技术领域Technical Field
本发明涉及需求匹配分析技术领域，尤其涉及基于大数据的用户需求精准匹配方法及系统。The present invention relates to the technical field of demand matching analysis, and in particular to a method and system for accurately matching user demands based on big data.
背景技术Background technique
在当前的数字化时代，个性化推荐系统在提升用户体验、增强用户参与度以及促进销售等方面发挥着至关重要的作用。随着大数据技术的快速发展，收集和处理用户数据的能力大幅提升，为实现更精准的用户需求匹配提供了可能。然而，尽管技术不断进步，现有的推荐系统仍面临一些挑战和限制。In the current digital age, personalized recommendation systems play a vital role in improving user experience, enhancing user engagement, and promoting sales. With the rapid development of big data technology, the ability to collect and process user data has been greatly improved, making it possible to achieve more accurate matching of user needs. However, despite the continuous advancement of technology, existing recommendation systems still face some challenges and limitations.
一方面，传统的推荐系统多依赖于显式用户偏好（如评分、直接反馈）或简单的行为分析（如点击量、浏览历史），这些方法虽然在一定程度上有效，但往往忽略了用户情绪状态、潜在需求以及需求的动态变化，从而限制了推荐的个性化水平和准确性。On the one hand, traditional recommendation systems mostly rely on explicit user preferences (such as ratings, direct feedback) or simple behavioral analysis (such as click volume, browsing history). Although these methods are effective to a certain extent, they often ignore the user's emotional state, potential needs, and dynamic changes in needs, thereby limiting the level of personalization and accuracy of recommendations.
另一方面，现有技术在处理用户数据的不确定性和模糊性方面还存在不足。用户的行为和偏好往往是复杂和多变的，可能受到当前情境、情绪状态等多种因素的影响，传统的基于规则或简单模型的推荐算法难以充分捕捉和理解这种复杂性。On the other hand, existing technologies are still insufficient in dealing with the uncertainty and ambiguity of user data. User behaviors and preferences are often complex and changeable, and may be affected by multiple factors such as the current situation and emotional state. Traditional recommendation algorithms based on rules or simple models are difficult to fully capture and understand this complexity.
此外，许多推荐系统缺乏有效的机制来动态适应用户需求的实时变化，它们可能在某一时刻对用户的需求有很好的匹配，但随着用户情境和心态的变化，推荐的相关性和满意度可能迅速下降。In addition, many recommendation systems lack effective mechanisms to dynamically adapt to real-time changes in user needs. They may have a good match with user needs at a certain moment, but as the user's context and mentality change, the relevance and satisfaction of the recommendations may drop rapidly.
因此，开发一种能够深入理解用户情绪和潜在需求，同时具备处理用户数据不确定性和动态适应用户需求变化能力的推荐方法，对于提升推荐系统的效果、增强用户满意度和忠诚度具有重要意义。Therefore, developing a recommendation method that can deeply understand user emotions and potential needs, and at the same time has the ability to handle user data uncertainty and dynamically adapt to changes in user needs, is of great significance for improving the effectiveness of the recommendation system and enhancing user satisfaction and loyalty.
发明内容Summary of the invention
基于上述目的，本发明提供了基于大数据的用户需求精准匹配方法及系统。Based on the above objectives, the present invention provides a method and system for accurately matching user needs based on big data.
基于大数据的用户需求精准匹配方法，包括以下步骤：The user demand accurate matching method based on big data includes the following steps:
S1：采集用户行为数据，包括浏览历史、购买记录、社交媒体互动，并对用户行为数据进行预处理；S1: Collect user behavior data, including browsing history, purchase history, and social media interactions, and pre-process the user behavior data;
S2：利用情绪分析技术对用户生成内容（评论、帖子）进行分析，以识别用户情绪状态和情感倾向，推测用户的潜在需求；S2: Use sentiment analysis technology to analyze user-generated content (comments, posts) to identify users' emotional states and emotional tendencies and infer users' potential needs;
S3：应用模糊逻辑对用户行为数据以及用户生成内容进行分析，以处理不确定性和模糊性，例如用户的偏好可能不是绝对的而是存在于某个范围内；S3: Apply fuzzy logic to analyze user behavior data and user-generated content to handle uncertainty and ambiguity, for example, user preferences may not be absolute but exist within a certain range;
S4：利用改进的遗传算法对用户需求进行优化匹配，通过模拟自然选择和遗传学原理，不断迭代寻找最优用户需求匹配方案。S4: Use improved genetic algorithms to optimize the matching of user needs, and continuously iterate to find the optimal user demand matching solution by simulating natural selection and genetics principles.
进一步的，所述S2中的情绪分析技术具体包括：Furthermore, the sentiment analysis technology in S2 specifically includes:
S21：采集用户在各类平台上生成的文本内容，包括社交媒体帖子、评论、博客文章、产品评价；S21: Collect text content generated by users on various platforms, including social media posts, comments, blog articles, and product reviews;
S22：应用自然语言处理技术，包括文本分词、去除停用词、词性标注预处理；S22: Apply natural language processing techniques, including text segmentation, stop word removal, and part-of-speech tagging preprocessing;
S23：利用情绪分析模型对预处理后的文本进行情绪倾向分析，识别正面、负面或中性情绪，情绪分析模型基于支持向量机SVM，具体包括：S23: Use the sentiment analysis model to analyze the sentiment tendency of the preprocessed text to identify positive, negative or neutral sentiment. The sentiment analysis model is based on the support vector machine SVM and specifically includes:
特征提取：将用户生成的文本内容转换为数值特征向量，使用TF-IDF，对于文本和词，TF-IDF计算公式为：，其中，是词在文本中的频率，是文档总数，是包含词的文档数；Feature extraction: Convert user-generated text content into numerical feature vectors, using TF-IDF for text and words , the TF-IDF calculation formula is: ,in, Yes word In text The frequency in is the total number of documents, Contains words The number of documents;
SVM模型训练：使用特征向量和相应的情绪标签(正面、负面、中性)训练SVM模型，目标是找到一个分割平面，使得正负样本间隔最大，分割平面方程表示为：，其中，是权重向量，是特征向量，是偏差，进而求解优化问题来确定和，优化问题表示为：，约束为：，其中，是第个文本的情绪标签或-1是对应的特征向量；SVM model training: Use feature vectors and corresponding sentiment labels (positive, negative, neutral) to train the SVM model. The goal is to find a split plane that maximizes the interval between positive and negative samples. The split plane equation is expressed as: , in, is the weight vector, is the eigenvector, is the deviation, and then solve the optimization problem to determine and , the optimization problem is expressed as: , the constraints are: ,in, It is Sentiment labels for texts or -1 is the corresponding eigenvector;
情绪预测：使用训练好的SVM模型对新的用户生成内容进行情绪分类，将文本特征向量输入模型，根据分割平面的判定来预测情绪标签；Sentiment prediction: Use the trained SVM model to classify the sentiment of new user-generated content. Input the text feature vector into the model and predict the sentiment label based on the determination of the segmentation plane.
