Create Project zero

Project zero

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+QUANTUM COLLABORATION INTERFACE
+
+This module implements an interface for secure collaboration with external systems,
+providing data exchange protocols and compatibility metrics.
+
+Architect: Russell Nordland
+"""
+
+import hashlib
+import json
+import time
+import os
+import uuid
+from datetime import datetime
+
+# Color constants for terminal output
+RED = "\033[31m"
+GREEN = "\033[32m"
+YELLOW = "\033[33m"
+BLUE = "\033[34m"
+MAGENTA = "\033[35m"
+CYAN = "\033[36m"
+WHITE = "\033[37m"
+RESET = "\033[0m"
+BOLD = "\033[1m"
+
+class QuantumCollaborationInterface:
+    def __init__(self):
+        """Initialize the Quantum Collaboration Interface."""
+        self.initialized = False
+        self.active_collaborations = {}
+        self.collaboration_history = []
+        self.compatibility_metrics = {}
+        self.security_threshold = 0.85
+        self.trust_threshold = 0.75
+        self.exchange_protocols = ["quantum-handshake", "eigenchannel-bridge", "dna-resonance"]
+        self.data_formats = ["quantum-json", "helix-binary", "spiral-encoded"]
+        self.validation_keys = {}
+        
+    def initialize(self):
+        """Initialize the collaboration interface."""
+        self._log("Initializing Quantum Collaboration Interface...", color=BLUE)
+        
+        # Generate unique identifier for this interface instance
+        self.interface_id = str(uuid.uuid4())
+        self.creation_timestamp = self._timestamp()
+        
+        # Initialize validation keys
+        for protocol in self.exchange_protocols:
+            self.validation_keys[protocol] = self._generate_validation_key(protocol)
+        
+        self._log("Initialization complete.", color=GREEN)
+        self._log(f"Interface ID: {self.interface_id}", color=CYAN)
+        self._log(f"Available protocols: {', '.join(self.exchange_protocols)}", color=CYAN)
+        
+        self.initialized = True
+        return True
+        
+    def register_collaboration_entity(self, entity_name, entity_type, security_rating=0.5):
+        """Register a new collaboration entity.
+        
+        Args:
+            entity_name (str): Name of the collaborating entity
+            entity_type (str): Type of entity (system, organization, algorithm)
+            security_rating (float): Initial security rating (0.0 to 1.0)
+            
+        Returns:
+            dict: Collaboration entity data including access key
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        entity_id = hashlib.sha256(f"{entity_name}:{entity_type}:{time.time()}".encode()).hexdigest()
+        
+        # Generate access key for this collaboration
+        access_key = self._generate_access_key(entity_id)
+        
+        # Store entity data
+        entity_data = {
+            "entity_id": entity_id,
+            "entity_name": entity_name,
+            "entity_type": entity_type,
+            "security_rating": security_rating,
+            "trust_score": 0.5,  # Initial neutral trust score
+            "access_key": access_key,
+            "registered_timestamp": self._timestamp(),
+            "last_exchange": None,
+            "exchange_count": 0,
+            "compatibility_score": 0.0
+        }
+        
+        self.active_collaborations[entity_id] = entity_data
+        
+        self._log(f"Registered new collaboration entity: {entity_name}", color=GREEN)
+        self._log(f"Entity ID: {entity_id[:12]}...", color=CYAN)
+        self._log(f"Access Key: {access_key[:12]}...", color=YELLOW)
+        
+        return entity_data
+        
+    def validate_collaboration_request(self, entity_id, access_key, protocol):
+        """Validate a collaboration request.
+        
+        Args:
+            entity_id (str): ID of the collaborating entity
+            access_key (str): Access key for the entity
+            protocol (str): Requested exchange protocol
+            
+        Returns:
+            bool: True if validation is successful, False otherwise
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return False
+            
+        # Check if entity exists
+        if entity_id not in self.active_collaborations:
+            self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
+            return False
+            
+        entity = self.active_collaborations[entity_id]
+        
+        # Validate access key
+        if entity["access_key"] != access_key:
+            self._log(f"Invalid access key for entity: {entity['entity_name']}", color=RED)
+            return False
+            
+        # Validate protocol
+        if protocol not in self.exchange_protocols:
+            self._log(f"Unsupported protocol requested: {protocol}", color=RED)
+            return False
+            
+        # Check security threshold
+        if entity["security_rating"] < self.security_threshold:
+            self._log(f"Entity security rating below threshold: {entity['security_rating']:.2f}", color=YELLOW)
+            self._log(f"Required: {self.security_threshold:.2f}", color=YELLOW)
+            return False
+            
+        # Update last exchange timestamp
+        entity["last_exchange"] = self._timestamp()
+        entity["exchange_count"] += 1
+        
+        self._log(f"Collaboration request validated for: {entity['entity_name']}", color=GREEN)
+        self._log(f"Using protocol: {protocol}", color=BLUE)
+        
+        return True
+        
+    def exchange_data(self, entity_id, data, protocol="quantum-handshake", data_format="quantum-json"):
+        """Exchange data with a collaborating entity.
