

@@ -71,48 +71,7 @@ public final class PatternScaler {
             // 配置读取异常时不施加上限
         }
 
-        // 构建压缩输入（将每个输入的 multiplier 翻倍，保留每个模板的原始数量）
+        // 仅使用 multiplier 构建轻量化 ScaledProcessingPattern（具体视图按需计算）
-        IInput[] scaledInputs = new IInput[baseInputs.length];
+        return new ScaledProcessingPattern(base, base.getDefinition(), multiplier);
-        for (int i = 0; i < baseInputs.length; i++) {
-            var in = baseInputs[i];
-            var template = in.getPossibleInputs();
-            GenericStack[] scaledTemplates = new GenericStack[template.length];
-            for (int j = 0; j < template.length; j++) {
-                scaledTemplates[j] = new GenericStack(template[j].what(), template[j].amount());
-            }
-            scaledInputs[i] = new ScaledProcessingPattern.Input(scaledTemplates, in.getMultiplier() * multiplier);
-        }
-
-        /* 4. 构建压缩输出 */
-        GenericStack[] scaledCondensedOutputs = new GenericStack[baseOutputs.length];
-        for (int i = 0; i < baseOutputs.length; i++) {
-            GenericStack out = baseOutputs[i];
-            if (out != null) {
-                scaledCondensedOutputs[i] = new GenericStack(out.what(), out.amount() * multiplier);
-            }
-        }
-
-        // 构建并打印稀疏表示（直接按 multiplier 放大）
-        GenericStack[] scaledSparseInputs = new GenericStack[baseSparseInputs.length];
-        for (int i = 0; i < baseSparseInputs.length; i++) {
-            var in = baseSparseInputs[i];
-            if (in != null) {
-                scaledSparseInputs[i] = new GenericStack(in.what(), in.amount() * multiplier);
-            }
-        }
-        GenericStack[] scaledSparseOutputs = new GenericStack[baseSparseOutputs.length];
-        for (int i = 0; i < baseSparseOutputs.length; i++) {
-            var out = baseSparseOutputs[i];
-            if (out != null) {
-                scaledSparseOutputs[i] = new GenericStack(out.what(), out.amount() * multiplier);
-            }
-        }
-
-        return new ScaledProcessingPattern(base,
-                base.getDefinition(),
-                scaledSparseInputs,
-                scaledSparseOutputs,
-                scaledInputs,
-                scaledCondensedOutputs);
     }
 }

src/main/resources/extendedae_plus.mixins.json

@@ -42,7 +42,6 @@
     "ae2.autopattern.CraftingTreeNodeMixin",
     "ae2.autopattern.CraftingTreeProcessMixin",
     "ae2.autopattern.PatternProviderLogicContainsRedirectMixin",
-    "ae2.autopattern.adaptation.AdvPatternProviderLogicContainsRedirectMixin",
     "ae2.helpers.PatternProviderLogicAdvancedMixin",
     "ae2.helpers.PatternProviderLogicDoublingMixin",
     "ae2.menu.ContainerPatternEncodingTermMenuMixin",