S24：基于主题建模的语义分析技术，理解用户生成内容的主题和上下文，推测用户的情绪状态和情感倾向。S24: Semantic analysis technology based on topic modeling to understand the theme and context of user-generated content and infer the user's emotional state and emotional tendency.
进一步的，所述主题建模具体包括：Furthermore, the topic modeling specifically includes:
为每个文档中的每个词随机分配一个主题，基于初始主题分配，计算文档-主题分布和词-主题分布；Randomly assign a topic to each word in each document, and calculate the document-topic distribution and word-topic distribution based on the initial topic assignment;
对于每个文档中的每个词，基于当前的文档-主题分布和词-主题分布，重新计算该词属于各个主题的概率，基于概率重新分配主题，迭代多次，直到分布稳定，文档中词属于主题的条件概率公式如下：For each word in each document, based on the current document-topic distribution and word-topic distribution, recalculate the probability that the word belongs to each topic, and reassign topics based on the probability. Repeat this process multiple times until the distribution is stable and the document Chinese words The conditional probability formula for belonging to a topic is as follows:
，其中，是文档中分配给主题的词的数量，是分配给主题的词的数量，和是先验参数，用于控制文档主题分布和词主题分布的稀疏程度： ,in, It is a document Assign to topic The number of words, is assigned to the subject Words quantity, and is a priori parameter used to control the sparsity of document topic distribution and word topic distribution:
主题提取：经过多次迭代后，得到每个文档的主题分布和每个主题的词分布，通过查看每个主题的高概率词，推断主题的语义内容。Topic extraction: After multiple iterations, we get the topic distribution of each document and the word distribution of each topic. By looking at the high-probability words of each topic, we can infer the semantic content of the topic.
进一步的，所述S3具体包括：Furthermore, the S3 specifically includes:
S31：定义系列模糊变量和相应的模糊集合来表示用户行为和内容的不同方面，如用户兴趣的强度可以分为“低”、“中”和“高”三个模糊集合；S31: Define a series of fuzzy variables and corresponding fuzzy sets to represent different aspects of user behavior and content. For example, the intensity of user interest can be divided into three fuzzy sets: "low", "medium" and "high";
S32：通过模糊化过程将用户行为数据和用户生成内容的量化指标转换为模糊集合中的隶属度，例如，用户对某个话题的兴趣程度可以通过其对相关内容的互动频率来量化，然后根据预设的规则转换为模糊集合“低”、“中”、“高”的隶属度；S32: converting the quantitative indicators of user behavior data and user-generated content into membership in a fuzzy set through a fuzzification process. For example, the user's interest in a topic can be quantified by the frequency of their interaction with related content, and then converted into membership of the fuzzy set "low", "medium", and "high" according to preset rules;
S33：应用模糊规则，模糊规则基于专家知识或数据分析得出，用于描述不同模糊变量之间的关系，例如：“如果用户对话题A的兴趣是高，并且对话题B的兴趣是中，则用户对产品X的偏好是高”；S33: Apply fuzzy rules. Fuzzy rules are based on expert knowledge or data analysis and are used to describe the relationship between different fuzzy variables. For example, "If the user's interest in topic A is high and the interest in topic B is medium, then the user's preference for product X is high."
S34：使用模糊推理器，根据模糊规则和输入的隶属度计算出用户对不同产品或服务的偏好隶属度，通过去模糊化过程将模糊推理结果转换为具体的推荐，包括使用质心方法计算偏好隶属度的加权平均，以确定最终推荐。S34: Using a fuzzy reasoner, the user's preference membership for different products or services is calculated based on the fuzzy rules and the input membership, and the fuzzy reasoning results are converted into specific recommendations through a defuzzification process, including using a centroid method to calculate a weighted average of the preference membership to determine the final recommendation.
进一步的，所述模糊推理器采用Mamdani模型，具体包括：Furthermore, the fuzzy inference engine adopts the Mamdani model, which specifically includes:
模糊化：对于每个输入变量，其对应模糊集合的隶属度计算公式为，其中是在模糊集合中的隶属函数;Fuzzification: For each input variable , which corresponds to the fuzzy set The membership calculation formula is: , in yes In fuzzy sets The membership function in ;
模糊规则应用:对于模糊规则“若是并且是，则是”的隶属度计算为：，其中，使用最小值操作符来模拟AND操作；Application of fuzzy rules: For the fuzzy rule "if yes and yes ,but yes The membership degree of ” is calculated as: , where the minimum operator is used to simulate the AND operation;
聚合：将所有模糊规则的输出聚合成单一的模糊集合，对于的每个值，其隶属度为所有规则对该值的隶属度的最大值：Aggregation: Aggregate the outputs of all fuzzy rules into a single fuzzy set. For each value of , its membership is the maximum of the memberships of all rules to that value:
； ;
去模糊化：将模糊输出转换为一个具体的数值，质心法去模糊化的计算为：，其中是最终的输出值。Defuzzification: Convert the fuzzy output into a specific value. The calculation of centroid defuzzification is: ,in is the final output value.