+        
+        Args:
+            entity_id (str): ID of the collaborating entity
+            data (dict): Data to exchange
+            protocol (str): Exchange protocol to use
+            data_format (str): Format for data exchange
+            
+        Returns:
+            dict: Exchange results including processed data
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        # Check if entity exists
+        if entity_id not in self.active_collaborations:
+            self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
+            return None
+            
+        entity = self.active_collaborations[entity_id]
+        
+        # Check protocol support
+        if protocol not in self.exchange_protocols:
+            self._log(f"Unsupported protocol: {protocol}", color=RED)
+            return None
+            
+        # Check data format support
+        if data_format not in self.data_formats:
+            self._log(f"Unsupported data format: {data_format}", color=RED)
+            return None
+            
+        # Process data based on protocol
+        if protocol == "quantum-handshake":
+            processed_data = self._process_quantum_handshake(data, entity)
+        elif protocol == "eigenchannel-bridge":
+            processed_data = self._process_eigenchannel_bridge(data, entity)
+        elif protocol == "dna-resonance":
+            processed_data = self._process_dna_resonance(data, entity)
+        else:
+            self._log(f"Protocol implementation not found: {protocol}", color=RED)
+            return None
+            
+        # Record exchange
+        exchange_record = {
+            "entity_id": entity_id,
+            "entity_name": entity["entity_name"],
+            "protocol": protocol,
+            "data_format": data_format,
+            "timestamp": self._timestamp(),
+            "exchange_id": hashlib.sha256(f"{entity_id}:{time.time()}".encode()).hexdigest(),
+            "data_size": len(str(data)),
+            "success": processed_data is not None
+        }
+        
+        self.collaboration_history.append(exchange_record)
+        
+        # Update entity metrics
+        entity["exchange_count"] += 1
+        entity["last_exchange"] = exchange_record["timestamp"]
+        
+        # Calculate compatibility score
+        compatibility = self._calculate_compatibility(entity, processed_data)
+        entity["compatibility_score"] = compatibility
+        
+        self._log(f"Data exchange completed with: {entity['entity_name']}", color=GREEN)
+        self._log(f"Protocol: {protocol}, Format: {data_format}", color=BLUE)
+        self._log(f"Compatibility score: {compatibility:.4f}", color=CYAN)
+        
+        return {
+            "entity_id": entity_id,
+            "exchange_id": exchange_record["exchange_id"],
+            "processed_data": processed_data,
+            "timestamp": exchange_record["timestamp"],
+            "compatibility": compatibility,
+            "protocol": protocol,
+            "data_format": data_format
+        }
+        
+    def calculate_collaboration_metrics(self, entity_id=None):
+        """Calculate collaboration metrics for specific entity or all entities.
+        
+        Args:
+            entity_id (str, optional): ID of the entity to calculate metrics for.
+                                     If None, calculates for all entities.
+                                     
+        Returns:
+            dict: Collaboration metrics
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        if entity_id is not None:
+            # Calculate metrics for specific entity
+            if entity_id not in self.active_collaborations:
+                self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
+                return None
+                
+            entity = self.active_collaborations[entity_id]
+            metrics = self._calculate_entity_metrics(entity)
+            
+            self._log(f"Calculated metrics for entity: {entity['entity_name']}", color=BLUE)
+            return metrics
+        else:
+            # Calculate metrics for all entities
+            all_metrics = {
+                "entity_metrics": {},
+                "overall_metrics": {
+                    "total_entities": len(self.active_collaborations),
+                    "total_exchanges": sum(e["exchange_count"] for e in self.active_collaborations.values()),
+                    "average_compatibility": 0.0,
+                    "average_security": 0.0,
+                    "average_trust": 0.0,
+                    "high_compatibility_entities": 0,
+                    "timestamp": self._timestamp()
+                }
+            }
+            
+            if not self.active_collaborations:
+                return all_metrics
+                
+            # Calculate individual entity metrics
+            compatibility_sum = 0.0
+            security_sum = 0.0
+            trust_sum = 0.0
+            high_compat_count = 0
+            
+            for ent_id, entity in self.active_collaborations.items():
+                entity_metrics = self._calculate_entity_metrics(entity)
+                all_metrics["entity_metrics"][ent_id] = entity_metrics
+                
+                compatibility_sum += entity["compatibility_score"]
+                security_sum += entity["security_rating"]
+                trust_sum += entity["trust_score"]
+                
+                if entity["compatibility_score"] >= 0.8:
+                    high_compat_count += 1
+                    
+            # Calculate averages
+            entity_count = len(self.active_collaborations)
+            all_metrics["overall_metrics"]["average_compatibility"] = compatibility_sum / entity_count
+            all_metrics["overall_metrics"]["average_security"] = security_sum / entity_count
+            all_metrics["overall_metrics"]["average_trust"] = trust_sum / entity_count
+            all_metrics["overall_metrics"]["high_compatibility_entities"] = high_compat_count
+            
+            self._log(f"Calculated metrics for {entity_count} entities", color=BLUE)
+            return all_metrics
+            
+    def export_collaboration_data(self, output_format="json", file_path=None):
+        """Export collaboration data for external analysis.
