Python示例，根据页面类型动态选择规则

openclaw openclaw官方 2026-04-09 1

我来介绍一下OpenClaw的动态适配机制。

Python示例，根据页面类型动态选择规则-第1张图片-OpenClaw开源下载|官方OpenClaw下载

OpenClaw动态适配概述

OpenClaw是一个基于规则的信息抽取框架,其动态适配主要体现在以下几个方面：

规则模板系统

// 定义动态规则模板
rule dynamic_extraction {
    pattern: "{{field_name}} is {{value_pattern}}"
    extract: {
        field: {{field_name}},
        value: {{value}}
    }
    constraints: [
        {{field_name}} in ["price", "quantity", "date"]
    ]
}

上下文感知规则

    rule_sets = {
        "ecommerce": ["price_rule", "product_rule"],
        "news": ["title_rule", "date_rule", "content_rule"],
        "forum": ["post_rule", "user_rule"]
    }
    # 根据页面特征动态组合规则
    selected_rules = []
    if "product" in url_pattern:
        selected_rules.extend(rule_sets["ecommerce"])
    if "news" in url_pattern:
        selected_rules.extend(rule_sets["news"])
    return selected_rules

自适应特征提取

// 动态识别页面结构特征
public class DynamicFeatureExtractor {
    public Map<String, Object> extractFeatures(HTMLDocument doc) {
        Map<String, Object> features = new HashMap<>();
        // 1. DOM结构特征
        features.put("table_count", doc.select("table").size());
        features.put("list_count", doc.select("ul, ol").size());
        // 2. 内容特征
        features.put("text_density", calculateTextDensity(doc));
        features.put("link_density", calculateLinkDensity(doc));
        // 3. 语义特征
        features.put("contains_price", containsPricePattern(doc.text()));
        features.put("contains_date", containsDatePattern(doc.text()));
        return features;
    }
}

规则权重动态调整

class AdaptiveRuleEngine:
    def __init__(self):
        self.rule_weights = {}
        self.rule_performance = {}
    def adjust_weights(self, extraction_result, ground_truth):
        """根据提取结果动态调整规则权重"""
        for rule_id, result in extraction_result.items():
            precision = self.calculate_precision(result, ground_truth)
            recall = self.calculate_recall(result, ground_truth)
            # 动态调整权重
            f1_score = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
            self.rule_weights[rule_id] = self.adaptive_update(
                self.rule_weights.get(rule_id, 1.0),
                f1_score
            )
    def adaptive_update(self, old_weight, performance):
        # 根据性能动态调整
        learning_rate = 0.1
        return old_weight + learning_rate * (performance - 0.8)

多策略融合

public class DynamicExtractionStrategy {
    // 1. 基于规则的抽取
    public ExtractionResult ruleBasedExtract(Document doc, RuleSet rules) {
        // 应用预定义规则
    }
    // 2. 基于机器学习的抽取
    public ExtractionResult mlBasedExtract(Document doc, MLModel model) {
        // 使用训练好的模型
    }
    // 3. 混合策略
    public ExtractionResult hybridExtract(Document doc) {
        // 动态选择策略
        if (isStructuredPage(doc)) {
            return ruleBasedExtract(doc, getStructureRules());
        } else if (hasSufficientTrainingData(doc)) {
            return mlBasedExtract(doc, getTrainedModel());
        } else {
            return fallbackExtract(doc);
        }
    }
}

在线学习机制

class OnlineLearningAdapter:
    def __init__(self):
        self.feedback_buffer = []
        self.rule_patterns = {}
    def receive_feedback(self, extraction_result, user_feedback):
        """接收用户反馈进行在线学习"""
        self.feedback_buffer.append({
            'result': extraction_result,
            'feedback': user_feedback
        })
        if len(self.feedback_buffer) >= BATCH_SIZE:
            self.update_rules_from_feedback()
    def update_rules_from_feedback(self):
        """从反馈中学习新规则"""
        positive_patterns = self.extract_patterns_from_feedback(positive=True)
        negative_patterns = self.extract_patterns_from_feedback(positive=False)
        # 更新规则库
        self.merge_patterns_to_rules(positive_patterns)
        self.add_constraints_from_negatives(negative_patterns)

配置化的适配参数

# dynamic_config.yaml
adaptive_settings:
  threshold_adjustment:
    enabled: true
    min_threshold: 0.3
    max_threshold: 0.9
    adjustment_step: 0.05
  rule_selection:
    strategy: "performance_based"  # options: fixed, round_robin, performance_based
    performance_window: 100
    decay_factor: 0.95
  fallback_mechanism:
    enabled: true
    fallback_order: ["xpath", "css", "regex", "ml"]
    timeout_ms: 5000

实时监控与调优

public class DynamicMonitor {
    private MetricsCollector metrics;
    private AlertManager alerts;
    public void monitorAndAdjust() {
        // 监控提取质量
        double current_precision = metrics.getPrecision();
        double current_recall = metrics.getRecall();
        // 自动调整参数
        if (current_precision < PRECISION_THRESHOLD) {
            adjustThresholds("increase");
        }
        if (current_recall < RECALL_THRESHOLD) {
            adjustThresholds("decrease");
        }
        // 检测模式漂移
        if (detectPatternDrift()) {
            triggerRetraining();
        }
    }
}

使用示例

from openclaw import OpenClaw, DynamicAdapter
# 创建动态适配的抽取器
claw = OpenClaw(
    adapter=DynamicAdapter(
        mode="adaptive",  # 自适应模式
        learning_rate=0.1,
        feedback_enabled=True
    )
)
# 配置动态规则
claw.configure({
    "dynamic_fields": {
        "price": {
            "patterns": ["\\$\\d+(\\.\\d{2})?", "\\d+(\\.\\d{2})?\\s*(USD|EUR)"],
            "confidence_threshold": 0.7,
            "adaptive": True
        }
    }
})
# 执行抽取（会自动适应不同页面结构）
results = claw.extract_from_urls(url_list)