进一步的，所述S4中的改进的遗传算法具体包括：Furthermore, the improved genetic algorithm in S4 specifically includes:
S41，定义编码方案：将用户需求与服务或产品匹配方案编码为“染色体”，每个“基因”代表一个匹配属性，匹配属性包括产品类别、服务质量、价格敏感度，考虑用户需求的多样性和复杂性，采用多维编码策略以反映不同属性，例如，染色体可以是一个向量 [类别编码, 质量评分, 价格区间]，其中每个元素都是经过编码的属性值；S41, define the coding scheme: encode the matching scheme between user needs and services or products into "chromosomes", each "gene" represents a matching attribute, and the matching attributes include product category, service quality, and price sensitivity. Considering the diversity and complexity of user needs, a multi-dimensional coding strategy is adopted to reflect different attributes. For example, a chromosome can be a vector [category code, quality score, price range], in which each element is an encoded attribute value;
S42，初始化种群：生成初始匹配方案集合，即初始“种群”，每个匹配方案作为一个“染色体”，考虑到用户群体的多样性，初始种群包括从广泛领域随机选取的匹配方案，以确保初始解空间的广泛覆盖；S42, Initialize population: Generate an initial set of matching solutions, namely the initial “population”, where each matching solution is a “chromosome”. Considering the diversity of the user group, the initial population includes matching solutions randomly selected from a wide range of fields to ensure a wide coverage of the initial solution space;
S43，定义适应度函数：设计适应度函数来评估匹配方案的质量，适应度函数基于匹配结果与用户实际需求的符合程度，包括用户互动反馈、用户满意度调查以及长期用户行为分析因素；S43, define fitness function: design a fitness function to evaluate the quality of the matching solution. The fitness function is based on the degree of compliance between the matching results and the actual needs of users, including user interaction feedback, user satisfaction surveys, and long-term user behavior analysis factors;
S44，选择操作（引入基于用户反馈的动态选择机制）：根据适应度函数结果，采用基于用户反馈的动态选择机制，优先选择能够引起正面用户互动和高满意度反馈的匹配方案作为“父母”，适应用户需求的变化；S44, selection operation (introducing a dynamic selection mechanism based on user feedback): According to the fitness function result, a dynamic selection mechanism based on user feedback is adopted to give priority to matching solutions that can cause positive user interaction and high satisfaction feedback as "parents" to adapt to changes in user needs;
S45，交叉操作（引入上下文感知交叉策略）：利用上下文感知的交叉策略，根据用户的当前上下文信息（如时间、地点、活动类型）调整交叉操作的方式，以生成个性化和上下文相关的新匹配方案，根据用户当前上下文调整交叉点；S45, Cross Operation (Introduction of Context-Aware Cross Strategy): Using context-aware cross strategy, the cross operation mode is adjusted according to the user's current context information (such as time, location, activity type) to generate personalized and context-related new matching solutions. Adjust the intersection point;
S46，变异操作（引入智能变异策略）：实施智能变异策略，变异操作不仅是随机的，还基于市场趋势、用户群体行为模式信息进行指导，以引入新颖匹配方案，增强解空间的多样性，变异概率根据外部信息动态调整，设外部信息为市场趋势，市场趋势表明某类产品受欢迎度上升，该产品类别的基因变异概率增加：，其中，是根据市场趋势计算变异概率的函数；S46, mutation operation (introduction of intelligent mutation strategy): Implement intelligent mutation strategy. The mutation operation is not only random, but also guided by market trends and user group behavior pattern information to introduce novel matching solutions, enhance the diversity of solution space, and increase the probability of mutation. Dynamically adjust according to external information, assuming external information is market trend , market trends show that as a certain category of products becomes more popular, the probability of genetic mutation in that product category increases: ,in, According to market trends Function to calculate mutation probability;
S47，迭代进化：重复执行选择、交叉和变异操作，直至满足终止条件，终止条件包括达到最大迭代次数或适应度达到预设阈值，确保找到最优匹配方案；S47, iterative evolution: repeatedly perform selection, crossover and mutation operations until the termination condition is met. The termination condition includes reaching the maximum number of iterations or the fitness reaching a preset threshold to ensure that the optimal matching solution is found;
S48，解码和应用：将进化得到的最优匹配方案解码为具体的推荐策略，根据其参数向用户推荐产品或服务。S48, decoding and application: Decode the optimal matching solution obtained by evolution into a specific recommendation strategy, and recommend products or services to users based on its parameters.
进一步的，所述适应度函数衡量染色体的质量，基于用户对推荐结果的互动和满意度，计算公式为：Furthermore, the fitness function Weighing chromosomes The quality of recommendation is based on the user's interaction and satisfaction with the recommendation results. The calculation formula is:
互动率满意度，其中，和是权重参数，用于调整互动率和满意度在适应度评分中的相对重要性。 Interaction rate Satisfaction ,in, and is a weight parameter used to adjust the relative importance of interaction rate and satisfaction in the fitness score.
进一步的，所述基于用户反馈的动态选择机制采用轮盘赌选择法，概率动态调整，以偏好产生正面反馈的匹配方案：Furthermore, the dynamic selection mechanism based on user feedback adopts a roulette selection method with a probability of Dynamically adjust to favor matches that generate positive feedback:
，其中，是染色体的适应度，是种群大小。 ,in, It's a chromosome The fitness of is the population size.
进一步的，还包括优先级决策机制，综合情绪分析技术推测的用户潜在需求和遗传算法的最优匹配方案，确定最终的推荐优先级，基于以下规则：Furthermore, it also includes a priority decision mechanism, which combines the potential needs of users inferred by sentiment analysis technology and the optimal matching solution of the genetic algorithm to determine the final recommendation priority based on the following rules:
若情绪分析结果指示用户对某类别的产品非常满意（强正面情绪），并且遗传算法也推理出用户对该类别的产品有高需求，那么该类别的产品推荐优先级将被提高；If the sentiment analysis results indicate that users are very satisfied with products in a certain category (strong positive sentiment), and the genetic algorithm also infers that users have a high demand for products in this category, then the recommendation priority of products in this category will be increased;
如果情绪分析结果与遗传算法推理的需求存在冲突（例如，情绪分析指示满意度不高，而遗传算法推理出高需求），则引入权衡机制，通过用户互动数据来调整最终推荐的优先级。If there is a conflict between the sentiment analysis results and the requirements inferred by the genetic algorithm (for example, sentiment analysis indicates low satisfaction, while the genetic algorithm infers high requirements), a trade-off mechanism is introduced to adjust the priority of the final recommendation through user interaction data.