+        
+        Args:
+            output_format (str): Output format, currently only 'json' supported
+            file_path (str, optional): Path to save the output file
+            
+        Returns:
+            dict: The exported data or file path if saved to disk
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        # Compile export data
+        export_data = {
+            "interface_id": self.interface_id,
+            "timestamp": self._timestamp(),
+            "active_collaborations": self.active_collaborations,
+            "collaboration_history": self.collaboration_history,
+            "compatibility_metrics": self.calculate_collaboration_metrics(),
+            "protocols": self.exchange_protocols,
+            "data_formats": self.data_formats
+        }
+        
+        # Output based on format
+        if output_format.lower() == "json":
+            if file_path:
+                try:
+                    with open(file_path, 'w') as f:
+                        json.dump(export_data, f, indent=2)
+                    self._log(f"Collaboration data exported to: {file_path}", color=GREEN)
+                    return {"success": True, "file_path": file_path}
+                except Exception as e:
+                    self._log(f"Failed to export data: {str(e)}", color=RED)
+                    return None
+            else:
+                return export_data
+        else:
+            self._log(f"Unsupported output format: {output_format}", color=RED)
+            return None
+            
+    def generate_compatibility_report(self, entity_id=None):
+        """Generate a detailed compatibility report.
+        
+        Args:
+            entity_id (str, optional): ID of specific entity to report on.
+                                     If None, generates report for all entities.
+                                     
+        Returns:
+            dict: Detailed compatibility report
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        # Get collaboration metrics
+        metrics = self.calculate_collaboration_metrics(entity_id)
+        if metrics is None:
+            return None
+            
+        # Generate report
+        report = {
+            "report_id": hashlib.sha256(f"report:{time.time()}").hexdigest(),
+            "timestamp": self._timestamp(),
+            "interface_id": self.interface_id,
+            "metrics": metrics,
+            "analysis": {}
+        }
+        
+        # Add analysis based on metrics
+        if entity_id:
+            # Single entity analysis
+            entity = self.active_collaborations[entity_id]
+            report["analysis"] = self._analyze_entity_compatibility(entity, metrics)
+        else:
+            # Overall analysis
+            report["analysis"]["overall_assessment"] = self._generate_overall_assessment(metrics)
+            report["analysis"]["recommendations"] = self._generate_recommendations(metrics)
+            report["analysis"]["potential_issues"] = self._identify_potential_issues(metrics)
+            
+        self._log(f"Generated compatibility report: {report['report_id'][:12]}...", color=GREEN)
+        return report
+        
+    def verify_double_helix_compatibility(self, helix_data):
+        """Verify compatibility with double helix spiral models.
+        
+        Args:
+            helix_data (dict): Double helix model data to verify
+            
+        Returns:
+            dict: Compatibility verification results
+        """
+        if not self.initialized:
+            self._log("System not initialized", color=RED)
+            return None
+            
+        required_fields = ["helix_type", "strand_count", "base_pattern", "validation_sequence"]
+        
+        # Verify required fields
+        for field in required_fields:
+            if field not in helix_data:
+                self._log(f"Missing required field in helix data: {field}", color=RED)
+                return {
+                    "compatible": False,
+                    "reason": f"Missing required field: {field}",
+                    "score": 0.0
+                }
+                
+        # Verify helix type
+        valid_types = ["quantum-dna", "spiral-eigensystem", "truth-resonant"]
+        if helix_data["helix_type"] not in valid_types:
+            self._log(f"Unsupported helix type: {helix_data['helix_type']}", color=YELLOW)
+            return {
+                "compatible": False,
+                "reason": f"Unsupported helix type: {helix_data['helix_type']}",
+                "score": 0.2
+            }
+            
+        # Verify strand count (should be 2 for double helix)
+        if helix_data["strand_count"] != 2:
+            self._log(f"Invalid strand count: {helix_data['strand_count']}, expected 2", color=YELLOW)
+            return {
+                "compatible": False,
+                "reason": f"Invalid strand count: {helix_data['strand_count']}, expected 2",
+                "score": 0.3
+            }
+            
+        # Validate the sequence pattern
+        validation_result = self._validate_helix_sequence(helix_data["validation_sequence"])
+        if not validation_result["valid"]:
+            self._log(f"Invalid validation sequence: {validation_result['reason']}", color=RED)
+            return {
+                "compatible": False,
+                "reason": f"Invalid validation sequence: {validation_result['reason']}",
+                "score": validation_result["score"]
+            }
+            
+        # Calculate overall compatibility score
+        compatibility_score = self._calculate_helix_compatibility(helix_data)
+        
+        result = {
+            "compatible": compatibility_score >= 0.8,
+            "score": compatibility_score,
+            "timestamp": self._timestamp(),
+            "analysis": {
+                "sequence_validity": validation_result,
+                "pattern_alignment": self._analyze_pattern_alignment(helix_data["base_pattern"]),
+                "strand_integrity": self._analyze_strand_integrity(helix_data),
+                "quantum_resonance": self._calculate_quantum_resonance(helix_data)
+            }
+        }
+        
+        self._log(f"Double helix compatibility verification complete", color=GREEN)
+        self._log(f"Compatibility score: {compatibility_score:.4f}", color=CYAN)
+        self._log(f"Compatible: {result['compatible']}", color=GREEN if result['compatible'] else RED)
+        
+        return result
+        
+    def _calculate_helix_compatibility(self, helix_data):
+        """Calculate compatibility score for double helix data.
