前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目 计算机毕业设计选题 💜💜
一.开发工具简介
开发语言：Java+Python（两个版本都支持） 后端框架：Spring Boot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+ElementUI+HTML 数据库：MySQL 系统架构：B/S 开发工具：IDEA（Java的）或者PyCharm（Python的）
二.系统内容简介
本Python去中心化知识图谱系统采用Django框架作为后端核心，结合Vue+ElementUI前端技术和MySQL数据库，构建了一个基于B/S架构的智能知识管理平台。系统的核心特色在于采用去中心化架构设计，打破传统集中式知识管理的局限性，通过分布式节点的方式实现知识的存储、检索和共享。主页提供知识图谱的可视化展示和快速检索入口，用户管理模块实现多角色权限控制和用户行为跟踪，知识类型管理对不同领域的知识进行分类组织和标签化处理。去中心化知识管理是系统的核心模块，通过图数据库技术构建知识实体间的关联关系，支持知识的动态添加、更新和关系推理，实现知识的网状分布和智能关联。试题库管理和试题管理模块基于知识图谱自动生成测试题目，根据知识点的关联度和难度层次进行智能组卷，知识测试管理提供自适应学习路径和个性化推荐功能。考试管理模块支持在线考试和成绩分析，系统管理提供平台配置和数据维护功能，个人中心记录用户的学习轨迹和知识掌握情况。整个系统通过Python的自然语言处理库和图计算框架，实现了知识的语义理解和智能推荐，为用户提供了一个相对完整的知识学习和管理环境。
三.系统功能演示
2026毕业设计选题Python知识图谱系统去中心化架构设计与实现
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.views import View
import json
import networkx as nx
import numpy as np
from collections import defaultdict, deque
import hashlib
import random
@method_decorator(csrf_exempt, name='dispatch')
class DecentralizedKnowledgeView(View):
def __init__(self):
self.spark = SparkSession.builder.appName("DecentralizedKnowledgeGraph").config("spark.executor.memory", "2g").getOrCreate()
self.knowledge_graph = nx.DiGraph()
self.node_registry = {}
def post(self, request):
data = json.loads(request.body)
action = data.get('action')
if action == 'add_knowledge':
knowledge_data = data.get('knowledge_data')
entity_id = self.generate_entity_id(knowledge_data['name'])
knowledge_node = {
'id': entity_id,
'name': knowledge_data['name'],
'description': knowledge_data['description'],
'category': knowledge_data['category'],
'attributes': knowledge_data.get('attributes', {}),
'node_hash': self.calculate_node_hash(knowledge_data),
'peers': [],
'confidence_score': 1.0
}
self.knowledge_graph.add_node(entity_id, **knowledge_node)
self.node_registry[entity_id] = knowledge_node
related_entities = data.get('related_entities', [])
for related in related_entities:
related_id = related['entity_id']
relationship = related['relationship']
weight = related.get('weight', 0.5)
if related_id in self.node_registry:
self.knowledge_graph.add_edge(entity_id, related_id, relationship=relationship, weight=weight)
self.update_peer_connections(entity_id, related_id)
distributed_nodes = self.distribute_knowledge_to_peers(knowledge_node)
result = {
'status': 'success',
'entity_id': entity_id,
'distributed_nodes': len(distributed_nodes),
'knowledge_integrity': self.verify_knowledge_integrity(entity_id)
}
return JsonResponse(result)
elif action == 'query_knowledge':
query_terms = data.get('query_terms', [])
search_results = self.decentralized_knowledge_search(query_terms)
return JsonResponse({'status': 'success', 'results': search_results})
@method_decorator(csrf_exempt, name='dispatch')
class KnowledgeTestView(View):
def __init__(self):
self.spark = SparkSession.builder.appName("KnowledgeTestSystem").config("spark.executor.memory", "2g").getOrCreate()
def post(self, request):
data = json.loads(request.body)
action = data.get('action')
if action == 'generate_adaptive_test':
user_id = data.get('user_id')
knowledge_domain = data.get('knowledge_domain')
difficulty_level = data.get('difficulty_level', 'medium')
user_profile = self.get_user_knowledge_profile(user_id)
knowledge_graph = self.load_knowledge_graph(knowledge_domain)
weak_knowledge_points = self.identify_weak_knowledge_points(user_profile, knowledge_graph)
test_questions = []
for weak_point in weak_knowledge_points[:10]:
related_concepts = list(knowledge_graph.neighbors(weak_point))
question_type = self.determine_question_type(weak_point, related_concepts)
if question_type == 'multiple_choice':
correct_answer = knowledge_graph.nodes[weak_point]['name']
distractors = self.generate_distractors(weak_point, related_concepts, knowledge_graph)
options = [correct_answer] + distractors[:3]
random.shuffle(options)
correct_index = options.index(correct_answer)
elif question_type == 'relationship':
source_concept = knowledge_graph.nodes[weak_point]['name']
target_concepts = [knowledge_graph.nodes[neighbor]['name'] for neighbor in related_concepts]
correct_answer = target_concepts[0] if target_concepts else "无关联概念"
options = target_concepts[:4] if len(target_concepts) >= 4 else target_concepts + ["选项A", "选项B"][:4-len(target_concepts)]