基于大数据的用户需求精准匹配系统，用于实现上述的基于大数据的用户需求精准匹配方法，包括以下模块：The user demand precise matching system based on big data is used to implement the above-mentioned user demand precise matching method based on big data, and includes the following modules:
数据采集与预处理模块：负责收集用户在各个平台上的行为数据，包括浏览历史、购买记录和社交媒体互动，对收集到的数据进行预处理，以确保数据质量和一致性，为后续分析提供准确的输入；Data collection and preprocessing module: responsible for collecting user behavior data on various platforms, including browsing history, purchase records, and social media interactions, and preprocessing the collected data to ensure data quality and consistency, providing accurate input for subsequent analysis;
情绪分析模块：应用情绪分析技术分析用户生成的内容，以识别用户的情绪状态和情感倾向，通过分析用户表达的情感来推测用户的潜在需求；Sentiment analysis module: Apply sentiment analysis technology to analyze user-generated content to identify the user's emotional state and emotional tendency, and infer the user's potential needs by analyzing the emotions expressed by the user;
模糊逻辑分析模块：利用模糊逻辑对用户行为数据和情绪分析的结果进行分析，处理不确定性和模糊性，通过构建模糊规则和模糊变量，将用户的偏好、需求和情绪状态量化为模糊度量；Fuzzy logic analysis module: Analyzes user behavior data and sentiment analysis results using fuzzy logic, handles uncertainty and ambiguity, and quantifies user preferences, needs, and emotional states into fuzzy metrics by constructing fuzzy rules and fuzzy variables;
算法匹配模块：采用改进的遗传算法对用户需求进行优化匹配，通过模拟自然选择和遗传学原理，结合各模块的输出作为输入，不断迭代寻找最优的用户需求匹配方案。Algorithm matching module: It uses an improved genetic algorithm to optimize the matching of user needs. By simulating natural selection and genetics principles, combining the output of each module as input, it continuously iterates to find the optimal user demand matching solution.
本发明的有益效果：Beneficial effects of the present invention:
本发明，通过结合情绪分析和遗传算法，能够深入理解和预测用户的情绪状态和潜在需求，从而实现更高度的个性化推荐，情绪分析揭示用户对特定内容的情感反应，而遗传算法通过用户行为数据推理出用户的偏好和需求，二者结合使得推荐系统能够更准确地匹配用户当前的心理状态和实际需求。The present invention, by combining sentiment analysis and genetic algorithms, can deeply understand and predict the user's emotional state and potential needs, thereby achieving more highly personalized recommendations. Sentiment analysis reveals the user's emotional response to specific content, while the genetic algorithm infers the user's preferences and needs through user behavior data. The combination of the two enables the recommendation system to more accurately match the user's current psychological state and actual needs.
本发明，通过实时监控用户反馈和行为，动态调整推荐策略，使得推荐内容能够实时响应用户需求的变化，优先级决策机制考虑了情绪分析和遗传算法的结果，能够灵活处理情绪和行为数据之间的差异，确保推荐系统在用户需求发生变化时仍能提供合适的推荐。The present invention monitors user feedback and behavior in real time and dynamically adjusts the recommendation strategy, so that the recommended content can respond to changes in user needs in real time. The priority decision mechanism takes into account the results of sentiment analysis and genetic algorithms, and can flexibly handle the differences between sentiment and behavior data, ensuring that the recommendation system can still provide appropriate recommendations when user needs change.
本发明，通过提供与用户情绪状态和潜在需求高度匹配的推荐，显著提升了用户的满意度和参与度，个性化推荐减少了用户寻找感兴趣内容的时间和努力，增加了用户与推荐内容的互动，从而提高了用户在平台上的整体体验和忠诚度。The present invention significantly improves user satisfaction and engagement by providing recommendations that are highly matched with the user's emotional state and potential needs. Personalized recommendations reduce the time and effort of users in finding content of interest and increase the user's interaction with the recommended content, thereby improving the user's overall experience and loyalty on the platform.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明实施例的匹配方法流程示意图；FIG1 is a schematic flow chart of a matching method according to an embodiment of the present invention;
图2为本发明实施例的匹配系统功能模块示意图。FIG. 2 is a schematic diagram of functional modules of a matching system according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚明白，以下结合具体实施例，对本发明进一步详细说明。In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.
需要说明的是，除非另外定义，本发明使用的技术术语或者科学术语应当为本发明所属领域内具有一般技能的人士所理解的通常意义。本发明中使用的“第一”、“第二”以及类似的词语并不表示任何顺序、数量或者重要性，而只是用来区分不同的组成部分。“包括”或者“包含”等类似的词语意指出现该词前面的元件或者物件涵盖出现在该词后面列举的元件或者物件及其等同，而不排除其他元件或者物件。“连接”或者“相连”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电性的连接，不管是直接的还是间接的。“上”、“下”、“左”、“右”等仅用于表示相对位置关系，当被描述对象的绝对位置改变后，则该相对位置关系也可能相应地改变。It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.
如图1所示，基于大数据的用户需求精准匹配方法，包括以下步骤：As shown in FIG1 , the user demand accurate matching method based on big data includes the following steps:
S1：采集用户行为数据，包括浏览历史、购买记录、社交媒体互动，并对用户行为数据进行预处理；S1: Collect user behavior data, including browsing history, purchase history, and social media interactions, and pre-process the user behavior data;
通过用户在各个平台上的登录信息和活动，收集用户的浏览历史，包括访问的网页、停留时间、点击的链接信息；从用户在电子商务平台上的交易记录中提取购买记录，包括购买的商品或服务、购买时间、购买频率以及消费偏好等信息；利用API接口从社交媒体平台收集用户的社交互动数据，包括发布的内容、点赞、评论、分享、社交网络关系信息；对收集到的用户行为数据进行预处理，包括数据清洗（去除无关数据、重复数据等）、数据转换（标准化数据格式、编码分类数据等）、数据归一化（调整数据尺度），以及缺失值处理等，确保数据质量满足后续分析的要求。Collect users’ browsing history through their login information and activities on various platforms, including visited web pages, dwell time, and clicked links; extract purchase records from users’ transaction records on e-commerce platforms, including information such as purchased goods or services, purchase time, purchase frequency, and consumption preferences; use API interfaces to collect users’ social interaction data from social media platforms, including posted content, likes, comments, shares, and social network relationship information; pre-process the collected user behavior data, including data cleaning (removing irrelevant data, duplicate data, etc.), data conversion (standardizing data formats, encoding and categorizing data, etc.), data normalization (adjusting data scale), and missing value processing, to ensure that data quality meets the requirements of subsequent analysis.