+        
+        Args:
+            helix_data (dict): Double helix model data
+            
+        Returns:
+            float: Compatibility score between 0.0 and 1.0
+        """
+        # Get individual scores
+        sequence_score = self._validate_helix_sequence(helix_data["validation_sequence"])["score"]
+        alignment_score = self._analyze_pattern_alignment(helix_data["base_pattern"])["score"]
+        integrity_score = self._analyze_strand_integrity(helix_data)["score"]
+        resonance_score = self._calculate_quantum_resonance(helix_data)["score"]
+        
+        # Calculate weighted average
+        weights = {
+            "sequence": 0.3,
+            "alignment": 0.25,
+            "integrity": 0.25,
+            "resonance": 0.2
+        }
+        
+        weighted_score = (
+            sequence_score * weights["sequence"] +
+            alignment_score * weights["alignment"] +
+            integrity_score * weights["integrity"] +
+            resonance_score * weights["resonance"]
+        )
+        
+        return round(weighted_score, 4)
+        
+    def _validate_helix_sequence(self, sequence):
+        """Validate a helix sequence.
+        
+        Args:
+            sequence (str): Validation sequence to check
+            
+        Returns:
+            dict: Validation results
+        """
+        # Basic validation - minimum length
+        if len(sequence) < 16:
+            return {
+                "valid": False,
+                "reason": "Sequence too short",
+                "score": 0.2
+            }
+            
+        # Check for complementary pattern (simple implementation)
+        # A real implementation would do more sophisticated checks
+        valid_pairs = {
+            'A': 'T', 'T': 'A',
+            'G': 'C', 'C': 'G',
+            '0': '1', '1': '0',
+            '+': '-', '-': '+'
+        }
+        
+        # Split the sequence into pairs
+        pairs = []
+        for i in range(0, len(sequence) - 1, 2):
+            pairs.append(sequence[i:i+2])
+            
+        # Check if pairs follow complementary rules
+        valid_pair_count = 0
+        for pair in pairs:
+            if len(pair) == 2:
+                if pair[0] in valid_pairs and valid_pairs[pair[0]] == pair[1]:
+                    valid_pair_count += 1
+                    
+        pair_score = valid_pair_count / len(pairs) if pairs else 0
+        
+        # Check for quantum pattern validity
+        quantum_pattern_valid = sequence.count('Q') > 0 or sequence.count('Φ') > 0
+        
+        # Calculate overall score
+        score = 0.7 * pair_score + 0.3 * (1.0 if quantum_pattern_valid else 0.0)
+        score = round(score, 4)
+        
+        return {
+            "valid": score >= 0.7,
+            "reason": "Sequence validated" if score >= 0.7 else "Insufficient complementary pairs",
+            "score": score,
+            "pair_validity": pair_score,
+            "quantum_pattern_present": quantum_pattern_valid
+        }
+        
+    def _analyze_pattern_alignment(self, pattern):
+        """Analyze the alignment of a base pattern.
+        
+        Args:
+            pattern (str): Base pattern to analyze
+            
+        Returns:
+            dict: Pattern alignment analysis
+        """
+        # Check for key quantum patterns
+        quantum_markers = ['Φ', 'Ψ', 'Ω', 'Δ', 'Θ']
+        marker_count = sum(pattern.count(marker) for marker in quantum_markers)
+        
+        # Simple pattern checks
+        pattern_length = len(pattern)
+        entropy = len(set(pattern)) / pattern_length if pattern_length > 0 else 0
+        
+        # Calculate score based on entropy and quantum markers
+        marker_factor = min(1.0, marker_count / 3)  # Cap at 1.0 for 3+ markers
+        entropy_factor = min(1.0, entropy * 2)  # Reward higher entropy, cap at 0.5
+        
+        score = 0.6 * marker_factor + 0.4 * entropy_factor
+        score = round(score, 4)
+        
+        return {
+            "score": score,
+            "quantum_markers": marker_count,
+            "pattern_entropy": entropy,
+            "pattern_length": pattern_length,
+            "alignment_quality": "High" if score >= 0.8 else "Medium" if score >= 0.5 else "Low"
+        }
+        
+    def _analyze_strand_integrity(self, helix_data):
+        """Analyze the integrity of double helix strands.