random.shuffle(options)
correct_index = options.index(correct_answer)
question = {
'question_id': f"Q_{len(test_questions)+1}",
'knowledge_point': weak_point,
'question_text': self.generate_question_text(question_type, weak_point, knowledge_graph),
'options': options,
'correct_answer': correct_index,
'difficulty': self.calculate_question_difficulty(weak_point, user_profile),
'explanation': knowledge_graph.nodes[weak_point]['description']
}
test_questions.append(question)
test_session = {
'test_id': f"TEST_{user_id}_{hash(str(test_questions))}",
'user_id': user_id,
'questions': test_questions,
'adaptive_parameters': {
'initial_difficulty': difficulty_level,
'adjustment_factor': 0.1,
'knowledge_weights': {kp: user_profile.get(kp, 0.5) for kp in weak_knowledge_points}
}
}
return JsonResponse({'status': 'success', 'test_session': test_session})
elif action == 'evaluate_answer':
test_id = data.get('test_id')
question_id = data.get('question_id')
user_answer = data.get('user_answer')
evaluation_result = self.evaluate_user_answer(test_id, question_id, user_answer)
return JsonResponse({'status': 'success', 'evaluation': evaluation_result})
@method_decorator(csrf_exempt, name='dispatch')
class QuestionManagementView(View):
def __init__(self):
self.spark = SparkSession.builder.appName("QuestionManagement").config("spark.executor.memory", "2g").getOrCreate()
def post(self, request):
data = json.loads(request.body)
action = data.get('action')
if action == 'intelligent_question_generation':
knowledge_graph_data = data.get('knowledge_graph')
target_concepts = data.get('target_concepts', [])
question_types = data.get('question_types', ['multiple_choice', 'true_false', 'short_answer'])
difficulty_distribution = data.get('difficulty_distribution', {'easy': 0.3, 'medium': 0.5, 'hard': 0.2})
graph = nx.DiGraph()
for node in knowledge_graph_data['nodes']:
graph.add_node(node['id'], **node)
for edge in knowledge_graph_data['edges']:
graph.add_edge(edge['source'], edge['target'], **edge)
generated_questions = []
for concept_id in target_concepts:
if concept_id not in graph.nodes:
continue
concept_info = graph.nodes[concept_id]
neighbors = list(graph.neighbors(concept_id))
predecessors = list(graph.predecessors(concept_id))
for question_type in question_types:
difficulty = self.select_difficulty_based_on_distribution(difficulty_distribution)
if question_type == 'multiple_choice':
question_text = f"关于{concept_info['name']}的描述，哪个是正确的？"
correct_option = concept_info['description']
wrong_options = []
for neighbor_id in neighbors[:3]:
neighbor_desc = graph.nodes[neighbor_id]['description']
modified_desc = self.modify_description_for_distractor(neighbor_desc, concept_info['name'])
wrong_options.append(modified_desc)
options = [correct_option] + wrong_options
random.shuffle(options)
correct_answer = options.index(correct_option)
elif question_type == 'relationship':
if neighbors:
related_concept = graph.nodes[neighbors[0]]
question_text = f"{concept_info['name']}与以下哪个概念关系最密切？"
correct_option = related_concept['name']
wrong_options = [graph.nodes[nid]['name'] for nid in random.sample(list(graph.nodes), 3) if nid != concept_id and nid != neighbors[0]]
options = [correct_option] + wrong_options
random.shuffle(options)
correct_answer = options.index(correct_option)
else:
continue
elif question_type == 'comprehension':
question_text = f"请解释{concept_info['name']}的主要特点和应用场景。"
reference_answer = f"{concept_info['description']}。主要应用于相关领域的具体场景中。"
options = []
correct_answer = -1
question_obj = {
'question_id': f"AUTO_{len(generated_questions)+1}",
'knowledge_point': concept_id,
'question_type': question_type,
'question_text': question_text,
'options': options,
'correct_answer': correct_answer,
'difficulty': difficulty,
'explanation': concept_info.get('detailed_explanation', concept_info['description']),
'tags': concept_info.get('tags', []),
'generation_method': 'graph_based_auto'
}
generated_questions.append(question_obj)
question_quality_scores = []
for question in generated_questions:
quality_score = self.evaluate_question_quality(question, graph)
question_quality_scores.append(quality_score)
question['quality_score'] = quality_score
high_quality_questions = [q for q in generated_questions if q['quality_score'] > 0.7]
return JsonResponse({
'status': 'success',
'generated_questions': high_quality_questions,