S2：利用情绪分析技术对用户生成内容（评论、帖子）进行分析，以识别用户情绪状态和情感倾向，推测用户的潜在需求；S2: Use sentiment analysis technology to analyze user-generated content (comments, posts) to identify users' emotional states and emotional tendencies and infer users' potential needs;
S3：应用模糊逻辑对用户行为数据以及用户生成内容进行分析，以处理不确定性和模糊性，例如用户的偏好可能不是绝对的而是存在于某个范围内；S3: Apply fuzzy logic to analyze user behavior data and user-generated content to handle uncertainty and ambiguity, for example, user preferences may not be absolute but exist within a certain range;
S4：利用改进的遗传算法对用户需求进行优化匹配，通过模拟自然选择和遗传学原理，不断迭代寻找最优用户需求匹配方案。S4: Use improved genetic algorithms to optimize the matching of user needs, and continuously iterate to find the optimal user demand matching solution by simulating natural selection and genetic principles.
S2中的情绪分析技术具体包括：The sentiment analysis techniques in S2 include:
S21：采集用户在各类平台上生成的文本内容，包括社交媒体帖子、评论、博客文章、产品评价；S21: Collect text content generated by users on various platforms, including social media posts, comments, blog articles, and product reviews;
S22：应用自然语言处理技术，包括文本分词、去除停用词、词性标注预处理；S22: Apply natural language processing techniques, including text segmentation, stop word removal, and part-of-speech tagging preprocessing;
S23：利用情绪分析模型对预处理后的文本进行情绪倾向分析，识别正面、负面或中性情绪，情绪分析模型基于支持向量机SVM，具体包括：S23: Use the sentiment analysis model to analyze the sentiment tendency of the preprocessed text to identify positive, negative or neutral sentiment. The sentiment analysis model is based on the support vector machine SVM and specifically includes:
特征提取：将用户生成的文本内容转换为数值特征向量，使用TF-IDF，对于文本和词，TF-IDF计算公式为：，其中，是词在文本中的频率，是文档总数，是包含词的文档数；Feature extraction: Convert user-generated text content into numerical feature vectors, using TF-IDF for text and words , the TF-IDF calculation formula is: ,in, Yes word In text The frequency in is the total number of documents, Contains words The number of documents;
SVM模型训练：使用特征向量和相应的情绪标签(正面、负面、中性)训练SVM模型，目标是找到一个分割平面，使得正负样本间隔最大，分割平面方程表示为：，其中，是权重向量，是特征向量，是偏差，进而求解优化问题来确定和，优化问题表示为：，约束为：，其中，是第个文本的情绪标签或-1是对应的特征向量；SVM model training: Use feature vectors and corresponding sentiment labels (positive, negative, neutral) to train the SVM model. The goal is to find a split plane that maximizes the interval between positive and negative samples. The split plane equation is expressed as: , in, is the weight vector, is the eigenvector, is the deviation, and then solve the optimization problem to determine and , the optimization problem is expressed as: , the constraints are: ,in, It is Sentiment labels for texts or -1 is the corresponding eigenvector;
情绪预测：使用训练好的SVM模型对新的用户生成内容进行情绪分类，将文本特征向量输入模型，根据分割平面的判定来预测情绪标签；Sentiment prediction: Use the trained SVM model to classify the sentiment of new user-generated content. Input the text feature vector into the model and predict the sentiment label based on the determination of the segmentation plane.
S24：基于主题建模的语义分析技术，理解用户生成内容的主题和上下文，推测用户的情绪状态和情感倾向。S24: Semantic analysis technology based on topic modeling to understand the theme and context of user-generated content and infer the user's emotional state and emotional tendency.
主题建模通过训练后可以得到每个文档的主题分布以及每个主题的词分布，通过分析这些分布，可以识别出用户生成内容中的主要主题，以及与每个主题最相关的关键词，主题建模具体包括：After training, topic modeling can obtain the topic distribution of each document and the word distribution of each topic. By analyzing these distributions, the main topics in user-generated content and the most relevant keywords for each topic can be identified. Topic modeling specifically includes:
为每个文档中的每个词随机分配一个主题，基于初始主题分配，计算文档-主题分布和词-主题分布；Randomly assign a topic to each word in each document, and calculate the document-topic distribution and word-topic distribution based on the initial topic assignment;
对于每个文档中的每个词，基于当前的文档-主题分布和词-主题分布，重新计算该词属于各个主题的概率，基于概率重新分配主题，迭代多次，直到分布稳定，文档中词属于主题的条件概率公式如下：For each word in each document, based on the current document-topic distribution and word-topic distribution, recalculate the probability that the word belongs to each topic, and reassign topics based on the probability. Repeat this process multiple times until the distribution is stable and the document Chinese words The conditional probability formula for belonging to a topic is as follows:
，其中，是文档中分配给主题的词的数量，是分配给主题的词的数量，和是先验参数，用于控制文档主题分布和词主题分布的稀疏程度： ,in, It is a document Assign to topic The number of words, is assigned to the subject Words quantity, and is a priori parameter used to control the sparsity of document topic distribution and word topic distribution:
主题提取：经过多次迭代后，得到每个文档的主题分布和每个主题的词分布，通过查看每个主题的高概率词，推断主题的语义内容，例如，如果一个主题包含了“购买”、“推荐”、“优惠”等高频词，则可能表示这是一个关于购物的主题。Topic extraction: After multiple iterations, we get the topic distribution of each document and the word distribution of each topic. By looking at the high-probability words of each topic, we can infer the semantic content of the topic. For example, if a topic contains high-frequency words such as "buy", "recommend", and "discount", it may mean that this is a topic about shopping.
：是条件概率，表示给定单词和文档的情况下，单词属于主题的概率。 : is the conditional probability, indicating that given a word and Documentation In the case of Belong to the theme The probability.
:表示在文档中分配给主题的单词的数量，这个计数不包括当前正在分配主题的那个单词，以确保算法的正确迭代更新。 : Indicates that in the document Assign to topic This count does not include the word that is currently being assigned a topic to ensure the correct iterative update of the algorithm.