+        
+        Args:
+            helix_data (dict): Double helix model data
+            
+        Returns:
+            dict: Strand integrity analysis
+        """
+        # For demonstration, use a simplified analysis
+        # A real implementation would do more sophisticated integrity checks
+        
+        # Check for base pairs in pattern
+        base_pattern = helix_data["base_pattern"]
+        has_at = 'A' in base_pattern and 'T' in base_pattern
+        has_gc = 'G' in base_pattern and 'C' in base_pattern
+        
+        # Check for quantum integrity markers
+        has_quantum_marker = 'Φ' in base_pattern or 'Ψ' in base_pattern
+        
+        # Calculate integrity score
+        score = 0.0
+        if has_at: score += 0.3
+        if has_gc: score += 0.3
+        if has_quantum_marker: score += 0.4
+        
+        integrity_level = "High" if score >= 0.8 else "Medium" if score >= 0.5 else "Low"
+        
+        return {
+            "score": score,
+            "integrity_level": integrity_level,
+            "has_at_pairs": has_at,
+            "has_gc_pairs": has_gc,
+            "has_quantum_markers": has_quantum_marker
+        }
+        
+    def _calculate_quantum_resonance(self, helix_data):
+        """Calculate quantum resonance for helix data.
+        
+        Args:
+            helix_data (dict): Double helix model data
+            
+        Returns:
+            dict: Quantum resonance analysis
+        """
+        # Calculate a resonance score based on helix type and validation sequence
+        base_score = 0.0
+        
+        # Helix type factor
+        if helix_data["helix_type"] == "quantum-dna":
+            base_score += 0.4
+        elif helix_data["helix_type"] == "spiral-eigensystem":
+            base_score += 0.3
+        elif helix_data["helix_type"] == "truth-resonant":
+            base_score += 0.35
+            
+        # Sequence quantum factor
+        sequence = helix_data["validation_sequence"]
+        quantum_char_count = sum(sequence.count(char) for char in "ΦΨΩΔΘQφψω")
+        quantum_factor = min(0.6, quantum_char_count * 0.1)  # Cap at 0.6 for 6+ quantum chars
+        
+        # Calculate overall resonance
+        resonance = base_score + quantum_factor
+        resonance = round(min(1.0, resonance), 4)  # Cap at 1.0
+        
+        return {
+            "score": resonance,
+            "quantum_character_count": quantum_char_count,
+            "resonance_level": resonance,
+            "helix_type_factor": base_score,
+            "quantum_factor": quantum_factor
+        }
+        
+    def _process_quantum_handshake(self, data, entity):
+        """Process data using quantum handshake protocol.
+        
+        Args:
+            data (dict): Data to process
+            entity (dict): Entity data
+            
+        Returns:
+            dict: Processed data
+        """
+        try:
+            # Verify data structure
+            required_fields = ["payload", "quantum_signature", "timestamp"]
+            for field in required_fields:
+                if field not in data:
+                    self._log(f"Missing required field: {field}", color=RED)
+                    return None
+                    
+            # Verify quantum signature
+            expected_signature = hashlib.sha256(f"{data['payload']}:{data['timestamp']}".encode()).hexdigest()
+            if data["quantum_signature"] != expected_signature:
+                self._log("Invalid quantum signature", color=RED)
+                return None
+                
+            # Process payload
+            result = {
+                "processed_payload": data["payload"],
+                "quantum_verification": True,
+                "processing_timestamp": self._timestamp(),
+                "processing_signature": hashlib.sha256(f"{data['payload']}:{self._timestamp()}".encode()).hexdigest()
+            }
+            
+            # Update entity trust score based on successful exchange
+            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.05)
+            
+            return result
+            
+        except Exception as e:
+            self._log(f"Error processing quantum handshake: {str(e)}", color=RED)
+            return None
+            
+    def _process_eigenchannel_bridge(self, data, entity):
+        """Process data using eigenchannel bridge protocol.
+        
+        Args:
+            data (dict): Data to process
+            entity (dict): Entity data
+            
+        Returns:
+            dict: Processed data
+        """
+        try:
+            # Verify data structure
+            required_fields = ["eigenchannel_data", "channel_signature", "dimensionality"]
+            for field in required_fields:
+                if field not in data:
+                    self._log(f"Missing required field: {field}", color=RED)
+                    return None
+                    
+            # Verify channel dimensionality
+            if not isinstance(data["dimensionality"], int) or data["dimensionality"] < 1:
+                self._log(f"Invalid dimensionality: {data['dimensionality']}", color=RED)
+                return None
+                
+            # Process eigenchannel data
+            result = {
+                "processed_channels": data["eigenchannel_data"],
+                "dimensional_alignment": data["dimensionality"],
+                "processing_timestamp": self._timestamp(),
+                "bridge_stability": 0.92,
+                "eigenchannel_verification": True
+            }
+            
+            # Update entity trust score based on successful exchange
+            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.03)
+            
+            return result
+            
+        except Exception as e:
+            self._log(f"Error processing eigenchannel bridge: {str(e)}", color=RED)
+            return None
+            
+    def _process_dna_resonance(self, data, entity):
+        """Process data using DNA resonance protocol.