'total_generated': len(generated_questions),
'high_quality_count': len(high_quality_questions),
'average_quality': np.mean(question_quality_scores)
})
def generate_entity_id(self, name):
return hashlib.md5(name.encode()).hexdigest()[:12]
def calculate_node_hash(self, knowledge_data):
content = f"{knowledge_data['name']}{knowledge_data['description']}{knowledge_data['category']}"
return hashlib.sha256(content.encode()).hexdigest()
def distribute_knowledge_to_peers(self, knowledge_node):
available_peers = list(self.node_registry.keys())
selected_peers = random.sample(available_peers, min(3, len(available_peers)))
for peer_id in selected_peers:
self.node_registry[peer_id]['peers'].append(knowledge_node['id'])
return selected_peers
def update_peer_connections(self, node1, node2):
if node1 in self.node_registry and node2 in self.node_registry:
if node2 not in self.node_registry[node1]['peers']:
self.node_registry[node1]['peers'].append(node2)
if node1 not in self.node_registry[node2]['peers']:
self.node_registry[node2]['peers'].append(node1)
def verify_knowledge_integrity(self, entity_id):
if entity_id not in self.node_registry:
return False
node = self.node_registry[entity_id]
peer_count = len(node['peers'])
expected_hash = node['node_hash']
return peer_count >= 2 and len(expected_hash) == 64
def decentralized_knowledge_search(self, query_terms):
search_results = []
for entity_id, node in self.node_registry.items():
relevance_score = 0
for term in query_terms:
if term.lower() in node['name'].lower():
relevance_score += 0.8
if term.lower() in node['description'].lower():
relevance_score += 0.5
if term.lower() in node['category'].lower():
relevance_score += 0.3
if relevance_score > 0:
search_results.append({
'entity_id': entity_id,
'name': node['name'],
'description': node['description'],
'relevance_score': relevance_score,
'peer_nodes': len(node['peers'])
})
return sorted(search_results, key=lambda x: x['relevance_score'], reverse=True)[:10]
def get_user_knowledge_profile(self, user_id):
return {'concept_1': 0.3, 'concept_2': 0.7, 'concept_3': 0.4}
def load_knowledge_graph(self, domain):
graph = nx.DiGraph()
sample_nodes = [
{'id': 'node_1', 'name': '基础概念', 'description': '领域基础知识'},
{'id': 'node_2', 'name': '进阶理论', 'description': '高级理论知识'},
{'id': 'node_3', 'name': '实践应用', 'description': '实际应用场景'}
]
for node in sample_nodes:
graph.add_node(node['id'], **node)
return graph
def identify_weak_knowledge_points(self, user_profile, knowledge_graph):
weak_points = []
for node_id in knowledge_graph.nodes:
if user_profile.get(node_id, 0.5) < 0.6:
weak_points.append(node_id)
return weak_points
def determine_question_type(self, knowledge_point, related_concepts):
return random.choice(['multiple_choice', 'relationship'])
def generate_distractors(self, target_concept, related_concepts, graph):
distractors = []
for concept in related_concepts:
if concept in graph.nodes:
distractors.append(graph.nodes[concept]['name'])
return distractors
def generate_question_text(self, question_type, knowledge_point, graph):
concept_name = graph.nodes[knowledge_point]['name']
if question_type == 'multiple_choice':
return f"关于{concept_name}，以下哪个描述是正确的？"
elif question_type == 'relationship':
return f"{concept_name}与以下哪个概念关系最密切？"
return f"请说明{concept_name}的主要特征。"
def calculate_question_difficulty(self, knowledge_point, user_profile):
user_mastery = user_profile.get(knowledge_point, 0.5)
if user_mastery < 0.3:
return 'easy'
elif user_mastery < 0.7:
return 'medium'
return 'hard'
def evaluate_user_answer(self, test_id, question_id, user_answer):
return {'correct': random.choice([True, False]), 'score': random.uniform(0.6, 1.0)}
def select_difficulty_based_on_distribution(self, distribution):
rand_val = random.random()
cumulative = 0
for difficulty, prob in distribution.items():
cumulative += prob
if rand_val <= cumulative:
return difficulty
return 'medium'
def modify_description_for_distractor(self, original_desc, target_concept):
return original_desc.replace('重要', '次要').replace('核心', '边缘')
def evaluate_question_quality(self, question, graph):
base_score = 0.5
if len(question['question_text']) > 10:
base_score += 0.2
if len(question['options']) >= 3:
base_score += 0.2
if question['explanation']:
base_score += 0.1
return min(base_score, 1.0)
六.系统文档展示
结束
💕💕文末获取源码联系 计算机程序员小杨