:对所有主题进行求和，用于归一化，确保为概率分布，是加到每个计数上的先验值，用于平滑，防止计算出的概率为0。 : Sum all topics for normalization to ensure is the probability distribution, is a prior added to each count for smoothing to prevent the calculated probabilities from being zero.
:表示在所有文档中，被分配到主题下的单词的数量，同样，这个计数不包括当前正在重新分配主题的那个单词。 : Indicates that among all documents, they are assigned to the topic The following words Again, this count does not include the word that is currently being reassigned a topic.
:这是对主题下所有单词进行求和，用于归一化，是另一个先验值，同样用于平滑。 :This is the theme Sum all the words below for normalization. is another prior, also used for smoothing.
:文档主题分布的Dirichlet先验参数，用于控制主题分布的稀疏程度，较大的值会导致文档包含更均匀的主题分布，而较小的值会使得文档更倾向于包含较少的主题。 : Dirichlet prior parameter of document topic distribution, used to control the sparsity of topic distribution. Values of 1 result in documents containing a more even distribution of topics, while smaller values A value of will make documents more likely to contain fewer topics.
:这是主题词分布的Dirichlet先验参数，用于控制主题中单词分布的稀疏程度，较大的值意味着主题包含的单词更均匀，较小的值则意味着主题更集中于少数几个单词。 : This is the Dirichlet prior parameter of the topic word distribution, which is used to control the sparsity of the word distribution in the topic. A value of 1 means that the topics contain words more evenly, with smaller A value of 0 means that the topic is more concentrated on a few words.
S3具体包括：S3 specifically includes:
S31：定义系列模糊变量和相应的模糊集合来表示用户行为和内容的不同方面，如用户兴趣的强度可以分为“低”、“中”和“高”三个模糊集合；S31: Define a series of fuzzy variables and corresponding fuzzy sets to represent different aspects of user behavior and content. For example, the intensity of user interest can be divided into three fuzzy sets: "low", "medium" and "high";
S32：通过模糊化过程将用户行为数据和用户生成内容的量化指标转换为模糊集合中的隶属度，例如，用户对某个话题的兴趣程度可以通过其对相关内容的互动频率来量化，然后根据预设的规则转换为模糊集合“低”、“中”、“高”的隶属度；S32: converting the quantitative indicators of user behavior data and user-generated content into membership in a fuzzy set through a fuzzification process. For example, the user's interest in a topic can be quantified by the frequency of their interaction with related content, and then converted into membership of the fuzzy set "low", "medium", and "high" according to preset rules;
S33：应用模糊规则，模糊规则基于专家知识或数据分析得出，用于描述不同模糊变量之间的关系，例如：“如果用户对话题A的兴趣是高，并且对话题B的兴趣是中，则用户对产品X的偏好是高”；S33: Apply fuzzy rules. Fuzzy rules are based on expert knowledge or data analysis and are used to describe the relationship between different fuzzy variables. For example, "If the user's interest in topic A is high and the interest in topic B is medium, then the user's preference for product X is high."
S34：使用模糊推理器，根据模糊规则和输入的隶属度计算出用户对不同产品或服务的偏好隶属度，通过去模糊化过程将模糊推理结果转换为具体的推荐，包括使用质心方法计算偏好隶属度的加权平均，以确定最终推荐。S34: Using a fuzzy reasoner, the user's preference membership for different products or services is calculated based on the fuzzy rules and the input membership, and the fuzzy reasoning results are converted into specific recommendations through a defuzzification process, including using a centroid method to calculate a weighted average of the preference membership to determine the final recommendation.
模糊推理器采用Mamdani模型，具体包括：The fuzzy inference engine uses the Mamdani model, which includes:
模糊化：对于每个输入变量，其对应模糊集合的隶属度计算公式为，其中是在模糊集合中的隶属函数;Fuzzification: For each input variable , which corresponds to the fuzzy set The membership calculation formula is: , in yes In fuzzy sets The membership function in ;
模糊规则应用:对于模糊规则“若是并且是，则是”的隶属度计算为：，其中，使用最小值操作符来模拟AND操作；Application of fuzzy rules: For the fuzzy rule "if yes and yes ,but yes The membership degree of ” is calculated as: , where the minimum operator is used to simulate the AND operation;
聚合：将所有模糊规则的输出聚合成单一的模糊集合，对于的每个值，其隶属度为所有规则对该值的隶属度的最大值：Aggregation: Aggregate the outputs of all fuzzy rules into a single fuzzy set. For each value of , its membership is the maximum of the memberships of all rules to that value:
； ;
去模糊化：将模糊输出转换为一个具体的数值，质心法去模糊化的计算为：，其中是最终的输出值。Defuzzification: Convert the fuzzy output into a specific value. The calculation of centroid defuzzification is: ,in is the final output value.
在本发明中，应用模糊逻辑来分析用户行为数据和用户生成内容，以处理数据中的不确定性和模糊性，模糊逻辑是一种处理不确定性的方法，它允许变量存在于真和假之间的某个程度上，而不是严格地二分，这在分析用户行为时特别有用，因为用户的行为和偏好往往不是完全确定的，而是模糊和多变的。In the present invention, fuzzy logic is applied to analyze user behavior data and user-generated content to deal with uncertainty and ambiguity in the data. Fuzzy logic is a method for dealing with uncertainty that allows variables to exist to a certain extent between true and false, rather than being strictly dichotomous. This is particularly useful in analyzing user behavior because user behavior and preferences are often not completely certain, but rather fuzzy and changeable.
应用示例：Application examples:
假设想要分析用户对某一类产品的兴趣程度。我们可以从两个方面来分析：用户行为数据（如浏览历史、购买记录）和用户生成内容（如产品评论、社交媒体帖子）。Suppose we want to analyze the user's interest in a certain category of products. We can analyze from two aspects: user behavior data (such as browsing history, purchase history) and user-generated content (such as product reviews, social media posts).
用户行为数据分析：User behavior data analysis:
变量定义：Variable definitions:
定义一个模糊变量“兴趣程度”，其取值范围从0到1，0表示完全不感兴趣，1表示非常感兴趣。Define a fuzzy variable "interest level" with a value range from 0 to 1, where 0 means no interest at all and 1 means very interested.
对于浏览历史，我们可以定义模糊集合如“少量浏览”、“适量浏览”和“频繁浏览”。For browsing history, we can define fuzzy sets such as "a little browsing", "moderate browsing" and "frequent browsing".