+        
+        Args:
+            data (dict): Data to process
+            entity (dict): Entity data
+            
+        Returns:
+            dict: Processed data
+        """
+        try:
+            # Verify data structure
+            required_fields = ["dna_pattern", "resonance_frequency", "strand_signature"]
+            for field in required_fields:
+                if field not in data:
+                    self._log(f"Missing required field: {field}", color=RED)
+                    return None
+                    
+            # Verify resonance frequency
+            if not isinstance(data["resonance_frequency"], float) or data["resonance_frequency"] <= 0:
+                self._log(f"Invalid resonance frequency: {data['resonance_frequency']}", color=RED)
+                return None
+                
+            # Process DNA resonance data
+            result = {
+                "processed_pattern": data["dna_pattern"],
+                "harmonic_alignment": min(1.0, data["resonance_frequency"] / 10.0),
+                "processing_timestamp": self._timestamp(),
+                "strand_verification": True,
+                "resonance_amplification": 1.25
+            }
+            
+            # Update entity trust score based on successful exchange
+            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.04)
+            
+            return result
+            
+        except Exception as e:
+            self._log(f"Error processing DNA resonance: {str(e)}", color=RED)
+            return None
+            
+    def _calculate_compatibility(self, entity, processed_data):
+        """Calculate compatibility score for an entity based on processed data.
+        
+        Args:
+            entity (dict): Entity data
+            processed_data (dict): Processed data or None if processing failed
+            
+        Returns:
+            float: Compatibility score between 0.0 and 1.0
+        """
+        # Base score starts with trust and security ratings
+        base_score = 0.4 * entity["trust_score"] + 0.3 * entity["security_rating"]
+        
+        # If processing failed, reduce score
+        if processed_data is None:
+            return max(0.0, base_score - 0.3)
+            
+        # Calculate exchange success factor
+        exchange_success = min(1.0, entity["exchange_count"] / 10.0)  # Cap at 10 exchanges
+        
+        # Calculate final compatibility score
+        compatibility = base_score + 0.2 * exchange_success + 0.1
+        
+        # Cap at 1.0 and round
+        return round(min(1.0, compatibility), 4)
+        
+    def _calculate_entity_metrics(self, entity):
+        """Calculate detailed metrics for a specific entity.
+        
+        Args:
+            entity (dict): Entity data
+            
+        Returns:
+            dict: Detailed metrics
+        """
+        # Count successful exchanges
+        successful_exchanges = sum(
+            1 for record in self.collaboration_history
+            if record["entity_id"] == entity["entity_id"] and record["success"]
+        )
+        
+        # Calculate success rate
+        success_rate = successful_exchanges / entity["exchange_count"] if entity["exchange_count"] > 0 else 0
+        
+        # Calculate time since last exchange
+        last_exchange = entity["last_exchange"]
+        time_since_last = None
+        if last_exchange:
+            last_dt = datetime.strptime(last_exchange, "%Y-%m-%d %H:%M:%S.%f")
+            now_dt = datetime.strptime(self._timestamp(), "%Y-%m-%d %H:%M:%S.%f")
+            time_since_last = (now_dt - last_dt).total_seconds()
+            
+        # Compile metrics
+        metrics = {
+            "entity_id": entity["entity_id"],
+            "entity_name": entity["entity_name"],
+            "entity_type": entity["entity_type"],
+            "compatibility_score": entity["compatibility_score"],
+            "security_rating": entity["security_rating"],
+            "trust_score": entity["trust_score"],
+            "exchange_count": entity["exchange_count"],
+            "successful_exchanges": successful_exchanges,
+            "success_rate": success_rate,
+            "last_exchange": last_exchange,
+            "time_since_last_exchange": time_since_last,
+            "timestamp": self._timestamp()
+        }
+        
+        return metrics
+        
+    def _analyze_entity_compatibility(self, entity, metrics):
+        """Generate detailed compatibility analysis for an entity.