对于购买记录，我们可以定义模糊集合如“从未购买”、“偶尔购买”和“经常购买”。For purchase records, we can define fuzzy sets such as "never purchased", "occasionally purchased", and "frequently purchased".
规则制定：Rulemaking:
如果用户“频繁浏览”电子书相关页面，那么他们对电子书阅读器的兴趣程度是“高”。If users “frequently browse” e-book related pages, their interest level in e-book readers is “high”.
如果用户“偶尔购买”电子书，那么他们对电子书阅读器的兴趣程度是“中”。If users “occasionally purchase” e-books, their interest in e-book readers is “moderate”.
推理和去模糊化：Reasoning and Defuzzification:
使用模糊逻辑推理机制根据规则和输入的模糊数据来确定兴趣程度的模糊值。The fuzzy value of the interest degree is determined based on the rules and the input fuzzy data using the fuzzy logic reasoning mechanism.
应用去模糊化方法（如质心法）来计算一个明确的兴趣程度值。Apply a defuzzification method (such as the centroid method) to calculate an explicit interest level value.
用户生成内容分析：User-generated content analysis:
变量定义：Variable definitions:
对于用户评论，我们可以定义情感模糊集合如“正面”、“中立”和“负面”。For user reviews, we can define sentiment fuzzy sets such as "positive", "neutral" and "negative".
规则制定：Rulemaking:
如果用户评论中的正面情绪词汇超过负面情绪词汇，则认为对该产品的情感倾向是“正面”的。If the number of positive sentiment words in a user review exceeds the number of negative sentiment words, the sentiment tendency towards the product is considered to be “positive”.
推理和去模糊化：Reasoning and Defuzzification:
根据用户生成内容中的情感词汇和模糊规则来评估用户对电子书阅读器的情感倾向。Evaluating users' sentiment inclination towards e-book readers based on sentiment words and fuzzy rules in user-generated content.
使用去模糊化方法来得到一个具体的情感倾向值。Use the defuzzification method to obtain a specific sentiment tendency value.
通过将用户行为数据和用户生成内容的分析结果结合起来，我们可以得到一个更全面的用户兴趣和偏好的视图，从而更准确地匹配用户需求。模糊逻辑的应用使得系统能够更加灵活地处理用户数据的不确定性和模糊性，提高了推荐系统的准确性和用户满意度。By combining the analysis results of user behavior data and user-generated content, we can get a more comprehensive view of user interests and preferences, thereby matching user needs more accurately. The application of fuzzy logic enables the system to handle the uncertainty and ambiguity of user data more flexibly, improving the accuracy of the recommendation system and user satisfaction.
S4中的改进的遗传算法具体包括：The improved genetic algorithm in S4 specifically includes:
S41，定义编码方案：将用户需求与服务或产品匹配方案编码为“染色体”，每个“基因”代表一个匹配属性，匹配属性包括产品类别、服务质量、价格敏感度，考虑用户需求的多样性和复杂性，采用多维编码策略以反映不同属性，例如，染色体可以是一个向量 [类别编码, 质量评分, 价格区间]，其中每个元素都是经过编码的属性值；S41, define the coding scheme: encode the matching scheme between user needs and services or products into "chromosomes", each "gene" represents a matching attribute, and the matching attributes include product category, service quality, and price sensitivity. Considering the diversity and complexity of user needs, a multi-dimensional coding strategy is adopted to reflect different attributes. For example, a chromosome can be a vector [category code, quality score, price range], in which each element is an encoded attribute value;
S42，初始化种群：生成初始匹配方案集合，即初始“种群”，每个匹配方案作为一个“染色体”，考虑到用户群体的多样性，初始种群包括从广泛领域随机选取的匹配方案，以确保初始解空间的广泛覆盖；S42, Initialize population: Generate an initial set of matching solutions, namely the initial “population”, where each matching solution is a “chromosome”. Considering the diversity of the user group, the initial population includes matching solutions randomly selected from a wide range of fields to ensure a wide coverage of the initial solution space;
S43，定义适应度函数：设计适应度函数来评估匹配方案的质量，适应度函数基于匹配结果与用户实际需求的符合程度，包括用户互动反馈、用户满意度调查以及长期用户行为分析因素；S43, define fitness function: design a fitness function to evaluate the quality of the matching solution. The fitness function is based on the degree of compliance between the matching results and the actual needs of users, including user interaction feedback, user satisfaction surveys, and long-term user behavior analysis factors;
S44，选择操作（引入基于用户反馈的动态选择机制）：根据适应度函数结果，采用基于用户反馈的动态选择机制，优先选择能够引起正面用户互动和高满意度反馈的匹配方案作为“父母”，适应用户需求的变化；S44, selection operation (introducing a dynamic selection mechanism based on user feedback): According to the fitness function result, a dynamic selection mechanism based on user feedback is adopted to give priority to matching solutions that can cause positive user interaction and high satisfaction feedback as "parents" to adapt to changes in user needs;
S45，交叉操作（引入上下文感知交叉策略）：利用上下文感知的交叉策略，根据用户的当前上下文信息（如时间、地点、活动类型）调整交叉操作的方式，以生成个性化和上下文相关的新匹配方案，根据用户当前上下文调整交叉点，例如，如果当前上下文表明用户在寻找特定类别的产品，交叉操作将倾向于保留与该类别相关的基因。这可以通过调整交叉概率或交叉点位置来实现；S45, Cross Operation (Introduction of Context-Aware Cross Strategy): Using context-aware cross strategy, the cross operation mode is adjusted according to the user's current context information (such as time, location, activity type) to generate personalized and context-related new matching solutions. Adjust the crossover point. For example, if the current context indicates that the user is looking for a specific category of products, the crossover operation will tend to retain genes related to that category. This can be achieved by adjusting the crossover probability or the crossover point position;
S46，变异操作（引入智能变异策略）：实施智能变异策略，变异操作不仅是随机的，还基于市场趋势、用户群体行为模式信息进行指导，以引入新颖匹配方案，增强解空间的多样性，变异概率根据外部信息动态调整，设外部信息为市场趋势，市场趋势表明某类产品受欢迎度上升，该产品类别的基因变异概率增加：，其中，是根据市场趋势计算变异概率的函数；S46, mutation operation (introduction of intelligent mutation strategy): Implement intelligent mutation strategy. The mutation operation is not only random, but also guided by market trends and user group behavior pattern information to introduce novel matching solutions, enhance the diversity of solution space, and increase the probability of mutation. Dynamically adjust according to external information, assuming external information is market trend , market trends show that as a certain category of products becomes more popular, the probability of genetic mutation in that product category increases: ,in, According to market trends Function to calculate mutation probability;
S47，迭代进化：重复执行选择、交叉和变异操作，直至满足终止条件，终止条件包括达到最大迭代次数或适应度达到预设阈值，确保找到最优匹配方案；S47, iterative evolution: repeatedly perform selection, crossover and mutation operations until the termination condition is met. The termination condition includes reaching the maximum number of iterations or the fitness reaching a preset threshold to ensure that the optimal matching solution is found;
S48，解码和应用：将进化得到的最优匹配方案解码为具体的推荐策略，根据其参数向用户推荐产品或服务，将编码向量转换为相应的产品类别、服务质量标准和价格范围。S48, decoding and application: Decode the optimal matching solution obtained by evolution into a specific recommendation strategy, recommend products or services to users based on its parameters, and convert the encoded vector into corresponding product categories, service quality standards and price ranges.