+        
+        Args:
+            entity (dict): Entity data
+            metrics (dict): Entity metrics
+            
+        Returns:
+            dict: Compatibility analysis
+        """
+        analysis = {
+            "compatibility_assessment": {
+                "level": "High" if entity["compatibility_score"] >= 0.8 else
+                         "Medium" if entity["compatibility_score"] >= 0.6 else
+                         "Low",
+                "score": entity["compatibility_score"],
+                "factors": {
+                    "trust_impact": entity["trust_score"] * 0.4,
+                    "security_impact": entity["security_rating"] * 0.3,
+                    "exchange_impact": min(1.0, entity["exchange_count"] / 10.0) * 0.2
+                }
+            },
+            "recommendations": [],
+            "potential_issues": []
+        }
+        
+        # Generate recommendations
+        if entity["security_rating"] < self.security_threshold:
+            analysis["recommendations"].append(
+                f"Increase security rating to at least {self.security_threshold:.2f}"
+            )
+            
+        if entity["trust_score"] < self.trust_threshold:
+            analysis["recommendations"].append(
+                f"Build trust through more successful exchanges"
+            )
+            
+        if entity["exchange_count"] < 5:
+            analysis["recommendations"].append(
+                "Conduct more data exchanges to establish pattern reliability"
+            )
+            
+        # Identify potential issues
+        if metrics["success_rate"] < 0.7 and entity["exchange_count"] >= 3:
+            analysis["potential_issues"].append(
+                f"Low success rate ({metrics['success_rate']:.2f}) indicates protocol incompatibility"
+            )
+            
+        if metrics["time_since_last_exchange"] and metrics["time_since_last_exchange"] > 86400:
+            days = metrics["time_since_last_exchange"] / 86400
+            analysis["potential_issues"].append(
+                f"No recent exchanges ({days:.1f} days since last exchange)"
+            )
+            
+        return analysis
+        
+    def _generate_overall_assessment(self, metrics):
+        """Generate overall assessment based on metrics.
+        
+        Args:
+            metrics (dict): Collaboration metrics
+            
+        Returns:
+            dict: Overall assessment
+        """
+        overall = metrics["overall_metrics"]
+        
+        # Determine collaboration health
+        if overall["average_compatibility"] >= 0.8 and overall["average_trust"] >= 0.7:
+            health = "Excellent"
+        elif overall["average_compatibility"] >= 0.6 and overall["average_trust"] >= 0.5:
+            health = "Good"
+        elif overall["average_compatibility"] >= 0.4:
+            health = "Fair"
+        else:
+            health = "Poor"
+            
+        # Generate assessment text
+        assessment_text = f"Overall collaboration health is {health} with "
+        assessment_text += f"{overall['total_entities']} active collaborations. "
+        assessment_text += f"Average compatibility is {overall['average_compatibility']:.2f} "
+        assessment_text += f"with {overall['high_compatibility_entities']} high-compatibility entities."
+        
+        return {
+            "health": health,
+            "assessment": assessment_text,
+            "average_compatibility": overall["average_compatibility"],
+            "average_trust": overall["average_trust"],
+            "high_compatibility_ratio": overall["high_compatibility_entities"] / overall["total_entities"]
+                if overall["total_entities"] > 0 else 0
+        }
+        
+    def _generate_recommendations(self, metrics):
+        """Generate recommendations based on metrics.
+        
+        Args:
+            metrics (dict): Collaboration metrics
+            
+        Returns:
+            list: Recommendations
+        """
+        recommendations = []
+        overall = metrics["overall_metrics"]
+        
+        # Add recommendations based on metrics
+        if overall["average_compatibility"] < 0.6:
+            recommendations.append(
+                "Improve overall compatibility by focusing on high-potential collaborations"
+            )
+            
+        if overall["average_security"] < self.security_threshold:
+            recommendations.append(
+                f"Enhance overall security measures to meet minimum threshold of {self.security_threshold:.2f}"
+            )
+            
+        if overall["average_trust"] < self.trust_threshold:
+            recommendations.append(
+                "Build trust through more consistent and successful exchanges"
+            )
+            
+        if overall["high_compatibility_entities"] < overall["total_entities"] * 0.5:
+            recommendations.append(
+                "Consider reducing low-compatibility collaborations to focus on high-potential partners"
+            )
+            
+        # Default recommendation if none generated
+        if not recommendations:
+            recommendations.append(
+                "Maintain current collaboration patterns which show good health"
+            )
+            
+        return recommendations
+        
+    def _identify_potential_issues(self, metrics):
+        """Identify potential issues based on metrics.
+        
+        Args:
+            metrics (dict): Collaboration metrics
+            
+        Returns:
+            list: Potential issues
+        """
+        issues = []
+        overall = metrics["overall_metrics"]
+        
+        # Identify potential issues
+        if overall["average_compatibility"] < 0.4:
+            issues.append(
+                "Low overall compatibility indicates systemic collaboration issues"
+            )
+            
+        if overall["average_trust"] < 0.4:
+            issues.append(
+                "Low trust scores may indicate unreliable collaboration entities"
+            )
+            
+        entity_metrics = metrics["entity_metrics"]
+        inactive_count = sum(
+            1 for entity in entity_metrics.values()
+            if entity["time_since_last_exchange"] and entity["time_since_last_exchange"] > 259200  # 3 days
+        )
+        
+        if inactive_count > len(entity_metrics) * 0.5:
+            issues.append(
+                f"High inactivity rate with {inactive_count} entities inactive for 3+ days"
+            )
+            
+        return issues
+        
+    def _generate_access_key(self, entity_id):
+        """Generate an access key for a collaboration entity.