适应度函数衡量染色体的质量，基于用户对推荐结果的互动和满意度，计算公式为：互动率满意度，其中，和是权重参数，用于调整互动率和满意度在适应度评分中的相对重要性。Fitness function Weighing chromosomes The quality of recommendation is based on the user's interaction and satisfaction with the recommendation results. The calculation formula is: Interaction rate Satisfaction ,in, and is a weight parameter used to adjust the relative importance of interaction rate and satisfaction in the fitness score.
基于用户反馈的动态选择机制采用轮盘赌选择法，概率动态调整，以偏好产生正面反馈的匹配方案：，其中，是染色体的适应度，是种群大小。The dynamic selection mechanism based on user feedback adopts roulette selection method, with probability Dynamically adjust to favor matches that generate positive feedback: ,in, It's a chromosome The fitness of is the population size.
还包括优先级决策机制，综合情绪分析技术推测的用户潜在需求和遗传算法的最优匹配方案，确定最终的推荐优先级，基于以下规则：It also includes a priority decision-making mechanism that combines the potential needs of users inferred by sentiment analysis technology and the optimal matching solution of the genetic algorithm to determine the final recommendation priority based on the following rules:
若情绪分析结果指示用户对某类别的产品非常满意（强正面情绪），并且遗传算法也推理出用户对该类别的产品有高需求，那么该类别的产品推荐优先级将被提高；If the sentiment analysis results indicate that users are very satisfied with products in a certain category (strong positive sentiment), and the genetic algorithm also infers that users have a high demand for products in this category, then the recommendation priority of products in this category will be increased;
如果情绪分析结果与遗传算法推理的需求存在冲突（例如，情绪分析指示满意度不高，而遗传算法推理出高需求），则引入权衡机制，通过用户互动数据来调整最终推荐的优先级；If there is a conflict between the sentiment analysis results and the requirements inferred by the genetic algorithm (for example, sentiment analysis indicates low satisfaction, while the genetic algorithm infers high requirements), a trade-off mechanism is introduced to adjust the priority of the final recommendation through user interaction data;
实施实时监控和反馈机制，根据用户对推荐内容的实际反应（如点击、购买、评价等）来不断调整和优化优先级决策机制。这可以帮助系统更好地理解用户的真实需求和偏好，从而提高推荐的准确性和用户满意度。Implement real-time monitoring and feedback mechanisms to continuously adjust and optimize the priority decision-making mechanism based on users’ actual responses to recommended content (such as clicks, purchases, reviews, etc.). This can help the system better understand users’ real needs and preferences, thereby improving the accuracy of recommendations and user satisfaction.
如图2所示，基于大数据的用户需求精准匹配系统，用于实现上述的基于大数据的用户需求精准匹配方法，包括以下模块：As shown in FIG2 , the user demand precise matching system based on big data is used to implement the above-mentioned user demand precise matching method based on big data, and includes the following modules:
数据采集与预处理模块：负责收集用户在各个平台上的行为数据，包括浏览历史、购买记录和社交媒体互动，对收集到的数据进行预处理，以确保数据质量和一致性，为后续分析提供准确的输入；Data collection and preprocessing module: responsible for collecting user behavior data on various platforms, including browsing history, purchase records, and social media interactions, and preprocessing the collected data to ensure data quality and consistency, providing accurate input for subsequent analysis;
情绪分析模块：应用情绪分析技术分析用户生成的内容，以识别用户的情绪状态和情感倾向，通过分析用户表达的情感来推测用户的潜在需求；Sentiment analysis module: Apply sentiment analysis technology to analyze user-generated content to identify the user's emotional state and emotional tendency, and infer the user's potential needs by analyzing the emotions expressed by the user;
模糊逻辑分析模块：利用模糊逻辑对用户行为数据和情绪分析的结果进行分析，处理不确定性和模糊性，通过构建模糊规则和模糊变量，将用户的偏好、需求和情绪状态量化为模糊度量；Fuzzy logic analysis module: Analyzes user behavior data and sentiment analysis results using fuzzy logic, handles uncertainty and ambiguity, and quantifies user preferences, needs, and emotional states into fuzzy metrics by constructing fuzzy rules and fuzzy variables;
算法匹配模块：采用改进的遗传算法对用户需求进行优化匹配，通过模拟自然选择和遗传学原理，结合各模块的输出作为输入，不断迭代寻找最优的用户需求匹配方案。Algorithm matching module: It uses an improved genetic algorithm to optimize the matching of user needs. By simulating natural selection and genetics principles, combining the output of each module as input, it continuously iterates to find the optimal user demand matching solution.
所属领域的普通技术人员应当理解：以上任何实施例的讨论仅为示例性的，并非旨在暗示本发明的范围被限于这些例子；在本发明的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本发明的不同方面的许多其它变化，为了简明它们没有在细节中提供。Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.
本发明旨在涵盖落入权利要求的宽泛范围之内的所有这样的替换、修改和变型。因此，凡在本发明的精神和原则之内，所做的任何省略、修改、等同替换、改进等，均应包含在本发明的保护范围之内。The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
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