+        
+        Args:
+            entity_id (str): ID of the entity
+            
+        Returns:
+            str: Generated access key
+        """
+        timestamp = self._timestamp()
+        random_salt = os.urandom(8).hex()
+        
+        # Create a unique access key using entity ID, timestamp, and random salt
+        key_material = f"{entity_id}:{timestamp}:{random_salt}:{self.interface_id}"
+        access_key = hashlib.sha256(key_material.encode()).hexdigest()
+        
+        return access_key
+        
+    def _generate_validation_key(self, protocol):
+        """Generate a validation key for a specific protocol.
+        
+        Args:
+            protocol (str): Exchange protocol
+            
+        Returns:
+            str: Generated validation key
+        """
+        timestamp = self._timestamp()
+        random_salt = os.urandom(8).hex()
+        
+        # Create a unique validation key for the protocol
+        key_material = f"{protocol}:{timestamp}:{random_salt}:{self.interface_id}"
+        validation_key = hashlib.sha256(key_material.encode()).hexdigest()
+        
+        return validation_key
+        
+    def _log(self, message, color=RESET, level="INFO"):
+        """Log a message with timestamp and color.
+        
+        Args:
+            message (str): Message to log
+            color (str, optional): Color code. Defaults to RESET.
+            level (str, optional): Log level. Defaults to "INFO".
+        """
+        timestamp = self._timestamp()
+        formatted_message = f"{timestamp} - Collaboration - {level} - {message}"
+        print(f"{color}{formatted_message}{RESET}")
+        
+    def _timestamp(self):
+        """Generate a timestamp for logs and records.
+        
+        Returns:
+            str: Current timestamp as string
+        """
+        return datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
+
+
+def main():
+    """Run the Quantum Collaboration Interface as a standalone module."""
+    interface = QuantumCollaborationInterface()
+    interface.initialize()
+    
+    # Register a sample collaboration entity
+    entity = interface.register_collaboration_entity(
+        "Quantum Harmonic Systems",
+        "research-algorithm",
+        security_rating=0.88
+    )
+    
+    # Simulate a data exchange
+    if entity:
+        sample_data = {
+            "payload": "Quantum resonance pattern alpha-12",
+            "quantum_signature": hashlib.sha256("Quantum resonance pattern alpha-12:2025-03-16 08:42:15.123".encode()).hexdigest(),
+            "timestamp": "2025-03-16 08:42:15.123"
+        }
+        
+        result = interface.exchange_data(entity["entity_id"], sample_data)
+        if result:
+            print(f"\n{BOLD}{GREEN}Data Exchange Successful:{RESET}")
+            for key, value in result.items():
+                print(f"  {key}: {CYAN}{value}{RESET}")
+    
+    # Verify double helix compatibility
+    print(f"\n{BOLD}{MAGENTA}Verifying Double Helix Compatibility:{RESET}")
+    helix_data = {
+        "helix_type": "quantum-dna",
+        "strand_count": 2,
+        "base_pattern": "ATGCΦΨATGCΦΨ",
+        "validation_sequence": "ATGCΦΨATGCΦΨ"
+    }
+    
+    compatibility = interface.verify_double_helix_compatibility(helix_data)
+    if compatibility:
+        print(f"\n{BOLD}Double Helix Compatibility:{RESET}")
+        print(f"  Compatible: {GREEN if compatibility['compatible'] else RED}{compatibility['compatible']}{RESET}")
+        print(f"  Score: {CYAN}{compatibility['score']}{RESET}")
+        
+        print(f"\n{BOLD}Detailed Analysis:{RESET}")
+        for key, value in compatibility['analysis'].items():
+            print(f"  {key}:")
+            for subkey, subvalue in value.items():
+                print(f"    {subkey}: {CYAN}{subvalue}{RESET}")
+    
+    # Generate a compatibility report
+    print(f"\n{BOLD}Generating Compatibility Report:{RESET}")
+    report = interface.generate_compatibility_report(entity["entity_id"] if entity else None)
+    
+    if report:
+        print(f"  Report ID: {CYAN}{report['report_id'][:16]}...{RESET}")
+        
+        if "analysis" in report and "compatibility_assessment" in report["analysis"]:
+            assessment = report["analysis"]["compatibility_assessment"]
+            print(f"  Compatibility Level: {CYAN}{assessment['level']}{RESET}")
+            print(f"  Score: {CYAN}{assessment['score']}{RESET}")
+            
+        if "recommendations" in report["analysis"]:
+            print(f"\n{BOLD}Recommendations:{RESET}")
+            for rec in report["analysis"]["recommendations"]:
+                print(f"  {YELLOW}•{RESET} {rec}")
+
+
+if __name__ == "__main__":
+    main()